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Assessing the impact of eucalyptus trees on soil chemical properties in rice fields
Continuity of care interventions for people with stroke and their caregivers: A scoping review protocol
Background The needs of people with stroke and caregivers’ change as they transition across the stroke care continuum from initial symptom onset to community living. Continuity of care interventions may support these changing needs. Continuity of care refers to care provided to individuals that is coherent, connected, and consistent across settings. Interventions have been developed to enhance continuity of care, but to date, no reviews have identified the core components of these interventions specific to various care transitions. Objective To examine the literature on continuity of care interventions that address the needs of people with stroke and/or their caregivers specific to stroke care transitions. Methods The study is guided by the Joanna Briggs Institute methodological framework for scoping reviews. The search will be conducted on Ovid MEDLINE, CINAHL Plus with Full Text, PsycINFO, and EMBASE, restricting to articles in English. Core concepts of stroke (e.g., ischemic or hemorrhagic stroke) and continuity of care (e.g., early supported discharge) and their synonyms will be used as search terms. Two reviewers will assess articles for inclusion. Data will be extracted and synthesised using quantitative descriptive analysis and deductive content analysis. A steering committee, consisting of people with lived experiences, researchers, and healthcare professionals, will discuss the charted data, and identify any gaps in the literature. Discussion This research will identify the components of continuity of care interventions, the specific transitions that they address, and any variability in components across different transitions. This research will identify gaps in the continuity of care literature such as where transition interventions are needed.
Charge and discharge scheduling method for large-scale electric vehicles in V2G mode via MLGCSO
Matching plasma and tissue miRNA expression analysis to detect viable ovarian germ cell tumors
Purpose MicroRNAs (miRNAs) are emerging as circulating biomarkers in germ cell tumors (GCT) with potential to guide management. Their role and expression patterns are more established in testicular GCTs, while lesser data exist in ovarian GCTs (OGCT). Methods Patients diagnosed with OGCT with plasma and tumor tissue available in our provincial biobank were included. Total RNA was extracted, and RT-qPCR was performed to measure miR-371–3 and miR-302/367 levels. Healthy plasma and ovarian tissue served as controls. Statistical analyses were performed using ANOVA and the Mann-Whitney U test. Clinicopathologic data was collected by chart review. Results From 2007 to 2022, 23 patients with OGCT were identified: 13 with viable non-teratoma germ cell (VNTGC) and 10 with immature teratoma germ cell (ITGC) tumors. Compared to healthy controls, all patients with VNTGC but not ITGC tumors had significantly higher miRNA levels in preoperative plasma and tumor tissue. Plasma miRNA kinetics correlated with disease burden, decreasing to undetectable levels following treatment, and increasing significantly upon relapse. Conclusion MiR-371–3 and miR-302/367 are highly expressed in ovarian VNTGC but not ITGC tumors, and their plasma levels correlate with disease burden. Future studies validating these findings in a larger cohort are needed to develop miRNAs as circulating biomarkers for clinical use.
Autonomous air combat decision making via graph neural networks and reinforcement learning
Structural analysis of social behavior: Using cluster analysis to examine personality profile associated with diabetes onset
Objective In this study we tested whether some intrapsychic behaviors of structure of personality can be associated with the onset of Type 2 diabetes (T2-DM). Methods T2-DM Patients (n. 257) and Healthy subjects (n = 258). Test: Social schedule (demographic variables); SASB–Form- Questionnaire A (describing intrapsychic behaviors of personality structure - 8 Clusters-Cl -). Results From the logistic model emerged that in subjects with profile 2 “Low Affiliation and Autonomy” compared to profile 2 “Low Autonomy and Self-Care” considering age, education and living conditions effects, the association to the onset of diabetes increases (OR: 1.668). Subjects with profile 2 “Low Affiliation and Autonomy” show low assertiveness and autonomy (SASB-Cl 1); have a medium-low ability to accept and support themselves (medium-low-SASB-Cl 3,4); do not improve their leisure activities or interpersonal relationships because they are too scheduled by things to do (SASB-Cl 4, Cl5); have self-critical behavior (SASB-Cl 6 medium) and self-neglectful behavior - ignore illnesses at an emotional and physical level (Cl8). They occasionally incur in self-destructive behaviors (Cl 7). They may experience self-exhaustion and mild to moderate depression. These are the intrapsychic behaviors associated with onset of diabetes. Another variable associated with the onset of diabetes is high educational level. Conclusions These results suggest that certain personality traits have a great association with T2-DM onset. Furthermore the analysis of intrapsychic modalities in association with the onset of diabetes could constitute a further screening aimed at primary prevention.
Phase behavior of nanoconfined continental shale oil and reservoir fluid phases in the Gulong shale of the Songliao basin
Determinant of zinc deficiency in orthopaedic inpatients
Zinc is vital for over 300 enzymes in major metabolic pathways, and deficiency can lead to serious conditions, especially post-surgery. This study aimed to investigate predictive factors of zinc deficiency in orthopaedic inpatients. A retrospective case-control study was conducted on patients admitted to Teikyo University Chiba Medical Center from 15 February to 31 August 2022. Patients were divided into zinc deficiency (< 60 µg/dL) and non-deficiency groups. Data included demographics, comorbidities, hospitalisation reasons, fracture details, medication use, and laboratory values. Fisher’s exact test and two-sample t-tests were used for analysis. Of 156 patients, 47 (30.1%) had zinc deficiency. The case group had higher fracture rates (68.1% vs. 33.9%; p < 0.001), and lower rates of spinal disease (2.1% vs. 31.2%; p < 0.001) and osteoarthritis (8.5% vs. 22.9%; p = 0.04). Fragility and hip fractures were more common in the case group. Anaemia, hip fracture, and hypoalbuminaemia were independent predictive factors of zinc deficiency.
Solutions behavior of mechanical oscillator equations with impulsive effects under Power Caputo fractional operator and its symmetric cases
Genome-wide association study of biological nitrogen fixation traits in mini-core cowpea germplasm
Biological Nitrogen Fixation (BNF) efficiency in legume crops such as cowpea (Vigna unguiculata L. Walp) has been less documented yet is key in improving yield performance and restoring soil fertility in sub-Saharan Africa. Nevertheless, little progress has been made in understanding the gene control of the BNF traits in cowpea to sustain the development of smart agriculture in this part of the world. This study aimed to identify cowpea genotypes and map genomic regions for BNF traits for developing high nitrogen-fixing cultivars. A total of 241 mini-core cowpea genotypes were inoculated with Bradyrhizobium spp in a screen house for two cycles. Phenotypic data collected on the number of nodules (NN) per plant, nodule efficiency (NE) in percentage, and nodule dry weight (NDW) per plant revealed significant differences implying high genetic variability in the mini-core population for nodulation capacity. Fifteen significant association signals were identified for BNF traits on nine chromosomes except Vu02 and Vu09 when two multi-locus models were considered. Markers accounting for over 15% variation for BNF traits included 2_31410 (2.32Mb) on Vu05 and 2_45545 (24.93Mb) on Vu06 for NN, 2_06530 (56.64Mb) and 2_27028 (34.31Mb) on Vu01 for NE and 2_50837 (10.07Mb) on Vu01 and 2_11699 (34.41Mb) on Vu07 for NDW, respectively. Additionally, positional candidate genes near the peak markers that encode genes associated with BNF in cowpea included Vigun06g121800, Vigun01g160600, Vigun10g014400, Vigun07g221500, Vigun07g221300 and Vigun11g096700. The genotype TVu-1477 was identified to have favorable alleles for both three studied traits. The significant markers identified in this study can be converted to Kompetitive Allele Specific-PCR (KASP) markers to accelerate the development of high-yielding cowpea varieties that also enhance soil fertility.
Prospective multicenter study identifying prognostic biomarkers and microbial profiles in severe CAP using BALF, blood mNGS, and PBMC transcriptomics
Enhancing student career guidance and sentimental analysis: A performance-driven hybrid learning approach with feature ranking
Choosing the appropriate career path poses a significant hurdle for students, especially when time is constrained. This research addresses the challenge of career prediction by introducing a method that integrates additional attributes, refines feature prioritization, and streamlines feature selection to enhance prediction precision. The key objectives of this study are to pinpoint pertinent features, accurately rank them, and enhance prediction accuracy by eliminating non-essential features. To accomplish these aims, three methodologies are employed: Feature Fusion and Normalization (FFN) for precise data identification, Average Feature Ranking (AFR) utilizing a blend of Random Forest (RF) and Linear Regression (LR) for feature prioritization, and Improved Prediction with Weighted Characteristics (PWF) which integrates Principal Component (PC) analysis for feature reduction. The prediction performance is assessed using a hybrid Multilayer Perceptron (MLP) classifier with 5-fold cross-validation. The outcomes reveal that the hybrid approach yields a superior feature set for prediction. The top twelve ranked features are determined by averaging each feature’s RF scores and coefficients. The achieved accuracy (ACC), precision (P), recall (R), and F1 scores stand at 87%, 87%, 86%, and 86%, respectively, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) value of 92%. These findings underscore the efficacy of the proposed hybrid learning technique in accurately forecasting career trajectories.
High-content screening (HCS) workflows for FAIR image data management with OMERO
Abstract High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. However, it generates massive amounts of metadata, making HCS experiments a unique data management challenge. This data includes images, reagents, protocols, analytic outputs, and phenotypes, all of which must be stored, linked, and made accessible to users, scientists, collaborators, and the broader community to ensure sharable results. This study showcases different approaches using Workflow Management Systems (WMS) to create reusable semi-automatic workflows for HCS bioimaging data management, leveraging the image data management platform OMERO. The three developed workflows demonstrate the transition from a local file-based storage system to an automated and agile image data management framework. These workflows facilitate the management of large amounts of data, reduce the risk of human error, and improve the efficiency and effectiveness of image data management. We illustrate how applying WMS to HCS data management enables us to consistently transfer images across different locations in a structured and reproducible manner, reducing the risk of errors and increasing data consistency and reproducibility. Furthermore, we suggest future research direction, including developing new workflows and integrating machine learning algorithms for automated image analysis. This study provides a blueprint for developing efficient and effective image data management systems for HCS experiments and demonstrates how different WMS approaches can be applied to create reusable, semi-automated workflows for HCS bioimaging data management using OMERO.
Preparation of Ni-Mn ferrites magnetic nanoparticles through the ethanol solution combustion-calcination process for the adsorption of methyl blue
Ni-Mn ferrites magnetic nanoparticles (MNPs) were successfully prepared through the ethanol solution combustion-calcination process, and characterized by SEM, TEM, XRD, VSM, BET, and FTIR techniques. For smaller particle size and suitable magnetic property, the optimum element ratio of the material was Ni0.9Mn0.1Fe2O4, and the optimal preparation conditions were appropriate ethanol dosage to attain Fe3+ concentrations of approximately 0.85 M, calcination temperature of 400 °C, and calcination time of 2 h, their specific surface area was 136.5 m2/g, and their average particle size and saturation magnetization were 35 nm and 21.66 emu/g, respectively. The adsorption process of methyl blue (MB) onto Ni0.9Mn0.1Fe2O4 MNPs conformed to the pseudo-second-order adsorption kinetic model in the initial concentrations of 100–250 mg/L. In comparison with Langmuir and Freundlich adsorption isotherm models, the Temkin model (R2 = 0.9865) was observed to better demonstrate the state of MB onto Ni0.9Mn0.1Fe2O4 MNPs, revealing that the adsorption mechanism of MB onto Ni0.9Mn0.1Fe2O4 MNPs was the multi-molecular chemical process. The adsorption capacity of Ni0.9Mn0.1Fe2O4 MNPs for MB still maintained about 90% of the initial adsorbance after 6 times cyclic utilization of the nanoparticles by recalcination method, suggesting that Ni0.9Mn0.1Fe2O4 MNPs had excellent regeneration performance. In general, these results coupled with its environmental friendliness attributed the potential candidates for effluent remediation.
Effect of calcium content on geopolymer consolidation of saline soils in the seasonally frozen zone
Perceived stress, coping mechanisms, and influential factors among undergraduate nursing students during ICU clinical placements: A cross-sectional study
Background Intensive Care Unit (ICU) exposes nursing students to high workloads, emotional demands, and high-risk performance. Understanding perceived stress, coping strategies, and influential factors may enhance students’ clinical experiences and outcomes. Aim To assess perceived stress levels, identify coping mechanisms, and explore associations between stress, coping mechanisms, and demographic factors among nursing students during their Intensive Care Unit (ICU) clinical. Setting The study was conducted across three campuses of the Higher Colleges of Technology (HCT) in the United Arab Emirates (UAE). Methods A cross-sectional study design with a total sample of 127 undergraduate nursing students was conducted. Data were collected using the Perceived Stress Scale (PSS) and Coping Behavior Inventory (CBI). Results Students reported a moderate level of perceived stress (mean = 1.87, SD = 0.80). The highest-ranking stressors reported included assignments and workload, with (mean = 2.12, SD = 0.91), followed by peer-related stress (mean = 1.98, SD = 1.03). The most reported coping mechanism among students was the problem-solving mechanism (mean = 2.23, SD = 0.95), followed by the transference mechanism (mean = 2.17, SD = 1.00), and staying optimistic (mean = 2.15, SD = 0.95). Stepwise regression showed that the significant predictors of overall stress were avoidance coping, β = 0.65, p < 0.001, and transference coping, β = 0.24, p < 0.001, explaining 63% of variance, R² = 0.63. Problem-focused coping negatively predicted environmental stress, (β = -0.21, p = 0.021), highlighting its protective role. Conclusion This research underscores the nursing students’ moderate stress experienced during ICU nursing placements due to workload and peer pressure. Use of problem-focused coping strategies reduced stress, while maladaptive avoidance coping strategies increased stress. Stress was strongly predicted by avoidance and transference coping confirming the necessity for coping skills instruction in nursing. Teaching stress coping and resilience building in clinical education will improve students’ well-being, performance, and preparedness for critical care nursing.
NEV battery recycling innovation strategy considering pro-social behavior from the game theory perspective
Image segmentation algorithm based on improved YOLOv8 model and its application in underground coal and gangue recognition
Coal and gangue recognition technology is one of the key technologies in the intelligent construction of coal mines. With the deepening of the research, only the coal and gangue recognition, the pixel segmentation of the coal gangue image is needed. Aiming at the gangue segmentation algorithm with low accuracy, easy to miss detection, wrong detection and large amount of detection data, slow detection speed and other problems. A coal gangue segmentation model based on improved YOLOv8 is proposed to achieve fast and accurate recognition of coal gangue images, and the overall computational volume of the model is not large, which has achieved better application results. Using the YOLOv8 model as the base model, the standard convolutional modules in the first, second & third C2f modules were replaced with depth separable convolution (DSC) modules in the YOLOv8 model backbone network, reducing the overall computational effort of the model. Adding the CBAM module before the second convolution of the up-sampling module and down-sampling stage in the model neck network improves the differentiation of the model for gangue and enhances the recognition accuracy. The original dataset was expanded from 1980 to 11,265 sheets using data expansion techniques and some hyperparameters were adjusted. Results show that the improved YOLOv8 model has an accuracy (Precision) of 95.67%, a recall (Recall) of 95.74%, a transmitted frames per second (FPS) of 32.11 frames/s, and a mean average precision (mAP) of 96.88%, which is an improvement of 5.6% in accuracy, 7.12% in recall, and the mean average precision (mAP) is improved by 4.65% and FPS is improved by 8.83 frames/s. By comparing with YOLOv3, YOLOv5, YOLOv7, and YOLOv8 models, the improved model is optimal in terms of accuracy and speed. Finally, the model is successfully applied to underground coal gangue image segmentation through transfer learning, and the effect of coal gangue image segmentation is good, which verifies the re-liability of the algorithm.