Abstract

Parkinson’s Disease (PD) neurodegenerative disorder which lacks reliable early diagnostic tests. In this study, we present a method for PD prediction that uses multimodal data from the Parkinson Progression Marker Initiative (PPMI), specifically clinico-demographic, biospecimen, and genetic data, to improve predictive accuracy and facilitate timely interventions via a multimodal machine learning approach. We evaluated data obtained from 598 participants (171 healthy controls and 427 PD patients) in three different modalities: 29 clinical features, five cerebrospinal fluid biomarkers, and 154 SNPs (single nucleotide polymorphisms), which were selected through a biology-driven feature selection method. We utilized three multimodal integration strategies—early, intermediate, and late—and trained various machine learning models, including LightGBM and Multilayer Perceptron (MLP), each of which was optimized by hyperparameter tuning and cross-validation. In early integration, we combined feature sets from all modalities into a single set, leveraging complementary information to increase predictive power. The intermediate integration method made use of autoencoders to encode features into a single 12 dimensional vector as the input into a Neural Network classifier. Late integration combined outputs from the top-performing models for each modality using ensemble techniques such as Voting Classifier and Stacking. We found that early integration achieved the highest performance, with Support Vector Machines with 90% accuracy, 0.98 AUC-ROC, 0.99 precision, and 0.93 F1 score. Intermediate integration with the Neural Network classifier followed with an AUC-ROC of 0.84 and an F1-score of 0.85. Our feature importance analysis identified two clinical scores, namely UPSIT (related to sensory decline) and SCOPA (measuring autonomic dysfunction), along with two SNPs (chr4 90755939 A G and chr4 90646886 G A, both previously linked to PD susceptibility) as crucial predictors, emphasizing their well established relevance in PD diagnosis. Early diagnosis and treatment of PD patients are facilitated by the integration of multiple data modalities, which also greatly increases the predicted accuracy for the disease while also providing us with a thorough understanding of its complexity.

Abstract

The constant shifts in social and political contexts, driven by emerging social movements and political events, lead to new forms of hate content and previously unrecognized hate patterns that machine learning models may not have captured. Some recent literature proposes data augmentation-based techniques to enrich existing hate datasets by incorporating samples that reveal new implicit hate patterns. This approach aims to improve the model's performance on out-of-domain implicit hate instances. It is observed, that further addition of more samples for augmentation results in the decrease of the performance of the model. In this work, we propose a Knowledge Transfer-driven Concept Refinement method that distills and refines the concepts related to implicit hate samples through novel prototype alignment and concept losses, alongside data augmentation based on concept activation vectors. Experiments with several publicly available datasets show that incorporating additional implicit samples reflecting new hate patterns through concept refinement enhances the model's performance, surpassing baseline results while maintaining cross-dataset generalization capabilities.

Abstract

The metaverse, a collection of virtual worlds offering a variety of social activities mirroring real-life interactions, is garnering increasing attention. Consequently, the need to ensure security and privacy within these spaces has become paramount. Metaverse users can create multiple avatars, a feature that can be exploited to deceive or threaten others, leading to significant internal security concerns. Additionally, users in the metaverse are susceptible to several external security threats due to the public nature of their communications with service providers.To address these challenges, we propose a novel quantum authentication scheme leveraging blockchain technology, decentralized identifiers, and verifiable credentials. This scheme enables secure identity verification and authentication for metaverse users. By allowing users to independently prove their identity without relying on service providers, the proposed approach mitigates privacy concerns associated with the management of personal information. Furthermore, our scheme achieves mutual authentication between the user and the service provider, while also being resilient against a variety of attacks, including unconditional security breaches, replay attacks, eavesdropping, man-in-the-middle attacks, and impersonation attempts. With a computation time of 17.4608 ms and a communication cost of 872 bits, the suggested protocol outperforms current solutions and provides superior security for the metaverse environment during data transmission and storage. Furthermore, we examine the operational capabilities and security features using the latest technology and techniques.

Abstract

The Ib Valley Coalfield in Odisha, one of India’s major coal-producing regions, has the third-highest coal reserves in the country. The study assesses how mining expansion and associated developments have reshaped the landscape over nearly five decades. Guided by the research goal of quantifying the impacts of coal mining activity on land use and land cover (LULC) changes, the study leverages multi-spectral/multi-temporal satellite imagery from multiple Landsat missions (2, 5, 7, 8, and 9) acquired between 1976 and 2024. The images were processed and divided into six primary LULC classes—Vegetation, Waterbodies, Mining Regions, Settlements, Industries, and Others—which were identified by employing visual interpretation techniques alongside using advanced band indices, namely, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to validate the actual features on the ground, along with Google Earth Engine for ground truth. Comparative analysis reveals significant shifts in LULC composition over the study period, during which the expansion of coal mining activities emerged as a dominant driver of change. Results show a pronounced expansion in Mining Regions, from 0.12 km² in 1976 to 49.49 km² in 2024, reflecting the increasing dominance of open-cast mining. Concurrently, Settlements grew from 10.67 km² to 97.06 km², underscoring rapid urbanisation. By contrast, Vegetation declined from 1304.08 km² to 1142.14 km², underscoring pressures on natural ecosystems. Waterbodies exhibited modest fluctuations, influenced by reservoir management and rainfall patterns. Moreover, the open land available for agriculture was severely affected as 111.84 km2 of open land was converted into mines, settlements and industries. The transition matrix of the findings highlights the substantial environmental transformations occurring in the Ib Valley Coalfield, which shows how Vegetation experienced significant loss, primarily due to urbanization and mining, indicating ecological stress from human activities. Similarly, the conversion of water bodies into agricultural land highlights the growing human demand for cultivable areas, often resulting in the degradation of natural ecosystems. These land use transitions, particularly the expansion of extractive industries and urban settlements, collectively reflect the mounting pressures of industrialization and urbanization on the region’s ecological balance. This also highlights severe air and water pollution from open-cast mining operations with implications for habitat integrity, water resource management, and regional socio-economic dynamics—environmental assessments. Overall, this study underscores the far-reaching ecological and socio-economic consequences of coal extraction in Ib Valley.

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.