bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-protecting CoFeAl-layered double hydroxides enable stable and efficient brine oxidation at 2 A cm−2
Authors: Liu Wei , Yu Jiage , Li Tianshui , Li Shihang , Ding Boyu , Guo Xinlong , Cao Aiqing , Sha Qihao , Zhou Daojin , Kuang Yun , Sun Xiaoming 
Published: June 2024

AbstractLow-energy consumption seawater electrolysis at high current density is an effective way for hydrogen production, however the continuous feeding of seawater may result in the accumulation of Cl−, leading to severe anode poisoning and corrosion, thereby compromising the activity and stability. Herein, CoFeAl layered double hydroxide anodes with excellent oxygen evolution reaction activity are synthesized and delivered stable catalytic performance for 350 hours at 2 A cm−2 in the presence of 6-fold concentrated seawater. Comprehensive analysis reveals that the Al3+ ions in electrode are etched off by OH− during oxygen evolution reaction process, resulting in M3+ vacancies that boost oxygen evolution reaction activity. Additionally, the self-originated Al(OH)n− is found to adsorb on the anode surface to improve stability. An electrode assembly based on a micropore membrane and CoFeAl layered double hydroxide electrodes operates continuously for 500 hours at 1 A cm−2, demonstrating their feasibility in brine electrolysis.
A computational fluid dynamics study to assess the impact of coughing on cerebrospinal fluid dynamics in Chiari type 1 malformation
Authors: Vandenbulcke Sarah , Condron Paul , Safaei Soroush , Holdsworth Samantha , Degroote Joris , Segers Patrick 
Published: June 2024

AbstractChiari type 1 malformation is a neurological disorder characterized by an obstruction of the cerebrospinal fluid (CSF) circulation between the brain (intracranial) and spinal cord (spinal) compartments. Actions such as coughing might evoke spinal cord complications in patients with Chiari type 1 malformation, but the underlying mechanisms are not well understood. More insight into the impact of the obstruction on local and overall CSF dynamics can help reveal these mechanisms. Therefore, our previously developed computational fluid dynamics framework was used to establish a subject-specific model of the intracranial and upper spinal CSF space of a healthy control. In this model, we emulated a single cough and introduced porous zones to model a posterior (OBS-1), mild (OBS-2), and severe posterior-anterior (OBS-3) obstruction. OBS-1 and OBS-2 induced minor changes to the overall CSF pressures, while OBS-3 caused significantly larger changes with a decoupling between the intracranial and spinal compartment. Coughing led to a peak in overall CSF pressure. During this peak, pressure differences between the lateral ventricles and the spinal compartment were locally amplified for all degrees of obstruction. These results emphasize the effects of coughing and indicate that severe levels of obstruction lead to distinct changes in intracranial pressure.
Biomass-derived carbon dots as fluorescent quantum probes to visualize and modulate inflammation
Authors: Kumar Mahima , Chinnathambi Shanmugavel , Bakhori Noremylia , Abu Norhidayah , Etezadi Fatemeh , Thangavel Vaijayanthi , Packwood Daniel , Sivaniah Easan , Pandian Ganesh N. 
Published: June 2024

AbstractQuantum dots, which won the Nobel Prize in Chemistry, have recently gained significant attention in precision medicine due to their unique properties, such as size-tunable emission, high photostability, efficient light absorption, and vibrant luminescence. Consequently, there is a growing demand to identify new types of quantum dots from various sources and explore their potential applications as stimuli-responsive biosensors, biomolecular imaging probes, and targeted drug delivery agents. Biomass-waste-derived carbon quantum dots (CQDs) are an attractive alternative to conventional QDs, which often require expensive and toxic precursors, as they offer several merits in eco-friendly synthesis, preparation from renewable sources, and cost-effective production. In this study, we evaluated three CQDs derived from biomass waste for their potential application as non-toxic bioimaging agents in various cell lines, including human dermal fibroblasts, HeLa, cardiomyocytes, induced pluripotent stem cells, and an in-vivo medaka fish (Oryzias latipes) model. Confocal microscopic studies revealed that CQDs could assist in visualizing inflammatory processes in the cells, as they were taken up more by cells treated with tumor necrosis factor-α than untreated cells. In addition, our quantitative real-time PCR gene expression analysis has revealed that citric acid-based CQDs can potentially reduce inflammatory markers such as Interleukin-6. Our studies suggest that CQDs have potential as theragnostic agents, which can simultaneously identify and modulate inflammatory markers and may lead to targeted therapy for immune system-associated diseases.
Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm
Authors: Hu Zhuo , Guo Weihao , Zhou Kege , Wang Lei , Wang Fu , Yuan Jinliang 
Published: June 2024

AbstractThis study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.
Clinical outcomes and predictive factors of stent grafts treatment for symptomatic central venous obstruction in end stage kidney disease patients with arteriovenous access
Authors: Liu Yamin , Wang Yufei , Wang Xinfang , Zhang Beihao , Lu Xiaoqing , Liang Xianhui , Wang Pei 
Published: June 2024

AbstractTo assess the efficacy of stent grafts (SGs) in managing central venous obstruction disease (CVOD) in hemodialysis (HD) patients with arteriovenous (AV) access, and to identify predictive factors influencing the SG treatment outcomes. HD subjects with CVOD who underwent SGs placement at our center between August 2018 and June 2022 were enrolled. Survival curve analysis using the Kaplan–Meier method and log-rank test was performed. Cox proportional hazards regression analysis was employed to identify predictive factors associated with outcomes. A total of 59 SG implantation procedures for CVOD were analyzed, comprising 30 cases of stenosis and 29 cases of occlusion. The access circuit primary patency (ACPP) at 6, 12, and 24 months post-SG placement were 80.9%, 53.8%, and 31.4%, respectively, while, the target lesion primary patency (TLPP) were 91.3%, 67.6%, and 44.5%, respectively. Subgroup analysis revealed higher TLPP in the stenosis group compared to the occlusion group, although the difference was not statistically significant (P = 0.165). The TLPP was significantly improved by SG placement in those who had antecedent balloon dilations (P < 0.001). Cox proportional hazards regression identified target lesion length ≥ 30 mm and procedure defects as independent predictors of lower TLPP after SG treatment for CVOD in HD patients. SG placement demonstrates safety and efficacy in managing CVOD among HD patients, leading to improved TLPP of endovascular therapy (EVT) for CVOD. Notably, long target lesions (≥ 30 mm) and procedure defects emerged as predictive factors influencing TLPP.
An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases
Authors: Smelik Martin , Zhao Yelin , Li Xinxiu , Loscalzo Joseph , Sysoev Oleg , Mahmud Firoj , Mansour Aly Dina , Benson Mikael 
Published: June 2024

AbstractMultiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min–max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65–0.86) and 0.84 (0.70–0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.
Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden
Authors: Gorlov Ivan P. , Gorlova Olga Y. , Tsavachidis Spyridon , Amos Christopher I. 
Published: June 2024

AbstractSingle nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the “all histology together” analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.
Depression recognition using voice-based pre-training model
Authors: Huang Xiangsheng , Wang Fang , Gao Yuan , Liao Yilong , Zhang Wenjing , Zhang Li , Xu Zhenrong 
Published: June 2024

AbstractThe early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.
The UF-5000 Atyp.C parameter is an independent risk factor for bladder cancer
Authors: Zhang Tong , Zhu Jianhong , Li Zhaoxing , Zhao Ya , Li Yan , Li Jing , He Qian , Geng Yan , Lu Wei , Zhang Lei , Li Zhenzhen 
Published: June 2024

AbstractBladder carcinoma (BC) accounts for > 90% of all urothelial cancers. Pathological diagnosis through cytoscopic biopsy is the gold standard, whereas non-invasive diagnostic tools remain lacking. The “Atyp.C” parameter of the Sysmex UF-5000 urine particle analyzer represents the ratio of nucleus to cytoplasm and can be employed to detect urinary atypical cells. The present study examined the association between urinary Atyp.C values and BC risk. This two-center, retrospective case–control study identified clinical primary or newly recurrent BC (study period, 2022–2023; n = 473) cases together with controls with urinary tract infection randomly matched by age and sex (1:1). Urinary sediment differences were compared using non-parametric tests. The correlations between urinary Atyp.C levels and BC grade or infiltration were analyzed using Spearman’s rank correlation. The BC risk factor odds ratio of Atyp.C was calculated using conditional logistic regression, and potential confounder effects were adjusted using stepwise logistic regression (LR). Primary risk factors were identified by stratified analysis according to pathological histological diagnosis. The mean value of urinary Atyp.C in BC cases (1.30 ± 3.12) was 8.7 times higher than that in the controls (0.15 ± 0.68; P < 0.001). Urinary Atyp.C values were positively correlated with BC pathological grade and invasion (r = 0.360, P < 0.001; r = 0.367, P < 0.001). Urinary Atyp.C was an independent risk factor for BC and closely related with BC pathological grade and invasion. Elevated urinary Atyp.C values was an independent risk factor for BC. Our findings support the use of Atyp.C as a marker that will potentially aid in the early diagnosis and long-term surveillance of new and recurrent BC cases.
Structural optimization and flow field analysis of turbomolecular pump based on a new performance prediction algorithm
Authors: Sun Kun , Deng Haishun , Wang Cheng , Zhang Shiwei 
Published: June 2024

AbstractIn the present study, a new turbomolecular pump (TMP) performance prediction algorithm is proposed according to the variable surface combined blade row (VSCBR) geometric model. The simulation calculation program is designed to perform structural optimization and flow field analysis. Research on the pumping performance of the traditional straight blade row (TSBR) indicates that when the blade velocity ratio is greater than 1, the increase in the pumping speed and compression ratio of the TMP gradually tends to stabilize. Response surface methodology is used to optimize the structural parameters of the first four stages of the combined blade row. The optimized VSCBR increases the pumping speed by 18.2% compared to that of the TSBR. The flow field analysis based on the optimized VSCBR shows that gas molecules reaching the rear blades are likely to approach the outlet, and the proportion of gas molecules in this region exceeds 50%. Therefore, the blades we designed should be conducive to additional gas molecules reaching the outlet.
SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues
Authors: Zhang Bin , Hou Zilong , Yang Yuning , Wong Ka-chun , Zhu Haoran , Li Xiangtao 
Published: June 2024

AbstractProteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452.
Tuning interfacial fluidity and colloidal stability of membranized coacervate protocells
Authors: Ji Yanglimin , Qiao Yan 
Published: June 2024

AbstractThe cell membrane not only serves as the boundary between the cell’s interior and the external environment but also plays a crucial role in regulating fundamental cellular behaviours. Interfacial membranization of membraneless coacervates, formed through liquid-liquid phase separation (LLPS), represents a reliable approach to constructing hierarchical cell-like entities known as protocells. In this study, we demonstrate the capability to modulate the interfacial membrane fluidity and thickness of dextran-bound coacervate protocells by adjusting the molecular weight of dextran or utilizing dextranase-catalyzed hydrolysis. This modulation allows for rational control over colloidal stability, interfacial molecular transport and cell-protocell interactions. Our work opens a new avenue for surface engineering of coacervate protocells, enabling the establishment of cell-mimicking structures and behaviours.
Substrate scope expansion of 4-phenol oxidases by rational enzyme selection and sequence-function relations
Authors: Eggerichs Daniel , Weindorf Nils , Weddeling Heiner G. , Van der Linden Inja M. , Tischler Dirk 
Published: June 2024

AbstractEnzymes are natures’ catalysts and will have a lasting impact on (organic) synthesis as they possess unchallenged regio- and stereo selectivity. On the downside, this high selectivity limits enzymes’ substrate range and hampers their universal application. Therefore, substrate scope expansion of enzyme families by either modification of known biocatalysts or identification of new members is a key challenge in enzyme-driven catalysis. Here, we present a streamlined approach to rationally select enzymes with proposed functionalities from the ever-increasing amount of available sequence data. In a case study on 4-phenol oxidoreductases, eight enzymes of the oxidase branch were selected from 292 sequences on basis of the properties of first shell residues of the catalytic pocket, guided by the computational tool A2CA. Correlations between these residues and enzyme activity yielded robust sequence-function relations, which were exploited by site-saturation mutagenesis. Application of a peroxidase-independent oxidase screening resulted in 16 active enzyme variants which were up to 90-times more active than respective wildtype enzymes and up to 6-times more active than the best performing natural variants. The results were supported by kinetic experiments and structural models. The newly introduced amino acids confirmed the correlation studies which overall highlights the successful logic of the presented approach.
The study of climate change: the need to “bring the state back in”
Authors: Chudnovsky Mariana , Fernandez José Carlos 
Published: June 2024

AbstractHow to address a “super wicked problem” like climate change is not only a policy sciences discussion but also a public administration one. Surprisingly, climate change has received little attention from the public administration field and public policy literature has given marginal attention to the role of the state apparatus in climate action. Especially, at the local level where it is crucial to address most of the adaptation agenda. This a serious problem since Latin America faces an especially challenging situation since the organizational capacity at the local level in the public sector is poor. State apparatuses with a low organizational capacity to process the complexity of certain public policies may distort and even ruin well-designed climate policies. Furthermore, empirical research on the role of public administrations in addressing climate change at the local level, despite its importance, remains extremely limited. Much of the discussion focuses on the design of policies to achieve this goal. If the organizational capacity of the agencies of the state is built only around specific policies to address very local challenges, we will miss the fact that they are tied up with systemic and intractable organizational practices and capacities. To examine the organizational capacity at the Latin American public sector local level to address climate challenges is as important as designing technically accurate policies and the debate on state capacity can shed light on how to do so. Finally, this article aims to open an agenda for research and a claim for local action.
A comprehensive review of sustainable materials and toolpath optimization in 3D concrete printing
Authors: Zhuang Zicheng , Xu Fengming , Ye Junhong , Hu Nan , Jiang Liming , Weng Yiwei 
Published: June 2024

AbstractThe construction sector has experienced remarkable advancements in recent years, driven by the demand for sustainable and efficient building practices. Among these advancements, 3D concrete printing has emerged as a highly promising technology that holds the potential to revolutionize the construction industry. This review paper aims to provide a comprehensive analysis of the latest developments in three vital areas related to 3D concrete printing: sustainable materials, structural optimization, and toolpath design. A systematic literature review approach is employed based on established practices in additive manufacturing for construction to explore the intersections between these areas. The review reveals that material recycling plays a crucial role in achieving sustainable construction practices. Extensive research has been conducted on structural optimization methodologies to enhance the performance and efficiency of 3D printed concrete structures. In the printing process, toolpath design plays a significant role in ensuring the precise and efficient deposition of concrete. This paper discusses various toolpath generation strategies that take factors such as geometric complexity, printing constraints, and material flow control into account. In summary, the insights presented in this paper may serve as guidelines for researchers, engineers, and industry professionals towards sustainable and efficient construction practices using 3D concrete printing technology.