Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

The CMG Helicase Bypasses DNA-Protein Cross-Links to Facilitate Their Repair.

Covalent DNA-protein cross-links (DPCs) impede replication fork progression and threaten genome integrity. Using Xenopus egg extracts, we previously showed that replication fork collision with DPCs causes their proteolysis, followed by translesion DNA synthesis. We show here that when DPC proteolysis is blocked, the replicative DNA helicase CMG (CDC45, MCM2-7, GINS), which travels on the leading strand template, bypasses an intact leading strand DPC. Single-molecule imaging reveals that GINS does not dissociate from CMG during bypass and that CMG slows dramatically after bypass, likely due to uncoupling from the stalled leading strand. The DNA helicase RTEL1 facilitates bypass, apparently by generating single-stranded DNA beyond the DPC. The absence of RTEL1 impairs DPC proteolysis, suggesting that CMG must bypass the DPC to enable proteolysis. Our results suggest a mechanism that prevents inadvertent CMG destruction by DPC proteases, and they reveal CMG's remarkable capacity to overcome obstacles on its translocation strand.

Health and equity implications of individual adaptation to air pollution in a changing climate.

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone's need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

An intersectional genetic approach for simultaneous cell type-specific labelling and gene knockout in the mouse.

Precise genome manipulation in specific cell types and subtypes in vivo is crucial for neurobiological research because of the cellular heterogeneity of the brain. Site-specific recombinase systems in the mouse, such as Cre-loxP, improve cell type-specific genome manipulation; however, undesirable expression of cell type-specific Cre can occur. This could be due to transient expression during early development, natural expression in more than one cell type, kinetics of recombinases, sensitivity of the Cre reporter, and disruption in cis-regulatory elements by transgene insertion. Moreover, cell subtypes cannot be distinguished in cell type-specific Cre mice. To address these issues, we applied an intersectional genetic approach in mouse using triple recombination systems (Cre-loxP, Flp-FRT and Dre-rox). As a proof of principle, we labelled heterogeneous cell subtypes and deleted target genes within given cell subtypes by labelling neuropeptide Y (NPY)-, calretinin (calbindin 2) (CR)- and cholecystokinin (CCK)-expressing GABAergic neurons in the brain followed by deletion of RNA-binding Fox-1 homolog 3 (Rbfox3) in our engineered mice. Together, our study applies an intersectional genetic approach in vivo to generate engineered mice serving dual purposes of simultaneous cell subtype-specific labelling and gene knockout.

TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Progress in modeling dynamic systems for sustainable development.

This Perspective evaluates recent progress in modeling nature-society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice-defining purpose, selecting components, analyzing interactions, and assessing interventions-we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature-society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.

Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.

Climate oscillation impacts on water supply augmentation planning.

Climate oscillations ranging from years to decades drive precipitation variability in many river basins globally. As a result, many regions will require new water infrastructure investments to maintain reliable water supply. However, current adaptation approaches focus on long-term trends, preparing for average climate conditions at mid- or end-of-century. The impact of climate oscillations, which bring prolonged and variable but temporary dry periods, on water supply augmentation needs is unknown. Current approaches for theory development in nature-society systems are limited in their ability to realistically capture the impacts of climate oscillations on water supply. Here, we develop an approach to build middle-range theory on how common climate oscillations affect low-cost, reliable water supply augmentation strategies. We extract contrasting climate oscillation patterns across sub-Saharan Africa and study their impacts on a generic water supply system. Our approach integrates climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning-based advances in stochastic dynamic control. We find that longer climate oscillations often require greater water supply augmentation capacity but benefit more from dynamic approaches. Therefore, in settings with the adaptive capacity to revisit planning decisions frequently, longer climate oscillations do not require greater capacity. By building theory on the relationship between climate oscillations and least-cost reliable water supply augmentation, our findings can help planners target scarce resources and guide water technology and policy innovation. This approach can be used to support climate adaptation planning across large spatial scales in sectors impacted by climate variability.

Ventral Pallidum and Amygdala Cooperate to Restrain Reward Approach under Threat.

Foraging decisions involve assessing potential risks and prioritizing food sources, which can be challenging when confronted with changing and conflicting circumstances. A crucial aspect of this decision-making process is the ability to actively overcome defensive reactions to threats and focus on achieving specific goals. The ventral pallidum (VP) and basolateral amygdala (BLA) are two brain regions that play key roles in regulating behavior motivated by either rewards or threats. However, it is unclear whether these regions are necessary in decision-making processes involving competing motivational drives during conflict. Our aim was to investigate the requirements of the VP and BLA for foraging choices in conflicts involving overcoming defensive responses. Here, we used a novel foraging task and pharmacological techniques to inactivate either the VP or BLA or to disconnect these brain regions before conducting a conflict test in male rats. Our findings showed that BLA is necessary for making risky choices during conflicts, whereas VP is necessary for invigorating the drive to obtain food, regardless of the presence of conflict. Importantly, our research revealed that the connection between VP and BLA is critical in controlling risky food-seeking choices during conflict situations. This study provides a new perspective on the collaborative function of VP and BLA in driving behavior, aimed at achieving goals in the face of dangers.

Neurotopographical Transformations: Dissecting Cortical Reconfigurations in Auditory Deprivation.

Within the intricate matrices of cognitive neuroscience, auditory deprivation acts as a catalyst, propelling a cascade of neuroanatomical adjustments that have, until now, been suboptimally articulated in extant literature. Addressing this gap, our study harnesses high-resolution 3 T MRI modalities to unveil the multifaceted cortical transformations that emerge in tandem with congenital auditory deficits. We conducted a rigorous cortical surface analysis on a cohort of 90 congenitally deaf individuals, systematically compared with 90 normoacoustic controls. Our sample encompassed both male and female participants, ensuring a gender-inclusive perspective in our analysis. Expected alterations within prototypical auditory domains were evident, but our findings transcended these regions, spotlighting modifications dispersed across a gamut of cortical and subcortical structures, thereby epitomizing the cerebral adaptive dynamics to sensory voids. Crucially, the study's innovative methodology integrated two pivotal variables: the duration of auditory deprivation and the extent of sign language immersion. By intersecting these metrics with structural changes, our analysis unveiled nuanced layers of cortical reconfigurations, elucidating a more granulated understanding of neural plasticity. This intersectional approach bestows a unique advantage, allowing for a discerning exploration into how varying durations of sensory experience and alternative communication modalities modulate the brain's morphological terrain. In encapsulating the synergy of neuroimaging finesse and incisive scientific rigor, this research not only broadens the current understanding of adaptive neural mechanisms but also paves the way for tailored therapeutic strategies, finely attuned to individual auditory histories and communicative repertoires.

2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease" provides recommendations to guide clinicians in the treatment of patients with lower extremity peripheral artery disease across its multiple clinical presentation subsets (ie, asymptomatic, chronic symptomatic, chronic limb-threatening ischemia, and acute limb ischemia). METHODS: A comprehensive literature search was conducted from October 2020 to June 2022, encompassing studies, reviews, and other evidence conducted on human subjects that was published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through May 2023 during the peer review process, were also considered by the writing committee and added to the evidence tables where appropriate. STRUCTURE: Recommendations from the "2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with peripheral artery disease have been developed.

PlantFUNCO: Integrative Functional Genomics Database Reveals Clues into Duplicates Divergence Evolution.

Evolutionary epigenomics and, more generally, evolutionary functional genomics, are emerging fields that study how non-DNA-encoded alterations in gene expression regulation are an important form of plasticity and adaptation. Previous evidence analyzing plants' comparative functional genomics has mostly focused on comparing same assay-matched experiments, missing the power of heterogeneous datasets for conservation inference. To fill this gap, we developed PlantFUN(ctional)CO(nservation) database, which is constituted by several tools and two main resources: interspecies chromatin states and functional genomics conservation scores, presented and analyzed in this work for three well-established plant models (Arabidopsis thaliana, Oryza sativa, and Zea mays). Overall, PlantFUNCO elucidated evolutionary information in terms of cross-species functional agreement. Therefore, providing a new complementary comparative-genomics source for assessing evolutionary studies. To illustrate the potential applications of this database, we replicated two previously published models predicting genetic redundancy in A. thaliana and found that chromatin states are a determinant of paralogs degree of functional divergence. These predictions were validated based on the phenotypes of mitochondrial alternative oxidase knockout mutants under two different stressors. Taking all the above into account, PlantFUNCO aim to leverage data diversity and extrapolate molecular mechanisms findings from different model organisms to determine the extent of functional conservation, thus, deepening our understanding of how plants epigenome and functional noncoding genome have evolved. PlantFUNCO is available at https://rocesv.github.io/PlantFUNCO.

Emotion recognition and regulation in males: Role of sex and stress steroids.

Understanding emotions in males is crucial given their higher susceptibility to substance use, interpersonal violence, and suicide compared to females. Steroid hormones are assumed to be critical biological factors that affect and modulate emotion-related behaviors, together with psychological and social factors. This review explores whether males' abilities to recognize emotions of others and regulate their own emotions are associated with testosterone, cortisol, and their interaction. Higher levels of testosterone were associated with improved recognition and heightened sensitivity to threatening faces. In contrast, higher cortisol levels positively impacted emotion regulation ability. Indirect evidence from neuroimaging research suggested a link between higher testosterone levels and difficulties in cognitive emotion regulation. However, this notion must be investigated in future studies using different emotion regulation strategies and considering social status. The present review contributes to the understanding of how testosterone and cortisol affect psychological well-being and emotional behavior in males.

Genetic Networks in Plant Vascular Development.

Understanding the development of vascular tissues in plants is crucial because the evolution of vasculature enabled plants to thrive on land. Various systems and approaches have been used to advance our knowledge about the genetic regulation of vasculature development, from the scale of single genes to networks. In this review, we provide a perspective on the major approaches used in studying plant vascular development, and we cover the mechanisms and genetic networks underlying vascular tissue specification, patterning, and differentiation.

Linear: a framework to enable existing software to resolve structural variants in long reads with flexible and efficient alignment-free statistical models.

Alignment is the cornerstone of many long-read pipelines and plays an essential role in resolving structural variants (SVs). However, forced alignments of SVs embedded in long reads, inflexibility of integrating novel SVs models and computational inefficiency remain problems. Here, we investigate the feasibility of resolving long-read SVs with alignment-free algorithms. We ask: (1) Is it possible to resolve long-read SVs with alignment-free approaches? and (2) Does it provide an advantage over existing approaches? To this end, we implemented the framework named Linear, which can flexibly integrate alignment-free algorithms such as the generative model for long-read SV detection. Furthermore, Linear addresses the problem of compatibility of alignment-free approaches with existing software. It takes as input long reads and outputs standardized results existing software can directly process. We conducted large-scale assessments in this work and the results show that the sensitivity, and flexibility of Linear outperform alignment-based pipelines. Moreover, the computational efficiency is orders of magnitude faster.

Estimation of non-equilibrium transition rate from gene expression data.

The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder.

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.