Version Of Database Research Articles

Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv

ABSTRACT Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

Identification of Hub Genes and Pathways of Middle Cerebral Artery Occlusion in Aged Rats Using the Gene Expression Omnibus Database.

Stroke remained the leading cause of disability in the world, and the most important non-modifiable risk factor was age. The treatment of stroke for elder patients faced multiple difficulties due to its complicated pathogenesis and mechanism. Therefore, we aimed to identify the potential differentially expressed genes (DEGs) and singnalling pathways for aged people of stroke. To compare the DEGs in the aged rats with or without middle cerebral artery occlusion (MCAO) and to analyse the important genes and the key signaling pathways involved in the development of cerebral ischaemia in aged rats. The Gene Expression Omnibus (GEO) analysis tool was used to analyse the DEGs in the GSE166162 dataset of aged MCAO rats compared with aged sham rats. Differential expression analysis was performed in aged MCAO rats and sham rats using limma. In addition, the 74 DEGs (such as Fam111a, Lcn2, Spp1, Lgals3 and Gpnmb were up-regulated; Egr2, Nr4a3, Arc, Klf4 and Nr4a1 were down-regulated) and potential compounds corresponding to the top 20 core genes in the Protein-Protein Interaction (PPI) network was constructed using the STRING database (version 12.0). Among these 30 compounds, resveratrol, cannabidiol, honokiol, fucoxanthin, oleandrin and tyrosol were significantly enriched. These DEGs were subjected to Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the most significantly enriched pathway in aged MCAO rats. Moreover, innate immune response, the complement and coagulation cascades signaling pathway, the IL-17 and other signaling pathways were significantly correlated with the aged MCAO rats. Our study indicates that multiple genes and pathological processes involved in the aged people of stroke. The immune response might be the key pathway in the intervention of cerebral infarction in aged people.

Development of interactive maps of geolocations of native plants of Kazakhstan and the living collections of the Mangyshlak experimental botanical garden

The purpose of this article is to identify problems, demonstrate results and assess the prospects for compiling interactive digital maps of native plants of Kazakhstan and the MEBG living collections, to be distributed through the Internet. Here we present four interactive maps: MEBG collections in the 10th sub-district of Aktau; herbarium specimens of plants from the Garden’s gene pool; samples of the herbarium of the natural flora of Western Kazakhstan and plants of the natural flora of Kazakhstan. Standard web programming languages are used to compile Internet maps. The creation of html pages and server versions of databases is carried out automatically in specially developed computer program modules. They are compiled in MEBS for the dry conditions of Mangistau.

Influence of data selection on aquatic ecotoxicity characterization factors for selected persistent and mobile substances

PurposeThis paper addresses the lack of ecotoxicity characterization factors (CFs) for persistent and mobile (PM) chemicals in life cycle assessment. The specific aims are (1) to provide CFs for 64 selected chemicals and benchmark them against the USEtox database, (2) to propose an ecotoxicity data harmonization strategy, and (3) to analyze the influence of ecotoxicity data sources and data harmonization strategies.MethodsSixty-four per- and polyfluoroalkyl substances (PFAS), triazines, and triazoles were selected. An ecotoxicity data harmonization strategy was developed to ensure data consistency. A screening strategy for including transformation products was also developed. Existing CFs in the USEtox organic substances database (version 2.01) were identified, and new CFs were calculated based on available data sources. The USEtox model (version 2.13) was used for calculations, and the collected ecotoxicity data from different sources were varied to test their influence.Results and discussionThe ecotoxicity data harmonization strategy excluded a considerable amount of incompatible data. To the list of 64 chemicals, 3 transformation products were added. Out of the 67 chemicals, experimental ecotoxicity data were available for 47, leading to the use of in silico tools and average values to fill data gaps for 20 chemicals. CFs for 67 chemicals could thus be provided. Comparing CFs calculated based on experimental and estimated ecotoxicity data reveals that the estimated data leads to considerably different CF values.ConclusionsThe paper provides strategies for ecotoxicity data harmonization and the inclusion of transformation products. The limited availability of experimental ecotoxicity data and differing results of the applied estimation methods highlight the need for further development of estimation methods. In addition, further development in including transformation products is recommended, which is particularly relevant for PM chemicals.

Nationally Representative Trends in Incidence of Procedures Done Concomitantly With Primary and Revision Total Ankle From 2012 to 2020

Background As new literature emerges and practice patterns fluctuate, there is a significant potential for variation with regard to adjunctive procedures performed with primary total ankle arthroplasty (pTAA) and revision total ankle arthroplasty (rTAA). Our study aims to evaluate yearly trends in the incidence of concomitant procedures and compare the incidence of adjunctive procedures between pTAA and rTAA. Methods The 2012-2020 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all pTAA and rTAA. The number of concurrent procedures was compared between pTAA and rTAA and compared across years available in the most current version of the NSQIP database (2012-2020). Results Patients undergoing pTAA had a higher mean age than patients undergoing rTAA (64 vs 61 years; P < .001), and patients undergoing rTAA had higher wound class (P < .001), indicating higher levels of contamination. Concurrent procedures were performed significantly less frequently with pTAA than rTAA (mean procedures 0.82 vs 1.30; P < .001). Removal of deep implants was more commonly coded with rTAA than pTAA (9.0% vs 17.7%; P < .001). Gastrocnemius recession was more frequently performed with pTAA than rTAA (10.4% vs 3.0%; P = .001). Conclusion As pTAA and rTAA increase in incidence throughout the United States, there is increased importance on furthering our understanding of these procedures. This study provides a nationally representative analysis of adjunctive procedures with pTAA and rTAA from 2012 to 2020. Generally, more adjunctive procedures are performed with rTAA versus pTAA, confirming the high complexity of rTAA. Levels of Evidence: Level III

Nurses Job Satisfaction, Patients Satisfaction, and Quality of Care in Nnamdi Azikiwe Teaching Hospital Nnewi, Anambra State, Nigeria

Nurses are the largest workforce in healthcare facilities, providing 50% - 80% of healthcare services. They play important role in providing quality care to patients, which is important in healthcare. This study determined job satisfaction, patient satisfaction and quality of care of nurses in Nnamdi Azikiwe University Teaching Hospital Nnewi, Anambra State. A descriptive cross-sectional survey was used. A self-administered questionnaire was used to obtain information from 208 nurses, 138 patients and 36 head nurses using simple randomization technique. A pilot testing was carried out with a reliability index of 0.81. Data analysis was performed using Statistical Software for Social Sciences (SPSS) database version 25. Chi square was used to test the significance hypotheses of p < 0.05.The majority of nurses 82 (39.42%) were between ages 41-50 while most of them, 48 (23.08%) had between 11-15 years of experience. Most of the recruited patients (43.48%) were between the ages of 31-46 years and most respondents were female (52.17%). The results also show that the majority of nurses (55.3%) were dissatisfied with their jobs. 84.8% of the patients were satisfied with the quality of nursing care. According to head nurses reports, nurses provided quality care to patients. Communication and team building, training, leadership and management, salary, work itself, work environment, interpersonal relationship were significantly positively associated with overall job satisfaction. Job satisfaction of nurses in this research area was slightly low. On the other hand, patients are quite satisfied with the level of care. Despite nurses’ job satisfaction, they still provide quality care to patients. It recommended that hospital management and government should consider all components of job satisfaction.

The impacts of future sectoral change on the greenhouse gas emissions of construction materials for Swiss residential buildings

Life Cycle Assessment (LCA) is a comprehensive method for evaluating building sustainability. However, most studies have relied on generic or national LCA databases, overlooking the systematic consideration of future scenarios and sectoral developments due to data and tool constraints. Moreover, there's a gap in providing a standardized workflow for such evaluations and understanding their impact on the greenhouse gas (GHG) emissions of buildings on a national level. Addressing these gaps, our study incorporates scenarios from two Integrated Assessment Models (IAMs), considering the future developments of the electricity, cement, and steel sectors, to assess embodied GHG emissions of Swiss residential buildings from 2015 to 2055. Our findings reveal that incorporating these future considerations can lead up to a 25% GHG emissions reduction by 2055 compared to 2015, primarily due to GHG emission reductions in concrete and brick as a result of these considerations. This is much less than the emissions reduction potential of some specific construction materials and far from the goal of net zero. It also underscores the importance of exploring alternative strategies for net-zero GHG emissions at the building stock level. Consequently, we present reproducible workflows using open-source tools and future versions of the Swiss national building LCA databases, offering a blueprint for similar future studies in other countries.

The role of intracranial pressure variability as a predictor of intracranial hypertension and mortality in critically ill patients.

Intracranial pressure (ICP) monitoring is a widely utilized and essential tool for tracking neurosurgical patients, but there are limitations to the use of a solely ICP-based paradigm for guiding management. It has been suggested that ICP variability (ICPV), in addition to mean ICP, may be a useful predictor of neurological outcomes, as it represents an indirect measure of intact cerebral pressure autoregulation. However, the current literature regarding the applicability of ICPV shows conflicting associations between ICPV and mortality. Thus, the authors aimed to investigate the effect of ICPV on intracranial hypertensive episodes and mortality using the eICU Collaborative Research Database version 2.0. The authors extracted from the eICU database 1,815,676 ICP readings from 868 patients with neurosurgical conditions. ICPV was computed using two methods: the rolling standard deviation (RSD) and the absolute deviation from the rolling mean (DRM). An episode of intracranial hypertension was defined as at least 25 minutes of ICP > 22 mm Hg in any 30-minute window. The effects of mean ICPV on intracranial hypertension and mortality were computed using multivariate logistic regression. A recurrent neural network with long short-term memory was used for time-series predictions of ICP and ICPV to prognosticate future episodes of intracranial hypertension. A higher mean ICPV was significantly associated with intracranial hypertension using both ICPV definitions (RSD: aOR 2.82, 95% CI 2.07-3.90, p < 0.001; DRM: aOR 3.93, 95% CI 2.77-5.69, p < 0.001). ICPV was significantly associated with mortality in patients with intracranial hypertension (RSD: aOR 1.28, 95% CI 1.04-1.61, p = 0.026, DRM: aOR 1.39, 95% CI 1.10-1.79, p = 0.007). In the machine learning models, both definitions of ICPV achieved similarly good results, with the best F1 score of 0.685 ± 0.026 and an area under the curve of 0.980 ± 0.003 achieved with the DRM definition over 20 minutes. ICPV may be useful as an adjunct for the prognostication of intracranial hypertensive episodes and mortality in neurosurgical critical care as part of neuromonitoring. Further research on predicting future intracranial hypertensive episodes with ICPV may help clinicians react expediently to ICP changes in patients.

Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5)

Abstract. Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Identification of Candidate mRNA Genes and Their Potential MicroRNA Targets in Lung Cancer Induced by Smoking Tobacco.

Smoking is considered the single highest risk factor for lung cancer and has been suggested to be associated with accelerated somatic mutations in respiratory mucosa that lead to the development of lung cancer. MicroRNAs serve as modulators in smoking-induced mRNA gene expression changes in the human airway epithelium and are linked to the development of lung cancer. The thermodynamics in the microRNA (miRNA)-mRNA interactions may be affected in tobacco smokers, consequently, leading to phenotypic variations in lung cancer patients. Therefore, this study aimed to investigate the impact of smoking tobacco on somatic mutations in mRNA genes and assess their potential impact on miRNA-mRNA interactions in lung cancers. The clinically significant pathogenic variants in mRNA genes in the dataset in lung cancer cases linked to smoking tobacco (n = 330) were obtained from the Cancer Atlas database (TCGA, http://cancergenome.nih.gov/) and used to assess the potential role of tobacco consumption in driving the genetic alterations in proto-oncogenes associated with lung cancer. The analysis of the miRNA interaction with the top five altered mRNA proto-oncogenes in lung cancer cases due to tobacco consumption was performed using the target prediction function in the miRDP program (Database version 5.2.3.1, https://mirdb.org/). We identified the top five mRNA proto-oncogenes enriched with simple somatic mutations (SSM) in lung cancer were TP53, EGFR, KRAS, FAT4, and KMT2D. Interestingly, we observed the highest incidence of SSM in the Tumor Protein p53 (TP53) gene at 63.64%. Similarly, the SSM incidence was 23.94% in the Epidermal Growth Factor Receptor (EGFR), 22.12% in the Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), 18.48% in the FAT Atypical Cadherin 4 (FAT4), and 14.24% in the Lysine (K)-Specific Methyltransferase 2D (KMT2D) genes. Subsequently, we used a bioinformatics approach to assess the effect of miRNA-mRNA interactions in lung cancer among the top five SSM-enriched mRNA proto-oncogenes. Among the top 20 identified and selected miRNAs, we observed 18 unique microRNAs that bind specifically to TP53, KRAS, and FAT4 genes and 17 and 19 microRNAs that exclusively bind with the EGFR and KMT2D genes, respectively. Our study found that the top five SSM-enriched mRNA proto-oncogenes in lung cancers among tobacco smokers were TP53, EGFR, KRAS, FAT4, and KMT2D. Further, our results provide an important insight into the involvement of the intricate network of mRNA-miRNA interactions in the development of lung cancer.

Leaf carbon and nitrogen stoichiometric variation along environmental gradients

Abstract. Leaf stoichiometric traits are central to ecosystem function and biogeochemical cycling, yet no accepted theory predicts their variation along environmental gradients. Using data in the China Plant Trait Database version 2, we aimed to characterize variation in leaf carbon and nitrogen per unit mass (Cmass, Nmass) and their ratio and to test an eco-evolutionary optimality model for Nmass. Community-mean trait values were related to climate variables by multiple linear regression. Climatic optima and tolerances of major genera were estimated; Pagel's λ was used to quantify phylogenetic controls, and Bayesian phylogenetic linear mixed models to assess the contributions of climate, species identity, and phylogeny. Optimality-based predictions of community-mean Nmass were compared to observed values. All traits showed strong phylogenetic signals. Climate explained only 18 % of C:N ratio variation among species but 45 % among communities, highlighting the role of taxonomic replacement in mediating community-level responses. Geographic distributions of deciduous taxa were separated primarily by moisture and evergreens by temperature. Cmass increased with irradiance but decreased with moisture and temperature. Nmass declined with all three variables. C:N ratio variations were dominated by Nmass. The coefficients relating Nmass to the ratio of maximum carboxylation capacity at 25 ∘C (Vcmax25) and leaf mass per area (Ma) were influenced by leaf area index. The optimality model captured 68 % and 53 % of variation between communities for Vcmax25 and Ma, respectively, and 21 % for Nmass. We conclude that stoichiometric variations along climate gradients are achieved largely by environmental selection among species and clades with different intraspecific trait values. Variations in leaf C:N ratio are mainly determined by Nmass, and optimality-based modelling shows useful predictive ability for community-mean Nmass. These findings should help to improve the representation of C:N coupling in ecosystem models.

Association between chronic obstructive pulmonary disease and 28-day mortality in patients with sepsis: a retrospective study based on the MIMIC-III database

BackgroundSepsis is a common cause of mortality in critically ill patients, and chronic obstructive pulmonary disease (COPD) is one of the most common comorbidities in septic patients. However, the impact of COPD on patients with sepsis remained unclear. Therefore, the purpose of this study aimed to assess the effect of COPD on the prognosis of septic patients based on Medical Information Mart for Intensive Care (MIMIC-III) database.MethodsIn this retrospective study based on the (MIMIC)-III database version 1.4 (v1.4), we collected clinical data and 28-day all-cause mortality from patients with sepsis in intensive care unit (ICU) and these patients met the diagnostic criteria of Sepsis 3 on ICU admission between 2008 and 2012. International Classification of Diseases (ICD-9) (4660, 490, 4910, 4911, 49120, 49121, 4918, 4919, 4920, 4928, 494, 4940, 4941, 496) was used to identified COPD. We applied Kaplan–Meier analysis to compare difference of 28-day all-cause mortality between septic patients with and without COPD. Cox proportional-hazards model was applied to explore the risk factor associated with 28-day all-cause mortality in patients with sepsis.ResultsSix thousand two hundred fifty seven patients with sepsis were included in this study, including 955 (15.3%) patients with COPD and 5302 patients without COPD (84.7%). Compared with patients without COPD, patients with COPD were older (median: 73.5 [64.4, 82.0] vs 65.8 [52.9, 79.1], P < 0.001), had higher simplified acute physiology score II (SAPSII) (median: 40.0 [33.0, 49.0] vs 38.0 [29.0,47.0], P < 0.001) and greater proportion of mechanical ventilatory support (MV) (55.0% vs 48.9%, P = 0.001). In our study, septic patients with COPD had higher 28-day all-cause mortality (23.6% vs 16.4%, P < 0.001) than patients without COPD. After adjusting for covariates, the results showed that COPD was an independent risk factor for the 28-day all-cause mortality of patients with sepsis (HR 1.30, 95%CI: 1.12–1.50, P = 0.001).ConclusionsCOPD was an independent risk factor of 28-day all-cause mortality in septic patients. Clinically, septic patients with COPD should be given additional care.

Using Next Generation Sequencing to Identify Trackable Clonotypic Sequences for Minimal Residual Disease Testing in AL Amyloidosis

Introduction: Recent studies have shown that deeper free light chain responses are associated with improved outcomes in patients with systemic light chain (AL) amyloidosis, yet the optimal long-term goal remains unclear.(N Engl J Med 2018; 378:518-528) Unlike multiple myeloma, where the role of minimal residual disease (MRD) testing is well established, in AL amyloidosis the methods to best identify MRD, and whether achieving MRD would benefit disease outcomes, warrant further investigation. (Blood Cancer J. 2021 Jun 21;11(6):117; PLoS ONE 15(7): e0235713. https://doi.org/10.1371/journal.pone. 0235713) In AL amyloidosis, the toxic misfolded oligomers and amyloid deposits are both due to clonal immunoglobulin light chains and cause organ damage and death. Hence, the tracking of clonotypic light chain sequences could present a promising approach for detecting the maintenance of profound therapeutic responses. Our study aimed to assess the frequency with which next generation sequencing (NGS) detects light-chain clones consistent with the patient's clonal light-chain isotype and to determine whether the light-chain sequences identified were encoded by light-chain variable region genes considered AL-related.(Journal of Clinical Oncology 2019 37:15_suppl, 8010; Blood (2017) 129 (3): 299-306; PLoS One 2022 Feb 25;17(2):e0264407) Methods: Ninety patients diagnosed with AL amyloidosis between September 2016 and July 2023 at Tufts Medical Center and University of California San Francisco were included. Bone marrow samples from the 90 patients were sent to Adaptive Biotechnologies Inc. (Seattle, WA) for initial clone identification using the clonoSEQ Assay. IMGT/BlastSearch database (Version 1.2.1) was used to identify the clonotypic immunoglobulin light chain variable domains detected by the clonoSEQ assay. Samples were then divided in to two groups based on the consistency of paraprotein isotype with the trackable sequences, and Mann-Whitney U test was used to compare the difference of the numbers of clonotypic sequences. Results: Of the 90 patients with bone marrow samples sent for clone identification, 85 (94.4%) had at least one trackable clonotypic sequence, with a median number of 3 and a maximum of 11. Of the 85 patients with trackable sequences, 67 (78.8%) had an AL paraprotein isotype consistent with the detected clonotypic sequence isotype, 48 with light chain and 19 with heavy chain isotypes. Compared to the inconsistent ones, patients who had consistency in paraprotein and trackable sequences exhibited higher numbers of clonotypic sequences ( P = 0.001). With respect to marrow plasma cell infiltrations, both consistent and inconsistent groups had a median of 15% marrow plasma cells with no statistically significant difference between them by two-tailed Mann-Whitney. Of note, consistent with the restricted pattern of germline gene use in AL, the 48 patients also used AL-related variable region Ig light chain genes; the variable region light chain genes identified were AL-related and included IGKV1-33 (8 identified sequences), IGKV4-1 (4), IGKV1-16 (3), IGKV1-5 (1), GKV1-13 (1), IGLV6-57 (8), IGLV3-21 (6), IGLV1-44 (5), IGLV3-1 (4), IGLV1-40 (3), IGLV2-14 (3), IGLV1-51 (2). Conclusion: In patients with AL amyloidosis, the ClonoSEQ assay can identify a sequence consistent with the patient's light or heavy chain isotype almost 80% of the time. Of those cases in which a clonal light chain sequence was consistent with the patient's isotype, the genes identified were from the well described AL-related light chain variable region gene repertoire. These findings indicate that the size of the AL clone does not affect this outcome and suggest that other factors related to the adaptive immune dysregulation of AL or to the multiplex PCR process may be involved. ClonoSEQ can identify trackable clonotypic light chain sequences that are AL-related 56% of the time in patients with AL, a finding that may be relevant to patients with myeloma.

MiRiQ Database: A Platform forIn Silico Rice Mutant Screening.

Genetic studies using mutant resources have significantly contributed to elucidating plant gene function. Massive mutant libraries sequenced by next-generation sequencing technology facilitate mutant identification and functional analysis of genes of interest. Here, we report the creation and release of an open-access database (https://miriq.agr.kyushu-u.ac.jp/index.php), called Mutation-induced Rice in Kyushu University (MiRiQ), designed for in silico mutant screening based on a whole-genome-sequenced mutant library. This database allows any user to easily find mutants of interest without laborious efforts such as large-scale screening by PCR. The initial version of the MiRiQ database (version 1.0) harbors a total of 1.6 million single-nucleotide variants (SNVs) and InDels of 721 M1 plants that were mutagenized by N-methyl-N-nitrosourea treatment of the rice cultivar Nipponbare (Oryza sativa ssp. japonica). The SNVs were distributed among 87% of all 35,630 annotated protein-coding genes of the Nipponbare genome and were predicted to induce missense and nonsense mutations. The MiRiQ database provides built-in tools, such as a search tool by keywords and JBrowse for mutation searches. Users can request mutant seeds in the M2 or M3 generations from a request form linked to this database. We believe that the availability of a wide range of gene mutations in this database will benefit the plant science community and breeders worldwide by accelerating functional genomic research and crop improvement.

Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches. Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality. Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts. We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.

The Russia-Ukraine War and Its Impact on the Food Security of Korea, China, and Japan: A CGE Approach

Purpose - In 2021, Ukraine and Russia exported 12% and 17% of global wheat, being significant grain producers and exporters. However, Russia’s invasion of Ukraine greatly impacts Ukraine’s wheat production and trade. In this regard, this study examines the impact of Russia’s invasion of Ukraine on global food security using the computable general equilibrium approach.&#x0D; Design/Methodology/Approach - We utilized the global multi-sector standard GTAP model, specifically the GTAP Database Version 11, with a base year of 2017. For this research purpose, the original 160 regions and 65 sectors of the GTAP database were aggregated into 23 regions and 24 sectors. Due to the uncertain nature of the conflict duration between Russia and Ukraine, we designed three scenarios: Slight, Medium, and Severe. Each scenario represents varying degrees of wheat production, trade disruption, and an increase in wheat production by third producers for shocks.&#x0D; Findings - The study reveals several effects: real GDP decreases for Ukraine, Russia, Turkey, India, and Georgia, while countries like Egypt, Turkey, Azerbaijan, India, Korea, and Japan experience diminished economic welfare. Russia’s invasion can affect wheat imports of countries like Turkey, India, Mongolia, and Egypt. Ukraine, Egypt, Turkey, and India registered trade surpluses, while others recorded deficits&#x0D; Research Implications - The ongoing conflict complicates assessing long-term consequences on global food security. Policymakers are advised to monitor closely, implement strategies to mitigate disruptions, and foster international collaboration to alleviate adverse effects on trade, economies, and food security, particularly of nations heavily reliant on Ukraine’s wheat exports, like Egypt, Turkey, Mongolia, Georgia, and Azerbaijan.

The antiSMASH database version 4: additional genomes and BGCs, new sequence-based searchesand more.

Many microorganisms produce natural products that are frequently used in the development of medicines and crop protection agents. Genome mining has evolved into a prominent method to access this potential. antiSMASH is the most popular tool for this task. Here we present version 4 of the antiSMASH database, providing biosynthetic gene clusters detected by antiSMASH 7.1 in publicly available, dereplicated, high-quality microbial genomes via an interactive graphical user interface. In version 4, the database contains 231534 high quality BGC regions from 592 archaeal, 35726 bacterialand 236 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers

BackgroundVenous thromboembolism (VTE) is a severe complication in critically ill patients, often resulting in death and long-term disability and is one of the major contributors to the global burden of disease. This study aimed to construct an interpretable machine learning (ML) model for predicting VTE in critically ill patients based on clinical features and laboratory indicators.MethodsData for this study were extracted from the eICU Collaborative Research Database (version 2.0). A stepwise logistic regression model was used to select the predictors that were eventually included in the model. The random forest, extreme gradient boosting (XGBoost) and support vector machine algorithms were used to construct the model using fivefold cross-validation. The area under curve (AUC), accuracy, no information rate, balanced accuracy, kappa, sensitivity, specificity, precision, and F1 score were used to assess the model's performance. In addition, the DALEX package was used to improve the interpretability of the final model.ResultsThis study ultimately included 109,044 patients, of which 1647 (1.5%) had VTE during ICU hospitalization. Among the three models, the Random Forest model (AUC: 0.9378; Accuracy: 0.9958; Kappa: 0.8371; Precision: 0.9095; F1 score: 0.8393; Sensitivity: 0.7791; Specificity: 0.9989) performed the best.ConclusionML models can be a reliable tool for predicting VTE in critically ill patients. Among all the models we had constructed, the random forest model was the most effective model that helps the user identify patients at high risk of VTE early so that early intervention can be implemented to reduce the burden of VTE on the patients.

Gene Transfer-Based Phylogenetics: Analytical Expressions and Additivity via Birth-Death Theory.

The genomic era has opened up vast opportunities in molecular systematics, one of which is deciphering the evolutionary history in fine detail. Under this mass of data, analyzing the point mutations of standard markers is often too crude and slow for fine-scale phylogenetics. Nevertheless, genome dynamics (GD) events provide alternative, often richer information. The synteny index (SI) between a pair of genomes combines gene order and gene content information, allowing the comparison of genomes of unequal gene content, together with order considerations of their common genes. Recently, genome dynamics has been modeled as a continuous-time Markov process, and gene distance in the genome as a birth-death-immigration process. Nevertheless, due to complexities arising in this setting, no precise and provably consistent estimators could be derived, resulting in heuristic solutions. Here, we extend this modeling approach by using techniques from birth-death theory to derive explicit expressions of the system's probabilistic dynamics in the form of rational functions of the model parameters. This, in turn, allows us to infer analytically accurate distances between organisms based on their SI. Subsequently, we establish additivity of this estimated evolutionary distance (a desirable property yielding phylogenetic consistency). Applying the new measure in simulation studies shows that it provides accurate results in realistic settings and even under model extensions such as gene gain/loss or over a tree structure. In the real-data realm, we applied the new formulation to unique data structure that we constructed-the ordered orthology DB-based on a new version of the EggNOG database, to construct a tree with more than 4.5K taxa. To the best of our knowledge, this is the largest gene-order-based tree constructed and it overcomes shortcomings found in previous approaches. Constructing a GD-based tree allows to confirm and contrast findings based on other phylogenetic approaches, as we show.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/.