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The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.

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Randomness is a fundamental property of human behavior. It occurs both in the form of intrinsic random variability, say when repetitions of a task yield slightly different behavioral outcomes, or in the form of explicit randomness, say when a person tries to avoid being predicted in a game of rock, paper and scissors. Randomness has frequently been studied using random sequence generation tasks (RSG). A key finding has been that humans are poor at deliberately producing random behavior. At the same time, it has been shown that people might be better randomizers if randomness is only an implicit (rather than an explicit) requirement of the task. We therefore hypothesized that randomization performance might vary with the exact instructions with which randomness is elicited. To test this, we acquired data from a large online sample (n = 388), where every participant made 1,000 binary choices based on one of the following instructions: choose either randomly, freely, irregularly, according to an imaginary coin toss or perform a perceptual guessing task. Our results show significant differences in randomness between the conditions as quantified by conditional entropy and estimated Markov order. The randomization scores were highest in the conditions where people were asked to be irregular or mentally simulate a random event (coin toss) thus yielding recommendations for future studies on randomization behavior.

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Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution, and structure-based measures. For large protein families, our synthetic sequences have similar or better properties compared to sequences generated by Potts models, including experimentally validated ones. Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models. Our method also more accurately reproduces the higher-order statistics and the distribution of sequences in sequence space of natural data than Potts models. MSA Transformer is thus a strong candidate for protein sequence generation and protein design.

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Molecule generation is the procedure to generate initial novel molecule proposals for molecule design. Molecules are first projected into continuous vectors in chemical latent space, and then, these embedding vectors are decoded into molecules under the variational autoencoder (VAE) framework. The continuous latent space of VAE can be utilized to generate novel molecules with desired chemical properties and further optimize the desired chemical properties of molecules. However, there is a posterior collapse problem with the conventional recurrent neural network-based VAEs for the molecule sequence generation, which deteriorates the generation performance. We investigate the posterior collapse problem and find that the underestimated reconstruction loss is the main factor in the posterior collapse problem in molecule sequence generation. To support our conclusion, we present both analytical and experimental evidence. What is more, we propose an efficient and effective solution to fix the problem and prevent posterior collapse. As a result, our method achieves competitive reconstruction accuracy and validity score on the benchmark data sets.

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We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that the hippocampal generative model is endowed with inductive biases to identify individual items of experience (first hierarchical layer), organize them into sequences (second layer) and cluster them into maps (third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, which supports the continual learning of multiple sequential experiences. We show that the model learns and efficiently retains multiple spatial navigation trajectories, by organizing them into spatial maps. Furthermore, the model reproduces flexible and prospective aspects of hippocampal dynamics that are challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination.

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The flexible escape behavior exhibited by C. elegans in response to threats relies on a combination of feedback and feedforward circuits.

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Complex animal behaviors arise from a flexible combination of stereotyped motor primitives. Here we use the escape responses of the nematode Caenorhabditis elegans to study how a nervous system dynamically explores the action space. The initiation of the escape responses is predictable: the animal moves away from a potential threat, a mechanical or thermal stimulus. But the motor sequence and the timing that follow are variable. We report that a feedforward excitation between neurons encoding distinct motor states underlies robust motor sequence generation, while mutual inhibition between these neurons controls the flexibility of timing in a motor sequence. Electrical synapses contribute to feedforward coupling whereas glutamatergic synapses contribute to inhibition. We conclude that C. elegans generates robust and flexible motor sequences by combining an excitatory coupling and a winner-take-all operation via mutual inhibition between motor modules.

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What would it mean to explain the mind in neural terms? Neural accounts of the mind are often sought in a reductionistic spirit in which neural mechanisms explain cognition. Because an individual's thoughts and behaviors are not reproducible without careful control of task, stimulus, and behavioral history, laws of the mind are the currency of psychology. Reduction may thus have to take the form familiar from physics: deriving macroscopic laws from microscopic laws. I argue that the metaphor of reduction from non-equilibrium physics may be the most appropriate. Macroscopic patterns of neural activity, which cause behavior and thought, are slow dynamical variables that dominate the fast microscopic dynamics of individual neurons and synapses. I outline a theoretical framework in which strongly recurrent neural networks, described by neural dynamics, generate neural representations as attractor states that are embedded in low-dimensional feature spaces. Instabilities of these states are instrumental in decision-making and the generation of sequences of mental states that are the basis for higher cognition. Networks of such neural population dynamics form neural cognitive architectures that capture the laws of the mind.

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Neurally inspired robotics already has a long history that includes reactive systems emulating reflexes, neural oscillators to generate movement patterns, and neural networks as trainable filters for high-dimensional sensory information. Neural inspiration has been less successful at the level of cognition. Decision-making, planning, building and using memories, for instance, are more often addressed in terms of computational algorithms than through neural process models. To move neural process models beyond reactive behavior toward cognition, the capacity to autonomously generate sequences of processing steps is critical. We review a potential solution to this problem that is based on strongly recurrent neural networks described as neural dynamic systems. Their stable states perform elementary motor or cognitive functions while coupled to sensory inputs. The state of the neural dynamics transitions to a new motor or cognitive function when a previously stable neural state becomes unstable. Only when a neural robotic system is capable of acting autonomously does it become a useful to a human user. We demonstrate how a neural dynamic architecture that supports autonomous sequence generation can engage in such interaction. A human user presents colored objects to the robot in a particular order, thus defining a serial order of color concepts. The user then exposes the system to a visual scene that contains the colored objects in a new spatial arrangement. The robot autonomously builds a scene representation by sequentially bringing objects into the attentional foreground. Scene memory updates if the scene changes. The robot performs visual search and then reaches for the objects in the instructed serial order. In doing so, the robot generalizes across time and space, is capable of waiting when an element is missing, and updates its action plans online when the scene changes. The entire flow of behavior emerges from a time-continuous neural dynamics without any controlling or supervisory algorithm.

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BACKGROUND: Assessing the risk of bias (RoB) in included studies is one of the key methodological aspects of systematic reviews. Cochrane systematic reviews appraise RoB of randomised controlled trials (RCTs) with the Cochrane RoB tool. Detailed instructions for using the Cochrane RoB tool are provided in the Cochrane Handbook for Systematic Reviews of Interventions (The Cochrane Handbook). The purpose of this study was to analyse whether Cochrane authors use adequate judgments about the RoB for random sequence generation of RCTs included in Cochrane reviews. METHODS: We extracted authors' judgments (high, low or unclear RoB) and supports for judgments (comments accompanying judgments which explain the rationale for a judgment) for random sequence generation of included RCTs from RoB tables of Cochrane reviews using automated data scraping. We categorised all supporting comments, analysed the number and type of various supporting comments and assessed adequacy of RoB judgment for randomisation in line with recommendations from the Cochrane Handbook. RESULTS: We analysed 10,103 RCTs that were included in 704 Cochrane reviews. For 5,706 RCTs, randomisation was not described, but for the remaining RCTs, it was indicated that randomisation was performed using computer/software/internet (N = 2,850), random number table (N = 883), mechanical method (N = 359) or it was incomplete/inappropriate (N = 305). Overall, 1,220/10,103 trials (12%) did not have a RoB judgment in line with Cochrane Handbook guidance about randomisation. The highest proportion of misjudgements was found for trials with high RoB (28%), followed by those with low (20%) or unclear (3%). Therefore, one in eight judgments for the analysed domain in Cochrane reviews was not in line with Cochrane Handbook, and one in four if the judgment was "high risk". CONCLUSION: Authors of Cochrane reviews often make judgments about the RoB related to random sequence generation that are not in line with instructions given in the Cochrane Handbook, which compromises the reliability of the systematic reviews. Our results can help authors of both Cochrane and non-Cochrane reviews which use Cochrane RoB tool to avoid making common mistakes when assessing RoB in included trials.

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DNA nanotechnology enables the design and assembly of DNA nanostructures with unprecedented control over their size and shape. Additionally, the programmable base-pairing alphabet of DNA allows the incorporation of responsive units within these DNA nanostructures. Here, we describe a general design strategy to construct responsive DNA prisms that can encapsulate and selectively release an encapsulated siRNA upon recognition of an oligonucleotide trigger. This prismatic DNA scaffold design is adaptable and can encapsulate oligonucleotides of any length and type. Moreover, the prism can be made to respond to an oligonucleotide trigger of interest like a messenger RNA (mRNA) or a microRNA (miRNA), thus enabling dual or synergistic therapeutic strategies. We present an overview of the design strategy used to access these DNA nanostructures, followed by the steps involved in DNA sequence generation, assembly, and validation of this construct.

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BACKGROUND: Systematic reviews of randomised trials guide policy and healthcare decisions. Yet, we observed that some reviews judge randomised trials as high or unclear risk of bias (ROB) for sequence generation, potentially introducing bias. However, to date, the extent of this issue has not been well examined. We evaluated the consistency in the ROB assessment for sequence generation of randomised trials in Cochrane and non-Cochrane reviews, and explored the reviewers' judgement of the quality of evidence for the related outcomes. METHODS: Cochrane intervention reviews (01/01/2017-31/03/2017) were retrieved from the Cochrane Database of Systematic Reviews. We also searched for systematic reviews in ten general medical journals with highest impact factors (01/01/2016-31/03/2017). We examined the proportion of reviews that rated the sequence generation domain as high, low or unclear risk of selection bias. For reviews that had rated any randomised trials as high or unclear risk of bias, we examined the proportion that had assessed the quality of evidence. RESULTS: Overall, 100 systematic reviews were included in our analysis. We evaluated 64 Cochrane reviews which comprised of 984 randomised trials; 0.8% (n = 8) and 52.2% (n = 514) were rated as high and unclear ROB for sequence generation respectively. We further evaluated 36 non-Cochrane reviews which comprised of 1376 trials; 5.8% (n = 80) and 39.6% (n = 545) were rated as high and unclear ROB respectively. Ninety percent (n = 10) of non-Cochrane reviews which rated randomised trials as high ROB for sequence generation did not report an underlying reason. All Cochrane reviews assessed the quality of evidence (GRADE). For the non-Cochrane reviews, only just over half had assessed the quality of evidence. CONCLUSION: Systematic reviews of interventions frequently rate randomised trials as high or unclear ROB for sequence generation. In general, Cochrane reviews were more transparent than non-Cochrane reviews in ROB and quality of evidence assessment. The scientific community should more strongly promote consistent ROB assessment for sequence generation to minimise selection bias and support transparent quality of evidence assessment. Consistency ensures that appropriate conclusions are drawn from the data.

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Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (robust nucleus of the arcopallium, or primary motor cortex) or Area X (basal ganglia), which play important roles in song production or song learning, respectively. In addition to these long-range outputs, HVC neurons also send local axon collaterals throughout that nucleus. Despite their implications for a range of circuit models, these local processes have never been completely reconstructed. Here, we use in vivo single-neuron Neurobiotin fills to examine 40 projection neurons across 31 birds with somatic positions distributed across HVC. We show that HVC(RA) and HVC(X) neurons have categorically distinct dendritic fields. Additionally, these cell classes send axon collaterals that are either restricted to a small portion of HVC ("local neurons") or broadly distributed throughout the entire nucleus ("broadcast neurons"). Overall, these processes within HVC offer a structural basis for significant local processing underlying behaviorally relevant population activity.

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OBJECTIVE: We investigated the associations between risk of bias judgments from Cochrane reviews for sequence generation, allocation concealment and blinding, and between-trial heterogeneity. STUDY DESIGN AND SETTING: Bayesian hierarchical models were fitted to binary data from 117 meta-analyses, to estimate the ratio λ by which heterogeneity changes for trials at high/unclear risk of bias compared with trials at low risk of bias. We estimated the proportion of between-trial heterogeneity in each meta-analysis that could be explained by the bias associated with specific design characteristics. RESULTS: Univariable analyses showed that heterogeneity variances were, on average, increased among trials at high/unclear risk of bias for sequence generation (λˆ 1.14, 95% interval: 0.57-2.30) and blinding (λˆ 1.74, 95% interval: 0.85-3.47). Trials at high/unclear risk of bias for allocation concealment were on average less heterogeneous (λˆ 0.75, 95% interval: 0.35-1.61). Multivariable analyses showed that a median of 37% (95% interval: 0-71%) heterogeneity variance could be explained by trials at high/unclear risk of bias for sequence generation, allocation concealment, and/or blinding. All 95% intervals for changes in heterogeneity were wide and included the null of no difference. CONCLUSION: Our interpretation of the results is limited by imprecise estimates. There is some indication that between-trial heterogeneity could be partially explained by reported design characteristics, and hence adjustment for bias could potentially improve accuracy of meta-analysis results.

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Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

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OBJECTIVE: To determine if adequacy of randomisation and allocation concealment is associated with changes in effect sizes (ES) when comparing physical therapy (PT) trials with and without these methodological characteristics. DESIGN: Meta-epidemiological study. PARTICIPANTS: A random sample of randomised controlled trials (RCTs) included in meta-analyses in the PT discipline were identified. INTERVENTION: Data extraction including assessments of random sequence generation and allocation concealment was conducted independently by two reviewers. To determine the association between sequence generation, and allocation concealment and ES, a two-level analysis was conducted using a meta-meta-analytic approach. PRIMARY AND SECONDARY OUTCOME MEASURES: association between random sequence generation and allocation concealment and ES in PT trials. RESULTS: 393 trials included in 43 meta-analyses, analysing 44,622 patients contributed to this study. Adequate random sequence generation and appropriate allocation concealment were accomplished in only 39.7% and 11.5% of PT trials, respectively. Although trials with inappropriate allocation concealment tended to have an overestimate treatment effect when compared with trials with adequate concealment of allocation, the difference was non-statistically significant (ES=0.12; 95% CI -0.06 to 0.30). When pooling our results with those of Nuesch et al, we obtained a pooled statistically significant value (ES=0.14; 95% CI 0.02 to 0.26). There was no difference in ES in trials with appropriate or inappropriate random sequence generation (ES=0.02; 95% CI -0.12 to 0.15). CONCLUSIONS: Our results suggest that when evaluating risk of bias of primary RCTs in PT area, systematic reviewers and clinicians implementing research into practice should pay attention to these biases since they could exaggerate treatment effects. Systematic reviewers should perform sensitivity analysis including trials with low risk of bias in these domains as primary analysis and/or in combination with less restrictive analyses. Authors and editors should make sure that allocation concealment and random sequence generation are properly reported in trial reports.

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Attention is the process of focusing mental resources on a specific cognitive/behavioral task. Such brain dynamics involves different partially overlapping brain functional networks whose interconnections change in time according to the performance stage, and can be stimulus-driven or induced by an intrinsically generated goal. The corresponding activity can be described by different families of spatiotemporal discrete patterns or sequential dynamic modes. Since mental resources are finite, attention modalities compete with each other at all levels of the hierarchy, from perception to decision making and behavior. Cognitive activity is a dynamical process and attention possesses some universal dynamical characteristics. Thus, it is time to apply nonlinear dynamical theory for the description and prediction of hierarchical attentional tasks. Such theory has to include the analyses of attentional control stability, the time cost of attention switching, the finite capacity of informational resources in the brain, and the normal and pathological bifurcations of attention sequential dynamics. In this paper we have integrated today's knowledge, models and results in these directions.

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Sensory feedback is crucial for learning and performing many behaviors, but its role in the execution of complex motor sequences is poorly understood. To address this, we consider the forebrain nucleus HVC in the songbird, which contains the premotor circuitry for song production and receives multiple convergent sensory inputs. During singing, projection neurons within HVC exhibit precisely timed synaptic events that may represent the ongoing motor program or song-related sensory feedback. To distinguish between these possibilities, we recorded the membrane potential from identified HVC projection neurons in singing zebra finches. External auditory perturbations during song production did not affect synaptic inputs in these neurons. Furthermore, the systematic removal of three sensory feedback streams (auditory, proprioceptive, and vagal) did not alter the frequency or temporal precision of synaptic activity observed. These findings support a motor origin for song-related synaptic events and suggest an updated circuit model for generating behavioral sequences.

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