bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference
Authors: Peng Hui , Wang He , Kong Weijia , Li Jinyan , Goh Wilson Wen Bin 
Published: May 2024

AbstractIdentification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew’s correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.
Bulk transparent supramolecular glass enabled by host–guest molecular recognition
Authors: Cai Changyong , Wu Shuanggen , Zhang Yunfei , Li Fenfang , Tan Zhijian , Dong Shengyi 
Published: May 2024

AbstractSupramolecular glass is a non-covalently cross-linked amorphous material that exhibits excellent optical properties and unique intrinsic structural features. Compared with artificial inorganic/organic glass, which has been extensively developed, supramolecular glass is still in the infancy stage, and itself is rarely recognized and studied thus far. Herein, we present the development of the host–guest molecular recognition motifs between methyl-β-cyclodextrin and para-hydroxybenzoic acid as the building blocks of supramolecular glass. Non-covalent polymerization resulting from the host–guest complexation and hydrogen bonding formation enables high transparency and bulk state to supramolecular glass. Various advantages, including recyclability, compatibility, and thermal processability, are associated with dynamic assembly pattern. Short-range order (host–guest complexation) and long-range disorder (three dimensional polymeric network) structures are identified simultaneously, thus demonstrating the typical structural characteristics of glass. This work provides a supramolecular strategy for constructing transparent materials from organic components.
Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding
Authors: Jankowski Wojciech , Surov Stepan S. , Hernandez Nancy E. , Rawal Atul , Battistel Marcos , Freedberg Daron , Ovanesov Mikhail V. , Sauna Zuben E. 
Published: May 2024

AbstractDirect oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.
Bicontinuous RuO2 nanoreactors for acidic water oxidation
Authors: Chen Ding , Yu Ruohan , Yu Kesong , Lu Ruihu , Zhao Hongyu , Jiao Jixiang , Yao Youtao , Zhu Jiawei , Wu Jinsong , Mu Shichun 
Published: May 2024

AbstractImproving activity and stability of Ruthenium (Ru)-based catalysts in acidic environments is eager to replace more expensive Iridium (Ir)-based materials as practical anode catalyst for proton-exchange membrane water electrolyzers (PEMWEs). Here, a bicontinuous nanoreactor composed of multiscale defective RuO2 nanomonomers (MD-RuO2-BN) is conceived and confirmed by three-dimensional tomograph reconstruction technology. The unique bicontinuous nanoreactor structure provides abundant active sites and rapid mass transfer capability through a cavity confinement effect. Besides, existing vacancies and grain boundaries endow MD-RuO2-BN with generous low-coordination Ru atoms and weakened Ru-O interaction, inhibiting the oxidation of lattice oxygen and dissolution of high-valence Ru. Consequently, in acidic media, the electron- and micro-structure synchronously optimized MD-RuO2-BN achieves hyper water oxidation activity (196 mV @ 10 mA cm−2) and an ultralow degradation rate of 1.2 mV h−1. A homemade PEMWE using MD-RuO2-BN as anode also conveys high water splitting performance (1.64 V @ 1 A cm−2). Theoretical calculations and in-situ Raman spectra further unveil the electronic structure of MD-RuO2-BN and the mechanism of water oxidation processes, rationalizing the enhanced performance by the synergistic effect of multiscale defects and protected active Ru sites.
Effects of C-reactive protein rapid testing and communication skills training on antibiotic prescribing for acute cough. A cluster factorial randomised controlled trial
Authors: Llor Carl , Trapero-Bertran Marta , Sisó-Almirall Antoni , Monfà Ramon , Abellana Rosa , García-Sangenís Ana , Moragas Ana , Morros Rosa 
Published: May 2024

AbstractThis cluster randomised clinical trial carried out in 20 primary care centres in Barcelona was aimed at assessing the effect of a continuous intervention focused on C-reactive protein (CRP) rapid testing and training in enhanced communication skills (ECS) on antibiotic consumption for adults with acute cough due to lower respiratory tract infection (LRTI). The interventions consisted of general practitioners and nurses’ use of CRP point-of-care and training in ECS separately and combined, and usual care. The primary outcomes were antibiotic consumption and variation of the quality-adjusted life years during a 6-week follow-up. The difference in the overall antibiotic prescribing between the winter seasons before and after the intervention was calculated. The sample size calculated could not be reached due to the COVID-19 outbreak. A total of 233 patients were recruited. Compared to the usual care group (56.7%) antibiotic consumption among patients assigned to professionals in the ECS group was significantly lower (33.9%, adjusted odds ratio [aOR] 0.38, 95% CI 0.15–0.94, p = 0.037), whereas patients assigned to CRP consumed 43.8% of antibiotics (aOR 0.70, 95% CI 0.29–1.68, p = 0.429) and 38.4% in the combined intervention group (aOR 0.45, 95% CI, 0.17–1.21; p = 0.112). The overall antibiotic prescribing rates in the centres receiving training were lower after the intervention compared to those assigned to usual care, with significant reductions in β-lactam rates. Patient recovery was similar in all groups. Despite the limited power due to the low number of patients included, we observed that continuous training achieved reductions in antibiotic consumption.
Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace
Authors: Tayyebi Zakieh , Pine Allison R. , Leslie Christina S. 
Published: May 2024

AbstractStandard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.
A dataset of 40’000 trees with section-wise measured stem diameter and branch volume from across Switzerland
Authors: Didion Markus , Herold Anne , Thürig Esther , Topuz Serra , Vulovic Zeljka , Abegg Meinrad , Nitzsche Jens , Stillhard Jonas , Glatthorn Jonas 
Published: May 2024

AbstractEstimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40’349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.
CASi: A framework for cross-timepoint analysis of single-cell RNA sequencing data
Authors: Wang Yizhuo , Flowers Christopher R. , Wang Michael , Huang Xuelin , Li Ziyi 
Published: May 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints’ scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.
Experimental study of organic enrichment on meiofaunal diversity
Authors: Mohammad Deyaaedin A. , AL-Farga Ammar , Sami Mahmoud 
Published: May 2024

AbstractThe organic enrichment effects on the meiofauna and nematofauna were assessed for field sediment and other experimental ones enriched with organic matters conducted in the laboratory for 4 weeks. Also, dissolved oxygen (DO) and pH were monitored for each one. The abundance and diversity of meiofaunal groups and nematofauna varied. Strong significant correlations were found between DO and the studied items. Nematoda was the most abundant group in the field sediment and other experimental ones; their counts increased with the increase in organic enrichments and were dominated by deposit feeders. Amphipoda, Ostracoda and predator/omnivore nematodes disappeared in highly organic-enriched sediments. Changes in DO and organic enrichments might be the more attributable reasons for the alteration of the meiobenthic assemblages. The generic compositions of Nematoda provide a good indicator for environmental alterations.
Optimization of ultrasonic pretreatment and analysis of chlorogenic acid in potato leaves
Authors: Wang Xin , Gong Xianyun , Lin Binbin 
Published: May 2024

AbstractChlorogenic acid (CA) is an effective ingredient that can strengthen immunity during following the COVID-19 era. The current cost of CA is high owing to its complex purification process and low yield (approximately 2%). In this study, a one-step path orthogonal experiment was designed based on the results from Gauss calculation, which consisted of acidity, coordination, and hydrolysis in molecules. The optimized extraction conditions were 60 ℃, 60 min, 1:20 liquid ratio, and 40% ethanol in a nitrogen atmosphere controlled using a device of our own design, which led to CA yields of up to 6.35% from potato leaves. The purified CA was analyzed using high-performance liquid chromatography, thin-layer chromatography, ultraviolet–visible spectroscopy, and molecular fluorescence. This accurate and reproducible method can not only be used to obtain high yields of CA but can also be used for the quality control of active plant products and their isomers.
Development and evaluation of a lyophilization protocol for colorimetric RT-LAMP diagnostic assay for COVID-19
Authors: Prado Nayra Oliveira , Marin Anelis Maria , Lalli Larissa Araujo , Sanchuki Heloisa Bruna Soligo , Wosniaki Denise Kusma , Nardin Jeanine Marie , Morales Hugo Manoel Paz , Blanes Lucas , Zanette Dalila Luciola , Aoki Mateus Nóbrega 
Published: May 2024

AbstractMolecular diagnostics involving nucleic acids (DNA and RNA) are regarded as extremely functional tools. During the 2020 global health crisis, efforts intensified to optimize the production and delivery of molecular diagnostic kits for detecting SARS-CoV-2. During this period, RT-LAMP emerged as a significant focus. However, the thermolability of the reagents used in this technique necessitates special low-temperature infrastructure for transport, storage, and conservation. These requirements limit distribution capacity and necessitate cost-increasing adaptations. Consequently, this report details the development of a lyophilization protocol for reagents in a colorimetric RT-LAMP diagnostic kit to detect SARS-CoV-2, facilitating room-temperature transport and storage. We conducted tests to identify the ideal excipients that maintain the molecular integrity of the reagents and ensure their stability during room-temperature storage and transport. The optimal condition identified involved adding 5% PEG 8000 and 75 mM trehalose to the RT-LAMP reaction, which enabled stability at room temperature for up to 28 days and yielded an analytical and diagnostic sensitivity and specificity of 83.33% and 90%, respectively, for detecting SARS-CoV-2. This study presents the results of a lyophilized colorimetric RT-LAMP COVID-19 detection assay with diagnostic sensitivity and specificity comparable to RT-qPCR, particularly in samples with high viral load.
A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data
Authors: Siva Nanda K. , Singh Yashbir , Hathaway Quincy A. , Sengupta Partho P. , Yanamala Naveena 
Published: May 2024

AbstractTo provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the “pattern” of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.
Research on social and economic factors influencing regional mortality patterns in China
Authors: Li Tiantian , Zhang Shuyin , Li Handong 
Published: May 2024

AbstractRegional population mortality correlates with regional socioeconomic development. This study aimed to identify the key socioeconomic factors influencing mortality patterns in Chinese provinces. Using data from the Seventh Population Census, we analyzed mortality patterns by gender and urban‒rural division in 31 provinces. Using a functional regression model, we assessed the influence of fourteen indicators on mortality patterns. Main findings: (1) China shows notable gender and urban‒rural mortality variations across age groups. Males generally have higher mortality than females, and rural areas experience elevated mortality rates compared to urban areas. Mortality in individuals younger than 40 years is influenced mainly by urban‒rural factors, with gender becoming more noticeable in the 40–84 age group. (2) The substantial marginal impact of socioeconomic factors on mortality patterns generally becomes evident after the age of 45, with less pronounced differences in their impact on early-life mortality patterns. (3) Various factors have age-specific impacts on mortality. Education has a negative effect on mortality in individuals aged 0–29, extending to those aged 30–59 and diminishing in older age groups. Urbanization positively influences the probability of death in individuals aged 45–54 years, while the impact of traffic accidents increases with age. Among elderly people, the effect of socioeconomic variables is smaller, highlighting the intricate and heterogeneous nature of these influences and acknowledging certain limitations.
Computational study of diffraction image formation from XFEL irradiated single ribosome molecule
Authors: Stransky Michal , E Juncheng , Jurek Zoltan , Santra Robin , Bean Richard , Ziaja Beata , Mancuso Adrian P. 
Published: May 2024

AbstractSingle particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation.
Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy
Authors: Ma Liuhuan , Yang Jiong 
Published: May 2024

AbstractThe integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.