1. Aug 2024
    1. AB_397880

      DOI: 10.1002/cne.23683

      Resource: (BD Biosciences Cat# 610518, RRID:AB_397880)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_397880


      What is this?

    2. AB_528264

      DOI: 10.1002/cne.23683

      Resource: (DSHB Cat# GAD-6, RRID:AB_528264)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_528264


      What is this?

    3. AB_2314563

      DOI: 10.1002/cne.23683

      Resource: (Tocris Bioscience Cat# 2063, RRID:AB_2314563)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2314563


      What is this?

    4. AB_2079751

      DOI: 10.1002/cne.23683

      Resource: (Millipore Cat# AB144P, RRID:AB_2079751)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2079751


      What is this?

    5. AB_10000340

      DOI: 10.1002/cne.23683

      Resource: (Swant Cat# CB 38, RRID:AB_10000340)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10000340


      What is this?

    6. AB_398225

      DOI: 10.1002/cne.23683

      Resource: (BD Biosciences Cat# 610908, RRID:AB_398225)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_398225


      What is this?

    1. AB_2171328

      DOI: 10.1002/cne.23672

      Resource: (Santa Cruz Biotechnology Cat# sc-7604, RRID:AB_2171328)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_2171328


      What is this?

    2. AB_477257

      DOI: 10.1002/cne.23672

      Resource: (Sigma-Aldrich Cat# N0142, RRID:AB_477257)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_477257


      What is this?

    3. AB_2314660

      DOI: 10.1002/cne.23672

      Resource: (Vector Laboratories Cat# AS-2104, RRID:AB_2314660)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_2314660


      What is this?

    4. AB_572217

      DOI: 10.1002/cne.23672

      Resource: (ImmunoStar Cat# 24112, RRID:AB_572217)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_572217


      What is this?

    5. AB_572266

      DOI: 10.1002/cne.23672

      Resource: (ImmunoStar Cat# 20064, RRID:AB_572266)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_572266


      What is this?

    6. nif-0000-30467

      DOI: 10.1002/cne.23672

      Resource: ImageJ (RRID:SCR_003070)

      Curator: @gabimpine

      SciCrunch record: RRID:SCR_003070


      What is this?

    7. RGD_1566440

      DOI: 10.1002/cne.23672

      Resource: (RGD Cat# 1566440,RRID:RGD_1566440)

      Curator: @gabimpine

      SciCrunch record: RRID:RGD_1566440


      What is this?

    1. AB_2224402

      DOI: 10.1002/cne.23667

      Resource: (Abcam Cat# ab5076, RRID:AB_2224402)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2224402


      What is this?

    2. AB_2114471

      DOI: 10.1002/cne.23667

      Resource: (Agilent Cat# M0823, RRID:AB_2114471)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2114471


      What is this?

    3. AB_2157554

      DOI: 10.1002/cne.23667

      Resource: (R and D Systems Cat# AF2418, RRID:AB_2157554)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2157554


      What is this?

    4. AB_887872

      DOI: 10.1002/cne.23667

      Resource: (Synaptic Systems Cat# 131 011, RRID:AB_887872)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_887872


      What is this?

    5. AB_10123643

      DOI: 10.1002/cne.23667

      Resource: AB_10123643

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10123643


      What is this?

    6. AB_477010

      DOI: 10.1002/cne.23667

      Resource: (Sigma-Aldrich Cat# G3893, RRID:AB_477010)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_477010


      What is this?

    7. AB_10000343

      DOI: 10.1002/cne.23667

      Resource: (Swant Cat# 235, RRID:AB_10000343)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10000343


      What is this?

    8. AB_10000342

      DOI: 10.1002/cne.23667

      Resource: (Swant Cat# CG1, RRID:AB_10000342)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10000342


      What is this?

    9. AB_10000347

      DOI: 10.1002/cne.23667

      Resource: (Swant Cat# 300, RRID:AB_10000347)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10000347


      What is this?

    1. AB_10563566

      DOI: 10.1002/cne.23668

      Resource: (Thermo Fisher Scientific Cat# A-11036, RRID:AB_10563566)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10563566


      What is this?

    2. AB_10562368

      DOI: 10.1002/cne.23668

      Resource: AB_10562368

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10562368


      What is this?

    3. AB_10373124

      DOI: 10.1002/cne.23668

      Resource: (Thermo Fisher Scientific Cat# A-31565, RRID:AB_2536178)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10373124


      What is this?

    4. AB_2307442

      DOI: 10.1002/cne.23668

      Resource: (Phoenix Pharmaceuticals Cat# H-029-30, RRID:AB_2307442)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2307442


      What is this?

    5. AB_2298772

      DOI: 10.1002/cne.23668

      Resource: (Millipore Cat# MAB377, RRID:AB_2298772)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2298772


      What is this?

    6. rid_000081

      DOI: 10.1002/cne.23668

      Resource: GraphPad Prism (RRID:SCR_002798)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_002798


      What is this?

    7. AB_631746

      DOI: 10.1002/cne.23668

      Resource: (Santa Cruz Biotechnology Cat# sc-2004, RRID:AB_631746)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_631746


      What is this?

    8. AB_2092129

      DOI: 10.1002/cne.23668

      Resource: (Novus Cat# 26630002, RRID:AB_2092129)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2092129


      What is this?

    9. RGD_1302714

      DOI: 10.1002/cne.23668

      Resource: (RGD Cat# 1302714,RRID:RGD_1302714)

      Curator: @jcabotaj

      SciCrunch record: RRID:RGD_1302714


      What is this?

    10. RGD_2312511

      DOI: 10.1002/cne.23668

      Resource: (RGD Cat# 2312511,RRID:RGD_2312511)

      Curator: @jcabotaj

      SciCrunch record: RRID:RGD_2312511


      What is this?

    1. SciRes_000137

      DOI: 10.1002/cne.23664

      Resource: Fiji (RRID:SCR_002285)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_002285


      What is this?

    2. RGD_737929

      DOI: 10.1002/cne.23664

      Resource: (RGD Cat# 737929,RRID:RGD_737929)

      Curator: @jcabotaj

      SciCrunch record: RRID:RGD_737929


      What is this?

    3. ZIRC_ZL1

      DOI: 10.1002/cne.23664

      Resource: (ZIRC Cat# ZL1,RRID:ZIRC_ZL1)

      Curator: @jcabotaj

      SciCrunch record: RRID:ZIRC_ZL1


      What is this?

    4. IMSR_JAX:000664

      DOI: 10.1002/cne.23664

      Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

      Curator: @jcabotaj

      SciCrunch record: RRID:IMSR_JAX:000664


      What is this?

    1. AB_664696

      DOI: 10.1002/cne.23665

      Resource: (FUJIFILM Wako Pure Chemical Corporation Cat# 544-10001-WAKO, RRID:AB_664696)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_664696


      What is this?

    2. AB_727049

      DOI: 10.1002/cne.23665

      Resource: (Abcam Cat# ab41489, RRID:AB_727049)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_727049


      What is this?

    3. AB_880078

      DOI: 10.1002/cne.23665

      Resource: (Abcam Cat# ab32423, RRID:AB_880078)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_880078


      What is this?

    4. nif-0000-30467

      DOI: 10.1002/cne.23665

      Resource: ImageJ (RRID:SCR_003070)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_003070


      What is this?

    5. AB_2336066

      DOI: 10.1002/cne.23665

      Resource: (Thermo Fisher Scientific Cat# S-21374, RRID:AB_2336066)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2336066


      What is this?

    6. AB_954958

      DOI: 10.1002/cne.23665

      Resource: (Abcam Cat# ab6876, RRID:AB_954958)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_954958


      What is this?

    7. AB_2313737

      DOI: 10.1002/cne.23665

      Resource: (Innovative Research Cat# A11058, RRID:AB_2313737)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2313737


      What is this?

    8. AB_10564074

      DOI: 10.1002/cne.23665

      Resource: AB_10564074

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10564074


      What is this?

    9. AB_10561706

      DOI: 10.1002/cne.23665

      Resource: AB_10561706

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_10561706


      What is this?

    1. nif-0000-30467

      DOI: 10.1113/jphysiol.2014.281014

      Resource: ImageJ (RRID:SCR_003070)

      Curator: @gabimpine

      SciCrunch record: RRID:SCR_003070


      What is this?

    2. AB_11180183

      DOI: 10.1113/jphysiol.2014.281014

      Resource: (Thermo Fisher Scientific Cat# A10042, RRID:AB_2534017)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_11180183


      What is this?

    3. AB_2340476

      DOI: 10.1113/jphysiol.2014.281014

      Resource: (Jackson ImmunoResearch Labs Cat# 706-605-148, RRID:AB_2340476)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_2340476


      What is this?

    4. AB_2283325

      DOI: 10.1113/jphysiol.2014.281014

      Resource: (Neuromics Cat# GP10108, RRID:AB_2283325)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_2283325


      What is this?

    5. AB_2040054

      DOI: 10.1113/jphysiol.2014.281014

      Resource: (Alomone Labs Cat# APR-003, RRID:AB_2040054)

      Curator: @gabimpine

      SciCrunch record: RRID:AB_2040054


      What is this?

    1. AB_2313566

      DOI: 10.1002/glia.22756

      Resource: AB_2313566

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2313566


      What is this?

    2. rid_000089

      DOI: 10.1002/glia.22756

      Resource: MED PC (RRID:SCR_012156)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_012156


      What is this?

    3. rid_000087

      DOI: 10.1002/glia.22756

      Resource: SMART JUNIOR (RRID:SCR_012154)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_012154


      What is this?

    4. rid_000088

      DOI: 10.1002/glia.22756

      Resource: LightCycler Software (RRID:SCR_012155)

      Curator: @jcabotaj

      SciCrunch record: RRID:SCR_012155


      What is this?

    5. AB_2223041

      DOI: 10.1002/glia.22756

      Resource: (Millipore Cat# MAB1501, RRID:AB_2223041)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2223041


      What is this?

    6. AB_2313567

      DOI: 10.1002/glia.22756

      Resource: (Jackson ImmunoResearch Labs Cat# 111-035-003, RRID:AB_2313567)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2313567


      What is this?

    7. AB_2122301

      DOI: 10.1002/glia.22756

      Resource: (Cell Signaling Technology Cat# 2678, RRID:AB_2122301)

      Curator: @jcabotaj

      SciCrunch record: RRID:AB_2122301


      What is this?

    1. Cross validation

      Should this be applied to the GBM approach? Would it prevent it collapsing onto a spike at 0.42?

    1. 1 Matching Annotations

      This is an annotation.

    1. most importantly, all of them give the same answer to within an accuracy of about 10%. As we will see, this means we may finally be able to make reliable estimates of the size of the universe.

      This is definitely very exciting, but 10% is a lot when it comes to space, that could be a distance of lightyears. I am curious what is missing from the instruments currently available to be able to make more precise measurements.

    2. most of the material in the universe cannot at present be observed directly in any part of the electromagnetic spectrum. An understanding of the properties and distribution of this invisible matter is crucial to our understanding of galaxies.

      Challenging the idea of "seeing is believing" and the idea of invisible matter must have been very controversial at the time and confusing to lots of people, but is now something we take for granted, despite the fact that we don't have a full understanding of it.

    3. Several simple ideas of this kind were tried, some by Hubble himself, but none stood the test of time (and observation). Because no simple scheme for evolving one type of galaxy into another could be found, astronomers then tended to the opposite point of view. For a while, most astronomers thought that all galaxies formed very early in the history of the universe and that the differences between them had to do with the rate of star formation.

      I saw a quote the other day that loosely said that everything sounds crazy until it's proven by science, and I think that really applies here. I also find it interesting that what people think is an impossibility at one point can very quickly become fact later. The idea that science is always evolving and changing is really interesting to me.

    1. In some cases the accumulations is found inside structures and often it is mixed with other material for example collapsed material

      I would suggest adjusting punctuation for better readability. "In some cases, the accumulations are found inside structures, and often it is mixed with other material; for example, collapsed material".

    1. Among Finns, Estonians, and related northern European cultures, the Milky Way is regarded as the “pathway of birds” across the night sky. Having noted that birds seasonally migrate along a north-south route, they identified this byway with the Milky Way. Recent scientific studies have shown that this myth is rooted in fact: the birds of this region use the Milky Way as a guide for their annual migrations.

      How many other birds in different regions used the Milky Way to guide themselves but can't anymore? Although I'm curious how much this affects those birds, as they probably know where to go anyways instinctually or because of the many generations of birds flying south.

    2. Go back a few centuries, and these starlit sights would have been the norm rather than the exception.

      I am curious when the conscious decision was made to have protected spaces where development would be kept to a minimum in order to preserve the viewing of the sky.

    3. In 1785, William Herschel (Figure 25.2) made the first important discovery about the architecture of the Milky Way Galaxy. Using a large reflecting telescope that he had built, William and his sister Caroline counted stars in different directions of the sky. They found that most of the stars they could see lay in a flattened structure encircling the sky, and that the numbers of stars were about the same in any direction around this structure.

      I'm curious how many before Herschel and his sister tried and failed to discover how the milky way is laid out, and what set them apart from those before them, allowing them to get the job done.

    1. walls one

      It should be "the walls were one and a half mud bricks" wide.

    2. an other

      It should be "another," instead of "an other," i.e., one word, no space.

    1. development of a flow cytometry optimized GFP variant, gfpmut3 (Cormack et al., 1996), which is still extensively used for monitoring the fate of plasmids in natural environments.

      what about the variant causes it to be optimized for flow cyt?

    1. Init weight torch.Size([8, 4]) Init weight torch.Size([1, 8])

      两个结果的原因是 net 有两个线性层

    2. *[(name, param.shape) for name, param in m.named_parameters()][0]

      [(name, param.shape) for name, param in m.named_parameters()][0] 首先作为一个整体,获取列表的第一个元素,即第一个 (name, param.shape) 元组

      然后对这个元祖进行解包!

    3. net

      net 是 net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))

    4. if type(m) == nn.Linear

      nn.Linear 是一个全连接层(也称为线性层或密集层)。如果代码中有 if type(m) == nn.Linear: 这样的语句,它的意思是检查变量 m 是否是一个全连接层。

    1. Plasmid transfer was detected both in abundant and rare taxa of the initial recipient community

      were there any OTUs that did not appear in the initial recipient community at detectable levels??

    1. This corresponds to more sequences than sorted transconjugants for most samples (Supplementary Table 1), providing an adequate picture of the observed plasmid transfer range.

      # of final reads / # of Tc sorted > 1 is that they mean

    2. gate for only particles of bacterial size

      How do you determine where the "bacterial" sized particles should appear?

      Set a gate for bacterial size on a bivariate SSC-A vs FSC-A plot for events of bacterial size by including the donor strain and excluding all events caused by a sterile pyrophosphate buffer control. Source: Klumper, 2018 Spring protocol handbook

    1. "Did you put your name into the Goblet of Fire, Harry?" Dumbledore asked calmly. Reference: book.

      And, the transition to the scene 😂

    1. Part of the Part am I, once All, in primal Night,— Part of the Darkness which brought forth the Light

      Part of the whole?

    2. I am the Spirit that Denies! And justly so: for all things, from the Void Called forth, deserve to be destroyed:

      Metal af

    3. ’Tis written: “In the Beginning was the Word.” Here am I balked: who, now can help afford? The Word?

      Is he misunderstanding? Is this itself a translation problem?

    4. Since all that’s coarse provokes my enmity. This fiddling, shouting, ten-pin rolling I hate,—these noises of the throng: They rave, as Satan were their sports controlling. And call it mirth, and call it song!

      oh, chill out

    5. A thunder-word hath swept me from my stand

      thunder-word sounds like melville

    6. He art thou, who, my presence breathing, seeing, Trembles through all the depths of being, A writhing worm, a terror-stricken form

      insults him?

    7. Spirit I invoke

      What spirit?

    8. How each the Whole its substance gives, Each in the other works and lives!

      The Whole = Creation = God?

    9. Am I a God?—so clear mine eyes

      "little god"

    10. I do not pretend to aught worth knowing, I do not pretend I could be a teacher To help or convert a fellow-creature

      Woes of the educator

    11. He cannot choose but err.

      ?? Free will?

    12. How men torment themselves, is all I’ve noted. The little god o’ the world sticks to the same old way, And is as whimsical as on Creation’s day. Life somewhat better might content him, But for the gleam of heavenly light which Thou hast lent him: He calls it Reason—thence his power’s increased

      Man is "little god" and has divine Reason

    1. net(X)

      这是python的一个语法糖,net()实际上调用net.call(),而__call__()调用了forward

    2. F.relu

      nn.ReLU()是构造了一个ReLU对象,并不是函数调用,而F.ReLU()是函数调用

      这里也可以写成 return self.out(nn.ReLU()(self.hidden(X)) ,但是没有必要

      In the provided code, the ReLU layer is applied as a function within the forward method, using F.relu(self.hidden(X)). This means that the ReLU activation is not explicitly recorded as a separate layer in the model's structure. Instead, it is applied directly to the output of the hidden layer during the forward pass.

      If you want to explicitly include the ReLU layer in the model's structure, you can define it as a separate layer in the __init__ method and then use it in the forward method. Here's an example:

      ```python class MLP(nn.Module): def init(self): super().init() self.hidden = nn.Linear(20, 256) # 隐藏层 self.relu = nn.ReLU() # ReLU 层 self.out = nn.Linear(256, 10) # 输出层

      def forward(self, X):
          return self.out(self.relu(self.hidden(X)))
      

      ```

      In this version, the ReLU layer is explicitly defined and included in the model's structure.

  2. pressbooks.library.virginia.edu pressbooks.library.virginia.edu
    1. AI Tools

      Overall, I like the AI tools chapters and they are easy to read. I do think that they might need to be re-organized for a more logical progression of content, and that we can evaluate if some chapters that focus on only one specific tool should be maintained. If yes, do we need to find or write chapters for the (main) tools that are missing? Also, because they are coming from different sources, there is some repetition that we might need to address.

    1. Students can ask clarifying questions and receive instant feedback.  In math, a student could ask, “what is the formula for calculating the surface area of a triangular prism?”  In science, a student could ask “how much of the Earth’s surface is covered in water?”

      Can it be used for higher ed?

    2. High School

      Higher ed focus?

    1. Introduction

      Other coding specific tools might need to be included, such as GitHub Copilot, Debuild, etc.

    2. 65% of school aged children

      Higher ed focus?

    1. Supporting Career Exploration and Guidance with AI-Powered Tools

      I would probably end with this chapter, since it is more mentoring related than teaching/instruction related.

    1. Grades 5 & 6

      Higher ed focus?

    2. Introduction

      There are other platforms that could be mentioned in the area of creative storytelling: Character AI, Runway, Colossyan, etc...

    1. Our classrooms are also growing daily with little ones who are new to Canada.

      Country-specific reference?

    2. children

      Higher ed focus?

    1. Introduction

      I like to have a chapter for video tools, but maybe not just focusing on one? Or have other videos tools listed and summarized in the introductory chapters?

    1. Leveraging ChatGPT as a Teacher and Student Resource

      I like this chapter a lot because it focus on both the instructor side and the student side.

    1. schöne diagnose, gut gebrüllt... und jetzt? wo ist der lösungsvorschlag?

    2. 10:00 "nach aussen für demokratie, aber sie machen immer genau das gegenteil."<br /> ihre lügen sind too big to fail. if you must lie then lie big. Greenwood - 180 Degrees.<br /> scheinheilig. sich immer rausreden auf "fahrlässigkeit" und "zufall".

    1. for Research and Effective Source Identification

      Great and important chapter! I wonder if there are other tools to be listed under the 'research and effective source ID'.

    1. The Wittliff Collections, Texas State University

      Located on 7th floor of Alkek Library

    1. Reviewer #2 (Public Review):

      Summary:

      In the manuscript "Metabolic heterogeneity of colorectal cancer as a prognostic factor: insights gained from fluorescence lifetime imaging" by Komarova et al., the authors used fluorescence lifetime imaging and quantitative analysis to assess the metabolic heterogeneity of colorectal cancer. Generally, this work is logically well-designed, including in vitro and in vivo animal models and ex vivo patient samples. Although the key parameter (BI index) used in this study was already published by this group, it was shown that heterogeneity of patients' samples had associations with clinical characteristics of tumors. Additional samples from 8 patients were added to the data pool during the revision process, which is helpful and important for the conclusions that the authors are trying to draw. Overall, the revisions that the authors have made greatly strengthen this study.

      Strengths:

      (1) Solid experiments are performed and well-organized, including in vitro and in vivo animal models and ex vivo patient samples;

      (2) Attempt and efforts to build the association between the metabolic heterogeneity and prognosis for colorectal cancer.

      Weaknesses:

      (1) Although additional data acquired from 8 patients were collected, maybe more patients should be involved in the future for reliable diagnosis and prognosis.

    2. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Komarova et al. investigate the clinical prognostic ability of cell-level metabolic heterogeneity quantified via the fluorescence lifetime characteristics of NAD(P)H. Fluorescence lifetime imaging microscopy (FLIM) has been studied as a minimally invasive approach to measure cellular metabolism in live cell cultures, organoids, and animal models. Its clinical translation is spearheaded through macroscopic implementation approaches that are capable of large sampling areas and enable access to otherwise constrained spaces but lack cellular resolution for a one-to-one transition with traditional microscopy approaches, making the interpretation of the results a complicated task. The merit of this study primarily lies in its design by analyzing with the same instrumentation and approach colorectal samples in different research scenarios, namely in vitro cells, in vivo animal xenografts, and tumor tissue from human patients. These conform to a valuable dataset to explore the translational interpretation hurdles with samples of increasing levels of complexity. For human samples, the study specifically investigates the prediction ability of NAD(P)H fluorescence metrics for the binary classification of tumors of low and advanced stage, with and without metastasis, and low and high grade. They find that NAD(P)H fluorescence properties have a strong potential to distinguish between high- and low-grade tumors and a moderate ability to distinguish advanced-stage tumors from low-stage tumors. This study provides valuable results contributing to the deployment of minimally invasive optical imaging techniques to quantify tumor properties and potentially migrate into tools for human tumor characterization and clinical diagnosis.

      Strengths:

      The investigation of colorectal samples under multiple imaging scenarios with the same instrument and approach conforms to a valuable dataset that can facilitate the interpretation of results across the spectrum of sample complexity.

      The manuscript provides a strong discussion reviewing studies that investigated cellular metabolism with FLIM and the metabolic heterogeneity of colorectal cancer in general.

      The authors do a thorough acknowledgement of the experimental limitations of investigating human samples ex vivo, and the analytical limitation of manual segmentation, for which they provide a path forward for higher throughput analysis.

      Weaknesses:

      To substantiate the changes in fluorescence properties at the examined wavelength range (associated with NAD(P)H fluorescence) in relationship to metabolism, the study would strongly benefit from additional quantification of metabolic-associated metrics using currently established standard methods. This is especially interesting when discussing heterogeneity, which is presumably high within and between patients with colorectal cancer, and could help explain the particularities of each sample leading to a more in-depth analysis of the acquired valuable dataset.

      In order to address this issue, we have performed immunohistochemical staining of the available tumor samples for the two standard metabolic markers GLUT3 and LDHA.

      The results are included in Supplementary (Fig.S4). Discussion has been extended.

      Additionally, NAD(P)H fluorescence does not provide a complete picture of the cell/tissue metabolic characteristics. Including, or discussing the implications of including fluorescence from flavins would comprise a more compelling dataset. These additional data would also enable the quantification of redox metrics, as briefly mentioned, which could positively contribute to the prognosis potential of metabolic heterogeneity.

      We agree with the Reviewer that fluorescence from flavins could be helpful to obtain more complete data on cellular metabolic states. However, we lack to detect sufficiently intensive emission from flavins in colorectal cancer cells and tissues. The paragraph about flavins was added in Discussion and representative images - in Supplementary Material (Figure S5).

      In the current form of the manuscript, there is a diluted interpretation and discussion of the results obtained from the random forest and SHAP analysis regarding the ability of the FLIM parameters to predict clinicopathological outcomes. This is, not only the main point the authors are trying to convey given the title and the stated goals, but also a novel result given the scarce availability of these type of data, which could have a remarkable impact on colorectal cancer in situ diagnosis and therapy monitoring. These data merit a more in-depth analysis of the different factors involved. In this context, the authors should clarify how is the "trend of association" quantified (lines 194 and 199).

      We thank the Reviewer for this suggestion. The section has been updated with SHAP analysis using different parameters (dispersion D of t2, a1, tm and bimodality index BI of t2, a1, tm). It is now more clear that D-a1 is more strongly associated with clinicopathological outcomes compared with other variables. We have also added some biological interpretation of these results in the Discussion.

      Reviewer #2 (Public Review):

      Summary:

      In the manuscript "Metabolic heterogeneity of colorectal cancer as a prognostic factor: insights gained from fluorescence lifetime imaging" by Komarova et al., the authors used fluorescence lifetime imaging and quantitative analysis to assess the metabolic heterogeneity of colorectal cancer. Generally, this work is logically well-designed, including in vitro and in vivo animal models and ex vivo patient samples. However, since the key parameter presented in this study, the BI index, is already published in a previous paper by this group (Shirshin et al., 2022), and the quantification method of metabolic heterogeneity has already been well (and even better) described in previous studies (such as the one by Heaster et al., 2019), the novelty of this study is doubted. Moreover, I am afraid that the way of data analysis and presentation in this study is not well done, which will be mentioned in detail in the following sections.

      Strengths:

      (1) Solid experiments are performed and well-organized, including in vitro and in vivo animal models and ex vivo patient samples.

      (2) Attempt and efforts to build the association between the metabolic heterogeneity and prognosis for colorectal cancer.

      Weaknesses:

      (1) The human sample number (from 21 patients) is very limited. I wonder how the limited patient number could lead to reliable diagnosis and prognosis;.

      Additional 8 samples of patients’ tumors collected while the manuscript was under review were added to the present data. We agree that the number is still limited to conclude about the prognostic value of cell-level metabolic heterogeneity. But at this point we can expect that this parameter will become a metric for prognosis. We will continue this study to collect more samples of colorectal tumors and expand the approach to different cancer types.

      (2) The BI index or similar optical metrics have been well established by this and other groups; therefore, the novelty of this study is doubted.

      The purpose of this research was to quantify and compare the cellular metabolic heterogeneity across the systems of different complexity - commercial cell lines, tumor xenografts and patients’ tumors - using previously established FLIM-based metrics. For the first time, using FLIM, it was shown that heterogeneity of patients’ samples is much higher than of laboratory models and that it has associations with clinical characteristics of the tumors - the stage and the grade. In addition, this study provides evidence that bimodality (BI) in the distribution of metabolic features in the cell population is less important than the width of the spread (the dispersion value D).

      Some corrections have been made in the text on this point.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The following comments should be addressed to strengthen the rigor and clarity of the manuscript.

      (1) The ethical committee that approved the human studies should also be mentioned in the methods section, as was done with the animal studies.

      Information about the ethics committee has been added in the Manuscript.

      The study with the use of patients’ material was approved by the ethics committee of the Privolzhsky Research Medical University (approval № 09 from 30.06.2023).

      (2) The captions in Figures 2 and 3 must be revised. In Figure 2, it seems the last 2 sentences for the description of (C) do not belong there, and instead, the last sentence in the description of (D) may need to be included in (C) instead. Figure 3 is similar.

      The captions were revised.

      (3) From supplement Figure S2 it seems that EpCam and vimentin staining were only done in two of the mouse tumor types. No further mention is made in the results or methods section. Is there any reason this was not performed in the other tumor types? Were the histology and IHC protocols the same for the mouse and human tumors?

      The data on other tumor types and patients’ tumors have been added in Figure S3. Discussion was extended with the following paragraph.

      One of the possible reasons for metabolic heterogeneity could be the presence of stromal cells or diversity of epithelial and mesenchymal phenotypes of cancer cells within a tumor. Immunohistochemical staining of tumors for EpCam (epithelial marker) and vimentin (mesenchymal marker) showed that the fraction of epithelial, EpCam-positive, cells was more than 90% in tumor xenografts and on average 76±10 % in patients’ tumors (Figure S3). However, the ratio of EpCam- to vimentin-positive cells in patients’ samples neither correlated with D-a1 nor with BI-a1, which means that the presence of cells with mesenchymal phenotype did not contribute to metabolic heterogeneity of tumors identified by NAD(P)H FLIM.

      (4) Clarify the design of the experiments: The results come from 50 - 200 cells in each sample (except 30 in the CaCo2 cell culture) that were counted from 5 - 10 images acquired from each sample. There were 21 independent human samples. How many independent samples were included in the cell culture experiments and the mouse tumor models? Why is there an order of magnitude fewer cells included in the CaCo2 group compared to the other groups (Figure 1)? From the image (Figure 1A - CaCo2), it seems to be a highly populated type of sample, yet only 30 cells were quantified. What prevents the inclusion of the same number of cells to be quantified in each group for a more systematic evaluation?

      We thank the Reviewer for this comment.

      Cell culture experiments included two independent replicates for each cell line, the data from which were then combined. In animal experiments measurements were made in three mice (numbered 1-3 in Figure 2C) for each tumor type. We have made calculations for additional >100 cells of CaCo2 cell line. In the revised version the number of Caco2 cells is 146.

      The text of the Manuscript was revised accordingly.

      (5) Regarding references: Some claims throughout the text would benefit from an additional reference. For example: line 70 "Metabolic heterogeneity [...] is believed to have prognostic value"; line 121 " [...] the uniformity of cell metabolism in a culture, which is consistent with the general view on standard cell lines [...]". The clinical translational aspect (i.e., paragraph in line 255) warrants the inclusion of the efforts already done with FLIM imaging in the clinical setting both in vivo and ex vivo with point-spectroscopy and macroscopy imaging (e.g., Jo Lab, Marcu Lab, French Lab, and earlier work by Mycek and Richards-Kortum in colorectal cancer to name a few).

      Additional references were added.

      Reviewer #2 (Recommendations For The Authors):

      (1) In the Introduction, line 85, the authors mention that "Specifically, the unbound state of NAD(P)H has a short lifetime (~0.4 ns) and is associated with glycolysis, while the protein-bound state has a long lifetime (~1.7-3.0 ns) and is associated with OXPHOS". I do not think this claim is appropriate. One cannot simply say that the unbound state is associated with glycolysis, nor that the bound state is associated with OXPHOS; both unbound and bound state are associated with almost all the metabolic pathways. Instead, the expression of "glycolytic/ OXPHOS shift", as authors used in other sections of this manuscript, is a more appropriate one in this case.

      The text of the Introduction was revised.

      (2) What are the biological implications of the bimodality index (BI)? Please provide specific insights.

      Bimodal distribution indicates there are two separate and independent peaks in the population data. In the metabolic FLIM data, this indicates that there are two sub-populations of cells with different metabolic phenotypes. Previously, we have observed bimodal distribution in the population of chemotherapy treated cancer cells, where one sub-population was responsive (shifted metabolism) and the second - non-responsive (unchanged metabolism) [Shirshin et al., PNAS, 2022]. In the naive tumor, a number of factors have an impact on cellular metabolism, including genetics features and microenvironment, so it is difficult to determine which ones resulted in bimodality. Our data on correlation of bimodality (BI) with clinical characteristics of the tumors show that there are no associations between them. What really matters is the width of the parameter spread in the population. The early-stage tumors (T1, T2) were metabolically more heterogeneous than the late-stage ones (T3, T4). A degree of heterogeneity was also associated with differentiation state, a stage-independent prognostic factor in colorectal cancer where the lower grade correlates with better the prognosis. The early-stage tumors (T1, T2) and high-grade (G3) tumors had significantly higher dispersion of NAD(P)H-a1, compared with the late-stage (T3, T4) and low-grade ones (G1, G2). From the point of view of biological significance of heterogeneity, this means that in stressful and unfavorable conditions, to which the tumor cells are exposed, the spread of the parameter distribution in the population rather than the presence of several distinct clusters (modes) matters for adaptation and survival. The high diversity of cellular metabolic phenotypes provided the survival advantage, and so was observed in more aggressive (undifferentiated or poorly differentiated) and the least advanced tumors.

      The discussion has been expanded on this account.

      (3) Have you run statistics in Figure 1B? If yes, do you find any significance? The same question also applies to Figures 2C and 3C.

      We performed statistical analysis to compare different cell lines in in vitro and in vivo models, the results obtained are presented in Table S4.

      (4) Line 119, why is the BI threshold set at 1.1?

      When setting the BI threshold at 1.1, we relied on the work by Wang et al, Cancer Informatics, 2009. The authors recommended the 1.1 cutoff as more reliable to select bimodally expressed genes. Further, we validated this BI threshold to identify chemotherapy responsive and non-responsive sub-populations of cancer cells (Shirshin et al. PNAS, 2022)

      (5) Line 123, what does the high BI of mean lifetime stand for? Please provide biological implications and insights.

      The sentence was removed because inclusion of additional CaCo2 cells (n=146) for quantification NAD(P)H FLIM data showed no bimodality in this cell culture.

      (6) In the legend for Figure 2C, the authors mention that "the bimodality index (BI-a1) is shown above each box"; however, I do not see such values. It is also true for Figure 3C.

      The legends for Fig. 2 and 3 were corrected.

      (7) In Figure 2, t1-t3 were not explained and mentioned in the main text. What do they mean? Do they mean different time points or different tumors?

      t1-t3 means different tumors in a group. Changes have been made to the figure - individual tumors are indicated by numbers.

      (8) In Figure 3, what do p13, p15 and p16 mean? It is not clearly explained. If they just represent patients numbered 13, 15, and 16, then why are these patients chosen as representatives? Do they represent different stages or are they just chosen randomly?

      Figure 3 was revised. Representative images were changed and a short description for each representative sample was included. In the revised version, representatives have been selected to show different stages and grades.

      (9) In Figure 3, instead of showing the results for each patient, I would suggest that authors show representative results from tumors at different stages; or, at least, clearly indicate the specific information for each patient. I do not think that providing the patient number only without any patient-specific information is helpful.

      Figure 3 was revised.

      (10) The sample number (21 patients) is very limited. I wonder how the limited patient number could lead to reliable diagnosis and prognosis.

      Additional eight samples were added. The text, figures and tables were revised accordingly.

      (11) In Discussion, it would be helpful to compare the BI index used in this study with the previously developed OMI-index (Line 275).

      We believe that BI index and OMI index describe different things and, therefore, it is hard to compare them. While BI index is used to describe the degree of the metabolic heterogeneity, OMI index is an integral parameter that includes redox ratio, mean fluorescence lifetimes of NAD(P)H and FAD, and rather indicates the metabolic state of a cell. In this sense it is more relevant to compare it with conventional redox ratio or Fluorescence Lifetime Redox Ratio (FLIRR) (H. Wallrabe et al., Segmented cell analyses to measure redox states of autofluorescent NAD(P)H, FAD & Trp in cancer cells by FLIM, Sci. Rep. 2018; 8: 79). The assessment of the heterogeneity of the FLIM parameters has been previously reported using the weighted heterogeneity (wH) index (Amy T. Shah et al, In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment, Neoplasia 17, pp. 862–870 (2015). To the best of our knowledge, this is the only metric to quantify metabolic heterogeneity on the basis of FLIM data for today. A comparison of BI with the wH-index showed that the value of wH-index provides results similar to BI in the heterogeneity evaluation as demonstrated in our earlier paper (E.A. Shirshin et al, Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity, PNAS 119 (9) e2118241119 (2022).  Yet, the BI provides dimensionless estimation on the inherent heterogeneity of a sample, and therefore it can be used to compare heterogeneity assessed by different decay parameters and FLIM data analysis methods. The limitation of using the OMI index for FLIM data analysis is the low intensity of the FAD signal, which was the case in our experiments.

    3. eLife assessment

      This study presents a valuable finding on the heterogeneity of tumour metabolism using fluorescence lifetime imaging, measured across 4 cell lines, 4 tumour types of in vivo mouse models, and 29 patient samples. The indication is that the level of heterogeneity of cellular metabolism increases with model complexity and demonstrates high heterogeneity at a clinical level. The evidence supporting the claims of the authors is solid, and at the revision stage, the authors have included additional samples from 8 patients in the data pool, which is helpful for the conclusions that the authors are trying to draw. The work will be of interest to medical biologists developing methods for quantifying metabolic heterogeneity.

    4. Reviewer #1 (Public Review):

      Summary:

      In this study, Komarova et al. investigate the clinical prognostic ability of cell-level metabolic heterogeneity quantified via the fluorescence lifetime characteristics of NAD(P)H. Fluorescence lifetime imaging microscopy (FLIM) has been studied as a minimally invasive approach to measure cellular metabolism in live cell cultures, organoids, and animal models. Its clinical translation is spearheaded though macroscopic implementation approaches that are capable of large sampling areas and enable access to otherwise constrained spaces but lack cellular resolution for a one-to-one transition with traditional microscopy approaches, making the interpretation of the results a complicated task. The merit of this study primarily lies in its design by analyzing with the same instrumentation and approach colorectal samples in different research scenarios, namely in vitro cells, in vivo animal xenografts, and ex vivo tumor tissue from human patients. These conform to a valuable dataset to explore the translational interpretation hurdles with samples of increasing levels of complexity. For human samples, which exhibited the highest degree of heterogeneity from the experiments presented, the study specifically investigates the prediction ability of NAD(P)H fluorescence metrics for the binary classification of tumors of low and advanced stage, with and without metastasis, and low and high grade. They find that NAD(P)H fluorescence properties have a strong potential to distinguish between high- and low-grade tumors and a moderate ability to distinguish advanced stage tumors from low stage tumors. This study provides valuable results contributing to the deployment of minimally invasive optical imaging techniques to quantify tumor properties and potentially migrating into tools for human tumor characterization and clinical diagnosis.

      Strengths:

      The investigation of colorectal samples under multiple imaging scenarios with the same instrument and approach conforms to a valuable dataset that can facilitate interpretation of results across the spectrum of sample complexity.

      The manuscript provides a strong discussion reviewing studies that investigated cellular metabolism with FLIM and the metabolic heterogeneity of colorectal cancer in general.

      The authors do a thorough acknowledgement of the experimental limitations of investigating human samples ex vivo, and the analytical limitation of manual segmentation, for which they provide a path forward for higher throughput analysis.

      Weaknesses:

      NAD(P)H fluorescence provides a partial picture of the cell/tissue metabolic characteristics. Including fluorescence from flavins would comprise a more compelling dataset. These additional data should enable the quantification of redox metrics, which could positively contribute to the prognosis potential of metabolic heterogeneity. The authors did attempt to incorporate flavin fluorescence, unfortunately they could not find strong enough signal to proceed with the analysis.

    1. Reviewer #1 (Public Review):

      Summary:

      Starting from an unbiased search for somatic mutations (from COSMIC) likely disrupting binding of clinically approved antibodies the authors focus on mutations known to disrupt binding between two ERBB2 mutations and Pertuzamab. They use a combined computational and experimental strategy to nominate position which when mutated could result in restoring the therapeutic activity of the antibody. Using in vitro assays the authors confirm that the engineered antibody binds to the mutant ERBB2 and prevents ERBB3 phosphorylation

      Strengths:

      (1) In my assessment, the data sufficiently demonstrates that a modified version of Pertuzamab can bind both the wild-type and S310 mutant forms of ERBB2.

      (2) The engineering strategy employed is rational and effectively combines computational and experimental techniques.

      (3) Given the clinical activity of HER2-targeting ADCs, antibodies unaffected by ERBB2 mutations would be desired

      Weaknesses:

      (1) There is no data showing that the engineered antibody is equally specific as Pertuzamab i.e. that it does not bind to other (non-ERBB2) proteins.

      (2) There is no data showing that the engineered antibody has the desired pharmacokinetics/pharmacodynamics properties or efficacy in vivo.

      (3) Computational approaches are only used to design a phage-screen library, but not used to prioritize mutations that are likely to improve binding (e.g. based on predicted impact on the stability of the interaction). A demonstration how computational pre-screening or lead optimization can improve the time-intensive process would be a welcome advance.

      Comments on revised version:

      I have nothing to add beyond my first review, because no substantial changes, additional experiments and/or data, have been made to the manuscript.

    2. eLife assessment

      In this important manuscript, the authors used unbiased approaches to identify somatic mutations in publicly available databases that would disrupt clinically approved antibodies targeting HER2. Using a solid combination of both computational and experimental approaches they identify mutations that could restore therapeutic antibody sensitivity in a series of disease-relevant model systems. Additional cell-based and in vivo assays would strengthen the work and increase the translational and potential clinical relevance of the proposed work.

    1. for - search - google - participatory system mapping and MuSIASEM

      search - google - participatory system mapping and MuSIASEM - https://www.google.com/search?q=participatory+system+mapping+and+MuSIASEM&sca_esv=de3e428f524f6eaa&sxsrf=ADLYWIK8vYVFLcmHv4nSxvSg-qEGT2lXQg%3A1722527927661&ei=t7CrZqWBKPqmhbIPl62nkAk&ved=0ahUKEwjluPnJlNSHAxV6U0EAHZfWCZIQ4dUDCBA&uact=5&oq=participatory+system+mapping+and+MuSIASEM&gs_lp=Egxnd3Mtd2l6LXNlcnAaAhgCIilwYXJ0aWNpcGF0b3J5IHN5c3RlbSBtYXBwaW5nIGFuZCBNdVNJQVNFTTIFECEYoAFIoIaJBVAAWMuCiQVwCXgBkAEAmAHdBKABsowBqgEJMy0zNS4xMC4yuAEDyAEA-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-AUYigUYiwPCAhcQLhiABBjwAxixAxiDARioAxiLAxibA8ICFBAuGIAEGLEDGIMBGKgDGIsDGJsDwgIHEAAYAxiLA8ICERAuGIAEGLEDGKgDGIsDGJsDwgINEAAYgAQYsQMYRhj5AcICJxAAGIAEGLEDGEYY-QEYlwUYjAUY3QQYRhj5ARj0Axj1Axj2A9gBAsICCxAAGIAEGIYDGIoFwgIGEAAYFhgewgIIEAAYFhgeGA_CAggQABiABBiiBMICBRAhGJ8FwgIHECEYoAEYCpgDA7oGBggBEAEYAboGBggCEAEYE5IHCzkuMy0zNS4xMS4xoAfflwM&sclient=gws-wiz-serp

      search results returned - of interest

    1. Generating Images with Morpheus AI to Enhance Learning Outcomes and Inspire Creativity

      I would not focus an entire chapter to one image AI tool. It would be more consistent to have a chapter for image tools where we list and briefly describe all of them.

    1. I blogs, attending AI conferences, and joining AI communities

      Should we list some resources for blogs, conferences, etc.?

    1. Generating Prompts:

      I did an excellent LinkedIn Learning course only to generate ChatGBT prompts. Can we list resources like that?

    2. Generating Research Ideas

      Would be useful to lost which tools can be used to 'generate research ideas', which tools can be used to 'find relevant information', etc...

    1. ($8.33 per month).

      Are listed prices updated? Do we need to mention the price or just say that there are free and paid subscriptions?

    2. Grammarly

      Because the chapters are coming from different sources, there is some repetition, such as ChatGPT and Grammarly. Leave like that or not?

    3. ChatGPT 3.5 (free version)

      Current free version is ChatGBT 4o

    4. July 3, 2023

      Update to 2024?

    1. Coding

      For coding, should mention: GitHub Copilot and Debuild

    2. Other AI Writing Products

      Other writing tools that could be mentioned and/or included: Copy.ai, Andi, Twain, Compose AI, etc.

    3. The free version (ChatGPT  3.5)

      Current free version is ChatGBT 4o

    4. Summer 2023

      Should be updated to Summer 2024, and add additional tools on the list accordingly.

    1. Bronisław Kasper Malinowski (.mw-parser-output .IPA-label-small{font-size:85%}.mw-parser-output .references .IPA-label-small,.mw-parser-output .infobox .IPA-label-small,.mw-parser-output .navbox .IPA-label-small{font-size:100%}Polish: [brɔˈɲiswaf maliˈnɔfskʲi]; 7 April 1884 – 16 May 1942) was a Polish-British[a] anthropologist and ethnologist
    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      We would like to see the major conclusions constrained to better fit the data presented in the manuscript. Speed is only a single performance metric of a very complicated, very diverse system of locomotion.

      If the authors would like to maintain the broader conclusions, the study should be repeated with a number of different performance metrics to shore up the manuscript's results. Particularly with efficiency, speed is not a reliable measure of efficiency to begin with, so this needs to be explored in a more targeted and appropriate manner.

      We agree with Reviewer 1 that we should be more precise about the fitness metrics used and more constrained about the conclusions. Considering the points raised in each paragraph, we’ve modified the text as follows:

      - [line 17] “... to test the necessity of both traits for sustained and effective displacement on the ground.”

      - [starting on line 105] “We generate the robot’s sample using an artificial evolutionary process that selects for better locomotion ability - defined as higher average speed as it is a proxy for organisms with sustained and effective displacement.”

      - [starting on line 287] “We also found that different gravitational environments require different shape structures to optimize locomotion average speed.”

      - [starting on line 311] “This consistency is evidence that a small number of sparsely connected modules is a morphological computation principle for an organism’s optimized average speed.”

      - [starting on line 348] “Beyond that, extending the tests for other important aspects of locomotion behavior - as noise on the ground, energetic costs, and maneuverability - by using other locomotion metrics - as energy efficiency, stability margin, and dissipated power (Paez and Melo, 2014; Aoi et al., 2016 ) - would also be relevant to evaluate the principle’s robustness.”

      - [starting on line 524] “As the robots with the highest average speed are the ones that succeed in maximizing displacement and having robust dynamics (they will not tumble with time), we defined $\bar s$ as the fitness value using it as a proxy of successful directed locomotion. Selecting for bodies that maximize speed is a common locomotion bias in natural selection, as both predators and prey and thus fecundity and mortality depend on it (Alexander, 2006). Other measures - such as energy efficiency - can capture distinct important aspects of the locomotion complexity (Paez and Melo, 2014) and would be worthy of investigating in future work.”

      Paper Premise/Mission Statement: As defined in the abstract and also called out in the text starting on line 59 is "investigate whether symmetry and modularity are features of an organism's shape need [authors italics] to have for better-directed locomotion..."

      If we understood correctly the reviewer is asking for more precision in the statement. We modified the respective sentence in the following way:

      - [line 62] “... need to have for optimizing average speed on the ground,”

      Reviewer #2 (Recommendations For The Authors):

      i) a lot of details that are in the captions should be moved in the main text;

      Thank you for this comment. We reviewed all the captions and text making modifications to ensure that all the information in the captions is also present in the main text. Below, we highlighted some of the changes:

      - [line 57] “Thus, locomotion on the ground is present in phylogenetically distant species (such as the maned wolf and frogfish in Figure 1A) and depends upon … “

      - [starting on line 64] “Figure 1B shows a schematic representation of symmetry and modularity on the maned wolf and frogfish bodies.”

      - [starting on line 277] “There is a negative correlation between the proportion of feet voxels and the robot’s locomotion transference capability when the robots go to an environment with higher gravity, i.e., water to mars (dark blue in Figure 5C), water to earth (light blue), and mars to earth (red) - with a Spearman correlation coefficients of r = -0.39, r = -0.43, and r = -0.32, respectively, all with p < 1e-08.”

      ii) hypotheses should be spelled out more clearly;

      We verified the experiments and certified that every experiment had a clear hypothesis statement in the original manuscript. Before each section defining the hypothesis and describing the experiment, we added the following statement:

      - [starting on line 119] “ With this sample, we tested the hypotheses about the relationships between locomotion performance and body modularity and symmetry (Figure 1I).”

      iii) performance metrics and other features should be better defined using mathematical terms if possible (for example, instability);

      Thank you for the comment. We added a definition for instability in the text:

      - [starting on line 218] “Nonetheless, locomotion requires a minimum instability - the dynamic possibility of translating the center of mass - in the direction axis to generate the necessary forward displacement (Bruijn et al., 2013; Nagarkar et al., 2021).”

      Despite the different definitions of instability in literature (Bruijn et al., 2013, Paez and Melo, 2014; Aoi et al., 2016, Nagarkar et al., 2021), we didn’t find one mathematical definition that fits perfectly in our context.

      Following the reviewer's comment, when necessary we expanded the definition for other features:

      - [starting on line 199] “... the distribution of body weight. As the robots do not have sensory feedback abilities, the weight balance is defined as the body’s movement due to gravity forces (consequences of the weight distribution and surface contact points) (Benda et al., 1994). We hypothesized that the robots with the best directed locomotion ability would tend to have a symmetric body shape. A robot with a low XY shape symmetry (XY shape symmetry < 0.5) has a higher chance of having a poor weight balance, increasing the chance of the body tipping over, thus leading it to a lousy locomotion performance (blue dotted line in Figure 3C). “

      iv)  more details regarding the simulations should be included;

      We thank the reviewer for this comment. If we understood correctly the Reviewer 2 is asking for more details regarding: “a) the adequacy of the spatial resolution, whereby I failed to see a compelling argument regarding the completeness of 64 voxels; b) the realism of the oscillatory patterns, whereby all the voxels are set to oscillate at the same, constant, frequency of 2Hz; and c) the accuracy of simulations in water where added mass effects seem to be neglected.”. We modified the text to better satisfy these concern:

      a) [starting on line 96] “We choose to first explore exhaustively the $4^3$ space dimension, as it is the minimal possible space that allows meaningful body plans. We also did control experiments within 6^3 and 8^3 to check for dimension size effects.”

      - [starting on line 432] “We did control experiments with robots within 6³ and 8³ dimensions to check for dimension size effects - and we found that the results found in 4³ remained valid. We choose to focus our analysis in the 4³ design space because we consider it the minimum coarse-grain to approach the biological question about the contingency of shape outcomes pressured for locomotion. Smaller spaces do not allow sufficient complexity in the body structures, and increasing spatial resolution reduces the extensiveness of the investigated search space.”

      b) [starting on line 451] “… we used a fixed oscillation frequency of 𝑓 = 2 Hz (Kriegman et al.,2020). A fixed frequency value reduces the number of degrees of freedom in the search for solutions, but in return, it narrows the direct connection between the simulated organisms and animals. Exploring different frequency values in future work would be important to investigate the impact of varied oscillatory frequencies in the shape solutions for directed locomotion.”

      c) The environment we call “water” is not an accurate modeling of aquatic habitats as we didn’t simulate essential forces such as draff effects. This choice is explained in text starting on line 110: “In the water-like environment the bodies have nullifying body weight but do not have drag effects. We did not add drag in our simulations because our aim is to study just the body weight influences in locomotion independently of other forces.”

      v) a full paragraph about limitations should be included in the discussions, focusing on both simulation aspects (for example, the use of simple spring elements in the voxels) and theoretical assumptions (for example, addressing the potential role of non-locomotion-related aspects).

      We thank the reviewer for the comment. We edited some paragraphs of the discussion section to make more explicit some limitations of our work:

      [starting on line 398] “We expect that including other important aspects of an animal's body as a developmental process and sensory functions could influence the shape's outcomes with other layers of principles. Although we based our simulations on an already successful transference of \textit{in silico} behavior to organisms made of biological tissue

      \citep{kriegman_scalable_2020}, there is an intrinsic gap between spring-mass robots modeling and animal’s bodies that is worthy of exploring to ensure the generality of our results. Other methods, such as the inclusion of rigid body elements in the simulation (possible in Voxelyze), the use of finite element modeling (FEM) (Coevoet et al., 2019), and the construction of physical robots (Aguilar et al., 2016), are important complements to this work. Beyond that, principles on other scales as in the genotypes (Johnston et al., 2022) and in other behavioral phenotypes (Gomez-Marin et al., 2016) could also be investigated.”

      To address the potential role of non-locomotion-related aspects, we revised the section

      “Discussion - Contingency of evolutionary outcomes” where we discussed other functional and biological roles:

      [starting on line 354 ] “Here we investigate how a specific functional cause - optimization of average speed during directed locomotion on the ground - externally defines the phenotypic space of shape possibilities.”

      [starting on line 359] “For simplification purposes, we choose to not explicitly control other important factors of locomotion (i.e., energy consumption, maneuverability) that nonlinearly interact during locomotion. In future studies, it would be important to conduct similar studies on a wider range of factors to study the shape and dynamic principles in different conditions.“

    1. Reviewer #2 (Public Review):

      This work makes substantial progress towards understanding physical aspects of formation locomotion, notably the hydrodynamic stability of groups of flappers and the modifications to energy costs associated with flow interactions.

      Major strengths pertain to the fact that this topic is timely, interesting and complex, and the authors have advanced the understanding through their characterizations.

      The weaknesses may relate to the many idealizations employed in the simulations and models, which may raise questions about how to interpret their results and whether the outcomes hold generally. But given the complexity of the problem, simplifications are necessary. The authors have certainly provided a clear presentation with appropriate details and caveats that will help the reader extract the main messages and form their own conclusions.

      Overall, the work is a positive addition to the growing set of studies into schooling, flocking and related problems where unsteady flow interactions lead to interesting collective effects.

    1. Marcus said that the Toledo Hem Sealer application is impressive and would be good website content

    1. How different is Bob Doto's A System for Writing from Antinet Zettelkasten?

      reply to u/IamOkei at https://new.reddit.com/r/Zettelkasten/comments/1eg8jhe/how_different_is_bob_dotos_a_system_for_writing/?%24deep_link=true&correlation_id=f06f9a20-472b-4d1d-9820-65a28e4efb04&post_fullname=t3_1eg8jhe&post_index=0&ref=email_digest&ref_campaign=email_digest&ref_source=email&utm_content=post_title&%243p=e_as&utm_medium=Email%20Amazon%20SES

      Doto's book is far more focused, well-written, and actually edited. Scheper's less well written and in need of heavy editing. Save yourself the extra 400 pages and spend that time practicing the craft instead.

      If you were starting from scratch without any knowledge of the area, I would highly recommend Doto's work, possibly mention Ahrens in passing, and not suffer anyone to mention Scheper's book. If you're an academic, I would recommend Umberto Eco or perhaps if a historian, one of the many books on historical method like Barzun, Gottschalk, or Goutor.

      As background, both Scheper and Doto sent me pre-publication drafts of their work-in-progress to read. I've also read the majority of other books, papers, articles, and works in the space over the past 150+ years including nearly 100% of the references footnoted the two texts referenced. See also: https://boffosocko.com/research/zettelkasten-commonplace-books-and-note-taking-collection/

    1. ernst wolff for president ...<br /> aber ernsthaft, was würde ernst wolff machen wenn er alle freiheiten hätte?<br /> was würde er machen gegen übervölkerung, rohstoffmangel, degeneration?

    1. Twitter Scraper fue altamente eficaz en la recolección de datos textuales y en la identificación de patrones en los tweets. Su enfoque en el contenido textual permitió una cobertura integral de los temas discutidos, operando de manera eficiente y procesando grandes cantidades de datos textuales en un tiempo reducido.

      Decirlo de manera más clara.

    2. Su capacidad para manejar grandes volúmenes de datos permitió una recolección exhaustiva, y mostró un rendimiento eficiente en la ejecución de tareas de scraping, completando la recolección de datos en un tiempo razonable sin comprometer la calidad

      Decirlo con palabras más llanas y claras.

    3. Fue eficiente en obtener una gran cantidad de datos textuales, capturando un volumen significativo de tweets y hashtags, así como menciones y enlaces. Esta densidad de información textualmente rica facilitó la identificación de temas y sentimientos predominantes en el discurso político.

      Aclarar o quitar

    4. más exitosa

      con mayor calidad del dato

    5. Para garantizar que las conclusiones sean sólidas. Compararemos en profundidad las herramientas.

      Para garantizar que las conclusiones sean sólidas, compararemos ~~en profundidad~~ las herramientas.

    6. Este marco teórico proporciona la base conceptual para abordar los desafíos y oportunidades que presentan las nuevas tecnologías en el análisis de datos sociales y políticos.

      Borrar.

    7. , demuestra características innovadoras de suma significación en términos de procesos y resultados esperados, entendiendo estos últimos como el impacto de la investigación. Además
    8. Nuestro objetivo es gestionar y organizar el volumen masivo de datos textuales creados en X/Twitter durante la candidatura a la alcaldía de Bogotá en X/Twitter utilizando técnicas de minería de textos. Con el uso de esta metodología innovadora, podemos examinar las ramificaciones políticas de la difusión o proliferación de información en la plataforma y producir modelos útiles que pueden aplicarse para mejorar la toma de decisiones políticas y estratégicas.

      Este ya no es nuestro objetivo. Sino el de analizar la calidad de los microdatos extraídos.

    9. ==Se discuten los desafíos asociados con la reproducibilidad en la investigación de datos==. {{dónde}}
    1. Deposition: introduction

      When I clicked on "introduction" in the A16 unit book, it jumps to MZ Sitewide, and users need to use the backwards button to return to A16. This is consistent with the way in which other pages are open and users may get lost. I suggest to keep it consistent, i.e., open link in tab.

    2. .

      Delete one period

    1. These sign configurations exhibited an average reduction of 16% in mean force coefficients, with a range of 9% to 22% compared to single-faced sign force coefficients given by the preceding equation. These tests also showed that the eccentricity of 0.2 times the width of the structure is overly conservative. Eccentricities reported in the study ranged from 0.039 to 0.105 times the width of the structure, with an average of 0.061. Testing by Giannoulis et al. (2012) and Meyer et al. (2017) supports the findings in Mehta et al. (2012).

      This commentary and the testing referenced is Yesco's justification for calculating and applying Case B wind load with much less eccentricity than Figure 29.3-1 requires.

    2. The 0.85 term in the denominator modifies the wind tunnel-derived force coefficients into a format where the gust-effect factor, as defined in Section 26.11, can be used.

      Yesco has suggested that because the force coefficients were divided by 0.85 (normal gust factor), when determining the pressure for flexible signs with frequency less than 1 Hz, we should multiply by 0.85.

    1. Citizens, not users

      Citizens, not users.

      I like that. I've been grasping for a generic alternative to 'user' that is less generic than 'person' and this hits a sweet spot.

    1. 4:00 "gewaltfantasien laut aussprechen ist eine straftat"<br /> nein, zu sagen "ich werde den scheiss habek wegballern" ist KEINE straftat.<br /> deutsche sind nur zu blöd für redefreiheit... hirntotes scheissvolk >: (

    1. Grammar

      In this section, it is unclear what it mean by "markers" and "control points." Since the grammar is in progress, it is probably a good idea to explain briefly here.

    1. With just a fraction of the caffeine found in coffee, you get focus, natural energy and immune support without the jitters, crash or dependency.

      Change this

    2. Hello

    1. Sections

      In this section, it is unclear what the lines and arrows on the maps mean. I suppose it will be explained in the Grammar, but since it is in progress, it might be good idea to briefly explain it here.

    1. X3: B1 thermocouple cooking cabinet

      The combi-steamer checks the temperature value of temperature sensor B1 every second (in all modes). If the temperature is below 350°C [662°F], the combi-steamer is fully functional.

      If the temperature rises above 350 degrees Service 28.2 shows and the oven has no function.

      To protect the unit from damage due to overtemperature, the unit cannot be operated again until the temperature sensor B1 has cooled below 350°C [662°F]

      Thermocouple B1 also controls steam production for steam requirements up to local boiling point by monitoring the cooking cabinet temperature up to steam saturation point.

      (Steam saturation point is when all internal cabinet metalwork (including B1) reaches the local boiling point temperature).

      Upon reaching steam saturation point B1 causes the processor to switch off steam heating.

      When product is placed in the oven the cabinet temperature reduces and steam production starts once again until the product, cabinet metalwork and B1 reaches steam saturation point..

    2. X2: B3.1 to 3.6 thermocouples core probe

      The ICP Core probe has 6 separate thermocouples, five in the stem and one in the handle.

      The probe when used in a process should be completely embedded in the product to be cooked, such that the 5 thermocouples in the stem are recording a temperature inside the product .

      The processor calulates the average temperature of the 5 thermocouples and uses this value in order to aciieve an accurate core temperature.

      The handle of the Core probe should be pushed against the body of the product to be cooked as the thermocuople situated in the handle is used in the process for product browning and therefore should be in contact with the product surface.

    1. Reviewer #2 (Public Review):

      Summary:

      This was a well-executed and well-written paper. The authors have provided important new datasets that expand on previous investigations substantially. The discovery that changes in diet are not so closely correlated with the presence of alkaloids (based on the expanded sampling of non-defended species) is important, in my opinion.

      Strengths:

      Provision of several new expanded datasets using cutting edge technology and sampling a wide range of species that had not been sampled previously. A conceptually important paper that provides evidence for the importance of intermediate stages in the evolution of chemical defense and aposematism.

      Weaknesses:

      There were some aspects of the paper that I thought could be revised. One thing I was struck by is the lack of discussion of the potentially negative effects of toxin accumulation, and how this might play out in terms of different levels of toxicity in different species. Further, are there aspects of ecology or evolutionary history that might make some species less vulnerable to the accumulation of toxins than others? This could be another factor that strongly influences the ultimate trajectory of a species in terms of being well-defended. I think the authors did a good job in terms of describing mechanistic factors that could affect toxicity (e.g. potential molecular mechanisms) but did not make much of an attempt to describe potential ecological factors that could impact trajectories of the evolution of toxicity. This may have been done on purpose (to avoid being too speculative), but I think it would be worth some consideration.

      In the discussion, the authors make the claim that poison frogs don't (seem to) suffer from eating alkaloids. I don't think this claim has been properly tested (the cited references don't adequately address it). To do so would require an experimental approach, ideally obtained data on both lifespan and lifetime reproductive success.

    2. eLife assessment

      This important study sheds light on how poison frogs gain their toxins, with surprising new data on low levels of toxins in previously non-toxic frogs. The authors propose a new theory for evolution of toxicity based on convincing evidence, but the manuscript needs restructuring to be clearer. While the manuscript will benefit from improved presentation, this research has the potential to greatly impact our understanding of animal defense mechanisms.

    1. We propose a variant of autoencoderswhich can work with two views of the data, while being explicitly trained to achieve all

      The goal is to build an autoencoder that learns a common representation of a single object when given multiple perspectives during training.

    2. "Correlational neural networks" - looking at learning from multiple perspectives of the same thing to increase representation learning.

      @article{chandar2016neuralcompjour, author = {Chandar, Sarath and Khapra, Mitesh M and Larochelle, Hugo and Ravindran, Balaraman}, date-added = {2024-08-01 10:47:30 -0400}, date-modified = {2024-08-01 10:50:01 -0400}, journal = {Neural Computation}, keywords = {correlation-learning, machine-learning, inductive-bias, autoencoders}, number = {2}, pages = {257--285}, pdf = {https://www.researchgate.net/profile/Balaraman-Ravindran/publication/275588055_Correlational_Neural_Networks/links/55ed84d308ae21d099c75c00/Correlational-Neural-Networks.pdf}, publisher = {MIT Press}, title = {Correlational neural networks}, venue-short = {NeuralCompJour}, volume = {28}, year = {2016}}

    1. Главное цыганское гетто Европы

      Это видео о том, что в словакий, во первых много цыган, у них есть свой поселение, которых безумно сильно остают от всего мира: 1. электричества, света 2. дома сделаны в ручную, некоторые без крыш как в Африке, или в видео про Сеул. во вторых, правительство Словакий очень помогает им с условиями жизни. строит дома, или проводит электричество.

      в этом видео Леша посещяет одно из таких населений Цыган, которое считается самым современным, в этом месте просто куча детей. условно у каждого человека есть, как минимум, 5 братьев и сестер.

      оно такое цивильное, потому в этом поселений когда-то родился будущий управляшющий этого района. именно он сделал его ухоженным, практический без мусора и относительно приятным для проживания. хотя до этого было просто тьма мусора по всему району

      он в детстве видел, как всё плохо. закончил вуз и получил диплом и стал дипломатом ради того, чтоб сделать жизнь людей в месте, где он родился лучше

    1. eLife assessment

      This valuable study examines whether the BMP signaling pathway has a role in H3.3K27M DMG tumors, regardless of the presence of ACRVR1 activating mutations. The authors provide solid evidence that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. Although this work could be further enhanced by the inclusion of additional models, the study overall points to BMP2/7 as a potential target for future therapies in this deadly cancer.

    2. Reviewer #1 (Public Review):

      Summary:

      Mutational analysis of diffuse midline glioma (DMG) found that ACVR1 mutations, which up-regulate BMP signaling pathway are found in most H3.1K27M, but not H3.3K27M DMG cases. In this manuscript, Huchede et al attempted to determine whether the BMP signaling pathway has any role in H3.3K27M DMG tumors. They found that the BMP signaling is activated to a similar level in H3.3K27M DMG cells with wild type ACVR1 compared to ACVR1 DMG cells, likely due to the expression of BMP7 or BMP2. They went on to test whether cells treated with BMP7 or BMP2 treatments affected the gene expression and cell fitness of tumor cells with H3.3K27M mutation. They concluded that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. The major issue for this conclusion is that the authors did not use the right models/controls to obtain results to support this conclusion as detailed below. Therefore, in order to strengthen the conclusion, the authors need to address the major concerns below.

      Strength:<br /> Address an important question in DMG field.

      Major concerns/weakness:<br /> (1) All the results in Fig. 2 utilized two glioma lines SF188 and Res259. The authors should repeat all these experiments in a couple of H3.3K27M DMG lines by deleting H3.3K27M mutation first.<br /> (2) Fig. 3. The experiments of BMP2 treatment should be repeated in another H3.3K27M DMG line using H3.1K27M ACVR1 mutant tumor lines as controls.

      Minor concerns<br /> Fig.2A. BMP2 expression increased in H3.3K27M SF188 cells. Therefore, the statement "whereas BMP2 and BMP4 expressions are not significantly modified (Figure 2A and Figure 2-figure supplement A-B)"is not accurate

      Comments on revised version:

      I had three issues listed above on the initial version. The authors did not address my major concerns of #1 and #2, which are re-listed above.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1

      Summary:

      Mutational analysis of diffuse midline glioma (DMG) found that ACVR1 mutations, which up-regulate the BMP signaling pathway are found in most H3.1K27M, but not H3.3K27M DMG cases. In this manuscript, Huchede et al attempted to determine whether the BMP signaling pathway has any role in H3.3K27M DMG tumors. They found that the BMP signaling is activated to a similar level in H3.3K27M DMG cells with wild-type ACVR1 compared to ACVR1 DMG cells, likely due to the expression of BMP7 or BMP2. They went on to test whether cells treated with BMP7 or BMP2 treatments affected the gene expression and cell fitness of tumor cells with H3.3K27M mutation. They concluded that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. The major issue for this conclusion is that the authors did not use the right models/controls to obtain results to support this conclusion as detailed below. Therefore, in order to strengthen the conclusion, the authors need to address the major concerns below.

      Strength:

      This paper addresses an important question in the DMG field.

      Major concerns/weakness:

      (1) All the results in Fig. 2 utilized two glioma lines SF188 and Res259. The authors should repeat all these experiments in a couple of H3.3K27M DMG lines by deleting the H3.3K27M mutation first.

      We thank the referee for his/her comments that have helped us to strengthen our conclusions. Although we were rather interested in studying how the BMP pathway can participate in installing a particular cell state at the time of expression of the K27M mutation, we have now included the characterization of the native H3.3K27M BT245 and SU-DIPGXIII cell lines, and their counterparts in which the mutation was reverted by CRISPRCas9 (Harutyunyan et al., 2019). As shown in Figure 3-figure supplement D, the growth arrest induced by BMP2 seems indeed to be specific of the K27M epigenetic context, which could also be required to settle a positive regulation loop to activate the BMP pathway, as mentioned in the Discussion.

      (2) Fig. 3. The experiments of BMP2 treatment should be repeated in other H3.3K27M DMG lines using H3.1K27M ACVR1 mutant tumor lines as controls.

      The use of mutant ACVR1 lines is interesting, but their control status seems questionable, as the addition of BMPs could have a cumulative effect on the effect of the mutation, notably by activating other receptors in the pathway. But we have now included 3 different cell lines (HSJD-DIPG-014, BT245 and SU-DIPGXIII), and observed similar impact of BMP2 with growth arrest as a readout (Figure 3-figure supplement C-D)

      Minor concerns

      Fig.2A. BMP2 expression increased in H3.3K27M SF188 cells. Therefore, the statement "whereas BMP2 and BMP4 expressions are not significantly modified (Figure 2A and Figure 2-figure supplement A-B)" is not accurate.

      The referee is absolutely right, and we have corrected this statement.

      Reviewer #2 (Public Review):

      The manuscript by Huchede et al investigates the BMP pathway in H3K27M-mutant gliomas carrying or not activating mutations in ALK2 (ACVR1). Their results in cell lines and in datasets acquired from the literature on patient tumors indicate that the BMP signaling pathway is activated at similar levels between ACVR1 wild-type and mutant tumors. The group further identifies BMP2 and BMP7 as possibly the main activators of the pathway in cells. They then show that BMP2 and 7 crosstalk with the H3 mutation and synergize to induce transcriptomic rewiring leading to an invasive cell state.

      The paper is well-written and easy to follow with a robust experimental plan and datasets supporting the claims. While previous work (acknowledged by the authors) indicated activation of BMP in H3K27M tumors, wild type for the ACVR1 mutation this paper is a nice addition and provides further mechanistic cues as to the importance of the BMP pathway and specific members in these deadly brain cancers. The effect of these BMPs in quiescence and invasion is of particular interest.

      We thank the referee for his/her supportive comments.

      A few suggestions to clarify the message are provided below 1- In thalamic diffuse midline gliomas, the BMP pathway should not be activated as it is in the pons. The authors should identify thalamic tumors in the datasets they explored and patients-derived cell lines from thalamic tumors available to investigate whether this pathway is active across all H3.3K27M mutants in the brain midline or specifically in tumors from the pons.

      The inter-patient variability observed in the level of activation of the BMP pathway may indeed be due, at least in part, to different tumor locations. However, we failed to find this information in the publicly available datasets that we used. We however included this element in the Discussion part.

      (2) There are ~20% H3.3K27M tumors that carry an ACVR1 mutation and similar numbers of H3.1K27M that are wild type for this gene. Can the authors identify these outliers in their datasets and assess the activation of BMP2 and 7 or other BMP pathway members in this context?

      We have now included the outliers present in our datasets in the legends of Figure 1B and Figure 1-figure supplement B and F. From the few samples available to document these outliers in the cohorts that we used, we have not observed major differences regarding the expression levels of BMP2/7 or BMP pathway members and have discussed the fact that it may result from the establishment in all cases of a feedback loop of activation.

      In all this is an interesting paper that provides meaningful data to pursue clinical targeting of the BMP pathway, which would be a nice addition to the field.

      We thank the reviewer for his/her supportive comments.

    1. eLife assessment

      This important study extends existing sequentially Markovian coalescent approaches to include the combined use of SNPs and hypervariable loci such as epimutations. This is an intriguing addition to infer population size history in the recent past, and the authors provide solid validation of their methods via simulation and analysis of empirical data in Arabidopsis thaliana. Given the increasing availability of such data, this work is a timely contribution and represents a foundation for further developments to explore when and where these methods will be best used.

    2. Reviewer #1 (Public Review):

      The authors developed an extension to the pairwise sequentially Markov coalescent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

      Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

      Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process.

    3. Reviewer #2 (Public Review):

      A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors developed an extension to the pairwise sequentially Markov coalescent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes. Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

      Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

      Major comments:

      - For all of the simulated demographic inference results, only plots are presented. This allows for qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

      We believe this comment was addressed in the previous revision (Sup Table 6-10) by adding Root Mean Square Errors for the demographic estimates (and RMSE for recent versus past portions of the demography). 

      - 434: The discussion downplays the really odd result that inputting the true value of the mutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour. (Comment addressed in revision. Still, I find the explanation added at 449ff to be somewhat puzzling -- shouldn't the results of the regional HMM scan only improve if the true mutation rate is given?)

      We do understand that our results and explanation can appear counter-intuitive. As acknowledged by the reviewer, in the previous round of revision we have at length clarified this puzzling behaviour by the discrepancy in assessing methylation regions using the HMM method which then differs from the HMM for the SMC inference. We are happy to clarify further in response to the new question of reviewer 1:

      If the Reviewer #1 means the SNP mutations (e.g. A → T), knowing the true mutation rate does not help the HMM to recover the region level methylation status. 

      If the Reviewer #1 means the epimutations (whether it is the region, site or both), knowing the true epimutations rates could theoretically help the HMM to recover the region level methylation status. However, at present, our method does not leverage information from epimutation rates to infer the region level methylation status. As inferring the epimutations rates is one of the goals of this study in the SMC inference, and that region level methylation status is required to infer those rates, we suspect that using epimutations rates to infer the region level methylation status could be statistically inappropriate (generating some kind of circular estimations). Instead, our HMM uses only the proportion of methylated and unmethylated sites (estimated from the genome) to determine whether or not a region status is most-likely to be methylated or unmethylated. We now explicit this fact in the HMM for methylation region in the method section.

      We acknowledge that our HMM to infer region level methylation status could be improved, but this would be a complete project and study on its own (due to the underlying complexity of the finite site and the lack of a consensus model for epimutations at evolutionary time scale). We believe our HMM to have been the best compromise with what was known from methylation and our goals when the study was conducted, and future work is definitely worth conducting on the estimation of the methylation regions.

      - As noted at 580, all of the added power from integrating SMPs/DMRs should come from improved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases. (Comment addressed in revision via Supp. Table 7.).

      - A general remark on the derivations in Section 2 of the supplement: I checked these formulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

      We believe this comment was acknowledged in the previous revision (line 649), and we thank the reviewer for this interesting insight.

      - Most (all?) of the SNP-only SMC methods allow for binning together consecutive observations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

      We believe this comment was addressed in the previous revision and was added to the manuscript in the methods Section (subsection :  SMC optimization function).

      - 486: The assumed site and region (de)methylation rates listed here are several OOM different from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533? (Comment addressed in revision.)

      Reviewer #2 (Public Review):

      A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

      We thank again the reviewer #2 for his positive comments.  

      Reviewer #3 (Public Review):

      I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems reasonable and in principle the inclusion of highly mutable sties is a nice advance. This is an exciting new avenue for thinking about inference from genomic data. I remain a bit concerned about how well this will work in systems where much less is understood about methylation,

      The authors include some good caveats about applying this approach to other systems, but I think it would be helpful to empiricists outside of thaliana or perhaps mammalian systems to be given some indication of what to watch out for. In maize, for example, there is a nonbimodal distribution of CG methlyation (35% of sites are greater than 10% and less than 90%) but this may well be due to mapping issues. The authors solve many of the issues I had concerns with by using gene body methylation, but this is only briefly mentioned on line 659. I'm assuming the authors' hope is that this method will be widely used, and I think it worth providing some guidance to workers who might do so but who are not as familiar with these kind of data.

      We thank the reviewer #3 for his positive comments. And we agree with Reviewer #3 concerning the application to data and that our approach needs to be carefully thought before applied. Our results clearly show that methylation processes are not well enough understood to apply our approach as we initially (maybe naively) designed it. Further investigations need to be conducted and appropriate theoretical models need to be developed before reliable results can be obtained. And we hope that our discussion points this out. However, our approach, the theoretical models and the additional tools contained in this study can be used to help researchers in their investigations to whether or not use different genomic markers to build a common (potentially more reliable) ancestral history. We enhanced the discussion in this second revision by clarifying also the use of the methylation from genic regions to avoid  confusion (lines 700-731).

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      In added Supp. Table 7, I don't think these are in log10 units as stated in the caption.

      Well Spotted! Indeed, the RMSE is not in log10 scale, we corrected the caption. We also added that the TMRCA used for MRSE calculations is in generations units to avoid potential confusion.  

      Reviewer #3 (Recommendations for The Authors):

      I very much appreciate the authors' attention to previous questions. I would ask that a bit more is spent in the discussion on concerns/approaches empiricists should keep in mind -- I am wary of this being uncritically applied to data from non-model species. It was not clear to me, for example (only mentioned on line 659 in the discussion) that the thaliana data is only using gene-body methylation. This poses potential issues with background selection that the authors acknowledge appropriately, but also assuages many of my concerns about using genome-wide data. I think text with recommendations for data/filtering/etc or at least cautions of assumptions empiricists should be aware of would help.

      We apologize for the confusion at line 659. As written in the other section of the manuscript we meant CG sites in genic regions (and not only gene body methylated regions).

      Due to the manuscript’s structure, the data from Arabidopsis thaliana is only described at the very end of the manuscript (line 900+). However, a brief description could also be found line 291-296. We however added a sentence in the introduction (line 128) for clarity. 

      We however agree with the comment made by reviewer #3 concerning the application to data. We pointed in the discussion the risk of applying our approach on ill-understood (or illprepared) data and stressed the current need of studies on the epimutations processes at evolutionary time scale ( i.e. at Ne time scale) (line 700-703).

    1. eLife assessment

      This work presents valuable information on the structure of the spirosome's native extended conformation as the active form of the aldehyde-alcohol dehydrogenase (AdhE) enzyme. The evidence is solid, although the work does not provide a mechanistic understanding of the function and dynamics of AdhE.

    2. Reviewer #1 (Public Review):

      Clostridium thermocellum serves as a model for consolidated bioprocessing (CBP) in lignocellulosic ethanol production. The primary ethanol production pathway involves the enzyme aldehyde-alcohol dehydrogenase (AdhE), which exhibits complex regulation, forming long oligomeric structures known as spirosomes.

      The present study describes the cryo-EM structure of C. thermocellum AdhE, resolved at 3.28 Å resolution. By integrating cryo-EM data with molecular dynamics simulations, this study showed that the aldehyde intermediate resides longer in the channel of the extended form, supporting the mechanistic model in which the extended spirosome conformation represents the active form of AdhE.

      These findings advance the understanding of the function and regulation of AdhE, a key enzyme involved in the ethanol biosynthesis pathway in Clostridium thermocellum, a model organism for ethanol production in consolidated bioprocessing.

    3. Reviewer #2 (Public Review):

      Summary:

      The manuscript by Ziegler et al, entitled 'Structural characterization and dynamics of AdhE ultrastructure from Clostridium thermocellum: A containment strategy for toxic intermediates?" presents the atomic resolution cryo-EM structure of C. thermocellum AdhE showing that it show dominantly an extended form while E.coli AdhE shows dominantly a compact form. With comparative analysis of their C. thermocellum structure and the previous E.coli AdhE structure, they tried to reveal the mechanism by which C.thermocellum and E.coli show different dominant conformations. In addition, they also analyzed the substrate channel by comparative and computational approaches. Lastly, their computational analysis using CryoDRGN reveals conformational heterogeneity in the sample. Despite this the manuscript is very descriptive and does not provide a mechanistic understanding by which AdhE works, this work will provide structural frame works to further investigate the function and mechanism of AdhE dynamics.

      Strengths:

      This manuscript provides the first C. thermocellum (Ct) AdhE structure and comparatively analyzed this structure with E.coli AdhE.

      Weaknesses:

      This work is very descriptive and does not provide mechanistic understanding of the function and dynamics of AdhE.

    4. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary:

      Clostridium thermocellum serves as a model for consolidated bioprocess (CBP) in lignocellulosic ethanol production, but yet faces limitations in solid contents and ethanol titers achieved by engineered strains thus far. The primary ethanol production pathway involves the enzyme aldehydealcohol dehydrogenase (AdhE), which forms long oligomeric structures known as spirosomes, previously characterized via the 3.5 Å resolution E. coli AdhE structure using single-particle cryoEM. The present study describes the cryo-EM structure of the C. thermocellum ortholog, sharing 62% sequence identity with E. coli AdhE, resolved at 3.28 Å resolution. Detailed comparative structural analysis, including the Vibrio cholerae AdhE structure, was conducted. Integrating cryoEM data with molecular dynamics simulations indicated that the aldehyde intermediate resides longer in the channel of the extended form, supporting the hypothesis that the extended spirosome represents the active form of AdhE. 

      Strengths: 

      The study conducts a comprehensive structural comparative analysis of oligomerization interfaces and the acetaldehyde channel across compact and extended conformations. Structural and computational results suggest the extended spirosome as the most likely active state of AdhE. 

      Weaknesses: 

      The overall resolution of the C. thermocellum structure is similar to the E. coli ortholog, which shares 62% sequence identity, and the oligomerization interfaces and the acetaldehyde channel were previously described. 

      Reviewer #2 (Public Review): 

      Summary: 

      The manuscript by Ziegler et al, entitled 'Structural characterization and dynamics of AdhE ultrastructure from Clostridium thermocellum: A containment strategy for toxic intermediates?" presents the atomic resolution cryo-EM structure of C. thermocellum AdhE showing that it show dominantly an extended form while E. coli AdhE shows dominantly a compact form. With comparative analysis of their C. thermocellum structure and the previous E. coli AdhE structure, they tried to reveal the mechanism by which C. thermocellum and E. coli show diXerent dominant conformations. In addition, they also analyzed the substrate channel by comparative and computational approaches. Lastly, their computational analysis using CryoDRGN reveals conformational heterogeneity in the sample. Although this manuscript suggests a potential mechanism of the diXerent features of AdhEs, this manuscript is very descriptive and does not provide suXicient data to support the authors' conclusions, which may be due to the lack of experimental data to support their findings from the computational analysis. 

      Strengths: 

      This manuscript provides the first C. thermocellum (Ct) AdhE structure and comparatively analyzed this structure with E. coli AdhE. 

      Weaknesses: 

      Their main conclusions obtained mostly by computational and comparative analysis are not supported by experimental data. 

      Reviewer #3 (Public Review): 

      This study describes the first structure of Gram-positive bacterial AdhE spirosomes that are in a native extended conformation. All the previous structures of AdhE spirosomes obtained come from Gram-negative bacterial species with native compact spirosomes (E. coli, V. cholerae). In E. coli, AdhE spirosomes can be found in two diXerent conformational states, compact and extended, depending on the substrates and cofactors they are bound to. 

      The high-resolution cryoEM structure of the extended C. thermocellum AdhE spirosomes produced in E. coli in an apo state (without any substrate or cofactors) is compared to the E. coli extended and compact AdhE spirosomes structures previously published. The authors have modeled (in Swiss-Model) the structure of compact C. thermocellum AdhE spirosomes, using E. coli compact AdhE spirosome conformation as a template, and performed molecular dynamics simulations. They have identified a channel in which the toxic reaction intermediate aldehyde could transit from the aldehyde dehydrogenase active site to the alcohol dehydrogenase active site, in an analogous manner to E. coli spirosomes. These findings are in line with the hypothesis that the extended spirosomes could correspond to the active form of the enzyme. 

      In this work, the authors speculate that the C. thermocellum AdhE spirosomes could switch from the native extended conformation to a compact conformation, in a way that is inverse of E. coli spirosomes. Although attractive, this hypothesis is not supported by the literature. Amazingly, in some Gram-positive bacterial species (S. pneumoniae, S. sanguinis or C. di8icile...), AdhE spirosomes are natively extended and have never been observed in a compact conformation. On the opposite, E. coli (and other Gram-negative bacteria) native AdhE spirosomes are compact and are able to switch to an extended conformation in the presence of the cofactors (NAD+, coA, and iron). The data presented as they are now are not convincing to confirm the existence of C. thermocellum AdhE spirosomes in a compact conformation. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      Major points: 

      (1) The claim of achieving the highest resolution AdhE structure lacks strong support since the E. coli structure was solved at 3.5A, whereas the C. thermocellum was solved at 3.28A. Conducting a local resolution analysis could provide insights into distinct structural interpretations, enhancing the strength of the claim. 

      We have modified the sentence claiming this as the highest resolution AdhE structure to say, “In this study, we presented and analyzed a high-resolution structure of the AdhE spirosome from C. thermocellum.” We have included the local resolution map in Figure 2C – all structural analysis was performed in regions from the center of the molecule, where the highest resolution information was determined.

      (2) The comparative structural analysis of the oligomerization interface is thorough, yet it could benefit from greater conciseness. Focusing on highlighting major findings would streamline the presentation and enhance clarity. 

      We altered a few places in the comparative structural analysis in response to other reviewers. We also divided the main structure section into two subsections (spirosome interfaces and AdhE active sites) to enhance clarity.

      Reviewer #2 (Recommendations For The Authors): 

      (1) The authors should change the tile containing "?". Does it mean that the conclusions that the authors made are still in question? 

      We have removed the question mark to indicate that our results point to a channeling mechanism.

      (2) Figure 1B: Clarify Ct Fwd. Is this adding NADH, and Ct Rev adding NAD+? 

      This information is described in the text in lines 98-100. It is also at the bottom of figure 1B.

      (3) Line 131: Please revise accordingly for clarity: "The extended dimer interfaces" è "The extended E.coli dimer interface". 

      This has been edited for clarity. We have added the following sentence resulting to indicate which interfaces that are being discussed: “Both the E. coli and C. thermocellum extended dimer interfaces bury ~5000 Å2. While the compact C. thermocellum compact dimer interface buries a similar surface area of ~4800 Å2, the E. coli dimer interface buries ~3800 Å2.”

      (4) Line 133-136: Why that does not seem to be the case? These sentences are not clear what the authors exactly mean. 

      We altered the text to say, “One would expect the compact structure in E. coli to have a larger buried surface area due to it being the predominant form when it is examined without additives, but that is not the case; further corroborating that factors other than buried surface area must impact the apo state of the spirosome.” We hope this clarifies our intent.

      (5) Line 138-145: The authors should provide a logic for how the diXerent distribution of the charged residues would change the form of AdhE. It may just be a diXerent distribution nothing to do with the conformational change. 

      After further analysis of the interface amino acid distribution, we agree that the distribution may have nothing to do with the conformational change. We have changed this section to end with the sentence “Analysis of the residues buried in these interfaces reveals that while many of the residues are identical in the C. thermocellum and E. coli extended structures, there are some diXerences in amino acid type distribution, although nothing that directly indicates control of conformer state (Supplemental Figure 3).” 

      (6) Line 169: Kim et al. è Cho et al.

      We have corrected this error.

      (7) Line 122-235: The whole section is just describing the diXerence between Ct and Ec AdhE suggesting that this diXerence may contribute to the conformational diXerence without any evidence. The author cannot say that the diXerences in the interface, active sites cofactor pockets, etc explain why two AdhE (Ct, Ec) have diXerent domain conformers unless they provide experimental data. 

      We did not conclude that any diXerences we observed structurally were responsible for the conformation change. The purpose of this section was solely to compare the structures to determine if we could find a structural basis for the diXerence between E. coli and C. thermocellum conformation – we stated a few times throughout the section and in the discussion that there were no immediate structural reasons for this diXerence in shape. We have added a few sentences in the discussion to address whether Gram-positive vs. Gram-negative is influencing the shape, addressed in reviewer #3 comment #4. 

      (8) Line 237: The whole section "Identification..." analyzed the substrate channel by computational analysis. The author should provide experimental evidence that these residues identified are critical for channeling by generating mutants and measuring their activity. 

      We agree that mutagenesis is the next logical step for these results, however it is outside the scope of work of this paper as this study will not be that straightforward. We have included a sentence in the discussion to indicate our plans for further investigation to the channel that says, “Future mutagenesis studies will be needed to confirm whether the spirosome exists to control the reaction flux in high-reactant conditions.”

      Reviewer #3 (Recommendations For The Authors): 

      (1) The capacity of C. thermocellum AdhE spirosomes to switch from a natively extended conformation to a compact conformation is not demonstrated in this manuscript, as it is now. Because this would be the first time that Gram-positive bacterial AdhE spirosomes are observed in a compact conformation, the authors should provide a clear demonstration of their existence by presenting reliable and good images of C. thermocellum compact spirosomes. 

      We have modified Figure 1A to zoom in on one compact and extended spirosome that we have identified from each C. thermocellum sample. We have included triangles of the same size and shape to indicate the proximity of a turn of a helix, showing that the identified compact spirosomes have a tighter conformation than extended spirosomes.

      (2) The authors should show at least an image of the compact C. thermocellum spirosomes, that they claim to observe in the presence of NADH or in the forward reaction conditions mentioned in Figure 1. The authors have added diXerent reactants to the extended C. thermocellum spirosomes and visualized their conformation by negative stain. An image of each condition tested would be valuable and would nicely complete the distribution of compact versus extended spirosomes presented in Figure 1. 

      We have created a new supplemental figure with spirosomes circled for all of the experimental conditions for C. thermocellum (Supplemental figure 1). We have added a reference to supplemental figure 1 in the text to direct the reader to these images.

      (3) The cryoEM classes presented in Figure 8 are not convincing and could correspond to dimers or rosettes of AdhE or to E. coli endogenous AdhE. CryoEM classes showing longer compact C. thermocellum spirosomes should be shown. The percentage of these compact spirosomes visualized in the micrographs should be added and discussed in the text as it would increase confidence in these findings and confirm that C. thermocellum compact spirosomes exist. Heterologous production of C. thermocellum AdhE in E. coli depleted for its endogenous AdhE would be required to definitively prove that these are compact C. thermocellum AdhE spirosomes in the cryoEM. 

      We included the pictures of the theoretical compact spirosomes, as generated from the 8-mer of E. coli AdhE (6AHC) to address the possibility of rosettes. We have now indicated in the text that there were 6.7% of the particles in the compact conformation, which is less than seen by negative stain. We further mentioned that the compact spirosome is less compact than that seen in E. coli. We added a sentence to the discussion about the possibility of contaminating E. coli spirosomes (though this is very unlikely ) in our compact spirosome analysis: “While these compact spirosomes could result from expression in E. coli, though this is very unlikely, we also identified compact spirosomes in a native C. thermocellum lysate, which would not have similar contamination issues.”

      (4) The authors should include and discuss in the text previous findings (among which Laurenceau et al., 2015...) describing the diXerences between Gram-positive and Gram-negative spirosomes. AdhE spirosomes are natively extended in most Gram-positive bacterial species (S. pneumoniae, S. sanguinis or C. diXicile...), and have never been observed in a compact conformation. On the opposite, E. coli (and other Gram-negative bacteria) native AdhE spirosomes are compact and are able to switch to an extended conformation in the presence of the cofactors (NAD+, coA, and iron). 

      We have added the following sentences to the discussion to address this comment: “This could potentially be due to the diXerences between Gram-positive and Gram-negative bacteria. In previous studies, compact spirosomes have only been isolated from Gram-negatives while solely extended spirosomes have been isolated from Gram-positives. Furthermore, while the compact spirosomes can transition to extended in the presence of cofactors, the reverse has not been previously observed with an extended spirosome.”

      (5) The authors have spotted some diXerences between the E. coli and C. thermocellum structures, that they believe could explain the intrinsic capacity of these spirosomes to be natively extended or compact. It would be interesting to confirm this hypothesis by measuring C. thermocellum extended AdhE spirosome activity and comparing it to E. coli extended spirosomes. The impact of mutations in the regions proposed by the authors to be important in the capacity of C. thermocellum AdhE to be extended (especially the GxGxxG motif and the D494 position) would be appreciated to confirm this hypothesis. 

      We agree that this would be an interesting avenue of research although it is currently outside the scope of this paper. We are looking into experiments that we can perform where we can track both activity and conformation but have not found an ideal experiment at this time.

      (6) Many statements and result interpretations are overstated in several parts of the manuscript and would need to be rewritten to balance the absence of clear evidence of C. thermocellum compact spirosomes. 

      We have shown that we have identified compact spirosomes, addressed in multiple comments above. We have adjusted the language of the paper to indicate more uncertainty that will be followed up in future mutagenesis experiments. However, these mutations are not that simple to identify and this research would require a fairly large study that is better suited for a follow up manuscript.

      (7) The Figure 7 legend would need to be corrected.

      We are unsure as to what needs to be corrected in the figure 7 legend based on this comment.