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Novel fabrication techniques for HKUST-1 adsorptive nanocomposite membrane for heavy metals removal from wastewater
Integration of the reptile search algorithm and the adaptive neuro-fuzzy inference system enhances standardized precipitation evapotranspiration index forecasting
Expanding the V1-MT model to the estimation of perceived fluid direction
Abstract Humans can readily perceive the direction of liquid flow, yet computational modeling of this process remains challenging due to the complexity of non-rigid motion. Previous models based on neural activities in the primary visual cortex (V1) and the middle temporal area (MT) have been effective in explaining rigid motion perception. In this study, we extend the V1-MT model to address the perception of liquid flow direction. Participants observed video clips of liquid flow and reported the perceived direction, while the V1-MT model was used to predict these perceptions. The winner-take-all approach failed to accurately capture the observed perceptions. In contrast, a weighted mean of directional energies yielded strong predictions, highlighting that the human visual system spatially integrates directional energies from non-rigid motion components. These findings broaden the applicability of the V1-MT model to non-rigid motion and provide insights into how the visual system bridges the gap between computational models of rigid and non-rigid motion perception.
Unravel subseafloor hydrothermal leaching and magmatic degassing during chimney formation at Kolumbo volcano
Abstract Hydrothermal chimneys are the upmost expression of fluids, metals and ligands transfer from the subseafloor to the hydrosphere, eventually forming seafloor massive sulfides. In volcanic arc settings, both magmatic and hydrothermal fluids occur together. While each fluid reflects different metal mobilizing mechanisms (i.e. magmatic degassing and hydrothermal leaching of subseafloor lithologies), it is unclear which metals they respectively provide to the budget of the chimneys. We investigate the metal sources and mobilizing mechanisms associated with a gold-rich hydrothermal field from Kolumbo volcano (South Aegean Volcanic Arc, Greece) by comparing Pb isotope ratios of ore minerals from a chimney with those of potential source rocks. Four key findings result from our study: (1) Kolumbo volcanic rocks are the main source of Pb for the chimneys; (2) Magmatic assimilation of Cycladic Basement allows to track magmatic differentiation and identify metal mobilizing mechanisms for Pb and metals with similar behavior. At Kolumbo, magmatic degassing mobilizes As, Ag, Au, Cu, Hg, Sb, Sn and Zn along with Pb, while hydrothermal leaching of rhyolite provides Tl and likely some base metals to the chimneys; (3) Magmatic fluids contributed to galena and Sb-Pb sulfosalts formation while pyrite formed from hydrothermal fluids leaching rhyolite; (4) Galena growth zones in pyrite reveal episodic pulses of magmatic fluids during the chimney growth. The combined use of Pb isotopes on ore minerals and source rocks provides an additional tool to discriminate between magmatic and hydrothermal fluids contribution during seafloor massive sulfide formation, especially in arc settings where magmatic assimilation of crustal material with distinct isotopic signature is more likely to occur.
Assessing groundwater drought in Iran using GRACE data and machine learning
Finite element analysis of mandibular fracture fixation authenticated by 3D printed mandible mechanical testing
RFID-embedded mattress for sleep disorder detection for athletes in sports psychology
The cervical microbiome of ewe breeds with known divergent fertility following artificial insemination with frozen-thawed semen
Abstract The use of artificial insemination (AI) with frozen-thawed semen in sheep is limited internationally due to low pregnancy rates. An exception is Norway, where high success rates routinely occur following vaginal deposition of frozen-thawed semen during natural estrus. Previous research suggests that breed-specific differences in pregnancy rates may result from impaired cervical sperm transport. This study compared cervical microbiomes among sheep breeds with known differences in pregnancy rates after AI. Cervical samples were collected from Suffolk (low fertility) and Belclare (medium fertility) breeds in Ireland, and Norwegian white sheep (NWS) and Fur breeds (both high fertility) in Norway, during the follicular phase of both natural and synchronized estruses, and the luteal phase of synchronized estrus. Amplicon sequencing revealed significantly higher bacterial abundance during the follicular phase in the low-fertility Suffolk breed compared to high-fertility breeds. Alpha diversity was higher in Suffolk and Belclare breeds, especially during the natural follicular phase, coinciding with pronounced beta diversity differences among breeds. Genus Histophilus was the top feature leading to microbial differences between ewe breeds and types of cycle. Ewe breed was the main driver of cervical microbial composition; increased microbial load in lower-fertility breeds may negatively impact sperm survival/transport, hampering AI success.
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.