DOI: 10.1101/2024.06.26.600791

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DOI: 10.1101/2024.06.26.600791

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DOI: 10.1101/2024.06.26.600791

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DOI: 10.3390/plants13131765

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DOI: 10.1101/2024.06.21.599877

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DOI: 10.1101/2024.06.21.599974

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DOI: 10.1101/2024.06.21.599974

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DOI: 10.1101/2024.06.21.599974

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DOI: 10.1101/2024.06.21.599974

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DOI: 10.1101/2024.06.21.599974

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DOI: 10.1101/2024.06.25.600671

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DOI: 10.1101/2024.06.23.600144

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What is this?

DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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What is this?

DOI: 10.3390/cancers16132367

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What is this?

DOI: 10.3390/cancers16132367

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What is this?

DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.3390/cancers16132367

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DOI: 10.1101/2024.06.24.600360

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DOI: 10.1101/2024.06.24.600360

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DOI: 10.1101/2024.06.24.600360

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DOI: 10.1101/2024.06.24.600360

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DOI: 10.1101/2024.06.27.600913

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DOI: 10.1101/2024.06.27.600913

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DOI: 10.1101/2024.06.27.600913

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DOI: 10.1101/2024.06.26.600909

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DOI: 10.1371/journal.ppat.1012343

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DOI: 10.1101/2024.06.22.600169

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DOI: 10.1101/2024.06.22.600169

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DOI: 10.1101/2024.06.27.600564

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DOI: 10.1101/2024.06.27.600564

Resource: (RRID:CVCL_9772)

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What is this?

DOI: 10.3390/v16071036

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DOI: 10.3390/v16071036

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DOI: 10.3390/v16071036

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DOI: 10.3390/v16071036

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DOI: 10.1101/2024.06.24.600539

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DOI: 10.1101/2024.06.24.600539

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DOI: 10.1101/2024.06.28.601229

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DOI: 10.1101/2024.06.12.598660

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DOI: 10.1101/2024.06.18.599588

Resource: MMRRC_067175-UCD

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What is this?

Dose this mean a fly in a lab setting could mate less than a true wild fruit fly? (assuming lab flies produce less dopamine compared to their wild counterpart)

Using this knowledge, I wonder if eye color could also effect the chances of mating in these fruit flies.

This leads me to question if females mate more to visual cues rather than chemical cues.

...

aha. random.

///

I can see note here.

Addition of an H5P.

Keep with box when you move it obviously. Do these need to be separate sentences? Must they be so far away from the box. They do not appear to go together at first glance, which is important to making information on webpages digestible/accessible. same above.

this has to come earlier; either above (preferably) or right under the map. It would have saved me wondering and asking time with some (!) of my earlier questions. This goes for the box above as well.

How in relation/comparison to the 5-star facilities we started with? This also goes for the map above as well.

Why are you starting here rather than with the 5-star facilities? This goes for the statement above as well.

I know it makes sense numerically to start with the lowest ratings but that isn't why people will come to your site. They want to know which facility is ranked highest. So reverse this scale to reflect that. This goes for the scale above as well.

So, where are they? Please include a map that shows Oregon, the borders of its major areas, cities and/or towns and the number of facilities in each.

Maybe you are attempting to say too much but sacrificing clarity in this heading? what does "overall star" mean; are your referring to ratings, no of facilities in an area on the map...? If it's ratings, what does overall mean? is it different from the 5-star we've been working with previously? if so, why? how?

"One dot"? which dot? the yellow, gray or black one?

Please make the map clear, user friendly. add the borders and place names for major areas or cities/towns. Not everyone has memorized Oregon's geographical layout. What is someone from Washington or Idaho were viewing this? Help them to know where things are.

Reverse these so they are the same as above and make sense for the subheading "Where..."

clarify - what does "overall one star" - are you referring to ratings or something else? "with one dot per resident" per facility or city/town? Maybe you are attempting to say too much in this heading?

Can you make this map clearer? Include the borders for cities/towns. Include (some) place names. Not everyone is that familiar with places in oregon and what if someone in washington were viewing this page? Help them know where places are.

change to "approximately"

Thread migration from auto-NUMA Balancing; Potential source of conflicts against CPU Load Balancer's decisions

This resonated strongly with me. We have discussed a great deal about having student centered classrooms, but I am not sure if we have actually considered the student voice and ideas in designing classrooms that focus on them.

nice

Link to previous section, growth mindset.

https://boffosocko.com/2016/09/30/complexity-isnt-a-vice-10-word-answers-and-doubletalk-in-election-2016/

Discussed as part of "storytelling vs. logic" at FoTL

Edmund Gröpl's concept map of Adler & Van Doren's How to Read a Book via https://forum.zettelkasten.de/discussion/comment/20668#Comment_20668:

My main takeaway from this quote is, if you work hard, you will be successful. That is true when it comes to many things. However, when if comes to the livelihood of marginalized groups of people… Not so much. I have seen individuals work extremely hard and not reach the level of success that matches their work ethic due to discrimination, lack of resources, and no support. This quote makes me sad due to the context that it is being used in.

I have had several conversations with friends and family about the phrase “the rich get richer, and the poor get poorer. We don’t get to choose the family we are born in. When you are born into a poor family with no resources you are already at a disadvantage from birth. Impoverish stricken individuals focus is survival. They do not have the skill set and educational background to land a job where they can afford healthcare and nice housing. Understanding the phrase above is crucial for developing policies to create a more equitable and inclusive society.

In my opinion as a DCFS Child Welfare Specialist, I believe benefits should be made eligible to all. The term “able-bodied” is just not inclusive. There is no equity in education, housing opportunities, medical care and etc. to simply allow individuals who are able but suffering to go without support.

Mental health, for example, is often under treated and can be truly debilitating in impacting someone's job performance or attendance. I believe not everyone has the learned coping skills to deal with adversity and self-maintenance and need assistance to help them with survival and change. I believe the assistance should be guided, monitored, goal oriented and be programmatic so that we see progress and not stagnate their dependency without giving the skill set to achieve personal growth. I do believe adjustment should be made during the time the assistance is being utilized. I see many able-bodied individuals that were born into poverty and has zero support and need assistance just as much as a person that has a disability.

**MOST IMPORTANT: Essential questions allow more inquiry. It also allows the learning to mbe transferable between content areas/units. It allows a focus on concepts rather then a focus on content **

should be framed as a question, not a topic. An ESSENTIAL QUESTION!

Most important: Planning ahead and plannning backwards!!!

think about where the learning is going after one lesson

think about how we will assess the inquiries

plan ahead AND plan backwards with the inquiry process. Have things in mind for the lesson, but if the students questioning takes a turn, allow the lesson to switch gears

"accidentally" getting to the curriculum through inquiry!

keep curriculum requirements in mind when completing

Teachers should dive deep into these documents before "framing the inquiry"

should continue to learn over time! Grow on past knowledge

remove teacher guidance and move towards student guided learning

How do we do this? Should we not refer to the process at all?

dialogue is key when it comes to inquiry

Inquiry & questioning is a messy process! There is no one right way to approach it.

Love this idea

https://www.urbandictionary.com/define.php?term=pking

pking - player killing

stop

I really love this method. it is important to understand how others perceive a concept.

Very interesting method, and way of looking at the signal. "What makes a document exceptional because something is common within itself and uncommon without".

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Genomics.<br /> See the peer reviews and the recommendation.

I really enjoy these types of activities that the person decided what action to take because you can feels part of the story, game etc..

This its a perfect example of Mathematical sublime because its infinite and its vast.

É por isso que no âmbito deste módulo da microcredencial uma etapa (neste caso uma semana) está dividida apenas em uma e-atividade que se subdivide em fases? Quando faz sentido dividir em e-atividade 1 e e-atividade 2, existe um critério definido?

Totalmente de acordo. Tenho por hábito referir aos meus formandos que "para quem não sabe para onde vai qualquer caminho serve" (relembrando as sábias palavras do Coelho em Alice do País das Maravilhas) e daí a importância de uma planificação adequada que obrigatoriamente envolve o desenvolvimento de e-atividades com intencionalidade pedagógica.

A formulação de questões, a investigação e o desenvolvimento do pensamento crítico tem sido apontada como um ponto fundamental nos processos de ensino-aprendizagem, na medida em que promove um tipo de raciocínio de complexidade e qualidade superiores potenciado a tomada de decisões a vários níveis e normalmente associada a resultados positivos.

it wold be useful to have a link to synopsis

I think the author mentions kurtz is accompanied by half caste to point out racial beliefs they had during that time. Since he mentions multiple times.

a person whose parents are of different races, in particular, with a European father and an Indian mother.

he is so rude

clever

difference in description again

annoying, irritating

[dadasnake vs Qiime2] on clusters

Where does this overhead come from?

https://www.youtube.com/watch?v=a6CDK4wJFsg

https://www.reddit.com/r/Wikipediavideos

GitHub Copilot vs 豆包 MarsCode

Good idea!

Author response:

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

eLife assessment:

This useful modeling study explores how the biophysical properties of interneuron subtypes in the basolateral amygdala enable them to produce nested oscillations whose interactions facilitate functions such as spike-timing-dependent plasticity. The strength of evidence is currently viewed as incomplete because of insufficient grounding in prior experimental results and insufficient consideration of alternative explanations. This work will be of interest to investigators studying circuit mechanisms of fear conditioning as well as rhythms in the basolateral amygdala.

We disagree with the overall assessment of our paper. The current reviews published below focus on two kinds of perceived inadequacies. Reviewer 1 (R1) was concerned that the fear conditioning paradigm used in the model is not compatible with some of the experiments we are modeling. The reviewer helpfully suggested in the Recommendations for the Authors some papers, which R1 believed exposed this incompatibility. In our reading, those data are indeed compatible with our hypotheses, as we will explain in our reply. Furthermore, the point raised by R1 is an issue for the entire field. We will suggest a solution to that issue based on published data.

Reviewer 2 (R2) said that there is no evidence that the BLA is capable of producing, by itself, the rhythms that have been observed during fear conditioning in BLA and, furthermore, that the paper we cited to support such evidence, in fact, refutes our argument. We believe that the reasoning used by reviewer 2 is wrong and that the framework of R2 for what counts as evidence is inadequate. We spell out our arguments below in the reply to the reviewers.

Finally, we believe this work is of interest far beyond investigators studying fear conditioning. The work shows how rhythms can create the timing necessary for spike-timing-dependent plasticity using multiple time scales that come from multiple different kinds of interneurons found both in BLA and, more broadly, in cortex. Thus, the work is relevant for all kinds of associative learning, not just fear conditioning. Furthermore, it is one of the first papers to show how rhythms can be central in mechanisms of higher-order cognition.

Reviewer #1

We thank Reviewer 1 for his kind remarks about our first set of responses and their understanding of the importance of the work. There was only one remaining point to be addressed:

Deficient in this study is the construction of the afferent drive to the network, which does elicit activities that are consistent with those observed to similar stimuli. It still remains to be demonstrated that their mechanism promotes plasticity for training protocols that emulate the kinds of activities observed in the BLA during fear conditioning.

It is true that some fear conditioning protocols involve non-overlapping US and CS, raising the question of how plasticity happens or whether behavioral effects may happen without plasticity. This is an issue for the entire field (Sun et al., F1000Research, 2020). Several papers (Quirk, Repa and LeDoux, 1995; Herry et al, 2007; Bordi and Ledoux 1992) show that the pips in auditory fear conditioning increase the activity of some BLA neurons: after an initial transient, the overall spike rate is still higher than baseline activity. The question remains as to whether the spiking is sustained long enough and at a high enough rate for STDP to take place when US is presented sometime after the stop of the CS.

Experimental recordings cannot speak to the rate of spiking of BLA neurons during US due to recording interference from the shock. However, evidence seems to suggest that ECS activity should increase during the US due to the release of acetylcholine (ACh) from neurons in the basal forebrain (BF) (Rajebhosale et al., 2024). Pyramidal cells of the BLA robustly express M1 muscarinic ACh receptors (Muller et al., 2013; McDonald and Mott, 2021) and M1 receptors target spines receiving glutamatergic input (McDonald et al., 2019). Thus, ACh from BF should elicit a long-lasting depolarization in pyramidal cells. Indeed, the pairing of ACh with even low levels of spiking of BLA neurons results in a membrane depolarization that can last 7 – 10 s (Unal et al., 2015). This implies that the release of ACh can affect the consequences of the CS in successive trials. This should include higher spiking rates and more sustained activity in the ECS neurons after the first presentation of US, thus ensuring a concomitant activation of ECS and fear (F) neurons necessary for STDP to take place. Hence, we suggest that a solution to the problem raised by R1 may be solved by considering the role of ACh release by BF. To the best of our knowledge, there is nothing in the literature that contradicts this potential solution. The model we have may be considered a “minimal” model that puts in by hand the higher frequency due to the cholinergic drive without explicitly modeling it. As R1 says, it is important for us to give the motivation of that higher frequency; in the next revision, we will be explicit about how the needed adequate firing rate can come about without an overlap of CS and US in any given trial.

Reviewer #2

The authors of this study have investigated how oscillations may promote fear learning using a network model. They distinguished three types of rhythmic activities and implemented an STDP rule to the network aiming to understand the mechanisms underlying fear learning in the BLA.

After the revision, the fundamental question, namely, whether the BLA networks can or cannot intrinsically generate any theta rhythms, is still unanswered. The author added this sentence to the revised version: "A recent experimental paper, (Antonoudiou et al., 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone." In the cited paper, the authors studied gamma oscillations, and when they applied 10 uM Gabazine to the BLA slices observed rhythmic oscillations at theta frequencies. 10 uM Gabazine does not reduce the GABA-A receptor-mediated inhibition but eliminates it, resulting in rhythmic populations burst driven solely by excitatory cells. Thus, the results by Antonoudiou et al., 2022 contrast with, and do not support, the present study, which claims that rhythmic oscillations in the BLA depend on the function of interneurons. Thus, there is still no convincing evidence that BLA circuits can intrinsically generate theta oscillations in intact brain or acute slices. If one extrapolates from the hippocampal studies, then this is not surprising, as the hippocampal theta depends on extra-hippocampal inputs, including, but not limited to the entorhinal afferents and medial septal projections (see Buzsaki, 2002). Similarly, respiratory related 4 Hz oscillations are also driven by extrinsic inputs. Therefore, at present, it is unclear which kind of physiologically relevant theta rhythm in the BLA networks has been modelled.

Reviewer 2 (R2) says “the fundamental question, namely, whether the BLA networks can or cannot intrinsically generate any theta rhythms, is still unanswered.” In our revision, we cited (Antonoudiou et al., 2022), who showed that BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings. R2 pointed out that this paper produces such theta under conditions in which the inhibition is totally removed. R2 then states that the resulting rhythmic populations burst at theta “are driven solely by excitatory cells. Thus, the results by (Antonoudiou et al., 2022) contrast with, and do not support, the present study, which claims that rhythmic oscillations in the BLA depend on the function of interneurons. Thus, there is still no convincing evidence that BLA circuits can intrinsically generate theta oscillations in intact brain or acute slices.”

This reasoning of R2 is faulty. With all GABAergic currents omitted, the LFP is composed of excitatory currents and intrinsic currents. Our model of the LFP includes all synaptic and membrane currents. In our model, the high theta comes from the spiking activity of the SOM cells, which increase their activity if the inhibition from VIP cells is removed. We are including a new simulation, which models the activity of the slice in the presence of kainate (as done in Antonoudiou et al., 2022), providing additional excitation to the network. If the BLA starts at high excitation, our model produces an ongoing gamma in the VIP cells that suppress SOM cells and allows a PING gamma to form between PV and F cells; with Gabazine (modeled as the removal of all the GABAergic synapses), this PING is no longer possible and so the gamma rhythm disappears. As expected, the simulation shows that the model produces theta with Gabazine; the model also shows that a PING rhythm is produced without Gabazine, and that this rhythm goes away with Gabazine because PING requires feedback inhibition (see Author response image 1). Thus, the theta increase with Gabazine in the (Antonoudiou et al., 2022) paper can be reproduced in our model, so that paper does support the model.

Author response image 1.

Spectral properties of the BLA network without (black) versus with Gabazine (magenta). Power spectra of the LFP proxy, which is the linear sum of AMPA, GABA (only present in the absence of Gabazine, D-, NaP-, and H-currents. Both power spectra are represented as mean and standard deviation across 10 network realizations. Bottom: inset between 35 and 50 Hz.

Nevertheless, we agree that this paper alone is not sufficient evidence that the BLA can produce a low theta. We have recently learned of a new paper (Bratsch-Prince et al., 2024) that is directly related to the issue of whether the BLA by itself can produce low theta, and in what circumstances. In this study, intrinsic BLA theta is produced in slices with ACh stimulation (without needing external glutamate input) which, in vivo, would be produced by the basal forebrain (Rajebhosale et al., eLife, 2024) in response to salient stimuli. The low-theta depends on muscarinic activation of CCK interneurons, a group of interneurons that overlaps with the VIP neurons in our model (Krabbe 2017; Mascagni and McDonald, 2003).

We suspect that the low theta produced in (Bratsch-Prince et al., 2024) is the same as the low theta in our model. We do not explicitly include ACh modulation of BLA in our paper, but in current work with experimentalists, we aim to show that ACh is essential to the theta by activating the BLA VIP cells. In our re-revised version, we will discuss Bratsch-Prince et al., 2024 and its connection to our hypothesis that the theta oscillations can be produced within the BLA.

Note that we have already included a paragraph stating explicitly that our hypothesis in no way contradicts the idea that inputs to the BLA may include theta oscillations. Indeed, the following paragraphs in the revised paper describe the complexity of trying to understand the origin of brain rhythms in vivo. R2 did not appear to take this complexity, and the possible involvement of neuromodulation, into account in their current position that the theta rhythms cannot be produced intrinsically in the BLA.

From revised paper: “Where the rhythms originate, and by what mechanisms. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. They draw this conclusion in mice by removing the hippocampus, which can volume conduct to BLA, and noticing that other nearby brain structures did not display any oscillatory activity. Our model also supports the idea that intrinsic mechanisms in the BLA can support the generation of the low theta, high theta, and gamma rhythms.

Although the BLA can produce these rhythms, this does not rule out that other brain structures also produce the same rhythms through different mechanisms, and these can be transmitted to the BLA. Specifically, it is known that the olfactory bulb produces and transmits the respiratory-related low theta (4 Hz) oscillations to the dorsomedial prefrontal cortex, where it organizes neural activity (Bagur et al., 2021). Thus, the respiratory-related low theta may be captured by BLA LFP because of volume conduction or through BLA extensive communications with the prefrontal cortex. Furthermore, high theta oscillations are known to be produced by the hippocampus during various brain functions and behavioral states, including during spatial exploration (Vanderwolf, 1969) and memory formation/retrieval (Raghavachari et al., 2001), which are both involved in fear conditioning. Similarly to the low theta rhythm, the hippocampal high theta can manifest in the BLA. It remains to understand how these other rhythms may interact with the ones described in our paper.”

We believe our current paper is important to show how detailed biophysical modeling can unearth the functional implications of physiological details (such as the biophysical bases of rhythms), which are often (indeed, usually) ignored in models, and why rhythms may be essential to some cognitive processes (including STDP). Indeed, for evaluating our paper it is necessary to go back to the purpose of a model, especially one such as ours, which is “hypothesis/data driven”. The hypotheses of the model serve to illuminate the functional roles of the physiological details, giving meaning to the data. Of course, the hypotheses must be plausible, and we think that the discussion above easily clears that bar. Hypotheses should also be checked experimentally, and a model that explains the implications of a hypothesis, such as ours, provides motivation for doing the hard work of experimental testing. We think that R1 understands this and has been very helpful.

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

eLife assessment

This useful modeling study explores how the biophysical properties of interneuron subtypes in the basolateral amygdala enable them to produce nested oscillations whose interactions facilitate functions such as spike-timing-dependent plasticity. The strength of evidence is currently viewed as incomplete because the relevance to plasticity induced by fear conditioning is viewed as insufficiently grounded in existing training protocols and prior experimental results, and alternative explanations are not sufficiently considered. This work will be of interest to investigators studying circuit mechanisms of fear conditioning as well as rhythms in the basolateral amygdala. 

Most of our comments below are intended to rebut the sentence: “The strength of evidence is currently viewed as incomplete because the relevance to plasticity induced by fear conditioning is viewed as insufficiently grounded in existing training protocols and prior experimental results, and alternative explanations are not sufficiently considered”. 

We believe this work will be interesting to investigators interested in dynamics associated with plasticity, which goes beyond fear learning. It will also be of interest because of its emphasis on the interactions of multiple kinds of interneurons that produce dynamics used in plasticity, in the cortex (which has similar interneurons) as well as BLA. We note that the model has sufficiently detailed physiology to make many predictions that can be tested experimentally. Details are below in the answer to reviewers.

Reviewer #1 (Public Comments):  

(1) … the weakness is that their attempt to align with the experimental literature (specifically Krabbe et al. 2019) is performed inconsistently. Some connections between cell types were excluded without adequate justification (e.g. SOM+ to PV+). 

In order to constrain our model, we focused on what is reported in (Krabbe et al., 2019) in terms of functional connectivity instead of structural connectivity. Thus, we included only those connections for which there was strong functional connectivity. For example, the SOM to PV connection is shown to be small (Krabbe et al., 2019, Supp. Fig. 4, panel t). We also omitted PV to SOM, PV to VIP, SOM to VIP, VIP to excitatory projection neurons; all of these are shown in (Krabbe et al. 2019, Fig. 3 (panel l), and Supp. Fig. 4 (panels m,t)) to have weak functional connectivity, at least in the context of fear conditioning. 

We reply with more details below to the Recommendations for the Authors, including new text.

(2) The construction of the afferent drive to the network does not reflect the stimulus presentations that are given in fear conditioning tasks. For instance, the authors only used a single training trial, the conditioning stimulus was tonic instead of pulsed, the unconditioned stimulus duration was artificially extended in time, and its delivery overlapped with the neutral stimulus, instead of following its offset. These deviations undercut the applicability of their findings.  

Regarding the use of a single long presentation of US rather than multiple presentations (i.e., multiple trials): in early versions of this paper, we did indeed use multiple presentations. We were told by experimental colleagues that the learning could be achieved in a single trial. We note that, if there are multiple presentations in our modeling, nothing changes; once the association between CS and US is learned, the conductance of the synapse is stable. Also, our model does not need a long period of US if there are multiple presentations.  

We agree that, in order to implement the fear conditioning paradigm in our in-silico network, we made several assumptions about the nature of the CS and US inputs affecting the neurons in the BLA and the duration of these inputs. A Poisson spike train to the BLA is a signal that contains no structure that could influence the timing of the BLA output; hence, we used this as our CS input signal. We also note that the CS input can be of many forms in general fear conditioning (e.g., tone, light, odor), and we wished to de-emphasize the specific nature of the CS. The reference mentioned in the Recommendations for authors, (Quirk, Armony, and LeDoux 1997), uses pulses 2 seconds long. At the end of fear conditioning, the response to those pulses is brief. However, in the early stages of conditioning, the response goes on for as long as the figure shows. The authors do show the number of cells responding decreases from early to late training, which perhaps reflects increasing specificity over training. This feature is not currently in our model, but we look forward to thinking about how it might be incorporated. Regarding the CS pulsed protocol used in (Krabbe et al., 2019), it has been shown that intense inputs (6kHz and 12 kHz inputs) can lead to metabotropic effects that last much longer than the actual input (200 ms duration) (Whittington et al., Nature, 1995). Thus, the effective input to the BLA may indeed be more like Poisson.

Our model requires the effect of the CS and US inputs on the BLA neuron activity to overlap in time in order to instantiate fear learning. Despite paradigms involving both overlapping (delay conditioning, where US coterminates with CS (Lindquist et al., 2004), or immediately follows CS (e.g., Krabbe et al., 2019)) and non-overlapping (trace conditioning) CS/US inputs existing in the literature, we hypothesized that concomitant activity in CS- and US-encoding neuron activity should be crucial in both cases. This may be mediated by the memory effect, as suggested in the Discussion of our paper, or by metabotropic effects as suggested above, or by the contribution from other brain regions. We will emphasize in our revision that the overlap in time, however instantiated, is a hypothesis of our model. It is hard to see how plasticity can occur without some memory trace of US. This is a consequence of our larger hypothesis that fear learning uses spiketiming-dependent plasticity; such a hypothesis about plasticity is common in the modeling literature. 

We reply with more details below to the Recommendations for the Authors, including new text.

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) This paper draws extensively from Krabbe et al. 2019, but it does not do so consistently. The paper would be strengthened if it tried to better match the circuit properties and activations.

Specifically: 

a. Krabbe found that PV interneurons were comparably activated by the US (see Supp Fig 1). Your model does not include that. The basis for the Krabbe 2019 claim that PV US responses are weaker is that they have a slightly larger proportion of cells inhibited by the US, but this is not especially compelling. In addition, their Fig 2 showed that VIP and SOM cells receive afferents from the same set of upstream regions. 

b. The model excluded PV-SOM connections, but this does not agree with Krabbe et al. 2019, Table 2. PV cells % connectivity and IPSC amplitudes were comparable to those from VIP interneurons. 

c. ECS to PV synapses are not included. This seems unlikely given the dense connectivity between PV interneurons and principal neurons in cortical circuits and the BLA (Woodruff and Sah 2007 give 38% connection probability in BLA). 

We thank the Reviewer for raising these points, which allow us to clarify how we constrained our model and to do more simulations. Specifically: 

a. (Wolff et al., Nature, 2014), cited by (Krabbe et al. 2018), reported that PV and SOM interneurons are on average inhibited by the US during the fear conditioning. However, we agree that (Krabbe et al., 2019) added to this by specifying that PV interneurons respond to both CS+ and US, although the fraction of US-inhibited PV interneurons is larger. As noted by the Reviewer, in the model we initially considered the PV interneurons responding only to CS+ (identified as “CS” in our manuscript). For the current revision, we ran new simulations in which the PV interneuron receives the US input, instead of CS+. It turned out that this did not affect the results, as shown in the figure below: all the network realizations learn the association between CS and fear. In the model, the PING rhythm between PV and F is the crucial component for establishing fine timing between ECS and F, which is necessary for learning. Having PV responding to the same input as F, i.e., US, facilitates their entrainment in PING and, thus, successful learning. 

As for afferents of VIP and SOM from upstream regions, in (Krabbe et al., 2019) is reported that “[…] BLA SOM interneurons receive a different array of afferent innervation compared to that of VIP and PV interneurons, which might contribute to the differential activity patterns observed during fear learning.” Thus, in the model, we are agnostic about inputs to SOM interneurons; we modeled them to fire spontaneously at high theta.

To address these points in the manuscript, we added some new text in what follows:

(1) New Section “An alternative network configuration characterized by US input to PV, instead of CS, also learns the association between CS and fear” in the Supplementary information:

“We constrained the BLA network in Fig. 2 with CS input to the PV interneuron, as reported in (Krabbe et al., 2018). However, (Krabbe et al., 2019) notes that a class of PV interneurons may be responding to US rather than CS. Fig. S3 presents the results obtained with this variation in the model (see Fig. 3 A,B for comparison) and shows that all the network realizations learn the association between CS and fear. In the model, the PING rhythm between PV and F is the crucial component for establishing fine timing between ECS and F, which is necessary for learning. Having PV responding to the same input as F, i.e., US, facilitates their entrainment in PING and, thus, successful fear learning.

We model the VIP interneuron as affected by US; in addition, (Krabbe et al. 2019) reports that a substantial proportion of them is mildly activated by CS. Replacing the US by CS does not change the input to VIP cells, which is modeled by the same constant applied current. Thus, the VIP CS-induced activity is a bursting activity at low theta, similar to the one elicited by US in Fig. 2.”

(2) Section “With the depression-dominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning” in Results: “Finally, since (Krabbe et al., 2019) reported that a fraction of PV interneurons are affected by US, we have also run the simulations for single neuron network with the PV interneuron affected by US instead of CS. In this case as well, all the network realizations are learners (see Fig. S3). ”

(3) Section “Conditioned and unconditioned stimuli” in Materials and Methods: “To make Fig. S3, we also considered a variation of the model with PV interneurons affected by US, instead of CS, as reported in (Krabbe et al. 2019).”

b. Re the SOM to PV connection: As reported in the reply to the public reviews, we considered the prominent functional connections reported in (Krabbe et al., 2019), instead of structural connections. That is, we included only those connections for which there was strong functional connectivity. For example, the SOM to PV connection is shown to be small (Supp. Fig. 4, panel t, in (Krabbe et al., 2019)). We also omitted PV to SOM, PV to VIP, SOM to VIP, and VIP to excitatory projection neurons; all of these are shown in (Krabbe et al. 2019, Fig. 3 (panel l), and Supp. Fig. 4 (panels m,t)) to have weak functional connectivity, at least in the context of fear conditioning.

In order to clarify this point, in Section “Network connectivity and synaptic currents” in Materials and Methods, we now say:

“We modeled the network connectivity as presented in Fig. 2B, derived from the prominent functional, instead of structural, connections reported in (Krabbe et al., 2019).”

c. Re the ECS to PV synapses: We thank the Reviewer for the reference provided; as the Reviewer says, the ECS to PV synapses are not included. Upon adding this connection in our network, we found that, unlike the connection suggested in part a above, introducing these synapses would, in fact, change the outcome. Thus, the omission of this connection must be considered an implied hypothesis. Including those synapses with a significant strength would alter the PING rhythm created by the interactions between F and PV, which is crucial for ECS and F fine timing. Thanks very much for showing us that this needs to be said. Our hypothesis does not contradict the dense connections mentioned by the Reviewer; such dense connectivity does not mean that all pyramidal cells connect to all interneurons. This hypothesis may be taken as a prediction of the model.

The absence of this connection is now discussed at the end of a new Section of the Discussion entitled “Assumptions and predictions of the model”, which reads as follows:

“Finally, the model assumes the absence of significantly strong connections from the excitatory projection cells ECS to PV interneurons, unlike the ones from F to PV. Including those synapses would alter the PING rhythm created by the interactions between F and PV, which is crucial for ECS and F fine timing. We note that in (Woodruff and Sah, 2007) only 38% of the pyramidal cells are connected to PV cells. The functional identity of the connected pyramidal cells is unknown. Our model suggests that successful fear conditioning requires F to PV connections and that ECS to PV must be weak or absent.”

(2) Krabbe et al. 2019 and Davis et al. 2017 were referenced for the construction of the conditioned and unconditioned stimulus pairing protocol. The Davis citation is not applicable here because that study was a contextual, not cued, fear conditioning paradigm. Regarding Krabbe, the pairing protocol was radically different from what the authors used. Their conditioned stimulus was a train of tone pips presented at 0.9 Hz, which lasted 30 s, after which the unconditioned stimulus was presented after tone offset. The authors should determine how their network behaves when this protocol is used. Also, note that basolateral amygdala responses to tone stimuli are primarily brief onset responses (e.g. Quirk, Armony, and LeDoux 1997), and not the tonic activation used in the model.  

We replied to this point in our responses to the Reviewer’s Public Comments as follows:

“We agree that, in order to implement the fear conditioning paradigm in our in-silico network, we made several assumptions about the nature of the CS and US inputs affecting the neurons in the BLA and the duration of these inputs. A Poisson spike train to the BLA is a signal that contains no structure that could influence the timing of the BLA output; hence, we used this as our CS input signal. We also note that the CS input can be of many forms in general fear conditioning (e.g., tone, light, odor), and we wished to de-emphasize the specific nature of the CS. The reference mentioned in the Recommendations for authors, (Quirk, Armony, and LeDoux 1997), uses pulses 2 seconds long. At the end of fear conditioning, the response to those pulses is brief. However, in the early stages of conditioning, the response goes on for as long as the figure shows. The authors do show the number of cells responding decreases from early to late training, which perhaps reflects increasing specificity over training. This feature is not currently in our model, but we look forward to thinking about how it might be incorporated. Regarding the CS pulsed protocol used in (Krabbe et al., 2019), it has been shown that intense inputs (6kHz and 12 kHz inputs) can lead to metabotropic effects that last much longer than the actual input (200 ms duration) (Whittington et al., Nature, 1995). Thus, the effective input to the BLA may indeed be more like

Poisson.”

Current answer to the Reviewer:

There are several distinct issues raised by the Reviewer in the more detailed critique. We respectfully disagree that the model is not applicable to context-dependent fear learning where the context acts as a CS, though we should have been more explicit. Specifically, our CS input can describe both the cue and the context. We included the following text in the Results section “Interneuron rhythms provide the fine timing needed for depression-dominated STDP to make the association between CS and fear”:

“In our simulations, the CS input describes either the context or the cue in contextual and cued fear conditioning, respectively. For the context, the input may come from the hippocampus or other non-sensory regions, but this does not affect its role as input in the model.”

The second major issue is whether the specific training protocols used in the cited papers need to be exactly reproduced in the signals received by the elements of our model; we note that there are many transformations that can occur between the sensory input and the signals received by the BLA. In the case of auditory fear conditioning, a series of pips, rather than individual pips, are considered the CS (e.g., (Stujenske et al., 2014; Krabbe et al. 2019)). Our understanding is that a single pip does not elicit a fear response; a series of pips is required for fear learning. This indicates that it is not the neural code of a single pip that matters, but rather the signal entering the amygdala that incorporates any history-dependent signaling that could lead to spiking throughout the sequence of pips.  Also, as mentioned above, intense inputs at frequencies about 6kHz and 12kHz can lead to metabotropic effects that last much longer than each brief pip (~200 ms), thus possibly producing continuous activity in neurons encoding the input. Thus, we believe that our use of the Poisson spike train is reasonable. 

However, we are aware that the activity of neurons encoding CS can be modulated by the pips: neurons encoding auditory CS display a higher firing rate when each pip is presented and a Poisson-like spike train between pips (Herry et al., Journal of Neuroscience, 2007). Here we confirm that potentiation is present even in the presence of the fast transient response elicited by the pips. We said in the original manuscript that there is learning for a Poisson spike train CS input at ~50 Hz; this describes the neuronal activity in between pips. For the revision, we asked whether learning is preserved when CS is characterized by higher frequencies, which would describe the CS during and right after each pip. We show in the new Fig. S4 that potentiation is ensured for a range of CS frequencies. The figure shows the learning speed as a function of CS and US frequencies. For all the CS frequencies considered, i) there is learning, ii) learning speed increases with CS frequency. Thus, potentiation is present even when pips elicit a faster transient response.

To better specify this in the manuscript, 

We added the following sentences in the Results section “With the depressiondominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning”: 

“We note that the CS and US inputs modeled as independent Poisson spike trains represent stimuli with no structure. Although we have not explicitly modeled pulsating pips, as common in auditory fear conditioning (e.g., (Stujenske 2014; Krabbe 2019)), we show in Fig. S4 that potentiation can be achieved over a relatively wide range of gamma frequencies. This indicates that overall potentiation is ensured if the gamma frequency transiently increases after the pip.”

We added the section “The full network potentiates for a range of CS frequencies“ and figure S4 in the Supplementary Information:

We included in Materials and Methods “Conditioned and unconditioned stimuli” the following sentences:

“Finally, for Fig.S4, we considered a range of frequencies for the CS stimulus. To generate the three Poisson spike trains with average frequencies from 48 to 64 Hz in Fig. S4, we set 𝜆 = 800, 1000, 1200.”

Finally, to address the comment about the need for CS and US overlapping in time to instantiate fear association, we added the following text in the Results section “Assumptions and predictions of the model”:

“Finally, our model requires the effect of the CS and US inputs on the BLA neuron activity to overlap in time in order to instantiate fear learning. Despite paradigms involving both overlapping (delay conditioning, where US co-terminates with CS (e.g., (Lindquist et al., 2004)), or immediately follows CS (e.g., Krabbe et al., 2019)) and non-overlapping (trace conditioning) CS/US inputs exist, we hypothesized that concomitant activity in CS- and US-encoding neuron activity should be crucial in both cases. This may be mediated by the memory effect due to metabotropic effects (Whittington et al., Nature, 1995) as suggested above, or by the contribution from other brain regions (see section “Involvement of other brain structures” in the Discussion). The fact that plasticity occurs with US memory trace is a consequence of our larger hypothesis that fear learning uses spike-timing-dependent plasticity; such a hypothesis about plasticity is common in the modeling literature.”

(3) As best as I could tell, only a single training trial was used in this study. Fair enough, especially given that fear learning can occur with a single trial. However, most studies of amygdala fear conditioning have multiple trials (~5 or more). How does the model perform when multiple trials are given?  

The association between CS and fear acquired after one trial, i.e., through a potentiated ECS to F connection, is preserved in the presence of multiple trials.  Indeed, the association would be weakened or erased (through depression of the ECS to F connection) only if ECS and F did not display good fine timing, i.e., F does not fire right after ECS most of the time. However, the implemented circuit supports the role of interneurons in providing the correct fine timing, thus preventing the association acquired from being erased.  

In the second paragraph of the Results section “With the depression-dominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning”, we made the above point by adding the following text:

“We note that once the association between CS and fear is acquired, subsequent presentations of CS and US do not weaken or erase it: the interneurons ensure the correct timing and pauses in ECS and F activity, which are conducive for potentiation.”

(4) The LFP calculations are problematic. First, it is unclear how they were done. Did the authors just take the transmembrane currents they included and sum them, or were they scaled by distance from the 'electrode' and extracellular conductivity (as one would derive from the Laplace equation)? Presumably, the spatial arrangement of model neurons was neglected so distance was not a factor. 

Second, if this is the case, then the argument for excluding GABAergic conductances seems flawed. If the spatial arrangement of neurons is relevant to whether to include or exclude GABAergic conductances, then wouldn't a simulation without any spatial structure not be subject to the concern of laminar vs. nuclear arrangement? 

Moreover, to the best I can tell, the literature the authors use to justify the exclusion of

GABAergic currents does not make the case for a lack of GABAergic contribution in non-laminar structures. Instead, those studies only argue that in a non-laminar structure, AMPA currents are detectable, not that GABA cannot be detected. Thus, the authors should either include the GABAergic currents when calculating their simulated LFP, or provide a substantially better argument or citation for their exclusion. 

We thank the Reviewer for pointing this out; this comment helped us rethink how to model the LFP. The origin of the LFP signal in BLA has not been fully determined, but factors thought to be important include differences in the spatial extension of the arborization in excitatory and inhibitory neurons, in the number of synaptic boutons, and spatial distributions of somata and synapses (Lindén et al 2011; Łęski 2013; Mazzoni et al. 2015). In the first version of the manuscript, we excluded the GABAergic currents because it is typically assumed that they add very little to the extracellular field as the inhibitory reversal potential is close to the resting membrane potential. For the revision, we re-ran the simulations during pre and post fear conditioning and we modeled the LFP as the sum of the AMPA, GABA and NaP-/H-/D- currents. With this new version of the LFP, we added a new Fig. 6 showing that there is a significant increase in the low theta power, but not in the high theta power, with fear learning (Fig. 6 C, D, E). This increase in the low theta power was mainly due to the AMPA currents created by the newly established connection from ECS to F, which allowed F to be active after fear conditioning in response to CS. 

However, as the Reviewer mentioned, our network has no spatial extent: neurons are modeled as point cells. Thus, our current model does not include the features necessary to model some central aspects of the LFP. Despite that, our model does clearly demonstrate how rhythmic activity in the spike timing of neurons within the network changes due to fear learning (Fig. 6B). The spiking outputs of the network are key components of the inputs to the LFP, and thus we expect the rhythms in the spiking to be reflected in more complex descriptions of the LFP. But we also discovered that different LFP proxies provide different changes in rhythmic activity comparing pre- and post-fear learning; although we have no principled way to choose a LFP proxy, we believe that the rhythmic firing is the essential finding of the model.

We have added the following to the manuscript:

(1) In the new version of Fig. 6, we present the power spectra of the network spiking activity (panel B), along with the power spectra of the LFP proxy that includes the GABA, AMPA, and NaP-/H-/D- currents (panels C, D, E). 

(2) We modified the conclusion of the Results section entitled “Increased low-theta frequency is a biomarker of fear learning” by saying:

“In this section, we explore how plasticity in the fear circuit affects the network dynamics, comparing after fear conditioning to before. We first show that fear conditioning leads to an increase in low theta frequency power of the network spiking activity compared to the pre-conditioned level (Fig. 6 A,B); there is no change in the high theta power. We also show that the LFP, modeled as the linear sum of all the AMPA, GABA, NaP-, D-, and H- currents in the network, similarly reveals a low theta power increase and no significant variation in the high theta power (Fig. 6 C,D,E). These results reproduce the experimental findings in (Davis et al., 2017), and (Davis et al., 2017), and Fig 6 F,G show that the low theta increase is due to added excitation provided by the new learned pathway. The additional unresponsive ECS and F cells in the network were included to ensure we had not biased the LFP towards excitation. Nevertheless, although both the AMPA and GABA currents contribute to the power increase in the low theta frequency range (Fig. 6F), the AMPA currents show a dramatic power increase relative to the baseline (the average power ratio of AMPA and GABA post- vs pre-conditioning across 20 network realizations is 3*103 and 4.6, respectively). This points to the AMPA currents as the major contributor to the low theta power increase. Specifically, the newly potentiated AMPA synapse from ECS to F ensures F is active after fear conditioning, thus generating strong currents in the PV cells to which it has strong connections (Fig. 6G). Finally, the increase in power is in the low theta range because ECS and F are allowed to spike only during the active phase of the low theta spiking VIP neurons. We have also explored another proxy for the LFP (see Supplementary Information and Fig. S6).”

In the Supplementary Information, we included a figure and some text in the new section entitled “A higher low theta power increase emerges in LFP approximated with the sum of the absolute values of the currents compared to their linear sum”:

“Given that our BLA network comprises a few neurons described as single-compartment cells with no spatial extension and location, the LFP cannot be computed directly from our model’s read-outs. In the main text, we choose as an LFP proxy the linear sum of the AMPA, GABA, and P-/H-/D-currents. We note that if the LFP is modeled as the sum of the absolute value of the currents, as suggested by (Mazzoni et al. 2008; Mazzoni et al. 2015), an even higher low theta power increase arises after fear conditioning compared to the linear sum. Differences in the power spectra also arise if other LFP proxies (e.g., only AMPA currents, only GABA currents) are considered. A principled description of an LFP proxy would require modeling the three-dimensional BLA anatomy, including that of the interneurons VIP and SOM; this is outside the scope of the current paper. (See (Feng et al. 2019) for a related project in the BLA.)”

(3) We updated the Materials and Methods section “Local field potentials and spectral analysis” to explain how we compute the LFP in the revised manuscript: 

“We considered as an LFP proxy as the linear sum of all the AMPA, GABA, NaP, D, and H currents in the network. The D-current is in the VIP interneurons, and NaP-current and H-current are in SOM interneurons.”

Although it is beyond the scope of the current work, an exploration of the most accurate proxy of the LFP in the amygdala is warranted. Such a study could be accomplished by adopting a similar approach as in (Mazzoni et al., 2015), where several LFP proxies based on point-neuron leaky-integrate and fire neuronal network were compared with a “groundtruth” LFP obtained in an analogous realistic three-dimensional network model. 

To explicitly mention this issue in the paper, we add a paragraph in the “Limitations and caveats” section in the Discussion, which reads as follows:

“LFPs recorded in the experiments are thought to be mainly created by transmembrane currents in neurons located around the electrode and depend on several factors, including the morphology of the arborization of contributing neurons and the location of AMPA and GABA boutons (Katzner et al. 2009; Lindén et al 2011; Łęski 2013; Mazzoni et al. 2015). Since our model has no spatial extension, we used an LFP proxy; this proxy was shown to reflect the rhythmic output of the network, which we believe to be the essential result (for more details see Results “Increased low-theta frequency is a biomarker of fear learning”, and Supplementary Information “A higher low theta power increase emerges in LFP approximated with the sum of the absolute values of the currents compared to their linear sum”).”

(4)     We have removed the section “Plasticity between fear neuron and VIP slows down overall potentiation” in Results and sections “Plasticity between the fear neuron (F) and VIP slows down overall potentiation” and “Plastic F to VIP connections further increase lowtheta frequency power after fear conditioning” in the Supplementary Information. This material is extraneous since we are using a new proxy for LFP.

Minor points: 

(1) In Figure 3C, the y-axis tick label for 0.037 is written as "0.37."

We thank the reviewer for finding this typo; we fixed it.

(2) Figure 5B is unclear. It seems to suggest that the added ECS and F neurons did not respond to either the CS or UCS. Is this true? If so, why include them in the model? How would their inclusion change the model behavior? 

It is correct that the added ECS and F neurons did not respond to the CS or US (UCS); they are constructed to be firing at 11 Hz in the absence of any connections from other cells.  These cells were included to be part of our computation of the LFP.  Specifically, adding in those cells would make the LFP take inhibition into account more, and we wanted to make sure that were not biasing our computation away from the effects of inhibition.  As shown in the paper (Fig. 6B), even with inhibition onto these non-responsive cells, the LFP has the properties claimed in the paper concerning the changes in the low theta and high-theta power, because the LFP is dominated by new excitation rather than the inhibition. 

First, in the Results section “Network with multiple heterogeneous neurons can establish the association between CS and fear”, we commented on the added ECS and F neurons that do not respond to either CS or US by saying the following:

“The ECS cells not receiving CS are inhibited by ongoing PV activity during the disinhibition window (Fig. 5B); they are constructed to be firing at 11 Hz in the absence of any connections from other cells. The lack of activity in those cells during fear conditioning implies that there is no plasticity from those ECS cells to the active F. Those cells are included for the calculation of the LFP (see below in “Increased low-theta frequency is a biomarker of fear learning”.)”

Furthermore, we add the following sentence in the Results section “Increased low-theta frequency is a biomarker of fear learning”: 

“The additional unresponsive ECS and F cells in the network were included to ensure we had not biased the LFP towards excitation.”

(3) Applied currents are given as current densities, but these are difficult to compare with current levels observed from whole-cell patch clamp recordings. Can the currents be given as absolute levels, in pA/nA. 

In principle, it is possible to connect current densities with absolute levels, as requested. However, we note that the number of cells in models is orders of magnitude smaller than the number being modeled. It is common in modeling to adjust physiological parameters to achieve the qualitative properties that are important to the model, rather than trying to exactly match particular recordings.

We added to the Methods description why we choose units per unit area, rather than absolute units. 

“All the currents are expressed in units per area, rather than absolute units, to avoid making assumptions about the size of the neuron surface.”

(4) Regarding: "We note that the presence of SOM cells is crucial for plasticity in our model since they help to produce the necessary pauses in the excitatory projection cell activity. However, the high theta rhythm they produce is not crucial to the plasticity: in our model, high theta or higher frequency rhythms in SOM cells are all conducive to associative fear learning. This opens the possibility that the high theta rhythm in the BLA mostly originates in the prefrontal cortex and/or the hippocampus (Stujenske et al., 2014, 2022)." The chain of reasoning in the above statement is unclear. The second sentence seems to be saying contradictory things. 

We agree that the sentence was confusing; thank you for pointing it out. We have revised the paragraph to make our point clearer. The central points are: 1) having the SOM cells in the BLA is critical to the plasticity in the model, and 2) these cells may or may not be the source of the high theta observed in the BLA during fear learning.

We deleted from the discussion the text reported by the Reviewer, and we added the following one to make this point clearer:

“We note that the presence of SOM cells is crucial for plasticity in our model since they help to produce the necessary pauses in the excitatory projection cell activity. The BLA SOM cells do not necessarily have to be the only source of the high theta observed in the BLA during fear learning; the high theta detected in the LFP of the BLA also originates from the prefrontal cortex and/or the hippocampus (Stujenske et al., 2014, 2022).”

(5) Regarding: "This suggests low theta power change is not just an epiphenomenon but rather a biomarker of successful fear conditioning." Not sure this is the right framing for the above statement. The power of the theta signal in the LFP reflects the strengthening of connections, but it itself does not have an impact on network activity. Moreover, whether something is epiphenomenal is not relevant to the question of whether it can serve as a successful biomarker. A biomarker just needs to be indicative, not causal. 

We intended to say why the low theta power change is a biomarker in the sense of the Reviewer. That is: experiments have shown that, with learning, the low theta power increases. The modeling shows in addition that, when learning does not take place, the low power does not increase. That means that the low theta power increases if and only if there is learning, i.e., the change in low theta power is a biomarker. To make our meaning clearer, we have changed the quoted sentences to read: 

“This suggests that the low theta power change is a biomarker of successful fear conditioning: it occurs when there is learning and does not occur when there is no learning.”

Reviewer #2 (Public Comments): 

We thank the Reviewer for raising these interesting points. Below are our public replies and the changes we made to the manuscript to address the Reviewer’s objections.

(1) Gamma oscillations are generated locally; thus, it is appropriate to model in any cortical structure. However, the generation of theta rhythms is based on the interplay of many brain areas therefore local circuits may not be sufficient to model these oscillations.

Moreover, to generate the classical theta, a laminal structure arrangement is needed (where neurons form layers like in the hippocampus and cortex)(Buzsaki, 2002), which is clearly not present in the BLA. To date, I am not aware of any study which has demonstrated that theta is generated in the BLA. All studies that recorded theta in the BLA performed the recordings referenced to a ground electrode far away from the BLA, an approach that can easily pick up volume conducted theta rhythm generated e.g., in the hippocampus or other layered cortical structure. To clarify whether theta rhythm can be generated locally, one should have conducted recordings referenced to a local channel (see Lalla et al., 2017 eNeuro). In summary, at present, there is no evidence that theta can be generated locally within the BLA. Though, there can be BLA neurons, firing of which shows theta rhythmicity, e.g., driven by hippocampal afferents at theta rhythm, this does not mean that theta rhythm per se can be generated within the BLA as the structure of the BLA does not support generation of rhythmic current dipoles. This questions the rationale of using theta as a proxy for BLA network function which does not necessarily reflect the population activity of local principal neurons in contrast to that seen in the hippocampus.

In both modeling and experiments, a laminar structure does not seem to be needed to produce a theta rhythm. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. The authors draw this conclusion by looking at mice ex vivo slices. The currents that generate these rhythms are in the BLA, since the hippocampus was removed to eliminate hippocampal volume conduction and other nearby brain structures did not display any oscillatory activity. Also, in the modeling literature, there are multiple examples of the production of theta rhythms in small networks not involving layers; these papers explain the mechanisms producing theta from non-laminated structures (Dudman et al., 2009, Kispersky et al., 2010, Chartove et al. 2020).  We are not aware of any model description of the mechanisms of theta that do require layers.

We added the following text in the introduction of the manuscript to make this point clearer:  “A recent rodent experimental study (Antonoudiou et al. 2022) suggests that BLA can intrinsically generate theta oscillations (3-12 Hz).”

(2) The authors distinguished low and high theta. This may be misleading, as the low theta they refer to is basically a respiratory-driven rhythm typically present during an attentive state (Karalis and Sirota, 2022; Bagur et al., 2021, etc.). Thus, it would be more appropriate to use breathing-driven oscillations instead of low theta. Again, this rhythm is not generated by the BLA circuits, but by volume conducted into this region. Yet, the firing of BLA neurons can still be entrained by this oscillation. I think it is important to emphasize the difference.

Many rhythms of the nervous system can be generated in multiple parts of the brain by multiple mechanisms. We do not dispute that low theta appears in the context of respiration; however, this does not mean that other rhythms with the same frequencies are driven by respiration. Indeed, in the response to question 1 above, we showed that theta can appear in the BLA without inputs from other regions. In our paper, the low theta is generated in the BLA by VIP neurons. Using intrinsic currents known to exist in VIP neurons (Porter et al., 1998), modeling has shown that such neurons can intrinsically produce a low theta rhythm. This is also shown in the current paper. This example is part of a substantial literature showing that there are multiple mechanisms for any given frequency band. 

To elaborate more on this in the manuscript, we added the following new section in the discussion:

“Where the rhythms originate, and by what mechanisms. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. They draw this conclusion in mice by removing the hippocampus, which can volume conduct to BLA, and noticing that other nearby brain structures did not display any oscillatory activity. Our model also supports the idea that intrinsic mechanisms in the BLA can support the generation of the low theta, high theta, and gamma rhythms. 

Although the BLA can produce these rhythms, this does not rule out that other brain structures also produce the same rhythms through different mechanisms, and these can be transmitted to the BLA. Specifically, it is known that the olfactory bulb produces and transmits the respiratory-related low theta (4 Hz) oscillations to the dorsomedial prefrontal cortex, where it organizes neural activity (Bagur et al., 2021). Thus, the respiratory-related low theta may be captured by BLA LFP because of volume conduction or through BLA extensive communications with the prefrontal cortex. Furthermore, high theta oscillations are known to be produced by the hippocampus during various brain functions and behavioral states, including during spatial exploration (Vanderwolf, 1969) and memory formation/retrieval (Raghavachari et al., 2001), which are both involved in fear conditioning. Similarly to the low theta rhythm, the hippocampal high theta can manifest in the BLA. It remains to understand how these other rhythms may interact with the ones described in our paper.”

We also note that the presence of D-currents in the BLA VIP interneurons should be confirmed experimentally, and that the ability of VIP interneurons to generate the BLA low theta rhythm constitutes a prediction of our computational model. These points are specified in the first paragraph in the Discussion entitled “Assumptions and predictions of the model”:

“The interneuron descriptions in the model were constrained by the electrophysiological properties reported in response to hyperpolarizing currents (Sosulina et al., 2010). Specifically, we modeled the three subtypes of VIP, SOM, and PV interneurons displaying bursting behavior, regular spiking with early spike-frequency adaptation, and regular spiking without spike-frequency adaptation, respectively. Focusing on VIP interneurons, we were able to model the bursting behavior by including the D-type potassium current. This current is thought to exist in the VIP interneurons in the cortex (Porter et al., 1998), but whether this current is also found in the VIP interneurons the BLA is still unknown. Similarly, we endowed the SOM interneurons with NaP- and H-currents, as the OLM cells in the hippocampus. Due to these currents, the VIP and SOM cells are able to show  low- and high-theta oscillations, respectively. The presence of these currents and the neurons’ ability to exhibit oscillations in the theta range during fear conditioning and at baseline in BLA, which are assumptions of our model, should be tested experimentally.”

(3) The authors implemented three interneuron types in their model, ignoring a large fraction of GABAergic cells present in the BLA (Vereczki et al., 2021). Recently, the microcircuit organization of the BLA has been more thoroughly uncovered, including connectivity details for PV+ interneurons, firing features of neurochemically identified interneurons (instead of mRNA expression-based identification, Sosulina et al., 2010), synaptic properties between distinct interneuron types as well as principal cells and interneurons using paired recordings. These recent findings would be vital to incorporate into the model instead of using results obtained in the hippocampus and neocortex. I am not sure that a realistic model can be achieved by excluding many interneuron types.

The interneurons and connectivity that we used were inspired by the functional connectivity reported in (Krabbe et al., 2019) (see above answer to Reviewer #1). As reported in (Vereczki et al., 2021), there are multiple categories and subcategories of interneurons; that paper does not report on which ones are essential for fear conditioning. We did use all the highly represented categories of the interneurons, except NPYcontaining neurogliaform cells.

The Reviewer says “I am not sure that a realistic model can be achieved by excluding many interneuron types”. We agree with the Reviewer that discarding the introduction of other interneurons subtypes and the description of more specific connectivity (soma-, dendrite-, and axon-targeting connections) may limit the ability of our model to describe all the details in the BLA. However, this work represents a first effort towards a biophysically detailed description of the BLA rhythms and their function. As in any modeling approach, assumptions about what to describe and test are determined by the scientific question; details postulated to be less relevant are omitted to obtain clarity. The interneuron subtypes we modeled, especially VIP+ and PV+, have been reported to have a crucial role in fear conditioning (Krabbe et al., 2019). Other interneurons, e.g. cholecystokinin and SOM+, have been suggested as essential in fear extinction. Thus, in the follow-up of this work to explain fear extinction, we will introduce other cell types and connectivity. In the current work, we have achieved our goals of explaining the origin of the experimentally found rhythms and their roles in the production of plasticity underlying fear learning. Of course, a more detailed model may reveal flaws in this explanation, but this is science that has not yet been done.

We elaborate more on this in a new section in the Discussion entitled “Assumptions and predictions of the model”. The paragraph related to this point reads as follows:

“Our model, which is a first effort towards a biophysically detailed description of the BLA rhythms and their functions, does not include the neuron morphology, many other cell types, conductances, and connections that are known to exist in the BLA; models such as ours are often called “minimal models” and constitute the majority of biologically detailed models. Such minimal models are used to maximize the insight that can be gained by omitting details whose influence on the answers to the questions addressed in the model are believed not to be qualitatively important. We note that the absence of these omitted features constitutes hypotheses of the model: we hypothesize that the absence of these features does not materially affect the conclusions of the model about the questions we are investigating. Of course, such hypotheses can be refuted by further work showing the importance of some omitted features for these questions and may be critical for other questions. Our results hold when there is some degree of heterogeneity of cells of the same type, showing that homogeneity is not a necessary condition.”

(4) The authors set the reversal potential of GABA-A receptor-mediated currents to -80 mV. What was the rationale for choosing this value? The reversal potential of IPSCs has been found to be -54 mV in fast-spiking (i.e., parvalbumin) interneurons and around -72 mV in principal cells (Martina et al., 2001, Veres et al., 2017).

A GABA-A reversal potential around -80 mV is common in the modeling literature (Jensen et al., 2005; Traub et al., 2005; Kumar et al., 2011; Chartove et al., 2020). Other computational works of the amygdala, e.g. (Kim et al., 2016), consider GABA-A reversal potential at -75 mV based on the cortex (Durstewitz et al., 2000). The papers cited by the reviewer have a GABA-A reversal potential of -72 mV for synapses onto pyramidal cells; this is sufficiently close to our model that it is not likely to make a difference. For synapses onto PV+ cells, the papers cited by the reviewer suggest that the GABA-A reversal potential is -54 mV; such a reversal potential would lead these synapses to be excitatory instead of inhibitory. However, it is known (Krabbe et al., 2019; Supp. Fig. 4b) that such synapses are in fact inhibitory. Thus, we wonder if the measurements of Martina and Veres were made in a condition very different from that of Krabbe. For all these reasons, we consider a GABA-A reversal potential around -80 mV in amygdala to be a reasonable assumption.

In section “Network connectivity and synaptic currents” in “Materials and Methods” we provided references to motivate our choice of considering a GABA-A reversal potential around -80 mV:

“The GABAa current reversal potential (𝐸!) is set to −80        𝑚𝑉, as common in the modeling literature (Jensen et al., 2005; Traub et al., 2005; Kumar et al., 2011; Chartove et al., 2020).”

(5) Proposing neuropeptide VIP as a key factor for learning is interesting. Though, it is not clear why this peptide is more important in fear learning in comparison to SST and CCK, which are also abundant in the BLA and can effectively regulate the circuit operation in cortical areas.

Other peptides seem to be important in overall modulation of fear, but VIP is especially important in the first part of fear learning, the subject of our paper. Re SST: we hypothesize that SST interneurons are critical in fear extinction and preventing fear generalization, but not to initial fear learning. The peptide of the CCK neurons, which overlap with VIP cells, has been proposed to promote the switch between fear and safety states after fear extinction (Krabbe al. 2018). Thus, these other peptides are likely more important for other aspects of fear learning.  

In the Discussion, we have added:

“We hypothesize that SST peptide is critical in fear extinction and preventing fear generalization, but not to initial fear learning. Also, the CCK peptide has been proposed to promote the switch between fear and safety states after fear extinction (Krabbe al. 2018).”

Reviewer #2 (Recommendations For The Authors): 

We note that Reviewer #2’s Recommendations For The Authors have the same content as the Public Comments. Thus, the changes to the manuscript we implemented above address also the private critiques listed below.

(1) As the breathing-driven rhythm is a global phenomenon accompanying fear state, one might restrict the analysis to this oscillation. The rationale beyond this restriction is that the 'high' theta in the BLA has an unknown origin (since it can originate from the ventral hippocampus, piriform cortex etc.). 

In response to point 4 made by Reviewer 1 (Recommendations for the Authors) (p. 13), referring to high theta in the BLA, we previously wrote: 1) having the SOM cells in the BLA is critical to the plasticity in the model, and 2) these cells may or may not be the source of the high theta observed in the BLA during fear learning.

In the Public Critiques, Reviewer 2 relates the respiratory rhythm to the low theta. We answered this point in point 2 of the Reviewer’s Public Comments (at p. 15).

(2) I would include more interneurons in the network model incorporating recent findings. 

This point was answered in our response to point 3 of the Reviewer’s Public Comments.

(3) The reversal potential for GABA-A receptor-mediated currents would be good to set to measured values. In addition, I would use AMPA conductance values that have been measured in the BLA. 

We addressed this objection in our response to point 4 of the Reviewer’s Public Comments.

Reviewer #3 (Public comments):

Weaknesses: 

(1) The main weakness of the approach is the lack of experimental data from the BLA to constrain the biophysical models. This forces the authors to use models based on other brain regions and leaves open the question of whether the model really faithfully represents the basolateral amygdala circuitry. 

(2) Furthermore, the authors chose to use model neurons without a representation of the morphology. However, given that PV+ and SOM+ cells are known to preferentially target different parts of pyramidal cells and given that the model relies on a strong inhibition form SOM to silence pyramidal cells, the question arises whether SOM inhibition at the apical dendrite in a model representing pyramidal cell morphology would still be sufficient to provide enough inhibition to silence pyramidal firing.

3) Lastly, the fear learning relies on the presentation of the unconditioned stimulus over a long period of time (40 seconds). The authors justify this long-lasting input as reflecting not only the stimulus itself but as a memory of the US that is present over this extended time period. However, the experimental evidence for this presented in the paper is only very weak.

We are repeating here the answers we gave in response to the public comments, adding further relevant points.

(1) Our neurons were constrained by electrophysiology properties in response to hyperpolarizing currents in the BLA (Sosulina et al., 2010). We can reproduce these electrophysiological properties by using specific membrane currents known to be present in similar neurons in other brain regions (D-current in VIP interneurons in the cortex, and NaP- and H-currents in OLM/SOM cells in the hippocampus). Also, though a much more detailed description of BLA interneurons was given in (Vereczki et al., 2021), it is not clear that this level of detail is relevant to the questions that we were asking, especially since the experiments described were not done in the context of fear learning.

(2) It is true that we did not include the morphology, which undoubtedly makes a difference to some aspects of the circuit dynamics. Furthermore, it is correct that the model relies on a strong inhibition from SOM and PV to silence the excitatory projection neurons. We agree that the placement of the SOM inhibition on the pyramidal neurons can make a difference on some aspects of the circuit behavior. We are assuming that the inhibition from the SOM cells can inhibit the pyramidal cells firing, which can be seen as a hypothesis of our model. It is well known that VIP cells disinhibit pyramidal cells through inhibition of SOM and PV cells (Krabbe et al. 2019); hence, this hypothesis is generally believed. This choice of parameters comes from using simplified models: it is standard in modeling to adjust parameters to compensate for simplifications.

Re points 1) and 2), in a new paragraph (“Assumptions and predictions of the model”) in the Discussion reported in response to Reviewer #2 (public comments)’s point 3, we stated that modeling requires the omission of many details to bring out the significance of other details.

(3) 40 seconds is the temporal interval we decided to use to present the results. In the Results, we also showed that there is learning over a shorter interval of time (15 seconds) where CS and US/memory of US should both be present. Thus, our model requires 15 seconds over a single or multiple trials for associative learning to be established. We included references to additional experimental papers to support our reasoning in the last paragraph of section “Assumptions and predictions of the model” in the Discussion, also reported in response to Reviewer #1 point 2 (Recommendations for the Authors). We said there that some form of memory or overlap in the activity of the excitatory projection neurons is necessary for spike-timing-dependent plasticity.

The authors achieved the aim of constructing a biophysically detailed model of the BLA not only capable of fear learning but also showing spectral signatures seen in vivo. The presented results support the conclusions with the exception of a potential alternative circuit mechanism demonstrating fear learning based on a classical Hebbian (i.e. non-depression-dominated) plasticity rule, which would not require the intricate interplay between the inhibitory interneurons. This alternative circuit is mentioned but a more detailed comparison between it and the proposed circuitry is warranted.

Our model accounts for the multiple rhythms observed in the context of fear learning, as well as the known involvement of multiple kinds of interneurons. We did not say explicitly enough why our complicated model may be functionally important in ways that cannot be fulfilled with a simpler model with the non depression-dominated Hebbian rule. To explain this, we have added the following in the manuscript discussion: 

“Although fear learning can occur without the depression-dominated rule, we hypothesize that it is necessary for other aspects of fear learning and regulation. That is, in pathological cases, there can be overgeneralization of learning. We hypothesize that the modulation created by the involvement of these interneurons is normally used to prevent such overgeneralization. However, this is beyond the scope of the present paper.”

We have also written an extra paragraph about generalization in the Discussion “Synaptic plasticity in our model”:

“With the classical Hebbian plasticity rule, we show that learning can occur without the involvement of the VIP and SOM cells. Although fear learning can occur without the depressiondominated rule, we hypothesize that the latter is necessary for other aspects of fear learning and regulation. Generalization of learning can be pathological, and we hypothesize that the modulation created by the involvement of VIP and SOM interneurons is normally used to prevent such overgeneralization. However, in some circumstances, it may be desirable to account for many possible threats, and then a classical Hebbian plasticity rule could be useful. We note that the involvement or not of the VIP-SOM circuit has been implicated when there are multiple strategies for solving a task (Piet et al., 2024). In our situation, the nature of the task (including reward structure) may determine whether the learning rule is depression-dominated and therefore whether the VIP-SOM circuit plays an important role.”

Reviewer #3 (Recommendations For The Authors): 

We thank the Reviewer for all the recommendations. We replied to each of them below.

In general, there are some inconsistencies in the naming (e.g. sometimes you write PV sometimes PV+,...), please use consistent abbreviations throughout the manuscript. You also introduce some of the abbreviations multiple times. 

We modified the manuscript to remove all the inconsistencies in the naming. 

Introduction: 

- In the last section you speak about one recent study but actually cite two articles. 

We removed the reference to (Perrenoud and Cardin, 2023), which is a commentary on the Veit et al. article.

Results: 

- 'Brain rhythms are thought to be encoded and propagated largely by interneurons' What do you mean by encoded here? 

We agree with the Reviewer that the verb “to encode” is not accurate. We modified the sentence as follows:

“Brain rhythms are thought to be generated and propagated largely by interneurons”.

- The section 'Interneurons interact to modulate fear neuron output' could be clearer. Start with describing the elements of the circuit, then the rhythms in the baseline. 

We reorganized the section as follows:

“Interneurons interact to modulate fear neuron output. Our BLA network consists of interneurons, detailed in the previous section, and excitatory projection neurons (Fig. 2A). Both the fear-encoding neuron (F), an excitatory projection neuron, and the VIP interneuron are activated by the noxious stimulus US (Krabbe et al., 2019). As shown in Fig. 2A (top, right), VIP disinhibits F by inhibiting both SOM and PV, as suggested in (Krabbe et al., 2019). We do not include connections from PV to SOM and VIP, nor connections from SOM to PV and VIP, since those connections have been shown to be significantly weaker than the ones included (Krabbe et al., 2019). The simplest network we consider is made of one neuron for each cell type. We introduce a larger network with some heterogeneity in the last two sections of the Results.

Fig. 2A (bottom) shows a typical dynamic of the network before and after the US input onset, with US modeled as a Poisson spike train at ~50 Hz; the network produces all the rhythms originating from the interneurons alone or through their interactions with the excitatory projection neurons (shown in Fig. 1). Specifically, since VIP is active at low theta during both rest and upon the injection of US, it then modulates F at low theta cycles via SOM and PV. In the baseline condition, the VIP interneuron has short gamma bursts nested in low theta rhythm. With US onset, VIP increases its burst duration and the frequency of low theta rhythm. These longer bursts make the SOM cell silent for long periods of each low theta cycle, providing F with windows of disinhibition and contributing to the abrupt increase in activity right after the US onset. Finally, in Fig. 2A, PV lacks any external input and fires only when excited by F. Thanks to their reciprocal interactions, PV forms a PING rhythm with F, as depicted in Fig.1C.”

- Figure 3C: The lower dashed line has the tick label '0.37' which should read '0.037'. 

We fixed it.

- The section describing the network with multiple neurons could be clearer, especially, it is not really clear how these different ECS and F neurons receive their input. 

We answered the same objection in the reply to Reviewer #1 in point 2 under “minor issues.”

Discussion: 

- The paragraph 'It has also been suggested that ventral tegmental area has a role in fear expression (Lesas et al.,2023). Furthermore, it has been reported that the prelimbic cortex (PL) modulates the BLA SOM cells during fear retrieval, and the latter cells are crucial to discriminate non-threatening cues when desynchronized by the PL inputs (Stujenske et al., 2022).' is merely stating facts but I don't see how they relate to the presented work. 

We thank the Reviewer for pointing out that this was confusing. What we meant to emphasize was that later stages of fear conditioning and extinction appear to require more than the BLA. We specifically mention the discrimination of non-threatening cues at the end of the paragraph, which now reads as follows:

“Other brain structures may be involved in later stages of fear responsiveness, such as fear extinction and prevention of generalization. It has been reported that the prelimbic cortex (PL) modulates the BLA SOM cells during fear retrieval, and the latter cells are crucial to discriminate non-threatening cues when desynchronized by the PL inputs (Stujenske et al., 2022). Brain structures such as the prefrontal cortex and hippocampus have been documented to play a crucial role also in fear extinction, the paradigm following fear conditioning aimed at decrementing the conditioned fearful response through repeated presentations of the CS alone. As reported by several studies, fear extinction suppresses the fear memory through the acquisition of a distinct memory, instead of through the erasure of the fear memory itself (Harris et al., 2000; Bouton, 2002; Trouche et al., 2013; Thompson et al., 2018). Davis et al., 2017 found a high theta rhythm following fear extinction that was associated with the suppression of threat in rodents. Our model can be extended to include structures in the prefrontal cortex and the hippocampus to further investigate the role of rhythms in the context of discrimination of non-threatening cues and extinction. We hypothesize that a different population of PV interneurons plays a crucial role in mediating competition between fearful memories, associated with a low theta rhythm, and safety memories, associated with a high theta rhythm; supporting experimental evidence is in (Lucas et al., 2016; Davis et al., 2017; Chen et al., 2022).”

- The comparison to other models BLA is quite short and seems a bit superficial. A more indepth comparison seems warranted. 

We thank the reviewer for suggesting that a more in-depth comparison between our and other models in the literature would improve the manuscript. We rewrote entirely the first paragraph of that section. The new content reads as follows:

“Comparison with other models. Many computational models that study fear conditioning have been proposed in the last years; the list includes biophysically detailed models (e.g., (Li 2009; Kim et al., 2013a)), firing rate models (e.g., Krasne 2011; Ball 2012; Vlachos 2011), and connectionist models (e.g., Moustafa 2013; Armony 1997; Edeline 1992) (for a review see (Nair et al., 2016)). Both firing rate models and connectionist models use an abstract description of the interacting neurons or regions. The omission of biophysical details prevents such models from addressing questions concerning the roles of dynamics and biophysical details in fear conditioning, which is the aim of our model.  There are also biophysically detailed models (Li 2009; Kim 2013; Kim 2016; Feng 2019), which differ from ours in both the physiology included in the model and the description of how plastic changes take place.  One main difference in the physiology is that we differentiated among types of interneurons, since the fine timing produced for the latter was key to our use of rhythms to produce spike-time dependent plasticity. The origin of the gamma rhythm (but not the other rhythms) was investigated in Feng et al 2019, but none of these papers connected the rhythms to plasticity.

The most interesting difference between our work and that in (Li 2009; Kim 2013; Kim 2016) is the modeling of plasticity.  We use spike-time dependent plasticity rules.  The models in (Li 2009; Kim 2013; Kim 2016) were more mechanistic about how the plasticity takes place, starting with the known involvement of calcium with plasticity.  Using a hypothesis about back propagation of spikes, the set of papers together come up with a theory that is consistent with STDP and other instantiations of plasticity (Shouval 2002a; Shouval 2002b).  For the purposes of our paper, this level of detail, though very interesting, was not necessary for our conclusions.  By contrast, in order for the rhythms and the interneurons to have the dynamic roles they play in the model, we needed to restrict our STDP rule to ones that are depression-dominated.  Our reading of (Shouval 2002) suggests to us that such subrules are possible outcomes of the general theory.  Thus, there is no contradiction between the models, just a difference in focus; our focus was on the importance of the much-documented rhythms (Seidenbecher et al., 2003; Courtin et al., 2014b; Stujenske et al., 2014; Davis et al., 2017) in providing the correct spike timing.  We showed in the Supplementary Information (“Classical Hebbian plasticity rule, unlike the depression-dominated one, shows potentiation even with no strict pre and postsynaptic spike timing”) that if the STDP rule was not depression dominated, the rhythms need not be necessary.  We hypothesize that the necessity of strict timing enforced by the depression-dominated rule may foster the most appropriate association with fear at the expense of less relevant associations.”

- The paragraph 'This could happen among some cells responding to weaker sensory inputs that do not lead to pre-post timing with fear neurons. This timing could be modified by the "triconditional rule", as suggested in (Grewe et al., 2017).' is not very clear. What exactly is 'this' in the first sentence referring to? If you mention the 'tri-conditional rule' here, please briefly explain it and how it would solve the issue at hand here.  

We apologize that the sentence reported was not sufficiently clear. “This” refers to “depression”. We meant that, in our model, depression during fear conditioning happens every time there is no pre-post timing between neurons encoding the neutral stimuli and fear cells; poor pre-post timing can characterize the activity of neurons responding to weaker sensory inputs and does not lead to associative learning. We modified that paragraph as follows:

“The study in (Grewe et al., 2017) suggests that associative learning resulting from fear conditioning induces both potentiation and depression among coactive excitatory neurons; coactivity was determined by calcium signaling and thus did not allow measurements of fine timing between spikes. In our model, we show how potentiation between coactive cells occurs when strict pre-post spike timing and appropriate pauses in the spiking activity arise. Depression happens when one or both of these components are not present. Thus, in our model, depression represents the absence of successful fear association and does not take part in the reshaping of the ensemble encoding the association, as instead suggested in (Grewe et al., 2017). A possible follow-up of our work involves investigating how fear ensembles form and modify through fear conditioning and later stages. This follow-up work may involve using a tri-conditional rule, as suggested in (Grewe et al. 2017), in which the potential role of neuromodulators is taken into account in addition to the pre- and postsynaptic neuron activity; this may lead to both potentiation and depression in establishing an associative memory.”

- In the limitations and caveats section you mention that the small size of the network implies that they represent a synchronous population. What are the potential implications for the proposed rhythm-dependent mechanism? What are your expectations for larger networks? 

We apologize if we were not adequately clear. We are guessing that the Reviewer thought we meant the entire population was synchronous, which it is not. We meant that, when we use a single cell to represent a subpopulation of cells of that type, that subpopulation is effectively synchronous. For larger networks in which each subtype is represented by many cells, there can be heterogeneity within each subtype. We have shown in the paper that the basic results still hold under some heterogeneity; however, they may fail if the heterogeneity is too large.

We mentioned in a new section named “Assumptions and predictions of the model” in response to point 3 made by Reviewer #2.

- The discussion is also missing a section on predictions/new experiments that can be derived from the model. How can the model be confirmed, what experiments/results would break the model? 

To answer this question, we put in a new section in the Discussion entitled “Assumptions and predictions of the model”. The first paragraph of this section is in the reply to Reviewer #2 point 2; the second paragraph is in the reply to Reviewer #2 point 3; the last paragraph is in the Reply to Reviewer #1 point c; the rest of the section reads as follows:

“Our study suggests that all the interneurons are necessary for associative learning provided that the STDP rule is depression-dominated. This prediction could be tested experimentally by selectively silencing each interneuron subtype in the BLA: if the associative learning is hampered by silencing any of the interneuron subtypes, this validates our study. Finally, the model prediction could be tested indirectly by acquiring more information about the plasticity rule involved in the BLA during associative learning. We found that all the interneurons are necessary to establish fear learning only in the case of a depression-dominated rule. This rule ensures that fine timing and pauses are always required for potentiation: interneurons provide both fine timing and pauses to pyramidal cells, making them crucial components of the fear circuit. 

The modeling of the interneurons assumes the involvement of various intrinsic currents; the inclusion of those currents can be considered hypotheses of the model. Our model predicts that blockade of D-current in VIP interneurons (or silencing VIP interneurons) will both diminish low theta and prevent fear learning. Finally, the model assumes the absence of significantly strong connections from the excitatory projection cells ECS to PV interneurons, unlike the ones from F to PV. Including those synapses would alter the PING rhythm created by the interactions between F and PV, which is crucial for fine timing between ECS and F needed for LTP.”

moin

könnt ihr mal die abod website reparieren?

https://abod.de/<br /> bringt den fehler "clock error"<br /> weil das SSL zertifikat ist abgelaufen seit 2023-09-27

ich suche das begleitmaterial zum hörbuch<br /> Albrecht Müller - Die Revolution ist fällig<br /> angeblich zu finden auf<br /> https://abod.de/revolution

https://www.youtube.com/watch?v=Pet-8Jkmbgw&feature=youtu.be

r/Wikipediavideos

https://www.reddit.com/r/Wikipediavideos

図番は8-1？

他の章ではコロン：があるが、ここにはない。

他の章にはコロン：があるが、ここにはない。

are you idiots looking for the people who made "the bomb" ... because it's us.

if you are looking for the people who made the bomb the thing that is related to "supernovae" and ... stardust ... and "#starheart" I mean, that's me/us/also

در ساده ترین روشش این است که فقط حاوی title و text باشد

دقت داشته باش که با استفاده از این panels می تونی چنتا panel داشته باشی

The link is the wrong one

remove this part

all digital technologies.

replace with businesses

delete. Deployment is enough

Same changes as in the other page

F

capitalize L

This is so true. I find that in the classroom it helps you gain respect of the kids when you show you care to find their interests.

Overall Rating (5/5)

Impact (5/5) This paper describes studies aimed at solving the mystery of the role of EBER1 in Epstein-Barr virus (EBV) infections. As noted by the authors, EBV has been studied extensively and the importance of EBER1 has been known for over 40 years but how it aids in EBV infections has remained elusive due to the fact that it has been impervious to attempts at knockdown using conventional methods to date. Now, using CRISPR, the authors have been able to knock down EBER1 by over 90% and see the effects. In their interesting findings they see that unlike most RNA modulators, EBER1 does not work directly on the genome but instead, acts as a translation modifier by inhibiting the ribosomal protein L22 which then allows for the upregulation of its paralog, L22L1. The effect of this on cellular function is to increase oxidative phosphorylation, an event which supports cellular growth and transformation. This finding has high impact and with further work can lead to finding targets to limit the spread of cancers that are EBV based. One suggestion is to change the title to “Epstein-Barr virus non-coding RNA EBER1 promotes the expression of the ribosomal protein paralog L22L1 to boost oxidative phosphorylation” to increase search engine hits.

Methods (4/5) The authors used standard cell culture methods with the use of CRISPR to knock down EBER1 in EBV infected BJAB-B1 cells. BJAB cells are an EBV negative tumor cell line often used in oncology studies. For these studies they infected these cells with EBV so as to have controls and steady state EBV levels. Immunoblotting was used to confirm increases in L22L1.The methods were all cell based and appropriate. Next steps, although not for this particular study, would be to produce a mouse model of EBER1 conditional knockdown and see if introduction of EBV led to EBV based cancers or other diseases.

Results (5/5) The results clearly show that loss of EBER1 causes an increase in L22L1 within ribosomes. Overexpression of L22L1 in ribosomes led to the expression of mRNAs associated with oxidative phosphorylation. Examination of ribosomal subunits in the EBER1 knockdown cells confirmed that loss of EBER1 led to a similar pattern of mRNAs expression associated with oxidative phosphorylation. Interestingly, if L22L1 was knocked down in these cells, colony formation was inhibited suggesting that a role for oxidative phosphorylation in the formation of growth and potentially transformation.

Discussion (5/5) This paper gives an intriguing look into the pathway by which EBV can lead to cancer formation, something which has eluded researchers for decades. Thus, this study has the potential to be very high impact. The study identifies a key step by which EBER1, a known protein involved in EBV function, leads to cellular growth by activating L22L1 which is a paralog to the ribosomal protein L22. Activation of L22L1 stimulated oxidative phosphorylation pathways that are normally quiescent which in turn allows for cellular growth. There are still many holes in the story, but this paper plugs a big one. It would be nice to see the next steps taken in determining how these particular oxidative phosphorylation pathways stimulate cancer growth. Also, as noted above, moving this into a mouse model would be a great step, although not needed for the publication of this particular article.

Reviewer Information The reviewer (Dr. Heather Duffy) is the Chair of Biotechnology at the Franklin Cummings Technical Institute. Her PhD is in neuroscience, but her work is as a protein biochemist working on inflammation, signal transduction, and cell-cell communication. She has worked in both industry and academia for over 20 years.

Dr. Heather Duffy on ResearchHub: https://www.researchhub.com/user/1790894/overview

ResearchHub Peer Review Statement: This peer review has been uploaded from ResearchHub as part of a paid peer review initiative. ResearchHub aims to accelerate the pace of scientific research using novel incentive structures.

这个地方很重要。

ここも　もっと前の　のほうがニュアンスが伝わりそうです

もっと前の　のほうがニュアンスが伝わりそうです

不用意、だと「うっかり」みたいな語感ですよね。ここのケースはむしろわざとだと思うのでちょっと違和感がありました。

代案

意図しない

わかりにくいと感じました

代案

Djangoにはログアウトを処理する機能があります

原文はたしかにTestingですが、ユニットテスト的な意味合いでのテストに読めてしまいます。

代案（「確認する」と迷ったけど軽い「見る」を使った）

テンプレート内でuser.is_authenticatedを見ることで

前半（体言止め）と合ってないので不自然に感じます。

代案

既存のモデルの変更をしたとき

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Paleontology.<br /> See the peer reviews and the recommendation.

https://link.springer.com/book/10.1007/978-3-031-63498-7

https://link.springer.com/chapter/10.1007/978-3-031-63498-7_12

LCTRS

delete

test

delete it

Our leadership team consists of seasoned executives from varied backgrounds—including asset management, banking, technology, and consulting—ensuring alignment between our collective expertise and your business objectives.

Banks, Hedge Funds, Private Equity and Debt Funds, Logistics and Retails

delete this word

There should be an option to reject cookies while still seeing the website

delete

主引导记录中最多四个表项。可以是四个主要分区，亦可以是两个主分区三个扩展分区，数量不定的，如果是四个主要分区那么磁盘的容量会被分配完。

逻辑分区由扩展分区切分出来的，可以进行格式化，而唯一的扩展分区不能进行格式化。同时需要注意的是，逻辑分区在linux中的命名与主分区以及扩展分区是不连续的，如上面高亮的一样

由于只能三个扩展分区可能不够用，由此引入了逻辑分区的概念，同时限制了扩展分区只能用一个，另外两个0填充，再对唯一的扩展分区进行逻辑分区

globalの訳語にはないですが、地理的なデータセット　のほうが伝わりやすそう

意味がわかりにくかったです。

代案

追加するプロットは　

もしくは次の節と表現をそろえて

追加のプロットは～使用して作成する必要があります

日本語だと「開発者ツール」のほうが一般的な気がします。

Chromeでは「デベロッパーツール」、Edgeでは「開発者ツール」でした

ブラウザの新しいタブ　でしょうか

を作成　でしょうか

AI-Assisted Sensemaking

Sensemaking with AI: Leveraging Machine Intelligence

sensemaker

Oui, c'est une question de régime d'écriture. C'est ce qu'a démontré efficacement Annie Combes, à laquelle on pourrait renvoyer.

Ce sont des choses qui ont été largement commentées par la critique, il y a plus de trente ans.

la construction est syntaxiquement impossible. "remés" est un participe, pour construire la phrase avec "choisi" il faudrait un infinitif.

"estre" ?

on se demande si c'est vraiment le bon terme. Il est blessé et pas du tout serein.

"à laquelle" ?

expliquer qu'il ne s'agit pas d'Agravain, fils de Lot ?

"planent"

"contraste"

Ce qui est à peu près normal puisque ce sont les guerres qui prennent alors le relais. Toujours en l'absence l'alternative, après l'achèvement de la Quête du Graal.

Ici, on a un peu un raccourci: s'il s'agit bien d'une "agitation des corps", elle s'explique non par opposition avec la quiétude, mais bien plus par le fait que, en effet, la Quête se soit achevée et le Graal ait disparu. Il n'y a plus de merveille, plus d'aventure. En d'autres termes, l'existence chevaleresque est privée de son but spirituel et doit (re)-changer de boussole. C'est le tournoi qui tient lieu d'aventure. Cela a été amplement mis en avant par la critique.

je supprimerais "être"

je propose "repousser"

"eux"

Cela va un peu vite. Personne ne peut nier que la reine a donné la pomme au chevalier, il y a donc ce lien de cause à effet qui fait que Gauvain, par exemple, se refuse de combattre. Ce que dit Lancelot —et l'argument marche aussi en théologie — c'est que l'intention n'y était pas.

eLife assessment

This study provides insights into the mechanism of axonal directional changes, utilizing the pacemaker neurons of the circadian clock, the sLNVs, as a model system. The data were collected and analysed using solid methodology, resulting in valuable data on the interplay of signalling pathways and the growth of the axon. The study holds potential interest for neurobiologists focusing on axonal growth and development.

に置き換えると　でしょうか

には　でしょうか

typo figsize

この辺、本文と揃えて訳語に置き換えたほうがよさそう

でも日本語対応の設定をしてないと豆腐になるのが悩ましい

意味が伝わりにくいと感じました。

代案

この書き方をするのが一般的です。

We would like to thank you and the reviewers for your thoughtful comments that assisted us to improve the manuscript. We carefully followed the reviewers’ recommendations and provide a detailed point-by-point account of our responses to the comments. 

Please find below the important changes in the updated manuscript.

(1) We changed the title according to the comments provided by reviewer #1.

(2) We edited the introduction, results, and discussion to improve the link between the objectives of the study, the findings, and their discussion, as reviewer #2 recommended.

(3) We clarified the link between camouflage and fitness, which is now presented as a hypothesis, as reviewer #1 suggested.

(4) We added new analyses and figures in the main text and in the supplementary materials to better emphasize sex differences in landing force, foraging strategies and hunting success, following reviewer #1 suggestion.

(5) According to reviewer #2 comments, we edited the results adding key information about methods to help the reader understand the findings without reading the Methods section.

(6) We added important details about the model selection approach along with a discussion of the low R-square values reported in our analyses on hunting success, as reviewer #2 suggested.

eLife assessment 

This fundamental work substantially advances our understanding of animals' foraging behaviour, by monitoring the movement and body posture of barn owls in high resolution, in addition to assessing their foraging success. With a large dataset, the evidence supporting the main conclusions is convincing. This work provides new evidence for motion-induced sound camouflage and has broad implications for understanding predator-prey interactions. 

Public Reviews: 

Reviewer #1 (Public Review): 

In this paper, Schalcher et al. examined how barn owls' landing force affects their hunting success during two hunting strategies: strike hunting and sit-and-wait hunting. They tracked tens of barn owls that raised their nestlings in nest boxes and utilized high-resolution GPS and acceleration loggers to monitor their movements. In addition, camcorders were placed near their nest boxes and used to record the prey they brought to the nest, thus measuring their foraging success. 

This study generated a unique dataset and provided new insights into the foraging behavior of barn owls. The researchers discovered that the landing force during hunting strikes was significantly higher compared to the sit-and-wait strategy. Additionally, they found a positive relationship between landing force and foraging success during hunting strikes, whereas, during the sit-and-wait strategy, there was a negative relationship between the two. This suggests that barn owls avoid detection by generating a lower landing force and producing less noise. Furthermore, the researchers observed that environmental characteristics affect barn owls' landing force during sit-and-wait hunting. They found a greater landing force when landing on buildings, a lower landing force when landing on trees, and the lowest landing force when landing on poles. The landing force also decreased as the time to the next hunting attempt decreased. These findings collectively suggest that barn owls reduce their landing force as an acoustic camouflage to avoid detection by their prey. 

The main strength of this work is the researchers' comprehensive approach, examining different aspects of foraging behavior, including high-resolution movement, foraging success, and the influence of the environment on this behavior, supported by impressive data collection. The weakness of this study is that the results only present a partial biological story contained within the data. The focus is on acoustic camouflage without addressing other aspects of barn owls' foraging strategy, leaving the reader with many unanswered questions. These include individual differences, direct measurements of owls' fitness, a detailed analysis of the foraging strategy of males and females, and the collective effort per nest box. However, it is possible that these data will be published in a separate paper. 

We greatly appreciate your recognition of the comprehensive approach and extensive data collection. Our primary objective was to study the role of acoustic camouflage. Nonetheless, the manuscript now includes a detailed analysis of the foraging strategy and hunting success of males and females (lines 164-225).

The results presented support the authors' conclusion that lower landing force during sit-andwait hunting increases hunting success, likely due to a decreased probability of detection by their prey, resulting in acoustic camouflage. The authors also argue that hunting success is crucial for survival, and thus, acoustic camouflage has a direct link to fitness. While this statement is reasonable, it should be presented as a hypothesis, as no direct evidence has been provided here.

Thank you for the comment. We agree and thus have edited the language accordingly.  

However, since information about nestling survival is typically monitored when studying behavior during the breeding period, the authors' knowledge of the effect of acoustic camouflage on owls' fitness can probably be provided. Furthermore, it will be interesting to further examine the foraging strategies used by different individuals during foraging, the joint foraging success of both males and females within each nest box, and the link between landing force and foraging success if the data are available.

We are currently writing a manuscript on these topics. We are aware that several scientific questions regarding the foraging ecology of the barn owl still need our attention. Regarding the link between landing force and foraging success, we believe that our revised manuscript addresses this specific topic, please see specific responses below.

However, even without this additional analysis on survival, this paper provides an unprecedented dataset and the first measurement of landing force during hunting in the wild. It is likely to inspire many other researchers currently studying animal foraging behavior to explore how animals' movements affect foraging success.

Reviewer #2 (Public Review): 

Summary: 

The authors provide new evidence for motion-induced sound camouflage and can link the hunting approach to hunting success (detailing the adaptation and inferring a fitness consequence). 

Strengths: 

Strong evidence by combining high-resolution accelerometer data with a ground-truthed data set on prey provisioning at nest boxes. A good set of co-variates to control for some of the noise in the data provides some additional insights into owl hunting attempts. 

Weaknesses: 

There is a disconnect between the hypotheses tested and the results presented, and insufficient detail is provided on the statistical approach. R2 values of the presented models are very small compared to the significance of the effect presented. Without more detail, it is impossible to assess the strength of the evidence.

In the revised manuscript, we changed the way results are presented and we improved the link between the hypotheses and the results. The R2 values are indeed small. It is however important to keep in mind that we are assessing the outcome of one specific behavior (i.e. landing force during sit-and-wait hunts) on hunting success in a wild environment, where many complex ecological interactions likely influence hunting success. Nonetheless, the coefficients (as reported in the results) show that for every 1 N increase in landing force, there is a 15% reduction in hunting success, which is substantial. In the discussion we also note that 50 Hz is a relatively low sampling frequency for estimating the peak ground reaction force. We have gone back over the presentation of our results and made our discussion more nuanced to acknowledge this aspect. 

We have also added a detailed description about our model selection process in the methods section and provide a model selection table for each analysis in the supplementary materials.

The authors seem to overcome persisting challenges associated with the validation and calibration of accelerometer data by ground-truthing on-board measures with direct observations in captivity, but here the methods are not described any further and sample sizes (2 owls - how many different loggers were deployed?) might be too small to achieve robust behavioural classifications.

Thank you for the comment. Details of our methods of behavioural identification are provided in lines 385 – 429. There are two reasons why our results should not be limited by the sample size. First, we used the temporal sequence of changes in acceleration, and rates of change in acceleration data, which make the methods robust to individual differences in acceleration values. Furthermore, our methods for behavioural identification were not based on machine learning. Instead, we use a Boolean based approach (as described in Wilson et al. 2018. MEE), which is more robust to small differences in absolute values that might occur e.g. in relation to slight changes in device position. 

Recommendation for the authors:

Reviewer #1 (Recommendations For The Authors): 

Comment 1. This study provides new insights into animals' foraging behavior and will probably inspire other researchers to examine foraging behavior in such high resolution.

We hope so, thank you.

Comment 2. However, it is necessary to describe better the measured landing force and the hunting strike and perching behavior so the readers can understand these methods when reading the results (and without reading the Methods).

We have now changed the text in the “Results” to help the reader understand the key methods while reading the results.

Comment 3. In addition, make sure you use the same terminology for hunting strategies during the entire paper and especially in all figures and corresponding result descriptions.

We now use consistent terminology throughout the text and figures. We hope that this is now clear in the revised manuscript.

Comment 4. In addition, although I find your statement about the link between acoustic camouflage and fitness reasonable, it should be described as a hypothesis or examined if you want to keep the direct link statement. I believe showing a direct link can add an additional outstanding aspect to this paper, but I also understand that it can be addressed in a separate paper.

We agree that the relationship between hunting success and barn owl fitness is an important topic, but it necessitates a consideration of both hunting strategies, including hunting on the wing, which extends beyond the limits of our current study. Indeed, our primary objective was to conduct a detailed examination of the interplay between acoustic camouflage and the success of the sit-and-wait technique.

However, we have edited the manuscript to explicitly describe the link between acoustic camouflage and fitness as a hypothesis. We believe this adjustment provides a more accurate representation of our approach. We hope this clarifies the specific emphasis of our work and its contribution to the understanding of barn owl hunting behavior.

Here are my detailed comments about the paper: 

Comment 5. Title: Consider changing the title to "Acoustic camouflage predicts hunting success in a wild predator." 

We would like to thank you for your nice proposition. However, we opted for a different title, which is now “Landing force reveals new form of motion-induced sound camouflage in a wild predator”.

Comment 6. Line 91-93: Please provide additional information about the collected dataset, including: 

Description of the total period of observations, an average and standard deviation of perching and hunting attempt events per individual per night, number of foraging trips per individual per night, details about the geographic location and characteristics of the habitat, season, and reproductive state. 

The revised manuscript now includes detailed information about the collected dataset (i.e. study area, reproductive state, etc…). “We used GPS loggers and accelerometers to record high resolution movement data during two consecutive breeding seasons (May to August in 2019 and 2020) from 163 wild barn owls (79 males and 84 females) breeding in nest boxes across a 1,000 km² intensive agricultural landscape in the western Swiss plateau.” Results section, lines 79 – 82

Details about the number of foraging trips per individuals and per night are now presented in the results: “Sexual dimorphism in body mass was marked among our sampled individuals. Males were lighter than females (84 females, average body mass: 322 ± 22.6 g; 79 males, average body mass 281 ± 16.5 g, Fig S6) and provided almost three times more prey per night than females (males: 8 ± 5 prey per night; females: 3 ± 3 prey per night; Fig.S7). Males also displayed higher nightly hunting effort than females (Males: 46 ± 16 hunting attempts per night, n= 79; Females: 25 ± 11 hunting attempts per nights, n=84; Fig. 3A, Fig S8). However, females were more likely to use a sit and wait strategy than males (females: 24% ± 15%, males: 13% ± 10%, Fig.S9). As a result, the number of perching events per night was similar between males and females (Females: 76 ± 23 perching events per nights; Males: 69 ± 20 perching events per night; Fig S8).” (lines 165 – 174) 

Comment 7. In addition, state if the information describes breeding pairs of males and females and provides statistics on the number of tracked pairs and the number of nest boxes.

The revised manuscript now includes a description of the number of tracked breeding pairs and the number of nest boxes. “Of these individuals, 142 belonged to pairs for which data were recovered from both partners (71 pairs in total, 40 in 2019, 31 in 2020). The remaining 21 individuals belonged to pairs with data from one partner (11 females and 1 male in 2019; 4 females and 5 males in 2020).” (lines 82 – 85.)

Comment 8. Line 93: Briefly define the term "landing force" and explain how it was measured (and let the reader know that there is a detailed description in the Methods).

We now include a brief definition of the “landing force” along with a brief explanation of how it was measured in the results section. “We extracted the peak vectoral sum of the raw acceleration during each landing and converted this to ground reaction force (hereafter “landing force”, in Newtons) using measurements of individual body mass (see methods for detailed description).” (lines 92 – 95).

Comment 9. Line 94: All definitions, including "pre-hunting force," need to be better described in the Results section.

Thank you for this suggestion. We now provided a better description of those key definitions directly in the results section: 

Measurement of landing force: “Barn owls employing a sit-and-wait strategy land on multiple perches before initiating an attack, with successive landings reducing the distance to the target prey (Fig. 2C). 

We used the acceleration data to identify 84,855 landings. These were further categorized into perching events (n = 56,874) and hunting strikes (n = 27,981), depending whether barn owls were landing on a perch or attempting to strike prey on the ground (Fig. 1A and B, see methods for specific details on behavioral classification).” (lines 88 – 95)

Pre-hunt perching force predicts hunting success: “Finally, we analyzed whether the landing force in the last perching event before each hunting attempt (i.e. pre-hunt perching force) predicted variation in hunting success” (lines 229 – 230)

Comment 10. Line 102: Remove "Our analysis of 27,981 hunting strikes showed that" and add "n = 27,981" after the statistics. You have already stated your sample size earlier. There is no need to emphasize it again, although your sample size is impressive.

We modified the text in the results section as suggested.

Comment 11. Line 104: The results so far suggest that the difference in landing force between males and females is an outcome of their different body masses. However, it is not clear what is the reason for the difference in the number of hunting strike attempts between males and females (Lines 104-106). Can you compare the difference in landing force between males and females with similar body mass (females from the lower part of the distribution and males from the upper part)? Is there still a difference?

Thank you, following your comment we made some new analyses that clarified the situation around landing force involved in perching and hunting strike events between sexes. But firstly, we wanted to clarify why there is a difference in number of hunting attempts between males and females. During the breeding season, females typically perform most of the incubation, brooding, and feeding of nestlings in the nest, while the male primarily hunts food for the female and chicks. The female supports the male providing food in a very irregular way, and this changes from pair to pair (paper in prep.). The differences in number of hunting attempts between males and females reflects this asymmetry in food provisioning between sexes during this specific period. We specified this in the revised version of the manuscript (lines 164 – 174). 

We also provide a new analysis to investigate sex differences in mass-specific landing force (force/body mass). We found that males and females produce similar force per unit of body mass during perching events. This demonstrates that the overall higher perching force in females (see Fig. 4C in the manuscript) is therefore driven by their higher body mass. (lines 194 – 199)

Comment 12. Line 154: I believe Boonman et al. (2018) is relevant to this part of the discussion. Boonman, Arjan, et al. found that barn owl noise during landing and taking off is worth considering. ["The sounds of silence: barn owl noise in landing and taking off."

Behavioral Processes 157 (2018): 484-488.]

We now cited this paper in the discussion.

Comment 13. Line 164: Your results do not directly demonstrate a link to fitness, although they potentially serve as a proxy for fitness (add a reference). However, you might have information regarding nestlings' survival - that will provide a direct link for fitness. Change your statement or add the relevant data.

We appreciated your feedback, and we adjusted the language accordingly.

Comment 14. Line 213: If the poles are closer to the ground - is it possible that the higher trees and buildings serve for resting and gathering environmental information over greater distances? For example, identifying prey at farther distances or navigating to the next pole?

Yes, this is indeed the most likely explanation for the fact that owls land more on buildings and trees than on poles until the last period (about 6 minutes) before hunting. In these last minutes, barn owls preferentially use poles, as we showed in figure 2B. The revised manuscript now includes this explanation in the discussion (lines 269 – 284).

Comment 15. Line 250: The product "AXY-Trek loggers" does not appear on the Technosmart website (there are similar names, but not an exact match). Are you sure this is the correct name of the tracking device you used? 

Thank you for pointing out this detail that we missed. The device we used is now called "AXY-Trek Mini" (https://www.technosmart.eu/axy-trek-mini/). We have corrected this error directly in the revised manuscript.

Comment 16. Line 256: Please explain how the devices were recovered. Did you recapture the animals? If so, how? Additionally, replace "after approximately 15 days" with the exact average and standard deviation. Furthermore, since you have these data, please state the difference in body mass between the two measurements before and after tagging.

The birds were recaptured to recover the devices. Adults barn owls were recaptured at their nest sites, again using automatic sliding traps that are activated when birds enter the nest box. The statement "after approximately 15 days" was replaced by the exact mean and standard deviation, which were 10.47 ± 2.27 days. Those numbers exclude five individuals from the total of 163 individuals included in this study. They could not be recaptured in the appropriate time window but were re-encountered when they initiated a second clutch later in the season (4 individuals) or a new clutch the year after (1 individual).

We integrated this previously missing information in the revised manuscript (lines 370 – 372).

Comment 17. Line 259: What was the resolution of the camera? What were the recording methods and schedule? How did you analyze these data? 

The resolution was set to 3.1 megapixel. Motion sensitive camera traps were installed at the entrance to each nest box throughout the period when the barn owls were wearing data loggers, and each movement detected triggered the capture of three photos in bursts. The photos recorded were not analyzed as such for this study, but were used to confirm each supply of prey, which had previously been detected from the accelerometer data. We added these details in the revised manuscript (lines 377 – 380)

Comment 18_1. Figure 1: 

Panel A) Include the sex of the described individual. 

The sex of the described individual is now included in the figure caption.

Comment 18_2. It would be interesting to show these data for both males and females from the same nest box (choose another example if you don't have the data for this specific nest box). 

Although we agree that showing tracks of males and females from the same nest is very interesting, the purpose of this figure was to illustrate our data annotation process and we believe that adding too many details on this figure will make it appear messy. However, the revised manuscript now includes a new figure (Fig. 3A) which shows simultaneous GPS tracks of a male and a female during a complete night, with detailed information about perching and hunting behaviors.

Comment 18_3. Add the symbol of the nest box to the legend. 

Done

Comment 18_4. Provide information about the total time of the foraging trip in the text below. 

The duration of the illustrated foraging trip has been included in the figure caption.

Comment 18_5. To enhance the figure’s information on foraging behavior, consider color coding the trajectory based on time and adding a background representing the landscape. Since this paper may be of interest to researchers unfamiliar with barn owl foraging behavior, it could answer some common questions. 

For similar reasons explained in our answer above (Comment 18_2), we would rather keep this figure as clean as possible. However, we followed your recommendations and included these details in the new Figure 3 described above. In this new figure, GPS tracks are color coded according to the foraging trip number and includes a background representing the landscape. To provide even more detail about the landscape, we added another figure in the supplementary materials (Fig. S2) which provides illustration of barn owls foraging ground and nest site that we think might be of interest for people unfamiliar with barn owls.

Comment 18_6. Inset panels) provide a detailed description of the acceleration insert panels. 

Done

Comment 18_7. Color code the acceleration data with different colors for each axis, add x and y axes with labels, and ensure the time frame on the x-axis is clear. How was the self-feeding behavior verified (should be described in the methods section)? 

We kept both inset panels as simple as possible since they serve here as examples, but a complete representation of these behaviors (with time frame, different colors and labels) is provided in the supplementary materials (figure S3). We included this statement in the figure caption and added a reference to the full representations from the supplementary materials: 

In the Figure caption: “Inset panels show an example of the pattern of the tri-axial acceleration corresponding to both nest-box return and self-feeding behaviors (but see Fig S3for a detailed representation of the acceleration pattern corresponding to each behavior).” 

In the Method section: “Self-feeding was evident from multiple and regular acceleration peaks in the surge and heave axes (resulting in peaks in VeDBA values > 0.2 g and < 0.9 g, Fig.S3D), with each peak corresponding to the movement of the head as the prey was swallowed whole.”.

Comment 18_8. Panel B) Note in the caption that you refer to the acceleration z-axis.

We believe that keeping the statement “the heave acceleration…” in the figure caption is more informative than referring to the “z-axis” as it describes the real dimension to which we are referring. The use of the x, y and z axes can be misleading as they can be interchanged depending on the type and setting of recorders used.

Comment 18_9. Present the same time scale for both hunting strategies to facilitate comparison. You can achieve this by showing only part of the flight phase before perching. 

Done

Comment 18_10. Panel C) Presenting the data for both hunting strategy and sex would provide more comprehensive information about the results and would be relatively easy to implement. 

We agree with your comment. We present the differences in landing force for both landing contexts and sexes in the new Figure 3 as well as in the supplementary materials (Figure S10) of this revised manuscript.

Comment 19. Figure 2: Please provide an explanation of the meaning of the circles in the figure caption.  

Done

Comment 20. Figure 3: 

Panel A) It is unclear how the owl illustration is relevant to this specific figure, unlike the previous figures where it is clear. Also, suggest removing the upper black line from the edge of the figure or add a line on the right side. 

Done (now in Figure 2).

Panel B) "Density" should be capitalized. 

Done

Panel C) Add a scale in meters, and it would be helpful to include an indication of time before hunting for each data point. 

Done

Comment 21. Figure S1: Mark the locations of the nest boxes and ensure that trajectories of different individuals and sexes can be identified. 

The purpose of this figure was to show the spatial distribution of the data. We think that adding nest locations and coloring the paths according to individuals and/or sex will make the figure less clear. However, the new Figure 3 highlights those details.

Comment 22. Figure S2: Show the pitch angle similarly to how you showed the acceleration axes, and explain what "VeDBA" stands for. Provide a description of the perching behavior, clearly indicating it on the figure. Add axes (x, y, z) to the illustration of the acceleration explanation. 

We edited this figure (now figure S3) to show the pitch angle and provide an explanation of what “VeDBA” stands for in the figure caption. The figure caption now also provides a better description of the perching behavior. For the axes (i.e. X, Y, Z), we prefer to refer to the heave, surge, and sway as this is more informative and refers to what is usually reported in studies working with tri-axial accelerometers.

Comment 23. Table S1: Improve the explanation in the caption and titles of the table. 

Done

Reviewer #2 (Recommendations For The Authors): 

Comment 1. From the public review and my assessment there, the authors can be assured that I thoroughly enjoyed the read and am looking forward to seeing a revised and improved version of this paper. 

We thank the reviewer for this comment. We revised the manuscript according to their comments.

Comment 2. In addition to my major points stated above, I would like to add the following recommendations: 

The manuscript is overall well written, but it uses a very pictorial language (a little as if we were in a David Attenborough documentary) that I find inappropriate for a research paper (especially in the abstract and introduction, "remarkable" (2x), "sophisticated" (are there any unsophisticated adaptations? We are referring to something under selection after all) etc.

We appreciated that you found the paper overall well written, and we understand the comment about pictorial language. We therefore slightly changed the text to make sure that the adjective used to describe adaptive strategies are not over-emphasized.

Comment 3. Abstract 

"While the theoretical benefits of predator camouflage are well established, no study has yet been able to quantify its consequences for hunting success." - This claim is actually not fully true: 

Nebel Carina, Sumasgutner Petra, Pajot Adrien and Amar Arjun 2019: Response time of an avian prey to a simulated hawk attack is slower in darker conditions, but is independent of hawk colour morph. Soc. open sci.6:190677 

We edited our claim to specify that the consequences of predator camouflage on hunting success has never been quantified in natural conditions and cited the reference in the introduction.

Comment 4. Line 23. Rephrase to: "We used high-resolution movement data to quantify how barn owls (Tyto alba) conceal their approach when using a sit-and-wait strategy, as well as the power exerted during strikes." 

We edited this sentence in the abstract, as suggested.

Comment 5. Results 

There is a disconnect between the objectives outlined at the end of the introduction and the following results that should be improved. 

The authors state: "Using high-frequency GPS and accelerometer data from wild barn owls (Tyto alba), we quantify the landing dynamics of this sit-and-wait strategy to (i) examine how birds adjust their landing force with the behavioral and environmental context and (ii) test the extent to which the magnitude of the predator cue affects hunting success." But one of the first results presented are sex differences. 

This is a fair point. We have now changed our statement in the end of the introduction as well as the order of the results to improve the link between the objectives outlined in the introduction and the way result are presented. 

Comment 6. At this stage, the reader does not even know yet that we are presented with a size-dimorphic species that also has very different parental roles during the breeding season. This should be better streamlined, with an extra paragraph in the introduction. And these sex differences are then not even discussed, so why bring them up in the first place (and not just state "sex has been fitted as additional co-variate to account for the size-dimorphism in the species" without further details). 

We edited the way the objectives are outlined in the introduction to cover the size dimorphism (lines 70 – 76). We also completely changed the way the sex differences are presented in the results, including a new analysis that we believe provides a better comprehensive understanding of barn owl foraging behavior (lines 164 – 206). Finally, we added a new paragraph in the discussion to consider those results (lines 319 – 339).

Comment 7. It is not clear to me where and how high-resolution GPS data were used? The results seem to concentrate on ACC – why GPS was used and how it features should be foreshadowed in a few lines in the introduction. I definitively prefer having the methods at the end of a manuscript, but with this structure, it is crucial to give the reader some help to understand the storyline. 

GPS data were used to validate some behavioral classifications (prey provisioning for example), but most importantly they were used to link each landing event with perch types. We edited the text in the result section to clarify where GPS and/or ACC data were used.

Comment 8. Discussion 

Move the orca example further down, where more detail can be provided to understand the evidence. 

After our extensive edits in the discussion, we felt this example was interrupting the flow. We now cite this study in the introduction. 

Comment 9. Size dimorphism and evident sex differences are not discussed. 

The revised manuscript now includes a new paragraph in the discussion in which sex differences are discussed (lines 319 – 339).

Comment 10. Be more precise in the terminology used (for example, land use seems to be interchangeable with habitat characteristics?). 

We modified “land use” with “habitat data” in the revised manuscript.

Comment 11. Methods 

Please provide a justification for the very high weight limit (5%; line 256). This limit is outdated and does not fulfill the international standard of 3% body weight. I assume the ethics clearance went through because of the short nature of the study (i.e., the birds were not burdened for life with the excess weight? But a line is needed here or under the ethics considerations to clarify this). 

The 5% weight limit was considered acceptable due to the short deployment period, and we now edited the ethics statement to emphasize this point. However, it is important to note that there is no real international standard, with both 3% and 5% weight limits being commonly used. Both limits are arbitrary and the impact of a fixed mass on a bird varies with species and flight style. All owls survived and bred similarly to the non-tagged individuals in the population (lines 373 – 376 & lines 558 – 561)

EDITORIAL COMMENT: We strongly encourage you to provide further context and clarification on this issue, as suggested by the Reviewer. On a related point, the ethics statement refers to GPS loggers, rather than GPS and ACC devices; we encourage you to clarify wording here.

Thank you for highlighting this point that indeed needed some clarifications.

Although we have used the terminology "GPS recorders", the authorization granted by the Swiss authorities for this study effectively covers the entire tracking system, which combines both GPS and ACC recorders in the same device. We have therefore changed the wording used in the ethics statement to avoid any misunderstanding (lines 373 – 376 & lines 558 – 561)

Comment 12. Please provide more information on the model selection approach, what does "Non-significant terms were dropped via model simplification by comparing model AIC with and without terms." mean? Did the authors use a stepwise backward elimination procedure (drop1 function)? Or did they apply a complete comparison of several candidate models? I think a model comparison approach rather than stepwise selection would be more informative, as several rather than only one model could be equally probable. This might also improve model weights or might require a model averaging procedure - current reported R2values are very small and do not seem to support the results well. 

We apologize for the lack of details about this important aspect of the statistical analysis. We applied an automated stepwise selection using the dredge function from the R package “MuMin”, therefore applying a complete comparison of several candidate models. The final models were chosen as the best models since the number of candidate models within ∆AIC<2 was relatively low in each analysis and thus a model averaging was not appropriate here. We edited the methods section to ensure clarity, and added model selection tables for each analysis, ranked according to AICc scores, in the supplementary materials (lines 532 – 552)

In addition, we agree that the reported R-squared values in our analyses are quite low, specifically regarding the influence of pre-hunt perching force on hunting success (cond R2 = 0.04). Nonetheless, landing impact still has a notable effect size (an increase of 1N reduces hunting success by 15%). The reported values are indicative of the inherent complexity in studying hunting behavior in a wild setting where numerous variables come into play. We specifically investigated the hypothesis that the force involved during pre-hunt landings, and consequently the emitted noise, influences the success of the next hunting attempt in wild barn owls. Factors such as prey behavior and micro-habitat characteristics surrounding prey (such as substrate type and vegetation height) are most likely to be influential but hard, or nearly impossible, to model. We now cover this in a more nuanced way in the discussion (lines 266 – 268)

Comment 13. Please explain why BirdID was nested in NightID - this is not clear to me.

Probably here there is a misunderstanding because we wrote that we nested NightID in BirdID (and not BirdID in NightID). 

Comment 14. I hope the final graphs and legends will be larger, they are almost impossible to read. 

We enlarged the graphs and legends as much as possible to improve readability. However, looking at the graphs in the published version they seem clear and readable.

Comment 15. Figure S1: Does "representation" mean the tracks don't show all of the 163 owls? If so, be precise and tell us how many are illustrated in the figure. 

Figure S1 represent the tracks for each of the 163 barn owls used in the study. We changed the terminology used in the figure caption to avoid any misunderstanding.

Comment 16. Figure S4: Please adjust the y-axis to a readable format. 

Done

Reviewer #1 (Public Review):

In this paper, Schalcher et al. examined how barn owls' landing force affects their hunting success during two hunting strategies: strike hunting and sit-and-wait hunting. They tracked tens of barn owls that raised their nestlings in nest boxes and utilized high-resolution GPS and acceleration loggers to monitor their movement. In addition, camcorders were placed near their nest boxes and used to record the prey they brought to the nest, thus measuring their foraging success.

This study generated a unique dataset and provided new insights into the foraging behavior of barn owls. The researchers discovered that the landing force during hunting strikes was significantly higher compared to the sit-and-wait strategy. Additionally, they found a positive relationship between landing force and foraging success during hunting strikes, whereas, during the sit-and-wait strategy, there was a negative relationship between the two. This suggests that barn owls avoid detection by generating a lower landing force and producing less noise. Furthermore, the researchers observed that environmental characteristics affect barn owls' landing force during sit-and-wait hunting. They found a greater landing force when landing on buildings, a lower landing force when landing on trees, and the lowest landing force when landing on poles. The landing force also decreased as the time to the next hunting attempt decreased. These findings collectively suggest that barn owls reduce their landing force as an acoustic camouflage to avoid detection by their prey.

The main strength of this work is the researchers' comprehensive approach, examining different aspects of foraging behavior, including high-resolution movement, foraging success, and the influence of the environment on this behavior, supported by impressive data collection.

The results presented support the authors' conclusion that lower landing force during sit-and-wait hunting increases hunting success, likely due to a decreased probability of detection by their prey, resulting in acoustic camouflage. The authors also hypothesized that hunting success is crucial for survival, and thus, acoustic camouflage has a direct link to fitness. This paper provides an unprecedented dataset and the first measurement of landing force during hunting in the wild. It is likely to inspire many other researchers currently studying animal foraging behavior to explore how animals' movement affects foraging success.

eLife assessment

This fundamental work substantially advances our understanding of animals' foraging behaviour by monitoring the movement and body posture of barn owls in high resolution and assessing their foraging success. With a large dataset, the evidence supporting the main conclusions is compelling. This work provides new corroboration for motion-induced sound camouflage and has broad implications for understanding predator-prey interactions.

原文通りではあるのですが、かえってわかりにくい気がします。Pygameのチートシートということもあり、単に「グループ」としてもよいのでは

見出しであることが分かるようにしたいですね。

代案 「ゲームを開始する」以降

読みにくいと感じました

代案

jpg, png, gifなどのファイルを使用するようにシステムを設定することもできます。

意味がとりにくいと感じました。

代案

見た目をすっきりさせるために

どちらでもよいと思いますがひらがなが続くので「面白く」としてもよさそう

eLife assessment

The current study sheds important light on the role of sphingolipid metabolism on the maturation of Parkinson's disease-associated Synphilin-1 inclusion bodies (SY1 IBs) on the mitochondrial surface in a yeast model using Synthetic Genetic Array (SGA) and state-of-the-art imaging techniques. The authors provide compelling evidence that downregulating the sphingolipid biosynthesis pathway leads to mitochondrial dysfunction, defective maturation, and enhanced toxicity of SY1 IBs, and this effect is conserved from yeast to mammals. Altogether, this study implicates the role of sphingolipid metabolism in the detoxification process of misfolded proteins by facilitating large IB formation on the mitochondrial outer membrane.

Reviewer #1 (Public Review):

The authors have shown the following:

(1) SY1 aggregation enhances (in terms of number of aggregates) when Sphingolipid biosynthesis is blocked.<br /> (2) In a normal cell (where sphingolipid biosynthesis is not hampered), the aggregate of SY1 (primarily the Class I aggregate) is localized only on the mitochondrial endomembrane system.<br /> (3) The localization is due to the association of SY1 (aggregates) with mitochondrial proteins like Tom70, Tim44, etc. (Is the localization completely lost? What happens to the toxicity when the aggregates are not localized on mitochondria?)<br /> (4) This fuels the loss of mitochondrial function.<br /> (5) Mitochondrial function is further abrogated when there is a block in sphingolipid biosynthesis.<br /> (6) A similar phenomenon is conserved in mammalian cell lines.

Comments on the revised version

The authors have addressed all the issues raised and I am satisfied with the answers but with the following reservations.

(1) I still think that the authors need to set the importance of the differences in aggregation in the context of toxicity arising from protein misfolding/aggregation. While the authors state the limitation in the response, and I agree that a single manuscript cannot complete a field of investigation I still think that this is an important point missing from this manuscript.

(2) I retain my reservations about the fluorescence intensity data shown for Rho123, DCF, Jc1, and MitoSox. The errors are much lower than what we typically achieve in biological experiments in our as well as our collaborator's lab. A glimpse at published literature would also support our statement. Specifically, RHO123 shows a large difference in errors between Figure 5 and Figure 5 Supplement 2. The point to note is that the absolute intensities do not vary between these figures, but the errors are the order of magnitude lower in the main figures. I, therefore, accept these figures in good faith without further interrogation.<br /> I think the message from the manuscript is important and worth following up on.

Reviewer #2 (Public Review):

Summary:

The authors used a yeast model for analyzing Parkinson's disease-associated synphilin-1 inclusion bodies (SY1 IBs). In this model system, large SY1 IBs are efficiently formed from smaller potentially more toxic SY1 aggregates. Using a genome-wide approach (synthetic genetic array, SGA, combined with a high content imaging approach), the authors identified the sphingolipid metabolic pathway as pivotal for SY1 IBs formation. Disturbances of this pathway increased SY1-triggered growth deficits, loss of mitochondrial membrane potential, increased production of reactive oxygen species (ROS), and decreased cellular ATP levels pointing to an increased energy crisis within affected cells. Notably, SY1 IBs were found to be surrounded by mitochondrial membranes using state-of-the-art super-resolution microscopy. Finally, the effects observed in the yeast for SY1 IBs turned out to be evolutionary conserved in mammalian cells. Thus, sphingolipid metabolism might play an important role in the detoxification of misfolded proteins by large IBs formation at the mitochondrial outer membrane.

Strengths:

• The SY1 IB yeast model is very suitable for the analysis of genes involved in IB formation.<br /> • The genome-wide approach combining a synthetic genetic array (SGA) with a high content imaging approach is a compelling approach and enabled the reliable identification of novel genes. The authors tightly checked the output of the screen.<br /> • The authors clearly showed, including a couple of control experiments, that the sphingolipid metabolic pathway is crucial for SY1 IB formation and cytotoxicity.<br /> • The localization of SY1 IBs at mitochondrial membranes has been clearly demonstrated with state-of-the-art super-resolution microscopy and biochemical methods.<br /> • Pharmacological manipulation of the sphingolipid pathway influenced mitochondrial function and cell survival.

Weaknesses:

• It remains unclear how sphingolipids are involved in SY1 IB formation.

Author response:

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

Response to Reviewer #1 comments:

(1) SY1 aggregation enhances (in terms of number of aggregates) when Sphingolipid biosynthesis is blocked.

a. Line no 132-133: I agree that there is circumstantial evidence that the maturation pathway of SY1 IB is perturbed by knocking down sphingolipid biosynthesis. However, to prove this formally, a time course of IB maturation needs to be reported in the knock-down strains.

Please see Figure 2-figure supplement 1 for the time course of SY1 IB maturation in the knock-down strains. We have added the result to the manuscript, please see lines 129-131on page 5 in the revised version.

b. It will be good to have formal evidence that sphingolipids are indeed downregulated when these genes are downregulated (knocked down).

This issue has been clearly evidenced in previous reports, and we have added the appropriate references in the main text. For example, down-regulation of LCB1 or SPT in yeast decreased sphingolipid levels by Huang et al (https://doi.org/10.1371/journal.pgen.1002493). According to the report from Tafesse FG, et al (https://doi.org/10.1371/journal.ppat.1005188), in mammalian cells in which Sptlc2 was knocked down by CRISPR/Cas9, sphingolipid and glucosylceramide production is almost completely blocked. In addition, the levels of sphingosine, sphingomyelin, and ceramide were significantly lower compared to control cells. Please see lines 143-144 on pages 6 and lines 232-233 on pages 9 in the revised version.

(2) In a normal cell (where sphingolipid biosynthesis is not hampered), the aggregate of SY1 (primarily the Class I aggregate) is localized only on the mitochondrial endomembrane system. These results have been published for other aggregation-prone proteins and are partly explained in the literature. However, their role in the context of maturation is relatively unclear. The authors however provide no strong evidence to show if mitochondria are preferentially involved in any of the stages of IB maturation. Specifically:

a. Line 166-167: It is not clear from Figure 4B that this is indeed the case. Only the large IB seems to colocalize in all three panels (Class I, 2, 3) with Mitotracker. The smaller IBs in 2 and 3 do not show any obvious co-localization. It is also possible that they do co-localize, but it is not clear from the images. I would appreciate it if the authors either provide stronger evidence (better image) or revise this statement. This point is crucial in some claims made later in the manuscript. (pls see comment #5A).

Based on the reviewer's suggestion, we replaced the images in Figure 4B. In addition, we added the 3D reconstruction results of the interrelationship between Class 3 and Mitotracker in Figure 4-figure supplement 1B, to further show their relationship.

(3) The localization is due to the association of SY1 (aggregates) with mitochondrial proteins like Tom70, Tim44 etc. There are some critical points (that can strengthen the manuscript) that are not addressed here. Primarily, the important role of mitochondria in the context of toxicity is neglected. Although the authors have mentioned in the discussion that it was not their main focus, I believe that this is the novel part of the manuscript and this part is potentially a beautiful addition to literature. The questions I found unanswered are:

a. Is the localization completely lost upon deleting these genes? I see only a partial loss in shape/localization. This is not properly explained in the manuscript. The shape of the IB seems to remain intact while the localization is slightly altered. This indicates that even when sphingolipid is present, SY1 localization is dictated by the (lipid-raft embedded) proteins. Interestingly, it shows that even in the absence of mitochondrial localization the shape of the aggregates is not altered in these deletion strains! How do the authors explain this if mitochondrial surface sphingolipids are important for IB maturation? (the primary screen found that sphingolipid biosynthesis promotes the formation of Class I IBs).

We agree that mutation in one mitochondrial binding protein only a partial loss in shape/localization, and we have replaced “association” with “surrounding” in the manuscript. Please see lines 163-166 on page 6 in the revised version. In mutants that interact with SY1, we counted the proportion of Class 3 aggregates formed by SY1 and found an increase in the proportion of SY1 Class 3 aggregates in the deletion mutants compared to controls, partially lost interaction of SY1 with mitochondria has effect on shape of aggregates, as detailed in line 184 on page 7 and Figure 4-figure supplement 1D. We think that SY1 interactions with mitochondrial proteins are important for the localization of SY1 IB in mitochondria, whereas sphingolipids play an important role in facilitating the formation of Class 1 IBs from Class 3 aggregates.

b. What happens to the toxicity when the aggregates are not localized on mitochondria?

We thank the reviewer for the comments, however to investigate this issue, since a single mutant can only partially affect the phenotype, it may be necessary to construct groups of mutants of different genes to observe the effect, which we will further elucidate in our future studies. What we want to show in this work is that SY1 achieves binding to mitochondria by interacting with these mitochondrial proteins.

c. It is important to note that sphingolipids may affect the whole process indirectly by altering pathways involved in protein quality control or UPR. UPR may regulate the maturation of IBs. It is therefore important to test if any of the effects seen could be of direct consequence.

We agree with the reviewer's comments, but there was no significant enrichment for protein quality control or UPR-related pathways in our genome-wide screen, so it is unlikely that sphingolipids indirectly cause maturation of IBs by affecting these two pathways. We addressed this issue in our discussion. Please see lines 325-328 on page 12 in the revised version.

d. In Figure 4D, the authors find SY1 when they pull down Tom70, Tom37 or Tim44. Tim44 is a protein found in the mitochondrial matrix, how do the authors explain that this protein is interacting with a protein outside the mitochondrial outer membrane?

This interaction could be potentially due to that some of the soluble SY1 enter the mitochondrial matrix and interact with Tim44.

e. Is it possible that the authors are immunoprecipitating SY1 since IBs have some amount of unimported mitochondrial proteins in aggregates formed during proteotoxic stress (https://doi.org/10.1073/pnas.2300475120) (Liu et al. 2023).

Our Co-IP experiments were performed in the soluble state supernatant, so mitochondrial proteins in aggregates were not detected.

f. Line 261 (Discussion): Does deletion of Tom70 or one of the anchors increase Class III aggregation and increase toxicity? Without this, it is hard to say if mitochondria are involved in detoxification.

We thank the reviewer for the comments, please see our response to comment 3b.

(4) This fuels the loss of mitochondrial function.

a. Line 218-219: Although the change is significant, the percentage change is very slight. Is this difference enough to be of physiological relevance in mitochondrial function? In our hands, the DCF fluorescence is much more variable.

We agree with the reviewer that there is a small difference (but significant). To which extend such a difference be of physiological relevance in mitochondrial function need to be further investigated.

b. Is SY1-induced loss of mitochondrial function less in knockouts of Tom70 or the other ones found to be important for localizing the SY1 aggregate to mitochondria?

We examined mitochondrial membrane potential (indicated by Rho 123 fluor intensity) in tom70Δ, tom37Δ and control his3Δ strains and found that the knocking out of Tom70 or Tom37 reduced the mitochondrial toxicity caused by SY1 expression. Please see lines 212-214 on page 8 in the revised version, and Figure 5-figure supplement 2.

(5) Mitochondrial function is further abrogated when there is a block in sphingolipid biosynthesis.

a. Myriosin acted like the deletion strains that showed less structured aggregates. There were more aggregates (Class 3) but visually they seemed to be spread apart. The first comment (#2A) on aggregate classes and their interaction with mitochondria may become relevant here.

According to a recent review article (https://doi.org/10.3389/fcell.2023.1302472), sphingolipids are present in the mitochondrial membrane, bind to many mitochondrial proteins and have emerged as key regulators of mitochondrial morphology, distribution and function. Dysregulation of sphingolipid metabolism in mitochondria disrupts many mitochondrial processes, leading to mitochondrial fragmentation, impaired bioenergetics and impaired cellular function. Myriocin treatment, which affects sphingolipid metabolism, causes mitochondria to become more fragmented, which may explain why the aggregates appear visually spread apart. Regarding the interaction with mitochondria, we counted the proportion of SY1 aggregates surrounded by mitochondria after treatment with myriocin, and the results were not significantly different compared to the control. Please see lines 168-169 on page 6 in the revised version, and Figure 4-figure supplement 1C.

(6) A similar phenomenon is conserved in mammalian cell lines.

a. Line 225-226: Did the authors confirm that this was the only alteration in the genome? Or did they complement the phenotype, genetically?

We performed SPTLC2 gene complementation experiments in knockout cell lines and found that SPTLC2 gene complementation was able to reduce the number of cells forming IBs and the percentage of dispersed irregular IBs compared to controls. Please see lines 240-242 on page 9 in the revised version, and Figure 6-figure supplement 2B.

b. Line 241-245: One of the significant phenotypes observed by downregulating sphingolipid biosynthesis in yeast and mammalian cells, was the increase in the number of aggregates. This is not shown in myriocin treatment in mammalian cells. This needs to be shown to the main concordance with the original screen and the data presented with the KO mammalian cell line.

Please see Figure 7-figure supplement 1A for the data on the proportion of cells forming SY1 IBs after myriocin treatment in mammalian cells, and myriocin treatment in mammalian cells was the same as in the KO mammalian cell line.

Minor Comments:

Line 273-275: How is this statement connected to the previous statement? Was it observed that aggregate fusion was advantageous to the cells?

Yes, aggregate/oligomer fusion is advantageous to the cells, and we have modified the previous statement. Please see line 280 on page 10 in the revised version.

Line 293-294: I am not sure I understand this statement.

We have modified this statement. Please see lines 302-303 on page 11 in the revised version.

Line 295-296: But the authors have commented at multiple places that mitochondria detoxify the cell from SY1 aggregates. I find this link fascinating and worth investigating. Most of the current work has some known links in literature (not everything). The mitochondrial connection being the most fascinating one.

We have removed this sentence. We have added a validation experiment for the role of mitochondrial activity in SY1 IB maturation in the revised version.

Line 318: Do the authors mean: The open question is...

Thanks to the reviewer, we have corrected it.

Response to Reviewer #2 comments:

I recommend considering live cell microscopy to analyze whether sphingolipid-dependent formation of SY1 IB takes place at the mitochondrial outer membrane. The IBs could also be produced at other membranes and then transported to the mitochondrial outer membrane for storage.

As shown in Figure 4A, SY1 IB primarily interacts with mitochondria.

I recommend analyzing whether mitochondrial activity is needed for sphingolipid-dependent SY1 IB formation. Are these IBs localized to mitochondrial membrane solely as scaffold or are these organelles needed to provide the energy for driving IB formation in concert with sphingolipids? This point could be addressed with rho0 strains lacking mitochondrial DNA.

We thank the reviewer for this recommendation. We expressed SY1 protein in BY4741 rho0 strain as suggested and found that the maturation and mitochondrial surrounding state of SY1 IB was not affected by mitochondrial activity. Please see lines 185-187 on page 7 in the revised version, and Figure 4-figure supplement 1E and 1F.

The authors should be more precise in the statistical methods used in their study (method, pre-/post-tests, number of replicates...).

We thank the reviewer for the comment and we have provided a more precise description of the statistical methods. Please see lines 531-534 on page 19 and figure legends in the revised version.