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A novel water quality risk assessment framework for reservoir water bodies coupling key parameter selection and dynamic warning threshold determination
Scientific naming conventions should keep in step with contemporary science
Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN)
Abstract The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and survival. To enhance emergency preparedness, decision trees are used to analyze historical data and predict ambulance demand in specific locations over time. This helps in planning the necessary number of ambulances in advance. In situations where ambulance resources are limited, a support vector machine (SVM) evaluates patient data to optimize the distribution of available ambulances, ensuring that the most critical patients receive timely medical attention. For real-time route optimization, a convolutional neural network (CNN)-based deep learning model is used to adjust ambulance routes based on current traffic and road conditions, achieving an accuracy of 99.15%. By improving dispatch efficiency and communication, the proposed machine learning-based ambulance system reduces the burden on emergency services, enhancing overall effectiveness, particularly during peak demand periods.
Calcium modulates growth and biofilm formation of Lactobacillus acidophilus ATCC 4356 and Lactiplantibacillus plantarum ATCC 14917
Coping with five mismatches between policy and practice in hemiboreal forest stands and landscapes
Abstract Maintenance of forest ecosystems revolves around the long-term persistence and resilience of their components, structures and functions. Focusing on Europe’s hemiboreal forests, we evaluate mismatches between naturally dynamic forest ecosystems and current forest management systems forming obstacles for developing closer-to-nature forest management. Using Lithuania as a case study, we (i) quantify the main forest vegetation community types using soil types, ground layer flora, and tree and shrub species, (ii) review the relationships among these vegetation communities and their predicted natural disturbance regimes, (iii) analyse changes in tree species composition, (iv) compare the life expectancy of trees with harvest age, and (v) compare the contemporary stand age distributions with predicted natural disturbance regimes stand age distributions. Results show five mismatches between current practices and policy visions. Despite identifying 17 natural hemiboreal forest vegetation communities only eight dominant stand tree species were reported in current forestry reporting. The areal extents of three different natural disturbance regimes were: gap dynamics - mixed broadleaved forests on wet-mesic very fertile sites (22%), succession - mixed spruce forests on fertile sites (49%), and cohort dynamics - Scots pine forest on poor fertility sites (30%). Changes in tree species composition showed declines of primary tree species of 12–71% for the three disturbance regimes. The ratio of natural expected life expectancy to harvest age varied from two-fold to eight-fold across different tree species. Stand age distributions in naturally dynamic forests and managed forests revealed a current dramatic deficit of old-growth stands. Coping with the five identified mismatches between natural forests and current forest management requires multiple solutions: (1) closer-to-nature forest management that emulate natural disturbance regimes at tree and stand scales, (2) landscape planning, and (3) multi-level governance approaches.
Kidney disease is a worldwide killer. Treat it that way
Differential expression and functional analysis of circular RNAs and m6A modifications in children with Philadelphia chromosome-positive acute lymphoblastic leukemia
Abstract Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukaemia (ALL) in childhood is associated with dismal outcomes, in large part due to challenges in diagnosis and monitoring therapeutic efficacy. Recent studies suggest that circular RNAs (circRNAs) are potential diagnostic and prognostic biomarkers for various tumours. to indicate the potential role of circRNAs in identifying or serving as novel targets for treatments. Here, we analysed CircRNA expression profiles in samples from three Ph+ ALL patients at diagnosis (CK1 group), on day 19 after treatment (T1 group) and in first complete remission (day 46 after treatment, T2 group), as well as one Ph− ALL patient at diagnosis (CK2 group). A total of 922 differentially expressed circRNAs (DECs) potentially associated with RNA degradation, microRNAs in cancer, propanoate metabolism and ubiquitin-mediated proteolysis were found (626 upregulated and 296 downregulated) between the CK1 and CK2 groups. In addition, we identified 224 DECs (129 upregulated and 95 downregulated) between the CK1 and T1 groups and 225 DECs (136 upregulated and 89 downregulated) between the CK1 and T2 groups, including 136 for which their expression was upregulated and 89 for which their expression was downregulated. The levels of hsa_circ_0012152 and hsa_circ_0009024 were significantly increased in Ph+ ALL patients, the changes in the levels of these circRNAs were confirmed by qRT‒PCR, indicating their potential as diagnostic biomarkers. Most upregulated DECs underwent N6-methyladenosine (m6A) modification noting the specific roles that are now better understood based on the circRNAs and DECs identified, and ideally suggesting how the findings could impact the diagnosis and treatment of Ph+ ALL The findings of this study increase our understanding of the roles of m6A-modified circRNAs in the pathogenesis of Ph+ ALL.
Pathogenesis of bovine H5N1 clade 2.3.4.4b infection in macaques
An agent-based model of COVID- 19 in the food industry for assessing public health and economic impacts of infection control strategies
Abstract The COVID- 19 pandemic exposed challenges of balancing public health and economic goals of infection control in essential industries like food production. To enhance decision-making during future outbreaks, we developed a customizable agent-based model (FInd CoV Control) that predicts and counterfactually compares COVID- 19 transmission in a food production operation under various interventions. The model tracks the number of infections as well as economic outcomes (e.g., number of unavailable workers, direct expenses, production losses). The results revealed strong tradeoffs between public health and economic impacts of interventions. Temperature screening and virus testing protect public health but have substantial economic downsides. Vaccination, while inexpensive, is too slow as a reactive strategy. Intensive physical distancing and biosafety interventions prove cost-effective. The variability and bimodality in predicted impacts of counterfactual interventions, explained by the chance effects and early stochastic infection die-off, caution against relying on single-operation real-world data for decision-making. These findings underscore the need for a proactive infrastructure capable of rapidly developing integrated infection-economic mechanistic models for the essential industries to guide infection control, policy-making, and socially acceptable decisions.
Astaxanthin alleviates fipronil-induced neuronal damages in male rats through modulating oxidative stress, apoptosis, and inflammatory markers
Abstract Fipronil (FPN) is an effective pesticide for veterinary and agricultural use; however, it can induce neurotoxic effects on non-target organisms after accidental exposure. Astaxanthin (AST) is a dark red carotenoid with antioxidant, anti-inflammatory, neuroprotective, and antiapoptotic effects. This study investigated the ameliorative impact of AST against FPN-induced brain damage in rats. Thirty-two adult Wistar rats were allocated into four groups (n = 8): Control, AST (20 mg/kg bwt/day), fipronil (FPN) (20 mg/kg bwt/day), and AST + FPN group. Acetylcholine (ACh), dopamine, malondialdehyde (MDA), and proinflammatory cytokines, including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and inflammatory cytokine cyclooxygenase-2 (COX2) levels were enhanced in the FPN-administered group relative to the control group. In addition, a substantial reduction of acetylcholine esterase (AchE), gamma-aminobutyric acid (GABA), serotonin, reduced glutathione (GSH) levels, catalase (CAT), and total superoxide dismutase (T-SOD) enzyme activities were determined. FPN induced histopathological alterations in the cerebral and cerebellar tissues. Likewise, the histomorphometric image analysis of H and E-stained tissue sections was constant with FPN-induced neurotoxicity. Immunohistochemically, an intense positive immunohistochemical staining of apoptotic marker caspase-3 and astrocytes activation marker glial fibrillary acidic protein (GFAP) in the examined tissues was noticed. Inversely, the simultaneous administration of AST partially attenuated FPN impacts, ameliorating the severity of FPN-induced neuronal damage. These results were also established with the molecular docking findings. It could be suggested that AST has antioxidant, anti-inflammatory, and anti-apoptotic capabilities against FPN-induced neuronal damage via suppression of oxidative stress and pro-inflammatory cytokines, preservation of the neurotransmitters, and the cerebral and cerebellar histoarchitectures.
Which programming language should I use? A guide for early-career researchers
Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning
Trade tariffs could worsen deforestation in South America
Global classification of river morphology based on inland water dynamics characterization and digital elevation data
Abstract Classifying river morphology is crucial for fluvial geomorphology and hydrology. River morphology reflects hydrodynamic and sedimentary processes, providing critical insights into the diversity of global river systems. This study establishes a global framework for river morphology classification based on remote sensing and topographic data. Using the Global Inland Water Dynamics Characterization dataset and the global digital elevation model ASTER GDEM V3, a river spatial image decomposition process was developed, dividing global river data into tens of thousands of image blocks containing dynamic imagery and elevation information. A ResNet-50 deep neural network was employed to construct an image-elevation fusion classification model, classifying global rivers into five major types: meandering rivers, braided rivers, straight rivers, anastomosing rivers, and anabranching rivers. These types were further divided into 17 subtypes to capture finer morphological variations. The spatial distribution patterns and morphological features of these river types were analyzed, providing a comprehensive understanding of the global distribution of river planforms. This framework advances the knowledge of river systems at a global scale and lays the foundation for future studies in fluvial geomorphology and hydrology.