bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Cu doped RuO2 catalyst for efficient and durable acidic water oxidation
Authors: Liu Jingyu , Huang Jie , Wang Xi‐ao , Zhang Xiaoyan , Zhang Lixue 
Published: June 2024

AbstractProducing hydrogen by water electrolysis in acidic system is essential for advancing the proton‐exchange membrane water electrolyzer (PEMWE) and maintaining the global sustainability. However, excavating highly active and durable catalysts for anodic oxygen evolution reaction (OER) in acid medium remains a great challenge due to the sluggish kinetics of OER and the severe catalyst dissolution. In this work, we developed a Cu doped RuO2 catalyst (Cu‐RuO2@CP) through simple drop casting and low temperature pyrolysis procedures. The incorporation of Cu into RuO2 executes complicated responsibilities: improving OER activity by raising more oxygen vacancies and accelerating the charge transfer between solid‐liquid interface; strengthening the long‐term durability via suppressing the over‐oxidation of RuO2 into soluble RuO4; lowering the catalyst cost by replacing with less expensive Cu. Hence, the Cu‐RuO2@CP exhibits excellent activity and stability in 0.5 M H2SO4 electrolyte, requiring a low overpotential of 200 mV to achieve 10 mA cm−2 and preserving stability for almost 500 h. Moreover, the overpotential is as low as 397 mV even when operating under an industrial‐level current density of 1000 mA cm−2, paving a new way to develop high performance OER electrocatalyst in acidic environment to promote the large‐scale utilization of PEMWE.
Lithocarpus polystachyus Rehd. leaves aqueous extract inhibits learning and memory impairment in Alzheimer's disease rats: Involvement of the SIRT6/NLRP3 signaling pathway
Authors: Wu Wendan , Yan You , Yi Tingting , Wei Yu , Gao Jianmei , Gong Qihai 
Published: June 2024

AbstractAlzheimer's disease (AD) is a chronic and progressive neurodegenerative condition that is influenced by multiple factors along with neuroinflammation and oxidative stress. Our previous study proved that Lithocarpus polystachyus Rehd. aqueous extract (sweet tea aqueous extract, STAE) effectively inhibits hydrogen peroxide‐induced neuronal cell injury. However, it is not clear whether STAE can protect against AD, and its underlying mechanisms are still uncertain. Therefore, the present study was designed to evaluate the possible behavioral and neurochemical effects of STAE on Aβ25‐35‐induced AD rats administered STAE (20, 40, 80 mg/mL) for 14 days. We showed that STAE administration significantly and dose‐dependently ameliorated the cognitive deficits in the AD rat models, assessed in the Morris water maze (MWM) test, Y‐maze test, and novel object recognition (NOR) test. The results of hematoxylin and eosin (H&E) staining and Nissl staining showed that after treatment with STAE, the pathological damage to the hippocampal CA1, CA3, and dentate gyrus (DG) neurons of rats was significantly improved. Furthermore, STAE dose‐dependently inhibited microglia and astrocyte activation in the hippocampus of rats accompanied by increased protein expression of silent mating‐type information regulation 2 homolog 6 (SIRT6) and decreased protein expression of nod‐like receptor thermal protein domain‐associated protein 3 (NLRP3) and its downstream pyroptosis‐related genes after following Aβ25‐35. In summary, our findings indicate that STAE effectively inhibits Aβ25‐35‐induced learning and memory impairment in rats, and the mechanism is, at least partially, related to the regulation of SIRT6/NLRP3 signaling pathway.
Prediction of large‐for‐gestational‐age at birth using fetal biometry in type 1 and type 2 diabetes: A retrospective cohort study
Authors: Rathcke Sidsel L. , Sinding Marianne M. , Christensen Trine T. , Uldbjerg Niels , Christiansen Ole Bjarne , Kornblad Julia , Søndergaard Kamilla H. , Krogh Sofie , Sørensen Anne N. W. 
Published: June 2024

AbstractObjectiveTo compare ultrasound‐assessed fetal head circumference (HC), abdominal circumference (AC), HC/AC ratio, and estimated fetal weight (EFW) in prediction of large‐for‐gestational‐age (LGA) at birth in pregnancies affected by type 1 (T1DM) and type 2 (T2DM) diabetes.MethodsThis retrospective cohort study included all women with T1DM and T2DM giving birth to singletons between 2010 and 2019 at Aalborg University Hospital, Denmark. Ultrasound scans were performed at 16, 20, 28 and 34 weeks of pregnancy. LGA was defined as birth weight deviation of 15% or greater from the expected for gestational age (≥90th centile). Prediction of LGA was assessed by logistic regression adjusted for maternal characteristics and glycated hemoglobin (HbA1c) and area under the receiver operating characteristics curve (AUC).ResultsAmong 180 T1DM pregnancies, 118 (66%) had an LGA neonate at birth. At 28 weeks of pregnancy, they were predicted with AUCHC/AC = 0.67, AUCAC = 0.85, and AUCEFW = 0.86. The multivariate analysis did not improve the predictive performance of the HC/AC ratio or AC. Among 87 T2DM pregnancies, 36 (41%) had an LGA neonate at birth. At 28 weeks, they were predicted with AUCHC/AC = 0.73, AUCAC = 0.83, and AUCEFW = 0.87. In T2DM, the multivariate analysis significantly improved the predictive performance for both HC/AC ratio and AC from 20 weeks of pregnancy.ConclusionIn T1DM and T2DM pregnancies, LGA is characterized by a general fetal overgrowth including both AC and HC. Therefore, AC and EFW perform better than the HC/AC ratio in the prediction of LGA. In T2DM, as opposed to T1DM, the predictive performance was improved by the inclusion of maternal characteristics and HbA1c in the analysis.
Range‐wide prediction of habitat suitability for king cobras under current and future scenarios
Authors: Mukherjee Subha Shankar , Patra Debidas , Hossain Asif 
Published: June 2024

AbstractOphiophagus hannah, commonly known as the king cobra, is listed as vulnerable by the International Union for Conservation of Nature (IUCN) and is protected under national laws in most countries. This charismatic species faces multiple threats, including habitat loss, human persecution, illegal trafficking, and climate change. Due to the king cobra's sensitivity to environmental conditions, its population status and trends are barely understood. This study used the MaxEnt algorithm to predict the potential distribution of king cobras across Asia, a method that has been successfully implemented in modeling distributions of various species in the region. The findings showed that Evergreen Broadleaf Trees emerged as the most influential variable for the distribution of Ophiophagus hannah with a 27.3% contribution, followed by the Mean Diurnal Range and Urban/Built‐up areas. Jackknife analysis identified the Mean Diurnal Range as having the highest testing gain. Approximately 413,268 km2 were found to have the most suitable climatic conditions for sustaining this species. Countries such as India, Myanmar, Bangladesh, Vietnam, Cambodia, Indonesia, and Malaysia were identified as having favorable conditions. Under the future climate scenario SSP5‐8.5, the extent of suitable habitats (maximum) for the king cobra is projected to decrease in the periods 2041–2060 and 2081–2100. This report provides valuable insights that could inform conservation strategies for O. hannah in these regions.
Discovery and characterization of a transient chaetal gland during the development of Capitella teleta (Sedentaria: Annelida)
Authors: Tilic Ekin , Bartolomaeus Thomas , Seaver Elaine C. 
Published: June 2024

AbstractChaetae are among the most extensively studied structures in polychaetes, serving as a defining morphological trait for annelids. Capitella teleta stands out as one of the few established annelid models for developmental and morphological studies, thus receiving significant scholarly attention. In this study, we unveil a previously unnoticed glandular structure associated with chaetae within the larvae of C. teleta. Our investigations demonstrate the absence of comparable structures in the chaetal follicles of adults and juveniles (older than 1 week), as well as during active chaetogenesis, underscoring the transient nature of these glands. This indicates that larval chaetal follicles transform into a gland that later disappears. Utilizing histology and transmission electron microscopy, we characterized these glands. Our findings underscore the diversity of chaetal ultrastructure in annelids and show that, even in well‐studied species, novel morphological details can be found. We emphasize the importance of examining various life‐history stages to capture such transient morphological features. This work lays a crucial morphological foundation and deepens our understanding of chaetae and chaetogenesis in C. teleta, paving the way for more accurate interpretations of future experimental studies on chaetogenesis in this species.
Hyper‐parameter optimizedGPRmodel based on chaos game algorithm forRFpower transistors
Authors: Gao Zhiwei , Zhou Tao , Crupi Giovanni , Cai Jialin 
Published: June 2024

AbstractIn this paper, a new frequency domain behavior model for radio frequency (RF) power transistors based on a hyper‐parameter optimized Gaussian process regression (GPR) method is presented. The chaos game optimization (CGO) algorithm is used to optimize GPR hyperparameters, resulting in the CGO‐GPR model. The basic theory as well as the details of the modeling process are presented. Validation of the model is conducted using a 10‐watt GaN power transistor. Compared to the standard GPR model, the proposed model achieved a significant improvement. Furthermore, the comparison with the particle swarm optimization (PSO) based GPR model (PSO‐GPR) showed that the proposed model allows achieving superior performance, thereby confirming the effectiveness of the developed modeling technique.
Profitability in rural restaurants and owners' motivation to agritourism: A case from Nepal
Authors: Bhatta Kumar , Gajurel Ram Prasad , Tanaka Toshinori , Ohe Yasuo 
Published: June 2024

AbstractRural Restaurants (RR) are located in rural areas primarily to serve the owner's business interests. However, they also play a crucial role in providing economic opportunities for rural communities and are regarded as a center for rural economy. Rural areas often face challenges such as depopulation due to limited access to public services, inadequate infrastructure, geographical remoteness, economic hardships, educational barriers, and other factors. Thus, this study aims to investigate the profitability of RRs and owners' motivations for agritourism development, as profitability and business expansion are typically positively correlated. Data from 105 samples collected in rural Nepal in 2022 and 2023 were analyzed using Tobit and Ordered Logit regression models, respectively. The study conceptualizes and confirms various contributors to RR profitability, including factors related to the supply side, demand appeal, and those influenced by pandemic‐related factors. Additionally, we explored how owners' motivations for agritourism are influenced by the restaurant's characteristics, quality‐related factors, and other attributes. Policymakers need to support RR owners in developing agritourism initiatives and contribute to quality control through effective enforcement of consumer laws by a responsible and robust quality control team.
Quality of Life Outcomes for Parotid Malignancies
Authors: Tang Anthony , Li Jinhong , Scheff Nicole , Johnson Jonas T. , Contrera Kevin J. , Nilsen Marci L. 
Published: June 2024

BackgroundThis study describes patient‐reported outcome measures (PROMs) and associated factors in patients who underwent surgery for malignant parotid tumors (MPT).MethodsThis is a retrospective study of all surgically treated MPT patients in a multidisciplinary head and neck cancer (HNC) survivorship clinic (2017–2023). PROMs included University of Washington Quality of Life Questionnaire (UW‐QOL), Eating Assessment Tool (EAT‐10), Patient Health Questionnaire (PHQ‐8), Generalized Anxiety Disorder (GAD‐7), Neck Disability Index (NDI), and Insomnia Severity Index. Multivariable regression analysis was used to investigate clinical predictors associated with PROMs.ResultsIn 62 MPT patients, the prevalence of clinically relevant dysphagia symptoms (EAT‐10), elevated symptoms of depression (PHQ‐8), moderate/severe symptoms of anxiety (GAD‐7), moderate/severe neck pain with activities of daily living (NDI), and moderate/severe symptoms of insomnia at last follow‐up was 32.3%, 15.5%, 7.1%, 17.7%, and 7.2%, respectively. Nonparametric one‐sided test revealed that patients treated with adjuvant CRT had significantly worse physical QOL, social‐emotional QOL, and swallowing scores than patients treated with surgery alone (p = 0.01, p = 0.02, p = 0.03, respectively); that patients treated with surgery and adjuvant RT had significantly worse physical QOL and social‐emotional QOL than patients treated with surgery alone (p < 0.01, p = 0.01, respectively) and that patients treated with surgery and adjuvant CRT had significantly worse swallowing and neck pain than patients treated with surgery and adjuvant RT (p = 0.03, p = 0.05, respectively).ConclusionsIn patients with surgically treated MPT, adjuvant CRT and RT were associated with worse PROMs.Level of Evidence4 Laryngoscope, 134:4549–4556, 2024
A strong subadditivity‐like inequality for quantum entropy in semifinite von Neumann algebras
Authors: Łuczak Andrzej 
Published: June 2024

AbstractLet be a semifinite von Neumann algebra with a normal faithful semifinite trace , and let , , be its subalgebras such that and that restricted to any of these subalgebras is semifinite. Denote by , , and the normal conditional expectations from onto , and , respectively, such that is invariant with respect to any of them. The quadruple , , , is said to be a commuting square if
In this note, we show that the property of being a commuting square is characterized by a sort of the SSA‐like (strong subadditivity of entropy) inequality
for an arbitrary normal state on , where denotes the Segal entropy of the state . The situation when we have equality in the inequality above is also investigated, and various equivalent conditions are obtained, in an especially appealing form for finite von Neumann algebras. For such algebras, one more condition is obtained under the assumption of independence of the algebras and .
Predicting radiation‐induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy
Authors: Colen Jonathan , Nguyen Cam , Liyanage Seth W. , Aliotta Eric , Chen Joe , Alonso Clayton , Romano Kara , Peach Sean , Showalter Timothy , Read Paul , Larner James , Wijesooriya Krishni 
Published: June 2024

AbstractBackgroundStereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti‐tumor T cells and stimulate T cell infiltration into tumors. Radiation‐induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT‐induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections.PurposeRIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT.MethodsThis Python‐based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10‐fold cross validation.ResultsOur algorithm predicted post‐treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5 × 109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post‐treatment ALC < 0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38‐s end‐to‐end prediction time of post treatment ALC.ConclusionOur model performed well in predicting RIIS in patients treated using lung SBRT. With near‐real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.
Applications of machine learning in urodynamics: A narrative review
Authors: Liu Xin , Zhong Ping , Gao Yi , Liao Limin 
Published: June 2024

AbstractBackgroundMachine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models for different urodynamic research tasks.MethodsWe undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodynamics, and lower urinary tract symptoms. We identified three domains for assessment in advance of commencing the review. These were the applications of urodynamic studies examination, applications of diagnoses of dysfunction related to urodynamics, and applications of prognosis prediction.ResultsThe machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, which are urodynamic examination, diagnosis of urinary tract dysfunction and prediction of the efficacy of various treatment methods. Most of these studies were single‐center retrospective studies, lacking external validation, requiring further validation of model generalization ability, and insufficient sample size. The relevant research in this field is still in the preliminary exploration stage; there are few high‐quality multi‐center clinical studies, and the performance of various models still needs to be further optimized, and there is still a distance from clinical application.ConclusionsAt present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics. The purpose of this review is to summarize and classify the machine learning algorithms applied in this field and to guide researchers to select the appropriate algorithm model for different task requirements to achieve the best results.
Density functional theory‐based rapid and accurate estimation of reduction potentials of acridinium derivatives in ground and excited state
Authors: Choi Jiyoon , Kang Kyungtae , Park Ji Hun , Kim Hyungjun 
Published: June 2024

AbstractWe devised a quantum chemical simulation protocol that can rapidly and accurately predict ground and excited state reduction potentials (E0 and E*, respectively) of acridinium photoredox catalysts (PCs). To bypass time‐consuming excited state geometry optimizations, we used ground state equilibrium geometries to compute the electronic energy of excited states, which provides reasonable E0 and E* estimates. The contribution of Hartree‐Fock exchange (HFX) in density functionals was systematically varied to estimate E0 and E*. In addition to reproducing experimental results, physically sensible models, such as correct descriptions of exciton behavior, are highly necessary. Based on the exciton correlation values, the appropriate amount of HFX in the B3LYP functional was determined to be 30 % to yield physically sensible bound electron‐hole pairs for small organic molecules. We also investigated the impact of basis sets on the predictability of E0 and E*. Geometry optimizations with B3LYP‐D2(HFX 20 %)/6‐31G and single point energy refinement with (TD‐)B3LYP‐D2(HFX 30%)/6‐311++G(d,p) yielded the best results. The transferability of the suggested protocol was confirmed with a test set consisting of eight acridinium derivatives. This study can provide reasonably accurate results with relatively small amounts of computational resources, and it is therefore expected to greatly contribute to the development of new acridinium PCs.
A Hybrid Deep Learning‐Based Power Management Strategy for PV‐Assisted Desalination Plant
Authors: Almashnowi Majed Y. A. , AlGhamdi Sami , Alamier Waleed M. , Ali Syed Kashif , Bakather Omer Y. , Hassan Mohamed , Qudsieh Isam Y. , Imran Mohd , Alam Md Mottahir 
Published: June 2024

AbstractThe increasing global need for freshwater has led to the widespread implementation of photovoltaic (PV) assisted desalination facilities as a viable and environmentally friendly remedy. The necessity for these plants resides in executing an effective power management strategy to provide dependable and economically feasible water generation. This paper utilizes a mixed architecture consisting of a convolutional neural network (CNN) and a deep Q‐learning Network (DQN) to implement a hybrid deep learning‐based power management strategy. The presented approach is modeled and executed in matrix laboratory (MATLAB) language, and the experimental findings validated that this algorithm achieved a computational time of 0.2 s and an energy loss of 0.01 megawatts, which is lower than the conventional models. Furthermore, the proposed strategy achieved a remarkable fit with an accuracy rate of 0.99, demonstrating its effectiveness in handling diverse load power and solar profiles in power management. Implementing this hybrid approach holds promise for a substantial reduction in operational expenditures and the advancement of sustainability in freshwater generation, making a valuable contribution toward a more environmentally friendly and optimized future for the desalination sector.
Evaluating drug interaction potential from cytokine release syndrome using a physiologically based pharmacokinetic model: A case study of teclistamab
Authors: Willemin Marie‐Emilie , Wang Lin Shun Xin , De Zwart Loeckie , Wu Liviawati S. , Miao Xin , Verona Raluca , Banerjee Arnob , Liu Baolian , Kobos Rachel , Qi Ming , Ouellet Daniele , Goldberg Jenna D. , Girgis Suzette 
Published: June 2024

AbstractCytokine release syndrome (CRS) was associated with teclistamab treatment in the phase I/II MajesTEC‐1 study. Cytokines, especially interleukin (IL)‐6, are known suppressors of cytochrome P450 (CYP) enzymes' activity. A physiologically based pharmacokinetic model evaluated the impact of IL‐6 serum levels on exposure of substrates of various CYP enzymes (1A2, 2C9, 2C19, 3A4, 3A5). Two IL‐6 kinetics profiles were assessed, the mean IL‐6 profile with a maximum concentration (Cmax) of IL‐6 (21 pg/mL) and the IL‐6 profile of the patient presenting the highest IL‐6 Cmax (288 pg/mL) among patients receiving the recommended phase II dose of teclistamab in MajesTEC‐1. For the mean IL‐6 kinetics profile, teclistamab was predicted to result in a limited change in exposure of CYP substrates (area under the curve [AUC] mean ratio 0.87–1.20). For the maximum IL‐6 kinetics profile, the impact on omeprazole, simvastatin, midazolam, and cyclosporine exposure was weak to moderate (mean AUC ratios 1.90–2.23), and minimal for caffeine and s‐warfarin (mean AUC ratios 0.82–1.25). Maximum change in exposure for these substrates occurred 3–4 days after step‐up dosing in cycle 1. These results suggest that after cycle 1, drug interaction from IL‐6 effect has no meaningful impact on CYP activities, with minimal or moderate impact on CYP substrates. The highest risk of drug interaction is expected to occur during step‐up dosing up to 7 days after the first treatment dose (1.5 mg/kg subcutaneously) and during and after CRS.