bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facile synthesis process for preparing silicon carbide with unique honeycomb structure
Authors: Nguyen Quynh Thi , Luu Quy Son , Kim Jiwon , Do Uyen Thi , Park Yeeun , Kim Jihyun , Lee Youngbok 
Published: January 2024

AbstractThis study introduces a novel and versatile method for synthesizing honeycomb‐structured silicon carbide (SiC). The innovative approach utilizes a sucrose solution as the carbon source and nonporous silica spheres, which serve both as silicon precursors and templates, allowing for precise control over pore sizes. Notably, the process is characterized by its cost‐effectiveness, eco‐friendliness, and the utilization of milder conditions attributed to magnesiothermic reduction. The tunable pore sizes achieved through adjustments in the size of silica particles offer a versatile platform for customizing SiC materials to meet specific application requirements. Beyond its customizable nature, the method reduces the environmental footprint of SiC synthesis by utilizing eco‐friendly materials. Its combined attributes of accessibility, sustainability, and performance optimization underscore its potential for driving advancements in SiC‐based applications across various industrial and scientific domains.
Global prevalence of restless legs syndrome among hemodialysis patients: A systematic review and meta‐analysis
Authors: Zhou Xu‐Hua , Liu Yuan , Zhang Xin‐Rui , Wang Cong , Liu Shan‐Shan , Jiang Yan 
Published: January 2024

AbstractObjectives: Restless legs syndrome (RLS) is a common complaint in patients undergoing hemodialysis (HD). Despite the fact that the estimated prevalence of RLS among HD patients is widely reported, these results varied significantly in the relevant literature. Due to this limitation, the aim of this study was to determine the global prevalence of RLS among HD patients.Methods: This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐analyses 2020 statement. We searched the electronic databases: Cochrane Library, PubMed, Embase, Web of Science, Scopus, Cumulative Index to Nursing and Allied Health Literature, China Knowledge Resource Integrated Database, Wanfang Database, Chinese Biomedical Database, and Weipu Database. A random effects model was employed to calculate pooled prevalence rates.Results: The global pooled prevalence of RLS in HD patients was 27.2% (95% CI: 24.8–29.7). Stratified analyses demonstrated that included studies with sample size <100 had the highest pooled prevalence of RLS. The prevalence of RLS using clinical interviews and questionnaires was 28.7% (95% CI: 25.2–32.2) and 25.9% (95% CI: 22.8–29.1), respectively. RLS prevalence is higher in females (29.7%, 95% CI: 26.2–33.2) HD patients than in males (23.5%, 95% CI: 20.9–26.0), and the African region has the highest prevalence in the world when the diagnostic criteria were restricted to the 2003 version of International RLS Study Group criteria, the prevalence of RLS was highest (28.9%, 95% CI: 25.9–31.9).Conclusion: Our results revealed a high RLS prevalence in HD patients worldwide. However, the prevalence of RLS among HD patients varied significantly based on sample size, data collection method, gender, diagnostic criteria, and geographical region.
Implications of confounding from unmodeled interactions between explanatory variables when using latent variable regression models to make inferences
Authors: Kvalheim Olav M. , Vidar Warren S. , Baumeister Tim U. H. , Linington Roger G. , Cech Nadja B. 
Published: January 2024

AbstractWith linear dependency between the explanatory variables, partial least squares (PLS) regression is commonly used for regression analysis. If the response variable correlates to a high degree with the explanatory variables, a model with excellent predictive ability can usually be obtained. Ranking of variable importance is commonly used to interpret the model and sometimes this interpretation guides further experimentation. For instance, when analyzing natural product extracts for bioactivity, an underlying assumption is that the highest ranked compounds represent the best candidates for isolation and further testing. A problem with this approach is that in most cases, the number of compounds is larger than the number of samples (and usually much larger) and that the concentrations of the compounds correlate. Furthermore, compounds may interact as synergists or as antagonists. If the modeling process does not account for this possibility, the interpretation can be thoroughly wrong because unmodeled variables that strongly influence the response will give rise to confounding of a first‐order PLS model and send the experimenter on a wrong track. We show the consequences of this by a practical example from natural product research. Furthermore, we show that by including the possibility of interactions between explanatory variables, visualization using a selectivity ratio plot may provide model interpretation that can be used to make inferences.
Mechanistic Insights into the N‐Hydroxylations Catalyzed by the Binuclear Iron Domain of SznF Enzyme: Key Piece in the Synthesis of Streptozotocin
Authors: Li Rui‐Ning , Chen Shi‐Lu 
Published: January 2024

AbstractSznF, a member of the emerging family of heme‐oxygenase‐like (HO‐like) di‐iron oxidases and oxygenases, employs two distinct domains to catalyze the conversion of Nω‐methyl‐L‐arginine (L‐NMA) into N‐nitroso‐containing product, which can subsequently be transformed into streptozotocin. Using unrestricted density functional theory (UDFT) with the hybrid functional B3LYP, we have mechanistically investigated the two sequential hydroxylations of L‐NMA catalyzed by SznF's binuclear iron central domain. Mechanism B primarily involves the O−O bond dissociation, forming Fe(IV)=O, induced by the H+/e− introduction to the FeA side of μ‐1,2‐peroxo‐Fe2(III/III), the substrate hydrogen abstraction by Fe(IV)=O, and the hydroxyl rebound to the substrate N radical. The stochastic addition of H+/e− to the FeB side (mechanism C) can transition to mechanism B, thereby preventing enzyme deactivation. Two other competing mechanisms, involving the direct O−O bond dissociation (mechanism A) and the addition of H2O as a co‐substrate (mechanism D), have been ruled out.
Homochiral zeolites as chiral modifier for voltammetry sensors with high enantioselectivity
Authors: Vakulin Ivan V. , Zilberg Rufina A. , Galimov Ilnar I. , Sycheva Maria A. 
Published: January 2024

AbstractComposite enantioselective voltammetry sensors provide a well‐known solution for the analysis and discrimination of enantiomerical drugs by voltammetry. The design of such sensor presumes the use of chiral substances to impart chirality to the electrode surface. We present the results of applying a theoretical approach based on simulating the adsorption of analyte enantiomers for preparing high enantioselectivity of pioneering enantioselective sensors using homochiral zeolites as chiral modifiers.We investigated the ability of some homochiral zeolites like CZP, ITV, OSO, STW, MFI, and SOF to act as solid chiral modifiers. Using tryptophan and tyrosine as model objects, we found that the maximum difference between the adsorption energies (ΔEads) of enantiomers on zeolites occurs if enantiomers are placed into the pores of zeolites. The SOF zeolite demonstrates the best enantioselectivity to both amino acids. Moreover, we compared the theoretical results with the experimental enantioselectivity value for tryptophan on a sensor designed as a paste electrode with MFI as the chiral modifier. We found that the calculated value ΔEads(R‐S) = −11.0 kJ/mol for MFI, though it is not the best value in the zeolite set considered, it is still enough for the sensor with MFI as the chiral modifier to demonstrate a higher than usual enantioselectivity (ip1/ip2 = 1.58) in an experiment with R‐ and S‐ tryptophan as the analyte.
GELU‐LSTM‐encoder‐decoder fault prediction for batch processes based on the global–local percentile method
Authors: Gao Xuejin , Li Xuefeng , Li Bolun , Han Huayun , Gao Huihui , Qi Yongsheng 
Published: January 2024

AbstractFault prediction is a crucial strategy for ensuring the safety and efficiency of batch processes, and it aims to predict the degradation trend and isolate the fault. Operating mistakes or external environment changes can lead to the degradation of batch processes, eventually resulting in faults. The point at which degradation begins is known as the time to start prediction (TSP). Determining the TSP provides more reliable degradation information for fault prediction and enhances prediction accuracy. Nevertheless, it is difficult to obtain TSP in batch processes because of some complex data features, such as multi‐phase and strong dynamics, which result in a significant loss of fault prediction. To address these challenges, a fault prediction method is proposed based on the global–local percentile method applied to Gaussian error linear unit‐long short‐term memory‐encoder‐decoder (GELU‐LSTM‐encoder‐ED). Firstly, squared prediction error (SPE) is normalized to eliminate differences in data features. Secondly, a global–local percentile method is proposed to determine the TSP. The period is divided based on the threshold fluctuation to derive the model input. Then the global percentile model is employed to identify the interval to which the TSP belongs. Subsequently, the local percentile model is employed to analyze this interval and identify the TSP. Thirdly, the LSTM based on the encoder‐decoder model, enhanced with the GELU, is utilized to improve the performance of fault prediction. Finally, two fault datasets on the penicillin fermentation process and Escherichia coli fermentation process are employed to demonstrate the performance of the presented method compared with other shared methods.
Fatty acid‐binding protein‐3 and renal function decline in patients with chronic coronary syndrome
Authors: Yeh Jiunn‐Tyng , Huang Chin‐Chou , Leu Hsin‐Bang , Yin Wei‐Hsian , Tseng Wei‐Kung , Wu Yen‐Wen , Lin Tsung‐Hsien , Yeh Hung‐I , Chang Kuan‐Cheng , Wang Ji‐Hung , Wu Chau‐Chung , Chen Jaw‐Wen 
Published: January 2024

AbstractBackgroundRenal dysfunction is common in patients with coronary artery disease. Due to the shared vascular pathogenesis between the two conditions, novel biomarkers such as the fatty acid‐binding protein‐3 (FABP‐3) have been proposed for diagnosis and prognosis prediction. This multicentre prospective cohort study investigates the association between FABP‐3 and renal dysfunction.HypothesisWe hypothesized that higher FABP‐3 levels are correlated to worse renal outcome.MethodsPatients with chronic coronary syndrome were classified into three groups based on the initial serum FABP‐3 levels. The Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equation was used to estimate the patient's renal function. Renal events were defined as >25% and >50% decline in estimated glomerular filtration rate (eGFR). Cox multivariable regression was employed to delineate the correlation between FABP‐3 and renal dysfunction.ResultsA total of 1606 subjects were included. During a mean follow‐up of 35.9 months, there were 239 patients with eGFR >25% reduction and 60 patients with >50% reduction. In the Kaplan–Meier survival curve and log‐rank test, increased levels of FABP‐3 were significantly correlated with eGFR >25% reduction (p < .001) and >50% reduction (p < .001). Multivariate Cox regression model revealed that subjects with higher FABP‐3 exhibited a greater risk of eGFR >25% reduction (Group 2: hazard ratio [HR] = 2.328, 95% confidence interval [CI] = 1.521–3.562, p < .001; Group 3: HR = 3.054, 95% CI = 1.952–4.776, p < .001) and >50% reduction (Group 3: HR = 4.838, 95% CI = 1.722–13.591, p = .003).ConclusionsSerum FABP‐3 may serve as a novel biomarker to predict eGFR decline in patients with chronic coronary syndrome.
Impedance Response Analysis of Anion Exchange Membrane Electrolyzers for Determination of the Electrochemically Active Catalyst Surface Area
Authors: Watzele Sebastian A. , Kluge Regina M. , Maljusch Artjom , Borowski Patrick , Bandarenka Aliaksandr S. 
Published: January 2024

AbstractPolymer membrane electrolyzers benefit from high‐pressure operation conditions and low gas cross‐over and can either conduct protons (H+) or hydroxide ions (OH−). Both types of electrolyzers have a similar design, but differ in power density and the choice of catalysts. Despite the significant endeavor of their optimization, to date, there is no well‐established impedance model for detailed analysis for either type of these devices. This complicates the in‐situ characterization of electrolyzers, hindering the investigation of degradation mechanisms and electrocatalytic processes as a function of applied current density or time. Nevertheless, a detailed understanding of such individual processes and distinguishing the performance‐limiting factors are the keystones for sophisticated device optimization. In this work, an impedance model based on electrode processes has been developed for an anion exchange membrane electrolyzer utilizing iridium oxide anode and platinum cathode electrocatalysts. This model allows to deconvolute the measured impedances into constituents related to the individual electrode processes and to estimate actual physico‐chemical quantities such as the reaction kinetic parameters and double‐layer capacitances. We discuss the meaning of the fitting parameters and show that this model enables, for the first time, the estimation of the electrochemically active surface area of the anode electrocatalysts under reaction conditions.
ACAT2 may be a novel predictive biomarker and therapeutic target in lung adenocarcinoma
Authors: Wang Zhongchao , Cao Zhugen , Dai Zhaoxia 
Published: January 2024

AbstractBackgroundAcyl‐coenzyme A cholesterol acyltransferase (ACAT) is a membrane‐binding enzyme localized in the endoplasmic reticulum. ACAT2 can promote the development of colon cancer, but its efficacy in lung adenocarcinoma (LUAD) remains uncertain.MethodACAT2 expression was performed by using the TIMER2.0 database. The GEPIA database was utilized to analyze the correlation between ACAT2 expression and pathological stage of the tumor. Clinical prognosis was assessed through the Kaplan–Meier analysis. The CancerSEA database was employed to scrutinize the correlations between the ACAT2 expression and the functional status of various tumors, which were subsequently visualized as a heatmap. Furthermore, molecular interaction network analysis was performed by the STRING tool.ResultsHigh ACAT2 expression was associated with a poor DFS and OS in LUAD patients. Cox regression analysis indicated that the poor outcomes may be related to tumor stage, nodal stage, distant metastatic stage. ACAT2 was found to play a crucial role in various biological processes, including the cell cycle, DNA repair, DNA damage response, and proliferation. Enrichment pathway analysis revealed four ACAT2 related genes, ACOX1, EHHADH, OXCT1, and DLAT.ConclusionOur study showed that ACAT2 was upregulated in LUAD, and had a worse survival. ACAT2 could be a novel predictive biomarker and therapeutic target in LUAD.
Successful application of lorlatinib in a 23‐year‐old patient with anaplastic lymphoma kinase (ALK)‐positive lung cancer and multiple brain metastases
Authors: Murakami Yosuke , Kawashima Yosuke , Chiba Shinji , Hara Shuichiro , Yamazaki Yusuke , Doman Tsuyoshi , Saito Shin , Odaka Tetsuo , Ogasawara Takahiro , Shimizu Hisashi , Sugisaka Jun , Aiba Tomoiki , Toi Yukihiro , Yamanda Shinsuke , Kimura Yuichiro , Sugawara Shunichi 
Published: January 2024

AbstractBackgroundAnaplastic lymphoma kinase (ALK)‐positive lung cancer has a better long‐term prognosis with ALK‐inhibitor than other lung cancers. However, resistance to ALK‐inhibitors and the control of metastases in the central nervous system (CNS) remain to be a challenge in the management of ALK‐positive lung cancer.CaseWe present the case of a 23‐year‐old man who developed multiple brain metastases while receiving alectinib treatment for ALK‐positive lung cancer. After 3 months of lorlatinib initiation, brain metastases disappeared, and complete response (CR) was maintained.ConclusionWhile lorlatinib can be used as first line therapy, this drug may be considered as second line or later option for patients with multiple brain metastases if the patient has already been treated with other ALK‐inhibitors since lorlatinib is thought to have good CNS penetration. This treatment option should be verified by further research.
A Bioequivalence Comparison Between the Once‐Daily Extended‐Release Tablet and the Twice‐Daily Tablet Formulations of Deutetrabenazine at Steady State
Authors: Sunzel Eva‐Maria , Rabinovich‐Guilatt Laura , Iyengar Malini , Ruffo Debra , Birajdar Nagnath G. , Loupe Pippa , Gutierrez Maria , Gordon Mark Forrest , Ghibellini Giulia 
Published: January 2024

AbstractDeutetrabenazine is approved for the treatment of tardive dyskinesia and chorea associated with Huntington's disease. This study compared the exposure between the once‐daily (test) and twice‐daily (reference) formulations of deutetrabenazine under fed conditions. Using a randomized crossover design, healthy adults (n = 262) received the 24 mg of the test formulation once daily and 12 mg of the reference formulation twice daily, each for 7 days. Plasma concentrations were collected on Days 4‐6 before dose intake, and frequently for pharmacokinetic evaluation on Days 6 and 7 for determination of deutetrabenazine and active metabolites, deuterated α‐dihydrotetrabenazine (α‐HTBZ) and β‐dihydrotetrabenazine (β‐HTBZ). Geometric mean ratios (GMRs, test/reference) were computed for all analytes, and bioequivalence was tested for area under the plasma concentration‐time curve over 24 hours at steady state (AUC0‐24 h,ss) and for maximum plasma concentrations at steady state (Cmax,ss). The GMRs for AUC0‐24 h,ss were 115% for deutetrabenazine and 95% for deuterated total (α+β)‐HTBZ; and the GMR for Cmax,ss for deutetrabenazine was 95%. Relative bioavailability was assessed for Cmax,ss of the active metabolites; the GMR was 78% for total (α+β)‐HTBZ. At steady state, deutetrabenazine administered as the once‐daily formulation was bioequivalent to the twice‐daily formulation for both AUC and Cmax, and the active metabolites were bioequivalent with regard to AUC0‐24 h,ss.
Optimizing the Cray Graph Engine for performant analytics on cluster, SuperDome Flex, Shasta systems and cloud deployment
Authors: Rickett Christopher D. , Maschhoff Kristyn J. , Sukumar Sreenivas R. 
Published: January 2024

AbstractWe present updates to the Cray Graph Engine, a high performance in‐memory semantic graph database, which enable performant execution across multiple architectures as well as deployment in a container to support cloud and as‐a‐service graph analytics. This paper discusses the changes required to port and optimize CGE to target multiple architectures, including Cray Shasta systems, large shared‐memory machines such as SuperDome Flex (SDF), and cluster environments such as Apollo systems. The porting effort focused primarily on removing dependences on XPMEM and Cray PGAS and replacing these with a simplified PGAS library based upon POSIX shared memory and one‐sided MPI, while preserving the existing Coarray‐C++ CGE code base. We also discuss the containerization of CGE using Singularity and the techniques required to enable container performance matching native execution. We present early benchmarking results for running CGE on the SDF, Infiniband clusters and Slingshot interconnect‐based Shasta systems.
Special Issue on the pervasive nature of HPC (PN‐HPC)
Authors: Lapegna Marco , Mele Valeria , Montella Raffaele , Szustak Lukasz 
Published: January 2024

SummaryThis special issue on the Pervasive Nature of HPC (PN‐HPC) collects an extension of the most valuable works presented at the sixth Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP‐22), held in Gdansk (Poland) in September 2022, jointly with the 14th conference on Parallel Processing and Applied Mathematics (PPAM‐22). New original papers related to the workshop themes are also included. The final aim is to provide a glimpse of the current state of knowledge related to the development of efficient methodologies and algorithms for HPC systems with multiple forms of parallelism.
Transition metal phosphides: synthesis nanoarchitectonics, catalytic properties, and biomass conversion applications
Authors: Lu Xuebin , Yan Kai , Yu Zhihao , Wang Jingfei , Liu Runyu , Zhang Rui , Qiao Yina , Xiong Jian 
Published: January 2024

AbstractDeveloping inexpensive and efficient catalysts for biomass hydrogenation or hydrodeoxygenation (HDO) is essential for efficient energy conversion. Transition metal phosphides (TMPs), with the merits of abundant active sites, unique physicochemical properties, tunable component structures, and excellent catalytic activities, are recognized as promising biomass hydrogenation or HDO catalytic materials. Nevertheless, the biomass hydrogenation or HDO catalytic applications of TMPs are still limited by various complexities and inherent performance bottlenecks, and thus their future development and utilization remain to be systematically sorted out and further explored. This review summarizes the current popular strategies for the preparation of TMPs. Subsequently, based on the structural and electronic properties of TMPs, the catalytic activity origins of TMPs in biomass hydrogenation or HDO is elucidated. Additionally, the application of TMPs in efficient biomass hydrogenation or HDO catalysis, as well as highly targeted multiscale strategies to enhance the catalytic performance of TMPs, are comprehensively described. Finally, large‐scale amplification synthesis, rational construction of TMP‐based catalysts and in‐depth study of the catalytic mechanism are also mentioned as challenges and future directions in this research field. Expectedly, this review can provide professional and targeted guidance for the rational design and practical application of TMPs biomass hydrogenation or HDO catalysts.
A multisource energy harvesting circuit for vibration and light energy with MPPT
Authors: Zhu Danfeng , Wang Dashan , Qian Libo 
Published: January 2024

SummaryThe article presents a multisource energy harvesting circuit (MEHC) for vibration and light energy with maximum power point tracking (MPPT). The MEHC has a cold start capability and extracts energy from piezoelectric transducers (PZTs) and photovoltaic cells (PVs) simultaneously when the multi PZTs reach their peak open‐circuit (OC) voltage, detected by the peak voltage detector and the PV1 cell voltage exceeds the MPP voltage (MPP voltage is approximately 0.7 times the PV cell's OC voltage) during the off‐peak time of the PZTs. The experimental results indicate that the output power of the MEHC is 2.8 mW under certain conditions, and the maximum energy extraction efficiency of the PVs is about 75%. The output power of the MEHC is 4.59 times of that without PVs.
Cellular models in autoinflammatory disease research
Authors: Şen Başak , Balcı‐Peynircioğlu Banu 
Published: January 2024

AbstractSystemic autoinflammatory diseases are a heterogeneous group of rare genetic disorders caused by dysregulation of the innate immune system. Understanding the complex mechanisms underlying these conditions is critical for developing effective treatments. Cellular models are essential for identifying new conditions and studying their pathogenesis. Traditionally, these studies have used primary cells and cell lines of disease‐relevant cell types, although newer induced pluripotent stem cell (iPSC)‐based models might have unique advantages. In this review, we discuss the three cellular models used in autoinflammatory disease research, their strengths and weaknesses, and their applications to inform future research in the field.