The impact of negative factors of professional activities on the health of law enforcement officers.

OBJECTIVE: Aim: To study the study the impact of negative factors of professional activities on the health of law enforcement officers. PATIENTS AND METHODS: Materials and Methods: The research involved 89 law enforcement officers (58 men, and 31 women) who served for a year in practical police units after graduating from a higher educational institution. Methods: analysis and generalization of scientific and methodological literature, anthropometry, physiometry, testing, and statistical methods. The health status was assessed by body mass index, Robinson index, and vital index. RESULTS: Results: It was found that the body mass index of law enforcement officers for one year of service in practical units deteriorated in both men and women by 1.3 kg/m2 and 0.9 kg/m2, respectively. More negative changes were observed in men. At the same time, after one year of service, the number of men with overweight (27.5 %) and even obesity of the first (13.9 %) and second (5.2 %) degrees increased significantly. In women, the changes were less pronounced. The dynamics of Robinson and vital indices, as well as the level of physical fitness, were also negative. CONCLUSION: Conclusions: The results of the research indicate a negative impact of professional factors in terms of professional activities on the health of law enforcement officers. It has been found that adherence to the principles of a healthy lifestyle, in particular, a regimen of rationally organized motor activity, is an important area in combating the impact of negative factors of professional activities on the health of law enforcement officers.

Nuclear Receptors and the Hidden Language of the Metabolome.

Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact with their ligands to regulate downstream gene networks, along with the identification of ligands for orphan NHRs, could enable innovative approaches for cellular engineering, disease modeling and regenerative medicine. We review recent discoveries in the identification of physiologic ligands for NHRs. We propose new models of ligand-receptor co-evolution, the emergence of hormonal function and models of regulation of NHR specificity and activity via one-ligand and two-ligand models as well as feedback loops. Lastly, we discuss limitations on the processes for the identification of physiologic NHR ligands and emerging new methodologies that could be used to identify the natural ligands for the remaining 17 orphan NHRs in the human genome.

Draft genome sequences of Butyrivibrio hungatei DSM 14810 (JK 615T) and Butyrivibrio fibrisolvens DSM 3071 (D1T).

Here, we report the draft genome sequences of two Butyrivibrio-type strains isolated from rumen fluid. The genome sequence of Butyrivibrio hungatei DSM 14810 was 3.3 Mb with 3,093 predicted genes, while the Butyrivibrio fibrisolvens DSM 3071 genome sequence was 4.8 Mb with 4,132 predicted genes.

Draft genome sequence of Nitrobacter vulgaris DSM 10236T.

Here, we report the draft genome sequence of Nitrobacter vulgaris DSM 10236T, a nitrite-oxidizing bacterium isolated from a sewage system in Hamburg, Germany. The genome is 4.3 Mb in size with 4,585 predicted genes, including the full complement of genes necessary for growth on nitrite (narK, nxrA, nxrB, nxrC, and nxrD).

Airborne DNA reveals predictable spatial and seasonal dynamics of fungi.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores3. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms4,5.

Controlling Spatial Morphology of Microparticle Deposits via Thermocapillary Flows: Effect of Boundary Geometry.

The production of particle deposits with a desired distribution geometry has significant potential for materials science, printing, and coating technologies. Most methods for achieving well-defined assemblies rely on the spontaneous evaporation of colloidal solutions on substrates with predetermined properties, or on precise control of particle arrangement by external stimuli. Here, we present a combined method that enables the production of centimeter-scale microparticle deposits with a desired geometric shape. The method is based on controlling the massive transport of microparticles by thermocapillary flow in a layer of volatile liquid in a cell with borders of the desired geometry. Capillary forces cause the liquid to be distributed in the cell, forming corner wetting menisci and the flat layer in the central area. The formation of particle deposits occurs in two stages, determined by the flow regime. At the initial stage, the axisymmetric thermocapillary flow occurs in the flat part of the layer, resulting in the circular shape of the particle deposit. During the transition to the second stage of assembling thermocapillary flow is localized in the corner wetting menisci that results in reshaping the current particle deposit to match the geometry of the cell borders. Here, we demonstrated the creation of circular, square, and triangular shapes of the patterns of polystyrene microparticles using a point heater located at the geometric center of the cell. The proposed method is reliable, easy to implement, and potentially capable of producing a wide variety of deposit geometries, making it an attractive technique for patterning and modifying surface properties with particles of any type.

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.

Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA.

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.

Experimental evidence that root-associated fungi improve plant growth at high altitude.

Unravelling how species communities change along environmental gradients requires a dual understanding: the direct responses of the species to their abiotic surroundings and the indirect variation of these responses through biotic interactions. Here, we focus on the interactive relationships between plants and their symbiotic root-associated fungi (RAF) along stressful abiotic gradients. We investigate whether variations in RAF community composition along altitudinal gradients influence plant growth at high altitudes, where both plants and fungi face harsher abiotic conditions. We established a translocation experiment between pairs of Bistorta vivipara populations across altitudinal gradients. To separate the impact of shifting fungal communities from the overall influence of changing abiotic conditions, we used a root barrier to prevent new colonization by RAF following translocation. To characterize the RAF communities, we applied DNA barcoding to the root samples. Through the utilization of joint species distribution modelling, we assessed the relationship between changes in plant functional traits resulting from experimental treatments and the corresponding changes in the RAF communities. Our findings indicate that RAF communities influence plant responses to stressful abiotic conditions. Plants translocated from low to high altitudes grew more when they were able to associate with the resident high-altitude RAF compared to those plants that were not allowed to associate with the resident RAF. We conclude that interactions with RAF impact how plants respond to stressful abiotic conditions. Our results provide experimental support that interactions with RAF improve plant stress tolerance to altitudinal stressors such as colder temperatures and less nutrient availability.

Parasympathetic neurons derived from human pluripotent stem cells model human diseases and development.

Autonomic parasympathetic neurons (parasymNs) control unconscious body responses, including "rest-and-digest." ParasymN innervation is important for organ development, and parasymN dysfunction is a hallmark of autonomic neuropathy. However, parasymN function and dysfunction in humans are vastly understudied due to the lack of a model system. Human pluripotent stem cell (hPSC)-derived neurons can fill this void as a versatile platform. Here, we developed a differentiation paradigm detailing the derivation of functional human parasymNs from Schwann cell progenitors. We employ these neurons (1) to assess human autonomic nervous system (ANS) development, (2) to model neuropathy in the genetic disorder familial dysautonomia (FD), (3) to show parasymN dysfunction during SARS-CoV-2 infection, (4) to model the autoimmune disease Sjögren's syndrome (SS), and (5) to show that parasymNs innervate white adipocytes (WATs) during development and promote WAT maturation. Our model system could become instrumental for future disease modeling and drug discovery studies, as well as for human developmental studies.

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.

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.

Morphology and Phase Compositions of FePt and CoPt Nanoparticles Enriched with Noble Metal.

The article reveals for the first time the features of nanoparticle morphology, phase compositions, and their changes when heating FePt and CoPt nanoalloys. Nanoparticles were obtained by co-reduction of precursor solution mixtures with hydrazine hydrate. The features were found by a complex of methods of X-ray diffraction (in situ XRD and X-ray scattering), TEM HR, and cyclic voltammetry. In addition, adsorbometry results were obtained, and the stability of different nanocluster structures was calculated by the molecular dynamics method. There were only FCC solid solutions in the X-ray patterns of the FePt and CoPt nanoalloys. According to XRD, in the case of nanoparticle synthesis with Fe and Co content less than 10 at. %, the composition of solid solutions was close to or practically equal to the composition of the as-synthesized nanoparticles quantified by inductively coupled plasma optical emission spectrometry. For systems synthesis with Fe and Co content greater than the above, the solubility limits (SLs) of Fe and Co in Pt were set 11.4 ± 0.7 at. % and 17.5 ± 0.6 at. %, respectively. Therefore, there were non-registered XRD extra-phases (XRNDPh-1) in the systems when CFe,Co ≥ SL. This statement was supported by the results of TEM HR and X-ray scattering: the smallest nanocrystals (1-2 nm) and amorphous particles were found, which qualitatively agreed with the sorbometry and SAXS results. Molecular dynamics calculations of stability for FePt and CoPt alloys claimed the structures of the most stable phase corresponded to phase diagrams (A1 and L12). Specific peculiarities of the morphology and compositions of the solid solutions of nanoalloys were established: structural blockiness (domain) and composition heterogeneity, namely, platinum enrichment of internal (deep) layers and homogenization of the nanoalloy compositions at relatively low temperatures (130-200 °C). The suggested model of the formation of nanoalloys during the synthesis, qualitatively, was compliant with the results of electrochemical deposition of FePt films on the surface of various electrodes. When nanocrystals of solid solutions (C(Fe, Co) < SL) were heated above specific temperatures, there were phase transformations with the formation of two-phase regions, with solid solutions enriched with platinum or iron (non-registered XRD phase XRNDPh-2). The newly formed phase was most likely intermetallic compounds, FePt3, CoPt3. As a result of the study, the model was developed, taking into account the nanoscale of the particles: XRDPh (A1, FeaPt1-a) → XRDPh (A1, Fem×a-xPtm-m×a+x) + XRNDPh-2 (Fen×a+yPtn-n×a-y) (here, m + n = 1, m ≤ 1, n ≤ 1).

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Trends in Outcome of Hematopoietic Stem Cell Transplantation: 5000 Transplantations and 30 Years of Single-Center Experience.

In this single-center analysis, we evaluated the trends in 5185 hematopoietic cell transplantations performed between 1990 and 2022. The study group comprised 3237 allogeneic (alloHCT) and 1948 autologous (autoHCT) hematopoietic cell transplantations. In the multivariate analysis, there was an improvement in event-free-survival (EFS) after autoHCT (HR 0.6, 95% CI 0.4-0.7, p < 0.0001) due to reduced cumulative incidence of relapse in the last five years (56% in 2010-2014 vs. 38% in 2015-2022). An improvement in EFS after alloHCT over time was observed (HR 0.33, 95% CI 0.23-0.48, p < 0.0001), which was due to reduced non-relapse mortality. No difference in cumulative relapse incidence was observed over the last decade for allografted patients. Survival after autoHCT improved in Hodgkin's disease (HR 0.1, 95% CI 0.1-0.3), multiple myeloma (HR 0.4, 95% CI 0.2-0.7) and solid tumors (HR 0.2, 95% CI 0.2-0.4), while after alloHCT, improvement was observed in acute myeloid leukemia (HR 0.3, 95% CI 0.1-0.5), acute lymphoblastic leukemia (HR 0.2, 95% CI 0.1-0.5), Hodgkin's disease (HR 0.1, 95% CI 0.0-0.4), non-Hodgkin's lymphomas and chronic lymphocytic leukemia (HR 0.2, 95% CI 0.0-0.6), inborn diseases (HR 0.2, 95% CI 0.2-0.4) and acquired aplastic anemia with matched related donors and matched unrelated donors (HR 0.3, 95% CI 0.2-0.8).

Unraveling the functional dark matter through global metagenomics.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Genome sequences of key bacterial symbionts of entomopathogenic nematodes: Xenorhabdus cabanillasii DSM17905, Xenorhabdus ehlersii DSM16337, Xenorhabdus japonica DSM16522, Xenorhabdus koppenhoeferii DSM18168, and Xenorhabdus mauleonii DSM17908.

Xenorhabdus species are bacterial symbionts of entomopathogenic Steinernema nematodes, in which they produce diverse secondary metabolites implicated in pathogenesis. To expand resources for natural product prospecting and exploration of host-symbiont-pathogen relationships, the genomes of Xenorhabdus cabanillasi, Xenorhabdus ehlersii, Xenorhabdus japonica, Xenorhabdus koppenhoeferii, and Xenorhabdus mauleonii were analyzed.

Prediction of Distant Metastases in Patients with Kidney Cancer Based on Gene Expression and Methylation Analysis.

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive histological type of cancer in this location. Distant metastases are present in approximately 30% of patients at the time of first examination. Therefore, the ability to predict the occurrence of metastases in patients at early stages of the disease is an urgent task aimed at personalized treatment. Samples of tumor and paired histologically normal kidney tissue from patients with metastatic and non-metastatic ccRCC were studied. Gene expression was analyzed using real-time PCR. The level of gene methylation was evaluated using bisulfite conversion followed by quantitative methylation-specific PCR. Two groups of genes were analyzed in this study. The first group includes genes whose expression is significantly reduced during metastasis: CA9, NDUFA4L2, EGLN3, and BHLHE41 (p < 0.001, ROC analysis). The second group includes microRNA genes: MIR125B-1, MIR137, MIR375, MIR193A, and MIR34B/C, whose increased methylation levels are associated with the development of distant metastases (p = 0.002 to <0.001, ROC analysis). Based on the data obtained, a combined panel of genes was formed to identify patients whose tumors have a high metastatic potential. The panel can estimate the probability of metastasis with an accuracy of up to 92%.