bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanically resilient hybrid aerogels containing fibers of dual-scale sizes and knotty networks for tissue regeneration
Authors: Shahriar S. M. Shatil , McCarthy Alec D. , Andrabi Syed Muntazir , Su Yajuan , Polavoram Navatha Shree , John Johnson V. , Matis Mitchell P. , Zhu Wuqiang , Xie Jingwei 
Published: February 2024

AbstractThe structure and design flexibility of aerogels make them promising for soft tissue engineering, though they tend to come with brittleness and low elasticity. While increasing crosslinking density may improve mechanics, it also imparts brittleness. In soft tissue engineering, resilience against mechanical loads from mobile tissues is paramount. We report a hybrid aerogel that consists of self-reinforcing networks of micro- and nanofibers. Nanofiber segments physically entangle microfiber pillars, allowing efficient stress distribution through the intertwined fiber networks. We show that optimized hybrid aerogels have high specific tensile moduli (~1961.3 MPa cm3 g−1) and fracture energies (~7448.8 J m−2), while exhibiting super-elastic properties with rapid shape recovery (~1.8 s). We demonstrate that these aerogels induce rapid tissue ingrowth, extracellular matrix deposition, and neovascularization after subcutaneous implants in rats. Furthermore, we can apply them for engineering soft tissues via minimally invasive procedures, and hybrid aerogels can extend their versatility to become magnetically responsive or electrically conductive, enabling pressure sensing and actuation.
Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition
Authors: Guzman Ulises H. , Martinez-Val Ana , Ye Zilu , Damoc Eugen , Arrey Tabiwang N. , Pashkova Anna , Renuse Santosh , Denisov Eduard , Petzoldt Johannes , Peterson Amelia C. , Harking Florian , Østergaard Ole , Rydbirk Rasmus , Aznar Susana , Stewart Hamish , Xuan Yue , Hermanson Daniel , Horning Stevan , Hock Christian , Makarov Alexander , Zabrouskov Vlad , Olsen Jesper V. 
Published: February 2024

AbstractMass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.
Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules
Authors: Sun Jianfeng , Philpott Martin , Loi Danson , Li Shuang , Monteagudo-Mesas Pablo , Hoffman Gabriela , Robson Jonathan , Mehta Neelam , Gamble Vicki , Brown Tom , Brown Tom , Canzar Stefan , Oppermann Udo , Cribbs Adam P. 
Published: February 2024

AbstractUnique molecular identifiers are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are a source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing unique molecular identifiers using homotrimeric nucleotide blocks provides an error-correcting solution that allows absolute counting of sequenced molecules.
Development of Groundwater Levels Dataset for Chile since 1970
Authors: Venegas-Quiñones Héctor Leopoldo , Valdés-Pineda Rodrigo , García-Chevesich Pablo , Valdés Juan B. , Gupta Hoshin V. , Whitaker Martha P. L. , Ferré Ty P. A. 
Published: February 2024

AbstractAccess to accurate spatio-temporal groundwater level data is crucial for sustainable water management in Chile. Despite this importance, a lack of unified, quality-controlled datasets have hindered large-scale groundwater studies. Our objective was to establish a comprehensive, reliable nationwide groundwater dataset. We curated over 120,000 records from 640 wells, spanning 1970-2021, provided by the General Water Resources Directorate. One notable enhancement to our dataset is the incorporation of elevation data. This addition allows for a more comprehensive estimation of groundwater elevation. Rigorous data quality analysis was executed through a classification scheme applied to raw groundwater level records. This resource is invaluable for researchers, decision-makers, and stakeholders, offering insights into groundwater trends to support informed, sustainable water management. Our study bridges a crucial gap by providing a dependable dataset for expansive studies, aiding water management strategies in Chile.
Chia seeds ameliorate cardiac disease risk factors via alleviating oxidative stress and inflammation in rats fed high-fat diet
Authors: Aref Mohamed , FaragAllah Eman Mahmoud , Goda Nehal I. A. , Abu-Alghayth Mohammed H. , Abomughaid Mosleh M. , Mahboub Heba H. , Alwutayd Khairiah Mubarak , Elsherbini Hadeel A. 
Published: February 2024

AbstractObesity upsurges the risk of developing cardiovascular disease, primarily heart failure and coronary heart disease. Chia seeds have a high concentration of dietary fiber and increased concentrations of anti-inflammatoryand antioxidant compounds. They are used for weight loss plus enhancing blood glucose and lipid profile. The current perspective was commenced to examine the protective influence of chia seeds ingestion on cardiovascular disease risk factors in high-fat diet-fed rats. Forty male albino rats (with an initial body weight of 180–200 g) were used in this study. Rats were randomly and equally divided into 4 groups: Group I was the control group and group II was a control group with chia seeds supplementation. Group III was a high-fat diet group (HFD) that received HFD for 10 weeks and group IV was fed on HFD plus chia seeds for 10 weeks. In all groups Echocardiographic measurements were performed, initial and final BMI, serum glucose, AC/TC ratio, lipid profile, insulin (with a computed HOMA-IR), creatinine phosphokinase-muscle/brain (CPK-MB), CRP, and cardiac troponin I (cTnI) and MAP were estimated. Whole heart weight (WHW) was calculated, and then WHW/body weight (BW) ratio was estimated. Eventually, a histopathological picture of cardiac tissues was performed to assess the changes in the structure of the heart under Haematoxylin and Eosin and Crossmon’s trichrome stain. Ingestion of a high diet for 10 weeks induced a clear elevation in BMI, AC/ TC, insulin resistance, hyperlipidemia, CRP, CPK-MB, and cTnI in all HFD groups. Moreover, there was a significant increase in MAP, left ventricular end diastolic diameter (LVEDD), and left ventricular end systolic diameter (LVESD). Furthermore, histological cardiac examination showed structural alteration of the normal structure of the heart tissue with an increase in collagen deposition. Also, the Bcl-2 expression in the heart muscle was significantly lower, but Bax expression was significantly higher. Chia seeds ingestion combined with HFD noticeably ameliorated the previously-recorded biochemical biomarkers, hemodynamic and echocardiography measures, and histopathological changes. Outcomes of this report reveal that obesity is a hazard factor for cardiovascular disease and chia seeds could be a good candidate for cardiovascular system protection.
Elevated body temperature is associated with depressive symptoms: results from the TemPredict Study
Authors: Mason Ashley E. , Kasl Patrick , Soltani Severine , Green Abigail , Hartogensis Wendy , Dilchert Stephan , Chowdhary Anoushka , Pandya Leena S. , Siwik Chelsea J. , Foster Simmie L. , Nyer Maren , Lowry Christopher A. , Raison Charles L. , Hecht Frederick M. , Smarr Benjamin L. 
Published: February 2024

AbstractCorrelations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.
An observation of a negative effect of social cohesion on creativity in musical improvisation
Authors: Kempf Adrian , Benedek Mathias , Schiavio Andrea 
Published: February 2024

AbstractAlthough various social factors can significantly impact creative performance, it is still unclear how social cohesion (i.e., how close we feel to others) influences creativity. We therefore conducted two studies exploring the association between social cohesion and creativity within the domain of musical improvisation, a prime example of creative performance, which usually plays out in social contexts. The first study (n = 58 musical novices) showed that music-induced synchrony facilitates social cohesion. In our second study (n = 18 musical novices), we found that in two out of three experimental conditions, increased social cohesion is associated with less creative musical outcomes, as rated by nine expert musicians. In our subsequent analysis we related measures of social cohesion and creativity. This approach highlights how, within a musical setting, creativity unfolds in the context of social contingencies as social cohesion and related factors.
Biochar and conservation tillage affect the agronomic performance and fatty acid composition of Nigella sativa L. under both irrigated and dryland conditions
Authors: Kiani Hawre , Khalesro Shiva , Mokhatssi-Bidgoli Ali , Sharifi Zahed 
Published: February 2024

AbstractSoils in arid and semi-arid regions like Iran have suffered greatly from low organic matter content and low water availability. Traditional tillage and the overuse of chemical fertilizers are accelerating the problems in the region. So, sensible and sustainable strategies such as conservation tillage and natural organic inputs are becoming increasingly important to enhance organic matter and humidity in the soil and grow high-quality crops in agroecosystems. Thus, in 2019 and 2020, a split-split plot arrangement within a randomized complete block design was conducted in Iran to assess the effects of irrigated conditions, tillage systems, and biochar on the aforementioned traits. There were two irrigation conditions (irrigated and dryland) as the main plots, three tillage methods (conventional, minimum, and no-tillage) as sub-plots, and two application rates for biochar (0 and 15 ton ha−1) as sub-sub plots. The findings indicated that biochar application enhanced grain yield across all tillage methods under both irrigation conditions. Biochar with minimum tillage improved oil yield by 23% and 29% compared to those that did not use biochar under the dryland and irrigated conditions, respectively. Moreover, oil yield was higher in 2020 than in 2019 for all tillage systems and biochar rates. The main components of Nigella sativa L. oil belong to linoleic, oleic, and palmitic acids. Minimum tillage with biochar under irrigated conditions in 2020 and no-tillage without biochar under dryland conditions in 2019 had the most (59%) and the least linoleic acid (53%), respectively. Conventional, minimum, and no-tillage with biochar in dryland conditions significantly increased linoleic acid by 2%, 3%, and 5% compared to those without biochar in 2020, respectively. In general, adopting biochar with minimum tillage produced the best outcomes for Nigella sativa L. yield, and grain oil quality under both irrigation conditions. It is recommended that farmers incorporate these practices to produce high-quality Nigella sativa L. in sustainable agricultural systems.
The use of deep learning integrating image recognition in language analysis technology in secondary school education
Authors: Chu Liqing , Liu Yanlan , Zhai Yixi , Wang Dandan , Wu Yufei 
Published: February 2024

AbstractThis work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse’s speaking rate, speech intelligibility, average sentence length, and content similarity are −0.56, −0.71, −0.71, and −0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.
Fuzzy PID control of permanent magnet synchronous motor electric steering engine by improved beetle antennae search algorithm
Authors: Zhang Bu , Niu Pingjuan , Guo Xitong , He Jiyan 
Published: February 2024

AbstractThe accuracy of control in permanent magnet synchronous motor system significantly affects overall mechanical structure safety. To satisfy high-performance control for the position servo of the electric steering engine, this study selects a suitable vector control model for permanent magnet synchronous motor. Additionally, an enhanced beetle antennae search algorithm is designed and employed to optimize the fuzzy proportional–integral–derivative controller. The hybrid fuzzy proportional-integral-derivative controller is then implemented in the control model of the permanent magnet synchronous motor, resulting in the establishment of a novel control model for the electric steering engine driven by the permanent magnet synchronous motor. The test results showed that root-mean-square error of this control model was 0.03 mm and 0.02 mm respectively under the conditions of sinusoidal response, square wave response and step response, which was obviously shorter than all the selected control models. In addition, the standard deviation of the control model designed in this study accounted for less than 4% of root-mean-square error of electric steering engine position under the sinusoidal response condition, so the calculation stability was high. The research results show that the designed control model has a certain reference value for improving servo control performance of permanent magnet synchronous motor.
A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring
Authors: Bajcz Alex W. , Glisson Wesley J. , Doser Jeffrey W. , Larkin Daniel J. , Fieberg John R. 
Published: February 2024

AbstractTo efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.
Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features
Authors: Mahmud S. M. Hasan , Goh Kah Ong Michael , Hosen Md. Faruk , Nandi Dip , Shoombuatong Watshara 
Published: February 2024

AbstractDNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available athttps://deepwet-dna.monarcatechnical.com/. The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
Effect of ginger supplementation on the fecal microbiome in subjects with prior colorectal adenoma
Authors: Prakash Ajay , Rubin Nathan , Staley Christopher , Onyeaghala Guillaume , Wen Ya-Feng , Shaukat Aasma , Milne Ginger , Straka Robert J. , Church Timothy R. , Prizment Anna 
Published: February 2024

AbstractGinger has been associated with a decreased incidence of colorectal cancer (CRC) through reduction in inflammatory pathways and inhibition of tumor growth. Recent pre-clinical models have implicated changes in the gut microbiome as a possible mediator of the ginger effect on CRC. We hypothesized that, in adults previously diagnosed with a colorectal adenoma, ginger supplementation would alter the fecal microbiome in the direction consistent with its CRC-inhibitory effect. Sixty-eight adults were randomized to take either ginger or placebo daily for 6 weeks, with a 6-week washout and longitudinal stool collection throughout. We performed 16S rRNA sequencing and evaluated changes in overall microbial diversity and the relative abundances of pre-specified CRC-associated taxa using mixed-effects logistic regression. Ginger supplementation showed no significant effect on microbial community structure through alpha or beta diversity. Of 10 pre-specified CRC-associated taxa, there were significant decreases in the relative abundances of the genera Akkermansia (p < 0.001), Bacteroides (p = 0.018), and Ruminococcus (p = 0.013) after 6-week treatment with ginger compared to placebo. Ginger supplementation led to decreased abundances of Akkermansia and Bacteroides, which suggests that ginger may have an inhibitory effect on CRC-associated taxa. Overall, ginger supplementation appears to have a limited effect on gut microbiome in patients with colorectal adenomas.