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A facile synthesis of rGO–CuO nanocomposite was carried out using hydrothermal method. rGO–CuO nanocomposite was incorporated in aerosol forming composite (AFC) for the first time in order to develop the fast fire extinguishant materials. Synthesized nanocomposites were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), energy dispersive Xray spectroscopy (EDX) and transmission electron microscopy (TEM). Catalytic activity of rGO–CuO was analysed considering the parameters such as combustion temperature, activation energy, burn rate, combustion efficiency, minimum fire extinguishing concentration (MEC) and fire extinguishing time. Addition of rGO–CuO to base AFC remarkably enhanced the performance of AFC by increasing the combustion efficiency (11.36%) and decreasing the combustion temperature (16.14%) and MEC (19.78%). A tremendous increase in burn rate (47.1%) boosted the fast extinguishment of fire by reducing the fire extinguishment time 78.2%. Developed AFC found promising to meet the faster fire extinguishment requirement of futuristic fire suppression systems. [ABSTRACT FROM AUTHOR]

rGO/Fe2O3 nanocomposites were synthesized using facile and inexpensive hydrothermal route. For the first time, these nanocomposites were incorporated in aerosol forming composites (AFCs), characterized and investigated for ultra-fast fire extinguishing applications. The morphological and structural characteristics were examined using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transmission infrared spectroscopy (FTIR), Raman spectroscopy and energy dispersive X-ray spectroscopy (EDX). AFCs were also characterized for burn rate, combustion efficiency, minimum fire extinguishing concentration, fire extinguishing time, combustion temperature and activation energy. Characterization revealed successful synthesis of rGO/Fe2O3 nanocomposites having Fe2O3 nanoparticles (25–50 nm) over and in-between the graphene oxide nanosheets. Increase in combustion efficiency (9%) and burn rate (35%) and reduction in onset temperature (17.16%), combustion temperature (13.46%) and activation energy (37.71%) were observed on addition of 3% rGO/5.0Fe2O3 nanocomposite to base AFC. Increase in burn rate by 35% led to ultra-fast aerosol generation and faster (50% reduction in fire extinguishing time) fire extinguishment. AFCs found promising for incorporation in condensed aerosol-based fire extinguishing systems to comply futuristic quicker fire extinguishment requirements. [ABSTRACT FROM AUTHOR]

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user's permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud. [ABSTRACT FROM AUTHOR]

Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods. [ABSTRACT FROM AUTHOR]

Background: Neonatal sepsis significantly contributes to neonatal mortality and morbidity in developing countries. The delay in diagnosis due to the time-consuming gold standard blood culture necessitates rapid, accessible, and cost-effective diagnostic markers. Aims: This study aimed to evaluate the sensitivity and specificity of Micro-ESR for early neonatal sepsis diagnosis and compare its effectiveness with other standard markers. Methodology: Conducted at D.Y. Patil Hospital's Department of Pediatrics in Navi Mumbai, this prospective observational study included 200 neonates suspected of sepsis. Blood samples were analyzed for CBC, CRP, Micro-ESR, and bacterial culture before antibiotic initiation. Micro-ESR's diagnostic performance was assessed against other markers based on confirmed sepsis cases via blood culture. Results: Among 200 neonates, 16 had a positive blood culture. Micro-ESR showed a sensitivity of 87.5% and specificity of 28.80%. In comparison, Total Leucocyte Count (TLC) and Absolute Neutrophil Count (ANC) demonstrated higher specificity (91.25% and 70% respectively) but lower sensitivity. Platelet count and CRP indicated high specificity (94.56%) and sensitivity (100%) respectively, albeit with low predictive values. Conclusions: While CRP exhibited the highest sensitivity, TLC and platelet count showed superior specificity. Despite its lower specificity, Micro-ESR's high sensitivity suggests its utility as a supportive early diagnostic marker alongside CBC, CRP, and ANC. Blood culture remains the definitive diagnostic standard. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer's disease (AD), it does not necessarily show the obvious symptoms of AD. As a result, it becomes challenging to distinguish between mild cognitive impairment and cognitively normal. In this paper, we propose an ensemble of deep learners based on convolutional neural networks for the early diagnosis of Alzheimer's disease. The proposed approach utilises simple averaging ensemble and weighted averaging ensemble methods. The ensemble-based transfer learning model demonstrates enhanced generalization and performance for AD diagnosis compared to traditional transfer learning methods. Extensive experiments on the OASIS-3 dataset validate the effectiveness of the proposed model, showcasing its superiority over state-of-the-art transfer learning approaches in terms of accuracy, robustness, and efficiency. [ABSTRACT FROM AUTHOR]

In recent times Gallbladder cancer (GBC) incidences increased many folds in India and are being reported from arsenic hotspots identified in Bihar. The study aims to establish association between arsenic exposure and gallbladder carcinogenesis. In the present study, n = 200 were control volunteers and n = 152 confirmed gallbladder cancer cases. The studied GBC patient's biological samples-gallbladder tissue, gallbladder stone, bile, blood and hair samples were collected for arsenic estimation. Moreover, n = 512 gallbladder cancer patients blood samples were also evaluated for the presence of arsenic to understand exposure level in the population. A significantly high arsenic concentration (p < 0.05) was detected in the blood samples with maximum concentration 389 µg/L in GBC cases in comparison to control. Similarly, in the gallbladder cancer patients, there was significantly high arsenic concentration observed in gallbladder tissue with highest concentration of 2166 µg/kg, in gallbladder stones 635 µg/kg, in bile samples 483 µg/L and in hair samples 6980 µg/kg respectively. Moreover, the n = 512 gallbladder cancer patient's blood samples study revealed very significant arsenic concentration in the population of Bihar with maximum arsenic concentration as 746 µg/L. The raised arsenic concentration in the gallbladder cancer patients' biological samples—gallbladder tissue, gallbladder stone, bile, blood, and hair samples was significantly very high in the arsenic exposed area. The study denotes that the gallbladder disease burden is very high in the arsenic exposed area of Bihar. The findings do provide a strong link between arsenic contamination and increased gallbladder carcinogenesis. [ABSTRACT FROM AUTHOR]

Objective: Enrollment of patients of Black African ancestry with systemic lupus erythematosus (SLE) in phase II and phase III of the belimumab trials was not reflective of the racial distribution observed in the lupus population. This study was undertaken to assess the efficacy and safety of intravenous (IV) belimumab plus standard therapy in patients of self‐identified Black race. Methods: EMBRACE (GSK Study BEL115471; ClinicalTrials.gov identifier: NCT01632241) was a 52‐week multicenter, double‐blind, placebo‐controlled trial in adults of self‐identified Black race with active SLE who received monthly belimumab 10 mg/kg IV, or placebo, plus standard therapy. The optional 26‐week open‐label extension phase included patients who completed the double‐blind phase. The primary end point of the study was SLE Responder Index (SRI) response rate at week 52 with modified proteinuria scoring adapted from the SLE Disease Activity Index 2000 (SLEDAI‐2K) (SRI–SLEDAI‐2K). Key secondary end points included SRI response rate at week 52, time to first severe SLE flare, and reductions in prednisone dose. Results: The modified intent‐to‐treat population comprised 448 patients, of whom 96.9% were women and the mean ± SD age was 38.8 ± 11.42 years. The primary end point (improvement in the SRI–SLEDAI‐2K response rate at week 52) was not achieved (belimumab 48.7%, placebo 41.6%; odds ratio 1.40 [95% confidence interval 0.93, 2.11], P = 0.1068); however, numerical improvements favoring belimumab were observed, in which the SRI–SLEDAI‐2K response rates were higher in those who received belimumab compared with those who received placebo, especially in patients with SLE who had high disease activity or renal manifestations at baseline. The safety profile of belimumab was generally consistent with that observed in previous SLE trials. Adverse events were the primary reasons for double‐blind phase withdrawals (belimumab 5.4%, placebo 6.7%). Conclusion: The primary end point of this study was not achieved, but improvement with belimumab versus placebo was observed, suggesting that belimumab remains a suitable treatment option for SLE management in patients of Black African ancestry. [ABSTRACT FROM AUTHOR]

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi's entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi's entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi's entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity. [ABSTRACT FROM AUTHOR]