Abstract

When novice engineers (fresh or recent graduates with little industry experience) join a SaaS (Software-as-a-Service) product company, they are tasked with comprehending the product, especially its behavior and dynamics. We believe that they can comprehend more effectively if they know and understand the architecture patterns used in the product. Are the current architecture pattern descriptions of high quality? Do they fit the needs of novice engineers? We evaluated the pattern descriptions of Model-View-Controller (MVC) (a popular and important architecture pattern for cloud systems) from a quality and fitment perspective and found gaps. To address these gaps, we have built a System of Systems (SoS) model of MVC that uses a transition systems vocabulary and a set-theoretic notation. In the paper, we show that this SoS model provides a rich set of information about the behavior and dynamics of the MVC components and their interactions. The model bridges the gaps in the MVC pattern description. One of the contributions of the paper is to provide criteria to evaluate the pattern descriptions for quality and fitment for novice engineers. The paper proposes that we augment the benchmark pattern description of MVC with an SoS model. The paper also demonstrates a general approach to building SoS models for architecture patterns and recommends creating a catalog of SoS models for SaaS architecture patterns. We believe such a catalog will significantly help novice engineers in comprehension and other software engineering activities.

Abstract

In literature, various time-frequency representation methods were investigated for automatic detection of voice disorders. Stockwell-Transform (S-Transform) provides good time-frequency localization; hence, it may efficiently capture the voice disorder related information from speech signal. With this motivation, we investigated different variants of S-Transform for the classification of voice disorders. This study proposed the S-Transform based cepstral coefficients for voice disorder detection and assessment. The performance of the proposed feature was compared with baseline features on SVD and HUPA databases. Compared to baseline features, proposed features performed best in terms of classification accuracy of 80.2% and 79.8% on HUPA and SVD databases, respectively for voice disorder detection task. Also, the proposed features performed better in case of assessment task. Further, the experimental results reveal that combining cepstral coefficients derived from S-Transform with baseline features improved the performance of proposed systems by 8% and 4% for detection and assessment tasks, respectively which highlights complementary nature of the explored features. We also analysed the effectiveness of S-Transform based spectral representation in capturing the acoustic characteristics for various voice qualities like breathiness, harshness, creakiness, and falsetto phonations. This representation was also compared with other time-frequency based methods such as STFT, ZTW and SFF. It was observed that S-Transform effectively captures the acoustic variations associated with different voice qualities compared to other baseline methods, which may be due to better spectro-temporal resolution offered by the S-Transform.

Abstract

The IEEE International Conference on Software Architecture (ICSA) is the premier gathering of practitioners and researchers interested in software architecture, component-based software engineering, and quality aspects of complex software systems. The 21st IEEE International Conference on Software Architecture (ICSA 2024) continued the tradition of a working conference, where attendees met and where software architects were able to explain the challenges they face and try to influence the future of the field. Interactive working sessions were the place where researchers met practitioners to identify opportunities to shape the future of our field.

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 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