bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Generation of Out‐of‐Plane Polarized Spin Current in Polycrystalline Heavy Metal Devices with Broken Electric Symmetries
Authors: Liu Qianbiao , Lin Xin , Shaked Ariel , Nie Zhuyang , Yu Guoqiang , Zhu Lijun 
Published: August 2024

AbstractSpin currents of perpendicularly polarized spins (z spins) have received blooming interest for the potential in energy‐efficient spin–orbit torque switching of perpendicular magnetization in the absence of a magnetic field. However, generation of z spins is limited mainly to magnetically or crystallographically low‐symmetry single crystals that are hardly compatible with the integration to semiconductor circuits. This work reports efficient generation of z spins in sputter‐deposited polycrystalline heavy metal devices via a new mechanism of broken electric symmetries in both the transverse and perpendicular directions. Both the damping‐like and field‐like spin–orbit torques of z spins can be tuned significantly by varying the degree of the electric asymmetries via the length, width, and thickness of devices as well as by varying the type of the heavy metals. The presence of z spins also enables deterministic, nearly‐full, external‐magnetic‐field‐free switching of a uniform perpendicularly magnetized FeCoB layer, the core structure of magnetic tunnel junctions, with high coercivity at a low current density. These results establish the first universal, energy‐efficient, integration‐friendly approach to generate z‐spin current by electric asymmetry design for dense and low‐power spin‐torque memory and computing technologies and will stimulate investigation of z‐spin currents in various polycrystalline materials.
A Single‐Pixel Event Photoactive Device for Real‐Time, In‐Sensor Spatiotemporal Optical Information Processing
Authors: Kumar Mohit , Park Hayoung , Seo Hyungtak 
Published: August 2024

AbstractThe increasing demand for energy‐efficient, sophisticated optical sensing technologies in various applications, from machine vision to optical communication, highlights the necessity for innovations in spatiotemporal information sensing and processing at a nearly single‐pixel scale. Traditional methods, including multi‐pixel photodetector arrays and event‐based camera systems, often fail to provide rapid, real‐time detection and processing of dynamic events within the sensor. This shortcoming is particularly notable in handling high‐dimensional spatiotemporal data, where the dependency on sequential data input and external processing tools leads to latency, reduced throughput, and heightened energy consumption, thereby impeding real‐time parallel data processing capabilities. Here, a carrier‐selective, single‐pixel, position‐sensitive planar photoactive device that integrates spatiotemporal event sensing with inherent short‐term memory capabilities is introduced. The proof‐of‐concept single‐pixel event photoactive device enables in‐sensor spatiotemporal parallel optical information processing, efficiently managing multibit (>4 bit) data simultaneously and facilitating ultrafast (≈0.4 µs) recognition of input patterns with low energy consumption (25 femtojoules). Additionally, by adjusting the operating speed from continuous to pulsed light illumination, the sensor array can detect trajectories and absolute position of events, offering in‐sensor optical flow detection. This single‐pixel event photodetector marks significant advancement toward developing compact, energy‐efficient, ultrafast sensors suitable for a wide range of in sensor‐based photonic applications.
Association of polygenic risk scores with Alzheimer's disease and plasma biomarkers among Chinese older adults: A community‐based study
Authors: Hou Tingting , Liu Keke , Fa Wenxin , Liu Cuicui , Zhu Min , Liang Xiaoyan , Ren Yifei , Xu Shan , Wang Xiang , Tang Shi , Wang Yongxiang , Cong Lin , Tan Qihuan , Du Yifeng , Qiu Chengxuan 
Published: August 2024

AbstractINTRODUCTIONWe examined the associations of polygenic risk score (PRS) with Alzheimer's disease (AD) and plasma biomarkers in the Chinese population.METHODSThis population‐based study used baseline data from MIND‐China (2018; n = 4873) and follow‐up data from dementia‐free individuals (2014–2018; n = 2117). We measured AD‐related plasma biomarkers in a subsample (n = 1256). Data were analyzed using logistic and Cox regression models.RESULTSWe developed PRS with (PRSAPOE) and without (PRSnon‐APOE) apolipoprotein E (APOE) gene. In the longitudinal analysis, PRSAPOE was associated with a multivariable‐adjusted hazards ratio of 1.91 (95% CI = 1.13–3.23) for AD. PRSAPOE in combination with demographics yielded discriminative (area under the curve [AUC]) and predictive(C‐statistic) accuracy of 0.80 (95% confidence interval [CI] = 0.77–0.84) and 0.80 (0.77–0.82), respectively. PRSnon‐APOE showed an association with AD risk similar to PRSAPOE. PRSAPOE, but not PRSnon‐APOE, was associated with reduced plasma Aβ42/Aβ40 ratio and increased Neurofilament light chain (NfL) (p < 0.05).DISCUSSIONThe PRS with and without APOE gene, in combination with demographics, shows good discriminative and predictive ability for AD. The AD‐related pathologies underlie AD risk associated with PRSAPOE.Highlights
The PRSAPOE and PRSnon‐APOE were associated with AD risk in the Chinese population.
The PRSAPOE and PRSnon‐APOE, in combination with demographics, showed good discriminative and predictive ability for AD.
The AD‐related pathologies underlie the AD risk associated with PRSAPOE but not PRSnon‐APOE.
Inhibitory effect and mechanism analysis of modified coal gangue powder on the methane–air explosion
Authors: Yang Ke , Li Xuerui , Ji Hong , Xing Zhixiang , Jiang Juncheng , Ji Xinlong 
Published: August 2024

AbstractThe application of industrial solid waste coal gangue (CG) in gas explosion suppression is explored, which opens up a new way for the resource utilization of CG. Two modified CG anti‐explosion agents, first‐grade modified CG (RCG) and second‐grade modified CG (MCG), were prepared by roasting activation and acid–base synergistic excitation. The explosion suppression performance of CG, RCG, and MCG was investigated through a 2.5 L semi‐closed explosion pipe. The experimental results were compared and analyzed, and their pyrolysis characteristics, phase composition, and particle size were analyzed to reveal their explosion suppression mechanism. It was proved that MCG had the best explosion suppression effect. Under the condition of 9.5% methane–air, it was found that the explosion suppression effect was most significant when the powder mass of the three powders was 300, 360, and 360 mg, respectively. The peak explosion overpressure is reduced by 10.51%, 21.96%, and 32.66%, respectively, and the peak arrival time of flame velocity is extended by .14 times, .20 times, and 1.15 times, respectively. MCG can effectively inhibit methane explosion utilizing physical and chemical synergistic heat absorption, porous structure formation barrier, heat isolation, oxygen dilution, adsorption, and capture of free radicals.
Aerial Video Surveys and Spatial Prioritization Reveal Conservation Opportunities for Endangered Dugongs in New Caledonia
Authors: Mannocci Laura , Derville Solène , Seguin Raphael , Mouillot David 
Published: August 2024

ABSTRACT

The dugong population of New Caledonia was recently assessed as endangered by the IUCN due to its low genetic diversity, its limited number of mature individuals and the continuing threats from poaching, collisions, incidental capture and seagrass degradation. No‐take marine protected areas (MPAs) implemented in critical dugong habitats may efficiently safeguard the remaining population by directly or indirectly reducing these threats.

The goal of the present study was to develop scenarios of MPAs targeting dugongs in New Caledonia, focusing on the central west coast where they are known to aggregate during austral winter.

We combined 7 h of video surveys from a light airplane and habitat modelling to derive regional predictions of dugong density. We then used these predictions to (1) assess the proportion of dugong population abundance protected by existing no‐take MPAs and (2) develop spatial prioritization scenarios based on explicit dugong abundance targets.

We found that existing no‐take MPAs only protect 11% of the estimated dugong population in the study region, leaving most dugong hotspots unprotected. For the same protected area coverage, prioritization based on abundance would allow protecting nearly five times more dugong individuals.

Given the endangered status of the population, a 70% abundance prioritization scenario is warranted, requiring the expansion of existing MPAs toward reef passes and shallow nearshore areas where dugong abundance is the highest. Our study offers an opportunity for dynamic spatial management through the implementation of a seasonal closure strictly restricting human activities in areas where dugongs aggregate in winter.
Enhancing the Ecological Quality Assessment of River Floodplains Based on Benthic Invertebrates
Authors: Kokavec Igor , Navara Tomáš , Elexová Emília Mišíková , Lešťáková Margita , Mláka Miroslav , Ščerbáková Soňa , Vráblová Zuzana , Očadlík Miroslav 
Published: August 2024

ABSTRACT

Floodplains are crucial components of river landscape, essential for the biodiversity and ecological integrity of large rivers. However, they are often overlooked and underestimated when assessing the ecological status of the main river channel.

The main objective of this study was to identify the most relevant scheme for assessing the ecological status of side arms in the Danube floodplain (Slovakia) based on benthic invertebrates.

The calculation of ecological status for the Danube main channel was adapted to the environmental conditions of the floodplain as a measure of the evaluation accuracy of six taxonomically different floodplain indexes. The study was based on a comprehensive survey of benthic invertebrate diversity conducted at 19 sampling sites, covering eupotamal and parapotamal side arms.

Three floodplain indexes that significantly correlated with ecological status were categorised based on predicted values in relation to status class thresholds. The floodplain index based on Polychaeta, Oligochaeta, Mysida, Isopoda and Amphipoda (FI_P) showed the best result, aligning closely with the commonly used classification of lateral habitats. Although it divides the eupotamon into two subclasses, a main channel and a side arm, the inclusion of two different eupotamal habitats in the classification supports the geomorphological variability of side arms and their degree of ecological succession.

The shortcomings of the assessment based on the lateral habitat classification scheme are discussed, emphasising the importance of applying this method for the assessment of floodplain waters and their conservation management.
Independent and interdependent group contingencies to increase on‐task work behavior among adults with intellectual disabilities
Authors: Daly Svetlana , Phillips Katrina J. , Mudford Oliver C. , Sharp Rebecca A. 
Published: August 2024

AbstractWe applied independent and interdependent group contingencies in a counterbalanced reversal experimental design to the vocational on‐task behavior for two groups of adults with intellectual disabilities. On‐task behavior increased for all participants in both groups above a criterion of 80% of time on‐task during both treatment phases. Independent and interdependent group contingencies were equally effective in increasing on‐task behavior. Responding was maintained at the criterion level for all members of one group at a 4‐week follow‐up. No increases in negative social interactions were observed when the interdependent group contingency was in effect and participants reported both contingencies to be socially valid. We suggest that group contingencies can be an effective intervention in increasing on‐task behavior of individuals with intellectual disabilities in vocational settings.
A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer
Authors: Wang Jing , Song Pujiao , Zhang Meng , Liu Wei , Zeng Xi , Chen Nanshan , Li Yuxia , Wang Minghua 
Published: August 2024

AbstractBackgroundTo explore the efficacy of a prediction model based on diffusion‐weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC).MethodsThis study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network‐based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA).ResultsThe optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935–1.000) and 0.885 (95% CI: 0.731–0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC‐training = 0.671, 0.873, 0.833, and 0.814, AUC‐test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively.ConclusionsThe predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.
The efficacy of cognitive behavioral therapy for mental health and quality of life among individuals diagnosed with cancer: A systematic review and meta‐analysis
Authors: Dils Alexander T. , O'Keefe Kathryn , Dakka Nada , Azar Michelle , Chen Meiyan , Zhang Anao 
Published: August 2024

AbstractObjectiveIt has long been documented that cognitive behavioral therapy (CBT) has positive impacts on improving mental health (MH) and quality of life (QoL) in the general population, but investigations on its effect on cancer survivors remain limited, especially for QoL outcomes. The purpose of this meta‐analysis is to investigate the effects of CBT as compared to control on cancer patients' MH and QoL outcomes. Control is defined in this study as standard therapy, waitlist control, and active/alternative therapy.MethodsIn total, 154 clinical trials creating a sample size of 1627 individuals were collected. Analysis focusing on MH and QoL excluded 29 clinical trials resulting in a final analysis of 132 clinical trials (and 1030 effect sizes). R Statistical Software (version 4.2.2) and the robumeta package were utilized to complete analysis, which entailed robust variance estimation (RVE) in intercept‐only meta‐regression, and univariate meta‐regression (for moderator analysis).ResultsAcross 132 clinical trials and 1030 effect size estimates, we identified that CBT moderately improves MH and QoL in cancer patients d = 0.388, 95% CI 0.294–0.483, p < 0.001. Additionally, age and delivery format can influence the efficacy of CBT in this patient population.ConclusionsCBT statistically improves the MH and QoL psychosocial parameters in cancer patients with greater efficacy in younger patients. Important clinical and intervention‐related factors, that is, age and delivery, should be considered when oncologists consider CBT as a psychotherapeutic intervention for individuals with cancer.
Predictive biomarkers for metachronous gastric cancer development after endoscopic resection of early gastric cancer
Authors: Kim Bokyung , Chun Harim , Lee Jongwon , Park Miree , Kwak Yoonjin , Kim Jung Mogg , Kim Sang Gyun , Ryu Ji Kon , Choi Jungmin , Cho Soo‐Jeong 
Published: August 2024

AbstractObjectivesWe aimed to identify predictive markers for metachronous gastric cancer (MGC) in early gastric cancer (EGC) patients curatively treated with endoscopic submucosal dissection (ESD).Materials and MethodsFrom EGC patients who underwent ESD, bulk RNA sequencing was performed on non‐cancerous gastric mucosa samples at the time of initial EGC diagnosis. This included 23 patients who developed MGC, and 23 control patients without additional gastric neoplasms for over 3 years (1:1 matched by age, sex, and Helicobacter pylori infection state). Candidate differentially‐expressed genes were identified, from which biomarkers were selected using real‐time quantitative polymerase chain reaction and cell viability assays using gastric cell lines. An independent validation cohort of 55 MGC patients and 125 controls was used for marker validation. We also examined the severity of gastric intestinal metaplasia, a known premalignant condition, at initial diagnosis.ResultsFrom the discovery cohort, 86 candidate genes were identified of which KDF1 and CDK1 were selected as markers for MGC, which were confirmed in the validation cohort. CERB5 and AKT2 isoform were identified as markers related to intestinal metaplasia and were also highly expressed in MGC patients compared to controls (p < 0.01). Combining these markers with clinical data (age, sex, H. pylori and severity of intestinal metaplasia) yielded an area under the curve (AUC) of 0.91 (95% CI, 0.85‐0.97) for MGC prediction.ConclusionAssessing biomarkers in non‐cancerous gastric mucosa may be a useful method for predicting MGC in EGC patients and identifying patients with a higher risk of developing MGC, who can benefit from rigorous surveillance.
Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning
Authors: Zheng Qingyuan , Wang Xinyu , Yang Rui , Fan Junjie , Yuan Jingping , Liu Xiuheng , Wang Lei , Xiao Zhuoni , Chen Zhiyuan 
Published: August 2024

AbstractBackgroundTumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self‐supervised attention‐based multiple instance learning (SSL‐ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin‐stained histopathological images.MethodsWe obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL‐ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang‐ABMIL, Ciga‐ABMIL, and ImageNet‐MIL) for their ability to predict TMB and VHL alterations.ResultsWe first identified two groups of populations with high‐ and low‐TMB (cut‐off point = 0.9). In two independent cohorts, the Wang‐ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang‐ABMIL model paid the highest attention to tumor regions in high‐TMB patients, while in VHL mutation prediction, non‐tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes.ConclusionsOur results indicated that SSL‐ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.
It all comes down to one: A rare case of a single coronary artery
Authors: Christodoulou Konstantinos C. , Stougiannou Theodora M. , Rigatou Anastasia , Karangelis Dimos 
Published: August 2024

Key Clinical MessageSCA is a rare congenital anomaly that, under certain conditions, can pose a life‐threatening risk to the individual. It is crucial to fully understand the entire course of the vessel and its anatomical relationships before developing a personalized treatment plan.
Insights into Bimetallic Ag2Cu2O3 Precatalyst for Electrochemical CO2 Reduction to Ethanol
Authors: Wang Huan , Liu Yuan Wei , Li Xin Yan , Xu Yi Ning , Xu Xiaolei , He Jing Jing , Niu Qiang , Liu Peng Fei , Yang Hua Gui 
Published: August 2024

AbstractThe electrochemical CO2 reduction reaction (CO2RR) into valuable chemicals represents an effective approach for realizing carbon neutralization goals. Copper oxide‐derived catalysts are particularly promising due to their tunable electronic structures. In this study, we focused on investigating the Ag2Cu2O3 model catalyst and a mixture of CuO and Ag2O with an identical metal molar ratio (denoted an as M–CuAgO). Electrochemical CO2RR tests revealed that Ag2Cu2O3 exhibited selectivity towards ethanol, while M–CuAgO showed no selectivity towards multi‐carbon products. Characterizations of the post‐reaction materials showed differences in the specific crystal structures of the two catalysts. Further X‐ray photoelectron spectroscopy (XPS) analysis demonstrated that the Ag2Cu2O3 structure, after the reaction, facilitated the transfer of electrons from Cu to Ag, thereby promoting the formation of multi‐carbon products. This work underscores the significance of structural design in precatalysts and opens up new avenues for the design of high‐performance catalysts.
Synthesis and Evaluation of Reduced Graphene Oxide (rGO) Supported on Ni3O4 as Spinel‐Based Cathode Catalyst for the Effective Anion‐Exchange Membrane Fuel Cell Application
Authors: Santhosh Kumar Ramasamy , Gokulapriyan Ramasamy , Sakthivel Venkitesan , Sayfiddinov Dilmurod , Kim Ae Rhan , Arunkumar Iyappan , Yoo Dong Jin 
Published: August 2024

AbstractOxygen reduction reaction (ORR) stability and catalytic activity in high‐durability anion exchange membrane fuel cells (AEMFCs) can be improved using graphene‐supported spinel‐based Ni3O4 cathode catalysts. Here, we describe a simple and economical hydrothermal method for synthesizing reduced graphene oxide (rGO) supported on Ni3O4. The atomic‐level contribution of the Ni−Ni and Ni−O bonds to the chemical structure of nickel oxide was confirmed by X‐ray photoelectron and absorption spectroscopy studies. Due to the force of the void for oxygen created by nickel atoms, Ni3O4@rGO for the ORR exhibited enhanced stability and catalytic activity (E1/2=0.761 V and over 30,000 CV cycles). A single AEMFC cell achieved the greatest power density and long‐term durability using a Ni3O4@rGO cathode, suggesting improved endurance despite the minimal voltage decrease (power density 29.6 mW cm−2, endurance for 25 h). These findings offer insights and point to opportunities for developing metal oxide–based AEMFCs.