Specialized Networks for Social Cognition in the Primate Brain.

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

A framework for quantifying individual and collective common sense.

The notion of common sense is invoked so frequently in contexts as diverse as everyday conversation, political debates, and evaluations of artificial intelligence that its meaning might be surmised to be unproblematic. Surprisingly, however, neither the intrinsic properties of common sense knowledge (what makes a claim commonsensical) nor the degree to which it is shared by people (its "commonness") have been characterized empirically. In this paper, we introduce an analytical framework for quantifying both these elements of common sense. First, we define the commonsensicality of individual claims and people in terms of the latter's propensity to agree on the former and their awareness of one another's agreement. Second, we formalize the commonness of common sense as a clique detection problem on a bipartite belief graph of people and claims, defining [Formula: see text] common sense as the fraction [Formula: see text] of claims shared by a fraction [Formula: see text] of people. Evaluating our framework on a dataset of [Formula: see text] raters evaluating [Formula: see text] diverse claims, we find that commonsensicality aligns most closely with plainly worded, fact-like statements about everyday physical reality. Psychometric attributes such as social perceptiveness influence individual common sense, but surprisingly demographic factors such as age or gender do not. Finally, we find that collective common sense is rare: At most, a small fraction [Formula: see text] of people agree on more than a small fraction [Formula: see text] of claims. Together, these results undercut universalistic beliefs about common sense and raise questions about its variability that are relevant both to human and artificial intelligence.

Distilling dynamical knowledge from stochastic reaction networks.

Stochastic reaction networks are widely used in the modeling of stochastic systems across diverse domains such as biology, chemistry, physics, and ecology. However, the comprehension of the dynamic behaviors inherent in stochastic reaction networks is a formidable undertaking, primarily due to the exponential growth in the number of possible states or trajectories as the state space dimension increases. In this study, we introduce a knowledge distillation method based on reinforcement learning principles, aimed at compressing the dynamical knowledge encoded in stochastic reaction networks into a singular neural network construct. The trained neural network possesses the capability to accurately predict the state conditional joint probability distribution that corresponds to the given query contexts, when prompted with rate parameters, initial conditions, and time values. This obviates the need to track the dynamical process, enabling the direct estimation of normalized state and trajectory probabilities, without necessitating the integration over the complete state space. By applying our method to representative examples, we have observed a high degree of accuracy in both multimodal and high-dimensional systems. Additionally, the trained neural network can serve as a foundational model for developing efficient algorithms for parameter inference and trajectory ensemble generation. These results collectively underscore the efficacy of our approach as a universal means of distilling knowledge from stochastic reaction networks. Importantly, our methodology also spotlights the potential utility in harnessing a singular, pretrained, large-scale model to encapsulate the solution space underpinning a wide spectrum of stochastic dynamical systems.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

Knowledge Graphs for drug repurposing: a review of databases and methods.

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides.

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis.

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFß treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.

Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Monocyte-Macrophages and T Cells in Atherosclerosis.

Atherosclerosis is an arterial disease process characterized by the focal subendothelial accumulation of apolipoprotein-B-containing lipoproteins, immune and vascular wall cells, and extracellular matrix. The lipoproteins acquire features of damage-associated molecular patterns and trigger first an innate immune response, dominated by monocyte-macrophages, and then an adaptive immune response. These inflammatory responses often become chronic and non-resolving and can lead to arterial damage and thrombosis-induced organ infarction. The innate immune response is regulated at various stages, from hematopoiesis to monocyte changes and macrophage activation. The adaptive immune response is regulated primarily by mechanisms that affect the balance between regulatory and effector T cells. Mechanisms related to cellular cholesterol, phenotypic plasticity, metabolism, and aging play key roles in affecting these responses. Herein, we review select topics that shed light on these processes and suggest new treatment strategies.

Interdisciplinary college curriculum and its labor market implications.

This article sheds light on how to capture knowledge integration dynamics in college course content, improves and enriches the definition and measurement of interdisciplinarity, and expands the scope of research on the benefits of interdisciplinarity to postcollege outcomes. We distinguish between what higher education institutions claim regarding interdisciplinarity and what they appear to actually do. We focus on the core academic element of student experience-the courses they take, develop a text-based semantic measure of interdisciplinarity in college curriculum, and test its relationship to average earnings of graduates from different types of schools of higher education. We observe that greater exposure to interdisciplinarity-especially for science majors-is associated with increased earnings after college graduation.

Device-independent quantum randomness-enhanced zero-knowledge proof.

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain, and social networks. However, the heuristic is typically built upon the random oracle model that makes ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol. Here, we present a quantum solution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test and delivers them with postquantum cryptography (PQC) authentication. By employing this service, we conceive and implement NIZKP of the three-coloring problem. By bridging together three prominent research themes, quantum nonlocality, PQC, and ZKP, we anticipate this work to inspire more innovative applications that combine quantum information science and the cryptography field.

Neural Representations of Non-native Speech Reflect Proficiency and Interference from Native Language Knowledge.

Learning to process speech in a foreign language involves learning new representations for mapping the auditory signal to linguistic structure. Behavioral experiments suggest that even listeners that are highly proficient in a non-native language experience interference from representations of their native language. However, much of the evidence for such interference comes from tasks that may inadvertently increase the salience of native language competitors. Here we tested for neural evidence of proficiency and native language interference in a naturalistic story listening task. We studied electroencephalography responses of 39 native speakers of Dutch (14 male) to an English short story, spoken by a native speaker of either American English or Dutch. We modeled brain responses with multivariate temporal response functions, using acoustic and language models. We found evidence for activation of Dutch language statistics when listening to English, but only when it was spoken with a Dutch accent. This suggests that a naturalistic, monolingual setting decreases the interference from native language representations, whereas an accent in the listener's own native language may increase native language interference, by increasing the salience of the native language and activating native language phonetic and lexical representations. Brain responses suggest that such interference stems from words from the native language competing with the foreign language in a single word recognition system, rather than being activated in a parallel lexicon. We further found that secondary acoustic representations of speech (after 200 ms latency) decreased with increasing proficiency. This may reflect improved acoustic-phonetic models in more proficient listeners.Significance Statement Behavioral experiments suggest that native language knowledge interferes with foreign language listening, but such effects may be sensitive to task manipulations, as tasks that increase metalinguistic awareness may also increase native language interference. This highlights the need for studying non-native speech processing using naturalistic tasks. We measured neural responses unobtrusively while participants listened for comprehension and characterized the influence of proficiency at multiple levels of representation. We found that salience of the native language, as manipulated through speaker accent, affected activation of native language representations: significant evidence for activation of native language (Dutch) categories was only obtained when the speaker had a Dutch accent, whereas no significant interference was found to a speaker with a native (American) accent.

OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated with additional annotations found in published case reports. Despite their potential, real-world data such as electronic health records (EHRs) have not been fully exploited to derive rare disease annotations. Here, we present open annotation for rare diseases (OARD), a real-world-data-derived resource with annotation for rare-disease-related phenotypes. This resource is derived from the EHRs of two academic health institutions containing more than 10 million individuals spanning wide age ranges and different disease subgroups. By leveraging ontology mapping and advanced natural-language-processing (NLP) methods, OARD automatically and efficiently extracts concepts for both rare diseases and their phenotypic traits from billing codes and lab tests as well as over 100 million clinical narratives. The rare disease prevalence derived by OARD is highly correlated with those annotated in the original rare disease knowledgebase. By performing association analysis, we identified more than 1 million novel disease-phenotype association pairs that were previously missed by human annotation, and >60% were confirmed true associations via manual review of a list of sampled pairs. Compared to the manual curated annotation, OARD is 100% data driven and its pipeline can be shared across different institutions. By supporting privacy-preserving sharing of aggregated summary statistics, such as term frequencies and disease-phenotype associations, it fills an important gap to facilitate data-driven research in the rare disease community.

Update on human herpesvirus 7 pathogenesis and clinical aspects as a roadmap for future research.

Human herpesvirus 7 (HHV-7) is a common virus that is associated with various human diseases including febrile syndromes, dermatological lesions, neurological defects, and transplant complications. Still, HHV-7 remains one of the least studied members of all human betaherpesviruses. In addition, HHV-7-related research is mostly confined to case reports, while in vitro or in vivo studies unraveling basic virology, transmission mechanisms, and viral pathogenesis are sparse. Here, we discuss HHV-7-related literature linking clinical syndromes to the viral life cycle, epidemiology, and viral immunopathogenesis. Based on our review, we propose a hypothetical model of HHV-7 pathogenesis inside its host. Furthermore, we identify important knowledge gaps and recommendations for future research to better understand HHV-7 diseases and improve therapeutic interventions.

Tracing human life trajectory using gut microbial communities by context-aware deep learning.

The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-related factors remains unclear. The lack of context-awareness, insufficient data, and the existence of batch effect are the three major issues, making the life trajection of the host based on gut microbial communities problematic. Here, we used a novel computational approach (microDELTA, microbial-based deep life trajectory) to track longitudinal human gut microbial communities' alterations, which employs transfer learning for context-aware mining of gut microbial community dynamics at different life stages. Using an infant cohort, we demonstrated that microDELTA outperformed Neural Network for accurately predicting the age of infant with different delivery mode, especially for newborn infants of vaginal delivery with the area under the receiver operating characteristic curve of microDELTA and Neural Network at 0.811 and 0.436, respectively. In this context, we have discovered the influence of delivery mode on infant gut microbial communities. Along the human lifespan, we also applied microDELTA to a Chinese traveler cohort, a Hadza hunter-gatherer cohort and an elderly cohort. Results revealed the association between long-term dietary shifts during travel and adult gut microbial communities, the seasonal cycling of gut microbial communities for the Hadza hunter-gatherers, and the distinctive microbial pattern of elderly gut microbial communities. In summary, microDELTA can largely solve the issues in tracing the life trajectory of the human microbial communities and generate accurate and flexible models for a broad spectrum of microbial-based longitudinal researches.