Abstract

Leishmania donovani causes a neglected tropical disease called visceral leishmaniasis. Additionally, leishmaniasis also manifests opportunistically, under conditions of immune compromise. The continued non-availability of effectively curative interventions (drugs or vaccines) against this disease necessitates a deeper knowledge of Leishmania biology in order to evolve novel strategies against the disease. We have used a meta-analysis approach to analyse Leishmania’s composite genetic network rather than investigating individual candidate genes. We performed Weighted Gene Co-expression Network Analysis (WGCNA) on publicly available Leishmania donovani transcriptome data to identify co-regulatory genetic modules. This clustering of Leishmania donovani transcriptomes revealed that genes fall in 30 distinct co-regulated modules with 32 to 3012 genes. In order to analyse the distribution of genes in Leishmania gene modules, we queried the enrichment or depletion of various annotation-qualifiers in the modules. We observed that several modules are specifically and individually enriched or depleted for annotation qualifiers derived from GO-annotation and KEGG-pathways and are differentially associated with life-phases and experimental conditions. Additionally, modules are also enriched or depleted for sequence based genetic features such as chromosomal location, location on co-transcriptional segment, rank of transcript from initiation of transcription, skewed usage of known RNA Binding Protein motifs. Classification of uncharacterized transcripts into co-regulatory modules provides insights in their probable characteristics, aiding future empirical investigation. Strikingly, two of the modules have reciprocal features including individual associations with logarithmic or stationary growth phases of Leishmania, two important life-phases that simulate the vector-dwelling pro-cyclic and the pre-infective meta-cyclic forms. Collectively, our analyses of Leishmania co-regulated gene modules is suggestive of additional regulatory modes over the mere differential mRNA stabilization.

Abstract

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. Notably, transcription factors related to ER stress, immune response, and cell proliferation, along with hypoxia-inducible factor and sterol regulatory element-binding proteins were found to drive metabolic reprogramming. These findings provide new insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, offering potential avenues for targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests. Recent research has emphasized the importance of metabolic dysfunctions in the pathophysiology of ASD. This study investigates metabolic alterations associated with ASD by analyzing transcriptomic data obtained from the prefrontal cortex (bulk tissue and single-nucleus) and data from peripheral blood mononuclear cells (PBMC). We assessed the metabolic activity of each patient based on gene expression profiles, revealing significant downregulation of vital metabolic pathways, including glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation, indicative of hypometabolism. Our analysis also highlighted dysregulation in lipid, vitamin, amino acid, and heme metabolism, which may contribute to the neurodevelopmental delays associated with ASD. Cell-specific metabolic activities in the ASD brain showed altered pathways in astrocytes, oligodendrocytes, excitatory neurons, and interneurons. Furthermore, we identified critical metabolic pathways and genes from PBMC gene expression data that distinguish ASD patients from typically developing individuals. Our findings demonstrate a consistent pattern of metabolic dysfunction across brain and blood samples. This research provides a comprehensive understanding of metabolic alterations in ASD, paving the way for exploring potential therapeutic strategies targeting metabolic dysregulation.

Abstract

Cancer remains a leading cause of morbidity and mortality worldwide. Despite advances in genomics, identifying clinically relevant subtypes of cancer remains challenging due to its complex and heterogeneous nature. In this work, we propose DeepGraphMut (DGM), a novel graph-based deep-learning pipeline that integrates somatic mutation data with protein-protein interaction (PPI) networks. By employing a graph autoencoder with a graph attention layer and a node-level attention decoder, DGM generates patient-specific clinically relevant encodings for unsupervised and supervised tasks. We demonstrate the effectiveness of DGM across 16 cancer types comprising of 7352 samples from The Cancer Genome Atlas (TCGA). Unsupervised clustering reveals distinct subtypes with significant survival differences in 11 cancer types. In supervised analysis using a Cox regression model, DGM demonstrates excellent performance in predicting survival outcomes, achieving a high concordance index (c-index) value in the range of 0.7 across most cancers, underscoring its robust predictive performance using only somatic mutation data. Furthermore, DGM outperforms its lightweight variant and the network-based stratification method in both unsupervised and supervised analyses. In summary, this study presents a promising approach for cancer subtype identification and prognosis, especially in resource-limited settings where multi-omics data may not be readily available. By leveraging the strengths of graph learning and network biology, DGM offers a valuable tool for advancing personalized medicine.

Abstract

The efective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnifcation, this dataset is one of the largest in Asia, specifcally focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological fndings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classifcation and IHC biomarker identifcation underscore its potential to signifcantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across diferent populations.

Abstract

This study critically analyses portrayals of masculinity on Over The Top (OTT) platforms in India, which have seen a marked rise in viewership since the beginning of the COVID pandemic. Netflix, cur- rently the largest investor in original content in India, offers a range of curated material in 31 languages. Masculinity over the years in Indian cinema, particularly the Bollywood industry, has largely been por- trayed through hegemonic notions of normativity. Through simple distinctions between good and bad, a clear contrast is set up between a hero and an anti-hero. R. W. Connell suggests a hierarchy of mas- culinities, in the middle of which lies Marginalised Masculinity. This study constructs this marginalised masculinity as working-class. We contend that at the intersection of class and socioeconomic status lies a particularly neo-liberal expression of masculinity. The Netflix original productions considered for this study exempt the working-class man from ethical obligations, in his conquest to liberate himself from his class position. Aspirational capitalism makes it okay to be ethically dubious, spawning a troubled masculinity. We use a three-step coding method to substantiate qualitative discourse analysis of Jam- tara - Sabka Number Aayega (2020, Soumendra Padhi), Class (2023, Ashim Ahluwalia), and The White Tiger (2021, Ramin Bahrani).

Abstract

The pursuit of habit building is challenging, and most people struggle with it. Research on successful habit formation is mainly based on small human trials focusing on the same habit for all the participants as conducting long-term heterogonous habit studies can be logistically expensive. With the advent of self-help, there has been an increase in online communities and applications that are centered around habit building and logging. Habit building applications can provide large-scale data on real-world habit building attempts and unveil the commonalities among successful ones. We collect public data on stickk.com, which allows users to track progress on habit building attempts called commitments. A commitment can have an external referee, regular check-ins about the progress, and a monetary stake in case of failure. Our data consists of 742,923 users and 397,456 commitments. In addition to the dataset, rooted in theories like Fresh Start Effect, Accountablity, and Loss Aversion, we ask questions about how commitment properties like start date, external accountability, monitory stake, and pursuing multiple habits together affects the odds of success. We found that people tend to start habits on temporal landmarks, but that does not affect the probability of their success. Practices like accountability and stakes are not often used but are strong determents of success. Commitments of 6 to 8 weeks in length, weekly reporting with an external referee, and a monetary amount at stake tend to be most successful. Finally, around 40% of all commitments are attempted simultaneously with other goals. Simultaneous attempts of pursuing commitments may fail early, but if pursued through the initial phase, they are statistically more successful than building one habit at a time.

Abstract

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai

Abstract

Fake news being spread on social media platforms has a disruptive and damaging impact on our lives. Multimedia content improves the visibility of posts more than text data but is also being used for creating fake news. Previous multimodal works have tried to address the problem of modeling heterogeneous modalities in identifying fake news. However, these works have the following limitations: (1) inefficient encoding of inter-modal relations by utilizing a simple concatenation operator on the modalities at a later stage in a model, which might result in information loss; (2) training very deep neural networks with a disproportionate number of parameters on small multimodal datasets result in higher chances of overfitting. To address these limitations, we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework that allows granular interactions within and across different modalities to learn more robust data representations for multimodal fake news detection. We use two publicly available fake news datasets, Twitter and Weibo, for evaluations. GAME-ON outperforms on Twitter by an average of 11% and achieves state-of-the-art performance on Weibo while using 91% fewer parameters than the best comparable state-of-the-art baseline. For deployment in real-world applications, GAME-ON can be used as a lightweight model (less memory and latency requirements), which makes it more feasible than previous state-of-the-art models.

Abstract

Chemical reaction prediction, encompassing forward synthesis and retrosynthesis, stands as a fundamental challenge in organic synthesis. A widely adopted computational approach frames synthesis prediction as a sequence-to-sequence translation task, using the common SMILES representation for molecules. Current evaluation of machine learning methods for retrosynthesis assume perfect training data, overlooking imperfections in reaction equations in popular datasets, such as missing reactants, products, other physical and practical constraints such as temperature and cost, primarily driven by a focus on the target molecule. This limitation leads to an incomplete representation of viable synthetic routes, especially when multiple sets of reactants can yield a given desired product. In response to these shortcomings, this study examines the prevailing evaluation methods and introduces comprehensive metrics designed to address imperfections in the dataset. Our novel metrics not only assess absolute accuracy by comparing predicted outputs with ground truth but also introduce a nuanced evaluation approach. We provide scores for partial correctness and compute adjusted accuracy through graph matching, acknowledging the inherent complexities of retrosynthetic pathways. Additionally, we explore the impact of small molecular augmentations while preserving chemical properties and employ similarity matching to enhance the assessment of prediction quality. We introduce SynFormer, a sequence-to-sequence model tailored for SMILES representation. It incorporates architectural enhancements to the original transformer, effectively tackling the challenges of chemical reaction prediction. SynFormer achieves a top-1 accuracy of 53.2% on the USPTO-50k dataset, demonstrating an improvement over previous state-of-the-art language models while being more efficient and eliminating the need for pre-training.