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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.

SF3B1 thermostability as an assay for splicing inhibitor interactions

2024 FDA approvals exceed average number but have lower sales projections

Copper and iron as unique trace elements linked to fibromyalgia risk

Dilated cardiomyopathy variant R14del increases phospholamban pentamer stability, blunting dynamic regulation of calcium

Biopharma dealmaking in 2024

Exploring health related quality of life for women with breast cancer in Ireland and Québec, Canada throughout the COVID-19 pandemic

Abstract The long-term consequences from the COVID-19 pandemic on breast cancer (BC) is highly unknown, however persisting unmet needs and psychosocial difficulties are likely. The objectives of this study were to evaluate the change in health-related quality of life (HR-QoL) from the pandemic to post-pandemic for women living with a diagnosis of BC and to assess the association between COVID-19 stressor impact and HR-QoL in Ireland and Québec, Canada. Women with a diagnosis of BC were initially enrolled in the cohort study. HR-QoL was assessed during the pandemic (2020–2021) and post-pandemic periods (2022). COVID-19 stressor impact was computed post-pandemic, and change in HR-QoL during and post-pandemic was compared between Ireland and Québec using independent t-tests. Multivariable analysis of covariance (ANCOVA) was used to evaluate the association between COVID-19 stressor impact and changes in HR-QoL, and compare it between Ireland and Québec. 405 participants were included from both settings (Ireland n = 267; Québec n = 138). The average HR-QoL improved from the COVID-19 pandemic to post-pandemic, and there were no differences between Ireland and Québec. Women with high COVID-19 stressor impact (18.9% of participants) had a significantly smaller improvement in their overall HR-QoL compared to those with low COVID-19 stressor impact, and this was evident in Ireland (p < 0.004) and Québec (p < 0.0001) but there were no significant differences between Ireland and Québec (interaction p-value > 0.05). Overall, HR-QoL for women with BC improved from pandemic to post-pandemic period. However, similarly in both settings, women who experienced higher levels of COVID-19-related stress had a slower recovery in HR-QoL. These results can guide decisions about health services and policies to adequately address the on-going effect of the pandemic and also prepare for future health crises.