Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals.

Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.

Rhnull phenotype in an Indian patient due to a novel c.1138 + 2 t > a mutation in the RHAG gene.

BACKGROUND: The Rh system is an extremely important RBC antigen system with over 50 antigens, 5 of which (D, C, E, c and e) are considered most clinically significant. The rare Rhnull phenotype can result from mutations in the RHD and RHCE genes or the RHAG gene that affects their expression. This is a case report of the second type. CASE REPORT: This case reports a multiparous lady who had to be evaluated for a panreactive antibody. The discrepancy was first identified at the centre she reported to. A thorough immunohematological workup was performed at a second reference laboratory. Suspecting Rhnull phenotype, a third referral (molecular typing) was requested at International Blood Group Reference Laboratory (IBGRL), Bristol. RESULTS: A novel RHAG null allele (c.1138+2t>a), causing a Rhnull phenotype was identified. The antibody was most likely an anti-Rh 29 antibody. CONCLUSION: The novel c.1138+2 t > a mutation in the RHAG gene causing the Rhnull phenotype and development of a pan reacting antibody(ies) made the patient's pregnancy challenging. Confirmation of the diagnosis, an important step in her management, required use of both serological immunohematology and molecular techniques.

Metatranscriptomes of California grassland soil microbial communities in response to rewetting.

When very dry soil is rewet, rapid stimulation of microbial activity has important implications for ecosystem biogeochemistry, yet associated changes in microbial transcription are poorly known. Here, we present metatranscriptomes of California annual grassland soil microbial communities, collected over 1 week from soils rewet after a summer drought-providing a time series of short-term transcriptional response during rewetting.

DKCNN: Improving deep kernel convolutional neural network-based covid-19 identification from CT images of the chest.

BACKGROUND: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease. OBJECTIVE: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations. METHODS: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing. RESULTS: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches. CONCLUSION: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.

Phylogenomics and genetic analysis of solvent-producing Clostridium species.

The genus Clostridium is a large and diverse group within the Bacillota (formerly Firmicutes), whose members can encode useful complex traits such as solvent production, gas-fermentation, and lignocellulose breakdown. We describe 270 genome sequences of solventogenic clostridia from a comprehensive industrial strain collection assembled by Professor David Jones that includes 194 C. beijerinckii, 57 C. saccharobutylicum, 4 C. saccharoperbutylacetonicum, 5 C. butyricum, 7 C. acetobutylicum, and 3 C. tetanomorphum genomes. We report methods, analyses and characterization for phylogeny, key attributes, core biosynthetic genes, secondary metabolites, plasmids, prophage/CRISPR diversity, cellulosomes and quorum sensing for the 6 species. The expanded genomic data described here will facilitate engineering of solvent-producing clostridia as well as non-model microorganisms with innately desirable traits. Sequences could be applied in conventional platform biocatalysts such as yeast or Escherichia coli for enhanced chemical production. Recently, gene sequences from this collection were used to engineer Clostridium autoethanogenum, a gas-fermenting autotrophic acetogen, for continuous acetone or isopropanol production, as well as butanol, butanoic acid, hexanol and hexanoic acid production.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

Optimization of Electrochemical Sensitivity in Anticancer Drug Quantification through ZnS@CNS Nanosheets: Synthesis via Accelerated Sonochemical Methodology.

Zinc sulfide/graphitic Carbon Nitride binary nanosheets were synthesized by using a novel sonochemical pathway with high electrocatalytic ability. The as- obtained samples were characterized by various analytical methods such as Transmission Electron Microscopy (TEM), Field emission scanning electron microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction analysis (XRD), and X-ray photoelectron spectroscopy (XPS) to evaluate the properties of ZnS@CNS synthesized by this new route. Subsequently, the electrical and electrochemical performance of the proposed electrodes were characterized by using EIS and CV to establish an electroactive ability of the nanocomposites. The complete properties like structural and physical of ZnS@CNS were analyzed. As-prepared binary nanocomposite was applied towards the detection of anticancer drug (flutamide) by various electrochemical methods such as cyclic voltammetry (CV), differential pulse voltammetry (DPV) and amperometry. The glassy carbon electrode modified with a ZnS@CNS composite demonstrates a remarkable electrocatalytic efficiency for detecting flutamide in a pH 7.0 (PBS). The composite modified electrode shows synergistic effect of ZnS and CNS catalyst. The electrochemical sensing performance of the linear range was improved significantly due to high electroactive sites and rapid electron transport pathways. Crucially, the electrochemical method was successfully demonstrated in biological fluids which reveals its potential real-time applicability in the analysis of drug.

Whole community shotgun metagenomes of two biological soil crust types from the Mojave Desert.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

Pd-Catalyzed Sequential Electrophilic Cyclization/Selective C-H Annulation Cascade: Synthesis of Isoxazole-Phthalimide-Fused Poly-Heterocyclics.

Harnessing the organo-palladium intermediates generated from electrophilic cyclizations for tandem C-C bond construction is a challenging task but constitutes an excellent tool for constructing complex motifs from simpler substrates. We realize herein such a cyclative annulation of alkynyl-oxime ethers with maleimides for the facile construction of isoxazole-phthalimide hybrid motifs through Pd(II) catalysis. This protocol features excellent regio-selectivity in C-H selection, a broad substrate scope, good functional group tolerance, and scalability. Necessary KIE & labeling studies give insight into the reaction mechanism.

Metatranscriptomes of two biological soil crust types from the Mojave desert in response to wetting.

We present eight metatranscriptomic datasets of light algal and cyanolichen biological soil crusts from the Mojave Desert in response to wetting. These data will help us understand gene expression patterns in desert biocrust microbial communities after they have been reactivated by the addition of water.

IMG/PR: a database of plasmids from genomes and metagenomes with rich annotations and metadata.

Plasmids are mobile genetic elements found in many clades of Archaea and Bacteria. They drive horizontal gene transfer, impacting ecological and evolutionary processes within microbial communities, and hold substantial importance in human health and biotechnology. To support plasmid research and provide scientists with data of an unprecedented diversity of plasmid sequences, we introduce the IMG/PR database, a new resource encompassing 699 973 plasmid sequences derived from genomes, metagenomes and metatranscriptomes. IMG/PR is the first database to provide data of plasmid that were systematically identified from diverse microbiome samples. IMG/PR plasmids are associated with rich metadata that includes geographical and ecosystem information, host taxonomy, similarity to other plasmids, functional annotation, presence of genes involved in conjugation and antibiotic resistance. The database offers diverse methods for exploring its extensive plasmid collection, enabling users to navigate plasmids through metadata-centric queries, plasmid comparisons and BLAST searches. The web interface for IMG/PR is accessible at https://img.jgi.doe.gov/pr. Plasmid metadata and sequences can be downloaded from https://genome.jgi.doe.gov/portal/IMG_PR.

Performance of Empirical Mode Decomposition for Frequency Identification in SSVEP Based BCI.

Empirical mode decomposition based conventional correlation (EMDCC) method is proposed to identify the frequency components in steady state visual evoked potentials (SSVEP) in electroencephalogram(EEG).The main aim of the proposed EMDCC method is to recognise narrow band frequency components that are present in SSVEP. The study is evaluated on two datasets. The first one is a 40 target benchmark dataset obtained from 35 subjects and the second is a 4 class Inhouse dataset collected from 10 healthy participants. The mean detection accuracy of the conventional correlation method is 85.64 % for the benchmark dataset and it is improved to 93.79 % in the proposed method. The mean detection accuracy of the conventional correlation method is 67.5 % for the Inhouse dataset and it is increased to 82.5 % in the proposed method. The mean detection accuracy of the proposed EMDCC method is also compared to time-weighting canonical correlation analysis (TWCCA) for the benchmark dataset. The mean detection accuracy of TWCCA is 91.04 %. Hence the results show better detection accuracies in the proposed EMDCC method than the simple conventional correlation method and also the existing TWCCA method.

Ethyl Gallate: Promising Cytoprotective against HIV-1-Induced Cytopathy and Antiretroviral-Induced Cytotoxicity.

Introduction: HIV-1 infection in cell culture is typically characterized by certain cytopathic effects such as vacuolization of cells and development of syncytia, which further lead to cell death. In addition, the majority of drugs during HIV treatment exhibit serious adverse effects in patients, apart from their beneficial role. During the screening of cytoprotective agents to protect the cells from HIV-1-associated cell death and also drug-associated toxicity, antioxidants from a natural source are assumed to be a choice. A well-known antioxidant, ethyl gallate (EG), was selected for cytoprotection studies which have already been proven as an anti-HIV agent. Objective: The main objective of the study was to explore the cytoprotective potential of EG against HIV-1-induced cytopathic effect and antiretroviral drug toxicity. Methods: DPPH free radical scavenging assay was performed with EG to find the effective concentration for antioxidant activity. HIV-1infection-associated cytopathic effects and further rescue by EG were studied in MT-2 lymphocytes by the microscopic method and XTT cytopathic assays. The cellular toxicity of different antiretroviral drugs in different cell lines and the consequent cytoprotective effectiveness of EG were investigated using an MTT cell viability assay. Results: Like ascorbic acid, EG exhibited promising antioxidant activity. HIV-1 infection of MT2 cells induces cell death often referred to as the cytopathic effect. In addition, the usage of antiretroviral drugs also causes severe adverse effects like cytotoxicity. In this context, EG was tested for its cytoprotective properties against HIV-1-induced cytopathic effect and drug-mediated cellular toxicity. EG reclaimed back the MT2 cells from HIV-1-induced cell death. Antiretroviral drugs, such as ritonavir, efavirinz, AZT, and nevirapine, were tested for their toxicity and induced more cell death at higher concentrations in different tissue models such as the liver (THLE-3), lung (AEpiCM), colorectal (HT-29), and brain (U87 MG). Pretreated cells with EG were rescued from the toxic doses of ART. Conclusion: EG was found to be exhibited cytoprotection not only from HIV-1-linked cell death but also from the chemotoxicity of antiretroviral drugs. Evidently, EG could be a cytoprotective supplement in the management of AIDS along with its enormous antioxidant benefits.

Targeting telomerase utilizing zeolitic imidazole frameworks as non-viral gene delivery agents across different cancer cell types.

Telomerase, a ribonucleoprotein coded by the hTERT gene, plays an important role in cellular immortalization and carcinogenesis. hTERT is a suitable target for cancer therapeutics as its activity is highly upregulated in most of cancer cells but absent in normal somatic cells. Here, by employing the two Metal-Organic Frameworks (MOFs), viz. ZIF-C and ZIF-8, based biomineralization we encapsulate Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9 plasmid system that targets hTERT gene (CrhTERT) in cancer cells. When comparing the two biocomposites, ZIF-C shows the better loading capacity and cell viability. The loaded plasmid in ZIF-C is highly protected against enzymatic degradation. CrhTERT@ZIF-C is efficiently endocytosed by cancer cells and the subcellular release of CrhTERT leads to telomerase knockdown. The resultant inhibition of hTERT expression decreases cellular proliferation and causing cancer cell death. Furthermore, hTERT knockdown shows a significant reduction in tumour metastasis and alters protein expression. Collectively we show the high potential of ZIF-C-based biocomposites as a promising general tool for gene therapy of different types of cancers.

Discovery of a novel filamentous prophage in the genome of the Mimosa pudica microsymbiont Cupriavidus taiwanensis STM 6018.

Integrated virus genomes (prophages) are commonly found in sequenced bacterial genomes but have rarely been described in detail for rhizobial genomes. Cupriavidus taiwanensis STM 6018 is a rhizobial Betaproteobacteria strain that was isolated in 2006 from a root nodule of a Mimosa pudica host in French Guiana, South America. Here we describe features of the genome of STM 6018, focusing on the characterization of two different types of prophages that have been identified in its genome. The draft genome of STM 6018 is 6,553,639 bp, and consists of 80 scaffolds, containing 5,864 protein-coding genes and 61 RNA genes. STM 6018 contains all the nodulation and nitrogen fixation gene clusters common to symbiotic Cupriavidus species; sharing >99.97% bp identity homology to the nod/nif/noeM gene clusters from C. taiwanensis LMG19424T and "Cupriavidus neocalidonicus" STM 6070. The STM 6018 genome contains the genomes of two prophages: one complete Mu-like capsular phage and one filamentous phage, which integrates into a putative dif site. This is the first characterization of a filamentous phage found within the genome of a rhizobial strain. Further examination of sequenced rhizobial genomes identified filamentous prophage sequences in several Beta-rhizobial strains but not in any Alphaproteobacterial rhizobia.

Population-level impact of beliefs and attitudes on vaccine decision-making in South Africa: results from the COVID-19 Vaccine Survey (2021/2022).

OBJECTIVES: In addition to being home to more than seven million HIV-infected individuals, South Africa also has a high burden of COVID-19 and related comorbidities worldwide. We aimed to identify the most influential "beliefs" and "attitudes" on vaccine decision-making behavior. STUDY DESIGN: This study used panel data from cross-sectional surveys. METHODS: We used the data from Black South Africans who participated in the "COVID-19 Vaccine Surveys" (November 2021 and February/March 2022) in South Africa. Besides standard risk factor analysis, such as multivariable logistic regression models, we also used the modified version of population attributable risk percent and estimated the population-level impacts of beliefs and attitudes on vaccine decision-making behavior using the methodology in multifactorial setting. RESULTS: A total of 1399 people (57% men and 43% women) who participated in both surveys were analyzed. Of these, 336 (24%) reported being vaccinated in survey 2. Overall low perceived risk, concerns around efficacy, and safety were identified as the most influential factors and associated with 52%-72% (<40 years) and 34%-55% (40+ years) of the unvaccinated individuals. CONCLUSION: Our findings highlighted the most influential beliefs and attitudes on vaccine decision-making and their population-level impacts, which are likely to have significant public health implications exclusively for this population.

Nanoformulations as a strategy to overcome the delivery limitations of cannabinoids.

Medical cannabis has received significant interest in recent years due to its promising benefits in the management of pain, anxiety, depression and neurological and movement disorders. Specifically, the major phytocannabinoids derived from the cannabis plant such as (-) trans-Δ9 -tetrahydrocannabinol (THC) and cannabidiol (CBD), have been shown to be responsible for the pharmacological and therapeutic properties. Recently, these phytocannabinoids have also attracted special attention in cancer treatment due to their well-known palliative benefits in chemotherapy-induced nausea, vomiting, pain and loss of appetite along with their anticancer activities. Despite the enormous pharmacological benefits, the low aqueous solubility, high instability (susceptibility to extensive first pass metabolism) and poor systemic bioavailability restrict their utilization at clinical perspective. Therefore, drug delivery strategies based on nanotechnology are emerging to improve pharmacokinetic profile and bioavailability of cannabinoids as well as enhance their targeted delivery. Here, we critically review the nano-formulation systems engineered for overcoming the delivery limitations of native phytocannabinoids including polymeric and lipid-based nanoparticles (lipid nano capsules (LNCs), nanostructured lipid carriers (NLCs), nanoemulsions (NE) and self-emulsifying drug delivery systems (SEDDS)), ethosomes and cyclodextrins as well as their therapeutic applications.

Standardized naming of microbiome samples in Genomes OnLine Database.

The power of next-generation sequencing has resulted in an explosive growth in the number of projects aiming to understand the metagenomic diversity of complex microbial environments. The interdisciplinary nature of this microbiome research community, along with the absence of reporting standards for microbiome data and samples, poses a significant challenge for follow-up studies. Commonly used names of metagenomes and metatranscriptomes in public databases currently lack the essential information necessary to accurately describe and classify the underlying samples, which makes a comparative analysis difficult to conduct and often results in misclassified sequences in data repositories. The Genomes OnLine Database (GOLD) (https:// gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute has been at the forefront of addressing this challenge by developing a standardized nomenclature system for naming microbiome samples. GOLD, currently in its twenty-fifth anniversary, continues to enrich the research community with hundreds of thousands of metagenomes and metatranscriptomes with well-curated and easy-to-understand names. Through this manuscript, we describe the overall naming process that can be easily adopted by researchers worldwide. Additionally, we propose the use of this naming system as a best practice for the scientific community to facilitate better interoperability and reusability of microbiome data.

Indirect Response of the Temperature, Humidity, and Rainfall on the Spread of COVID-19 over the Indian Monsoon Region.

This article examines the role of the meteorological variable in the spread of the ongoing pandemic coronavirus disease 2019 (COVID-19) across India. COVID-19 has created an unprecedented situation for public health and brought the world to a standstill. COVID-19 had caused more than 1,523,242 deaths out of 66,183,029 confirmed cases worldwide till the first week of December 2020. We have examined the surface temperature, relative humidity, and rainfall over five cities: Delhi, Mumbai, Kolkata, Bengaluru, and Chennai, which were severely affected by COVID-19. It is found that the prevailing southwest (SW) monsoon during the pandemic has acted as a natural sanitizer in limiting the spread of the virus. The mean rainfall is ~ 20-40 mm over the selected cities, resulting in an average decrease in COVID cases by ~ 18-26% for the next 3 days after the rainfall. The day-to-day variations of the meteorological parameters and COVID-19 cases clearly demonstrate that both surface temperature and relative humidity play a vital role in the indirect transport of the virus. Our analysis reveals that most COVID-19 cases fall within the surface temperature range from 24 to 30 °C and relative humidity range from 50% to 80%. At a given temperature, COVID-19 cases show a large dependency on the relative humidity; therefore, the coastal environments were more prone to infections. Wavelet transforms coherence analysis of the daily COVID-19 cases with temperature and relative humidity reveals a significant coherence within 8 days.

Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9.

The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.