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Co-application of potassium and thiourea for mitigating salinity stress in wheat seedlings
TLK1 as a therapeutic target in TMZ resistant glioblastoma using small molecule inhibitor
Hybrid extended Kalman filter with Newton Raphson method for lifetime prediction of lithium-ion batteries
Abstract To advance the lithium-ion battery (LIB) technology more quickly, its lifetime should be predicted accurately. The precise prediction of LIB lifetime can help in producing new batteries, better use and operation of batteries. It is worthy for noting here that the LIB is a heavy nonlinear system suffering from battery fading, degradation, uncertainty and variability of operating conditions. Therefore, this article presents a hybrid extended Kalman filter with Newton Raphson method for lifetime prediction of lithium-ion batteries. The data analyses are based on commercial lithium iron phosphate/graphite cells cycled at fast charge. The cycle life expectancy is in the range of 150 to 2,300 cycles. The discharge voltage characteristics are used to present capacity degradation. The battery datasets are used with a hybrid Extended Kalman Filter (EKF) and Newton Raphson method to match the predicted cycle life and the actual cycle life of the battery. The effectiveness of the proposed method is verified by making a fair comparison with the linear regression-based machine-learning method. In the testing of 100 lifecycles, the test error and root mean square error record 3.26% and 10.93 compared with the linear regression that achieves 9.1% and 211, respectively. With the proposed hybrid approach, the lifetime prediction of LIBs can be further enhanced.
Allosteric coupling activation mechanism in histidine kinases
A computational framework for identifying cytoskeletal genes associated with age-related diseases
Abstract The cytoskeleton comprises polymers from protein filaments shaped in an organized structure. This structure contributes significantly to the cell’s function and viability. Decades of research have implicated that the cytoskeleton’s dynamic nature is associated with downstream signaling events that further regulate cellular activity and control aging and neurodegeneration. This study aims to investigate the transcriptional changes of the cytoskeletal genes and their regulators in five age-related diseases: Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer’s disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM). An integrative approach of machine learning-based models and differential expression analysis was employed to identify potential biomarkers based on the cytoskeletal genes. Multiple machine-learning algorithms were used, where the Support Vector machines (SVM) classifier achieved the highest accuracy. The study highlighted 17 genes involved in the cytoskeleton’s structure and regulation associated with age-related diseases. The results provide a holistic overview of the role of transcriptionally dysregulated cytoskeletal genes in age-related diseases. This study pinpoints cytoskeletal genes and regulators of the cytoskeleton that can be utilized as potential markers and drug targets.
Knowledge of pregnant women towards pre-eclampsia in South Gondar zone, 2023
Crotonylation deficiency of S100A7 K49 promotes psoriatic keratinocyte proliferation through enhanced interaction with RAGE
A cross-sectional study investigating the L-shaped relationship between urinary albumin creatinine ratio and overweight/obesity in children and adolescents
The significance of the depth of invasion and tumor size in resected pathologic T4a gingivobuccal squamous cell carcinoma
ALBI-sarcopenia score as a predictor of treatment outcomes in hepatocellular carcinoma
Abstract The recently developed ALBI-Sarcopenia score has demonstrated effectiveness in predicting mortality in hepatocellular carcinoma (HCC), emerging as a crucial factor in guiding treatment decisions. To assess the utility of the ALBI-Sarcopenia score in predicting the success of HCC treatment. A prospective study involving 262 liver cirrhosis with HCC patients were assigned to various treatment strategies according to Barcelona clinics of liver disease (BCLC) classification. Patients were followed up for 12 months reporting laboratory data, sarcopenia, ALBI-Sarcopenia score, and outcomes. Sarcopenia was prevalent in 43.1% (48.35% males and 31.25% females, P = 0.042). Most patients were HCV-positive (88.9%) and classified as CTP A (55.7%) or BCLC B (54.2%). Over the study period, TACE was the most administered treatment (41.2% at baseline), followed by a progressive shift toward best supportive care as disease severity increased. Complete response rates declined from 31.7% at 1 month to 21.4% at 12 months, while progressive disease rates increased from 21.8 to 37.8% over the same period. At 12 months, the ALBI-Sarcopenia score demonstrated the highest predictive accuracy for treatment response (AUC:0.69, p = 0.001), outperforming both the ALBI (AUC: 0.631, p = 0.001) and MELD (AUC:0.623, p = 0.003) scores. Logistic regression identified ALBI-Sarcopenia as a significant independent predictor of response at 1 month (OR:1.25, 95% CI:0.881–1.971, p = 0.002) and 12 months (OR:2.189, 95% CI:0.992–4.937, p = 0.001). The ALBI-Sarcopenia score is a robust predictor of treatment outcomes in HCC, offering superior prognostic accuracy compared to traditional scoring systems, and enhancing patient stratification for optimized treatment planning.
The effect of seismic air gun shots on physiology and behaviour of fish lake communities
Mantle flows driving tectonic escape around eastern Himalaya syntaxis
The efficiency of azelastine hydrochloride and fluticasone propionate nasal spray to improve PAP adherence in patients with obstructive sleep apnea
Preparation and ecological risk assessment of porous sewage sludge substrate for ecological restoration
Genomic, socio-environmental, and sequencing capability patterns in the surveillance of SARS-CoV-2 in Latin America and the Caribbean up to 2023
Predictive models and WTAP targeting for idiopathic pulmonary fibrosis (IPF)
Experimental and numerical study of tubular steel columns with/without demountable bolted shear connectors embedded in the concrete
Abstract Three push-out specimens were experimentally tested to investigate the behavior of tubular steel columns (TSC) with and without bolted shear connectors embedded in normal concrete (NC). Each specimen consisted of a tubular steel column (TSC) encased in a 250 × 250 × 200 mm concrete cube The embedment/the prominent height of TSC was 100 mm. Foam was used underneath the TSC to form free space. The study considered variables such as the presence of demountable shear studs and reinforcement. The failure modes, load-slip response, peak load/slip, and shear stiffness of the specimens were analyzed. Furthermore, a finite element model (FEM) was developed using ABAQUS software to simulate the behavior of the tested specimens and validated against the experimental results. The FEM was also employed to conduct further parametric investigations. The results indicate that demountable shear studs significantly improve shear capacity, with specimens exhibiting a 217% higher peak load than those without studs. Reinforcing the concrete block had a negligible effect on peak load but increased peak slip by 37.7% and shear stiffness by 18.7% compared to the unreinforced specimen. Furthermore, increasing the TSC thickness significantly enhances peak load, with a 154.31% increase observed as the thickness increases from one-third of the bolt diameter to the full bolt diameter. Additionally, using TSC thicknesses greater than half the bolt diameter helps prevent bearing failure. Increasing the concrete compressive strength from 25 to 50 MPa leads to a 24.6% increase in peak load, while slip capacity decreases by 19.77%. For applications requiring high ductility, excessively high-strength concrete should be avoided, as it reduces slip capacity. The results also demonstrate that the bolt diameter should not exceed twice the TSC web thickness to prevent bearing failure.
Mineralogical characteristics and color genesis of black quartzite jade from Linwu, Hunan, China
A novel mathematical framework for pedigree-based calculation of Y-STR match probabilities
Abstract Y-chromosomal short tandem repeat (Y-STR) markers are routinely used in forensic casework to identify male donors of biological traces left at crime scenes, particularly in sexual assault cases. However, the evidential value of a match between the Y-STR profile of a trace and a potential donor, usually a crime suspect, is difficult to quantify, and the common albeit inappropriate practise to equate Y-STR match probabilities with Y-STR profile frequencies estimated from population databases has been subject to scientific debate for decades. As a solution to this long-standing problem, we suggest an alternative approach to the calculation of Y-STR match probabilities that involves splitting the group of potential donors other than the suspect into two: (i) his close male relatives (termed his ‘pedigree’) and (ii) all other males. While an upper limit to the match probability is easily calculated for the second group, it is computationally challenging to derive for the first. We therefore developed a mathematical framework that uses importance sampling to reconstruct and evaluate the Y-STR profiles of untyped members of the suspect’s pedigree by way of simulation. Extensive testing with elementary pedigrees of different structure and complexity confirmed that both, the framework and its Python-based software implementation yield match probability estimates that approximate well the correct analytical results, depending upon the number of simulations performed. Our methodology thus facilitates a more appropriate and valid solution to the long-standing problem of interpreting Y-STR profile matches in forensic casework.