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Priming the primary motor cortex with transcranial direct current stimulation: Effect on learning the golf putt
Background Priming the primary motor cortex (M1) with transcranial direct current stimulation (tDCS) prior to motor practice modulates post-synaptic activity, thereby impacting learning of a motor skill. This effect has been shown for the acquisition of simple motor skills. It is not clear whether priming tDCS can impact the learning/retention of a more naturalistic motor task. Objective/Hypothesis We investigated the effects of priming M1 with tDCS on the performance on a golf putting task. We hypothesized that participants who receive tDCS with the cathode over M1 (C-M1) would show better skill acquisition and retention performance, relative to participants who receive tDCS with the anode over M1 (A-M1) or sham tDCS. Methods Thirty-six participants were randomized into three groups: C-M1, A-M1, and sham tDCS. Participants received tDCS (1mA, 20 minutes) prior to practicing golf putting across two days. Performance (error) was measured for each putt. Participants returned on the third day for a retention test. Results After accounting for baseline performance, the C-M1 group performed significantly better compared to A-M1 [p = 0.02] and sham tDCS [p = 0.01] at the retention test. There was no difference in retention performance between A-M1 and sham tDCS. Conclusion Our findings partially support the Bienenstock-Cooper-Munro rule of metaplasticity. C-M1 tDCS priming enhanced motor learning, while A-M1 tDCS priming had no effect, relative to sham.
Integrating human mobility and animal movement data reveals complex space-use between humans and white-tailed deer in urban environments
Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study
Context and background. Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental disorder detection can reduce costs for public health agencies and prevent other major comorbidities. Additionally, the shortage of specialized personnel is very concerning since depression diagnosis is highly dependent on expert professionals and is time-consuming. Research problems. Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present. These challenges include assessing alternatives in terms of data cleaning and pre-processing techniques, feature selection, and appropriate ML classification algorithms. Purpose of the study. This paper tackles such an assessment based on a case study that compares different ML classifiers, specifically in terms of data cleaning and pre-processing, feature selection, parameter setting, and model choices. Methodology. The experimental case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD. Major findings. Besides the assessment of alternative techniques, we were able to build models with accuracy levels around 84% with Random Forest and XGBoost models, which is significantly higher than the results from the comparable literature which presented the level of accuracy of 72% from the SVM model. Conclusions. More comprehensive assessments of ML classification algorithms and NLP techniques for depression detection can advance the state of the art in terms of improved experimental settings and performance.
A novel approach for multiclass sentiment analysis on Chinese social media with ERNIE-MCBMA
Diazinon residues levels in farm-gate Brassica oleracea var. acephala of Kimira-Oluch smallholder farm improvement project, Kenya
Diazinon insecticide, though associated with human health impacts, is popularly used in the production of Brassica oleracea var. acephala (kale) at the Kimira-Oluch Smallholder Farmers Improvement Project (KOSFIP), Kenya. Diazinon controls insect pests that lower quality and profitability of produce. The preharvest interval of diazinon in kale is 12 days which may not be observed by farmers with inadequate appreciation of Good Agricultural Practices (GAP). Since the extent of GAPs adoption at KOSFIP has not been evaluated, it remains unclear whether diazinon residues levels in kale of KOSFIP could be a health risk to the consumers. Diazinon residues levels and corresponding health risks in farm-gate kale at KOSFIP were determined. Cross-sectional survey based on snowball sampling identified 40 farms applying diazinon on the vegetable. Triplicate samples were collected from each farm for residue analysis, using the QuEChERS method, and LC-ESI-MS/MS analysis. Standard normal distribution function f(z) revealed ≈78% of farm-gate samples had detectable residual diazinon levels and 70% were above the Codex MRL of 0.05 mg/kg. Continued application of diazinon on kale at KOSFIP is exposing consumers to short-term health risks. Efforts must be intensified to ensure GAP are adopted. The estimated farm-gate samples with health risk indices for children and adults (HRIc and HRIA) >1.0 were 64% and 26%, respectively. The residual levels are therefore potential health risks to both children and adults. Farm-gate residual levels and resultant partial HRI were comparatively higher than findings of most previous studies. Inappropriate label PHI and malpractices against GAP may be responsible for high residual levels. There should be regular surveillance and trainings of farmers on GAP for sustainable production of kale in the Lake Victoria region. Use of diazinon on kale should be discouraged and intensive routine pesticide residue screening be enhanced for conventional vegetable produce.
A methodological framework for identifying traditional rural landscapes based on environmental, cultural, and socio-economic indicators - the case study of China
Predicting prostate cancer metastasis in Ghana: Comparison of multiparametric and PSA models
Background Prostate cancer is the most prevalent male malignancy in Ghana, with a high-risk of metastatic progression. Early detection and adequate disease severity stratification are crucial for timely intervention, comprehensive management, and improved outcomes. This study evaluates and compares the predictive abilities of a multiparametric model and a PSA-alone model in forecasting metastasis in prostate cancer patients. Objective To compare the performance of a multiparametric model and a PSA-alone model in predicting metastasis in prostate cancer patients in Ghana. Methodology Logistic regression analyses were conducted on a dataset of 426 prostate cancer cases. The multiparametric model included variables such as age, BMI, marital status, ethnicity, socioeconomic status, clinical stage by DRE findings, PSA levels, and Gleason score. The PSA-alone model focused solely on PSA levels. Model performance metrics included Pseudo R-Squared, AUC, sensitivity, specificity, accuracy, PPV, NPV, FPR, FNR, and F1-Score. The Hosmer-Lemeshow test assessed the goodness-of-fit for the multiparametric model. All analyses were conducted at a 5% level of significance. Results The multiparametric model achieved a Pseudo R-Squared of 71.17%, AUC of 97.18%, sensitivity of 93.20%, specificity of 96.21%, accuracy of 92.25%, PPV of 85.62%, NPV of 96.24%, FPR of 8.24%, FNR of 6.80%, and F1-Score of 81.02%. The Hosmer-Lemeshow test yielded a non-significant p-value of 0.2405. The PSA-alone model had sensitivity of 32.24%, specificity of 91.76%, accuracy of 88.03%, PPV of 77.47%, NPV of 92.02%, FPR of 3.79%, FNR of 67.76%, F1-Score of 45.76%, and AUC of 73.79%. The multiparametric model’s Prevalence Yield was 32.15% and Sensitivity Yield was 32.15%, compared to the PSA-alone model’s 6.95% and 13.32%, respectively. Conclusion Both models effectively predict metastasis in prostate cancer patients. The multiparametric model shows superior overall performance with higher Pseudo R-Squared, AUC, and a better balance in sensitivity, specificity, and accuracy. These results suggest the multiparametric model as a more robust tool for metastasis risk assessment in resource-poor settings. However, clinical context and patient characteristics should guide model choice for optimal outcomes.
Earthquake clustering and structural modelling unravel volcano-tectonic complexity beneath Mount Etna
Correlation cluster analysis of slope safety monitoring data in reservoir areas
Current predictive methods for dam failures in reservoirs remain limited, indicating that the underlying mechanisms of such failures are not yet fully understood. To further elucidate the interrelationships among safety monitoring data in the reservoir area, this study established 36 monitoring cross-sections distributed across upper, middle, and lower slope zones. Each cross-section was instrumented with eight different types of monitoring devices. A total of 4,320 samples were collected (432 samples per instrument type), resulting in an overall dataset of 34,560 measurements. The monitoring data were sequentially analyzed using: (1) descriptive statistics, (2) Welch/Brown-Forsythe post hoc One-way analysis of variance (ANOVA), and (3) cluster analysis. The results demonstrate that: (1) Significant correlations exist among monitoring variables, with the strongest positive correlation observed between loading and lean (r = 0.40), while the strongest negative correlation occurred between sedimentation and stress (r = -0.39). (2) Cluster analysis of the eight monitoring variables revealed two distinct clusters: soil displacement, stress, and water-level formed one cluster, while the remaining variables comprised the second cluster. In summary, variations in monitoring data and their correlations resulted from water-level and environmental changes in the reservoir area, with spatial differences across monitoring types. A thorough investigation of these variations and their causes will enable accurate safety assessments of the reservoir area and support tailored response strategies for different locations.
Prevalence, reasons, and factors associated with loss to follow-up in newly diagnosed glaucoma suspects and glaucoma patients in Thailand
Estimating the influence of dietary composition and management on nutrient intake and excretion and methane emission in different pig categories
The study aimed to estimate the effect of diet composition, pig production stage, in-housing conditions, and manure management on methane (CH4) emissions from enteric fermentation, manure stored in the barn, and the outdoor storage tank. For each pig category, an estimation for emissions was made for a standard Danish pig diet based on wheat, barley, and soybean meal. Within each category of pigs, emissions were also estimated for diets with different levels and types of dietary fiber from sugar beet pulp, wheat bran, oats, wheat, or soy hulls, which were included as a partial substitution for wheat or barley. In all diets within four pig categories, feed intake, excreted dry matter, feces mass, and urine volume (g/d per animal) increased in sugar beet pulp, wheat bran, oat, or soy hull diets compared to the average Danish diet. In grower-finisher pigs, the sum of CH4 emissions from enteric fermentation, manure stored in the barn, and the outdoor storage tank were 9.8, 10.2, 11.0, 11.0, and 11.2 (kg/year/animal place) for wheat diet, average Danish diet, oat diet, wheat bran diet, and sugar beet pulp diet, respectively, while in gestating sows, were 16.9, 17.5, 18.4, 19.6, 19.7, and 23.2 (kg/year/animal place) in wheat diet, average Danish diet, oat diet, sugar beet pulp diet, wheat bran diet, and soy hull diet, respectively. Contribution of CH4 emissions from manure stored in the barn plus outdoor storage tank for the average Danish diet accounted for 95, 90, 83, and 84% of total CH4 emissions in weaned pigs, grower-finisher pigs, lactating sows, and gestating sows, respectively. In conclusion, feed composition has a considerable impact on CH4 emissions. Enteric CH4 and CH4 emissions from manure stored in the barn and in the outdoor storage tank were increased by elevated concentration of residual fiber in all four pig categories except for enteric CH4 in weaned pigs.
Constraints on ore vectoring from geochemical fingerprints of porphyry style pyrite
Abstract The sulfur isotope compositions of three generations of pyrite originated from skarns, stockwork, and late-stage, post-hydrothermal veins from three various zones of the porphyry style Myszków Mo–Cu–W deposit (center, circum-deposit, and periphery) were investigated as a proxy for the mineralized core of porphyry system. Overall, the mode of δ34Spyrite decreases with time, from skarn- through main- up to late-stage of ore mineralization (with average values of + 6.13, + 5.65, and + 3.34 ‰, respectively). The gradual decrease in δ34S values outwards from the deposit core (av. 3.95 ‰), through circum-deposit (av. + 3.40‰) to distal zone (av. + 3.05 ‰) was detected only for late-stage pyrite. Both the temporal and lateral zonation of δ34Spyrite could be explained by the progressive temperature decrease of the mineralized system and the mixing of ore-forming solutions with more dilute meteoric waters. The trace geochemistry of late-stage pyrite shows relatively constant values of Tl (from 0.13 to 0.14 ppm), Ti (9.10–10.30 ppm), Cr (9.94–12.37 ppm), and Mn (6.94–7.59 ppm) regardless of the zone of the Myszków Mo–Cu–W deposit. While, As (24.96–184.80 ppm), Sb (0.50–13.52 ppm), Bi (0.57–1.54 ppm) in pyrite and Sb/Te (0.06–1.62), Co/Bi (3.32–34.23), and Ag/Ni (0.006–0.140) increase with the proximity to the ore, contrary to Ag/Co which rises towards the periphery of the deposit (0.04–0.13). Ultimately, these results indicate that sulfur isotope data supported by trace geochemistry of late-stage pyrite can be potentially used as vectoring proxies to predict the likely direction to the mineralized center of a porphyry system.
Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous disease caused by multiple etiologies. It is characterized by excessive fat accumulation in the liver. Without intervention, MASLD can progress from steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis and even to cirrhosis and hepatocellular carcinoma. However, the pathogenesis of MASH and the mechanism underlying the development of fibrosis remain poorly understood, posing challenges for accurate diagnosis of MASH and fibrosis. In this study, we analyzed tissue RNA-seq data and clinical information of healthy individuals and MASLD patients from multiple datasets, the key genes and pathways involved in the occurrence and progression of MASLD, MASH, and fibrosis were screened respectively. Our findings reveal that the development of MASLD, MASH and fibrosis is associated with lipid metabolism processes. Based on the RNA expression profiles of identified hub genes, we established three alternative diagnostic models for MASLD, MASH, and fibrosis. These models demonstrated excellent performance in the diagnosis of MASLD, MASH, and fibrosis, with AUC values exceeding 0.9, implicating its potential clinical values in disease diagnosis.
A de-embedding method based on combining time and frequency domains
AI linked to boom in biomedical papers, infrared contact lenses, and is Earth’s core leaking?
Effect of tailored, intensive prehabilitation for risky lifestyles before ventral hernia repair on postoperative outcomes, health, and costs – study protocol for a randomised controlled trial (STRONG-Hernia)
Background A substantial untapped potential for risk reduction may be fulfilled by applying intensive lifestyle interventions targeting the co-existing risky lifestyle factors Smoking, Nutrition (both malnutrition and obesity), risky Alcohol intake, and Physical inactivity (SNAP) before surgery. This trial will compare the effect of combined and individually tailored prehabilitation with standard care on postoperative outcomes, health, and cost-effectiveness in short and long term in participants undergoing ventral hernia repair. An interview study will be nested within the randomised trial. Methods The study is a multicenter, parallel-group, superiority randomised clinical trial. A total of 400 adult participants undergoing ventral hernia repair with ≥1 SNAP factor will be allocated to the individually tailored STRONG programme or standard care. The STRONG programme is initiated at least four weeks prior to surgery and consists of six sessions. It is delivered as one session a week, approximately, and includes patient education, motivational, and pharmaceutical supports. The primary outcome is postoperative complications requiring treatment within 30 days. Secondary outcomes address other surgical outcomes, changes in lifestyle, health, and cost-effectiveness. Follow-up takes place after 6 weeks (the end of intervention), at surgery, and 30 days, 90 days, and 6 months after surgery, respectively. Long-term data on health and costs will be obtained from nationwide registries after two years. Eligible trial participants will be invited to a semi-structured interview study at baseline. Their reflections on the STRONG programme and the choice of participating in the trial or not will be explored. Discussion Many patients have multiple SNAP factors adding to the risk of complications related to surgery. As these are modifiable, prehabilitation may be an area with great potential for risk reduction. Nevertheless, no well-acknowledged and evidence-based strategies exist in the preoperative period. The STRONG programme is tailored specifically to the individual patient’s preidentified needs including up to all five common risky SNAP factors and may tap into the large unused potential for risk reduction. Overall, the study will add important new knowledge on the effect of individually tailored prehabilitation on complications and other important outcomes in elective surgery, and also clarify if this intervention will have long-lasting implications. Trial registration www.clinicaltrials.gov (NCT06611462).
GLOBal river SALiniTy and associated ions (GlobSalt)
Abstract Freshwater salinization (FS) is a threat to freshwater ecosystems, but its impact remains relatively poorly understood compared to other stressors (e.g. nutrient pollution), with some regions (e.g. Asia, Africa) remaining poorly explored. To assess how pervasive this issue is globally and identify salinization hotspots, we compiled global data on river salinity and associated ions. We retrieved information from different sources, harmonized it and merged it with HydroATLAS watersheds. Our global data set (GlobSalt) features 13 parameters, including electrical conductivity (EC), major ions, and nutrients. GlobSalt contains approximately fifteen million records on a monthly scale for river stations from 1980 to 2023 from all continents except Antarctica. The global median EC was 509 ± 205 μS cm−1, with 60% of rivers falling in the range of 50 to 500 μS cm−1, which is within the salinity niche of most freshwater organisms. We found a large spatial variability in EC, with some regions such as the Mediterranean, the Midwest of the US, arid regions of Argentina and Chile and Southwestern Australia having high mean salinity values. Temporally, EC was fairly stable. GlobSalt represents a critical resource for improving our understanding of FS dynamics, identifying regions at high risk and informing management strategies.
Molecular blueprint of a cellular sorting centre
Effect of orally administered cannabidiol oil on daily tonometric curve in healthy Italian Saddle horses
Background Phytocannabinoids have the potential to lower intraocular pressure in both normal and glaucomatous eyes and they have been tested in different animal species, but not in the horse. The present paper describes the tonometric curve of healthy adult Italian Saddle horses after oral administration of cannabidiol oil (CBD). Methods CBD 20% was administered orally (oily solution) at the dose of 1 mg/kg to 8 adult horses and intraocular pressure (IOP) was evaluated by tonometric curve. Data were then compared to those of the same horses obtained the day before (blank) CBD administration. Results 15 minutes after CBD administration, IOP (time zero 27.3 ± 2.1 mmHg right eye; 24.6 ± 2.3 mmHg left eye) started to decrease (19.5 ± 5.2 mmHg right eye; 20.8 ± 2.4 mmHg left eye) and 1 hour later CBD it reached the minimum level in all horses (11.4 ± 7.5 mmHg right eye; 9.5 ± 5.8 mmHg left eye), remaining statistically significantly lower than normal values for the entire observation period (8 hours; 12.0 ± 7.9 mmHg right eye; 11.9 ± 7.8 mmHg left eye). Conclusions CBD 20% was effective to significantly reduce IOP in healthy adult Italian Saddle horses and may be an effective hypotensive agent to be implemented in case of primary or secondary glaucoma.