bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-cell transcriptome atlas of testes from mice with high-fat diets
Authors: Cao Wenbin , Zhang Yulin , Qi Jia , Zhang Yanru , Ding Ruike , Meng Bin , Zhao Juan , Luo Shiwei , Shen Chong , Duan Chenjin , Qin Hongyu , Ye Yun , Liu Enqi , Qu Pengxiang 
Published: June 2024

AbstractObesity is accompanied by multiple known health risks and increased morbidity, and obese men display reduced reproductive health. However, the impact of obesity on the testes at the molecular levels remain inadequately explored. This is partially attributed to the lack of monitoring tools for tracking alterations within cell clusters in testes associated with obesity. Here, we utilized single-cell RNA sequencing to analyze over 70,000 cells from testes of obese and lean mice, and to study changes related to obesity in non-spermatogenic cells and spermatogenesis. The Testicular Library encompasses all non-spermatogenic cells and spermatogenic cells spanning from spermatogonia to spermatozoa, which will significantly aid in characterizing alterations in cellular niches and the testicular microenvironment during high-fat diet (HFD)-induced obesity. This comprehensive dataset is indispensable for studying how HFD disrupts cell-cell communication networks within the testis and impacts alterations in the testicular microenvironment that regulate spermatogenesis. Being the inaugural dataset of single-cell RNA-seq in the testes of diet-induced obese (DIO) mice, this holds the potential to offer innovative insights and directions in the realm of single-cell transcriptomics concerning male reproductive injury associated with HFD.
Chromosome-level genome assembly of the snakefly Mongoloraphidia duomilia (Raphidioptera: Raphidiidae)
Authors: Shen Rongrong , Sylvester Terrence , Shin Na Ra , Zhan Zhihong , Jin Jianfeng , Yang Ding , McKenna Duane D. , Liu Xingyue 
Published: June 2024

AbstractRaphidioptera (snakeflies) are a holometabolan order with the least species diversity but play a pivotal role in understanding the origin of complete metamorphosis. Here, we provide an annotated, chromosome-level reference genome assembly for an Asian endemic snakefly Mongoloraphidia duomilia (Yang, 1998) of the family Raphidiidae, assembled using PacBio HiFi and Hi-C data from female specimens. The resulting assembly is 653.56 Mb, of which 97.90% is anchored into 13 chromosomes. The scaffold N50 is 53.50 Mb, and BUSCO completeness is 97.80%. Repetitive elements comprise 64.31% of the genome (366.04 Mb). We identified 599 noncoding RNAs and predicted 11,141 protein-coding genes in the genome (97.70% BUSCO completeness). The new snakefly genome will facilitate comparison of genome architecture across Neuropterida and Holometabola and shed light on the ecological and evolutionary transitions between Neuropterida and Coleopterida.
A longitudinal qualitative study on physician experience in managing multimorbidity across the COVID-19 pandemic in Odisha, India
Authors: Mahapatra Pranab , Sahoo Krushna Chandra , Pati Sanghamitra 
Published: June 2024

AbstractWhile many studies have documented adverse impact of multiple chronic conditions or multimorbidity on COVID-19 outcomes in patients, there is scarcity of report on how physicians managed these patients. We investigated the experiences and challenges of clinicians in managing patients with multimorbidity throughout the COVID-19 pandemic in Odisha state, India. To understand the factors influencing illness management and the adaptive responses of physicians alongside the evolving pandemic, we followed a longitudinal qualitative study design. Twenty-three physicians comprising general practitioners, specialists, and intensivists, were telephonically interviewed in-depth. Saldana’s longitudinal qualitative data analysis method was employed for data analysis. COVID-19 pandemic initially diverted the attention of health systems, resulting in reduced care. With time, the physicians overcame fear, anxiety, and feelings of vulnerability to COVID-19 and started prioritising patients with multimorbidity for treatment and vaccination. All physicians recommended teleconsultation and digital health records to benefit chronic illness care during future public health crises. The findings underscore the transformative potential of physician resilience and adaptation during the COVID-19 pandemic, emphasizing the importance of prioritizing patients with multimorbidity, incorporating teleconsultation, and implementing digital health records in healthcare systems to enhance chronic illness care and preparedness for future public health crises.
The role of embodied cognition in action language comprehension in L1 and L2
Authors: Garello Stefana , Ferroni Francesca , Gallese Vittorio , Ardizzi Martina , Cuccio Valentina 
Published: June 2024

AbstractIn this study we carried out a behavioral experiment comparing action language comprehension in L1 (Italian) and L2 (English). Participants were Italian native speakers who had acquired the second language late (after the age of 10). They performed semantic judgments on L1 and L2 literal, idiomatic and metaphorical action sentences after viewing a video of a hand performing an action that was related or unrelated to the verb used in the sentence. Results showed that responses to literal and metaphorical L1 sentences were faster when the action depicted was related to the verb used rather than when the action depicted was unrelated to the verb used. No differences were found for the idiomatic condition. In L2 we found that all responses to the three conditions were facilitated when the action depicted was related to the verb used. Moreover, we found that the difference between the unrelated and the related modalities was greater in L2 than in L1 for the literal and the idiomatic condition but not for the metaphorical condition. These findings are consistent with the embodied cognition hypothesis of language comprehension.
Parallel detection of multiple biomarkers in a point-of-care-competent device for the prediction of exacerbations in chronic inflammatory lung disease
Authors: Röckendorf Niels , Ramaker Katrin , Gaede Karoline , Tappertzhofen Kristof , Lunding Lars , Wegmann Michael , Horbert Peter , Weber Karina , Frey Andreas 
Published: June 2024

AbstractSudden aggravations of chronic inflammatory airway diseases are difficult-to-foresee life-threatening episodes for which advanced prognosis-systems are highly desirable. Here we present an experimental chip-based fluidic system designed for the rapid and sensitive measurement of biomarkers prognostic for potentially imminent asthma or COPD exacerbations. As model biomarkers we chose three cytokines (interleukin-6, interleukin-8, tumor necrosis factor alpha), the bacterial infection marker C-reactive protein and the bacterial pathogen Streptococcus pneumoniae—all relevant factors in exacerbation episodes. Assay protocols established in laboratory environments were adapted to 3D-printed fluidic devices with emphasis on short processing times, low reagent consumption and a low limit of detection in order to enable the fluidic system to be used in point-of-care settings. The final device demonstrator was validated with patient sample material for its capability to detect endogenous as well as exogenous biomarkers in parallel.
ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone
Authors: Morra Pierluigi , Meneveau Charles , Zaki Tamer A. 
Published: June 2024

AbstractData assimilation (DA) integrates experimental measurements into computational models to enable high-fidelity predictions of dynamical systems. However, the cost associated with solving this inverse problem, from measurements to the state, can be prohibitive for complex systems such as transitional hypersonic flows. We introduce an accurate and efficient deep-learning approach that alleviates this computational burden, and that enables approximately three orders of magnitude computational acceleration relative to variational techniques. Our method pivots on the deployment of a deep operator network (DeepONet) as an accurate, parsimonious and efficient meta-model of the compressible Navier–Stokes equations. The approach involves two main steps, each addressing specific challenges. Firstly, we reduce the computational load by minimizing the number of costly direct numerical simulations to construct a comprehensive dataset for effective supervised learning. This is achieved by optimally sampling the space of possible solutions. Secondly, we expedite the computation of high-dimensional assimilated solutions by deploying the DeepONet. This entails efficiently navigating the DeepONet’s approximation of the cost landscape using a gradient-free technique. We demonstrate the successful application of this method for data assimilation of wind-tunnel measurements of a Mach 6, transitional, boundary-layer flow over a 7-degree half-angle cone.
Combined effect of gallic acid and zinc ferrite nanoparticles on wheat growth and yield under salinity stress
Authors: Shao Qingqin , Ren Lantian , Ramzan Musarrat , Hussain Muhammad Baqir , Datta Rahul , Almoallim Hesham S. , Ansari Mohammad Javed , Ehsan Abdullah 
Published: June 2024

AbstractSalinity stress significantly impacts crops, disrupting their water balance and nutrient uptake, reducing growth, yield, and overall plant health. High salinity in soil can adversely affect plants by disrupting their water balance. Excessive salt levels can lead to dehydration, hinder nutrient absorption, and damage plant cells, ultimately impairing growth and reducing crop yields. Gallic acid (GA) and zinc ferrite (ZnFNP) can effectively overcome this problem. GA can promote root growth, boost photosynthesis, and help plants absorb nutrients efficiently. However, their combined application as an amendment against drought still needs scientific justification. Zinc ferrite nanoparticles possess many beneficial properties for soil remediation and medical applications. That’s why the current study used a combination of GA and ZnFNP as amendments to wheat. There were 4 treatments, i.e., 0, 10 µM GA, 15 μM GA, and 20 µM GA, without and with 5 μM ZnFNP applied in 4 replications following a completely randomized design. Results exhibited that 20 µM GA + 5 μM ZnFNP caused significant improvement in wheat shoot length (28.62%), shoot fresh weight (16.52%), shoot dry weight (11.38%), root length (3.64%), root fresh weight (14.72%), and root dry weight (9.71%) in contrast to the control. Significant enrichment in wheat chlorophyll a (19.76%), chlorophyll b (25.16%), total chlorophyll (21.35%), photosynthetic rate (12.72%), transpiration rate (10.09%), and stomatal conductance (15.25%) over the control validate the potential of 20 µM GA + 5 μM ZnFNP. Furthermore, improvement in N, P, and K concentration in grain and shoot verified the effective functioning of 20 µM GA + 5 μM ZnFNP compared to control. In conclusion, 20 µM GA + 5 μM ZnFNP can potentially improve the growth, chlorophyll contents and gas exchange attributes of wheat cultivated in salinity stress. More investigations are suggested to declare 20 µM GA + 5 μM ZnFNP as the best amendment for alleviating salinity stress in different cereal crops.
Postoperative bioactive adrenomedullin is associated with the onset of ARDS and adverse outcomes in patients undergoing open thoracoabdominal aortic surgery
Authors: Doukas Panagiotis , Hartmann Oliver , Frankort Jelle , Arlt Birte , Krabbe Hanif , Jacobs Michael Johan , Greiner Andreas , Frese Jan Paul , Gombert Alexander 
Published: June 2024

AbstractCytokine-mediated systemic inflammation after open thoracoabdominal aortic aneurysm (TAAA) repairs plays a pivotal role in disrupting circulatory homeostasis, potentially leading to organ dysfunction. The bioactive form of adrenomedullin (bio-ADM) is a peptide hormone with immunomodulatory and vasomotor effects, making it a potential diagnostic agent in these cases. This retrospective, bicentric study, conducted between January 2019 and December 2022, recruited 36 elective open TAAA repair patients in two German centres. Serum and plasma samples were collected at multiple time points to measure bio-ADM levels. The primary objective was to evaluate the association of bio-ADM levels with the onset of acute respiratory distress syndrome (ARDS), with secondary endpoints focusing on mortality and SIRS-related morbidity. Results showed a significant association between postoperative bio-ADM levels (12–48 h after surgery) and the onset of ARDS (p < .001), prolonged ventilation (p = .015 at 12h after surgery), atrial fibrillation (p < .001), and mortality (p = .05 at 24h). The biomarker was also strongly associated with sepsis (p = .01 at 12 h) and multi-organ dysfunction syndrome (MODS) (p = .02 at 24 h after surgery). The study underscores the potential utility of bio-ADM as a diagnostic tool for identifying patients at risk of postoperative complications following open TAAA repairs.
Phosphomolybdic acid supported on magnetic poly calix[4]resorcinarene-EDTA-chitosan network as a recyclable catalyst for the synthesis of 5-aroyl-NH-1,3-oxazolidine-2-ones
Authors: Moradi Setareh , Mozafari Roya , Ghadermazi Mohammad 
Published: June 2024

AbstractIn this work, a novel procedure for immobilization of phosphomolybdic acid (PMA) on Magnetic polycalix[4]resorcinarene grafted to chitosan by EDTA (calix-EDTA-Cs) was reported. The heterogeneous nanocomposite (CoFe2O4@calix-EDTA-Cs@PMA) was applied an acid nanocatalyst for the synthesis of 5-aroyl-NH-1,3-oxazolidine-2-ones through the reaction of α-epoxyketones with sodium cyanate (NaOCN) in polyethylene glycol (PEG) as a green solvent under ultrasonic irradiation conditions. Some features of this work include quick reaction time, high reaction yield, easy separation of the catalyst, thermal stability, and eco-friendly.
Influence of the non-equal aligned nozzles for fuel injection inside the supersonic combustion chamber
Authors: Fan Zhongmian , Wang Lingxiao , Xu Fei , Zhang Xiuli , Xie Baoling , Wen Yushi , Li He , Aminian Saman 
Published: June 2024

AbstractThe importance of fuel mixing for the progress of the scramjet engine is indisputable. The present article shows the importance of the non-equal multi-injector system for effective fuel distribution and flame holding inside the combustion segment of a scramjet engine. The supersonic air and fuel jet flow in the non-equal nozzle arrangement is simulated via computational fluid dynamic technique. Two injector types of circular and rectangular nozzle have been analyzed to attain flow characteristics of hydrogen jets at supersonic cross flow. Mach contour is also analyzed for these jet arrangements to show the interface of the jet in the non-equal jet arrangement. Besides, addition of internal air jet is also simulated and evaluated in this research. Our investigation shows that the diffusion height of the fuel jet is higher when a rectangular non-equal nozzle is applied. The circular nozzle is more active in the spreading of the fuel in the combustor and the use of an internal air jet effectively increases fuel in a combustor of the scramjet.
A novel framework based on explainable AI and genetic algorithms for designing neurological medicines
Authors: Singh Vishakha , Singh Sanjay Kumar , Sharma Ritesh 
Published: June 2024

AbstractThe advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.
Risk factors for bloodstream infection among patients admitted to an intensive care unit of a tertiary hospital of Shanghai, China
Authors: Cui Yingchao , Yi Changlin , Zhang Chaomin , Yang Chihui , Wang Xinyi , Chen Wenkai , Peng Yibing , Dai Jing 
Published: June 2024

AbstractBlood flow infections (BSIs) is common occurrences in intensive care units (ICUs) and are associated with poor prognosis. The study aims to identify risk factors and assess mortality among BSI patients admitted to the ICU at Shanghai Ruijin hospital north from January 2022 to June 2023. Additionally, it seeks to present the latest microbiological isolates and their antimicrobial susceptibility. Independent risk factors for BSI and mortality were determined using the multivariable logistic regression model. The study found that the latest incidence rate of BSI was 10.11%, the mortality rate was 35.21% and the mean age of patients with BSI was 74 years old. Klebsiella pneumoniae was the predominant bacterial isolate. Logistic multiple regression revealed that tracheotomy, tigecycline, gastrointestinal bleeding, shock, length of hospital stay, age and laboratory indicators (such as procalcitonine and hemoglobin) were independent risk factors for BSI. Given the elevated risk associated with use of tracheotomy and tigecycline, it underscores the importance of the importance of cautious application of tracheostomy and empirical antibiotic management strategies. Meanwhile, the independent risk factors of mortality included cardiovascular disease, length of hospital stay, mean platelet volume (MPV), uric acid levels and ventilator. BSI patients exhibited a significant decrease in platelet count, and MPV emerged as an independent factor of mortality among them. Therefore, continuous monitoring of platelet-related parameters may aid in promptly identifying high-risk patients and assessing prognosis. Moreover, monitoring changes in uric acid levels may serve as an additional tool for prognostic evaluation in BSI patients.
Association between health literacy and Framingham risk score
Authors: Rahimi Tahereh , Hashemi Seyedeh Soroor , Rezaei Fatemeh , Aune Dagfinn 
Published: June 2024

AbstractHigh health literacy (HL) plays a critical role in preventing or delaying the onset of cardiovascular diseases (CVDs) and can improve disease management and control. The present study aims to determine the association between HL and non-laboratory-based (office-based) Framingham 10-year risk score of CVD. This cross-sectional study was conducted on 648 people aged 30–65 in the health centers of Jahrom. The Health Literacy Instrument for Adults (HELIA) was used to assess HL. The non-laboratory-based Framingham risk score (FRS) was utilized to determine the 10-year risk of CVDs. Risk factors such as age, gender, diabetes, current smoking status, systolic blood pressure (SBP), hypertension (HTN) treatment, and body mass index (BMI) were applied in the non-laboratory-based model. The average age of the subjects was 44.7 ± 10.5 years, among which 49.2% were males. The prevalence of diabetes, HTN, and smoking equaled 8.5%, 15.7%, and 10%, respectively. In addition, the average BMI was 26.1 ± 3.6 kg/m2. Based on the non-laboratory-based Framingham 10-year risk score of CVD, 72.5%, 13.9%, and 13.6% of the subjects were in the low, moderate, and high risk groups, respectively. Based on the HL grouping, the levels of insufficient, borderline, sufficient, and excellent HL were 19.3%, 26.4%, 34.6%, and 19.7%, respectively. A significant association was observed between 10-year CVD risk and HL grouping. In addition, a negative correlation was reported at the individual level between HL and non-laboratory-based FRS among the whole population (r = − 0.39, p < 0.001), men (r = − 0.32, p < 0.001), and women (r = − 0.42, p < 0.001). A higher HL score is associated with a lower risk of CVD. In addition, the adjusted logistic regression analysis showed that there was a strong association between elevated CVD risk (≥ 10%) and HL (OR 6.1, 95% CI 2.9–12.6) among inadequate HL participants compared with excellent HL individuals. Thus, designing and implementing training programs to increase HL, especially among those who are at risk of CVDs, should be regarded as an important issue for the prevention of such diseases.
A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance
Authors: Singh Amrita , Rose Anish Preethu , Verma Aparna , Venkatesan Sivanthy , V Logamurugan , Ghaisas Smita 
Published: June 2024

AbstractContract governance ensures that the agreed outcomes between customers and vendors are fulfilled. Information Technology (IT) outsourcing organizations enter thousands of contractual relationships each month leading to a high volume of business-critical contractual text that must be reviewed and deciphered for effective governance. The key to effective governance of contracts is a model that facilitates assigning ownership of the obligations to the right departments in an organization and allocating their accountability to the right stakeholders. For this, the contractual obligations need to be identified and classified so that details such as actions to be taken by departments in an organization and their ownership as per a given clause are brought out for the purpose of governance. In this paper, we present our work on automated extraction and classification of obligations present in Software Engineering (SE) contracts for the purpose of contracts governance. We propose a novel data decomposition-based hierarchical classification method for a multi-label classification of contractual obligations. We conducted experiments for a fine-grained automated classification of more than 55,000 statements from 50 large real-life SE contract documents received from a large vendor organization into 152 governance-specific classes. The results indicate that the proposed method can bring about a 7–8% improvement in accuracies when compared to state-of-the-art classification baselines such as BERT, RoBERTa, and generative models such as GPT-2.