Hierarchical graphene oxide-Ni3S2 quantum dots nanocomposites modified glassy carbon electrode for electrochemical detection of dopamine and tyrosine

A facile synthetic strategy is demonstrated to generate nickel sulfide quantum dots (Ni3S2). The thus formed Ni3S2 quantum dots are assembled onto exfoliated graphene oxide sheets hydrothermally to form nickel sulfide-graphene oxide nanocomposite material (GO-Ni3S2). The microscopic and spectroscopic characterization of the GO-Ni3S2 nanocomposites revealed the shape, size, crystalline phases, and oxidation states (of elements) of the hybrid material. The GO-Ni3S2 nanocomposites are then coated onto the glassy carbon electrode by drop casting to form GO-Ni3S2@GCE. The modified electrode is then used to detect dopamine and tyrosine simultaneously. The effect of scan rate, analyte concentrations, pH, and interfering agents on the peak current are studied to establish a plausible mechanism for oxidizing dopamine and tyrosine at GO-Ni3S2@GCE. The GO-Ni3S2@GCE is stable for 3 weeks and ten cycles of washing with minimal loss in the peak current in each cycle. Dopamine with a concentration as low as 12 nM can be detected using the GO-Ni3S2@GCE system

Heterogeneous microbial oceanographic environments: Application of GIS technology in deciphering of microenvironment scenarios off the central west coast of India

In the vast oceanic microbial environment of 2468.83km 2, GIS modeling techniques involving sixty query steps, enabled the deciphering of Microenvironments as low as 1.19km 2 to 38.6 km 2 for the summer of 2004 and in case of summer 2005 where 84 query steps were involved to decipher Microenvironments of 10.55km 2 to 25.94km 2. Thirtythree sampling stations were established between Betul to Ankola off the central west coast of India accounting for a spatial coverage of 2468.83km 2. GIS query-modeling investigation was carried out using spatial layers of depth, optical parameters (k-Irradiance attenuation Coefficient, c-Beam attenuation coefficient), sediment size parameters (Sediment Mean Size and Sediment Sorting) and Benthic Foraminifera Suborders (Rotaliina, Textulariina, Miliolina, Lagenina). Foraminifera have been used as a surrogate parameter. However, any microbial parameter could proxy for foraminifers providing for the numerical deciphering of microenvironments. This is suggestive of the assimilation of GIS technology for a better appreciation of microbial oceanography

Human protein reference database—2006 update

Human Protein Reference Database (HPRD) () was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein–protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein–protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data

The Genetic Basis of Hepatosplenic T-cell Lymphoma

Hepatosplenic T cell lymphoma (HSTL) is a rare and lethal lymphoma; the genetic drivers of this disease are unknown. Through whole exome sequencing of 68 HSTLs, we define recurrently mutated driver genes and copy number alterations in the disease. Chromatin modifying genes including SETD2, INO80 and ARID1B were commonly mutated in HSTL, affecting 62% of cases. HSTLs manifest frequent mutations in STAT5B (31%), STAT3 (9%), and PIK3CD (9%) for which there currently exist potential targeted therapies. In addition, we noted less frequent events in EZH2, KRAS and TP53. SETD2 was the most frequently silenced gene in HSTL. We experimentally demonstrated that SETD2 acts as a tumor suppressor gene. In addition, we found that mutations in STAT5B and PIK3CD activate critical signaling pathways important to cell survival in HSTL. Our work thus defines the genetic landscape of HSTL and implicates novel gene mutations linked to HSTL pathogenesis and potential treatment targets

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14