Abstract

Aberrant genome-wide DNA methylation patterns is common in cancers. Understanding how these affect the transcriptome can provide insights into subtype specific development and progression of tumorigenesis. In this study we carried out genome-wide analysis of DNA methylation and gene expression profiles in TCGA-BRCA breast cancer samples to propose a novel set of 35 methylation-based prognostic markers that may provide insights to molecular subtype specific disease stratification. Gene-set enrichment and pathway analysis of the predicted markers using MSigDB and DAVID revealed their role in mammary gland development pathway, various signaling pathways (ERBB2, NOTCH, etc.), and other cancer pathways, and show clear association with genes affected by hormone receptor status. We further show the discriminative power of the proposed DNA methylation signature in classifying breast cancer samples into three molecular subtypes, viz., Luminal, HER2-enriched and Triple Negative. An accuracy of 94.12% and MCC of 0.87 is obtained in stratified 5-fold cross-validation for the three-class classification using SVM-RBF

Abstract

Motorized two-wheelers are a prevalent and economical means of transportation, particularly in the Asia-Pacific region. However, hazardous driving practices such as triple riding and non-compliance with helmet regulations contribute significantly to accident rates. Addressing these violations through automated enforcement mechanisms can enhance traffic safety. In this paper, we propose DashCop, an end-to-end system for automated E-ticket generation. The system processes vehicle-mounted dashcam videos to detect two-wheeler traffic violations. Our contributions include: (1) a novel Segmentation and Cross-Association (SAC) module to accurately associate riders with their motorcycles, (2) a robust cross-association-based tracking algorithm optimized for the simultaneous presence of riders and motorcycles, and (3) the RideSafe-400 dataset, a comprehensive annotated dashcam video dataset for triple riding and helmet rule violations. Our system demonstrates significant improvements in violation detection, validated through extensive evaluations on the RideSafe-400 dataset.

Abstract

In this work, we present IDD-CRS, a large-scale dataset focused on critical road scenarios, captured using Advanced Driver Assistance Systems (ADAS) and dash cameras. Unlike existing datasets that predominantly emphasize pedestrian safety and vehicle safety separately, IDD-CRS incorporates both vehicle and pedestrian behaviors, offering a more comprehensive view of road safety. The dataset includes diverse scenarios, such as high-speed lane changes, unsafe vehicle approaches to pedestrians and cyclists, and complex interactions between ego vehicles and other road agents. Leveraging ADAS technology allows us to accurately define the temporal boundaries of actions, resulting in precise annotations and more reliable safety analysis. With 90 hours of video footage, consisting of 5400 one-minute-long videos and 135,000 frames, IDD-CRS introduces new vehicle related classes and hard negative classes, establishing baselines for action recognition and long-tail action recognition tasks. Our benchmarks reveal the limitations of current models, pointing toward future advancements needed for improving road safety technology.

Abstract

This study uses a transcriptomic and machine learning-based approach to gain a comprehensive understanding of the genomic landscape of Plasmodium falciparum, a malaria-causing parasite. This enables us to understand this life-threatening disease better and thus identify new treatment targets and intervention methods. We used single-cell RNA sequencing data from the Malarial Cell Atlas with 5,177 genes across 37,624 cells. Our approach combined static analyses, including highly variable gene identification, differential expression analysis, and M3Drop (a feature selection method based on noise filtration), to obtain stage-specific markers along with trajectory analysis (Monocle3) to identify top genes active during stage progression. The feature selection analyses were done for each stage and lifecycle gene, which are genes that remain active throughout the lifecycle. These complementary approaches gave us distinct gene sets: static analysis revealed genes necessary for stage-specific functions, while dynamic trajectory analysis highlighted genes that play an important role in cellular development. A neural network classifier trained on these gene sets achieved high accuracy (Accuracy: 96%; F1-score: 0.96) using trajectory-derived genes, performing better than the model trained on the entire gene set, indicating that the gene set has captured stage-specific signatures. Further, to validate the biological significance of the shortlisted genes, we performed a Gene Ontology analysis. We found that our analysis revealed results that are in line with existing literature. The ring stage genes were linked to immune evasion, trophozoite genes to haemoglobin digestion, schizont genes to merozoite invasion, and gametocyte genes to sexual differentiation. We plan to build on this work by studying the behaviour of important genes using a Poisson-Beta model to infer the kinetics of key genes, which will help us better understand how they change throughout the parasite's life cycle.

Abstract

The rising cost of drug discovery, coupled with a stagnation in the approval of novel treatments, highlights the urgent need for innovative strategies such as drug re-purposing. Pharmaceutical companies invest roughly 10-15 years and $2.6 billion to get a single FDA-approved drug to market. The COVID-19 pandemic further underscored the necessity of quickly identifying existing drugs with potential efficacy against a fast-spreading virus to curtail the pandemic. In this study, we perform a comparative analysis of several Graph Neural Networks (GNNs) and recommendation system models to address drug re-purposing. We construct an integrated graph that combines Protein-Protein Interaction networks, Drug-Target Protein graphs, Disease-Protein associations, and Drug-Disease links. We leverage a network learning paradigm implemented over this complex graph via both node-agnostic and heterogeneous graph techniques for link prediction in drug-disease pairs. We implement a Heterogeneous Graph Transformer (HGT) model that processes three node types (drugs, diseases, proteins) and four edge types. The HGT achieved an AUC-ROC of 0.985 and an F1-score of 0.90, demonstrating its efficacy in predicting drug re-purposing candidates. Additionally, we compared several node-agnostic GNN architectures, including Graph Convolutional Networks, Graph Attention Networks, GraphSAGE, and Graph Isomorphism Networks. All architectures performed comparably, with an AUC-ROC of around 0.98. However, when framing the drug re-purposing task as a recommendation problem using Matrix Factorization with side information, we observed a significant drop in performance, with the AUC-ROC falling to 0.82. This performance degradation highlights the importance of incorporating Protein-Protein Interaction networks in the modelling process, as matrix factorization fails to capture these complex network effects critical for drug re-purposing. Our models ranked 6,158 drugs based on their predicted efficacy in treating COVID-19, providing a valuable tool for prioritizing clinical trials and further research. Beyond COVID-19, such an integrated framework can allow us to uncover drug re-purposing prospects for any other novel diseases in a significantly more efficient and cost-effective way.

Abstract

Gliomas, accounting for nearly one-third of all brain tumours, present themselves as significant medical challenges owing to their heterogeneous and aggressive nature. Given that traditional diagnostic techniques often fall short of capturing the entire genetic landscape, there are minimal options currently present in personalised medicine, thus adding to existing challenges. In order to aid precision oncology, we present a machine learning (ML) framework for glioma subtype classification and mutation prediction using RNA sequencing data. We used a two-step feature selection procedure that included XGBoost and LASSO, leveraging RNA sequencing and somatic mutation data of 667 glioma samples from the Cancer Genome Atlas (TCGA) Pan-Cancer project to identify key genes that are associated with glioma subtypes and important mutations. Metrics such as AUROC, F1-Score, and Matthews Correlation Coefficient (MCC) were used to train and evaluate the supervised machine learning models, including Random Forest and Stochastic Gradient Descent. Our framework performed robustly despite the class imbalance present in the dataset. We achieved a high classification accuracy for Diffuse Glioma (DG) subtypes (AUROC 0.90-1.00), distinguishing between low-grade gliomas (LGGs) and glioblastoma multiforme (GBM). Mutation status predictions for the key prognostic genes, including IDH, TP53, and ATRX, achieved high AUROC scores of 0.89-0.98 and MCC values of 0.72-0.97. Further, we used feature sets from our top-performing models to perform gene set enrichment as part of our post-analysis. This confirmed the biological significance of identified genes related to established carcinogenic pathways and further validated the clinical applicability of our technique. Our ML-driven framework presented offers a scalable, data-driven solution in computational oncology with potential applications across cancer types.

Abstract

This study investigates how cinematographic techniques influence viewer perception and contribute to the objectification of women, utilizing eye-tracking data from 91 participants. They watched a sexualized music video (SV) known for objectifying portrayals and a non-sexualized music video (TV). Using dynamic Areas of Interests (AOIs)—head, torso, and lower body—gaze metrics such as fixation duration, visit count, and scan paths were recorded to assess visual attention patterns. Participants were grouped according to their average fixations on sexualized AOIs. Statistical analyses revealed significant differences in gaze behavior between the videos and among the groups, with increased attention to sexualized AOIs in SV. Additionally, data-driven group differences in fixations identified specific segments with heightened objectification that are further analyzed using scan path visualization techniques. These findings provide strong empirical evidence of camera-driven gaze objectification, demonstrating how cinematic framing implicitly shapes objectifying gaze patterns, highlighting the critical need for mindful media representation.

Abstract

Leptoquarks (LQs) can contribute to the magnetic and electric dipole moments of charged leptons, which the current experiments have measured with good accuracy. We revisit the parameter spaces of TeV-scale vector LQs that contribute to these observables and study how these models fare against the LHC bounds. We show that significant portions of the parameter space are excluded when the current LHC data is utilized effectively. We find that only U1and V2 can explain the observed positive shift in (aμexp-aμSM) through a lepton chirality-flipping contribution with O(1) LQ-quark-lepton coupling. We also see how these two LQs can fit the electron dipole moment and atomic parity violation measurements. We find that the current electric dipole moment measurements of the muon cannot restrain the LQ-quark-lepton couplings within perturbative regions.

Abstract

Despite the progress seen in classification methods, current approaches for handling videos with distribution shifts in source and target domains remain source-dependent as they require access to the source data during the adaptation stage. In this paper, we present a self-training based source-free video domain adaptation approach to address this challenge by bridging the gap between the source and the target domains. We use the source pre-trained model to generate pseudo-labels for the target domain samples, which are inevitably noisy. Thus, we treat the problem of source-free video domain adaptation as learning from noisy labels and argue that the samples with correct pseudo-labels can help us in adaptation. To this end, we leverage the cross-entropy loss as an indicator of the correctness of the pseudo-labels and use the resulting small-loss samples from the target domain for fine-tuning the model. We further enhance the adaptation performance by implementing a teacher–student (TS) framework, in which the teacher, which is updated gradually, produces reliable pseudo-labels. Meanwhile, the student undergoes fine-tuning on the target domain videos using these generated pseudo-labels to improve its performance. Extensive experimental evaluations show that our methods, termed as CleanAdapt, CleanAdapt + TS, achieve state-of-the-art results, outperforming the existing approaches on various open datasets.

Abstract

There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claims to be iteratively refined over time. This process requires significant researcher effort, reasoning, and ingenuity. Yet current benchmarks for LMs predominantly assess their ability to generate solutions rather than challenge them. We advocate for developing benchmarks that evaluate this inverse capability — creating counterexamples for subtly incorrect solutions. To demonstrate this approach, we start with the domain of algorithmic problem solving, where counterexamples can be evaluated automatically using code execution. Specifically, we introduce REFUTE, a dynamically updating benchmark that includes recent problems and incorrect submissions from programming competitions, where human experts successfully identified counterexamples. Our analysis finds that the best reasoning agents, even OpenAI o3-mini (high) with code execution feedback, can create counterexamples for only < 9% of incorrect solutions in REFUTE, even though ratings indicate its ability to solve up to 48% of these problems from scratch. We hope our work spurs progress in evaluating and enhancing LMs’ ability to falsify incorrect solutions — a capability that is crucial for both accelerating research and making models self-improve through reliable reflective reasoning.