bluuebunny/crossref_metadata_2025_split huggingface dataset:
Host protein ARF1 is a proviral factor for SARS-CoV-2 and a candidate broad-spectrum therapeutic target
Authors: Ning Yun-Jia , Zhang Cunhuan , Min Yuan-Qin , Xue Heng , Liu Kunpeng , Tian Yichao , Yang Ziying , Zhao Zihan , Yang Hang , Shan Chao , Sun Xiulian 
Published: January 2024

Abstract
SARS-CoV-2 and its emerging variants pose continuing threats to public health, necessitating further research into detailed infection mechanisms and broad-spectrum antiviral therapies. SARS-CoV-2 assembles at the ER–Golgi intermediate compartment (ERGIC), where the viral membrane (M) protein highly accumulates to act as the central driver of the viral morphogenesis. However, which host factors are involved in the ERGIC accumulation of M and whether they could be exploited as broad-spectrum antiviral targets remain unclear. Here, we identified an M-interacting host protein ARF1 as a proviral factor that bolsters the propagation of SARS-CoV-2 and its variants in cultured cells and the viral infection and pathogenicity in K18-hACE2 mice. Mechanistically, by interacting with M, ARF1 facilitates M accumulation to the ERGIC and thus M-driven virion production. Consistently, pharmacological ARF1 inhibition by small molecules disrupted ARF1 and M concentration at the ERGIC, blocking virion assembly and propagation. Furthermore, a designed peptide mimicking the M-targeted motif of ARF1 competitively blocked the M-ARF1 interaction, M accumulation at the ERGIC, M-driven virion assembly, and SARS-CoV-2 and its variants’ propagation in vitro. Moreover, the peptidomimetic inhibitor exhibited a therapeutic efficacy against SARS-CoV-2 infection and pathogenicity in vivo. These findings provide critical insights into the basic biology of SARS-CoV-2 propagation and show the potential to develop pan-SARS-CoV-2 therapeutic strategies by targeting ARF1 and/or the ARF1-M interaction interface.
DAB2IP inhibits glucose uptake by modulating HIF-1a ubiquitination under hypoxia in breast cancer
Authors: Shen Jie , Dong Hongliang , Jia Weiyi , Meng Weijian , Zhang Rui , Qi Zhihong , Chen Zhuo , Xie Sophia , Min Jiang , Liu Liang 
Published: January 2024

Abstract

Metabolic reprogramming becomes more and more important in tumor biology. Among various metabolic type, glucose metabolism represents as the major energy source and is often dysregulated in breast cancer. DAB2IP is widely reported to be a tumor suppressor and act as a scaffold protein to suppress tumor malignancy in breast cancer. Interesting, DAB2IP was also found to be a potential regulator in glucose uptake, however, the concrete mechanism is still not delineated. In this present research, we found DAB2IP could inhibited glucose uptake under hypoxia condition in breast cancer cells through suppressing HIF-1a signals. Mechanically, DAB2IP could interact with E3 ubiquitin ligase, STUB1, via its PER domain, thus triggering STUB1 mediated HIF-1a ubiquitylation and degradation, and finally inhibit glucose metabolism and tumor progression. Deleting PER domain could abrogate DAB2IP-related inhibitory effect of glucose uptake, intracellular ATP production and lactic acid production in breast cancer. These findings exhibit the biological role of DAB2IP in cancer-related glucose metabolism, and unveils a novel mechanism of DAB2IP in regulating STUB1-driven HIF-1a ubiquitylated degradation in breast cancer.
Comparative analysis of data preprocessing methods and machine learning models for geographical origin prediction in an imbalanced Panax notoginseng dataset using near-infrared spectroscopy
Authors: Cheng XueFeng , Buhari Abudhahir , Liu Juan 
Published: January 2024

Abstract
This study explores the application of near-infrared spectroscopy (NIRS) and machine learning to accurately determine the geographical origin of Panax notoginseng (P. notoginseng), a critical component in traditional Chinese medicine. Given the complexity of P. notoginseng geographical origin identification, especially in the face of imbalanced datasets, the study systematically evaluates a range of data preprocessing methods, including autocorrelation, data standardization, Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay (S-G) smoothing, first-order derivative (1D), second-order derivative (2D), and Principal Component Analysis (PCA). Furthermore, it assesses various machine learning models such as Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and neural networks in this context. First by assembling and preparing a substantial dataset of NIRS of P. notoginseng from different geographical locations. The dataset's imbalance, reflective of real-world scenarios, necessitates specialized data handling strategies. The study meticulously applies each preprocessing technique to this dataset, followed by the deployment of different machine learning models. This dual approach allows for an in-depth comparison of how each combination influences the accuracy of geographical origin prediction. Findings of the study reveal that specific combinations of data preprocessing methods and machine learning models yield substantial improvements in predicting the geographical origin of P. notoginseng. These combinations are pivotal in addressing the imbalances inherent in the dataset, thereby enhancing the reliability of the predictions. The research contributes significantly to the field by not only providing a solution to the problem of geographical origin prediction in imbalanced datasets but also by laying down a methodological framework that can be adapted for similar challenges in the broader area of herbal medicine research. This study serves as a cornerstone in the intersection of traditional Chinese medicine and modern scientific methods, offering a robust, data-driven approach to ensuring the authenticity and quality of vital medicinal herbs like P. notoginseng. Its implications extend beyond this specific application, providing insights and methodologies that could revolutionize quality control and authentication processes in herbal medicine globally.
Numerical simulation of flow around the legs during walking in water with implications for hydrotherapeutic exercise
Authors: Patel Umang , Sup Frank , Modarres-Sadeghi Yahya 
Published: January 2024

Abstract

One of the common hydrotherapeutic exercises is walking in water because buoyancy reduces joint loading and increases mobility for a patient. The fluid drag forces (the forces that act on the person from the fluid in the direction opposing the direction of motion) cause changes in muscleactivations, as walking in water changes the forces that act on the leg compared with overground walking. Here, through a series of numerical simulations, we quantify how the flow forces that act on the leg due to its motion in water change over a walking gait cycle. We show that besides drag forces that act on the walking legs and peak when the leg is accelerated forward, relatively large lateral forces (in the direction perpendicular to the direction of motion) also act on the leg. Theseforces are caused by the rapid acceleration of the opposite leg when the two legs are close, creating an asymmetric pressure distribution around the leg. These results are unexpected and could have significant implications for designing hydrotherapeutic plans for patients by considering the lateral forces besides the drag forces that act on the body while walking in water.
Active optical phased array integrated within a micro-cantilever
Authors: Fowler Daivid , Guerber Sylvain , Mollard Laurent , Dieppedale Christel , Rhun Gwenael Le , Hamelin Antoine , Faugier-Tovar Jonathan , Abdoul-Carime Kim 
Published: January 2024

Abstract

Using wafer level fabrication techniques, we combine silicon photonics and MEMS technologies to demonstrate the integration of an active photonic beam-steering circuit into a piezoelectric actuated micro cantilever. An optical phased array (OPA), operating at a wavelength of 905nm, provides output beam scanning over a range of 17° in one dimension, while the inclination of the entire circuit and consequently the angle of the output beam in a second dimension can be independently modified over a range of up to 40° via the piezoelectric actuator. This combination of existing commercial silicon technologies can provide continuous, low-divergence, solid-state beam scanning for sensing applications, while avoiding the disadvantages associated with the use of a widely tunable source laser.
Automatic classification of retinal detachment using a deep transfer learning algorithm based on multimodal fundus images: a multicenter study
Authors: Gan Fan , Wan Qi , Fan Huimin , Chen Wanyun , Qin Weiguo , Li Xiaonan , Liu Teng , Cao Jian , Liu kangcheng , You Zhipeng 
Published: January 2024

Abstract

Background
Early diagnosis and accurate classification are pivotal for realizing precision medicine and personalized treatment in managing retinal detachment. In this study, we devised a multi-modal diagnostic system that leverages B-scan, OCT, and UWF imaging technologies to achieve a precise classification of retinal detachment.
Methods
From May 2020 to April 2023, we collected UWF, B-scan, and OCT images for each patient. An automatic model was developed to segment retinal detachment lesions on the UWF images. These images were then input into five transfer learning models for feature extraction. Using PCA, we reduced the dimensionality of the features from each model. Next, we sequentially fused the model features and modality features to create multi-modality feature subsets. Through Spearman correlation and lasso regression, we identified key features. Based on these key features, we developed five machine learning models and selected the top-performing one as our primary multi-modality model. This optimal algorithm was also used to construct single-modality models for B-scan, OCT, and UWF images. Lastly, we compared the performance of the multi-modality and single-modality models on various validation sets.
Results
The multi-modality model exhibited better performance than the best single-modality model (UWF model), showing improvements of 6.79% and 0.04 in accuracy and AUC on the internal validation set (90.3%, 0.96), and 6.54% and 0.03 on the external validation set (83.0%, 0.94) respectively.
Conclusion
We developed a multi-modal deep transfer learning model using UWF, B-scan, and OCT images and achieved excellent performance. This model could serve as a diagnostic tool in clinical practice, assisting ophthalmologists in the initial classification of retinal detachment cases.
Exposure to Gulf war illness-related chemicals exacerbates alcohol- induced liver damage in rodents
Authors: Petrescu Anca , Venter Julie , Danilenko Daria D , Medina Daniela , Grant Stephanie , An Su Yeon , Williams Elaina , Mireles Patrick , Tjahja Matthew , DeMorrow Sharon 
Published: January 2024

Abstract
Gulf War Illness (GWI) describes a series of symptoms suffered by veterans of the Gulf war consisting of cognitive, neurological and gastrointestinal dysfunctions. Two chemicals associated with GWI are the insecticide permethrin (PER) and the nerve gas prophylactic pyridostigmine-bromide (PB). In this study we assessed the effects of PER and PB exposure on pathology and subsequent alcohol (EtOH)-induced liver injury, and the influence of a macrophage depletor, PLX3397, on EtOH-induced liver damage in PER/PB- treated mice. Male C57BL/6 mice were injected daily with vehicle or PER/PB for 10 days, followed by 4 months recovery, then treatment with PLX3397 and a chronic-plus-single-binge EtOH challenge for 10 days. PER/PB exposure resulted in the protracted increase in liver transaminases in the serum and induced chronic low-level microvesicular steatosis and inflammation in GWI vs Naïve mice up to 4 months after cessation of exposure. Furthermore, prior exposure to PER/PB also resulted in exacerbated response to EtOH-induced liver injury, with enhanced steatosis, ductular reaction and fibrosis. The enhanced EtOH-induced liver damage in GWI-mice was attenuated by strategies designed to deplete macrophages in the liver. Taken together, these data suggest that exposure to GWI-related chemicals may alter the liver’s response to subsequent ethanol exposure.
Sweet corn association panel and genome-wide association analysis reveal loci for chilling-tolerant germination
Authors: Wu Zhenxing , Wang Tingzhen , Chen Jianjian , Zhang Yun , Lv Guihua 
Published: January 2024

Abstract

Sweet corn is highly susceptible to the deleterious effects of low temperatures during the initial stages of growth and development. Employing a 56k chip, high-throughput single-nucleotide polymorphism (SNP) sequencing was conducted on 100 sweet corn inbred lines. Subsequently, six germination indicators—germination rate, germination index, germination time, relative germination rate, relative germination index, and relative germination time—were utilized for genome-wide association analysis. Candidate genes were identified via comparative analysis of homologous genes in Arabidopsis and rice, and their functions were validated using quantitative real-time polymerase chain reaction (qRT-PCR). The findings revealed 38,613 high-quality SNPs, with 16 significantly correlated SNPs. Within 50 kb upstream and downstream of the identified SNPs, 46 associated genes were identified, of which six were confirmed as candidate genes. Their expression patterns indicated that ZM11ΒHSDL5 and ZM2OGO likely play negative and positive regulatory roles, respectively, in the low-temperature germination of sweet corn. Thus, we determined that these two genes are responsible for regulating the low-temperature germination of sweet corn. This study contributes valuable theoretical support for improving sweet corn breeding and may aid in the creation of specific germplasm resources geared towards enhancing low-temperature tolerance in sweet corn.
Low-Entropy-Penalty Synthesis of Giant Macrocycles for Good Self-Assembly and Emission Enhancement
Authors: Li Chunju , Sun Xiao-Na , Liu Ao , Xu Kaidi , Zheng Zhe , Xu Kai , Dong Ming , Li Jian , Zhang Zhi-Yuan 
Published: January 2024

Abstract
Macrocycles are key tools for molecular recognition and self-assembly. However, traditionally prevalent macrocyclic compounds exhibit specific cavities with diameters usually less than 1 nm, limiting their range of applications in supramolecular chemistry. The efficient synthesis of giant macrocycles remains a significant challenge because an increase in the monomer number results in cyclization-entropy loss. In this study, we developed a low-entropy-penalty synthesis strategy for producing giant macrocycles in high yields. In this process, long and rigid monomers possessing two reaction modules were condensed with paraformaldehyde via Friedel–Crafts reaction. A series of giant macrocycles with cavities of sizes ranging from 2.0 nm to 4.7 nm were successfully synthesized with cyclization yields of up to 72%. Experimental results and theoretical calculations revealed that extending the monomer length rather than increasing the monomer numbers could notably reduce the cyclization-entropy penalty and avoid configuration twists, thereby favoring the formation of giant macrocycles with large cavities. Significantly, the excellent self-assembly capacity of these giant macrocycles promoted their assembly into organogels in various solvents. The obtained xerogels exhibited enhanced photoluminescence quantum efficiencies of up to 83.1%. Mechanism investigation revealed that the excellent assembly capacity originated from the abundant π–π interactions sites of the giant macrocycles. The outstanding emission enhancement resulted from the restricted nonradiative decay processes of rotation/vibration and improved radiative decay process of fluorescence. This study provides an effective and general method for achieving giant macrocycles, thereby expanding the supramolecular toolbox for host–guest chemistry and assembly applications. Moreover, the intriguingly assembly and photophysical properties demonstrate the feasibility of developing novel and unique properties by expanding the macrocycle size.
Effects of Electronic Sports on the Cognitive Skills of Attention, Working Memory, and Cognitive Flexibility
Authors: Imanian Maryam , Khatibi Amin , Heydarinejad Sedighe , Saemi Esmaeel , Veisia Esmaeil 
Published: January 2024

Abstract
Increasing popularity of electronic sports (esports) prompted the authors of this paper to explore the effects of esports in the cognitive skills of attention, working memory, and cognitive skills among gamers. A total of 40 undergraduate physical education students with no history of playing esports participated in this study. The students were randomly assigned to two groups, namely Singe Players and CO-Players. The two groups played the esport FIFA in single player and co-player modes for a period of eight weeks. Stroop task was used to assess changes in attention while working memory was assessed through N-back task and cognitive flexibility was explored using Wisconsin card test. In addition, paired sample t-test was used to evaluate changes in both single-players and co-players while potential variations between the two groups were analyzed using two-way ANOVA. The findings suggested that FIFA can improve attention and working memory in both single-player and co-player groups. However, cognitive flexibility was only improved for the co-player group. These findings show that esports can improve a number of cognitive skills such as attention, working memory, and cognitive flexibility and that these techniques can be used towards different goals including learning and instruction.
Relationship between plasma atherogenic index of plasma (AIP) and Klotho protein: A cross-sectional study
Authors: Chen Chunwei , Chen Chengwen , Wang Liudi , Chen Lingyao , Wang Suwen , Yuan Xiang , Bao Wei , Gong Haibin 
Published: January 2024

Abstract
Background: The plasma atherogenic index of plasma (AIP) is an indicator that measures the degree of lipid abnormalities and the risk of atherosclerosis, while Klotho protein is a protein with anti-aging biological functions. However, the relationship between these two factors has not been studied.
Objective: The aim of this study was to evaluate the association between plasma atherogenic index of plasma (AIP) and serum anti-aging protein Klotho in the US population.
Methods: This study utilized a cross-sectional design using data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2016. The AIP index was calculated using TG and HDL-C, and the concentration of Klotho protein was measured using an ELISA kit. After adjusting for potential confounding variables, a multivariable linear regression model was used to estimate the relationship between AIP and Klotho protein. Additionally, smooth curve fitting and segmented regression models were applied to test threshold effects and calculate inflection points.
Results: The study included 6,155 eligible adults with a mean age of 57.37 ± 10.75 years, of which approximately 48.22% were male. The results of multivariable linear regression showed that after adjusting for potential confounders, AIP was independently negatively associated with Klotho protein (β = -77.91, 95% CI (-131.61, -24.20)), which was consistent with the results when AIP (quartiles) was treated as a categorical variable (p trend = 0.0048). A non-linear relationship was observed between AIP and Klotho protein, with an inflection point at 0.02. On the left side of the inflection point (AIP ≤ 0.02), for every unit increase in AIP, the level of Klotho protein decreased by 200. On the right side of the inflection point (AIP > 0.02), this relationship was not observed. Subgroup analyses showed a stronger association between AIP and Klotho protein levels in females and non-drinkers, with values of -170.31 (-255.62, -94.99) and -306.24 (-520.18, -92.30) respectively.
Conclusion: A non-linear relationship exists between AIP and Klotho protein. When AIP is less than 0.02, there is a negative correlation between AIP and Klotho.
FungiRegEx: A tool for patterns identification in Fungal Proteomic sequences using regular expressions
Authors: Terron-Macias Victor , Mejía-Miranda Jezreel , Canseco-Pérez Miguel , Muñoz-Mata Mirna , Terron-Hernández Miguel 
Published: January 2024

Abstract
In the context of genome-scale research, it is imperative to automatically analyze numerous species and sub-species to discern distinctive features present in multiple proteomes that contain specific sequences of interest since they provide specific properties. Complex sequences must be recognized within an organism’s complete set of proteomes to accomplish this. This study introduces FungiRegEx, a user-friendly software for automatic genome-scale proteome analysis of fungi organisms, addressing the limitations of existing tools. FungiRegEx utilizes real-time data retrieval of the different species from the JGI Mycocosm database without downloading any files. With a user-friendly GUI, the tool offers efficient regular expression searches across 2,402 fungal species from the JGI Mycocosm portal. Validation with the sequence AXSXG or effector RXRL demonstrates FungiRegEx’s effectiveness in identifying user-defined patterns in the retrieved sequences. FungiRegEx accelerates result retrieval compared to manual processes, providing a console-free and programming-free experience; this tool allows customization, result filtering, and the possibility of saving the results for future research. FungiRegEx offers a promising solution for researchers exploring specific sequences in the fungal proteomes. It combines speed, adaptability, and ease of use, displaying the results in a GUI and making it easy to read. Its architecture ensures optimized resource usage and deployment flexibility, allowing the customization of specific software parameters. The tool’s potential for future research and exploration is emphasized, providing a nuanced perspective on its practical use within the fungal genomics community.
Neutralizing and decoupling the effects of lithium medication
Authors: Ahmed Irfan , Khan Muhammad Shehzad , Magsi Hina , Rizvi Syed Maaz Ahmed , Aziz Tariq , Lashari Najeeb ur Rehman , Ali Zulfiqar , Zhang Yanpeng , Alda Martin , Bergink Veerle , Lau Condon 
Published: January 2024

Abstract

Lithium-induced hypothyroidism in the neonate is a growing concern for lactating mothers. Maternal hypothyroidism in the postpartum period could lead to hypothyroidism in the infant via maternal compromised thyroid hormones (likely T4) in breast milk, and lithium in breast milk could have a direct effect on the neonatal thyroid axis. We have investigated lactating dams and pups, lithium-treated, with and without iodine supplement and control dams. We employed Enzym-linked immunosorbent assay and inductively coupled plasma mass spectrometry to assess hormone profiles and intrathyroidal iodine content. The mechanism for supplemented iodine uptake in the presence of lithium is hypothesized by change in membrane potential across the blood vessel and follicular cell(lactocyte) caused by variation in the gradient concentration of negative iodide ion, positive lithium, sodium, and potassium ions. Interestingly, lithium administered directly to pups from control mothers (average dose 900 mg/50kg/24 hours), did not affect their weight, thyroid hormones, blood urea, and intrathyroidal iodine content despite traces of lithium found in their blood and thyroid. The iodine pathway in presence of lithium content in both thyroid follicular cell and lactocyte has been regulated by gradient concentration of negative (iodide) and positive ions (lithium, potassium, and sodium). The results also demonstrate that lithium administration in lactating dams alters thyroid hormones (T4) and blood urea in both dams and pups, which could be reversed by iodine supplement. In future, supplementing iodine may be potentially useful in clinical practices to address the neonate concerns of lactating mothers and their infants either caused by prolonged lithium medication or maternal iodine deficiency.