Browse Articles
Discover research articles across all indexed journals
The cobalamin processing enzyme of Trichoplax adhaerens
The impact of locus of control on somatic and psychological profiles of patients with irritable bowel syndrome engaging in aerobic exercise
Magnesium-dependent-protein phosphatase 1B regulates the protein arginine methyltransferase 5 through the modulation of myosin phosphatase
Changes in optic nerve head microvasculature following disc hemorrhage absorption in glaucomatous eyes
Abstract This study investigated the changes in optic nerve head (ONH) microvasculature, circumpapillary retinal nerve fiber layer (cpRNFL) thickness, and visual field (VF) sensitivity following the absorption of optic disc hemorrhage (DH). Intradisc vessel density (dVD) was calculated using a 3 × 3 mm optic disc scan in 60 eyes of 60 patients with primary open angle glaucoma and DH who had undergone two or more swept-source optical coherence tomography angiography exams. Clinical parameters at the time of DH occurrence and after absorption, as well as those between the subgroups based on DH recurrence and location, were compared. Linear regression analysis was performed to identify factors associated with changes in cpRNFL thickness in the DH-affected quadrant. Mean dVD, cpRNFL thickness, and VF sensitivity significantly decreased after DH absorption (all P < 0.05). The reduction in dVD was more pronounced in eyes with recurrent DH compared to those with a single episode (P = 0.032). Eyes with DH occurring within or at the margin of the disc cup showed a greater dVD reduction than those with DH occurring outside the disc cup (P = 0.049). The reduction in cpRNFL thickness in the DH-affected quadrant correlated with dVD reduction in the same quadrant (β = 0.370, P = 0.013) and DH recurrence (β = -2.617, P = 0.033). This finding suggests that DH pathogenesis may be associated with changes in optic disc vasculature, contributing to glaucomatous progression.
Familial Alzheimer’s disease mutations in amyloid precursor protein impair calcineurin signaling to NMDA receptors
Effect of thermal stress on the life of DC link capacitors for smart grid
Acute heat stress upregulates Akr1b3 through Nrf-2 to increase endogenous fructose leading to kidney injury
MSC-EVs and UCB-EVs promote skin wound healing and spatial transcriptome analysis
Prenylation-dependent membrane localization of a deubiquitinating enzyme and its role in regulating G protein–mediated signaling in yeast
A hybrid Prairie INFO fission naked algorithm with stagnation mechanism for the parametric estimation of solar photovoltaic systems
Abstract This paper presents a study to enhance the performance of a recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well as exploitation properties. A new set of algorithms, namely Prairie dog optimization algorithm, INFO, and Fission fusion optimization algorithm (FuFiO) are included in the fundamental framework of NMRA to enhance the exploration operation. The proposed algorithm is a hybrid algorithm based on four algorithms: Prairie Dog, INFO, Fission Fusion and Naked mole-rat (PIFN) algorithm. Five new mutation operators/inertia weights are exploited to make the algorithm self-adaptive in nature. Apart from that, a new stagnation phase is added for local optima avoidance. The proposed algorithm is tested for variable population, dimension size, and efficient set of parameters is analysed to make the algorithm self-adaptive in nature. Friedman as well as Wilcoxon rank-sum tests are performed to determine the effectiveness of the PIFN algorithm. On the basis of a comparison of outcomes, the PIFN algorithm is more effective and robust than the other optimization techniques evaluated by prior researchers to address standard benchmark functions (classical benchmarks, CEC 2017, and CEC-2019) and complex engineering design challenges. Furthermore, the effectiveness as well as reliability of the PIFN algorithm is demonstrated by testing using various PV modules, namely the RTC France Solar Cell (SDM, and DDM), Photowatt-PWP201, STM6- 40/36, and STP6-120/36 module. The results obtained from the PIFN algorithm are compared with various MH algorithms reported in the existing literature. The PIFN algorithm achieved the lowest root-mean-square error value, for RTC France Solar Cell (SDM) is 7.72E−04, RTC France Solar Cell (DDM) is 7.59E−04, STP6-120/36 module is 1.44E−02, STM6-40/36 module is 1.723E−03, and Photowatt-PWP201 module is 2.06E−03, respectively. In order to enhance the accuracy of the obtained results of parameter estimation of solar photovoltaic systems, we integrated the Newton-Raphson approach with the PIFN algorithm. Experimental and statistical results further prove the significance of the PIFN algorithm with respect to other algorithms.
Redox potential tuning by calcium ions in a novel c-type cytochrome from an anammox organism
Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition
Between scents and sterols: Cyclization of labdane-related diterpenes as model systems for enzymatic control of carbocation cascades
Association between cardiometabolic index and hypertension among US adults from NHANES 1999–2020
Disorder within order: Identification of the disordered loop of STAS domain as the inhibitory domain in SLC26A9 chloride channel
Biomass production and silage quality of ensiled BRS Capiaçu elephant grass at different regrowth ages and residue heights
Substrate and inhibitor specificity of Plasmodium nucleoside transporters ENT1 orthologs
Assessing hazard prediction and risk calibration skills in experienced and novice e-scooter riders
Abstract Less experienced e-scooter riders often exhibit risky riding behaviours. Despite this, no studies have examined how riders calibrate risk, respond to hazardous situations, and the impact of riding experience on these skills. To address this, this study assessed hazard prediction and risk calibration in e-scooter riders via bespoke video-based tests featuring real e-scooter footage filmed from the rider’s perspective. The first experiment assessed the ability of e-scooter riders to predict hazardous riding scenarios. The second experiment evaluated their proneness to engage in risky riding situations. The results indicated that increased riding experience did not improve riders’ hazard prediction skills or reduced their proneness to engage in risky riding. In fact, a higher riding frequency was linked to an increased tendency to engage in risky behaviour in certain scenarios. The results highlight that the typically short duration of e-scooter trips may limit riders’ exposure to a variety of hazards, hindering their ability to develop effective risk calibration skills. The observed high propensity to engage in risky riding scenarios, combined with average hazard prediction scores, emphasizes the need for targeted rider training focused on vigilance and risk awareness.