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It's a collaborative effort of JMI, AIIMS, and ICMR, New Delhi, India. "Dynamic Web Server for ESKAPEE Pathogen classification" is the deployment of a robust machine learning model which accepts raw clinical sequences i.e. SRA data in the fast/fq format, and classifies it into 8 classes of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, E. coli (i.e. ESKAPEE) and non-ESKAPEE that consists of Human DNA, Fungi, and bacteria other than ESKAPEE.

Data related to paper Lymphoma tissue fragments to anticipate response to targeted therapies Folder structure ├── 10xVisium # spatial transcriptomics data │ ├── LP03 │ │ ├── CTRL │ │ └── Parental │ └── LP08 │ ├── CTRL │ ├── IBR │ ├── IDL │ └── Parental └── scRNAseq # processed single RNA seq data ├── Spleen 1 │ └── qc_data.h5ad └── Spleen 2 └── qc_data.h5ad Each spatial transcriptomics data folders contains ├── NucleiPerSpot.txt ├── bayesprism_updatedReference.rdata ├── filtered_feature_bc_matrix │ ├── barcodes.tsv.gz │ ├── features.tsv.gz │ └── matrix.mtx.gz └── spatial ├── aligned_fiducials.jpg ├── detected_tissue_image.jpg ├── scalefactors_json.json ├── tissue_hires_image.png ├── tissue_lowres_image.png └── tissue_positions_list.csv

UNSTRUCTURED Confluent and reticulated papillomatosis (CARP) is a rare disorder mostly seen in young adults. It is characterized by persistent dull-brown, centrally confluent, peripherally reticulate macules and papules, which coalesce to form patches and plaques on the upper trunk and neck. It is commonly confused with pityriasis versicolor and acanthosis nigricans (AN). We report the case of a 15-year-old male with multiple pigmented confluent and reticulated patches and plaques on the neck, trunk, and chin for 3 years, which was successfully treated with oral minocycline, resulting in complete resolution of lesions within 2 weeks. The morphology of CARP resembles that of various other dermatological conditions such as AN and pityriasis versicolor, and, as a result, it is frequently misdiagnosed and mistreated, leading to social embarrassment for the patient. Therefore, it is prudent for dermatologists to carry out comprehensive clinical and histopathological assessments to facilitate prompt diagnosis and management of this condition.

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on nodes are decomposed on network’s Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

MotivationContinuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as ‘big data records’ to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of disease outcomes provide an excellent ground for identifying important mutations by developing machine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2.ResultsWe describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely ‘Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal’ were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a ‘temporal modeling approach’ to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on ‘past data’ in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern.Contactsharmila.mande@tcs.comSupplementary informationSupplementary data are enclosed.

Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a ‘Bag of Words’ to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported recurrent mutations and phylogenetic clades for genomes. Additionally, we report the geographical distribution of the identified mutation signatures in SARS-CoV-2 genomes on the global map. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

Drug-induced hypersensitivity syndrome (DIHS) is an unusual, potentially life-threatening, multi-organ adverse drug reaction. DIHS usually develops 2-6 weeks after drug initiation. We report a case of 21 years old female with maculopapular rash associated with fever and generalised lymphadenopathy, 15 days after intake of aceclofenac. Treatment with intravenous corticosteroids, antibiotics and fluids along with cessation of the offending drug resulted in successful resolution.

The importance of understanding microbe-microbe as well as microbe-disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations. In this communication, we present a tool called "EviMass" (Evidence based mining of human Microbial Associations), which can assist biologists to validate their predicted hypotheses from new microbiome studies. Users can interactively query the processed back-end database for microbe-microbe and disease-microbe associations. The EviMass tool can also be used to upload microbial association networks generated from a human "disease-control" microbiome study and validate the associations from biomedical literature. Additionally, a list of differentially abundant microbes for the corresponding disease can be queried in the tool for reported evidences. The results are presented as graphical plots, tabulated summary, and other evidence statistics. EviMass is a comprehensive platform and is expected to enable microbiome researchers not only in mining microbial associations, but also enriching a new research hypothesis. The tool is available free for academic use at https://web.rniapps.net/evimass.

Cutaneous larvae migrans or creeping eruption is a serpiginous cutaneous eruption caused by the accidental penetration and migration of animal hookworm (nematode) larvae through the epidermis and subcutaneous tissue. The infection occurs most frequently in warmer climates and is associated with pruritus and burning sensations. We report a case of 48 years old farmer with vesicular lesion on left ankle progressing into a serpiginous track on the skin surface without any characteristic symptoms. Treatment with oral and topical drugs led to successful resolution of lesions within 10 days.

The organic food industry in India is in the early stages of growth. Higher disposable income and greater health awareness have resulted in an increased domestic demand for organic food. There is huge premium in selling organic products, not only to export markets but also to affluent, health conscious domestic consumers. India is endowed with an abundance of labour and has diverse agroclimatic region that is well suited to year round agriculture. It still has strong traditional agricultural practices. Can India make use of this comparative advantage to introduce sustainable agriculture practices and at the same time improve incomes of small and marginal farmers?On the supply side, small and marginal farmers realize that there is an opportunity to get higher net incomes even if yields are low in organic agriculture. This is because the price of pesticides and chemicals has increased significantly over the last few decades resulting in a significant increase in the cost of production. Organic farming cost could be 50% to 60% less when compared to inorganic farming practices.In addition to domestic demand side, globalized markets provide significant opportunities for Indian agriculture to capture a larger share of the global demand for organic food. This paper analyzes the growth of the organic food industry in relation to domestic and export demand. We also look at the supply side to determine if organic farming and sustainable agricultural practices could help improve farmers’ income. Finally, this paper analyses existing policy framework towards organic agriculture and how small and marginal farmers could possibly benefit in this niche market.