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PKM2 accelerated the progression of chronic fatigue syndrome via promoting the H4K12la/ NF-κB induced neuroinflammation and mitochondrial damage
Exploring blood immune cells in the protective effects of gut microbiota on rheumatic heart disease based on Mendelian randomization analysis
Comparative analysis of daily global solar radiation prediction using deep learning models inputted with stochastic variables
Abstract Photovoltaic power plant outputs depend on the daily global solar radiation (DGSR). The main issue with DGSR data is its lack of precision. The potential unavailability of DGSR data for several sites can be attributed to the high cost of measuring instruments and the intermittent nature of time series data due to equipment malfunctions. Therefore, DGSR prediction research is crucial nowadays to produce photovoltaic power. Different artificial neural network (ANN) models will give different DGSR predictions with varying levels of accuracy, so it is essential to compare the different ANN model inputs with various sets of meteorological stochastic variables. In this study, radial basis function neural network (RBFNN), long short-term memory neural network (LSTMNN), modular neural network (MNN), and transformer model (TM) are developed to investigate the performances of these algorithms for the DGSR prediction using different combinations of meteorological stochastic variables. These models employ five stochastic variables: wind speed, relative humidity, minimum, maximum, and average temperatures. The mean absolute relative error for the transformer model with input variables as average, maximum, and minimum temperatures is 1.98. ANN models outperform traditional models in predictive accuracy.
Random mechanisms govern bacterial succession in bioinoculated beet plants
Abstract Plant colonization by microbes is an example of succession, with its distinct phases differing in community structure and diversity. This process needs to be studied to improve bioinoculation strategies. Here, we show that, regardless of bioinoculation, soil type and plant genotype, bacteria colonize the rhizosphere and tissues of axenic beets in two phases associated with taproot development. Communities remained stable after five weeks of growth in soil. Time, soil type and genotype determined community structure both in the rhizosphere and in the endosphere. Inoculation changed the community structure, and members of Pseudomonadota and Bacillota were recruited by beets. Axenic beet colonization runs through phases similar to colonization of a glacier forefront, and bacteria are recruited mostly randomly. The transition from the early to late phase involves a decrease in the bacterial load in plant tissues, which may be linked to plant growth and the arrest of bacterial cell division. Therefore, early inoculation seems to be favourable. Five weeks of growth in soil enabled formation of stable bacterial communities in both the rhizosphere and the endosphere. The influence of inoculation seems to be indirect, probably due to microbe-microbe interactions.
Generalizing the similitude approach for laboratory astrophysics through equivalence symmetries for the example of radiative waves
Mathematical analysis of scrub typhus seasonal infection with re-scaled transmission rate considering Northeast India reported data from 2010 to 2022
Effect of splitting the sub-lethal dose of glyphosate on plant growth shikimate pathway-related metabolites and antioxidant status in faba beans
Abstract Glyphosate exerts its herbicidal activity by inhibiting the shikimate pathway, the main source of many primary and secondary metabolites. Application of a low dose of glyphosate to faba bean plants was effective in controlling Orobanche crenata infestation, but some toxic effects on host plants can occur. Splitting the low glyphosate dose can serve as a mitigation strategy to reduce the host toxicity. Under parasitic-free conditions, a greenhouse experiment was conducted during two seasons to study the effect of dividing the recommended glyphosate dose (170 g a.i. ha-1) into two-five sprays on the growth, shikimate-related metabolites, and antioxidant status of three faba bean varieties. After 40 days, splitting treatments tended to cause cumulative inhibition effects on the growth and productivity traits of faba beans depending on the tested varieties, seasons, and the number of sprays applied. The maximum reduction effect was noticed for twice- sprayed treatment in the first season and for five-sprayed treatment in the second one. The cumulative effect of splitting glyphosate treatments on the shikimate pathway metabolites and the antioxidant status was measured after a week of spraying. Splitting treatments induced great increases in shikimic acid and phenylalanine contents compared with control. These treatments continued to exert their oxidative stress on faba bean plants by reducing antioxidant activity and antioxidant compounds such as total phenolics, flavonoids, and the detected phenolic acids (p-hydroxybenzoic, syringic, vanillic, coumaric, and ferulic). A significant increase in the activities of antioxidant enzymes (superoxide dismutase, peroxidase, and polyphenol oxidase) was recorded for all splitting treatments.
Subsequent primary cancer risk and mortality among premenopausal breast cancer survivors
Reliable numerical scheme for coupled nonlinear Schrödinger equation under the influence of the multiplicative time noise
Global MPPT optimization for partially shaded photovoltaic systems
Clinical profile of adult stroke patients in Angola: a cross-sectional study
Abstract Stroke is a leading cause of death and disability worldwide, and the greatest burden of this disease has been observed in low- and middle-income countries, which continue to face several challenges in stroke care. The objective of this study was to identify the clinical profile of Angolan stroke patients in a tertiary hospital in Luanda. A cross-sectional investigation was conducted at Clínica Sagrada Esperança in Angola a tertiary center, on stroke patients admitted to the neurology service. Data was collected from November 2022 to March 2023. We included all patients who were admitted to the emergency department. Patients under 18 years of age, who had a previous stroke ≥ 2 score on the modified Rankin scale (mRS), and had brain tumors were excluded. We identified 139 stroke patients. The mean age was 59.5 ± 13.5 years, most were male (65.5%). The ischemic stroke was the most prevalent subtype (73.4%). The mean length of stay was 8.3 ± 4.7 days. Higher mRS scores at discharge were associated with complications (p < 0.001) and prolonged hospitalization (p = 0.001). The most frequent risk factors were hypertension (96.5%), alcohol use (67.4%), and diabetes (29.6%). Our study provides crucial insights into the profile of stroke patients in Angola. The collected data are vital for informing effective preventive measures and improving patient care.
Propensity score matching analysis of perioperative outcomes during Hub&Spoke training program in hepato-biliary surgery
Nutritional value, antibacterial activity, ACE and DPP IV inhibitory of red pomegranate seeds protein and peptides
Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer
Saccades influence functional modularity in the human cortical vision network
Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems
Abstract This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due to the nonlinear nature of power generation in PV systems, influenced by fluctuating weather conditions, managing this nonlinear data effectively remains a challenge. As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial. To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. The model utilizes data from the PV unit’s technical specifications, allowing the algorithms to forecast maximum power, current, and voltage based on given irradiance and temperature inputs. Predicted data is also used to determine the boost converter’s duty cycle. The simulation was conducted on a 100 kW solar panel with an open-circuit voltage of 64.2 V and a short-circuit current of 5.96 A. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE). Additionally, the study assessed the correlation and feature importance to evaluate model compatibility and the factors impacting the predictive accuracy of the ML models. Results showed that the DTR algorithm outperformed others like LR, RR, Lasso R, BR, GBR, and ANN in predicting the maximum current (Im), voltage (Vm), and power (Pm) of the PV system. The DTR model achieved RMSE, MAE, and R2 values of 0.006, 0.004, and 0.99999 for Im, 0.015, 0.0036, and 0.99999 for Vm, and 2.36, 0.871, and 0.99999 for Pm. Factors such as the size of the training dataset, operating conditions of the PV system, model type, and data preprocessing were found to significantly influence prediction accuracy.
Effects of prehospital advanced airway management on cardiac arrest patients who underwent extracorporeal cardiopulmonary resuscitation
CT imaging features and diagnostic algorithm for hepatic cystic echinococcosis
Abstract To systematically analyze CT imaging features of hepatic cystic echinococcosis (CE), explore radiological-pathological correlations, and develop a diagnostic algorithm for accurate disease classification. This retrospective study included 48 pathologically confirmed cases of hepatic CE from two medical centers. CT imaging features were analyzed by two experienced radiologists, evaluating lesion characteristics including location, morphology, wall features, and calcification patterns. Imaging findings were correlated with pathological results. A diagnostic algorithm was developed and validated, with inter-observer agreement assessed using Fleiss kappa coefficient. Seven distinct CT imaging patterns were identified, corresponding to different pathological stages: unilocular cystic (25.0%), multivesicular (8.3%), collapsed inner wall (10.4%), partially solidified (10.4%), solidified (16.7%), and calcified (25.0%) types, with complicated cases (4.2%) showing additional features. The proposed diagnostic algorithm achieved 94.0% accuracy (451/480 classifications) in validation testing by ten junior radiologists, with excellent inter-observer agreement (quadratic-weighted Fleiss kappa coefficient = 0.740 [95% CI 0.577–0.902], Gwet’s AC2 coefficient = 0.768). Primary diagnostic challenges involved differentiating between CE2 and CE3b lesions, and between CE3b and CE4 lesions. This study explores the correlation between CT imaging patterns and pathological stages of hepatic CE, proposing a validated diagnostic algorithm. The findings provide valuable insights for CE classification, particularly in regions where the disease is emerging or underrecognized.