Learning attentional templates for value-based decision-making.

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

Smart active particles learn and transcend bacterial foraging strategies.

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.

Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty.

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design.

Recent advances in cancer immunotherapy have highlighted the potential of neoantigen-based vaccines. However, the design of such vaccines is hindered by the possibility of weak binding affinity between the peptides and the patient's specific human leukocyte antigen (HLA) alleles, which may not elicit a robust adaptive immune response. Triggering cross-immunity by utilizing peptide mutations that have enhanced binding affinity to target HLA molecules, while preserving their homology with the original one, can be a promising avenue for neoantigen vaccine design. In this study, we introduced UltraMutate, a novel algorithm that combines Reinforcement Learning and Monte Carlo Tree Search, which identifies peptide mutations that not only exhibit enhanced binding affinities to target HLA molecules but also retains a high degree of homology with the original neoantigen. UltraMutate outperformed existing state-of-the-art methods in identifying affinity-enhancing mutations in an independent test set consisting of 3660 peptide-HLA pairs. UltraMutate further showed its applicability in the design of peptide vaccines for Human Papillomavirus and Human Cytomegalovirus, demonstrating its potential as a promising tool in the advancement of personalized immunotherapy.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Predicting Cued and Oddball Visual Search Performance from fMRI, MEG, and DNN Neural Representational Similarity.

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing.

Value-Based Search Efficiency Is Encoded in the Substantia Nigra Reticulata Firing Rate, Spiking Irregularity and Local Field Potential.

Recent results show that valuable objects can pop out in visual search, yet its neural mechanisms remain unexplored. Given the role of substantia nigra reticulata (SNr) in object value memory and control of gaze, we recorded its single-unit activity while male macaque monkeys engaged in efficient or inefficient search for a valuable target object among low-value objects. The results showed that efficient search was concurrent with stronger inhibition and higher spiking irregularity in the target-present (TP) compared with the target-absent (TA) trials in SNr. Importantly, the firing rate differentiation of TP and TA trials happened within ∼100 ms of display onset, and its magnitude was significantly correlated with the search times and slopes (search efficiency). Time-frequency analyses of local field potential (LFP) after display onset revealed significant modulations of the gamma band power with search efficiency. The greater reduction of SNr firing in TP trials in efficient search can create a stronger disinhibition of downstream superior colliculus, which in turn can facilitate saccade to obtain valuable targets in competitive environments.

Neurochemical Predictors of Generalized Learning Induced by Brain Stimulation and Training.

Methods of cognitive enhancement for humans are most impactful when they generalize across tasks. However, the extent to which such "transfer" is possible via interventions is widely debated. In addition, the contribution of excitatory and inhibitory processes to such transfer is unknown. Here, in a large-scale neuroimaging individual differences study with humans (both sexes), we paired multitasking training and noninvasive brain stimulation (transcranial direct current stimulation, tDCS) over multiple days and assessed performance across a range of paradigms. In addition, we varied tDCS dosage (1.0 and 2.0 mA), electrode montage (left or right prefrontal regions), and training task (multitasking vs a control task) and assessed GABA and glutamate concentrations via ultrahigh field 7T magnetic resonance spectroscopy. Generalized benefits were observed in spatial attention, indexed by visual search performance, when multitasking training was combined with 1.0 mA stimulation targeting either the left or right prefrontal cortex (PFC). This transfer effect persisted for ∼30 d post intervention. Critically, the transferred benefits associated with right prefrontal tDCS were predicted by pretraining concentrations of glutamate in the PFC. Thus, the effects of this combined stimulation and training protocol appear to be linked predominantly to excitatory brain processes.

Tracking the Misallocation and Reallocation of Spatial Attention toward Auditory Stimuli.

Completely ignoring a salient distractor presented concurrently with a target is difficult, and sometimes attention is involuntarily attracted to the distractor's location (attentional capture). Employing the N2ac component as a marker of attention allocation toward sounds, in this study we investigate the spatiotemporal dynamics of auditory attention across two experiments. Human participants (male and female) performed an auditory search task, where the target was accompanied by a distractor in two-third of the trials. For a distractor more salient than the target (Experiment 1), we observe not only a distractor N2ac (indicating attentional capture) but the full chain of attentional dynamics implied by the notion of attentional capture, namely, (1) the distractor captures attention before the target is attended, (2) allocation of attention to the target is delayed by distractor presence, and (3) the target is attended after the distractor. Conversely, for a distractor less salient than the target (Experiment 2), although responses were delayed, no attentional capture was observed. Together, these findings reveal two types of spatial attentional dynamics in the auditory modality (distraction with and without attentional capture).

The influence of the number of tree searches on maximum likelihood inference in phylogenomics.

Maximum likelihood (ML) phylogenetic inference is widely used in phylogenomics. As heuristic searches most likely find suboptimal trees, it is recommended to conduct multiple (e.g., ten) tree searches in phylogenetic analyses. However, beyond its positive role, how and to what extent multiple tree searches aid ML phylogenetic inference remains poorly explored. Here, we found that a random starting tree was not as effective as the BioNJ and parsimony starting trees in inferring ML gene tree and that RAxML-NG and PhyML were less sensitive to different starting trees than IQ-TREE. We then examined the effect of the number of tree searches on ML tree inference with IQ-TREE and RAxML-NG, by running 100 tree searches on 19,414 gene alignments from 15 animal, plant, and fungal phylogenomic datasets. We found that the number of tree searches substantially impacted the recovery of the best-of-100 ML gene tree topology among 100 searches for a given ML program. In addition, all of the concatenation-based trees were topologically identical if the number of tree searches was ≥ 10. Quartet-based ASTRAL trees inferred from 1 to 80 tree searches differed topologically from those inferred from 100 tree searches for 6 /15 phylogenomic datasets. Lastly, our simulations showed that gene alignments with lower difficulty scores had a higher chance of finding the best-of-100 gene tree topology and were more likely to yield the correct trees.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Top-down modulation of DLPFC in visual search: a study based on fMRI and TMS.

Effective visual search is essential for daily life, and attention orientation as well as inhibition of return play a significant role in visual search. Researches have established the involvement of dorsolateral prefrontal cortex in cognitive control during selective attention. However, neural evidence regarding dorsolateral prefrontal cortex modulates inhibition of return in visual search is still insufficient. In this study, we employed event-related functional magnetic resonance imaging and dynamic causal modeling to develop modulation models for two types of visual search tasks. In the region of interest analyses, we found that the right dorsolateral prefrontal cortex and temporoparietal junction were selectively activated in the main effect of search type. Dynamic causal modeling results indicated that temporoparietal junction received sensory inputs and only dorsolateral prefrontal cortex →temporoparietal junction connection was modulated in serial search. Such neural modulation presents a significant positive correlation with behavioral reaction time. Furthermore, theta burst stimulation via transcranial magnetic stimulation was utilized to modulate the dorsolateral prefrontal cortex region, resulting in the disappearance of the inhibition of return effect during serial search after receiving continuous theta burst stimulation. Our findings provide a new line of causal evidence that the top-down modulation by dorsolateral prefrontal cortex influences the inhibition of return effect during serial search possibly through the retention of inhibitory tagging via working memory storage.

Visuospatial processing in early brain-based visual impairment is associated with differential recruitment of dorsal and ventral visual streams.

Visuospatial processing impairments are prevalent in individuals with cerebral visual impairment (CVI) and are typically ascribed to "dorsal stream dysfunction" (DSD). However, the contribution of other cortical regions, including early visual cortex (EVC), frontal cortex, or the ventral visual stream, to such impairments remains unknown. Thus, here, we examined fMRI activity in these regions, while individuals with CVI (and neurotypicals) performed a visual search task within a dynamic naturalistic scene. First, behavioral performance was measured with eye tracking. Participants were instructed to search and follow a walking human target. CVI participants took significantly longer to find the target, and their eye gaze patterns were less accurate and less precise. Second, we used the same task in the MRI scanner. Along the dorsal stream, activation was reduced in CVI participants, consistent with the proposed DSD in CVI. Intriguingly, however, visual areas along the ventral stream showed the complete opposite pattern, with greater activation in CVI participants. In contrast, we found no differences in either EVC or frontal cortex between groups. These results suggest that the impaired visuospatial processing abilities in CVI are associated with differential recruitment of the dorsal and ventral visual streams, likely resulting from impaired selective attention.

Neural and psychological correlates of post-traumatic stress symptoms in a community adult sample.

A multitude of factors are associated with the symptoms of post-traumatic stress disorder. However, establishing which predictors are most strongly associated with post-traumatic stress disorder symptoms is complicated because few studies are able to consider multiple factors simultaneously across the biopsychosocial domains that are implicated by existing theoretical models. Further, post-traumatic stress disorder is heterogeneous, and studies using case-control designs may obscure which factors relate uniquely to symptom dimensions. Here we used Bayesian variable selection to identify the most important predictors for overall post-traumatic stress disorder symptoms and individual symptom dimensions in a community sample of 569 adults (18 to 85 yr of age). Candidate predictors were selected from previously established risk factors relevant for post-traumatic stress disorder and included psychological measures, behavioral measures, and resting state functional connectivity among brain regions. In a follow-up analysis, we compared results controlling for current depression symptoms in order to examine specificity. Poor sleep quality and dimensions of temperament and impulsivity were consistently associated with greater post-traumatic stress disorder symptom severity. In addition to self-report measures, brain functional connectivity among regions commonly ascribed to the default mode network, central executive network, and salience network explained the unique variability of post-traumatic stress disorder symptoms. This study demonstrates the unique contributions of psychological measures and neural substrates to post-traumatic stress disorder symptoms.

Unveiling the Morphology of Carbon-Supported Ru Nanoparticles by Multiscale Modeling.

Simulating the behavior of metal nanoparticles on supports is crucial for boosting their catalytic performance and various nanotechnology applications; however, such simulations are limited by the conflicts between accuracy and efficiency. Herein, we introduce a multiscale modeling strategy to unveil the morphology of Ru supported on pristine and N-doped graphene. Our multiscale modeling started with the electronic structures of a supported Ru single atom, revealing the strong metal-support interaction around pyridinic nitrogen sites. To determine the stable configurations of Ru2-13 clusters on three different graphene supports, global energy minimum searches were performed. The sintering of the global minimum Ru13 clusters on supports was further simulated by ab initio molecular dynamics (AIMD). The AIMD data set was then collected for deep potential molecular dynamics to study the melting of Ru nanoparticles. This study presents comprehensive descriptions of carbon-supported Ru and develops modeling approaches that bridge different scales and can be applied to various supported nanoparticle systems.

VAIV bio-discovery service using transformer model and retrieval augmented generation.

BACKGROUND: There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery. MAIN BODY: We propose a novel biomedical neural search service called 'VAIV Bio-Discovery', which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25. CONCLUSION: As a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

Quantifying the Impact of the Peptide Identification Framework on the Results of Fast Photochemical Oxidation of Protein Analysis.

Fast Photochemical Oxidation of Proteins (FPOP) is a promising technique for studying protein structure and dynamics. The quality of insight provided by FPOP depends on the reliability of the determination of the modification site. This study investigates the performance of two search engines, Mascot and PEAKS, for the data processing of FPOP analyses. Comparison of Mascot and PEAKS of the hemoglobin--haptoglobin Bruker timsTOF data set (PXD021621) revealed greater consistency in the Mascot identification of modified peptides, with around 26% of the IDs being mutual for all three replicates, compared to approximately 22% for PEAKS. The intersection between Mascot and PEAKS results revealed a limited number (31%) of shared modified peptides. Principal Component Analysis (PCA) using the peptide-spectrum match (PSM) score, site probability, and peptide intensity was applied to evaluate the results, and the analyses revealed distinct clusters of modified peptides. Mascot showed the ability to assess confident site determination, even with lower PSM scores. However, high PSM scores from PEAKS did not guarantee a reliable determination of the modification site. Fragmentation coverage of the modification position played a crucial role in Mascot assignments, while the AScore localizations from PEAKS often become ambiguous because the software employs MS/MS merging.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis.

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.