same as 2 sentences before

Well written! You tell the reader what to look at and what to conclude.

unclear what "pattern of scores" refers to. I guess you mean something with morphing steps. plz clarify

Unclear if this has to be true or whether it was something observed in the data

It would be nice if you first describe the different variables. Morph level (seven steps from 0 to 100%), response options (one-dimensional, two-dimensional). It makes it easier to follow.

not sure I get this. You mean that each face was rated twice? Once on woman continuum and once one man continuum, and order of all trials was random? If so, please clarify

can you share these images? Would be concrete. Also, would save time for others instead of reinventing the wheel.

It's just nice to see people be interested in stuff, and have a group of like minded people that's also interested in the same stuff! What else is there to it all

Digital public service - thank you!!

eLife Assessment

This valuable study investigates how biologically plausible learning mechanisms can support assembly formation that encodes statistics of the environment, by enabling neural sampling that is based on within-assembly connectivity strength. It convincingly shows that assembly formation can emerge from predictive plasticity in excitatory synapses, while two types of plasticity in inhibitory synapses are required: inhibitory homeostatic (predictive) plasticity and inhibitory competitive (anti-predictive) plasticity.

Reviewer #1 (Public review):

The authors have successfully addressed most of the issues raised in the first review. Nevertheless, some of the mentioned problems require further attention, mostly regarding the formal derivation of the learning rules, as well as connections to previous research.

Regarding the derivations of learning rules: The authors have provided Goal functions for each of the plastic neural connections to give some insight into what these connections do. However, as I understand, this does not address the main concern raised in the previous review: Why do these rules lead to overall network dynamics that sample from the input distribution? Virtually all other work on neural sampling that I am aware of (e.g., from Maass Lab, Lengyel Lab, etc.) start from a single goal function for all connections that somehow quantifies the difference of network dynamics from the target distribution. In the presented work the authors specify different goal functions for the different weights, which does not make clear how the desired network dynamics are ultimately achieved.

This becomes especially evident looking at the two different recurrent connections (M and G). M minimizes the difference between network activity f and recurrent prediction DKL[f|phi(My)], but why is this alone not enough to ensure a good sampling? G minimizes the squared error [f-phi(Gy)]^2, but what does that mean? The problem is that the goal functions are self-consistent in the sense that both f and phi(Gy) depend on G, which makes an interpretation very difficult. Ultimately it's easier to interpret this by looking at the plasticity rule and see that it leads to a balance. For G the authors furthermore actually ignore the derived plasticity rule and switch to a rule similar to the one for M, meaning that the actual goal function for G is also something like DKL[f|phi(Gy)]. Overall, an overarching optimization goal for the entire network is missing, which makes the interpretation very difficult. I understand that this might be very difficult to provide at this stage, but the authors should at least point out this shortcoming as an open question for the proposed framework.

Regarding the relation to previous work the authors have provided a lot more detailed discussion, which very much clears up the contributions and novel ideas in their work. Still, there are some claims that are not consistent with the literature. Especially, in lines 767 ff. the authors state that Kappel et al "assumed plasticity only at recurrent synapses projecting onto the excitatory neurons. In addition, unlike our model, the cell assembly memberships need to be preconfigured in the [...] model." This is not correct, as Kappel et al learn both the feed-forward and recurrent connections, hence the main difference is that in Kappel et al sampling is sequential and not random. This is why I mentioned this work in the first review, as it speaks against the authors claims of novelty (719 ff.), which should be adjusted accordingly.

Reviewer #2 (Public review):

Summary:

The paper reconsiders the formation of Hebbian-type assemblies, with their spontaneous reactivation representing the statistics of the sensory inputs, in the light of predictive synaptic plasticity. It convincingly shows that not all plasticity rules can be predictive in the narrow sense. While plasticity for the excitatory synapses (the forward projecting and recurrent ones) are predictive, two types of plasticity in the recurrent inhibition is required: a homeostatic and competitive one.

Details:

Besides the excitatory forward and recurrent connections that are learned based on predictive synaptic plasticity, two types of inhibitory plasticity are considered. A first type of inhibition is homeostatic and roughly balances excitation within the cell assemblies. Plasticity in this type 1 inhibition is also predictive, analogous to the plasticity of the excitatory synapses. However, plasticity in type 2 inhibition is competitive and has a switched sign. Both types of inhibitory plasticity, the predictive (homeostatic) and the anti-predictive (competitive) one, work together with the predictive excitatory plasticity to form cell assemblies representing sensory stimuli. Only if the two types of homeostatic and competitive inhibitory plasticity are present, will the spontaneous replay of the assemblies reflect the statistics of the stimulus presentation.

Critical review:

The simulations include Dale's law, making them more biologically realistic. The paper emphasizes predictive plasticity and introduces type 1 inhibitory plasticity that, by construction, tries to fully explain away the excitatory input. In the absence of external inputs, however, due to the symmetry between the excitatory and inhibitory-type-1 plasticity rules, excitation and inhibition tend to fully cancel each other. Multiple options may solve the dilemma:

(1) As other predictive dendritic plasticity models assume, the presynaptic source for recurrent inhibition is typically less informative than the presynaptic source of excitation, so that inhibition is not able to fully explain away excitation.

(2) Beside the inhibitory predictive plasticity that mirrors the analogous excitatory predictive plasticity, and additional competitive plasticity can be introduced.

The paper chooses solution (2) and suggests and additional inhibitory recurrent pathway that is not predictive, but instead anti-predictive with a reversed sign. The combination of the two types of inhibitory plasticities lead to a stable formation of cell assemblies. The stable target activity of the plasticity rules in a memory recall is not anymore 0, as it would be with only type-1-inhibitory plasticity.<br /> Instead, the target activity of plasticity is now enhanced within a winning assembly, and also positive but reduced in the loosing assemblies.

Reviewer #3 (Public review):

Summary:

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to a cellular monitoring of membrane potential history.

Strengths:

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities.

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law).

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation.

Weaknesses:

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly specific statistics of h reflect these likelihoods. I find this to be a key issue.

(2) The authors model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or near fully silent (Fig. 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required, since runaway activity is not as damaging to network activity.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli?

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

In their manuscript, the authors propose a learning scheme to enable spiking neurons to learn the appearance probability of inputs to the network. To this end, the neurons rely on error-based plasticity rules for feedforward and recurrent connections. The authors show that this enables the networks to spontaneously sample assembly activations according to the occurrence probability of the input patterns they respond to. They also show that the learning scheme could explain biases in decision-making, as observed in monkey experiments. While the task of neural sampling has been solved before in other models, the novelty here is the proposal that the main drivers of sampling are within-assembly connections, and not between-assembly (Markov chains) connections as in previous models. This could provide a new understanding of how spontaneous activity in the cortex is shaped by synaptic plasticity. 

The manuscript is well written and the results are presented in a clear and understandable way. The main results are convincing, concerning the spontaneous firing rate dependence of assemblies on input probability, as well as the replication of biases in the decision-making experiment. Nevertheless, the manuscript and model leave open several important questions. The main problem is the unclarity, both in theory and intuitively, of how the sampling exactly works. This also makes it difficult to assess the claims of novelty the authors make, as it is not clear how their work relates to previous models of neural sampling. 

We agree with the reviewer that our previous manuscript was not clear regarding the mechanism of the model. We have performed additional simulations and included a derivation of the learning rule to address this, which we explain below.

Regarding the unclarity of the sampling mechanism, the authors state that withinassembly excitatory connections are responsible for activating the neurons according to stimulus probability. However, the intuition for this process is not made clear anywhere in the manuscript. How do the recurrent connections lead to the observed effect of sampling? How exactly do assemblies form from feedforward plasticity? This intuitive unclarity is accompanied by a lack of formal justification for the plasticity rules. The authors refer to a previous publication from the same lab, but it is difficult to connect these previous results and derivations to the current manuscript. The manuscript should include a clear derivation of the learning rules, as well as an (ideally formal) intuition of how this leads to the sampling dynamics in the simulation. 

We have included a derivation of our plasticity rules in lines 871-919 in the revised manuscript. Consistent with our claim that predictive plasticity updates the feedforward and the recurrent synapses to predict output firing rates, we have shown that the corresponding cost function measures the discrepancy among the recurrent prediction, feedforward prediction, and the output firing rate. The resultant feedforward plasticity is the same with our previous rule (Asabuki and Fukai, 2020), which segments the salient patterns embedded in the input sequence. The recurrent plasticity rule suggests that the recurrent prediction learns the statistical model of the evoked activity, enabling the network to replay the learned internal model.  

Similarly, for the inhibitory plasticity, we defined a cost function that evaluates the difference between the firing rate and inhibitory potential within each neuron. This rule is crucial for maintaining balanced network dynamics. See our response below for more details on the role of inhibitory plasticity.

Some of the model details should furthermore be cleared up. First, recurrent connections transmit signals instantaneously, which is implausible. Is this required, would the network dynamics change significantly if, e.g., excitation arrives slightly delayed? Second, why is the homeostasis on h required for replay? The authors show that without it the probabilities of sampling are not matched, but it is not clear why, nor how homeostasis prevents this. Third, G and M have the same plasticity rule except for G being confined to positive values, but there is no formal justification given for this quite unusual rule. The authors should clearly justify (ideally formally) the introduction of these inhibitory weights G, which is also where the manuscript deviates from their previous 2020 work. My feeling is that inhibitory weights have to be constrained in the current model because they have a different goal (decorrelation, not prediction) and thus should operate with a completely different plasticity mechanism. The current manuscript doesn't address this, as there is no overall formal justification for the learning algorithm. 

First, while the reviewer's suggestion to test with delayed excitation is intriguing and crucial for a more biologically detailed spiking neuron model, we have chosen to maintain the current model configuration. Our use of Poisson spiking neurons, which generate spikes based on instantaneous firing rates, does not heavily depend on precise spike timing information. Therefore, to preserve the simplicity of our results, we kept the model unchanged.

Second, we agree that our previous claim regarding the importance of the memory trace h for sampling may have been confusing. As shown in Supplementary Figure 7b in the revised manuscript, when we eliminated the dynamics of the memory trace, sampling performance did indeed decrease. However, we also observed that the assembly activity ratio continued to show a linear relationship with stimulus probabilities. Based on these findings, we have revised our claim in the manuscript to clarify that the memory trace is primarily critical for firing rate homeostasis, rather than directly influencing sampling within the learned network. We have explained this in ll. 446-448 in the revised manuscript.

Third, we explored a new architecture where all recurrent connections are either exclusively excitatory or inhibitory, keeping their sign throughout the learning process. This change addresses the reviewer's concern about our initial assumption that only the inhibitory connection G was constrained to non-negative values. We found that inhibition plays a crucial role in decorrelation and prediction, helping activate specific assemblies through competition while preventing runaway excitation within active assemblies. We have explained this in ll.560-593 in the revised manuscript.

Finally, the authors should make the relation to previous models of sampling and error-based plasticity more clear. Since there is no formal derivation of the sampling dynamics, it is difficult to assess how they differ exactly from previous (Markov-based) approaches, which should be made more precise. Especially, it would be important to have concrete (ideally experimentally testable) predictions on how these two ideas differ. As a side note, especially in the introduction (line 90), this unclarity about the sampling made it difficult to understand the contrast to Markovian transition models. 

As the reviewer pointed out, previous computational models have demonstrated that recurrent networks with Hebbian-like plasticity can learn appropriate Markovian statistics (Kappel et al., 2014; Asabuki and Clopath, 2024). However, our model differs conceptually from these previous models. While Kappel et al. showed that STDP in winner-take-all circuits can approximate online learning of hidden Markov models (HMMs), a key difference with our model is that their neural representations acquire sequences using Markovian sampling dynamics, whereas our model does not depend on such ordered sampling. Specifically, in their model, sequential sampling arises from learned structures in the off-diagonal elements of the recurrent connections (i.e., between-assembly connections). In contrast, our network learns to stochastically generate recurrent cell assemblies by relying solely on within-assembly connections. A similar argument can be made for Asabuki and Clopath paper as well. Further, while our model introduced plasticity rule for all types of connections, Asabuki and Clopath paper introduced plasticity for recurrent synapses projecting on the excitatory neurons only and the cell assembly memberships were preconfigured unlike our model. We have added additional clarifying sentences in ll. 757-772 of the revised manuscript to elaborate on this point.

There are also several related models that have not been mentioned and should be discussed. In 663 ff. the authors discuss the contributions of their model which they claim are novel, but in Kappel et al (STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning) similar elements seem to exist as well, and the difference should be clarified. There is also a range of other models with lateral inhibition that make use of error-based plasticity (most recently reviewed in Mikulasch et al, Where is the error? Hierarchical predictive coding through dendritic error computation), and it should be discussed how the proposed model differs from these. 

We have clarified the difference from previously proposed recurrent network model to perform Markovian sampling. Please see our reply above.

We have also included additional sentence in ll. 704-709 in the revised manuscript to discuss how our model differs from similar predictive learning models: “It should be noted that while several network models that perform errorbased computations like ours exploit only inhibitory recurrent plasticity (Mikulasch et al., 2021; Mackwood et al., 2021; Hertäg and Clopath., 2022; Mikulasch et al., 2023), our model learns the structured spontaneous activity to reproduce the evoked statistics by modifying both excitatory and inhibitory recurrent connections.”

Reviewer #2 (Public Review):

Summary: 

The paper considers a recurrent network with neurons driven by external input. During the external stimulation predictive synaptic plasticity adapts the forward and recurrent weights. It is shown that after the presentation of constant stimuli, the network spontaneously samples the states imposed by these stimuli. The probability of sampling stimulus x^(i) is proportional to the relative frequency of presenting stimulus x^(i) among all stimuli i=1,..., 5. 

Methods: 

Neuronal dynamics: 

For the main simulation (Figure 3), the network had 500 neurons, and 5 nonoverlapping stimuli with each activating 100 different neurons where presented. The voltage u of the neurons is driven by the forward weights W via input rates x, the inhibitory recurrent weights G, are restricted to have non-negative weights (Dale's law), and the other recurrent weights M had no sign-restrictions. Neurons were spiking with an instantaneous Poisson firing rate, and each spike-triggered an exponentially decaying postsynaptic voltage deflection. Neglecting time constants of the postsynaptic responses, the expected postsynaptic voltage reads (in vectorial form) as 

u = W x + (M - G) f (Eq. 5) 

where f =; phi(u) represents the instantaneous Poisson rate, and phi a sigmoidal nonlinearity. The rate f is only an approximation (symbolized by =;) of phi(u) since an additional regularization variable h enters (taken up in Point 4 below). The initialisation of W and M is Gaussian with mean 0 and variance 1/sqrt(N), N the number of neurons in the network. The initial entries of G are all set to 1/sqrt(N). 

Predictive synaptic plasticity: 

The 3 types of synapses were each adapted so that they individually predict the postsynaptic firing rate f, in matrix form 

ΔW ≈ (f - phi( W x ) ) x^T 

ΔM ≈ (f - phi( M f ) ) f^T 

ΔG ≈ (f - phi( M f ) ) f^T but confined to non-negative values of G (Dale's law). 

The ^T tells us to take the transpose, and the ≈ again refers to the fact that the ϕ entering in the learning rule is not exactly the ϕ determining the rate, only up to the regularization (see Point 4). 

Main formal result: 

As the authors explain, the forward weight W and the unconstrained weight M develop such that, in expectations, 

f =; phi( W x ) =; phi( M f ) =; phi( G f ) , 

consistent with the above plasticity rules. Some elements of M remain negative. In this final state, the network displays the behaviour as explained in the summary. 

Major issues: 

Point 1: Conceptual inconsistency 

The main results seem to arise from unilaterally applying Dale's law only to the inhibitory recurrent synapses G, but not to the excitatory recurrent synapses M. 

In fact, if the same non-negativity restriction were also imposed on M (as it is on G), then their learning rules would become identical, likely leading to M=G. But in this case, the network becomes purely forward, u = W x, and no spontaneous recall would arise. Of course, this should be checked in simulations. 

Because Dale's law was only applied to G, however, M and G cannot become equal, and the remaining differences seem to cause the effect. 

Predictive learning rules are certainly powerful, and it is reasonable to consider the same type of error-correcting predictive learning rule, for instance for different dendritic branches that both should predict the somatic activity. Or one may postulate the same type of error-correcting predictive plasticity for inhibitory and excitatory synapses, but then the presynaptic neurons should not be identical, as it is assumed here. Both these types of error-correcting and error-forming learning rules for same-branches and inhibitory/excitatory inputs have been considered already (but with inhibitory input being itself restricted to local input, for instance). 

The model presented above lacked biological plausibility in several key aspects. Specifically, we assumed that the recurrent connection M could change sign through plasticity and be either excitatory or inhibitory, while the inhibitory connection G was restricted to being inhibitory only. This initial setting does not reflect the biological constraint that synapses typically maintain a consistent excitatory or inhibitory type. Furthermore, due to this unconstrained recurrent connectivity M, the original model had two types of inhibitory connections (i.e., the negative part of M and the inhibitory connection G) without providing a clear computational role for each type of inhibition.

To address these limitations and to understand the role of the two types of inhibition, we explored a new architecture where all recurrent connections are either exclusively excitatory or inhibitory, keeping their sign throughout the learning process. This change addresses the reviewer's concern about our initial assumption that only the inhibitory connection G was constrained to non-negative values. We found that inhibition plays a crucial role in prediction and decorrelation, helping activate specific assemblies through competition while preventing runaway excitation within active assemblies. We have explained this in ll. 561593 in the revised manuscript.

Point 2: Main result as an artefact of an inconsistently applied Dale's law? 

The main result shows that the probability of a spontaneous recall for the 5 nonoverlapping stimuli is proportional to the relative time the stimulus was presented. This is roughly explained as follows: each stimulus pushes the activity from 0 up towards f =; phi( W x ) by the learning rule (roughly). Because the mean weights W are initialized to 0, a stimulus that is presented longer will have more time to push W up so that positive firing rates are reached (assuming x is non-negative). The recurrent weights M learn to reproduce these firing rates too, while the plasticity in G tries to prevent that (by its negative sign, but with the restriction to non-negative values). Stimuli that are presented more often, on average, will have more time to reach the positive target and hence will form a stronger and wider attractor. In spontaneous recall, the size of the attractor reflects the time of the stimulus presentation. This mechanism so far is fine, but the only problem is that it is based on restricting G, but not M, to non-negative values. 

As mentioned above, we have included an additional simulation where all weights are non-negative. We have demonstrated the new results in Figure 6 before presenting the two-population model in the revised manuscript (Figure 7), so that readers can follow the importance of two pathways of inhibitory connections.

Point 3: Comparison of rates between stimulation and recall. 

The firing rates with external stimulations will be considerably larger than during replay (unless the rates are saturated). 

This is a prediction that should be tested in simulations. In fact, since the voltage roughly reads as  u = W x + (M - G) f,  and the learning rules are such that eventually M =; G, the recurrences roughly cancel and the voltage is mainly driven by the external input x. In the state of spontaneous activity without external drive, one has  u = (M - G) f ,  and this should generate considerably smaller instantaneous rates f =; phi(u) than in the case of the feedforward drive (unless f is in both cases at the upper or lower ceiling of phi). This is a prediction that can also be tested. 

Because the figures mostly show activity ratios or normalized activities, it was not possible for me to check this hypothesis with the current figures. So please show non-normalized activities for comparing stimulation and recall for the same patterns. 

We agree with the reviewer that the activity levels of spontaneous and induced activity should be compared. We have shown the distributions of activity level of these activities in our new Figure 2d. As expected, we found that the evoked activity showed stronger activity compared to the spontaneous activity.  

Point 4: Unclear definition of the variable h. 

The formal definition of h = hi is given by (suppressing here the neuron index i and the h-index of tau) 

tau dh/dt = -h if h>u, (Eq. 10)  h = u otherwise. 

But if it is only Equation 10 (nothing else is said), h will always become equal to u, or will vanish, i.e. either h=u or h=0 after some initial transient. In fact, as soon as h>u, h is decaying to 0 according to the first line. If u is >0, then it stops at u=h according to the second line. No reason to change h=u further. If u<=0 while h>u, then h is converging to 0 according to the first line and will stay there. I guess the authors had issues with the recurrent spiking simulations and tried to fix this with some regularization. However as presented, it does not become clear how their regulation works. 

We apologize for the reviewer that our definition of h was unclear. As the reviewer pointed out, since the memory trace is always positive and larger than (or equal to) the membrane potential, it is possible that the membrane potential becomes always negative and the memory trace reach to 0 constantly. However, since the network is always balanced between excitatory and inhibitory inputs, and it does not happen that the membrane potential always diverges negatively. In fact, we trained without any manipulations other than the memory trace described in the manuscript, and the network was able to learn the assembly structure stably. 

BTW: In Eq. 11 the authors set the gain beta to beta = beta0/h which could become infinite and, putatively more problematic, negative, depending on the value of h. Maybe some remark would convince a reader that no issues emerge from this. 

We have mentioned in ll. 864-866 in the revised manuscript that no issues emerge from the slope parameter.

Added from discussions with the editor and the other reviewers: 

Thanks for alerting me to this Supplementary Figure 8. Yes, it looks like the authors did apply there Dale's law for both the excitatory and inhibitory synapses. Yet, they also introduced two types of inhibitory pathways converging both to the excitatory and inhibitory neurons. For me, this is a confirmation that applying Dale's law to both excitatory and inhibitory synapses, with identical learning rules as explained in the main part of the paper, does not work. 

Adding such two pathways is a strong change from the original model as introduced before, and based on which all the Figures in the main text are based. Supplementary Figure 8 should come with an analysis of why a single inhibitory pathway does not work. I guess I gave the reason in my Points 1-3. Some form of symmetry breaking between the recurrent excitation and recurrent inhibition is required so that, eventually, the recurrent excitatory connection will dominate. 

Making the inhibitory plasticity less expressive by applying Dale's law to only those inhibitory synapses seems to be the answer chosen in the Figures of the main text (but then the criticism of unilaterally applying Dale's law). 

Applying Dale's law to both types of synapses, but dividing the labor of inhibition into two strictly separate and asymmetric pathways, and hence asymmetric development of excitatory and inhibitory weights, seems to be another option. However, introducing such two separate inhibitory pathways, just to rescue the fact that Dale's law is applied to both types of synapses, is a bold assumption. Is there some biological evidence of such two pathways in the inhibitory, but not the excitatory connections? And what is the computational reasoning to have such a separation, apart from some form of symmetry breaking between excitation and inhibition? I guess, simpler solutions could be found, for instance by breaking the symmetry between the plasticity rules for the excitatory and inhibitory neurons. All these questions, in my view, need to be addressed to give some insights into why the simulations do work. 

The reviewer’s intuition is correct. To effectively learn cell assembly structures and replay their activities, our model indeed requires two types of inhibitory connections. Please refer to our response above for further details. 

Overall, Supplementary Figure 8 seems to me too important to be deferred to the Supplement. The reasoning behind the two inhibitory pathways should appear more prominently in the main text. Without this, important questions remain. For instance, when thinking in a rate-based framework, the two inhibitory pathways twice try to explain the somatic firing rate away. Doesn't this lead to a too strong inhibition? Can some steady state with a positive firing rate caused by the recurrence, in the absence of an external drive, be proven? The argument must include the separation into Path 1 and Path 2. So far, this reasoning has not been entered. 

In fact, it might be that, in a spiking implementation, some sparse spikes will survive. I wonder whether at least some of these spikes survive because of the other rescuing construction with the dynamic variable h (Equation 10, which is not transparent, and that is not taken up in the reasoning either, see my Point 4)

Perhaps it is helpful for the authors to add this text in the reply to them. 

We have moved the former Supplemental Figure 8 to the main Figure 7. Please see our response above about the role of dual inhibitory connection types.

Reviewer #3 (Public Review): 

Summary: 

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to cellular monitoring of membrane potential history. 

Strengths: 

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities. 

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law). 

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation. 

Weaknesses: 

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly-specific statistics of h reflect these likelihoods. I find this to be a key issue. 

We agree that our previous claim regarding the importance of the memory trace h for sampling may have been confusing. As shown in Supplementary Figure 7b, when we eliminated the dynamics of the memory trace, sampling performance did indeed decrease. However, we also observed that the assembly activity ratio continued to show a linear relationship with stimulus probabilities. Based on these findings, we revised our claim in the manuscript to clarify that the memory trace is primarily critical for learning to avoid trivial solutions, rather than directly influencing sampling within the learned network. We have explained this in ll. 446-448 in the revised manuscript.

(2) The authors' model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or nearly fully silent (Figure 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required since runaway activity is not as damaging to network activity. 

The reviewer's intuition is correct. The saturating nonlinearity is important for the network to form stable assembly structures. We have added an additional sentence in ll. 866-868 to mention this.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli? 

LIF neurons) may solve this problem by utilizing spike-timing statistics.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Minor issues: 

Figure 1: It would be helpful to display the equation for output rate here as well. 

We have included the equation in the revised Figure 1a.

Figure 3c: Typo "indivisual neurons". 

We have modified the typo. We thank the reviewer for their careful review.

Line 325: Do you mean Figure 3f,g? 

We repeated the task with different numbers of stimuli in Supplementary Figure 1c,d.

Line 398: Winner-take-all can be misunderstood, as it typically stands for competition in inference, not in learning. 

We have rephrased it as “unstable dynamics” in l. 400

Line 429: Are intra-assembly and within-assembly the same? If so please use consistent terminology. 

We have made the terminology consistent.

Line 792 ff.: Please mention that (t) was left away. 

We have included a sentence to mention it in ll. 847-848 in the revised manuscript.

Line 817: Should u_i be v_i? 

We have modified the term.

Methods: What is the value of tau_h? 

We have used 𝜏! \=10 s, which is mentioned in l. 853

yup

Ben Werdmüller makes an interesting distinction. Internet tech, and thus Silicon Valley, originated in defense (ARPA etc.), whereas the web originated in academia in a spirit of open academic debate (CERN). Now ARPA etc had deep ties w academia too, and it's mostly defense funding at play. Still there may be something to this distinction. You could also say perhaps it's an Atlantic divide, the web originated at CERN in Europe.

Network graphs, maps - it's why canvas is the UI du jour, to go beyond linearity, lists and trees

eLife Assessment

This timely and important study used functional near-infrared spectroscopy hyperscanning to examine the neural correlates of how group identification influences collective behavior. The work provides incomplete evidence to indicate that the synchronization of brain activity between different people underlies collective performance and that changes in brain activity patterns within individuals may, in turn, underlie this between-person synchrony. This study will be of interest to researchers investigating the neuroscience of social behaviour.

Reviewer #1 (Public review):

Summary:

The authors of this article have presented a timely and well-written study exploring the impact of group identification on collective behaviors and performance. The breadth of analyses is impressive and contributes significantly to our understanding of the collective performance. However, there are several areas where further clarification and revision would strengthen the study.

Strengths:

(1) Timeliness and Relevance:<br /> The topic is highly relevant, particularly in today's interconnected and team-oriented work environments. Triadic hyperscanning is important to understand group dynamics, but most previous work has been limited to dyadic work.

(2) Comprehensive Analysis:<br /> The authors have conducted extensive analyses, offering valuable insights into how group identification affects collective behaviors.

(3) Clear Writing:<br /> The manuscript is well-written and easy to follow, making complex concepts accessible.

Weaknesses (clarifications needed):

(1) Experimental Design:<br /> The study does not mention whether the authors examined sex differences or any measures of attractiveness or hierarchy among participants (e.g., students vs. teachers). Including these variables could provide a more nuanced understanding of group dynamics.

(2) fNIRS Data Acquisition:<br /> The authors' approach to addressing individual differences in anatomy is lacking in detail. Understanding how they identified the optimal channels for synchrony between participants would be beneficial. Was this done by averaging to find the location with the highest coherence?

(3) Behavioral Analysis:<br /> For group identification, the analysis currently uses a dichotomous approach. Introducing a regression model to capture the degree of identification could offer more granular insights into how varying levels of group identification affect collective behavior and performance.

(4) Single Brain Activation Analysis:<br /> The application of the General Linear Model (GLM) is unclear, particularly given the long block durations and absence of multiple trials. Further explanation is needed on how the GLM was implemented under these conditions.

(5) Within-group neural Synchrony (GNS) Calculation:<br /> The method for calculating GNS could be improved by using mutual information instead of pairwise summation, as suggested by Xie et al. (2020) in their study on fMRI triadic hyperscanning. Additionally, the explanation of GNS calculation is inconsistent. At one point, it is mentioned that GNS was averaged across time and channels, while elsewhere, it is stated that channels with the highest GNS were selected. Clarification on this point is essential.

(6) Placement of fNIRS Probes:<br /> The probes were only placed in the frontal regions, despite literature suggesting that the superior temporal sulcus (STS) and temporoparietal junction (TPJ) regions are crucial for triadic team performance. A justification for this choice or inclusion of these regions in future studies would be beneficial.

(7) Interpretation of fNIRS Data:<br /> Given that fNIRS signals are slow, similar to BOLD signals in fMRI, the interpretation of Figure 6 raises concerns. It suggests that it takes several minutes (on the order of 4-5 minutes) for people to collaborate, which seems implausible. More context or re-evaluation of this interpretation is needed.

Reviewer #2 (Public review):

Summary:

This study primarily aims to examine the relationship between collective performance and group identification. Additionally, the authors propose that inter-brain synchronization (IBS) underlies collective performance and that changes in intra-brain functional connectivity or single-brain activation may, in turn, underlie IBS. The topic addressed in this paper is of great importance in the field using hyperscanning. However, the details of the experiments and analysis described in the paper are unclear, and the hypothesis as to why IBS is thought to underlie collective performance is not clearly presented. In addition, some of the analysis seems to be inappropriate.

Strengths:

I find the model presented in Figure 7 to be intriguing. Understanding why inter-brain synchronization occurs and how it is supported by specific single-brain activations or intra-brain functional connectivity is indeed a critical area for researchers conducting hyperscanning studies to explore.

Understanding triadic-interaction is really important, while almost all hyperscanning neuroimaging focuses on the dyadic interaction. The exploring neural/behavioral/psychological basis behind triadic interaction is a promising method for understanding collective behavior and decision-making.

Weaknesses:

The authors need to clearly articulate their hypothesis regarding why neural synchronization occurs during social interaction. For example, in line 284, it is stated that "It is plausible that neural synchronization is closely associated with group identification and collective performance...", but this is far from self-evident. Neural synchronization can occur even when people are merely watching a movie (Hasson et al., 2004), and movie-watchers are not engaged in collective behavior. There is no direct link between the IBS and collective behavior. The authors should explain why they believe inter-brain synchronization occurs in interactive settings and why they think it is related to collective behavior/performance.

The authors state that "GNS in the OFC was a reliable neuromarker, indicating the influence of group identification on collective performance," but this claim is too strong. Please refer to Figure 4B. Do the authors really believe that collective performance can be predicted given the correlation with the large variance shown? There is a significant discrepancy between observing a correlation between two variables and asserting that one variable is a predictive biomarker for the other.

Why are the individual answers being analyzed as collective performance (See, L-184)? Although these are performances that emerge after the group discussion, they seem to be individual performances rather than collective ones. Typically, wouldn't the result of a consensus be considered a collective performance? The authors should clarify why the individual's answer is being treated as the measure of collective performance.

Performing SPM-based mapping followed by conducting a t-test on the channels within statistically significant regions constitutes double dipping, which is not an acceptable method (Kriegeskorte et al., 2011). This issue is evident in, for example, Figures 3A and 4A.

Please refer to the following source:<br /> https://www.nature.com/articles/nn.2303

In several key analyses within this study (e.g., single-brain activation in the paragraph starting from L398, neural synchronization in the paragraph starting from L393), the TPJ is mentioned alongside the DLPFC. However, in subsequent detailed analyses, the TPJ is entirely ignored.

The method for analyzing single-brain activation is unclear. Although it is mentioned that GLM (generalized linear model) was used, it is not specified what regressors were prepared, nor which regressor's β-values are reported as brain activity. Without this information, it is difficult to assess the validity of the reported results.

While the model illustrated in Figure 7 seems to be interesting, for me, it seems not to be based on the results of this study. This is because the study did not investigate the causal relationships among the three metrics. I guess, Figure 5D might be intended to explain this, but the details of the analysis are not provided, making it unclear what is being presented.

The details of the experiment are not described at all. While I can somewhat grasp what was done abstractly, the lack of specific information makes it impossible to replicate the study.

Author response:

We are appreciative of the editors’ and reviewers’ positive comments and constructive suggestions, which will help us to improve our manuscript. We will make changes as required by the reviewers. Our primary focus will be on revising and clarifying certain aspects:

First, recent research has revealed a strong correlation between brain synchronization and group decision-making, a key neural marker. We aim to bolster our hypothesis by reviewing additional literature, ensuring accuracy in terminology and appropriateness in phrasing.

Second, it is crucial to note that we will include additional methodological details, such as the details of the experiment, the significance of individual difference variables, and the details of the data analyses.

Third, despite introducing a novel perspective in our study, we acknowledge the utilization of the conventional fNIRS hyperscanning analyses, which are widely accepted within the research community. Our methodology entails the identification of significant channels via one-sample t-tests, subsequently complemented by either ANOVAs or independent sample t-tests, without the need for double dipping.

We will address all the issues raised by the reviewers.We believe that the manuscript will significantly benefit from the insightful suggestions and invaluable contributions made by the editors and reviewers.

eLife Assessment

This important study challenges conventional life-history theory by demonstrating that reproductive-survival trade-offs are minimal in birds, except when reproductive effort is experimentally exaggerated. The evidence is solid, drawing from a meta-analysis of over 30 bird species, and effectively separates the effects of individual quality from reproductive costs. The findings will be of broad interest to evolutionary biologists and ecologists studying life-history trade-offs and reproductive strategies.

Reviewer #4 (Public review):

Summary:

This is an important study that underscores that reproduction-survival trade-offs are not manifested (contrary to what generally accepted theory predicts) across a range of studies on birds. This has been studied by a meta-analytical approach, gathering data from a set of 46 papers (30 bird species). The overall conclusion is that there are no trade-offs apparent unless experimental manipulations push the natural variability to extreme values. In the wild, the general pattern for within-species variation is that birds with (naturally) larger clutches survive better.

Strengths:

I agree this study highlights important issues and provides good evidence of what it claims, using appropriate methods.

Weaknesses:

I also think, however, that it would benefit from broadening its horizon beyond bird studies. The conclusions can be reinforced through insights from other taxa. General reasoning is that there is positive pleiotropy (i.e. individuals vary in quality and therefore some are more fit (perform better) than others. Of course, this is within their current environment (biotic, abiotic, social. ...), with consequences of maintaining genetic variation across generations - outlined in Maklakov et al. 2015 (https://doi.org/10.1002/bies.201500025). This explains the outcomes of this study very well and would come to less controversy and surprise for a more general audience.

I have two fish examples in my mind where this trade-off is also discounted. Of course, given that it is beyond brood-caring birds, the wording in those studies is slightly different, but the evolutionary insight is the same. First, within species but across populations, Reznick et al. (2004, DOI: 10.1038/nature02936) demonstrated a positive correlation between reproduction and parental survival in guppies. Second, an annual killifish study (2021, DOI: 10.1111/1365-2656.13382) showed, within a population, a positive association between reproduction and (reproductive) aging.

In fruit flies, there is also a strong experimental study demonstrating the absence of reproduction-lifespan trade-offs (DOI: 10.1016/j.cub.2013.09.049).

I suggest that incorporating insights from those studies would broaden the scope and reach of the current manuscript.

Likely impact:

I think this is an important contribution to a slow shift in how we perceive the importance of trade-offs in ecology and evolution in general. While the current view still is that one individual excelling in one measure of its life history (i.e. receiving benefits) must struggle (i.e. pay costs) in another part. However, a positive correlation between all aspects of life history traits is possible within an individual (such as due to developmental conditions or fitting to a particular environment). Simply, some individuals can perform generally better (be of good quality than others).

Author response:

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

In the second round of reviews, Reviewer 2 made three specific comments. The first comment criticises us for not including a set of equations they had requested in their first review. We did, in fact, include the requested equations in our revised submission, which were in the Supplementary Information, and were also cited in the main text of our revised manuscript and our changes were made clear in our response to the reviewer. The second comment, the reviewer suggested adding one word to a sentence in the abstract. We have made this change (line 23). The third comment, the reviewer highlights a sentence where we agree we could have been more clear. The sentence can be rectified by adding one word to the current sentence, which we have done (line 232). We believe the changes required to our manuscript are very minor, and we have implemented these two suggested changes, which are highlighted in the revised manuscript.

The memory can sometimes be unreliable.

Author response:

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

Reviewer #1:

Summary:

In this study, Nishi et al. claim that the ratio of long-term hematopoietic stem cell (LT-HSC) versus short-term HSC (ST-HSC) determines the lineage output of HSCs and reduced ratio of ST-HSC in aged mice causes myeloid-biased hematopoiesis. The authors used Hoxb5 reporter mice to isolate LT-HSC and ST-HSC and performed molecular analyses and transplantation assays to support their arguments. How the hematopoietic system becomes myeloid-biased upon aging is an important question with many implications in the disease context as well. However, their study is descriptive with remaining questions.

Weaknesses:

Comment #1-1: The authors may need conceptual re-framing of their main argument because whether the ST-HSCs used in this study are functionally indeed short-term "HSCs" is questionable. The data presented in this study and their immunophenotypic definition of ST-HSCs (Lineage negative/Sca-1+/c-Kit+/Flk2-/CD34-/CD150+/Hoxb5-) suggest that authors may find hematopoietic stem cell-like lymphoid progenitors as previously shown for megakaryocyte lineage (Haas et al., Cell stem cell. 2015) or, as the authors briefly mentioned in the discussion, Hoxb5- HSCs could be lymphoid-biased HSCs.

The authors disputed the idea that Hoxb5- HSCs as lymphoid-biased HSCs based on their previous 4 weeks post-transplantation data (Chen et al., 2016). However, they overlooked the possibility of myeloid reprogramming of lymphoid-biased population during regenerative conditions (Pietras et al., Cell stem cell., 2015). In other words, early post-transplant STHSCs (Hoxb5- HSCs) can be seen as lacking the phenotypic lymphoid-biased HSCs.

Thinking of their ST-HSCs as hematopoietic stem cell-like lymphoid progenitors or lymphoidbiased HSCs makes more sense conceptually as well.

Response #1-1: We appreciate this important suggestion and recognize the significance of the debate on whether Hoxb5- HSCs are ST-HSCs or lymphoid-biased HSCs.

HSCs are defined by their ability to retain hematopoietic potential after a secondary transplantation1-2. If Hoxb5- HSCs were indeed lymphoid-biased HSCs, they would exhibit predominantly lymphoid hematopoiesis even after secondary transplantation. However, functional experiments demonstrate that these cells lose their hematopoietic output after secondary transplantation3 (see Fig. 2 in this paper). Based on the established definition of HSCs in this filed, it is appropriate to classify Hoxb5- HSCs as ST-HSCs rather than lymphoid-biased HSCs.

Additionally, it has been reported that myeloid reprogramming may occur in the early posttransplant period, around 2-4 weeks after transplantation, even in lymphoid-biased populations within the MPP fraction, due to high inflammatory conditions4. However, when considering the post-transplant hematopoiesis of Hoxb5- HSC fractions as ST-HSCs, they exhibit almost the same myeloid hematopoietic potential as LT-HSCs not only during the early 4 weeks after transplantation but also at 8 weeks post-transplantation3, when the acute inflammatory response has largely subsided. Therefore, it is difficult to attribute the myeloid production by ST-HSCs post-transplant solely to myeloid reprogramming.

References

(1) Morrison, S. J. & Weissman, I. L. The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673 (1994).

(2) Challen, G. A., Boles, N., Lin, K. K. Y. & Goodell, M. A. Mouse hematopoietic stem cell identification and analysis. Cytom. Part A 75, 14–24 (2009).

(3) Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016).

(4) Pietras, E. M. et al. Functionally Distinct Subsets of Lineage-Biased Multipotent Progenitors Control Blood Production in Normal and Regenerative Conditions. Cell Stem Cell 17, 35–46 (2015).

Comment #1-2: ST-HSCs come from LT-HSCs and further differentiate into lineage-biased multipotent progenitor (MPP) populations including myeloid-biased MPP2 and MPP3. Based on the authors' claim, LT-HSCs (Hoxb5- HSCs) have no lineage bias even in aged mice. Then these LT-HSCs make ST-HSCs, which produce mostly memory T cells. These memory T cell-producing ST-HSCs then produce MPPs including myeloid-biased MPP2 and MPP3.

This differentiation trajectory is hard to accept. If we think Hoxb5- HSCs (ST-HSCs by authors) as a sub-population of immunophenotypic HSCs with lymphoid lineage bias or hematopoietic stem cell-like lymphoid progenitors, the differentiation trajectory has no flaw.

Response #1-2: Thank you for this comment, and we apologize for the misunderstanding regarding the predominance of memory T cells in ST-HSCs after transplantation. 

Our data show that ST-HSCs are not biased HSCs that predominantly produce memory T cells, but rather, ST-HSCs are multipotent hematopoietic cells. ST-HSCs lose their ability to self-renew within a short period, resulting in the cessation of ST-HSC-derived hematopoiesis. As a result, myeloid lineage with a short half-life disappears from the peripheral blood, and memory lymphocytes with a long half-life remain (see Figure 5 in this paper). 

Comment #1-3: Authors' experimental designs have some caveats to support their claims. Authors claimed that aged LT-HSCs have no myeloid-biased clone expansion using transplantation assays. In these experiments, authors used 10 HSCs and young mice as recipients. Given the huge expansion of old HSC by number and known heterogeneity in immunophenotypically defined HSC populations, it is questionable how 10 out of so many old HSCs can faithfully represent the old HSC population. The Hoxb5+ old HSC primary and secondary recipient mice data (Figure 2C and D) support this concern. In addition, they only used young recipients. Considering the importance of the inflammatory aged niche in the myeloid-biased lineage output, transplanting young vs old LT-HSCs into aged mice will complete the whole picture.

Response #1-3: We appreciate the reviewer for the comments. We acknowledge that using ten HSCs may not capture the heterogeneity of aging HSCs.

However, although most of our experiments have used a small number of transplanted cells (e.g., 10 cells), we have conducted functional experiments across Figures 2, 3, 5, 6, S3, and S6, totaling n = 126, equivalent to over 1260 cells. Previous studies have reported that myeloid-biased HSCs constitute more than 50% of the aged HSC population1-2. If myeloidbiased HSCs increase with age, they should be detectable in our experiments. Our functional experiments have consistently shown that Hoxb5+ HSCs exhibit unchanged lineage output throughout life. In contrast, the data presented in this paper indicate that changes in the ratio of LT-HSCs and ST-HSCs may contribute to myeloid-biased hematopoiesis.

We believe that transplanting aged HSCs into aged recipient mice is crucial to analyzing not only the differentiation potential of aged HSCs but also the changes in their engraftment and self-renewal abilities. We aim to clarify further findings through these experiments in the future.

References

(1) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(2) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Comment #1-4: The authors' molecular data analyses need more rigor with unbiased approaches. They claimed that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid or lymphoid gene set enrichment but aged bulk HSCs, which are just a sum of LT-HSCs and ST-HSCs by their gating scheme (Figure 4A), showed the "tendency" of enrichment of myeloid-related genes based on the selected gene set (Figure 4D). Although the proportion of ST-HSCs is reduced in bulk HSCs upon aging, since ST-HSCs do not exhibit lymphoid gene set enrichment based on their data, it is hard to understand how aged bulk HSCs have more myeloid gene set enrichment compared to young bulk HSCs. This bulk HSC data rather suggests that there could be a trend toward certain lineage bias (although not significant) in aged LT-HSCs or ST-HSCs. The authors need to verify the molecular lineage priming of LT-HSCs and ST-HSCs using another comprehensive dataset.

Response #1-4: Thank you for pointing out that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid

or lymphoid gene set enrichment, although aged bulk HSCs showed a tendency towards enrichment of myeloid-related genes.

The actual GSEA result had an FDR > 0.05. Therefore, we cannot claim that bulk HSCs showed significant enrichment of myeloid-related genes with age. Consequently, we have revised the following sentences:

[P11, L251] Neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid/lymphoid gene set enrichment, while shared myeloid-related genes tended to be enriched in aged bulk-HSCs, although this enrichment was not statistically significant (Fig. 4, F and G).

In addition to the above, we also found that the GSEA results differ among myeloid gene sets (Fig. 4, D-F; Fig. 4S, C-D). These findings suggest that discussing lineage bias in HSCs using GSEA is challenging. We believe that functional experimental data is crucial. From our functional experiments, when the ratio of LT-HSC to ST-HSC was reconstituted to match the ratio in young Bulk-HSCs (LT= 2:8) or aged bulk-HSCs (LT= 5:5), myeloid-biased hematopoiesis was observed with the aged bulk-HSC ratio. Based on this data, the authors concluded that age-related changes in the ratio between LT-HSCs and ST-HSCs in bulkHSCs cause myeloid-biased hematopoiesis rather than an increase in myeloid gene expression in the aged bulk-HSCs.

Comment #1-5: Some data are too weak to fully support their claims. The authors claimed that age-associated extramedullary changes are the main driver of myeloid-biased hematopoiesis based on no major differences in progenitor populations upon transplantation of 10 young HSCs into young or old recipient mice (Figure 7F) and relatively low donor-derived cells in thymus and spleen in aged recipient mice (Figure 7G-J). However, they used selected mice to calculate the progenitor populations in recipient mice (8 out of 17 from young recipients denoted by * and 8 out of 10 from aged recipients denoted by * in Figure 7C). In addition, they calculated the progenitor populations as frequency in c-kit positive cells. Given that they transplanted 10 LT-HSCs into "sub-lethally" irradiated mice and 8.7 Gy irradiation can have different effects on bone marrow clearance in young vs old mice, it is not clear whether this data is reliable enough to support their claims. The same concern applies to the data Figure 7G-J. Authors need to provide alternative data to support their claims.

Response #1-5: Thank you for useful comments. Our claim regarding Fig. 7 is that age-associated extramedullary changes are merely additional drivers for myeloid-biased hematopoiesis are not the main drivers. But we will address the issues pointed out.

Regarding the reason for analyzing the asterisk mice

We performed two independent experiments for Fig. 7. In the first experiment, we planned to analyze the BM of recipients 16 weeks after transplantation. However, as shown in Fig. 7B, many of the aged mice died before 16 weeks. Therefore, we decided to examine the BM of the recipient mice at 12 weeks in the second experiment. Below are the peripheral blood results 11-12 weeks after transplantation for the mice used in the second experiment.

Author response image 1.

For the second experiment, we analyzed the BM of all eight all eight aged recipients. Then, we selected the same number of young recipients for analysis to ensure that the donor myeloid output would be comparable to that of the entire young group. Indeed, the donor myeloid lineage output of the selected mice was 28.1 ± 22.9%, closely matching the 23.5 ± 23.3% (p = 0.68) observed in the entire young recipient population. 

That being said, as the reviewer pointed out, it is considerable that the BM, thymus, and spleen of all mice were not analyzed. Hence, we have added the following sentences:

[P14, L327] We performed BM analysis for the mice denoted by † in Figure 7C because many of the aged mice had died before the analysis.

[P15, L338] The thymus and spleen analyses were also performed on the mice denoted by † in Figure 7C.

Regarding the reason for 8.7 Gy.

Thank you for your question about whether 8.7 Gy is myeloablative. In our previous report1, we demonstrated that none of the mice subjected to pre-treatment with 8.7 Gy could survive when non-LKS cells were transplanted, suggesting that 8.7 Gy is enough to be myeloablative with the radiation equipment at our facility.

Author response image 2.

Response #1-5:

Reference

(1)  Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

Regarding the normalization of c-Kit in Figure 7F.  

Firstly, as shown in Supplemental Figures S1B and S1C, we analyze the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in different panels. Therefore, normalization is required to assess the differentiation of HSCs from upstream to downstream. Additionally, the reason for normalizing by c-Kit+ is that the bone marrow analysis was performed after enrichment using the Anti-c-Kit antibody for both upstream and downstream fractions. Based on this, we calculated the progenitor populations as a frequency within the c-Kit positive cells.

Next, the results of normalizing the whole bone marrow cells (live cells) are shown below. 

Author response image 3.

Similar to the results of normalizing c-Kit+ cells, myeloid progenitors remained unchanged, including a statistically significant decrease in CMP in aged mice. Additionally, there were no significant differences in CLP. In conclusion, we obtained similar results between the normalization with c-Kit and the normalization with whole bone marrow cells (live cells).

However, as the reviewer pointed out, it is necessary to explain the reason for normalization with c-Kit. Therefore, we will add the following description.

[P21, L502] For the combined analysis of the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in Figures 1B and 7F, we normalized by c-Kit+ cells because we performed a c-Kit enrichment for the bone marrow analysis.

Reviewer #2:

Summary:  

Nishi et al, investigate the well-known and previously described phenomenon of ageassociated myeloid-biased hematopoiesis. Using a previously established HoxB5mCherry mouse model, they used HoxB5+ and HoxB5- HSCs to discriminate cells with long-term (LTHSCs) and short-term (ST-HSCs) reconstitution potential and compared these populations to immunophenotypically defined 'bulk HSCs' that consists of a mixture of LT-HSC and STHSCs. They then isolated these HSC populations from young and aged mice to test their function and myeloid bias in non-competitive and competitive transplants into young and aged recipients. Based on quantification of hematopoietic cell frequencies in the bone marrow, peripheral blood, and in some experiments the spleen and thymus, the authors argue against the currently held belief that myeloid-biased HSCs expand with age. 

Comment #2-1: While aspects of their work are fascinating and might have merit, several issues weaken the overall strength of the arguments and interpretation. Multiple experiments were done with a very low number of recipient mice, showed very large standard deviations, and had no statistically detectable difference between experimental groups. While the authors conclude that these experimental groups are not different, the displayed results seem too variable to conclude anything with certainty. The sensitivity of the performed experiments (e.g. Figure 3; Figure 6C, D) is too low to detect even reasonably strong differences between experimental groups and is thus inadequate to support the author's claims. This weakness of the study is not acknowledged in the text and is also not discussed. To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

Response #2-1: Thank you for your important remarks. The power analysis for this experiment shows that power = 0.319, suggesting that more number may be needed. On the other hand, our method for determining the sample size in Figure 3 is as follows:

(1) First, we checked whether myeloid biased change is detected in the bulk-HSC fraction (Figure S3). The results showed that the difference in myeloid output at 16 weeks after transplantation was statistically significant (young vs. aged = 7.2 ± 8.9 vs. 42.1 ± 35.5%, p = 0.01), even though n = 10.

(2) Next, myeloid biased HSCs have been reported to be a fraction with high self-renewal ability (2004, Blood). If myeloid biased HSCs increase with aging, the increase in myeloid biased HSCs in LT-HSC fraction would be detected with higher sensitivity than in the bulk-HSC fraction used in Figure S3.

(3) However, there was no difference not only in p-values but also in the mean itself, young vs aged = 51.4±31.5% vs 47.4±39.0%, p = 0.82, even though n = 8 in Figure 3. Since there was no difference in the mean itself, it is highly likely that no difference will be detected even if n is further increased.

Regarding Figure 6, we obtained a statistically significant difference and consider the sample size to be sufficient. 

In addition, we have performed various functional experiments (Figures 2, 5, 6 and S6), and have obtained consistent results that expansion of myeloid biased HSCs does not occur with aging in Hoxb5+HSCs fraction. Based on the above, we conclude that the LT-HSC fraction does not differ in myeloid differentiation potential with aging.

Comment #2-2: As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

Response #2-2: Thank you for the comments. As the reviewer pointed out, we hope we could reconfirm our results using single-cell level technology in the future.

On the other hand, we have reported that the ratio of myeloid to lymphoid cells in the peripheral blood changes when the number of HSCs transplanted, or the number of supporting cells transplanted with HSCs, is varied1-2. Therefore, single-cell transplant data need to be interpreted very carefully to determine differentiation potential.

From this viewpoint, future experiments will combine the Hoxb5 reporter system with a lineage tracing system that can track HSCs at the single-cell level over time. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. We have reflected this comment by adding the following sentences in the manuscript.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system3-4. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Sakamaki T, Kao KS, Nishi K, Chen JY, Sadaoka K, Fujii M, et al. Hoxb5 defines the heterogeneity of self-renewal capacity in the hematopoietic stem cell compartment. Biochem Biophys Res Commun [Internet]. 2021;539:34–41. Available from: https://doi.org/10.1016/j.bbrc.2020.12.077

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(4) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet].

2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

Comment #2-3: It is also unclear why the authors believe that the observed reduction of ST-HSCs relative to LT-HSCs explains the myeloid-biased phenotype observed in the peripheral blood. This point seems counterintuitive and requires further explanation.

Response #2-3: Thank you for your comment. We apologize for the insufficient explanation. Our data, as shown in Figures 3 and 4, demonstrate that the differentiation potential of LT-HSCs remains unchanged with age. Therefore, rather than suggesting that an increase in LT-HSCs with a consistent differentiation capacity leads to myeloid-biased hematopoiesis, it seems more accurate to highlight that the relative decrease in the proportion of ST-HSCs, which remain in peripheral blood as lymphocytes, leads to a relative increase in myeloid cells in peripheral blood and thus causes myeloid-biased hematopoiesis.

However, if we focus on the increase in the ratio of LT-HSCs, it is also plausible to explain that “with aging, the proportion of LT-HSCs capable of long-term myeloid hematopoiesis increases. As a result, from 16 weeks after transplantation, the influence of LT-HSCs maintaining the long-term ability to produce myeloid cells becomes relatively more significant, leading to an increase in the ratio of myeloid cells in the peripheral blood and causing myeloid-biased hematopoiesis.”

Comment #2-4: Based on my understanding of the presented data, the authors argue that myeloid-biased HSCs do not exist, as<br /> a) they detect no difference between young/aged HSCs after transplant (mind low n-numbers and large std!); b) myeloid progenitors downstream of HSCs only show minor or no changes in frequency and c) aged LT-HSCs do not outperform young LT-HSC in myeloid output LT-HScs in competitive transplants (mind low n-numbers and large std!).

Response #2-4: We appreciate the comments. As mentioned above, we will correct the manuscript regarding the sample size.

Regarding the interpreting of the lack of increase in the percentage of myeloid progenitor cells in the bone marrow with age, it is instead possible that various confounding factors, such as differentiation shortcuts or changes in the microenviroment, are involved.

However, even when aged LT-HSCs and young LT-HSCs are transplanted into the same recipient mice, the timing of the appearance of different cell fractions in peripheral blood is similar (Figure 3 of this paper). Therefore, we have not obtained data suggesting that clear shortcuts exist in the differentiation process of aged HSCs into neutrophils or monocytes. Additionally, it is currently consensually accepted that myeloid cells, including neutrophils and monocytes, differentiate from GMPs1. Since there is no changes in the proportion of GMPs in the bone marrow with age, we concluded that the differentiation potential into myeloid cells remains consistent with aging.

Reference

(1) Akashi K and others, ‘A Clonogenic Common Myeloid Progenitor That Gives Rise to All Myeloid Lineages’, Nature, 404.6774 (2000), 193–97.

Strengths: 

The authors present an interesting observation and offer an alternative explanation of the origins of aged-associated myeloid-biased hematopoiesis. Their data regarding the role of the microenvironment in the spleen and thymus appears to be convincing. 

Weaknesses: 

Comment #2-5: "Then, we found that the myeloid lineage proportions from young and aged LT-HSCs were nearly comparable during the observation period after transplantation (Figure 3, B and C)."<br /> Given the large standard deviation and low n-numbers, the power of the analysis to detect differences between experimental groups is very low. Experimental groups with too large standard deviations (as displayed here) are difficult to interpret and might be inconclusive. The absence of clearly detectable differences between young and aged transplanted HSCs could thus simply be a false-negative result. The shown experimental results hence do not provide strong evidence for the author's interpretation of the data. The authors should add additional transplants and include a detailed power analysis to be able to detect differences between experimental groups with reasonable sensitivity.

Response #2-5: Thank you for providing these insights. Regarding the sample size, we have addressed this in Response #2-1.

Comment #2-6: Line 293: "Based on these findings, we concluded that myeloid-biased hematopoiesis observed following transplantation of aged HSCs was caused by a relative decrease in ST-HSC in the bulk-HSC compartment in aged mice rather than the selective expansion of myeloid-biased HSC clones."<br /> Couldn't that also be explained by an increase in myeloid-biased HSCs, as repeatedly reported and seen in the expansion of CD150+ HSCs? It is not intuitively clear why a reduction of ST-HSCs clones would lead to a myeloid bias. The author should try to explain more clearly where they believe the increased number of myeloid cells comes from. What is the source of myeloid cells if the authors believe they are not derived from the expanded population of myeloid-biased HSCs?

Response #2-6: Thank you for pointing this out. We apologize for the insufficient explanation. We will explain using Figure 8 from the paper.

First, our data show that LT-HSCs maintain their differentiation capacity with age, while ST-HSCs lose their self-renewal capacity earlier, so that only long-lived memory lymphocytes remain in the peripheral blood after the loss of self-renewal capacity in ST-HSCs (Figure 8, upper panel). In mouse bone marrow, the proportion of LT-HSCs increases with age, while the proportion of STHSCs relatively decreases (Figure 8, lower panel and Figure S5). 

Our data show that merely reproducing the ratio of LT-HSCs to ST-HSCs observed in aged mice using young LT-HSCs and ST-HSCs can replicate myeloid-biased hematopoiesis. This suggests that the increase in LT-HSC and the relative decrease in ST-HSC within the HSC compartment with aging are likely to contribute to myeloid-biased hematopoiesis.

As mentioned earlier, since the differentiation capacity of LT-HSCs remain unchaged with age, it seems more accurate to describe that the relative decrease in the proportion of STHSCs, which retain long-lived memory lymphocytes in peripheral blood, leads to a relative increase in myeloid cells in peripheral blood and thus causes myeloid-biased hematopoiesis.

However, focusing on the increase in the proportion of LT-HSCs, it is also possible to explain that “with aging, the proportion of LT-HSCs capable of long-term myeloid hematopoiesis increases. As a result, from 16 weeks after transplantation, the influence of LT-HSCs maintaining the long-term ability to produce myeloid cells becomes relatively more significant, leading to an increase in the ratio of myeloid cells in the peripheral blood and causing myeloid-biased hematopoiesis.”

Reviewer #3:

Summary:

In this manuscript, Nishi et al. propose a new model to explain the previously reported myeloid-biased hematopoiesis associated with aging. Traditionally, this phenotype has been explained by the expansion of myeloid-biased hematopoietic stem cell (HSC) clones during aging. Here, the authors question this idea and show how their Hoxb5 reporter model can discriminate long-term (LT) and short-term (ST) HSC and characterized their lineage output after transplant. From these analyses, the authors conclude that changes during aging in the LT/ST HSC proportion explain the myeloid bias observed. 

Although the topic is appropriate and the new model provides a new way to think about lineage-biased output observed in multiple hematopoietic contexts, some of the experimental design choices, as well as some of the conclusions drawn from the results could be substantially improved. Also, they do not propose any potential mechanism to explain this process, which reduces the potential impact and novelty of the study. Specific concerns are outlined below. 

Major 

Comment #3-1: As a general comment, there are experimental details that are either missing or not clear. The main one is related to transplantation assays. What is the irradiation dose? The Methods sections indicates "recipient mice were lethally irradiated with single doses of 8.7 or 9.1 Gy". The only experimental schematic indicating the irradiation dose is Figure 7A, which uses 8.7 Gy. Also, although there is not a "standard", 11 Gy split in two doses is typically considered lethal irradiation, while 9.5 Gy is considered sublethal.

Response #3-1: We agree with reviewer’s assessment about whether 8.7 Gy is myeloablative. To confirm this, it would typically be necessary to irradiate mice with different dose and observe if they do not survive. However, such an experiment is not ethically permissible at our facility. Instead, in our previous report1, we demonstrated that none of the mice subjected to pretreatment with 8.7 Gy could survive when non-LKS cells were transplanted, suggesting that

8.7 Gy is enough to be myeloablative with the radiation equipment at our facility.

Reference

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

Comment #3-2:  Is there any reason for these lower doses? Same question for giving a single dose and for performing irradiation a day before transplant. 

Response #3-2: We appreciate the reviewer for these important comments. Although the 8.7 Gy dose used at our facility is lower than in other reports, we selected this dose to maintain consistency with our previous experiments. For the same reason, we used a single irradiation, not split.  Regarding the timing of irradiation, the method section specifies that irradiation timing is 12-24 hours prior to transplantation. In most experiments, irradiation is performed at 12 hours. However, due to experimental progress, there were occasional instances where nearly 24 hours elapsed between irradiation and transplantation. We provide this information to ensure accuracy.

Comment #3-3: The manuscript would benefit from the inclusion of references to recent studies discussing hematopoietic biases and differentiation dynamics at a single-cell level (e.g., Yamamoto et. al 2018; Rodriguez-Fraticelli et al., 2020). Also, when discussing the discrepancy between studies claiming different biases within the HSC pool, the authors mentioned that Montecino-Rodriguez et al. 2019 showed preserved lymphoid potential with age. It would be good to acknowledge that this study used busulfan as the conditioning method instead of irradiation.

Response #3-3: We agree with this comment and have incorporated this suggestion into the manuscript

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. Additionally, in this report we purified LT-HSCs by Hoxb5 reporter system. In contrast, various LT-HSC markers have been previously reported2-3.  Therefore, it is ideal to validate our findings using other LT-HSC makers.

[P16, L368] Other studies suggest that blockage of lymphoid hematopoiesis in aged mice results in myeloid-skewed hematopoiesis through alternative mechanisms. However, this result should be interpreted carefully, since Busulfan was used for myeloablative treatment in this study4.   

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

(3) Sanjuan-Pla A, Macaulay IC, Jensen CT, Woll PS, Luis TC, Mead A, et al. Plateletbiased stem cells reside at the apex of the haematopoietic stem-cell hierarchy. Nature.

2013;502(7470):232–6. 

(4) Montecino-Rodriguez E, Kong Y, Casero D, Rouault A, Dorshkind K, Pioli PD. Lymphoid-Biased Hematopoietic Stem Cells Are Maintained with Age and Efficiently Generate Lymphoid Progeny. Stem Cell Reports. 2019 Mar 5;12(3):584–96. 

Comment #3-4: When representing the contribution to PB from transplanted cells, the authors show the % of each lineage within the donor-derived cells (Figures 3B-C, 5B, 6B-D, 7C-E, and S3 B-C). To have a better picture of total donor contribution, total PB and BM chimerism should be included for each transplantation assay. Also, for Figures 2C-D and Figures S2A-B, do the graphs represent 100% of the PB cells? Are there any radioresistant cells?

Response #3-4: Thank you for highlighting this point. Indeed, donor contribution to total peripheral blood (PB) is important information. We have included the donor contribution data for each figure above mentioned.

Author response image 4.

In Figure 2C-D and Figure S2A-B, the percentage of donor chimerism in PB was defined as the percentage of CD45.1-CD45.2+ cells among total CD45.1-CD45.2+ and CD45.1+CD45.2+ cells as described in method section.

Comment #3-5: For BM progenitor frequencies, the authors present the data as the frequency of cKit+ cells. This normalization might be misleading as changes in the proportion of cKit+ between the different experimental conditions could mask differences in these BM subpopulations. Representing this data as the frequency of BM single cells or as absolute numbers (e.g., per femur) would be valuable.

Response #3-5: We appreciate the reviewer's comment on this point. 

Firstly, as shown in Supplemental Figures S1B and S1C, we analyze the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in different panels. Therefore, normalization is required to assess the differentiation of HSCs from upstream to downstream. Additionally, the reason for normalizing by c-Kit+ is that the bone marrow analysis was performed after enrichment using the Anti-c-Kit antibody for both upstream and downstream fractions. Based on this, we calculated the progenitor populations as a frequency within the c-Kit positive cells. Next, the results of normalizing the whole bone marrow cells (live cells) are shown in Author response image 2. 

Similar to the results of normalizing c-Kit+ cells, myeloid progenitors remained unchanged, including a statistically significant decrease in CMP in aged mice. Additionally, there were no significant differences in CLP. In conclusion, similar results were obtained between the normalization with c-Kit and the normalization with whole bone marrow cells (live cells).

However, as the reviewer pointed out, it is necessary to explain the reason for normalization with c-Kit. Therefore, we will add the following description.

[P21, L502] For the combined analysis of the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in Figures 1B and 7F, we normalized by c-Kit+ cells because we performed a c-Kit enrichment for the bone marrow analysis.

Comment #3-6: Regarding Figure 1B, the authors argue that if myeloid-biased HSC clones increase with age, they should see increased frequency of all components of the myeloid differentiation pathway (CMP, GMP, MEP). This would imply that their results (no changes or reduction in these myeloid subpopulations) suggest the absence of myeloid-biased HSC clones expansion with age. This reviewer believes that differentiation dynamics within the hematopoietic hierarchy can be more complex than a cascade of sequential and compartmentalized events (e.g., accelerated differentiation at the CMP level could cause exhaustion of this compartment and explain its reduction with age and why GMP and MEP are unchanged) and these conclusions should be considered more carefully.

Response #3-6: We wish to thank the reviewer for this comment. We agree with that the differentiation pathway may not be a cascade of sequential events but could be influenced by various factors such as extrinsic factors.

In Figure 1B, we hypothesized that there may be other mechanisms causing myeloidbiased hematopoiesis besides the age-related increase in myeloid-biased HSCs, given that the percentage of myeloid progenitor cells in the bone marrow did not change with age. However, we do not discuss the presence or absence of myeloid-biased HSCs based on the data in Figure 1B. 

Our newly proposed theories—that the differentiation capacity of LT-HSCs remains unchanged with age and that age-related myeloid-biased hematopoiesis is due to changes in the ratio of LT-HSCs to ST-HSCs—are based on functional experiment results. As the reviewer pointed out, to discuss the presence or absence of myeloid-biased HSCs based on the data in Figure 1B, it is necessary to apply a system that can track HSC differentiation at single-cell level. The technology would clarify changes in the self-renewal capacity of individual HSCs and their differentiation into progenitor cells and peripheral blood cells. The authors believe that those single-cell technologies will be beneficial in understanding the differentiation of HSCs. Based on the above, the following statement has been added to the text.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

Comment #3-7: Within the few recipients showing good donor engraftment in Figure 2C, there is a big proportion of T cells that are "amplified" upon secondary transplantation (Figure 2D). Is this expected?

Response #3-7: We wish to express our deep appreciation to the reviewer for insightful comment on this point. As the reviewers pointed out, in Figure 2D, a few recipients show a very high percentage of T cells. The authors had the same question and considered this phenomenon as follows:

(1) One reason for the very high percentage of T cells is that we used 1 x 107 whole bone marrow cells in the secondary transplantation. Consequently, the donor cells in the secondary transplantation contained more T-cell progenitor cells, leading to a greater increase in T cells compared to the primary transplantation.

(2) We also consider that this phenomenon may be influenced by the reduced selfrenewal capacity of aged LT-HSCs, resulting in decreased sustained production of myeloid cells in the secondary recipient mice. As a result, long-lived memory-type lymphocytes may preferentially remain in the peripheral blood, increasing the percentage of T cells in the secondary recipient mice.

We have discussed our hypothesis regarding this interesting phenomenon. To further clarify the characteristics of the increased T-cell count in the secondary recipient mice, we will analyze TCR clonality and diversity in the future.

Comment #3-8: Do the authors have any explanation for the high level of variability within the recipients of Hoxb5+ cells in Figure 2C?

Response #3-8: We appreciate the reviewer's comment on this point. As noted in our previous report, transplantation of a sufficient number of HSCs results in stable donor chimerism, whereas a small number of HSCs leads to increased variability in donor chimerism1. Additionally, other studies have observed high variability when fewer than 10 HSCs are transplanted2-3. Based on this evidence, we consider that the transplantation of a small number of cells (10 cells) is the primary cause of the high level of variability observed.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Comment #3-9: Can the results from Figure 2E be interpreted as Hoxb5+ cells having a myeloid bias? (differences are more obvious/significant in neutrophils and monocytes).

Response #3-9: Thank you for your insightful comments. Firstly, we have not obtained any data indicating that young LT-HSCs are myeloid biased HSCs so far. Therefore, we classify young LT-HSCs as balanced HSCs1. Secondly, our current data demonstrate no significant difference in differentiation capacity between young and aged LT-HSCs (see Figure 3 in this paper). Based on these findings, we interpret that aged LT-HSCs are balanced HSCs, similar to young LT-HSCs.

Reference

(1)  Chen JY, Miyanishi M, Wang SK, Yamazaki S, Sinha R, Kao KS, et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature. 2016 Feb 10;530(7589):223–7. 

Comment #3-10: Is Figure 2G considering all primary recipients or only the ones that were used for secondary transplants? The second option would be a fairer comparison.

Response #3-10: We appreciate the reviewer's comment on this point. We considered all primary recipients in Figure 2G to ensure a fair comparison, given the influence of various factors such as the radiosensitivity of individual recipient mice1. Comparing only the primary recipients used in the secondary transplantation would result in n = 3 (primary recipient) vs. n = 12 (secondary recipient). Including all primary recipients yields n = 11 vs. n = 12, providing a more balanced comparison. Therefore, we analyzed all primary recipient mice to ensure the reliability of our results.

Reference

(1) Duran-Struuck R, Dysko RC. Principles of bone marrow transplantation (BMT): providing optimal veterinary and husbandry care to irradiated mice in BMT studies. J Am Assoc Lab Anim Sci. 2009; 48:11–22

Comment #3-11: When discussing the transcriptional profile of young and aged HSCs, the authors claim that genes linked to myeloid differentiation remain unchanged in the LT-HSC fraction while there are significant changes in the ST-HSCs. However, 2 out of the 4 genes shown in Figure S4B show ratios higher than 1 in LT-HSCs.

Response #3-11: Thank you for highlighting this important point. As the reviewer pointed out, when we analyze the expression of myeloid-related genes, some genes are elevated in aged LT-HSCs compared to young LT-HSCs. However, the GSEA analysis using myeloid-related gene sets, which include several hundred genes, shows no significant difference between young and aged LT-HSCs (see Figure S4C in this paper). Furthermore, functional experiments using the co-transplantation system show no difference in differentiation capacity between young and aged LT-HSCs (see Figure 3 in this paper). Based on these results, we conclude that LT-HSCs do not exhibit any change in differentiation capacity with aging.

Comment #3-12: When determining the lymphoid bias in ST-HSCs, the authors focus on the T-cell subtype, not considering any other any other lymphoid population. Could the authors explain this?

Response #3-12: We thank the reviewer for this comment. We conducted the experiments in Figure 5 to demonstrate that the hematopoiesis observed 16 weeks post-transplantation—when STHSCs are believed to lose their self-renewal capacity—is not due to de novo production of T cells from ST-HSCs. Instead, it is attributed to long-lived memory cells which can persistently remain in the peripheral blood.

As noted by the reviewer, various memory cell types are present in peripheral blood. Our analysis focused on memory T cells due to the broad consensus on memory T cell markers1. 

Our findings show that transplanted Hoxb5- HSCs do not continuously produce lymphoid cells, unlike lymphoid-biased HSCs. Rather, the loss of self-renewal capacity in Hoxb5- HSCs makes the presence of long-lived memory cells in the peripheral blood more apparent.

Reference

(1)  Yenyuwadee S, Sanchez-Trincado Lopez JL, Shah R, Rosato PC, Boussiotis VA. The evolving role of tissue-resident memory T cells in infections and cancer. Sci Adv. 2022;8(33). 

Comment #3-13: Based on the reduced frequency of donor cells in the spleen and thymus, the authors conclude "the process of lymphoid lineage differentiation was impaired in the spleens and thymi of aged mice compared to young mice". An alternative explanation could be that differentiated cells do not successfully migrate from the bone marrow to these secondary lymphoid organs. Please consider this possibility when discussing the data.

Response #3-13: We strongly appreciate the reviewer's comment on this point. In accordance with the reviewer's comment, we have incorporated this suggestion into our manuscript.

[P15, L343] These results indicate that the process of lymphoid lineage differentiation is impaired in the spleens and thymi of aged mice compared to young mice, or that differentiating cells in the bone marrow do not successfully migrate into these secondary lymphoid organs. These factors contribute to the enhanced myeloid-biased hematopoiesis in peripheral blood due to a decrease in de novo lymphocyte production.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Recommendation #2-1: To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

Response to Recommendation #2-1: Thank you for your important remarks. The power analysis for this experiment shows that power = 0.319, suggesting that more number may be needed. On the other hand, our method for determining the sample size in Figure 3 is as follows:

(1) First, we checked whether myeloid biased change is detected in the bulk-HSC fraction (Figure S3). The results showed that the difference in myeloid output at 16 weeks after transplantation was statistically significant (young vs. aged = 7.2 ± 8.9 vs. 42.1 ± 35.5%, p = 0.01), even though n = 10.

(2) Next, myeloid biased HSCs have been reported to be a fraction with high self-renewal ability (2004, Blood). If myeloid biased HSCs increase with aging, the increase in myeloid biased HSCs in LT-HSC fraction would be detected with higher sensitivity than in the bulk-HSC fraction used in Figure S3.

(3) However, there was no difference not only in p-values but also in the mean itself, young vs aged = 51.4±31.5% vs 47.4±39.0%, p = 0.82, even though n = 8 in Figure 3. Since there was no difference in the mean itself, it is highly likely that no difference will be detected even if n is further increased.

Regarding Figure S3, 5, 6, S6 and 7, we obtained a statistically significant difference and consider the sample size to be sufficient. 

Recommendation #2-2: As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

Response to Recommendation #2-2: Thank you for the comments. As the reviewer pointed out, we hope we could reconfirm our results using single-cell level technology in the future.

On the other hand, we have reported that the ratio of myeloid to lymphoid cells in the peripheral blood changes when the number of HSCs transplanted, or the number of supporting cells transplanted with HSCs, is varied1-2. Therefore, single-cell transplant data need to be interpreted very carefully to determine differentiation potential.

From this viewpoint, future experiments will combine the Hoxb5 reporter system with a lineage tracing system that can track HSCs at the single-cell level over time. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. We have reflected this comment by adding the following sentences in the manuscript.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty transplantation assays. Therefore, the current theory should be revalidated using single-cell technology. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Sakamaki T, Kao KS, Nishi K, Chen JY, Sadaoka K, Fujii M, et al. Hoxb5 defines the heterogeneity of self-renewal capacity in the hematopoietic stem cell compartment. Biochem Biophys Res Commun [Internet]. 2021;539:34–41. Available from: https://doi.org/10.1016/j.bbrc.2020.12.077

Minor points:

Recommendation #2-3: Figure 1: "Comprehensive analysis of hematopoietic alternations with age shows a discrepancy of age-associated changes between peripheral blood and bone marrow"

[Comment to the authors]: For clarity, the nature of the discrepancy should be stated clearly.

Response to Recommendation #2-3: Thank you for this important comment. Following the reviewer’s recommendation, we have revised the manuscript as follows

[P7, L139] Our analysis of hematopoietic alternations with age revealed that age-associated transition patterns of immunophenotypically defined HSC and CMP in BM were not paralleled with myeloid cell in PB (Fig. 1 C).

Recommendation #2-4: Figure 1B "(B) Average frequency of immunophenotypically defined HSC and progenitor cells in BM of 2-3-month mice (n = 6), 6-month mice (n = 6), 12-13-month mice (n = 6), {greater than or equal to} 23-month mice (n = 7).

[Comment to the authors]: It should be stated in the figure and legend that the values are normalized to the 2-3-month-old mice.

Response to Recommendation #2-4: Thank you for this comment. Figure 1B presents the actual measured values of each fraction in c-Kit positive cells in the bone marrow, without any normalization.

Recommendation #2-5: "We 127 found that the frequency of immunophenotypically defined HSC in BM rapidly increased 128 up to the age of 12 months. After the age, they remained plateaued throughout the 129 observation period (Fig. 1 B)."

[Comment to the authors]: The evidence for a 'plateau', where HSC numbers don't change after 12 months is weak. It appears that the numbers increase continuously (although less steep) after 12 months. I thus recommend adjusting the wording to better reflect the data.

Response to Recommendation #2-5: We thank the reviewer for the comments above and have incorporated these suggestions in our revision as follows. 

[P6, L126] We found that the frequency of immunophenotypically defined HSC in BM rapidly increased up to the age of 12 months. After the age, the rate of increase in their frequency appeared to slow down.

Recommendation #2-6: Figure 2G: [Comment to the authors]: Please add the required statistics, please check carefully all figures for missing statistical tests.

Response to Recommendation #2-6: Thank you for these important comments. In response, we have added the results of the significance tests for Figures 1A, 1C, 4C, and S5.

Recommendation #2-7: "If bulk-HSCs isolated from aged mice are already enriched by myeloid-biased HSC clones, we should see more myeloid-biased phenotypes 16 weeks after primary and the secondary transplantation. However, we found that kinetics of the proportion of myeloid cells in PB were similar across primary and the secondary transplantation and that the proportion of myeloid cells gradually decreased over time (Fig. 2 G). These results suggest the following two possibilities: either myeloid-biased HSCs do not expand in the LT-HSC fraction, or the expansion of myeloid-biased clones in 2-year-old mice has already peaked."

[Comment to the authors]: Other possible explanations include that the observed reduction in myeloid reconstitution over 16 weeks reflects the time required to return to homeostasis. In other words, it takes time until the blood system approaches a balanced output.

Response to Recommendation #2-7: We agree with the reviewer's comment. As the reviewer pointed out, the gradual decrease in the proportion of myeloid cells over time is not related to our two hypotheses in this part of the manuscript but rather to the hematopoietic system's process of returning to a homeostatic state after transplantation. Therefore, the original sentence could be misleading, as it is part of the section discussing whether age-associated expansion of myeloid-biased HSCs is observed. Based on the above, we have revised the sentence as follows.

[P8, L179] However, we found that kinetics of the proportion of myeloid cells in PB were similar across the primary and the secondary transplantation (Fig. 2 G). These results suggest the following two possibilities: either myeloid-biased HSCs do not expand in the LTHSC fraction, or the expansion of myeloid-biased clones in 2-year-old mice has already peaked.

Recommendation #2-8: It is also important to consider that the transplant results are highly variable (see large standard deviation), therefore the sensitivity to detect smaller but relevant changes is low in the shown experiments. As the statistical analysis of these experiments is missing and the power seems low these results should be interpreted with caution. For instance, it appears that the secondary transplants on average produce more myeloid cells as expected and predicted by the classical clonal expansion model.

Regarding "expansion of myeloid-biased clones in 2-year-old mice has already peaked". This is what the author suggested above. It might thus not be surprising that HSCs from 2-year-old mice show little to no increased myeloid expansion.

Response to Recommendation #2-8: Thank you for providing these insights. The primary findings of our study are based on functional experiments presented in Figures 2, 3, 5, 6, and 7. In Figure 3, there was no significant difference between young and aged LT-HSCs, with mean values of 51.4±31.5% and 47.4±39.0%, respectively (p = 0.82). Given the lack of difference in the mean values, it is unlikely that increasing the sample size would reveal a significant change. For ethical reasons, to minimize the use of additional animals, we conclude that LT-HSCs exhibit no change in lineage output throughout life based on the data in Figure 3. Statistically significant differences observed in Figures 2, 5, 6, and 7 further support our conclusions.

Additionally, because whole bone marrow cells were transplanted in the secondary transplantation, there may be various confounding factors beyond the differentiation potential of HSCs. Therefore, we consider that caution is necessary when evaluating the differentiation capacity of HSCs in the context of the second transplantation.

Recommendation #2-9: Figure 7C: [Comment to the authors]: The star * indicates with analyzed BM. As stars are typically used as indicators of significance, this can be confusing for the reader. I thus suggest using another symbol.

Response to Recommendation #2-9: We appreciate the reviewer for this comment and have incorporated the suggestion in the revised manuscript. We have decided to use † instead of the star*.

Reviewer #3 (Recommendations For The Authors):

Recommendation #3.1: In Figure 1A, the authors show the frequency of PB lineages (lymphoid vs myeloid) in mice of different ages. It would be great if they could show the same data for each subpopulation including these two main categories individually (granulocytes, monocytes, B cells, T cells...).

Response to Recommendation #3-1: We thank for this suggestion. We provide the frequency of PB lineages (granulocytes, monocytes, B cells, T cells, and NK cells) in mice of different ages.

Author response image 5.

Average frequency of neutrophils, monocytes, B cells, T cells, and NK cells in PB analyzed in Figure 1A. Dots show all individual mice. *P < 0.05. **P < 0.01. Data and error bars represent means ± standard deviation. 

Recommendation #3.2: It would be great if data from young mice could be shown in parallel to the graphs in Figure 2A.

Response to Recommendation #3-2: We thank the reviewer for the comments above and have incorporated these suggestions in Figure 2A. 

[P34, L916] (A) Hoxb5 reporter expression in bulk-HSC, MPP, Flk2+, and Lin-Sca1-c-Kit+ populations in the 2-year-old Hoxb5-tri-mCherry mice (Upper panel) and 3-month-old Hoxb5_tri-mCherry mice (Lower panel). Values indicate the percentage of mCherry+ cells ± standard deviation in each fraction (_n = 3). 

Recommendation #3.3: Do the authors have any explanation for the high level of variability within the recipients of Hoxb5+ cells in Figure 2C?

Response to Recommendation #3-3: Thank you for providing these insights. As noted in our previous report, transplantation of a sufficient number of HSCs results in stable donor chimerism, whereas a small number of HSCs leads to increased variability in donor chimerism1. Additionally, other studies have observed high variability when fewer than 10 HSCs are transplanted2-3. Based on this evidence, we consider that the transplantation of a small number of cells (10 cells) is the primary cause of the high level of variability observed.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Recommendation #3.4: Are the differences in Figure 3D statistically significant? If yes, please add statistics. Same for Figure 4C.

Response to Recommendation #3-4: Thank you for providing these insights. For Figure 3D, we performed an ANOVA analysis for each fraction; however, the results were not statistically significant. In contrast, for Figure 4C, we have added the results of significance tests for comparisons between Young LT-HSC vs. Young Bulk-HSC.

Recommendation #3.5: As a general comment, although the results in this study are interesting, the use of a Hoxb5 lineage tracing mouse model would be more valuable for this purpose than the Hoxb5 reporter used here. The lineage tracing model would allow for the assessment of lineage bias without the caveats introduced by the transplantation assays.

Response to Recommendation #3-5: We appreciate the reviewer for the important comments. Following the reviewer’s recommendation, we have revised the manuscript as follows

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

eLife Assessment

The manuscript provides useful findings to explore the heterogeneity of hematopoietic stem cells and age-related myeloid-biased hematopoiesis. The results presented in this study are incomplete and additional data are necessary to bolster the conclusions. Certain aspects of the methods, experimental design, and data analyses remain inadequate and only partially support the central claims.

Reviewer #1 (Public review):

Summary

In this study, Nishi et al. claim that the ratio of long-term hematopoietic stem cell (LT-HSC) versus short-term HSC (ST-HSC) determines the lineage output of HSCs and reduced ratio of ST-HSC in aged mice causes myeloid-biased hematopoiesis. Authors used Hoxb5 reporter mice to isolated LT-HSC and ST-HSC and performed molecular analyses and transplantation assays to support their arguments. How hematopoietic system becomes myeloid-biased upon aging is an important question with many implications in disease context as well. However, this study needs more definitive data.

(1) Authors' experimental designs have some caveats to definitely support their claims. Authors claimed that aged LT-HSCs have no myeloid-biased clone expansion using transplantation assays. In these experiments, authors used 10 HSCs and young mice as recipients. Given the huge expansion of old HSC by number and known heterogeneity in immunophenotypically defined HSC populations, it is questionable how 10 out of so many old HSCs (an average of 300,000 up to 500,000 cells per mouse; Mitchell et al., Nature Cell Biology, 2023) can faithfully represent old HSC population. The Hoxb5+ old HSC primary and secondary recipient mice data (Fig. 2C and D) support this concern. In addition, they only used young recipients. Considering the importance of inflammatory aged niche in the myeloid-biased lineage output, transplanting young vs old LT-HSCs into aged mice will complete the whole picture.

(2) Authors' molecular data analyses need more rigor with unbiased approaches. They claimed that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid or lymphoid gene set enrichment but aged bulk HSCs, which are just a sum of LT-HSCs and ST-HSCs by their gating scheme (Fig. 4A), showed the "tendency" of enrichment of myeloid-related genes based on the selected gene set (Fig. 4D). Although the proportion of ST-HSCs is reduced in bulk HSCs upon aging, since ST-HSCs do not exhibit lymphoid gene set enrichment based on their data, it is hard to understand how aged bulk HSCs have more myeloid gene set enrichment compared to young bulk HSCs. This bulk HSC data rather suggest that there could be a trend toward certain lineage bias (although not significant) in aged LT-HSCs or ST-HSCs. Authors need to verify the molecular lineage priming of LT-HSCs and ST-HSCs using another comprehensive dataset.

(3) Although authors could not find any molecular evidence for myeloid-biased hematopoiesis from old HSCs (either LT or ST), they argued that the ratio between LT-HSC and ST-HSC causes myeloid-biased hematopoiesis upon aging based on young HSC experiments (Fig. 6). However, old ST-HSC functional data showed that they barely contribute to blood production unlike young Hoxb5- HSCs (ST-HSC) in the transplantation setting (Fig. 2). Is there any evidence that in unperturbed native old hematopoiesis, old Hoxb5- HSCs (ST-HSC) still contribute to blood production? If so, what are their lineage potential/output? Without this information, it is hard to argue that the different ratio causes myeloid-biased hematopoiesis in aging context.

Reviewer #2 (Public review):

Summary:

Nishi et al, investigate the well-known and previously described phenomenon of age-associated myeloid-biased hematopoiesis. Using a previously established HoxB5mCherry mouse model, they used HoxB5+ and HoxB5- HSCs to discriminate cells with long-term (LT-HSCs) and short-term (ST-HSCs) reconstitution potential and compared these populations to immunophenotypically defined 'bulk HSCs' that consists of a mixture of LT-HSC and ST-HSCs. They then isolated these HSC populations from young and aged mice to test their function and myeloid bias in non-competitive and competitive transplants into young and aged recipients. Based on quantification of hematopoietic cell frequencies in the bone marrow, peripheral blood, and in some experiments the spleen and thymus, the authors argue against the currently held belief that myeloid-biased HSCs expand with age.

While aspects of their work are fascinating and might have merit, several issues weaken the overall strength of the arguments and interpretation. Multiple experiments were done with a very low number of recipient mice, showed very large standard deviations, and had no statistically detectable difference between experimental groups. While the authors conclude that these experimental groups are not different, the displayed results seem too variable to conclude anything with certainty. The sensitivity of the performed experiments (e.g. Fig 3; Fig 6C, D) is too low to detect even reasonably strong differences between experimental groups and is thus inadequate to support the author's claims. This weakness of the study is not acknowledged in the text and is also not discussed. To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

It is also unclear why the authors believe that the observed reduction of ST-HSCs relative to LT-HSCs explains the myeloid-biased phenotype observed in the peripheral blood. This point seems counterintuitive and requires further explanation.

Based on my understanding of the presented data, the authors argue that myeloid-biased HSCs do not exist, as<br /> a) they detect no difference between young/aged HSCs after transplant (mind low n-numbers and large std!!!); b) myeloid progenitors downstream of HSCs only show minor or no changes in frequency and c) aged LT-HSCs do not outperform young LT-HSC in myeloid output LT-HScs in competitive transplants (mind low n-numbers and large std!!!).<br /> However, given the low n-numbers and high variance of the results, the argument seems weak and the presented data does not support the claims sufficiently. That the number of downstream progenitors does not change could be explained by other mechanisms, for instance, the frequently reported differentiation short-cuts of HSCs and/or changes in the microenvironment.

Strengths:

The authors present an interesting observation and offer an alternative explanation of the origins of aged-associated myeloid-biased hematopoiesis. Their data regarding the role of the microenvironment in the spleen and thymus appears to be convincing.

Weaknesses:

"Then, we found that the myeloid lineage proportions from young and aged LT-HSCs were nearly comparable during the observation period after transplantation (Fig. 3, B and C)."<br /> [Comment to the authors]: Given the large standard deviation and low n-numbers, the power of the analysis to detect differences between experimental groups is very low. Experimental groups with too large standard deviations (as displayed here) are difficult to interpret and might be inconclusive. The absence of clearly detectable differences between young and aged transplanted HSCs could thus simply be a false-negative result. The shown experimental results hence do not provide strong evidence for the author's interpretation of the data. The authors should add additional transplants and include a detailed power analysis to be able to detect differences between experimental groups with reasonable sensitivity.

Line 293: "Based on these findings, we concluded that myeloid-biased hematopoiesis observed following transplantation of aged HSCs was caused by a relative decrease in ST-HSC in the bulk-HSC compartment in aged mice rather than the selective expansion of myeloid-biased HSC clones."<br /> Couldn't that also be explained by an increase in myeloid-biased HSCs, as repeatedly reported and seen in the expansion of CD150+ HSCs? It is not intuitively clear why a reduction of ST-HSCs clones would lead to a myeloid bias. The author should try to explain more clearly where they believe the increased number of myeloid cells comes from. What is the source of myeloid cells if the authors believe they are not derived from the expanded population of myeloid-biased HSCs?

Reviewer #3 (Public review):

In this manuscript, Nishi et al. propose a new model to explain the previously reported myeloid-biased hematopoiesis associated with aging. Traditionally, this phenotype has been explained by the expansion of myeloid-biased hematopoietic stem cell (HSC) clones during aging. Here, the authors question this idea and show how their Hoxb5 reporter model can discriminate long-term (LT) and short-term (ST) HSC and characterized their lineage output after transplant. From these analyses, the authors conclude that changes during aging in the LT/ST HSC proportion explain the myeloid bias observed.

Although the topic is appropriate and the new model provides a new way to think about lineage-biased output observed in multiple hematopoietic contexts, some of the experimental design choices, as well as some of the conclusions drawn from the results could be substantially improved. Also, they do not propose any potential mechanism to explain this process, which reduces the potential impact and novelty of the study.

The authors have satisfactorily replied to some of my comments. However, there are multiple key aspects that still remain unresolved.

It's difficult to teach when you've forgotten how you learned a thing yourself. It's hard to un-know a topic to empathize with the beginner.

This statement follows the broad thesis that imitation is far easier than innovation.

One of the major affordances of using a zettelkasten or card index for writing is that it forces the writer to break things down into their constituent parts, thereby making the entire process of writing far easier and less complex. One can separately focus their attention on the smaller steps of collecting, selecting, and arranging the material before beginning to actually write.

he capitalizes five words here to emphasize their importance... I wonder what he says in his section on capitalization???

the list of broad parts of writing an essay

Interesting to see this tennis analogy in writing in 1905 from a top-notch player who will win a silver metal in the 1908 Olympics...

Miles, Eustace Hamilton. How to Prepare Essays, Lectures, Articles, Books, Speeches and Letters, with Hints on Writing for the Press. London: Rivingtons, 1905. http://archive.org/details/howtoprepareessa00mileuoft.

Another takeaway from this chapter that piqued my interest was the discussion about how dramatic play and acting can be a good way of integrating knowledge. It says "Memory is enhanced when concepts can be acted out by the learner" (Harlan, Rivkin 12). This was interesting to me because one of the questions I get the most about acting is "How do you memorize all of those lines?" I could never really put it into a concise or logical way as to how I can do it, but reading about how acting out concepts can enhance memory makes total sense! In my case, the concept that is being acted out is the character I am playing, including their lines, emotions, and physical movements.

I like this quote because it tells people the drawback of having a school with lots of opportunities. kids don't have time to be kids and that's something that they can't get back. they work hard to compete against one another, striving to be better than on another that they forget to be themselves and do the things that they want to do. In another class that i was in, we talked about how competition has a certain limit and eventually everyone will be trying to reach goals that are impossible for someone to achieve. If every kid have these good grades and extracurricular activities, the disparity between people from these students and everyone else competing for top school admission will continue to grow.

Just fucking kill me.

Despite knowing this had to be the truth - unless God had finally answered at least one of my prayers about intervening directly with The Bluetooth Specification - I'd never actually plugged my QuietComfort 45s into any one of the devices upon which I've had Lossless audio turned on for years... until just now.

Yet we see this all the times, police officers taking course of their own actions and doing what they want. We saw this in the Geroge Floyd incident which officers took serious aggression without any harmful intent from Mr. Floyd. I think after that incident however, officers were more tamed given they recognized the amount of blacklashing the officers in the incident got.

Proactive policing can disrupt communal life by increasing surveillance and law enforcement presence, which may weaken trust and cooperation within communities. The social bonds can undermine informal social controls that lead to worse results.

eLife Assessment

This valuable study investigates both online responses to, and offline replay of, visual motion sequences. Sophisticated EEG analyses provide solid evidence for both feature-specific and non-specific sequence representations, though the explanation of the statistical methods used is currently incomplete.

Reviewer #1 (Public review):

Summary:

The study identifies two types of activation: one that is cue-triggered and non-specific to motion directions, and another that is specific to the exposed motion directions but occurs in a reversed manner. The finding that activity in the medial temporal lobe (MTL) preceded that in the visual cortex suggests that the visual cortex may serve as a platform for the manifestation of replay events, which potentially enhance visual sequence learning.

Strengths:

Identifying the two types of activation after exposure to a sequence of motion directions is very interesting. The experimental design, procedures, and analyses are solid. The findings are interesting and novel.

Weaknesses:

It was not immediately clear to me why the second type of activation was suggested to occur spontaneously. The procedural differences in the analyses that distinguished between the two types of activation need to be a little better clarified.

Reviewer #2 (Public review):

This paper shows and analyzes an interesting phenomenon. It shows that when people are exposed to sequences of moving dots (that is moving dots in one direction, followed by another direction, etc.), showing either the starting movement direction or ending movement direction causes a coarse-grained brain response that is similar to that elicited by the complete sequence of 4 directions. However, they show by decoding the sensor responses that this brain activity actually does not carry information about the actual sequence and the motion directions, at least not on the time scale of the initial sequence. They also show a reverse reply on a highly compressed time scale, which is elicited during the period of elevated activity, and activated by the first and last elements of the sequence, but not others. Additionally, these replays seem to occur during periods of cortical ripples, similar to what is found in animal studies.

These results are intriguing. They are based on MEG recordings in humans, and finding such replays in humans is novel. Also, this is based on what seems to be sophisticated statistical analysis. However, this is the main problem with this paper. The statistical analysis is not explained well at all, and therefore its validity is hard to evaluate. I am not at all saying it is incorrect; what I am saying is that given how it is explained, it cannot be evaluated.

eLife Assessment

This important study advances our understanding of the role of dopamine in modulating pair bonding in mandarin voles by examining dopamine signaling within the nucleus accumbens across various social stimuli using state-of-the-art causal perturbations. The evidence supporting the findings is compelling, particularly cutting-edge approaches for measuring dopamine release as well as the activity of dopamine receptor populations during social bonding. However, statistical analyses were found to lack rigor and clarity, and the lack of complementary experiments in females was noted as a weakness. Additionally, the manuscript would be strengthened by placing findings within a broader framework, such as by highlighting similarities and/or differences between mandarin and prairie voles.

Reviewer #1 (Public review):

These experiments are some of the first to assess the role of dopamine release and the activity of D1 and D2 MSNs in pair bond formation in Mandarin voles. This is a novel and comprehensive study that presents exciting data about how the dopamine system is involved in pair bonding. The authors provide very detailed methods and clearly presented results. Here they show dopamine release in the NAc shell is enhanced when male voles encounter their pair bonded partner 7 days after co-habitation. In addition, D2 MSN activity decreases whereas D1 MSN activity increases when sniffing the pair-bonded partner.

The authors do not provide justification for why they only use males in the current study, without discussing sex as a biological variable these data can only inform readers about one sex (which in pair-bonded animals by definition have 2 sexes). In addition, the authors do not use an isosbestic control wavelength in photometry experiments, although they do use EGFP control mice which show no effects of these interventions, a within-subject control such as an isosbestic excitation wavelength could give more confidence in these data and rule out motion artefacts within subjects.

There is an existing literature (cited in this manuscript) from Aragona et al., (particularly Aragona et al., 2006) which has highlighted key differences in the roles of rostral versus caudal NAc shell dopamine in pair bond formation and maintenance. Specifically, they report that dopamine transmission promoting pair bonding only occurs in the rostral shell and not the caudal shell or core regions. Given that the authors have targeted more caudally a discussion of how these results fit with previous work and why there may be differences in these areas is warranted.

The authors could discuss the differences between pair bond formation and pair bond maintenance more deeply.

The authors have successfully characterised the involvement of dopamine release, changes in D1 and D2 MSNs, and projections to the VP in pair bonding voles. Their conclusions are supported by their data and they make a number of very reasonable discussion points acknowledging various limitations.

Reviewer #2 (Public review):

Summary:

Using in vivo fiber-photometry the authors first establish that DA release when contacting their partner mouse increases with days of cohabitation while this increase is not observed when contacting a stranger mouse. Similar effects are found in D1-MSNs and D2-MSNs with the D1-MSN responses increasing and D2-MSN responses decreasing with days of cohabitation. They then use slice physiology to identify underlying plasticity/adaptation mechanisms that could contribute to the changes in D1/D2-MSN responses. Last, to address causality the authors use chemogenetic tools to selectively inhibit or activate NAc shell D1 or D2 neurons that project to the ventral pallidum. They found that D2 inhibition facilitates bond formation while D2 excitation inhibits bond formation. In contrast, both D1-MSN activation and inhibition inhibit bond formation.

Strengths:

The strength of the manuscript lies in combining in vivo physiology to demonstrate circuit engagement and chemogenetic manipulation studies to address circuit involvement in pair bond formation in a monogamous vole.

Weaknesses:

Weaknesses include that a large set of experiments within the manuscript are dependent on using short promoters for D1 and D2 receptors in viral vectors. As the authors acknowledge this approach can lead to ectopic expression and the presented immunohistochemistry supports this notion. It seems to me that the presented quantification underestimates the degree of ectopic expression that is observed by eye when looking at the presented immunohistochemistry. However, given that Cre transgenic animals are not available for Microtus mandarinus and given the distinct physiological and behavioral outcomes when imaging and manipulating both viral-targeted populations this concern is minor.

The slice physiology experiments provide some interesting outcomes but it is unclear how they can be linked to the in vivo physiological outcomes and some of the outcomes don't match intuitively (e.g. cohabitation enhances excitatory/inhibitory balance in D2-MSNs but the degree of contact-induced inhibition is enhanced in D2-MSN).

One interesting finding is that the relationship between D2-MSN and pair bond formation is quite clear (inhibition facilitates while excitation inhibits pair bond formation). In contrast, the role of D1-MSNs is more complicated since both excitation and inhibition disrupt pair bond formation. This is not convincingly discussed.

It seemed a missed opportunity that physiological readout is limited to males. I understand though that adding females may be beyond the scope of this manuscript.

Reviewer #3 (Public review):

Summary:

The manuscript is evaluating changes in dopamine signaling in the nucleus accumbens following pair bonding and exposure to various stimuli in mandarin voles. In addition, the authors present chemogenetic data that demonstrate excitation and inhibition of D1 and D2 MSN affect pair bond formation.

Strengths:

The experimental designs are strong. The approaches are innovative and use cutting-edge methods. The manuscript is well written.

Weaknesses:

The statistical results are not presented, and not all statistical analyses are appropriate. Additionally, some details of methods are absent.

An interesting point that was made was the fact that the developers have to put constraints on the data and the fields that can be used. They do this to fit their specific target audience, but it is also interesting that people who do not fit these data constraints must find ways to make themselves fit in in order to use social media apps. Developers should prioritize inclusiveness more.

I found that the protesting donkey is a perfect analogy for the situation with bots. It's hard to punish the protesting donkeys, who are, in this case, the people using the bots that were programmed by programmers for using bots that they don't understand the purpose of.

The United States was and still is a major global economic superpower. The American Great Depression was so extreme, it did more than just damage the United States.

Did the beginning of the mass consumerism era start when the industrial revolution started to take off? How is American consumer culture different today than it was when it started?

How did this affect the way that women were viewed through the lens of misogyny? Did this help shatter the barrier between what was considered traditionally male and traditionally female?

Religion was also used to justify and bring the need for a reform. Using Christianity, the religion the country and government was based on

Publication and reports brought more attention to the situation causing the government and people to take action.

The New Deal did little to exercise rights to African Americans

I also agree with this statement. Because without this knowledge, we tend to undervalue the effort required to advance existing technology into something more modern and improved.

Der kurz vor der COP16 zur Biodiversität veröffentlichte Living Planet Index zeigt das Ausmaß des Biodiversitätsverlusts in den vergangenen 50 Jahren, auch wenn an den dabei angewendeten statistischen Verfahren starke Zweifel bestehen. Die Wirbeltier-Populationen haben nach diesem Index um 73% abgenommen, am stärksten in Lateinamerika und der Karibik. Die wichtigste Ursache ist die veränderte Landnutzung. https://www.theguardian.com/environment/2024/oct/10/collapsing-wildlife-populations-points-no-return-living-planet-report-wwf-zsl-warns

The Time I Met New York’s Patron Saint of Typewriters by [[Thaisa Frank]] March 12, 2019

Stanley Adelman of Osner's Typewriter Repair in New York

(closed in 2001)

Transparent Peer Review

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This provides another example of the perspective that was commonly held by missionaries; that they were saving souls by converting them.

Question #1. Where is the location of the study and why do you think there are complex human dimension of wildlife conflicts higher than other locations in North America? - As we read the article we can learn that the location of the study is in a 23,700km^2 area of southwestern Alberta and was bounded by the HighWood River to the north, British Columbia to the west, and Montana to the South. In my opinion, I think that there are complex human dimensions of wildlife conflict higher here than in other locations in North America due to a over population that lack of protective barriers between humans and wildlife areas and the increase in urban zones or cities. What do you think could be a good solving solution to this problem between wildlife and humans?

Personal Question: How would this data change if we compared these species in a more remote ares with less human involvement vs an area that is frequently populated with people such as the Lake Tahoe area?

Question 2:

According to this paper, bears and wolves have very different diets. Bears are considered omnivores and will eat both vegetation and meat depending on the season and what is available. They also feed on food that has been set out by humans such as bird feed and garbage. Wolves on the other hand are strictly carnivorous and do not feed on vegetation.

Satirical

siehe 10.1038/d41586-022-04412-x

Argumente für die Verbindung von ecological economics, Dekolonisierung, degrowth, notwendiger ökologischer Transformation und Bioregionalismus. Der verbindende Faktor ist das Wachstum der Wirtschaft in den High Income Countries durch Ausbeutung von global verteilten ökologischen Ressourcen.

idk much about storing artifacts but I don't think it should be stored in a cardboard box.

What are the most important of the Quakers' beliefs?

Cite your sources.

What were these challenges?

Cite your sources.

Explain this chain! British tobacco merchants sold to Russia, yet it expanded the US market. How did this work?

Cite your sources.

Describe how and when industriallization took place in each country, Russia and the US.

Cite your sources.

When did slavery end in Russia and in the US? In what ways was the emancipation of slaves similar and different in each country?

Cite your sources.

How do these compare? How big (square miles) was the Russian Empire in the 18th Century? What about the United States?

Cite your sources.

an instruction that tells the computer to perform a specific action

This demonstrates the concept that we discussed in class that these missionaries thought of them selves as being sent by god to save the "savages". They genuinely thought that converting to Catholicism was the best thing that could be done for Indigenous peoples. This particular passage demonstrates the extremes that missionaries were willing to endure in order to achieve this goal.

De Paris references two other missionaries who had great success in converting many Indigenous people and highlights a key feature of both of these men; the fact that they could converse with the Indigenous peoples that they worked with. Having learned the language likely facilitated their abilities to gain trust and form relationships with these communities which likely enabled them to convert more people.

The figure title is too long. It simply needs to describe that the graph shows the relationship between Concentration of Sugar and Density.

An appropriate title would be "Relationship Between Sugar Concentration and Density"

There should have either been a tiny more explanation or just a period there. "...beaker. The..."

I don't think this is needed. The method should only state what you used.

Should specify the volume

Probably shouldnt say the 10 mL pipette measured out 10mL of fluid. just say itonce.

Reader does not have access to the lab packet.

The volume was in milliliters (mL).

The words longer than three letters should be capitalized.

It should be "Density of Solids and Liquids"

The type of balance used isn't specified.

There doesn't need to be a sub header introducing each step. Especially if the sub header is introducing new materials. It is better to just say, the 50mL beaker was filled with solution B.

"the volumetric pipette was filled with 10mL"

This is not passive voice because the author was pointing out a specified graph.

Unneeded. This could have started as: "The 50mL beaker was measured by an electronic balance."

What was its volume? What type of cylinder was it?

Present tense.

This should be passive voice.

This should have gone into the introduction section.

This is incorrect, as the students should have used the difference between the final volume and the initial volume, to get the volume of the rod.

I can research the #Enough campaign more

he receives constant backlash no matter the result

showcases the shift in just a few years and how important social media has been made

this is coming from arguably the best manager in the world

popular with the younger athletes

this can't be healthy for mental health

the fact that it's unfiltered interactions is probably the most important part here

observe the numbers on the angles are multiples of 30

Yes, I should have mentioned that.

need to flatten image; caption not quite matches the content, it is not a PtTe2 image, it is an image of a PtTe2 flake on top of a substrate.

Looking at the fit; you can see that it is a very poor fit, so the correlation length must be much less known. It is the average particle size, but here you only have one particle (flake), so the error would be quite large.

nanoAFMr is the correct spelling; needs citation!

This is not the topography, but the error image.

not needed in the report; again in the notebook, but in the report those details are "hidden" in the code portion.

Sample name is good for notebooks; however, in the report, we rename the sample usually to something simpler, such as the gold sample, etc.

s, also use 333.2 kHz (note the spacing)

Didn't know that all previous treaties were just ignored.

Didn't know there was military alliances with tribes during the colonial period

The calibration standard was determined by the class and it should not be mentioned where the data came from just how it was obtained

This part should be in past tense

This information could already have been interpreted from Figure 1

This isn't in past tense, and its also in a step by step process. Should be restated at "The steps are then repeated."

This title isn't the most formal, doesn't quite make sense.

This should be stated before adding the metal rod in.

the x value should be % sucrose

The graph should go in the R+D Section.

This is an equation disguised as a method, and thus should have gone into the introduction section.

You shouldn't be less specific. You should say something like, "The graduated cylinder was filled half way."

This should have gone in the introduction section, with the rest of the equations used in the lab.

Does not explain the measurement the "m" value is using

Still not passive voice

Not in passive voice.

should be in the passive voice

use the passive voice

Past tense, equation needs proper format and check grammar.

the end of sentence is repeated and can just be said with "Then the pipette was filled up to 10mL of 2% sugar." or some other replaceable sentence and remove first part.

Needs to be in past tense. Also use actual equation set up.

It goes from steps back to past tense. It doesn't entirely make sense given that this should all be in past tense and not in a step by step format like how this sentence describes. The first sentence is in present tense to begin with, instead it should be "The mass of the beaker was then recorded using a balance."

This should be written in past tense.

The methods section should be in the past tense. This section is in the present tense.

This section is in the present tense and should be in the past tense.

Nothing should "need" to be found out, the methods section should state that it was found and how,

these equations should be formatted rather than just written in regular text.

These sentences are not all complete sentences and not in passive tense.

nooo

?///

fire names

My initial reaction to this description of the infant was definitely shocking, and made me think more about societies standards for people because of the animal like description for this human. Also, looking closer at the second part of the sentence, the infant seems to feel free but terrified.

Pow wows are not religious in nature.

Great Sections for the paper.

The author is trying to convey the harsh reality of what injustice does to a person, and he is criticizing how many of his time do not see this suffering or choose to ignore it.

This quote conveys realism by showing the range of human emotions such a verdict would cause.

Possibly criticizing the Southern justice system during of time.

The author is implying a strong southern using dialect to implement realism values.

It's paradoxical how people with disabilities are barred from designing systems that are meant to support them. The stigma of people with disabilities as people who are lesser than, or less capable of designing is an outdated notion. Designing for the disabled population from a purely able bodied perspective is a myopic view of the issue, and is bound to overlook issues.

I don't understand quite yet how perverse incentives are avoided. All the author mentions is that tasks can be "tracked, weighted and exposed", but how, and will that be enough? What's the incentive to track other tasks? Aren't we already all too busy with our own tasks anyways? It depends a lot on how those tasks are actually created, who will have input and control etc...

The flaw of many systems both in technology and in business supply chains is the human aspect. While technical and cybersecurity systems become more and more effective year by year, the vulnerabilities in human behavior is usually the weak link.

I think there are a handful of incentives that social media companies have to protect privacy. If a company offers good privacy to its users, they will respect the company more and are more likely to use it. Also if the company protects privacy there is less chance of them having to pay fines for violating people's privacy.

My biggest concern regarding privacy on social media and the internet in general is when people's actions shift from offline to online. This is when people's actions on the internet and in real life get muddied and it becomes really dangerous and difficult to prosecute.

Ensuring the integrity of datasets is vital to ensure their applications are useful. An issue presented in this case where the population taking such surveys overrepresents a certain consumer segment means that the output of any model that uses such survey data wouldn't be representative of the wider population.

Social media sites are financially incentivized to collect our data to sell to advertised. However, this has some unintended consequences such as targeting gambling ads towards children and perpetuating addictions. Consumer rights regarding data is paramount to ensure such practices are used ethically.

The patron saint of typewriter collectors by [[Richard Polt]]

First of all this is hilarious. However, this does veer near vigilantism which raises some ethical questions. At what point should individuals/unregulated groups take justice into their own hands. The issue arises in who gets to define what justice is and is not.

All though these activities are trolling-adjacent I think there something special about truly trolling somebody on the internet. The fact that they don't know a thing about you and there's nothing about it is the cherry on top, which can never be recreated offline.

The idea that inauthentic interaction is still valuable is an interesting concept. The entire concept of acting and theater is predicated on the paradoxical concept of authentic inauthenticity. The basis of a medium of artistic expression is the imitation of authenticity, yet it is this basis that allows them to connect authentically with the audience.

The racism and hate were so extreme that many deaths and many lives lost. The suggestion that this was an organized attack just go to show how much hate they had.

Equality was in the grasp but still the was the impression that women are only useful in the home.

This decade allowed for women to do more things instead of just housework. Equality seemed more and more attainable in this decade as women had more opportunities just as the men.

Sports has a way of changing the focus on to only the sport and its outcome. It was a new form of entertainment and a way that different peoples can come together.

Most people aren't ready for a change and like things to stay the way they are, but I think people have to accept that life is always changing.

Harding actions for the wealthy to pay less taxes only made those who were not pay more to make up for it. Harding claimed to bring America back to normalcy but only pushed further away.

I know this is contentious territory in this group, but the relationship between "natural" and "ethical" is complicated. There are many things that are natural but unethical. The real question is: what are the criteria that we might use to judge natural things as being ethical or unethical? Singer seems to have some.

I see the comparison here, and think that there is merit to it. The "outsourcing" point is particularly strong--i.e. it is tempting to outsource moral actions to simplified optimization functions, which entice us to jump to conclusions about what is good. The question, though, is whether or not Singer encourages this. That is a tougher case. My sense is that Singer would NOT explicitly endorse that kind of behavior, even if he DOES implicitly endorse it, as a way to nudge people to become "more moral than they might otherwise be" according to his tastes.

Important point. People, especially those who are not philosophers, outsource their moral decision-making and opinion-forming to their "cave" of opinion, and moral habits formed therein.

Calling this out as the thrust of the piece. Is Rudy able to defend this point? (FWIW seems like it's still an interesting argument, even if the neolib/lib distinction is not make perfectly clear.)

AI drawbacks and issues

AI use described

Problem proposed and AI as the solution.

This is a predominate view of language even in anglophone Africa. I can confidently speak at least to my experience as a Ghanaian. Up until university, I was taught to aspire to a native-like (standard English) proficiency in speaking and writing which also equates to white-English. As an English major and a sociolinguist, I learned to deconstruct that notion. My class room experiences helped me challenge that supposed ideal of reaching 'native English' = 'Standard English'.

Doesn't UW have something like this? What do such policies mean? Are they in contrast to the translingual approach to pedagogy that we have been preaching?

This brings me to ask, 'Does UW have an explicit language policy? As instructors, how do we acknowledge the linguistic diversity in our classes and teaching materials?

Three sentences on what I just read,

I believe that BAP is aware of his place in history just like I assume Yuval does. Both authors are not writing as bots recording facts but provide a specialized idological prism to see our current civilization through. Each prisim has its own take, Yuval trying to articulate what the Silicon Valley "Open Conspiracy" types dream of. BAP trying to remind us what it was like to actually be part of history.

The "Why don't CEO's have Harrem's" is the one thing here that is going to stick with me. The women of today are so different from the past. In fact maybe the "Brave New World" reality of nameless sex with strangers and nude play as children is a more desireable world to the Incel, video game, monster can drinking of today.

It seems like the Puritan/Christian sexual practices have really shaped the west into becoming what it is today. I believe most men get their sense of identity from the type of sex they have access to. What's weird about modernity is that we are talking about sex rahter than women with the inherent consideration of children.

This population collapse, as articulated on the Georga Guide Stones, is going to be interesting.

What is the Opposite of "The Open Conspiracy"

I think this is why Curtis Yarvin's first instruction in Gray Mirror is to detach

• (9) #1: a general theory of collaboration - by Curtis Yarvin

I think we could use more "Bronce Age Mindset" right now. But if every other philsopher was promoting that and not just Bap and his little cult +Andrew Tate I agree we might have a problem.

Philosphers are a type of Tyrant?

They create memetic prisons that design people's behaviors.

They stop people from acting instread requiring people to think and think and think

Ah philosophers are the origional beurocrats

I wonder what Greek Philosophers would think of Ayn Rand Objectivism

A defensive use for cybersecurity from ChatGPT

A method of bypassing ChatGPT

Jailbreaking method

Jailbreaking ChatGPT to gain greater control

Defense

It’s the first study with enough data to do this kind of comparison, and it offers strong evidence about the factors that influence people’s chances of moving beyond the economic status they were born into. Some of the key factors include education, family structure, and the economic setup of the cities people live in.

This is a simile because there is a comparison God and him.

This is pathos and personification.

Connect Cephalus’s discussion of the value of wealth (329e ff. (Greek)) with Aristotle’s comments about the connection between wealth and happiness (eudaimonia) (NE I.4 1095a18-26 (Greek), I.5 1096a5-7 (Greek), I.8 1099a31-1099b9 (Greek), I.9 1099b26-29 (Greek)). (Christiana Olfert)

Sounds like we should be teaching people to skip the middleman (middle bot?) and just go to the “reliable sources,” then, huh?

It's also interesting to know that the first ever version of the internet was just a connection of multiple computers at first and then evolved into this network where users could only read, to now doing so many stuff with it.