Reviewer #3 (Public Review): 

The work 'A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation' by Wang et al. describes the importance of integrin mediated B-cell co-stimulation for IS formation in B-cells by fostering the formation of myosin II A driven actin arcs that are essential in the transport of IgM clusters towards the IS center. 

The work presented here, i.e. experiments and analysis, is very thoroughly done and includes tests and controls using different labelling strategies and constructs of myosin II A, multiple cell types including primary cells and a range of chemical inhibitors to rule out artefacts. 

The authors claim that the observation of actin arcs in B-cells co-stimulated by ICAM-1 - LFA-1 interaction is important for the efficient activation of B-cells in the presence of limiting levels of anti-IgM and this is very well supported by the experiments. However, it was a bit surprising that the paper did not draw much of parallels between the observed phenomenon and the reported actin arcs in activated T-cells even though some of the authors were very much involved in such work on T-cells. If there is a good reason to believe there is no ground to draw comparisons, this would then also need to be highlighted by the authors. 

The work on establishing the drivers of actin arc formation and dynamics is well done, but it is important to note that previous work has analyzed actin arc formation in other cell types. Work by Bershadsky has already established many 'ground rules' for the formation of actin arcs and the role of integrin adhesion, formin activity and myosin II in the process (Tee YH, Shemesh T, Thiagarajan V, Hariadi RF, Anderson KL, Page C, Volkmann N, Hanein D, Sivaramakrishnan S, Kozlov MM, Bershadsky AD. 2015. Cellular chirality arising from the self-organization of the actin cytoskeleton. Nat Cell Biol 17:445-457. doi:10.1038/ncb3137). It might be very instructive if the authors could put their findings in relation to this work. 

The formation of actin arcs is also well studied in U2OS cells and the results presented here could highlight interesting general features of this process observed in very different cell types (Tojkander S, Gateva G, Husain A, Krishnan R, Lappalainen P. 2015. Generation of contractile actomyosin bundles depends on mechanosensitive actin filament assembly and disassembly. Elife 4:1-28. doi:10.7554/eLife.06126; Bur-nette DT, Shao L, Ott C, Pasapera AM, Fischer RS, Baird MA, Der Loughian C, Delanoe-Ayari H, Paszek MJ, Davidson MW, Betzig E, Lippincott-Schwartz J. 2014. A contractile and counterbalancing adhesion system controls the 3D shape of crawling cells. J Cell Biol 205:83-96. doi:10.1083/jcb.201311104). 

In this regard, the findings about the importance of myosin II A activity, integrin adhesion and mDia1 in the formation of actin arcs is not that surprising and the authors might rather highlight the important role of these newly studied structures for co-stimulation in B-cells as this is the more novel and insightful bit of the work.

Author Response:

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve

Background: Myopia (short- or near-sightedness) is an ocular disorder of increasing concern to human individuals and health-care systems; these days one speaks of a "myopia epidemic" in developed countries. Usually it is due to excessive elongation of the optic axis of the eye during the ages of most rapid growth (ca. 5-16 years in humans), causing images of distant objects to be blurred at the retinal photoreceptors. The optical error can be corrected with lenses or corneal surgeries, but this does not reduce the risk of continued progression and vision loss. Despite extensive epidemiological and animal studies in the past several decades, the underlying causal mechanisms remain poorly known, and therapeutic options are limited. Therefore, further discovery of new candidate mechanisms, drug targets and drugs for inhibiting the onset and progression of myopia is urgently needed.

Rationale: The axial length of the eye is regulated mainly by qualities of the visual environment, including light intensity, spectrum, and spatiotemporal characteristics of images on the retina. Thus the retina encodes and integrates visual information over time, and ultimately sends regulatory "grow" or "stop" signals via the choroid - a vascular plexus behind the retina - to the sclera, the fibrous outer coat of the eye. Changes in size (area) of the sclera are responsible for changes in axial length, and thereby, refraction. The choroid is in a critical position, not only to relay "stop" or "go" messages to the sclera, but also potentially to critically modify those signals (or generate signals of its own) and further modulate ocular elongation and refraction. Importantly, very little is known about how the choroid fulfills either of these roles.

Aims of the Study: The authors' purpose was to test, in juvenile chicken models, whether the 'pro-inflammatory' cytokine, interleukin-6 (IL-6) - synthesized and released in the choroid - might play a key role in the developmental regulation of axial elongation and refraction of the eye.

Major strengths and weaknesses of the methods and results

Strengths:

1) The studies are focused on the choroid, which must be important in regulating ocular growth and refraction, but whose role is still not well understood

2) Expert use of front-line tools for quantifying mRNA and protein (microarray, RT-PCR, ELISA)

3) Immunohistochemistry: Good choice of antibody (raised to chicken IL-6), appropriate specificity control (preabsorption with chicken antigen)

4) IL-6 mRNA in choroid was impressively increased during recovery from form-deprivation myopia (FDM) (preliminary results, Fig. 2) - i.e., during strong positive (myopic) defocus - a defocus-dependent effect confirmed by a similar effect of lens-induced myopic defocus (Fig. 5).

5) Good data for the time-course of IL-6 mRNA content in choroid, with some confirmation of protein levels (though at only 2 treatment intervals) (Fig. 3)

6) Choroidal IL-6 mRNA also shown convincingly to increase, going from darkness to light (Fig. 4).

7) It's clever to compare the growth- and myopia-inhibiting effects of positive defocus, with those of other treatments known to do the same - in this case, atropine and nitric oxide (NO). The evidence shows that the effects of these agents on choroidal IL-6 mRNA are similar to the effect of positive defocus, with an NO-donor increasing the amounts of IL-6 mRNA and protein in isolated choroid (Fig. 7), and a NOS-inhibitor decreasing the mRNA levels at an intravitreal dose that inhibits scleral growth (Fig. 6).

8) If my calculations are correct, 0.1% atropine sulfate solution has a molarity of something like

1.3 mM. Since alpha-2A adrenoreceptors are present in the choroid, of mammals at least (e.g., Wikberg-Matsson et al., 1996, Exp Eye Res, 63(1):57-66), it might be interesting to explore the possibility that atropine is stimulating IL-6 production in the choroid by acting as agonist via these receptors (cf. Carr et al., 2018, IOVS, 59m2778-2791). The isolated choroid, with IL-6 mRNA and protein synthesis as read-outs, should be an exceptional (and novel) model for testing this and other possible signalling pathways in the choroid.

Weaknesses:

1) Immunolocalization of IL-6: The images (Fig. 1) are not good enough to identify cellular localization of immunoreactive structures; identification of RPE is questionable (no DAPI+ nuclei in labeled 'RPE'); nucleated erythrocytes should be visible in vessel lumina.

We have increased the magnification and resolution of images in Figure 1 to better distinguish immunoreactive cells. Additionally, we have included as Figure 1 - figure supplement 1, both H&E stained and immunolabelled images from adjacent serial sections (both longitudinal and cross sections) of control choroids in order to compare immunopositive cells with the histoarchitecture of the choroid. From these images, one can see that nucleated erythrocytes are located in some of the vessel lumina. The nuclei of the RPE label weakly as compared with those of choroidal fibroblasts or nucleated blood cells. In order to visualize the RPE nuclei, we had to increase the intensity of the DAPI channel (blue, 405 nm) to a level that is not optimal for viewing IL-6 immunolabelling (green). Therefore, we included an additional supplemental figure (Figure 1 – figure supplement 2) in which RPE nuclei are readily visible.

2) Many important details of methods have been left out. Spectral peaks of LED light-sources need to be given, lines 409-412; that's just one of many examples.

We have included graphical and tabular data describing the frequency spectra for each of the three LED light sources used in the present study (Figure 4 - figure supplement 1), and in Sheet 3 of the source data file for Supplementary figures. Additionally, we have added the method of obtaining the frequency spectra in the methods section (lines 499 – 501).

We have also included details of the antigen retrieval procedure performed during histological processing of ocular tissues and details on the methodology and analysis of microarray data (lines 544-547).

3) Intensity (illuminance) of "red" and "blue" lights seems unnecessarily low (58 and 111 lux, respectively, far below the "medium" and "high" intensities of white lights that were used; Fig. 4).

We agree with this comment. The intensities of the red and blue LED lights were limited by the red LED lights. We set them at their maximum intensity setting which registered at 58 lux. To compare their effect with that of the blue light, we felt we should set the blue LED lights at a similar setting, which required us to use its lowest setting, which registered at 111 lux. We realize that these intensities are low, compared with the medium and high settings of the white LED lights. Perhaps at higher intensities we might have observed a differential effect on IL-6 mRNA with red light compared to blue light. We have added this explanation and possibility in the Discussion section (lines 333 – 353).

4) Also, given that red and blue lights have been found to have opposite effects on FDM in chicks (e.g., Wang et al., 2018, IOVS, 59(11):4413-4424), the similarity of IL-6 responses to red and blue in the present study strikes me as a point against a role for IL-6 in regulation of eye growth.

We respectfully disagree with this statement. The Wang et al., paper reported that continuous exposure to red LED lights for five days had no significant effect on refraction or axial length in either control eyes or form deprived (myopic) eyes. In contrast, continuous exposure to blue LED light caused a significant hyperopic shift in refraction in both control and form deprived eyes, but had no significant effect on axial length in either control or form deprived eyes (although a trend toward a decrease in axial length was observed after five days in both control and form deprived eyes). Since refraction was significantly affected (in blue light-reared chicks), but axial length was only minimally affected, we suspect that continual exposure to blue light may have affected other ocular parameters (such as corneal curvature) that would have a significant impact on refraction. We predict that choroidal IL-6 expression is involved in the choroidal and scleral remodeling processes at the posterior pole of the eye that result in changes in vitreous chamber elongation, as opposed to having effects on the anterior segment of the eye. Our data shows short term (6 hr) exposure to red or blue LED light had no effect on IL-6 gene expression. If IL-6 gene expression is involved in the regulation of eye growth, we would expect that exposure to red or blue LED light would have no effect on scleral remodeling, vitreous chamber depth, or axial length, which in fact, is consistent with the results of the Wang et al., paper. We have added this interpretation in the Discussion section of the paper (lines 333 – 353).

5) I admire the thoroughness of confirming that some of the treatments did in fact have the predicted effects on ocular enlargement, by performing assays for scleral proteoglycan synthesis. This might not be essential to this work, although it is well done, and the scleral data won't detract from the value of the paper if retained. But the induction of opposite effects on eye (scleral) growth by such manipulations is well established, and much simpler (cheaper and faster) refraction and/or caliper measurements would have served the same purpose.

We elected to use scleral proteoglycan synthesis as a “read out” for axial elongation, since we can detect significant changes in scleral proteoglycan synthesis much earlier (within 6 hrs) than we and others can detect changes in axial length or vitreous chamber depth in chick eyes (≥2 days). Since our studies involved very short term exposure to myopic defocus (6 hrs), we felt measurements of scleral proteoglycan synthesis would be more likely to establish causal relationships between IL-6, nitric oxide and scleral remodeling.

6) I don't buy the argument that the source of NO is not in the choroid (lines 337-340), based on the failure of L-Arg to change significantly the amount of choroidal IL-6 mRNA (Fig. S1). Several thoughts come to mind here: (a) It is solidly established that the choroid is richly innervated with NO-synthesizing nerve fibres, and that its content of NOS is very high [e.g.: "NOS activity is widely distributed in the eye, (choroid > retina > CP > TM) …"; Geyer et al., 1997, Graefes Arch, 235(12):786-93; also (among others): Wu et al., 2007, Brain Res., 1186m155-63; Hashitani et al., 1998, J Physiol, 510(1):209-223; Fischer & Stell, 1999, cited in the present MS.]. So, there clearly are sources of NO within the choroid, in chicks as in mammals. (b) It's extremely unlikely that "NO, released from the retina … diffuses to the choroid to stimulate IL-6 synthesis", because NO is highly reactive and has a short half-life, restricting its diffusion. But yes, NO generated by iNOS in the RPE certainly could reach choroidal targets; is there anything in the literature to indicate that iNOS mRNA and protein are increased in the RPE, under conditions or treatments that inhibit axial elongation? (c) The critical experiment to test this idea - treating the isolated choroid with a NOS-inhibitor, to block synthesis of NO by cells in the choroid - was not performed here.

That would be a complicated and difficult exercise, however, requiring the invention of a way to stimulate NOS activity to a new base-level, and then being able to detect effects due to the inhibition of NO-synthesis. It would be good to discuss the issues raised in this point, but acceptable to suggest this as another of the questions that would be suitable to address by further experimentation beyond the scope of this paper.

Based on these comments (and the comments below) by Reviewer #1, we carried out additional experiments on isolated choroids using 50 mM KCl to depolarize the plasma membranes of choroidal cells in the presence of L-arginine (0.05 mM – 5 mM). We found that in the presence of KCl, treatment of choroids with L-arginine caused a significant increase in IL-6 mRNA. In contrast, L-arginine in the absence of KCl had no significant effect on IL-6 mRNA (as we found in our original experiments). We interpret these new results to indicate that choroidal IL-6 can be upregulated by endogenous sources of nitric oxide. We have included this data in new Figure 8 of the revised manuscript. We thank the reviewer for providing this insight!

7) Since it's overwhelmingly likely that NO is synthesized and released locally in the choroid, alternative explanations must be considered for why the NO-donor, PAPA-NONOate, caused increases in IL-6, while L-Arg didn't. Might it have been the case, for example, that the NOS- containing choroidal cells already were fully loaded with L-Arg, under these particular experimental conditions? or that the administered concentration of L-Arg was sub-optimal? or that the proportions of cellular mass to fluid volume in the choroidal samples were highly variable, causing high variance of the individual values? or that the parent compound PAPA- NONOate, however attractive 'his' name, had destinations in mind (molecular targets, actions) in addition to or other than sGC? Any one of these hypotheses might account for the fact that L-Arg reduced the mean level of IL-6 mRNA by almost 50%, but with p=0.14 despite the sample size n=16.

Please see explanation under item 6 above.

8) The results of the bulk assays - of whole choroids - are a good beginning, starting to build a map of largely uncharted territory; but they will never be completely satisfactory for constructing signalling pathways or networks for visual regulation of scleral expansion, and will leave one struggling to make sense of it all (cf. lines 345-347). Better immunolabeling, with better image definition and resolution, the addition of single-label images and bright-field images (to locate the RPE securely), and possibly FISH would be helpful for this. If you're rich, have great local resources, and/or are well connected with others who do, scRNA-seq of dissociated choroidal tissue (with RPE and sclera as controls) would have great potential here. If the tissue has been perfused intravascularly or well washed and drained, to get rid of blood cells, there shouldn't be very many cell types to characterize (but, my, wouldn't it be exciting and illuminating if there were!)

As stated under point 1 above, we have increased the magnification and resolution of images in Figure 1 to better distinguish immunoreactive cells and we have included both H&E stained and immunolabelled images from adjacent serial sections (both longitudinal and cross sections) of choroids in order to compare immunopositive cells with the histoarchitecture of the choroid in supplemental Figure 1 - figure supplement 1. We agree that scRNA-seq would yield valuable insights into gene expression changes amount individual cell populations within the choroid. We feel those studies are beyond the scope of this paper, but are ones that we are currently undertaking.

9) The relationships between the studies and outcomes reported in this manuscript, and the possible role of choroidal IL-6 and other inflammation-signalling molecules in myopia, is hardly touched upon at all - just a short, very general statement near the end of the Conclusions (lines 368-374).

We have added additional discussion regarding the possible role of inflammation in ocular growth regulation (lines 358-365, 370 – 372)

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve

Background: Myopia (short- or near-sightedness) is an ocular disorder of increasing concern to human individuals and health-care systems; these days one speaks of a "myopia epidemic" in developed countries. Usually it is due to excessive elongation of the optic axis of the eye during the ages of most rapid growth (ca. 5-16 years in humans), causing images of distant objects to be blurred at the retinal photoreceptors. The optical error can be corrected with lenses or corneal surgeries, but this does not reduce the risk of continued progression and vision loss. Despite extensive epidemiological and animal studies in the past several decades, the underlying causal mechanisms remain poorly known, and therapeutic options are limited. Therefore, further discovery of new candidate mechanisms, drug targets and drugs for inhibiting the onset and progression of myopia is urgently needed.

Rationale: The axial length of the eye is regulated mainly by qualities of the visual environment, including light intensity, spectrum, and spatiotemporal characteristics of images on the retina. Thus the retina encodes and integrates visual information over time, and ultimately sends regulatory "grow" or "stop" signals via the choroid - a vascular plexus behind the retina - to the sclera, the fibrous outer coat of the eye. Changes in size (area) of the sclera are responsible for changes in axial length, and thereby, refraction. The choroid is in a critical position, not only to relay "stop" or "go" messages to the sclera, but also potentially to critically modify those signals (or generate signals of its own) and further modulate ocular elongation and refraction. Importantly, very little is known about how the choroid fulfills either of these roles.

Aims of the Study: The authors' purpose was to test, in juvenile chicken models, whether the 'pro-inflammatory' cytokine, interleukin-6 (IL-6) - synthesized and released in the choroid - might play a key role in the developmental regulation of axial elongation and refraction of the eye.

Major strengths and weaknesses of the methods and results

Strengths:

1. The studies are focused on the choroid, which must be important in regulating ocular growth and refraction, but whose role is still not well understood

2. Expert use of front-line tools for quantifying mRNA and protein (microarray, RT-PCR, ELISA)

3. Immunohistochemistry: Good choice of antibody (raised to chicken IL-6), appropriate specificity control (preabsorption with chicken antigen)

4. IL-6 mRNA in choroid was impressively increased during recovery from form-deprivation myopia (FDM) (preliminary results, Fig. 2) - i.e., during strong positive (myopic) defocus - a defocus-dependent effect confirmed by a similar effect of lens-induced myopic defocus (Fig. 5).

5. Good data for the time-course of IL-6 mRNA content in choroid, with some confirmation of protein levels (though at only 2 treatment intervals) (Fig. 3)

6. Choroidal IL-6 mRNA also shown convincingly to increase, going from darkness to light (Fig. 4).

7. It's clever to compare the growth- and myopia-inhibiting effects of positive defocus, with those of other treatments known to do the same - in this case, atropine and nitric oxide (NO). The evidence shows that the effects of these agents on choroidal IL-6 mRNA are similar to the effect of positive defocus, with an NO-donor increasing the amounts of IL-6 mRNA and protein in isolated choroid (Fig. 7), and a NOS-inhibitor decreasing the mRNA levels at an intravitreal dose that inhibits scleral growth (Fig. 6).

8. If my calculations are correct, 0.1% atropine sulfate solution has a molarity of something like

1.3 mM. Since alpha-2A adrenoreceptors are present in the choroid, of mammals at least (e.g., Wikberg-Matsson et al., 1996, Exp Eye Res, 63(1):57-66), it might be interesting to explore the possibility that atropine is stimulating IL-6 production in the choroid by acting as agonist via these receptors (cf. Carr et al., 2018, IOVS, 59m2778-2791). The isolated choroid, with IL-6 mRNA and protein synthesis as read-outs, should be an exceptional (and novel) model for testing this and other possible signalling pathways in the choroid.

Weaknesses:

1. Immunolocalization of IL-6: The images (Fig. 1) are not good enough to identify cellular localization of immunoreactive structures; identification of RPE is questionable (no DAPI+ nuclei in labeled 'RPE'); nucleated erythrocytes should be visible in vessel lumina.

2. Many important details of methods have been left out. Spectral peaks of LED light-sources need to be given, lines 409-412; that's just one of many examples.

3. Intensity (illuminance) of "red" and "blue" lights seems unnecessarily low (58 and 111 lux, respectively, far below the "medium" and "high" intensities of white lights that were used; Fig. 4).

4. Also, given that red and blue lights have been found to have opposite effects on FDM in chicks (e.g., Wang et al., 2018, IOVS, 59(11):4413-4424), the similarity of IL-6 responses to red and blue in the present study strikes me as a point against a role for IL-6 in regulation of eye growth.

5. I admire the thoroughness of confirming that some of the treatments did in fact have the predicted effects on ocular enlargement, by performing assays for scleral proteoglycan synthesis. This might not be essential to this work, although it is well done, and the scleral data won't detract from the value of the paper if retained. But the induction of opposite effects on eye (scleral) growth by such manipulations is well established, and much simpler (cheaper and faster) refraction and/or caliper measurements would have served the same purpose.

6. I don't buy the argument that the source of NO is not in the choroid (lines 337-340), based on the failure of L-Arg to change significantly the amount of choroidal IL-6 mRNA (Fig. S1). Several thoughts come to mind here: (a) It is solidly established that the choroid is richly innervated with NO-synthesizing nerve fibres, and that its content of NOS is very high [e.g.: "NOS activity is widely distributed in the eye, (choroid > retina > CP > TM) ..."; Geyer et al., 1997, Graefes Arch, 235(12):786-93; also (among others): Wu et al., 2007, Brain Res., 1186m155-63; Hashitani et al., 1998, J Physiol, 510(1):209-223; Fischer & Stell, 1999, cited in the present MS.]. So, there clearly are sources of NO within the choroid, in chicks as in mammals. (b) It's extremely unlikely that "NO, released from the retina ... diffuses to the choroid to stimulate IL-6 synthesis", because NO is highly reactive and has a short half-life, restricting its diffusion. But yes, NO generated by iNOS in the RPE certainly could reach choroidal targets; is there anything in the literature to indicate that iNOS mRNA and protein are increased in the RPE, under conditions or treatments that inhibit axial elongation? (c) The critical experiment to test this idea - treating the isolated choroid with a NOS-inhibitor, to block synthesis of NO by cells in the choroid - was not performed here.

That would be a complicated and difficult exercise, however, requiring the invention of a way to stimulate NOS activity to a new base-level, and then being able to detect effects due to the inhibition of NO-synthesis. It would be good to discuss the issues raised in this point, but acceptable to suggest this as another of the questions that would be suitable to address by further experimentation beyond the scope of this paper.

7. Since it's overwhelmingly likely that NO is synthesized and released locally in the choroid, alternative explanations must be considered for why the NO-donor, PAPA-NONOate, caused increases in IL-6, while L-Arg didn't. Might it have been the case, for example, that the NOS- containing choroidal cells already were fully loaded with L-Arg, under these particular experimental conditions? or that the administered concentration of L-Arg was sub-optimal? or that the proportions of cellular mass to fluid volume in the choroidal samples were highly variable, causing high variance of the individual values? or that the parent compound PAPA- NONOate, however attractive 'his' name, had destinations in mind (molecular targets, actions) in addition to or other than sGC? Any one of these hypotheses might account for the fact that

L-Arg reduced the mean level of IL-6 mRNA by almost 50%, but with p=0.14 despite the sample size n=16.

8. The results of the bulk assays - of whole choroids - are a good beginning, starting to build a map of largely uncharted territory; but they will never be completely satisfactory for constructing signalling pathways or networks for visual regulation of scleral expansion, and will leave one struggling to make sense of it all (cf. lines 345-347). Better immunolabeling, with better image definition and resolution, the addition of single-label images and bright-field images (to locate the RPE securely), and possibly FISH would be helpful for this. If you're rich, have great local resources, and/or are well connected with others who do, scRNA-seq of dissociated choroidal tissue (with RPE and sclera as controls) would have great potential here. If the tissue has been perfused intravascularly or well washed and drained, to get rid of blood cells, there shouldn't be very many cell types to characterize (but, my, wouldn't it be exciting and illuminating if there were!)

9. The relationships between the studies and outcomes reported in this manuscript, and the possible role of choroidal IL-6 and other inflammation-signalling molecules in myopia, is hardly touched upon at all - just a short, very general statement near the end of the Conclusions (lines 368-374).

Whether the authors achieved their aims, and whether the results support their conclusions:

The authors have made a very convincing case that myopic defocus stimulates the synthesis of IL-6 mRNA and protein in the chick choroid. The effects of atropine and NO-related drugs further support the association between this action and the inhibition of excessive axial elongation and myopia.

Likely impact of the work on the field, and the utility of the methods and data to the community: This work - and in particular the use of assays for IL-6 in choroidal explants to assess the actions of candidate signalling molecules in the choroid - should be seen as an important step forward. The methods are up-to-date, but well established and straightforward, and could be easily duplicated by most workers in the field. The chick models for myopia induction and recovery have been used and refined for decades, so they are easy and almost foolproof (used successfully by many undergraduates in my lab). Once the foundations have been laid by studies in chicks, they can be translated rather easily for making similar studies in mammalian models such as guinea pigs and NHPs

this is a very important passage because it goes to show how different the black english is from regular english. it gives many places of where you would hear it.

Quale è il loop alla base del metodo Lean Startup?

Si tratta del loop B-M-L (Build, Measure e Learn), l'approccio con cui si segue questo loop è quello di affrontarlo nel minor tempo possibile. Alla fine di ogni iterazione del loop, infatti, ci ritroviamo con l'aver imparato qualcosa sul nostro target.

Più iterazioni facciamo di questo loop, nel modo più veloce possibile, e più ci ritroveremo con avere imparato qualcosa di utile sul target.

McIntyre, R. S., Lui, L. M., Rosenblat, J. D., Ho, R., Gill, H., Mansur, R. B., Teopiz, K., Liao, Y., Lu, C., Subramaniapillai, M., Nasri, F., & Lee, Y. (2021). Suicide reduction in Canada during the COVID-19 pandemic: Lessons informing national prevention strategies for suicide reduction. Journal of the Royal Society of Medicine, 01410768211043186. https://doi.org/10.1177/01410768211043186

Shih, S.-F., Wagner, A. L., Masters, N. B., Prosser, L. A., Lu, Y., & Zikmund-Fisher, B. J. (2021). Vaccine Hesitancy and Rejection of a Vaccine for the Novel Coronavirus in the United States. Frontiers in Immunology, 12, 558270. https://doi.org/10.3389/fimmu.2021.558270

Emary, K. R. W., Golubchik, T., Aley, P. K., Ariani, C. V., Angus, B., Bibi, S., Blane, B., Bonsall, D., Cicconi, P., Charlton, S., Clutterbuck, E. A., Collins, A. M., Cox, T., Darton, T. C., Dold, C., Douglas, A. D., Duncan, C. J. A., Ewer, K. J., Flaxman, A. L., … Pollard, A. J. (2021). Efficacy of ChAdOx1 nCoV-19 (AZD1222) vaccine against SARS-CoV-2 variant of concern 202012/01 (B.1.1.7): an exploratory analysis of a randomised controlled trial. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(21)00628-0

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)00628-0/fulltext

Sah, P., Fitzpatrick, M. C., Zimmer, C. F., Abdollahi, E., Juden-Kelly, L., Moghadas, S. M., Singer, B. H., & Galvani, A. P. (2021). Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis. Proceedings of the National Academy of Sciences, 118(34). https://doi.org/10.1073/pnas.2109229118

SciScore for 10.1101/2021.09.29.462326: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code.

Our study is also subject to some limitations. First, the samples were collected during the first COVID-19 wave in Madrid. It is unknown yet whether the emerging SARS-CoV-2 variants could lead to different metabolic consequences. Second, as expected, cases in the severe group were older and had more comorbidities than milder cases, so we considered potential confounders in our statistical approach. Third, in the subgroup analyses separated by clinical severity, the statistical power to detect differences in metabolite abundances was lower due to the smaller sample sizes. In summary, in this work examining for the first time the metabolic changes associated with COVID-19 by CE-MS, we report the discovery of new plasma biomarkers for COVID-19 that provide mechanistic explanations for the clinical consequences of SARS-CoV-2, including mitochondrial and liver dysfunction as a consequence of hypoxemia (citrulline, citrate and BAIBA), energy production and amino acid catabolism (L-glycine, L-alanine, L-serine, L-proline, L-aspartic acid and L-histidine), and endothelial dysfunction and thrombosis (citrulline, L-ADMA, 2-AB, and Neu5Ac), and we found interconnections between these pathways (Figure 5). These biomarkers deserve further attention as biomarkers of SARS-CoV-2 susceptibility and COVID-19 clinical severity and as potential targets for interventions.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.24.21264081: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

anchor modeling

Higdon, M. M., Wahl, B., Jones, C. B., Rosen, J. G., Truelove, S. A., Baidya, A., Nande, A. A., ShamaeiZadeh, P. A., Walter, K. K., Feikin, D. R., Patel, M. K., Knoll, M. D., & Hill, A. L. (2021). A systematic review of COVID-19 vaccine efficacy and effectiveness against SARS-CoV-2 infection and disease [Preprint]. Epidemiology. https://doi.org/10.1101/2021.09.17.21263549

SciScore for 10.1101/2021.09.27.462006: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Rodrigues, D. L., Zoppolat, G., Balzarini, R. N., & Slatcher, R. B. (2021). Security motives and personal well-being during the early months of the COVID-19 pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/xwtmy

SciScore for 10.1101/2021.09.27.21264163: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

There are several limitations of our study. First, our study includes substantially more female than male participants, reflecting the gender distribution among HCW at our institute. Second, the age distribution in the four groups is not identical. In particular, the AZD1222 group is, on average, considerably older as a consequence of restrictive use of the AZD1222 vaccine in individuals aged 60-64 years in the Netherlands. As immune responses tend to become weaker with higher age, this is a relevant factor when considering the weaker responses in the AZD1222 group. Finally, the samples we tested were taken at the expected peak of immunity. It will be relevant to study the durability of the neutralizing antibody responses after vaccination with each of these vaccines. Some studies suggest that immunity induced by adenovirus vaccines might be more durable than immunity from mRNA vaccines8,45. We have only analyzed known VOCs and VOIs and cannot predict how our results apply to future variants. One consideration is that current VOCs were probably selected based on increased fitness and/or transmissibility, while future variants may very well be selected based on escape from immunity when more and more people are vaccinated or have experienced COVD-19. Such escape variants may be more resistant to neutralizing antibodies induced by current vaccines than the VOCs and VOIs studied here and may render vaccines even less effective at preventing infection. However, while circulating ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.18.21263605: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.27.461930: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.25.21264117: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

I thank you, O my God, that you have acted wonderfully with dust, and with a vessel of clay you have worked so very powerfully. What am I that you have [inst]ructed me in the secret counsel of your truth and that you have given me insight into your wondrous deeds, that you have put thanksgiving into my mouth, pr[ai]se upon my tongue, and (made) the utterance of my lips as the foundation of jubilation, so that I might sing of your kindness and reflect on your strength all the day? Continually I bless your name, and I will recount your glory in the midst of humankind. In your great goodness 10. my soul delights. I know that your command is truth, that in your hand is righteousness, in your thoughts 11. all knowledge, in your strength all power, and that all glory is with you. In your anger are all punishing judgments, 12. but in your goodness is abundant forgiveness, and your compassion is for all the children of your good favor. Truly, you have made known to them the secret counsel of your truth 13. and given them insight into your wonderful mysteries. For the sake of your glory you have purified a mortal from sin, so that he may sanctify himself 14. for you from all impure abominations and from faithless guilt, so that he might be united with the children of your truth and in the lot with 15. your holy ones, so that a corpse-infesting maggot might be raised up from the dust to the council of [your] t[ruth], and from a spirit of perversion to the understanding that comes from you, 16. and so that he may take (his) place before you with the everlasting host and the [eternal] spirit[s], and so that he may be renewed together with all that i[s] 17. and will be and with those who have knowledge in a common rejoicing. vacat […] 18. [And, as for me,] I thank you, O my God, I exalt you, O my rock. And when you act wondrously[…] 19. […] that you have made known to me the secret counsel of truth and [you have given me insight] into [wonderful] myster[ies, and you have let me understand your wonders,] 20. [and] your [hid]den things you have revealed to me, so that I have gazed upon […]°° y kindness. And I know 21. [th]at righteousness belongs to you, and by your kindness [they] are judged […] h and destruction without your compassion. 22. As for me, a fount of bitter mourning was opened to me […]and trouble was not hidden from my eyes 23. when I knew the inclinations of humans, and I un[derstood] to what mortals return, [and I recognized the mour]nfulness of sin, and the anguish of 24. guilt. They entered my heart and they penetrated my bones l °[…]° ym and to utter an agonized moan 25a. and a groan to the lyre of lamentation for all gr[iev]ous mourning[…] 25. and bitter lament until the destruction of iniquity, when there is n[o more pain ]and no more affliction to make one weak. And then 26. I will sing upon the lyre of salvation, and the harp of jo[y, and the timbrel of rejoi]cing, and the flute of praise, without 27. ceasing. Who among all your creatures will be able to recount al[l] your [wonders]? With every mouth your name will be praised. 28. For ever and ever they will bless you according to [their] insight, [and at all tim]es they will proclaim together 29. with a joyous voice. There will be no sorrow or sighing, and iniquity [will be found] no [more]. Your truth will shine forth 30. for everlasting glory and eternal peace. Blessed are you,[O Lord, w]ho have given to your servant 31. insightful knowledge to understand your wondrous works and [a ready an]sw[er in order to] tell of the abundance of your kindness. 32. Blessed are you, God of compassion and grace, according to the great[ness of] your st[ren]gth and the magnitude of your truth, and the abundan[ce] 33. of your kindness with all your creatures. Gladden the soul of your servant through your truth, and purify me 34. by your righteousness. Even as I waited for your goodness, so for your kindness I hope. By your forgiveness[s] 35. you relieve my pains, and in my troubles you comfort me, for I depend on your compassion. Blessed are yo[u,] 36. O Lord, for you have done these things, and you have put into the mouth of your servant hymns of pr[a]is[e] 37. and a prayer of supplication, and a ready answer. You have established for me the wor[k of …] 38. And I reta[in strength] l […] b […] l […]°[…] 39. And you[…] 40. [your] truth[…] 41. and ’ […] 42. °°°[…]°

SciScore for 10.1101/2021.09.25.461776: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 36. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.24.461616: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 17. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Reavis, R. D., Ebbs, J. B., Onunkwo, A. K., & Sage, L. M. (2017). A self-affirmation exercise does not improve intentions to vaccinate among parents with negative vaccine attitudes (and may decrease intentions to vaccinate). PLoS ONE, 12(7), e0181368. https://doi.org/10.1371/journal.pone.0181368

SciScore for 10.1101/2021.09.18.21263550: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.20.21263837: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.23.21258047: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.14.21263556: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

A major limitation of this study is the inadequate follow up, which do not allow us to compare the persistence of antibody responses between PLWH and the healthy control. Despite of this limitation, our study demonstrates that the inactivated COVID-19 vaccine was immunogenic and safe in PLWH stable on cART.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.16.21263693: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

This journal article is divided into sections. It starts with an abstract and introduction in order to introduce the issue at hand. Then it is divided into sections about research. It starts with the theory, and then continues into how that is used. The theory of TPB is mentioned variously throughout the article. This shows how important it is to fast fashion.

Mentions acknowledgements, conflict of interests, and both authors contributions.

One table was used to back up information talked about in the article, includes different studies and their results. This table was summarized by the authors but the results within the chart were from secondary research.

To find a parametric equations for the line \(L\), we need to know what the point \(P = (x0,y0,z0)\) and the vector \(v = <a,b,c>\). \(P = OP1 = <1,2,-1>\) and \(v = P2P1 = <3,1,1>\). Thus the parametric equations of the line \(L\) are \(x(t) = x0+at = 1+3t, y(t) = y0+bt = 2+t, z(t) = z0+ct = -1 +t\)

Interesting and pessimistic perspective. What leads the writer to feel this?

Non Indians? Challenge ideas of slavery and ideas of modern California liberalism

What were the accusations? What was Kearney's relationship to McFadden? Was the author justified in calling him a "bully"?

Santa Ana winds

Key player?

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for their constructive and helpful comments on our manuscript and are delighted to find their consensus that the manuscript represents an important contribution to the field. We provide a detailed response to specific points below. In addition, we propose to include new data showing that our method can be applied to experimentally infected lung tissue. Namely, we show highly sensitive detection of SARS-CoV-2 RNA in infected hamster lung section.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer #1 **Major comments:**

The authors used approaches provided in FISH-quant (Mueller et al, Nat Methods 2013) and big-fish. However, these tools to analyze RNA aggregates were not designed and validated for such massive aggregations as observed by SARS-Cov-2. They were developed for cases such as transcription sites with much smaller aggregations, with a few tens to a hundred molecules. With a regular spot detection approach, usually a few thousand spots can be detected in a cell (e.g. King et al, J Virol 2018), but this depends also on the used microscope and the available cellular volume. Higher RNA concentrations cannot be resolved with a standard approach, because RNA spots start to overlap. Decomposing RNA aggregations can help but will not work reliably for the high RNA densities observed for SARS-Cov-2, especially at later infection time-points. The tools will then not provide accurate estimates anymore. To my knowledge, there is currently not accurate quantification method for such massive RNA levels in smFISH. What has been done in the past, is using cellular intensity as an approximation and perform calibrations with cells having lower and thus still resolvable RNA counts (Raj et al., PLO Biology; https://doi.org/10.1371/journal.pbio.0040309.sg003). The authors proposed three expression regimes (partially resistant, permissive, and super permissive). My concerns here apply mainly to the category super-permissive, where an accurate estimation can't be performed. Here a more cautious quantification should be applied. __To a lesser extent, this will also apply to some of quantifications of gRNAs per factory, with counts exceeding 100s of molecules. As mentioned above, this does not affect any of the conclusions, but would reflect more accurately what kind of reliable information can be drawn from such experiments.__

We agree with the reviewer that approaches like FISH-quant and Big-FISH cannot reliably quantify RNA spots with high spatial density such as our examples of “super-permissive” cells. Single molecule quantitation of such cases is likely to underestimate RNA expression as noted by us and King et al 2018 (doi: 10.1128/JVI.02241-17). Therefore, we integrated the combined smFISH signal intensity within entire cellular volumes and compared to the median intensity of single molecules in cells with lower infection density. We will (i) revise the methods and results sections to explain more carefully and explicitly the quantification of RNA in super-permissive cells. (ii) Provide a calibration plot for the quantitation as previously reported (Raj et al 2006, doi: 10.1371/journal.pbio.0040309).

We agree that high local RNA density has the potential to interfere with quantification of gRNAs within viral factories. We have used the “cluster.decomposition()” function of Big-FISH to quantify viral factories, which is conceptually similar to the “Integrated intensity” mode of FISH-quant. Applying this algorithm to non-super permissive cells allows us to use the mean intensity of a reference single-molecule spot to estimate the number of molecules in a cluster. We are confident such estimates are reliable in the majority of viral factories, which contain less than or equal to 200 single gRNA molecules. We will revise the methods section to clarify this method of analysis.

Reviewer #1 __**Minor comments:**__

1.Page 6; the authors state that "smFISH identifies ... cellular distribution .... within ER-like membranous structures". However, the authors didn't directly show such a localization, could they provide an experiment with an ER stain?

This text was based on previous light microscopy and EM studies that reported SARS-CoV-2 RNA in ER-derived membranes (termed Double Membrane Vesicles - DMVs) or co-localisation of anti-dsRNA (J2) with ER-markers (Cortese et al 2020; Hackstadt et al 202; Mendonca et al 2021)*. We propose to clarify the text on page 6 including the citation of these publications and to tone down our claim that the virus is located in ER-like membranous structures.

*Cortese et al 2020, doi: 10.1016/j.chom.2020.11.003

Hackstadt et al 2021, doi: 10.3390/v13091798

Mendonca et al 2021, doi: 10.1038/s41467-021-24887-y

2.It might be worthwhile pointing out that the probe-sets can be used in different host organisms (Vero - African green monkey; human cell lines).

We propose to revise the text to emphasise more clearly the applicability of SARS-CoV-2 probes for the study of many different host organisms.

3.I really liked the experiment, where the authors showed absence of signal when infecting with another virus & elegant control with the J2 AB. Maybe the authors could explain more clearly that the used a different coronavirus & that based on their sequence alignment no/little signal would be expected.

Thank you for this supportive comment. We plan to follow the reviewer’s suggestion and expand our explanation of the rationale of this experiment in the text.

7.The experiment with the isolated virions shows nicely that the smFISH approach has single-virus sensitivity. Did the authors compare the intensity of these isolated virions with the signal in Fig 1B? This might be a question of personal taste, but to me, this section might actually fit better in the first paragraph of page 4/5, where the authors describe single virions in cells.

Thank you for the interesting question. We have not performed a direct comparison of the spot intensities of intracellular genomic RNA molecules and those from the isolated virions, because isolated SARS-CoV-2 requires poly-L-lysine coating for the coverslip attachment while our infection strategy utilises cells growing on uncoated glass. Nonetheless, the isolated virion spot intensities follow a unimodal distribution, and their shape approximates to the point-spread function of the microscope. Since spots at 2 hpi are largely derived from non-replicative viral genomes and they are measured in the intracellular environment with the same background (autofluorescence), they are a better ‘single RNA molecule’ reference.

We also thank the reviewer for suggesting rearranging the text section. To address this point we plan to move the relevant text to the second paragraph of the Results section.

8.Page 6. The authors state "+ORF-N and +ORF-S single labelled spots, corresponding to sgRNAs, were more uniformly distributed throughout the cytoplasm than dual labelled gRNA". This is difficult to appreciate from the image. Is this something the authors could quantify, e.g. with the metrics proposed by Stueland et al, Scientific Reports 2019?

To address this point, we plan to: (i) present an alternative image illustrating a clearer example of differential spatial localisation of gRNA and sgRNA, and (ii) perform quantification of spatial dispersion indices for gRNA and sgRNA using the suggested method for our revision.

9.Page 6. The authors perform a FISH/IF experiment including a co-localization analysis, where a "limited overlap" with sgRNAs was observed. I was wondering if this overlap could actually be simply due to rather high density of the sgRNAs. Maybe a control analysis by slightly changing the RNA positions could provide insight here, and give a threshold for what's to be expected randomly at a given RNA density.

The reviewer’s comment is correct, in that a high density of sgRNAs and nucleocapsid protein could lead to signal overlap due to chance. This is why we excluded “super-permissive” cells from this analysis. Our co-localisation data showed that gRNA spots had a bimodal nucleocapsid immunofluorescence intensity distribution (data not shown), suggesting nucleocapsid-associated and “free” gRNAs, providing a threshold for this analysis. Nevertheless, we agree with the reviewer that the analysis of randomly positioned transcripts of the same density would provide a valuable control. In our revised MS we will include: (i) a random distribution analysis comparing the overlap between sgRNA and nucleocapsid in the “Observed” and a “Randomised” simulation, and (ii) a plot showing a full distribution of co-localised nucleocapsid immunofluorescence intensity for both genomic and sub-genomic viral RNAs.

10.I don't fully follow the argument about stability on page 8. The authors also see an increase in the RNA levels. Couldn't this increase compensate for loss of RNA due to degradation? Would it be possible to perform an experiment at a very high REMDESIVIR concentrations which would blocks transcription?

Remdesivir is a nucleoside analogue that inhibits viral RNA polymerase activity. While this drug inhibits viral replication, the inhibition is incomplete and using higher concentrations results in cellular toxicity. At the present time there are no stronger polymerase inhibitors available, so these experiments are the best approximation possible to assess viral RNA stability. We propose to revise the text to discuss the limitations of Remdesivir for modelling RNA stability.

12.How did the authors define/detect replication factories? I couldn't find information about this in the methods.

This is a good point raised by both the reviewers. Please see [Reviewer 2 General comment #1] for our response.

Reviewer #2 **General comments:**

1.The authors' definition of viral factories, in part as foci with at least 4 gRNA molecules, comes across as arbitrary. Perhaps a clearer explanation of this cutoff would be helpful to the readers' understanding of this definition. Additionally, confirmation of the functionality of such factories by immunofluorescence with anti-RdRp, for example, in addition to identifying staining of gRNAs and (-) sense viral RNAs at each focus could provide valuable support to the authors' conclusions.

We thank both reviewers for requesting further information on our explanation of viral factories. We defined viral factories as smFISH signals with spatially extended foci that exceed the size of the point spread function of the microscope and the intensity of a reference single molecule. We then filtered these candidate factories based on the radius of the signal foci with EM-measured radii of double-membrane vesicles and single-membrane vesicles formed by SARS-CoV-2 (150 nm pre-8hpi and 200 nm post-8hpi) (Cortese et al 2020; Mendoca et al 2021). Our terminology encompasses both replication and viral assembly sites. The threshold of 4 genomic RNA molecules was selected as a technical threshold to limit an over-estimation of viral factories at later timepoints. For our spinning-disk confocal imaging system, we found the threshold of 3-7 RNA molecules provided satisfactory results. We propose to revise both the Results and Methods sections to clarify our rationale for defining and quantifying viral factories.

As the reviewer mentioned, we have shown a partial overlap of positive sense genomic RNAs with negative sense genomic RNAs (Figure 2D, S2C), suggesting these viral factories represent double membrane vesicles. The use of antibodies against the viral polymerase (nsp12) is also a possibility to detect replication centres. However, replication centres are not the only ‘viral factories’ as there are also double-membrane structures where viral particles assemble (Mendoca et al 2021) and they, in principle, lack negative sense RNA and replication machinery, so neither smFISH probes against the negative strand nor a nsp12 antibody will comprehensively detect viral factories. We appreciate the valuable suggestion, but the classification of viral factories into replication and assembly sites would be challenging due to reagent availability and is beyond the scope of this manuscript.

2.The random distribution of super-permissive cells in each cell line was demonstrated early in the infection, primarily at 8 hpi. The authors do not show how this pattern changes over time (8, 10, 12, 16, 24 hpi, for example). Do clusters of super-permissive cells appear at later time points, or does the pattern of 'highly' infected cells remain random for each virus? Any strain-specific differences identified from such patterns may be important for understanding infection progression. Finally, the authors do acknowledge this point, but it cannot be overstated that these data were taken from cell culture systems that have limited similarities to the human respiratory epithelium. A better model for such studies might be primary cultured human bronchial epithelial cells, but of course, these cells are not as readily accessible as the cell lines used in this manuscript.

We share the same view that the presence and the spatial distribution of “super-permissive” cells can provide unique insights into SARS-CoV-2 infection dynamics. Our findings suggest that even at 24 hours post infection (hpi), not all cells become “super-permissive” and the culture maintains a heterogenous population of “partially resistant”, “permissive” and “super-permissive” cells (Figure 3C, S3C-D). We agree with the reviewer that the spatial distribution of “super-permissive” cells at later timepoints is of interest. To address this point, we plan to: (i) analyse the spatial distribution of “super-permissive” cells at 24 hpi, and (ii) compare the distribution of “super-permissive” cells at 24 hpi between VIC and B.1.1.7 strains.

We appreciate the comment on the limitations of the cell culture systems to the human respiratory tract. However, Calu-3 and A549-ACE2 lung epithelial cells have been used in many studies over the last year and we feel it is important to publish single cell quantitation with these models to enable comparison with the published literature. We believe our results provide valuable information on the intrinsic nature of host cell susceptibility to support viral replication. During the review of this manuscript, we applied our smFISH probes to detect SARS-CoV-2 RNA in infected Golden Syrian hamster lung sections, which show an uneven distribution of infected cells. While the identification and spatial characterisation of susceptible cell types in the lung are beyond the scope of this manuscript, we are excited to include this data in our revised paper to demonstrate the utility of this sensitive approach to track spatiotemporal viral infection dynamics.

3.The difference in early replication kinetics between the VIC and B.1.1.7 strains is an exciting finding that may have implications for clinical outcomes and transmissibility of these viruses. However, the authors did not clearly demonstrate how these differences in RNA production correlate to infectious viral load released from these cells (in bulk) at each time point. An explanation of this omission would be helpful.

We will provide data on the level of infectious virus secreted from VIC and B.1.1.7 infected cells at all time points in the revised paper.

In my opinion, findings related to specific cell lines are of much less importance (and are much less biologically relevant) that identification of replicative differences among strains. Such differences could be used, in part, to aid prediction of the transmissibility of VOC, for example. I think this point gets a bit 'lost in the weeds' of the rest of the paper.

To address this comment, we will revise text on the differential replication kinetics of the SARS-CoV-2 strains to make this more prominent in our paper.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer #1 __**Minor comments:**__

4.I might have missed this, but they authors could also mention the positive control data about but Calu3 infected with SARS-COv2. One thing I was wondering: why did the authors use two different cell lines for this experiment?

To address this point, we have added a sentence about a positive control visualising SARS-CoV-2 in Calu-3 cells using our probe set (page 5 – line 17).

The experiments with HCoV-229E were done in Huh-7.5 cells because SARS-CoV-2 and HCoV-229E have distinct cell preferences. Using the J2 antibody we show that the levels of the dsRNA derived from viral replication are similar in the two cell lines and with the two viruses. Therefore, the lack of smFISH signal in HCoV-229E infected cells supports the high specificity of the probe set.

5.Fig 1E. Would be nice to have the intensity scale for all time-points to permit a comparison of image intensities along the different time-points.

6.Fig 3B. Would be important to have intensity scale bars to judge the signal intensities across the different time-points.

The fluorescence intensity scale in Figure 1E is applicable to all timepoints, except for the lower panel at 24 hpi, which was intended to show wider dynamic contrast range. To address this point, we have provided intensity scales for all time-points studied in this figure and also Figure 3B.

11.Fig 3C. maybe indicate the two groups with dashed lines.

We have added a dashed line at the 102 mark in Figure 3C to visually differentiate “partially resistant” and “permissive” cells.

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

so close

Current vaccination policies

SciScore for 10.1101/2021.09.14.460338: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

One caveat to the previous studies that found FcγR activity to be critical for effective humoral immunity is that all of those studies were conducted with neutralizing IgG. Therefore, we cannot rule out that FcγR activation in the absence of a neutralizing response does not contribute to severe COVID-19 pathogenesis, as may be the case with individuals with anti-S2’FP dominant IgG profiles. Therefore, it will be important to dissect how IgG-targeting of neutralizing and non-neutralizing epitopes influences the relationship between FcγR activation and disease outcomes. Additionally, it will be important to examine the contribution of FcγR activation to systemic inflammation and control of virus replication in animal models that faithfully mimic human FcγR-signaling effects. In conclusion, the data presented here demonstrate that immunological history—particularly antibody repertoire from seasonal betacoronaviruses—predicts and potentially determines COVID-19 disease severity. Specifically, an anti-HR2-dominant Ab profile represents an efficient protective recall response, an anti-S2’FP dominant Ab profile suggests a deleterious inefficient recall response, and a predominantly anti-RBD profile likely reflects a protective de novo response. These data become particularly important in assessing epitope-targeting early in disease, which could allow earlier interventions with treatments like monoclonal Ab therapies, preventing progression to ARDS. Additionally, assessing the humora...

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SciScore for 10.1101/2021.09.15.460454: (What is this?)

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The main limitation lies in the quantification of the observed events in comparison to the reads without events which can be influenced by size dependency of some of the sample preparation steps as the full-size S protein cDNA is over 4kb long and many of the observed splicing events can be shorter than 200 bp and depleted by many sample preparation methods. Reported here are distributions based on the full length read population alone within each sample, but care should be applied before comparing these frequencies to other datasets with different sample preparation methods, such as the ChAdOx1 Nanopore RNA direct data (Sup Figure 8). Hundreds of randomly spaced splicing events characterized by the cDNA direct sequencing approach for the S protein and similar events seen in other proteins highlight the fact that even when the transgene’s CDS is apparently well designed and expression of full-length protein can be detected, the status quo design is just not optimal for RNA expression as transcript heterogeneity will inevitably impact both product levels (yield) and homogeneity. The impact of this will be dependent on the nature of the product, whether it is expressed in vitro or in vivo. In vitro expression offers opportunities to improve homogeneity by application of purification methods. Two thirds of cryptically spliced RNA molecules would be out-of-frame and would not just impact protein yield but any translation in vivo would generate novel peptides with potential immuno...

Results from TrialIdentifier: No clinical trial numbers were referenced.

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• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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About SciScore

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SciScore for 10.1101/2021.09.15.460487: (What is this?)

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Table 1: Rigor

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04639466</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">A Synthetic MVA-based SARS-CoV-2 Vaccine, COH04S1, for the P…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04977024</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">SARS-CoV-2 Vaccine (COH04S1) Versus Emergency Use Authorizat…</td></tr></table>

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About SciScore

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SciScore for 10.1101/2021.09.12.21263373: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.13.460111: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Because of this caveat associated with our mouse model, the time in which to intervene with a direct acting antiviral and sufficiently improve outcomes is curtailed in mice as compared to humans. Our recent study with RDV exemplifies this where we found the degree of therapeutic benefit in mice infected with SARS-CoV-2 MA10 was dependent on the time of initiation (17). Here, we show that oral administration with GS-621763 prevents body weight loss, loss of pulmonary function, severe lung pathology and virus replication when administered at 12 and 24 hrs after infection. While we observed improvement in some metrics with RDV initiated at 24 hr in our prior studies (17), GS-621763 therapy initiated at a similar time comparatively improved all metrics assessed. Thus, herein we provide proof-of-concept preclinical data that the orally bioavailable ester analog of RDV GS-621763 can exert potent antiviral effects in vivo during an ongoing SARS-CoV-2 infection. Lastly, we show that GS-621763 therapy provides similar levels of protection from SARS-CoV-2 pathogenesis as MPV, an oral nucleoside analog prodrug effective against SARS-CoV-2 in mice that is currently in human clinical trials. Future directions are focused on extending these studies to evaluate the efficacy of combinations of antivirals in our models of SARS-CoV-2 pathogenesis and in other models that can evaluate the blockade of transmission such as hamster and ferret. In summary, we provide preclinical data demonstrating ...

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About SciScore

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SciScore for 10.1101/2021.09.07.21262725: (What is this?)

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Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In this paper, the authors use a previously published method SHAP for interpreting deep learning (DL) models (specifically LSTMs) that are trained for predicting physicochemical attributes of peptides (such as antigenicity and collisional cross section). The paper shows that it's capable of identifying some amino acid residues contributing to the prediction results of the DL models. Reviewer #1 (Significance (Required)):

1. One main ideas of the paper is to use SHAP for determine the significant amino acids at each position (or pairs of AA at each position) contributing to the prediction. Some of the interpretation results are consistent with findings reported previously. This is very nice; however, most of these findings are statistical results such "XX is often present at the second position for the peptides with the positive outcome", which are relatively straightforward and may be derived by using some statistical methods without using DL models. We expect more complex patterns can be discovered in addition to these statistical observations.

We thank the reviewers for these comments.

First, to the point about discovering complex patterns, we note that one use of PoSHAP we discuss later in the paper is that PoSHAP enables interposition dependence analysis, which depends on interactions between residues and would not be reflected by summary statistics.

Second, we agree it is important to show whether PoSHAP produces different residue importance maps than simple statistical summaries of amino acids in each group. The strongest binding peptides, or the highest mobility for the CCS model, were determined by taking only peptides that fall above a linear regression best fit of the ranked experimental values. Statistical summary heatmaps were created and then compared to those from PoSHAP revealing some similarities but also many differences. We added the following text and new figure to the results section to illustrate these points:

“We wondered whether the patterns revealed by PoSHAP simply reflect the summary statistics for the high-binding or high-CCS subset of peptides. As expected, due to known differences in amino acid abundance across the proteome, the prevalence of amino acids was different across the training data and were also heterogeneous across positions (Figure 5A). To determine the subset of high CCS peptides, peptides were ordered in the training set by their CCS rank and then linear regression was performed to get the average trend line (Figure 5B). Any peptide above that trendline was defined as “high CCS”, and the frequency of amino acids at each position in this set was summarized using a heatmap (Figure 5C). Compared to the statistical amino acid frequencies, PoSHAP suggests a greater importance to arginine at both termini, the importance of tryptophan to increase CCS becomes apparent, and interior glutamic acid contributes less to high CCS than the frequencies would suggest (Figure 5D). The same analysis was repeated for MHC data (Supplementary Figures 9 and 10). This demonstrates that PoSHAP found non-linear relationships between the inputs and the outputs that are not present by simple correlation. “

Figure 5: Amino acid summary statistics differ from PoSHAP values for the CCS data. (A) Amino acid counts as a function of position for training data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis.

We also added the corresponding supplemental figures showing the same examples for the MAMU A001 model and human MHC models:

Supplemental Figure 9: Amino acid summary statistics differ from PoSHAP values for the A001 MAMU MHC I data. (A) Amino acid counts as a function of position for training data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis. For the MAMU model, the amino acid frequencies of the input peptides show no obvious preference for amino acid position, but some amino acids are over-represented overall. The presence of the “end” token is more likely to be a high binder statistically (C), but the PoSHAP reveals that this end token is not the main determinant of binding (D).

Supplemental Figure 10: Amino acid summary statistics differ from PoSHAP values for the human A1101 MHC I data. (A) Amino acid counts as a function of position for training data. The distribution of amino acids in this data. (B) Procedure for picking the ‘top peptides’ with the highest CCS. Linear regression was performed on the peptides ranked by their actual CCS value. Any peptide that fell above the trendline and overall mean were defined as ‘top peptides’. (C) Counts of amino acids for the top peptides were summarized in a heatmap. (D) Mean SHAP values across amino acids and positions from PoSHAP analysis. There are clear differences between the summary statistics of top peptides (C) and PoSHAP heatmap (D). For example, the end token is prominent in the summary statistics absent from the PoSHAP interpretation. Also, the preference for S/T/V at position two is tempered according to PoSHAP, but would be determined to be very important by the summary statistics.

Although the interpreting results reported in the paper largely agree with previous reports, the paper did not explicitly model the frequency of different amino acid in the training data. For instance, if the amino acid 'A' happens to be over-represented in the positive samples of peptides in the training data, the DL model may consider it as to contribute to the positive prediction, which may not be not true. This issue might become more serious when pairs of amino acids are considered. The authors may want to analyze this potential issue in their results.

We agree and understand the concern for the overrepresentation of amino acids that might skew the training of our models. To determine if this is an issue, as part of the response to the previous question, we looked at the amino acid counts for all peptides (Figure 5A, Supplemental Figures 9A and 10A). In general, the PoSHAP heatmaps (panel Ds in the same figures) look very different from the frequencies of amino acids (panel Cs in the figures), suggesting that amino acid frequencies have not caused any problem.

Even on a balanced training dataset, the LSTM model to be interpreted may still contain arbitrary bias due to invertible overfitting, which the authors did not discuss. It will be more convincing by training multiple models using different hyper-parameters and optimization algorithms, and then see if similar interpretation results can be reached among most or all of these models.

We assume the reviewer meant ‘inevitable overfitting’ instead of “invertible overfitting”? If so, the original manuscript did assess overfitting in Figure S4 based on the training and validation loss over training epochs.

We think the reviewer makes a good point that different models might produce different interpretations, so we trained new models without optimization and with different hyperparameters and with a different optimizer (RMS prop). We see essentially the same PoSHAP interpretations. We added the following text to the results section along with these three new supplemental figures:

“Given the dependence of the model interpretation results on the model used, the same model architecture trained with different parameters might result in different model interpretation. Given this, models for each of the three tasks mentioned here were retrained with different hyperparameters including the “RMS prop” optimizer. Each model produces similar or better prediction performance compared to the earlier version, and the model interpretation by PoSHAP was almost identical to the previous results in all three cases (Supplementary figures 12, 13, 14). This suggests that the model architecture drives the differences in interpretation, not the model training process.”

Supplemental Figure 12. PoSHAP Analysis of Mamu A001 With Unoptimized Hyperparameters and RMSprop. A new model for the Mamu data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of A001. (C) A dependence plot for A001 shows similar patterns to the Adam optimized model, including the positional dependence of proline at position two for high SHAP values of serine and threonine.

Supplemental Figure 13. PoSHAP Analysis of A:11*01 With Unoptimized Hyperparameters and RMSprop. A new model for the A:11*01 data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of A:11*01. (C) The SHAP ranges by position plot for A:11*01 shows similar patterns to the Adam optimized model, including the largest range of SHAP values at position two, nine, and ten.

Supplemental Figure 14. PoSHAP Analysis of CCS With Unoptimized Hyperparameters and RMSprop. A new model for the CCS data was trained using the same architectures but with different hyperparameters and RMSprop as the optimization algorithm. Loss was plotted as mean squared error compared to the validation data. (A) Similar metrics for MSE, r, and p-values were obtained (B). Similar patterns are also observed for the PoSHAP heatmap of CCS. (C) Dependence analysis was performed on the dataset and the combined distance-interaction type bar plot shows similar relationships between the groupings, notably charge repulsion’s split.

For the dependence analysis, it is not completely clear why the distance is used as the variable, while the relative position of the amino acid residue in the peptide is ignored. For example, if there is a strong interaction between the first and the last residues in the peptide, their distance changes depending on the peptide length. In figure 6, the authors showed strong interactions between amino acid that are 8-9 residues apart may suggest the peptide length actually plays a role here.

We used distance because as the dependence analysis is a calculation of the difference in means between two distributions of SHAP values, dependent of the amino acid at another position. We believe that the distance between these interacting points is a natural choice and among the most informative metrics to explain these interactions. We agree with the reviewer that peptide length is important to the magnitude of the interactions between amino acids. We also recognize that there may be interactions between the peptide termini that could be obscured by the interactions of the longer peptides. To better explore this possibility, we performed the dependence analysis on each of the different peptide lengths separately (8, 9, or 10 here) to see if this is the case. Unfortunately, given the smaller size of these data subsets, we were unable to show significant differences in the interaction groupings. Though, interestingly enough, the significant interactions for the peptides of length eight only occurred between neighboring amino acids or the termini. This may suggest an interaction between termini that could be explored in the future.

We added the following text and supplemental figure 11 to the results:

“Finally, to try to ask if the absolute positions of amino acids in the peptide are relevant for the interaction, the data was split into 8, 9, or 10mers before analysis (Supplemental Figure 11). This revealed that there may be interactions between the termini, but this effect may be difficult to observe because there are significantly fewer 8mers and 9mers in the CCS dataset.”

Supplemental Figure 11. SHAP Values of Collisional Cross Section by Peptide Length. The impact of peptide length on SHAP values was explored for the CCS data. The dataset was split into peptides of length 8, 9, and 10. All SHAP values were plotted as violin plots. The mean SHAP values were plotted in heatmaps by position and amino acid and standardized. Significant interactions by dependence analysis were plotted in bar charts by distance between interactions.

To further support our decision to use distance as an interaction metric, we have also now included an additional box plot for Figure 7, demonstrating the interactions between each of the categories combined with distance. We have found that some of the bimodality of the interaction categories are explained by the distance at which they interact. Most strikingly is charge repulsion that decreases CCS when neighboring but increases CCS when the interaction is further.

We added the following text and updated Figure 7 to the results section:

“Additionally, there are interesting differences in the interactions of the amino acid among the significant set of interactions (Figure 76B). All significant interactions from the CCS data (Supplemental Table 3, adj. p-value Though it is evident that the mean of each interaction type corresponds to the expected impact those interactions would have on CCS, each of the interaction dependence plots are bimodal, with some interactions increasing CCS and some decreasing it. To dissect this observation further, we combined the two methods of splitting the data to see if the bimodality of interaction types would be resolved by distance (Figure 7C). Though definitive conclusions cannot be made for most categories, likely due to the ever decreasing sample size by splitting, of note is the difference between neighboring charge repulsion and non-neighboring charge repulsion. Neighboring charge repulsion seems to decrease CCS while distant charge repulsion increases CCS (see adjusted p-value from Tukey’s posthoc test in Figure 7D). When distant, charge repulsion makes intuitive sense as the amino acids are forced apart, linearizing the peptide and increasing the surface area. When neighboring, it is possible that the repulsion causes a kink in the linear peptide, decreasing the cross section. Overall, these analyses demonstrate that the models were able to learn fundamental chemical properties of the amino acids and through PoSHAP analysis we were able to uncover them.”*

Figure 7. Dependence analysis of CCS model. (A) Significant (Bonferroni corr. P-value = charge repulsion, * = other, and δ = polar. For the distance analysis, interactions were grouped into three categories, neighboring (distance = 1), near (distance = 2, 3, 4, 5,6), and far (distance = 7, 8, 9). * indicates significance (ANOVA with Tukey’s post hoc test p-value

Also, it would be better to show that how the result looks like when applying this method to peptides in the negative samples (e.g., the peptides that are not bound by MHC in the antigenicity prediction experiment). Will the interpreting results also be negative?

We agree this is an interesting idea. We updated the supplemental figure showing PoSHAP of top peptide subsets to also show PoSHAP of bottom peptide subsets (supplemental figure 8). The results suggest that certain amino acid positions are detrimental to binding, for example D/E at various positions. We updated this section to add:

“We also performed the same analysis with the eight peptides with the lowest binding predictions (Supplemental Figure 8). These PoSHAP heatmaps are primarily composed of negative SHAP values, suggesting that using this subset reveals amino acids at certain positions that are detrimental to MHC binding.”

Supplemental Figure 8. Pooled PoSHAP for bottom and top predicted subsets of the data. The mean SHAP values for each amino acid at each position were calculated for the peptides with the bottom (A) or top (B) 0.013% predicted intensity (top 8 peptides) for the “A” Mamu alleles. Due to the small sample size, most of the amino acid positions have a value of zero. The positions with extreme values, however, illustrate important amino acids for prediction. Notably for A001 and A002, aspartic acid and glutamic acid contribute to low prediction along the peptide, suggesting charge may inhibit binding. For the top predictions, phenylalanine or leucine are important at the first position for both A001 and A008. A serine or threonine at position two is important for A001, A002, and A008. All alleles demonstrate the importance of a proline near the middle of the peptide.

Finally, it will be interesting to see the interpreting results when the method is applied to the DL models on more challenging tasks such as the prediction of tandem mass spectra of peptides. The authors may want to discuss these applications.

We agree it would be very interesting to apply this method to interpret predictions of tandem mass spectra. In this paper we already demonstrated PoSHAP on three different datasets with three different models, so we feel that adding a fourth model is out of the scope of this work. We do agree that we would like to explore this option in the future. We added this idea to the discussion section:

“Altogether the advances described herein are likely to find widespread use for interpreting models trained from biological sequences, including models not covered here such as those to predict tandem mass spectra (reviewed in 33).”

I am primarily interested in algorithmic and statistical problems in genomics and proteomics. We have develop deep learning models for predicting the full tandem mass spectrum of peptides, and am interested model interpretation methods to explain the fragmentation mechanism resulting in non-conventional fragment ions in tandem mass spectra of peptides. I review the paper in collaboration with my Ph.D students, who are developing deep learning models for computational mass spectrometry.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Comments to the Authors** In this study, the authors developed a framework named PoSHAP for the interpretation of neural networks trained on biological sequences. The current manuscript can be stronger if the following issues can be clearly addressed.

1. As interpreting model with SHAP is a vital part of this manuscript, it would be better to provide descriptions of the underlying principles of SHAP to enable the readers to understand the paper easily.

We recognize that understanding the principles of SHAP is vital. To better explain SHAP, we have added the following text to the introduction:

“SHAP is a perturbation-based explanation method where the contribution of an input is calculated by hiding that input and determining the effect on the output. SHAP expands this using the game theoretic approach of Shapely values that ensures the contributions of the inputs plus a calculated baseline sum to the predicted output.”

It is emphasized in the manuscript that PoSHAP is introduced to interpret neural networks trained on biological sequences. However, it is not clear why the authors choose the Model Agnostic Kernel SHAP, which is based on Linear LIME. Although it can be used for any model, the performance of which may not be optimal. In this regards, perhaps Deep SHAP or Gradient SHAP is more appropriate, both of which are designed for deep learning networks [1]. It would be better to provide some additional experiments on Deep SHAP and this work will be more convincing if the same or similar contribution of each position on each peptide as that of Kernel SHAP. [1] Lundberg, S., and S. I. Lee. "A Unified Approach to Interpreting Model Predictions." Nips 2017.

Our goal in using KernelExplainer was to demonstrate that PoSHAP was not dependent on model specific interpretation methods. However, we have realized that this intention may not have been clearly stated or demonstrated. To expand on this, we have included a new Figure 8, which shows PoSHAP analysis comparisons to other classes of machine learning models, all using Kernel Explainer. This result was interesting because it revealed that even though the XGboost model technically performed better at prediction (Figure 8A, reduced MSE and higher spearman rho), and produced a similar PoSHAP motif heatmap, the interpositional dependences from the perspective of distance (Figure 8C) or chemical interactions (Figure 8D) were substantially muted. This is also apparent with the other standard machine learning model ExtraTrees. This result shows that the choice of model architecture is important, and this direct comparison would not be possible if we used the DeepExplainer.

We added the following text and figure to the manuscript:

“ PoSHAP uses the SHAP KernelExplainer method, which is based on Local interpretable model-agnostic explanations (LIME). Using the general KernelExpplainer method enables direct comparison of interpretations produced by different models trained from the same data. To ask whether PoSHAP interpretation changes based on the model used, the CCS data was used to train XGboost or ExtraTrees models. Surprisingly, the XGboost model performed better than the LSTM model with regard to MSE and spearman rho between true and predicted values in the test set (Figure 8A). ExtraTrees was slightly worse than the other two models. The model interpretation heatmaps from PoSHAP were similar between the LSTM and XGboost, but the interpretation from the ExtraTrees model was missing the high average SHAP due to n-terminal histidine or arginine (Figure 8B). Even though XGboost produced a similar PoSHAP heatmap, the interpositional dependence with regard to distance (Figure 8C) and chemical interactions (Figure 8D) was muted. This shows that the choice of model is important for revealing amino acid interactions.”

Figure 8. CCS PoSHAP of Various Machine Learning Models. PoSHAP analysis was performed on two additional machine learning models, Extra Trees and Extreme Gradient Boosting (XGB). Predictions were plotted against experimental values and the Mean Squared Error and r values are reported for each model (A). PoSHAP heatmaps were created for each model (B), illustrating an increase in model complexity as more sophisticated models are used. Dependence analysis was performed on each model and the significant interactions are plotted by distance (C) and by combined distance and interaction type (D).

As described in the manuscript, "Correlations between true and predicted values were assessed by MSE, Spearman's rank correlation coefficient, and the correlation p-value." As an important indicator for evaluation, the exact p-values should be provided in the seven subgraphs in Figure 2, not p=0.0.

We agree with the reviewer that reporting accurate p-values can assist in evaluation. We have updated the figures to reflect the p-values as far as we were able to determine them. Unfortunately, we are limited by the nature of the double data type in python and so reported that the p-value was less than the minimum value allowed by a double in six of the seven graphs. Additionally, the scales have been marked symmetrically as you mentioned in comment 4.

It should be noted that the coordinate scales of Figure 2B and Figure 2C need to be marked symmetrically. And from Figure 2B, we can see that, the IC50 with smaller (0.8) values cannot be well predicted. Can the authors provide a detailed explanation about these results?

We understand the reviewer’s concern with poor prediction of extreme values. Figure B represents the IC50 prediction for the A1101 human allele which was the smallest of the datasets we used for training. It only consists of 4,522 entries, around 1/10 of the data used for the Mamu alleles and CCS. Because of this, it is likely that there were not enough examples of datapoints at the extremes to reliably train the model to account for them. However, given the limited size of the dataset, we were surprised with the satisfactory predictions. More importantly, the purpose of our paper is model interpretation not model prediction accuracy, and this shows that even when predictions are not perfect, the model interpretation by PoSHAP can still be effective. We thank the reviewer for noticing this and added the following statement to the results:

“Remarkably, this was achieved for A\11:01 using a total dataset of only 4,522 examples, which shows that PoSHAP can be effective with even less than 10,000 training examples. “*

References are needed in some descriptions in the manuscript. For example, "one might train a network to take an input of peptide sequence and predict chromatographic retention time", "RNNs have found extensive application to natural language processing, and by extension as a similar type of data, predictions from biological sequences such as peptides or nucleic acids".

We apologize for missing these references. We have now cited these statements and have added many additional references as part of our revision.

The description of the adopted three models in the section "Model architecture" is a bit confusing. As described in this section, "The LSTM layer outputs a 50x128 dimensional matrix to a dropout layer where a proportion of values are randomly set to 0", "a second LSTM layer outputs a tensor with length 128 and a second dropout layer then randomly sets a proportion of values to 0". But as shown in the Supplemental Figure 3, the output size of the first LSTM was 10x128. Also, as shown in Table 1, the dropout rates were not 0. Therefore, the section should be adjusted for clear clarification.

We apologize for the confusing wording. We meant that dropout layers randomly set values=0, not that the dropout proportion was 0. We reworded this part to read:

“The LSTM layer outputs a 10x128 dimensional matrix to a dropout layer where a proportion of values are randomly “dropped”, or set to 0. For the MHC models, a second LSTM layer outputs a tensor with length 128 to a second dropout layer. Then in all models, a dense layer reduces the data dimensionality to 64. For the MHC models, the data is then passed through a leaky rectified linear unit (LeakyReLU) activation before a final dropout layer, present in all models.”

Reviewer #2 (Significance (Required)): Pls refer to my comments provided as above.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** The main goal of the work is to provide the interpretation of Deep Neural Networks (LSTM in the paper) trained on biological sequences. For this purpose authors used the framework introduced earlier - SHapley Additive exPlanations (SHAP), in particular - the slight adaptation of this method called positional SHAP (PoSHAP), because they are interested in the impact of each position of the input sequence to the model output. They demonstrate this on three regression tasks that predict peptide properties. **Major comments** The main contribution, highlighted in the paper: authors showed how PoSHAP discloses amino acid motifs that influence MHC I binding. Further they described how PoSHAP enables understanding of interpositional dependence of amino acids that result in high affinity predictions. Also they argued that this work also contributes to a method for accurate prediction of peptide-MHC I affinity using peptide array data enabled by novel application of a neural network that combines amino acid embedding and LSTM layers.

There are some comments about the statements above: 1.Why was the LSTM model chosen? Recent publications showed the success of the Transformer model for biological sequences; however this direction was not covered in the related work overview. The architecture choice then should be better justified. Also the choice of LSTM for the biological sequences is not new and authors should better claim their statement about "novel application of a neural network that combines amino acid embedding and LSTM layers ". Where exactly is the novelty? Could the community use the pretrained embeddings for their purpose?

The reviewer is correct that transformer models are highly effective for making predictions from biological sequences. In fact, many models do well, and there is no single correct choice of model for this task. Though there are many models to choose from, our models are sufficiently accurate. Importantly, the main contribution of our manuscript is not to train the most accurate models, but rather to demonstrate a strategy for positional model interpretation based on SHAP. Related to that point, please note our response to reviewer #2’s second comment that our approach uses the kernel explainer and can be applied to any model. However, we do agree that we neglected coverage of the transformer model in the introduction and have added a paragraph to the introduction covering some of the recent work in this area:

“Many effective deep learning model architectures are available for making predictions from inputs of biological sequences, and there is currently no single correct choice. CNN models such as MHCflurry 2.0 (40) and LSTM models are effective at predicting MHC binding of peptides (41). Even simpler models, such as random forests, have been used to predict MHC binding (42,43). Prediction of other peptide properties like tandem mass spectra are often done with CNN or LSTM models (33). More recently, given the extraordinary performance of transformer models like BERT (44) and GPT-3 (45) for NLP, there is interest in transformer models for biological sequences (46).”

We also want to be sure we do not overstate the novelty of our contributions. We have updated our discussion to better reflect the nature of our contributions. We reworded the statement quoted above to read:

“Overall, the three modeling examples laid out herein serve as a tutorial for PoSHAP interpretation of almost any model trained from almost any biological sequence.”

The attention mechanism itself provides the great opportunity to interpret the model predictions. In the introduction section authors made a statement that attention layers may limit the flexibility of model architecture when designing new models. Could they better explain this limit? Because recent state of the art models successfully work with long biological sequences and show better results then any other models (one example could be found here: https://openreview.net/pdf?id=YWtLZvLmud7). Authors should cover these limits more, that also related to the motivation of the LSTM choice.

We added a paragraph to our introduction to expand on attention and its limitations:

“Attention mechanisms have been successful in recapitulating experimentally defined binding motifs, but require that the model be constructed with attention layers. This may limit the flexibility of model architecture when designing new models. For example, attention mechanisms are specific to neural networks. Simpler models, such as random forests and XGboost, may also be more suitable for some applications, and these cannot utilize attention. Also, while attention mechanisms are currently very effective, there is always a possibility that new architectures will emerge that make interpretations using attention infeasible. Beyond this, attention is a metric of the model itself, while SHAP values are calculated on a per input basis. By looking at the model through the lens of the inputs, we can understand the model’s “reasoning” behind any peptide. Attention mechanisms also do not enable dissection of interpositional dependencies between amino acids. Thus, new methods for model agnostic interpretation are desirable.”

Another statement was made about the PoSHAP - adaptation of the SHAP method. It is hard to follow through the explanation of this adaptation - it is not clear what exactly is this adaptation. For example, Kernel SHAP from the original paper computes feature importance, in this paper authors compute the impact of each position, that is basically also the feature importances. Thus authors should better explain the statement about PoSHAP novelty. Will it be possible to use PoSHAP for any other model trained for the same purpose? If yes, for better reproducibility, authors should provide the place where exactly in the repo is the code for this. Also mathematical notations are missing in the Positional SHAP (PoSHAP) section - it is better to explain the adaptation with them to increase the understanding of the section.

We apologize for the ambiguous wording in the abstract stating that “PoSHAP adapts SHAP”. We have reworded this statement to “PoSHAP utilizes SHAP”. The novelty of this approach is taking the feature importance values calculated by SHAP and structuring them to include each position’s index to allow for the interpretation of biological sequences. As we demonstrate here, this allows for novel interpretations of previously published data and will enable model interpretation in future studies that learn from biological sequences. Although this is practically very simple, we are not yet aware of any examples in the literature that do this.

The following two SHAP force plots demonstrate the difference between using SHAP as-is versus PoSHAP. There is a demonstrated need for such a framework, considering the dearth of biological sequence model interpretation using SHAP and the ambiguity within biological sequence SHAP interpretation. For example, Meier et al., Nature Communications, 2021 performed an analysis like our Figure S7C, which just shows the range of SHAP values per residue. Although we can learn something about which AAs are important based on the range of their SHAP values, SHAP as-is doesn’t reveal a motif. While our position indexing is a simple change, it enables all the rich, sequence dependent analysis we performed in this paper. We added the following text to the results section with this new supplementary figure:

“PoSHAP utilizes the standard SHAP package but adapts the analysis by simply appending an index to each input and maintaining positional information after the kernelExplainer interpretation, which enables tracking of each input postion’s contribution to an output prediction (supplementary figure 5, showing force plot with and without index).”

Supplemental Figure 5. SHAP Forceplots Demonstrating PoSHAP Indexing. Two forceplots were created with the SHAP forceplot method of the third peptide in the CCS testing set. (A) shows the plot with encoded inputs mapped to their amino acid. (B) shows the plot with the encoded inputs mapped to their amino acid and position. The addition of positional indexing removes the ambiguity of contributions, for example, glutamine having both a positive and a negative SHAP contribution to the prediction of the third peptide.

We have updated the repository to include a tutorial that demonstrates PoSHAP on provided data and explains how to use PoSHAP with your own model and data.

In the experimental section, authors first compare the results with previously known. For example, for the human MHC allele A*11:01 model PoSHAP analysis shows the similar results as was shown with another approach. Based on the provided explanation, it is not clear why PoSHAP is better than the previously published method. The advantage of the PoSHAP should be better explained.

We agree with the reviewer that the benefits of our approach should be as clear as possible. The referenced section of the paper is to validate our approach compared to another model interpretation technique. We added a new third paragraph to the discussion section to clearly explain the benefits of PoSHAP:

“There are several benefits of PoSHAP over competing methods. First, PoSHAP determines important residues despite biases in the frequencies of amino acids (Figure 5, Supplementary Figures 9 and 10). PoSHAP is also applicable to any model trained from sequential data (Figure 8), and enables dissection of interpositional dependencies (Figures 6 and 7). Finally, we include a clearly explained jupyter notebook on Github that will take any model and dataset and perform PoSHAP analysis.”

In the experimental section, after the PoSHAP performance verification, hypothesis generation was introduced. However, it is not clear how many hypotheses were generated; how many of them were known before; what kind of other categories are inside these hypotheses (unknown, possible and potentially interesting, etc).

We are unsure as to how to quantify the number of hypotheses generated by our approach. In a sense, the SHAP value of each amino acid at each position within a heatmap represents a hypothesis of the contribution of that amino acid to the metric being predicted. Each significant interaction listed in the first three supplemental tables represents a hypothesis of the interactions between two given amino acids at two positions. To make these into testable hypotheses requires some analysis, as we have discussed. i.e. the two binding motifs (L-T-P, F-S-P) of A001, or the distance-type interactions within the CCS.

The README section in the GitHub repo is not easily understandable. An additional explanation for each step is required (e.g., links to the folders where the calculated SHAP values, the trained models, all splits and all-important benchmarks are).

We have updated the README and repository to explain how to use PoSHAP, and explanations of each item in the repository.

**Minor comments**

1. The prior studies should be covered better (see Major comments).

We apologize for not better covering prior studies. We have significantly expanded the introduction by adding two new paragraphs and at least 10 additional citations.

The work consists of some typos, for example: "However, because many reports forgo model interpretation" - "t" is missed.

We did intend to use the word “forgo” not “forget” in that sentence. We have checked again thoroughly for spelling and grammar mistakes.

The hyperparameters table, hyperparameter search section should be moved to the supplemental material, that's technical details.

We moved this table to the supplementary materials.

Reviewer #3 (Significance (Required)): Interpretation of the model results is an important topic for biology. New findings here could lead to new interactions opening, new drugs development etc. That is relevant for the applied ML Researches and computational biologists. This paper aims to provide a way to do it. Because my field of interest and expertise lies in Machine Learning for healthcare, language modelling of biological sequences and Natural Language Processing, this work is of great interest to me. So I mostly evaluated ML methodology presented in the paper.

SciScore for 10.1101/2021.09.06.21262027: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

As alluded to in the introduction, one of the possible caveats of using a human cell line to express the S protein is that some blood samples will contain allo-reactive antibodies directed against that cell line, possibly as a consequence of a pregnancy, or past history of receiving a blood transfusion or organ transplant (Hickey et al. 2016; Karahan et al. 2020). The third column of Figure 1 shows examples of such samples containing marked levels of alloreactive antibodies, i.e. samples for which the J-R cells show significant levels of staining compared to the same cells labelled with just the secondary antibody. Based on our results collected on more than 350 clinical samples, we evaluate that ca. 30 % of samples will contain allo-reactive Abs that will result in levels of staining of Jurkat cells that are more than five-fold that of the signal obtained for the negative control (and 3-6 % more than ten-fold). Of note, we did not notice an increased frequency of allo-reactivity in samples from women compared to men, which suggests that allo-reactivity after pregnancy is not a major cause in the origin of those allo-reactions. A difficult question with all serological tests is that of where to set the threshold beyond which the specific signals detected can confidently be considered as positive, which will be directly linked to the balance between sensitivity and specificity of the assay. Based on the analyses of various cohorts of positive and negative samples (some of whic...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

https://www.youtube.com/watch?v=rhgwIhB58PA

Learning styles have been debunked.

Learning styles: V.A.R.K. model originated by Neil Flemiing stands for:

• visual
• auditory
• reading/writing
• kinesthetic

References:

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological science in the public interest, 9(3), 105-119. — https://ve42.co/Pashler2008

Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3), 266-271. — https://ve42.co/Willingham

Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style?. Learning and Individual Differences, 16(4), 321-335. — https://ve42.co/Massa2006

Riener, C., & Willingham, D. (2010). The myth of learning styles. Change: The magazine of higher learning, 42(5), 32-35.— https://ve42.co/Riener2010

Husmann, P. R., & O'Loughlin, V. D. (2019). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical sciences education, 12(1), 6-19. — https://ve42.co/Husmann2019

Snider, V. E., & Roehl, R. (2007). Teachers’ beliefs about pedagogy and related issues. Psychology in the Schools, 44, 873–886. doi:10.1002/pits.20272 — https://ve42.co/Snider2007

Fleming, N., & Baume, D. (2006). Learning Styles Again: VARKing up the right tree!. Educational developments, 7(4), 4. — https://ve42.co/Fleming2006

Rogowsky, B. A., Calhoun, B. M., & Tallal, P. (2015). Matching learning style to instructional method: Effects on comprehension. Journal of educational psychology, 107(1), 64. — https://ve42.co/Rogowskyetal

Coffield, Frank; Moseley, David; Hall, Elaine; Ecclestone, Kathryn (2004). — https://ve42.co/Coffield2004

Furey, W. (2020). THE STUBBORN MYTH OF LEARNING STYLES. Education Next, 20(3), 8-13. — https://ve42.co/Furey2020

Dunn, R., Beaudry, J. S., & Klavas, A. (2002). Survey of research on learning styles. California Journal of Science Education II (2). — https://ve42.co/Dunn2002

SciScore for 10.1101/2021.09.10.459749: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

This review reflects comments and contributions by Ricardo Carvalho, Omaya Dudin, Sónia Gomes Pereira, Samuel Lord, and Arthur Molines

The work by Lera-Ramirezet al. sheds light on the growth and sliding dynamics of microtubules during mitosis in pombe. The kymographs are beautiful and the figures are well laid-out. The data is generally convincing.

A comment on the overall organization of the paper. Figure 2 has a major location in the paper, but it seems that its main takeaway is that these MAPs aren't really involved in the main process this paper is probing. While these are important findings, it might be more satisfying to move some of the central results earlier.

A model schematic might drive home the main finding of the paper, and be particularly useful for readers who are not experts in microtubule or spindle dynamics. That said, the Discussion does an excellent job of summarizing the findings and explaining the takeaway message(s), even for the non-expert.

Specific comments

‘In higher eukaryotes’ - Suggest avoiding the terms higher and lower when describing organisms, and instead, directly defining which organisms, for instance in animals/metazoans that would be a better description.

Figure 1 E-F - It is hard to see the difference in the distribution, maybe a different color could be used instead of stars.

Figure 1 - Data shown in pink in G comes from 832 midzone length measurements during anaphase, from 60 cells in 10 independent experiments - The pink here does not correspond to the pink coding in D, consider colour choice for clarity across panels.

‘Finally, yeasts undergo closed mitosis’ - How does this relate to the findings in the Dey paper (cited here) which shows it was somewhat semi-closed or semi-open. According to the Dey paper, the membrane disassembles locally twice, at the SPB and the bridge.

Figure 1C - vertical comets in kymographs (Fig. 1C) do not correspond to non-growing microtubules, but rather microtubules that grow at a speed matching the sliding speed’ - For clarity, it might be nice to add: "(as the SPB moves away from the plus end in the kymograph)".

‘significantly shorter than in interphase, where growth events last more than 120 seconds on average [42,43]. Microtubule shrinking speed did not change during anaphase either (Fig. 1-Supplement 1D), and was on average 3.56±1.75 μm/min, also lower than in interphase (~8 min/μm)’ - This comment concerns the comparison of growth and shrinking rate as well as growth duration. The authors did not measure microtubule dynamics in interphase in this manuscript but compared their numbers to literature values. The comparison raises some questions for three reasons: 1) the microscopy method used is different in this paper and the two references provided, 2) the sample is mounted differently compared to the two references provided - 1) and 2) combined could lead to different levels of stress on the cells which could affect MT dynamics-, 3) (probably the most important caveat) the experiments are done at different temperatures: 27C in this paper versus 25C in the references provided. Microtubule dynamics are sensitive to temperature so this could explain part of the differences observed. Also, there are multiple values published for MT dynamics in interphase depending on the strain used and the microscopy method used. Suggest that the authors measure microtubule dynamics in interphase cells at 27C in SIM to ensure that the differences are not due to the technical parameters employed.

Small item - should ‘8 min/μm’ read “8 μm/min"?

‘we observed two populations of microtubules (fast and slow growing)’ - Does this statement about thistle fast and slow growing populations refer to the data in Fig. 1C and 2A?

‘In some cells, all microtubules seemed to switch to the slow growing phase simultaneously (Fig. 1C), while in others fast and slow growing microtubules co-existed (Fig. 2A)’ - This is a very interesting observation, could we know how many cells (%) were detected in each case? Is it that in 90% of the cells the switch is simultaneous, and hence the microtubule growth is somehow synchronized? Or is it more random, e.g. around 50%?

‘On such a plot, the data points visibly cluster in two separate clouds and the variation of growth speeds can be fitted by an error function (Fig. 1F)’ - It is unclear that there are two distinct clusters, maybe the assertion should be toned down, or some sort of cluster analysis provided.

‘speed of interphase microtubules (~2.3 μm/min)’ - It would be interesting to see the dynamics in a les1 mutant (Dey Nature 2020) paper. Just as a control for presence/absence of the bridge?

‘Figure 2, Transition from fast to slow microtubule growth occurs in the absence of known anaphase MAPs’ - It looks like the overlap zone is larger on the mal3 kymograph. Is the size of the midzone changed in some of the mutants? It could be important to report. Related to it, is the spindle length changed in some of the mutants? (It does not look like it from the kymographs displayed).

Additionally, adding the data about rescue localization in the mutant (equivalent of Fig 1 G) would be interesting to better describe the role of these different proteins.

Figure 2, Panel G to L - Could the authors indicate the value for the average +/- error in each bin for the WT and the mutants? Also, it is hard to say from the plots, but it looks like the WT average speed in the first bin is different in every panel, that would be good to know to have an idea of the reproducibility/variability.

The dots making up the "thick lines" are centered on 1.5/2.5/etc.. in some panels (G and K) and centered on 1/2/3/etc.. the others (I,J,L). Could the authors provide some clarification?

Figure 3 - Can the authors indicate the average values +/- error for each of the distributions in Fig. 3D? Maybe on the plot itself, in the legend or as a table. This would make them easily available without having to infer them from the Y axis. This comment is also valid for Fig 4I and 4J.

Figure 3E ‘Distance from the plus-end to the nuclear membrane bridge edge at rescue as a function of distance from the plus-end to the closest pole at rescue’ - The Y axis reads as "distance to the bridge edge" but it shows negative values, could this be "position to the bridge edge" instead? (same item throughout the text).

Figure 3 ‘Number of events: 442 (30 cells) wt, 260 (27 cells) klp9OE, 401 (35 cells) cdc25-22, from 3 independent experiments’ - P values this small raise a concern.

Presumably the number of degrees of freedom in the regression analysis should not exceed the number of independent experiments. Instead, the DoF listed under "error" in the analysis output is hundreds or thousands instead of 3. To address this, the regression analysis should use either the "Error" function in R or a linear mixed-effects model to account for the nesting of the repeated measurements within each independent experiment. Alternatively, it is also possible to just calculate summary means for each independent experiment, and calculate p values based on that N=3.

See: Lazic. Experimental Design for Laboratory Biologists. p. 157.

and the supplemental file of:https://doi.org/10.1371/journal.pbio.2005282

and the additional file 1 of:https://doi.org/10.1186/s12868-015-0228-5

and this for an alternative plotting approach:https://doi.org/10.1083/jcb.202001064

Recommend either recalculating the p values by one of the methods above or removing the reported p values from the paper. The large effects observed in many cases are self-evident without a significance metric, so eliminating the p values would be acceptable here.

(This comment applies to other figures through the paper that report p values based on number of cells or number of measurements instead of number of independent samples/experiments.)

Figure 4 - Nice experiment. It brings the question of how cell-shape affects all these dynamics (probably out of the scope of this work). But a for3 mutant for example?

‘Ase1 is required for microtubule growth speed to decrease during anaphase B, this is unlikely to be a direct effect’ - If it is unlikely to be a direct Ase1 effect is the title of the section accurate? "Ase1 is required for normal rescue distribution and for microtubule growth speed to decrease in anaphase B"

Figure 5 - What about an ase1 lem1 double mutant?

‘In summary, Ase1 is required for rescue organisation and for microtubule growth speed to decrease during anaphase B’- In this context it could make sense to discuss the observations from this paper (doi:10.1371/journal.pone.0056808) about the role of Ase1 ortholog's MAP65-1 in coordinating MT dynamics within bundles.

‘We initially set the microtubule growth velocity to 1.6 μm/min (early anaphase speed, Fig. 1F), and aimed to reproduce the experimental distribution of positions of rescue and catastrophe at early anaphase (spindle length < 6 μm’ - Kudos to the authors for detailing the model and its parameters in a way that even non-modelling experts can understand.

Discussion - ‘Our data suggests that microtubule growth speed is mainly governed by spatial cues’- Is it right to assume that in the cases where fast and slow growing microtubules were simultaneously observed, the fast microtubules were not/had not yet reached the midzone?

Methods - ‘PIFOC module (perfect image focus), and sCMOS camera’ - Is this Nikon's "Perfect Focus" autofocus, or some other manufacturer's system? And back-thinned sCMOS.

SciScore for 10.1101/2021.09.10.459744: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Limitations of this study: Our study used two different cell types and should be expanded using additional human cell types and also organoids. Moreover, to indeed make a claim on universality, additional variants should (and can) be tested using affinity/avidity measurements and neutralization assays.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04335136</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recombinant Human Angiotensin-converting Enzyme 2 (rhACE2) a…</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.09.459634: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.09.459664: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04750343</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Safety and Immunogenicity Study of SARS-CoV-2 Nanoparticle V…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04742738</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Safety and Immunogenicity Study of SARS-CoV-2 Nanoparticle V…</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.09.459577: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

One limitation of this study is that we could not evaluate whether the presence and abundance of the CoV2-miR-7a.2 correlates with disease progression or with impaired ISGs activation in patients with severe COVID-19 disease outcome. Thus, future studies will address the relevance of the CoV2-miR-7a in the progression of the COVID-19 disease. Furthermore, the recent evolution of the CoV2-miR-7a sequence in the SARS-CoV-2 genome may facilitate the development of specific therapeutic approaches to potentially target and dampen the virulence of SARS-CoV-2 infection in COVID-19 patients.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.01.21262969: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 60. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.08.459430: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Despite these limitations, the utilization of a large set of high-parameter techniques in one study to generate a time-resolved, multi-site profile of virus-induced innate and adaptive immune responses, has resulted in a comprehensive characterization of SARS-CoV-2 infection in the rhesus macaque model. Importantly, our findings highlight essential differences and similarities in the immune response to SARS-CoV-2 infection between rhesus macaques and humans that may be affected by age. As such, our study provides new insights into the age-related immune dynamics of SARS-CoV-2 infection and represents a substantial advance in available models of age-associated changes in immunity.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.08.459398: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.08.459408: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 72, 73, 74, 76 and 77. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.07.459123: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.01.21262715: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

There are limitations to the current study. Firstly, ETA samples were only collected from patients with severe disease requiring invasive oxygen support, therefore, it is unclear whether COVID-19 patients with milder symptoms had less robust immune responses in the respiratory tract. Additionally, most patients in the severe/critical group received dexamethasone, which could be an intercorrelating factor for the differences observed between severity groups. Moreover, while the non-COVID-19 controls provided insights onto the immune status in hospitalized individuals, the comparisons will benefit more if there were larger numbers of non-COVID patients with more homogenous diseases. Overall, innate and adaptive immune responses are generated in respiratory and blood samples of COVID-19 patients. While immunological features detected in the peripheral blood can be associated with robust immune responses and predict clinical outcomes, monitoring immune responses in the respiratory samples can be of a benefit prior to initiation of therapeutic interventions for COVID-19 patients.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.06.459206: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.02.21262995: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Another limitation of the GISST model, as we have presented it here, may be that it is missing important covariates in the regression model for the transmission rate (Eq 4). For example, we have not tried using other mobility metrics provided in the Google Community mobility reports. On the one hand, predicting future values of such covariates could be just as challenging as predicting future values of COVID-19 indicators without them. One might address this problem to some extent by applying a penalty proportional to the absolute value of the regression coefficient of these covariates, which would shrink the effect of unimportant covariates to zero. This approach would be readily achievable with the optimizer we used by choosing the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) algorithm instead of the LBFGS algorithm. The use of this option is what we alluded to in the introduction as the ability of GISST to search for important variables from a large space of candidate variables. Variables identified in this manner may sufficiently lead the indicators we wish to forecast that they improve forecasts just by being available up to or close to the date of forecast. Knowing which variables are important for predicting the transmission rate, even if they are difficult to forecast, could also be valuable for the design of forecasts.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Culture does not exist and cannot exist within a vacuum, so it would logically follow that the study of culture by extension cannot be studied apolitcally. To study Germany's culture (or any culture for that matter) one will inevitably discuss subject material that is political, as everything is political by nature of it existing. The only factor or question is how poltical is the subject matter? Or in other words, how heated is the debate?

too mathy

not exactly sure how a bunch of pages were cut short in "production version" i think I have to go through them all manually one by one. here's the markdown missing from this page:

I am accepting charitable donations,.\ ETH: 0x66e2871ef39334962fb75ce34407f825d67ec434 | BTC: 38B6vGaqNvMyTtoFEZPmNvMS7icV6ZnPMm | xDAI: 0x66e2871ef39334962fb75ce34407f825d67ec434

"ceruelad."

As a really brief aside prior to the introduction, the intersection of "Joseph's dream" and the stories of Atlantis and the Lost City of Gold ... among a great many other "municipal dreams" has long been (known or?) the primary motivation and drive behind spending so much time and effort on making this dream a reality. It also intersects a number of modern idioms, things like "all roads read to Rome" and "Rome wasn't built in a day" -- namely because I like to recall and recount how this vision of a city in the sky was truly re-written in a single day (which is why I haven't re-done it again or elaborated more on the things that ... I'm about to actually ... do (just to link further to El Dorado)).

It was written though ... to describe a place that could literally be built in a single day--using various tricks like "copying from our reality" the base framework of the island, and then using "computer science magic" ... here hidden in a place I call the Artificial Intelligence Samilicosm, little "tricks" that enable a single "interface class" to create a network of "island nodes" that would enable everyone Earth(s) to visit the attraction at exactly the same time, with concepts like K-nearest-nieghbor (k-NN) to create rooms that house millions or billions to appear to only have a small group of friends and family in them. That concept bleeds down into the boardroom concept, to help create a venue for a global-glactic conversation (GG-c?) on how the power of computer science can quickly show us how assimilation of this "demonstration" will quickly lead to a world without war and famine (and eventually absolution) by showing us how every war and argument fought over absolutely-falsely-scarce-resources have created dissension and conflict in a playce where the truth could build a much happier and healthier "venue" for interaction.

Though there are two primary pieces that connect in different ways to what I call the "hardware and software of the road" something that links to the words "sword" and "Asgard" ... among other perfect words like "hard drive." One is "OMEALFHT" and the other is the "Rod(s) of Hey-Seuss the Anti-King." Since this is still a sort of "decoder ring" for the hidden language, the link here to "hardware and software" and the letter "X" which connects the Xbox and "kisxmet" is that "cross-storm" (in T we're Macy's ... "intimacys") in a letter is also described by "gtk+" where you can see the "l" glyph of Brickell (off to see the Wizard) with half a X in ">lt" form. Since I probably haven't put it on the main WS ... Penrose sort of connects to the L's of Hell where it's now almost uncontastuble that the word Obelisk is pointing to a road aiming for the sky, as it is written, by the pen.

While it's probably no secret that those "ll"'s aren't the only version of paired L-words; it might be less known that I'm fairly certain "love and logic" are actually the best fit for why ... combining them ... keys the llave of Kurt Cobain's "hey, way" ... to open the gates of Hell and finally escape it. It's in special places like the "light of the Son's of Liberty" connecting both racism and sexism to name that ties together to show us that over time, our special history ended ... specifically slavery ... first by love--knowing it was the right thing to do, well before "logically" the jobs of picking cotton for instance would be replaced by gin. Though here we are again staring at "XIV's" c (see arxiv.org as "kiss ar hive") ... hive's hiding the fact that technology has well before now replaced the need for the kind of slavery we fail to see prevalent here in this place, a darkness caused by ... the same technology's' hidden use and keeping us from making very important connections between the illustrated teaching we call history and our present predicament.

\ NO DREAM, THE SECOND COMING WILL END RACISM, A VOW

Kiss me I'm fiVel; it's the same kind of darkness that caused some bright kids I met in California to tell me point blank there was no "dick" hidden in John Hancock's name or in Tricky Dick's or ... also in the Constitution and that sort of behavior is literally the cause of a slavery to lies and to watching Woodward and Burnstein's lack of fire extinguish our freedom to think clearly, to vote with clear thoughts, and in poignant and direct relation ... freedom of the press. We stand here refusing to see that our lack of action and seeming inability to discuss the "ridiculousness" of not seeing this information and my name on the news ins responsible for not seeing very clear evidence of mind control technology also on the news, and in our government's legislation and that's the cause of the slavery in the first place.

"Theyanthem" was a happy and bright reVelatory "so ... viandname" for me when I first landed on a sort or Elishan compound word describing the relationship between two anonymous "us's" or "we's" that played an early role in my introduction to the "red ties" of Gang-stalking and while Yusuf Islam's key clue of nomenclature probably only told a few people that Pine Crest school and the University of Florida had yet another very clear tie to vithename Adam and how songs play an integral role in keying and linking and intersexling the m essage of the Revelation of Jesus Christ with American History kisxes ... it was this new intersection that reaffirmed that link to the point where I see it needs to be made even more clear.

\ \\ I've noted before that it was very clear to me that the "oceans white with styrofoam" and the "good crowned with brotherhood" were very clear references to racism--and it takes not a logician to see that the simple Christian tenets that surround the foundation of America would tell you they were also a "thinly veited" admonishment of the same; it's through "logic" and advanced technology that we can be sure that the Second Coming and it's connection to pervasive "eyes to see" and "computer assisted intelligence" will almost immediately destroy the absolute stupidity of racism, jingoism, and the like. Pine Crest's school song--which is literally the only other "anthem" that I know of, though I'm sure m-any nations' will be added to the group of songs that are hallowed centuries and millennium into the future from this place that is the origen of ... something special ... an intergalactic network of races supporting goodness and morality. It's single related phrase "like our towers so tall and white" (in tempo, even, highlighting the strangeness of the addition of color) preluding the very clear reference and explanation to "how" ... our minds expand and ideas take flight. As an aside, it's the words "as the years go by we'll love you more" that I now see as a sort of ... appromise specifically to me, in this place where I feel hated, for all the wrong reasons.

The proof is "in the pudding' of course, but it's been so damned long already that I don't see the world changing by "intro-duction of floating city in sky" or by "magical neuro-nalpmi-napatms" without at least having a story break and some actual public and recorded discussions--even if that's really what I want, to see thing happen "swiftly" and without the possibility of it being "every day has it's day of being forgotten"--by everyone but songs.\ \

It's still ery clear, tho, that you don't understand the message, and the quasi-veiled-doublespeak-response from ... personas-non-识别 ... that what's happening all around us and without our input is very clearly tied to this same lack of acknowledgement that a working society has a "working press" and that failing to see this message on TV is undeniable proof that there isn't a single working government on the planeth Eart... and that should tell us that "the skies" too ... are staring down at us in sham.

It should be abundantly clear that even with the knowledge and even the very technology of "how to build Heaven" in hand ... you risk carelessly and without regard for the future the possibility that we will be finding ourselves in multiple-simultaneous-hells rather than any Heaven at all until we rectify the darkness surround "communication" and "government" that is so pronounced and obvious here--you should see it as a very clear lesson ... one that you should also see you have not learned.

\ \

So we're staring at the focal point of the intersection of a message that certainly wasn't written in a day, but is read as thousands of years of history--probably a very large "underestimate" of what it actually is based on and took to put together. Here we have ... "lore come alive" to show us the "nard" ... the salting of the road to Heaven is "explained" as a sky literally drugging me ... around the time of the dissemination of a message that probably would have "gone viral" and made the news if it wasn't for that ... "salting." It's really difficult to say today if that "salt" is warming a road or "preserving a message" as we see ... nearly ubiquitously that salt does good things for ... the dead ... and it's somewhat toxic to the living to the penultimate "wife of Lot" -- clear as day to me today that's just like this message. The story of "Casper pointing to a gate ... as a message all around us" is a godsend in Heaven and for "an Earth in Heaven" and without doubt poison were we ever "stuck" or "printed" or ... trashed into reality for no reason. Just like the "love and logic" of the end letters of Hell ... it was very clear early on that printing the Earth was not the point, but a sort of honeypot trap--and as the "hardware description" came to light much later it wasn't just because we'd be pissed about the message, but because it's simply the wrong path given the current state of "hidden technology." Hidden, I'm telling you, be'cause your silence and our lack of acti onon) here threatens civilization.

So it's the "nard" of my grandfather's name Bernard, of John 12:1 and of John Maynard Keynes that connect the "NES" and "salt" to the clear intersection of "Tea Parties" and Na-po-leon Bon-to-part-e with "taxation is the ft of our Christ" and "no taxation without representation" to ... Render to Caesar ..." why it is that "no representation" and "no free thought" are linked at the hip to "no free speech" and "no free press" and you not picking up the phone and calling a reporter. It's linked to "IRS" in the heart of "FIRST" as in ... the first to make the connection between "taxes" and an overabundance of food and "natural" resources ... brought to light by nothing more than acknowledging that this message is "important enough" to allow the world to actually progress instead of stagnating.

Below you can see that I'm putting together the pieces of software necessary to build a prototype "Sworpen of Caesarthor" ... (hear: see's Arthur) and I'm going to ahead and do it presumably all alone because nobody's (very few, anyway) "coming out of the woodwork to help build a platform that will end forever the power of any government to censor a message ... of this import (and obviously less important ... messages also). I need your help ... not being angry at the world for staring at me in stupified ignorance. rather than seeing (and building something with...) the light.

 On April 23rd, 2018, a curious transaction appeared on the Ethereum blockchain. An anonymous activist sent 0 ETH to themselves, but the transaction contained many extra bytes beyond the ones used to complete the transaction. These extra bytes were the text of a letter written by Yue Xin, a student at Peking University, detailing a pattern of intimidation and threats made against her by the school in response to her attempts to investigate claims of sexual assault made against a professor (you can read the full letter by clicking "view input as" and selecting utf-8 on the etherscan page). She had initially posted the letter on the social media site WeChat, where it was widely shared before censors began to purge all copies from the platform. Chinese censors have consistently targeted the #MeToo movement, forcing whistleblowers to find creative means of sharing their stories like esoteric hashtags such as #RiceBunny, or 米兔, which is pronounced similarly to "me too."

By using Ethereum, activists have found a new and unique avenue to disseminate information and resist censorship for Xin's letter. Because every computer running a full Ethereum node has the complete transaction history, Yue Xin's letter is replicated across thousands of independent computers. These computers are all controlled by individuals and organizations without any centralized oversight or shared government, making it virtually impossible to remove the letter's content from the network. The same technique was used again in late July to protect a censored story about corruption and negligence at a Chinese vaccine manufacturer.

[

](https://ipfs.io/ipns/fromthemachine.org/blog.joincivil.com/uncensored-content-on-ethereum-how-chinese-activists-inspired-civil-f09f095a9e91)

P.S. .... the preceDING world changing message is filled with gibberish.  Understand, "gibberish" is going to change the world.

Unless otherwise indicated, this work was written between the Christmas and Easter seasons of 2017 and 2020(A). The content of this page is released to the public under the GNU GPL v2.0 license; additionally any reproduction or derivation of the work must be attributed to the author, Adam Marshall Dobrin along with a link back to this website, fromthemachine dotty org.

That's a "." not "dotty" ... it's to stop SPAMmers. :/

This document is "living" and I don't just mean in the Jeffersonian sense. It's more alive in the "Mayflower's and June Doors ..." living Ethereum contract sense and literally just as close to the Depp/C[aster/Paglen (and honorably PK] 'D-hath Transundance**sense of the ... new meaning; as it is now published on Rinkeby, in "living contract" form. It is subject to change; without notice anywhere but here--and there--in the original spirit of the GPL 2.0. We are "one step closer to God" ... and do see that in that I mean ... it is a very real fusion of this document and the "spirit of my life" as well as the Spirit's of Kerouac's America and Vonnegut's Martian Mars and my Venutian Hotel ... and my fusion of Guy-A and GAIA; and the Spirit of the Earth .. and of course the God given and signed liberties in the Constitution of the United States of America. It is by and through my hand that this document and our X Commandments link to the Bill or Rights, and this story about an Exodus from slavery that literally begins here, in the post-apocalyptic American hartland. Written ... this day ... April 14, 2020 (hey, is this HADAD DAY?) ... in Margate FL, USA. For "official used-to-v TAX day" tomorrow, I'm going to add the "immultible incarnite pen" ... if added to the living "doc/app"--see is the DAO, the way--will initi8 the special secret "hidden level" .. we've all been looking for.

Nor do just mean this website or the totality of my written works; nor do I only mean ... this particular derivation of the GPL 2.0+ modifications I continually source ... must be "from this website." I also mean the thing that is built from ... bits and piece of blocks of sand-toys; from Ethereum and from Rust and from our hands and eyes working together ... from this place, this cornerstone of the message that is ... written from brick and mortar words and events and people that have come before this poit of the "sealed W" that is this specific page and this time. It's 3:28; just five minutes--or is it four, too layne.

This work is not to be redistributed according to the GPL unless all linked media on Youtube and related sites are intact--and historical references to the actual documented history of the art pieces (as I experience/d them) are also available for linking. Wikipedia references must be available for viewing, as well as the exact version of those pages at the time these pieces were written. All references to the Holy Bible must be "linked" (as they are or via ... impromptu in-transit re-linking) to the exact verses and versions of the Bible that I reference. These requirements, as well as the caveat and informational re-introduction to God's DAO above ... should be seen as material modifications to the original GPL2.0 that are retroactively applied to all works distributed under license via this site and all previous e-mails and sites. /s/ wso\ If you wanna talk to me get me on facebook, with PGP via FlowCrypt or adam at from the machine dotty org

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SciScore for 10.1101/2021.09.02.458667: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.03.458735: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.02.458740: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

A limitation of this study is the use of chimeric viruses rather than the use of clinically approved vaccine platforms for expressing variant spikes or clinical variant isolates for the challenge. The neutralizing profile elicited by chimeric viruses may differ from that elicited by the clinically approved vaccine platforms. In chimeric virus-immunized hamsters, immune responses to non-spike viral proteins may provide added protection when compared with animals immunized with spike-alone vaccines such as mRNA and adenovirus-expression platforms. Despite this limitation, it is conceivable that the relative rank of neutralizing levels would be preserved against different SARS-CoV-2 variants. In summary, increasing global immunization with the currently available safe and effective vaccines, together with boosters when needed, is the strategy to end the COVID-19 pandemic. The design of the booster vaccines depends on whether the newly emerged variants can escape the immunity generated by the current vaccines or natural infections. Potential immune escape of any new variants should be closely monitored by laboratory studies and real-world breakthroughs in vaccinated and infected individuals.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.01.458653: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Despite the promises demonstrated in this study, one limitation is that only pseudovirus-containing culture media were used to characterize the fusion proteins for proof-of-concept. Therefore, it is necessary to further evaluate our approach in real specimens such as blood from COVID-19 patients in the near future.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 32. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.09.01.458544: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Kraemer, M. U. G., Hill, V., Ruis, C., Dellicour, S., Bajaj, S., McCrone, J. T., Baele, G., Parag, K. V., Battle, A. L., Gutierrez, B., Jackson, B., Colquhoun, R., O’Toole, Á., Klein, B., Vespignani, A., COVID-19 Genomics UK (COG-UK) Consortium‡, Volz, E., Faria, N. R., Aanensen, D. M., … Pybus, O. G. (2021). Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science, 373(6557), 889–895. https://doi.org/10.1126/science.abj0113

SciScore for 10.1101/2021.08.20.21261687: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

This just shows that different enviorments either help or doesn't help for a students own academics and be more akined to what helps a child during their development.

This is a great last say to the entire article. The whole point of the article was to show the importance of technological clubhouses in communities that don't have the same opportunities as other privileged communities.

Luis's skills were not seen as valuable through the school curriculum. Digital work is the future and his skills were not being utilized properly and wasnt given that same support from the school as the clubhouse.

Okay lets be real here, the idea that Luis is included in field trips to the game design company Electronic Arts is pretty cool.

This can also be an issue with the education system itself. He needed this sort of development, but school wasn't giving it to him

This is actually a great opportunity to get when your young, even college students struggle to get this opportunity

The ever-increasing expansion of technology is leading to newer ways to collaborate with other students, which allows them not only to work with other classmates, but potentially students in different counties, states, or countries.

I knew this point would come up!