bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-intensity pulsed ultrasound delays the progression of osteoarthritis by regulating the YAP–RIPK1–NF-κB axis and influencing autophagy
Authors: Pan Chunran , Lu Fan , Hao Xiaoxia , Deng Xiaofeng , Liu Jiawei , Sun Kai , Hou Wenjie , Shang Xingru , Chi Ruimin , Guo Fengjing , Xu Tao 
Published: March 2024

Abstract
Background
Osteoarthritis (OA) is a degenerative disease characterized by chronic inflammation of the joint. As the disease progresses, patients will gradually develop symptoms such as pain, physical limitations and even disability. The risk factors for OA include genetics, gender, trauma, obesity, and age. Unfortunately, due to limited understanding of its pathological mechanism, there are currently no effective drugs or treatments to suspend the progression of osteoarthritis. In recent years, some studies found that low-intensity pulsed ultrasound (LIPUS) may have a positive effect on osteoarthritis. Nonetheless, the exact mechanism by which LIPUS affects osteoarthritis remains unknown. It is valuable to explore the specific mechanism of LIPUS in the treatment of OA.

Methods
In this study, we validated the potential therapeutic effect of LIPUS on osteoarthritis by regulating the YAP–RIPK1–NF-κB axis at both cellular and animal levels. To verify the effect of YAP on OA, the expression of YAP was knocked down or overexpressed by siRNA and plasmid in chondrocytes and adeno-associated virus was injected into the knee joint of rats. The effect of LIPUS was investigated in inflammation chondrocytes induced by IL-1β and in the post-traumatic OA model.

Results
In this study, we observed that YAP plays an important role in the development of osteoarthritis and knocking down of YAP significantly inhibited the inflammation and alleviated cartilage degeneration. We also demonstrated that the expression of YAP was increased in osteoarthritis chondrocytes and YAP could interact with RIPK1, thereby regulating the NF-κB signal pathway and influencing inflammation. Moreover, we also discovered that LIPUS decreased the expression of YAP by restoring the impaired autophagy capacity and inhibiting the binding between YAP and RIPK1, thereby delaying the progression of osteoarthritis. Animal experiment showed that LIPUS could inhibit cartilage degeneration and alleviate the progression of OA.

Conclusions
These results showed that LIPUS is effective in inhibiting inflammation and cartilage degeneration and alleviate the progression of OA. As a result, our results provide new insight of mechanism by which LIPUS delays the development of osteoarthritis, offering a novel therapeutic regimen for osteoarthritis.
An automatic system for recognizing fly courtship patterns via an image processing method
Authors: Chen Ching-Hsin , Lin Yu-Chiao , Wang Sheng-Hao , Kuo Tsung-Han , Tsai Hung-Yin 
Published: March 2024

AbstractFruit fly courtship behaviors composed of a series of actions have always been an important model for behavioral research. While most related studies have focused only on total courtship behaviors, specific courtship elements have often been underestimated. Identifying these courtship element details is extremely labor intensive and would largely benefit from an automatic recognition system. To address this issue, in this study, we established a vision-based fly courtship behavior recognition system. The system based on the proposed image processing methods can precisely distinguish body parts such as the head, thorax, and abdomen and automatically recognize specific courtship elements, including orientation, singing, attempted copulation, copulation and tapping, which was not detectable in previous studies. This system, which has high identity tracking accuracy (99.99%) and high behavioral element recognition rates (> 97.35%), can ensure correct identification even when flies completely overlap. Using this newly developed system, we investigated the total courtship time, and proportion, and transition of courtship elements in flies across different ages and found that male flies adjusted their courtship strategy in response to their physical condition. We also identified differences in courtship patterns between males with and without successful copulation. Our study therefore demonstrated how image processing methods can be applied to automatically recognize complex animal behaviors. The newly developed system will largely help us investigate the details of fly courtship in future research.
Maximizing efficiency in sunflower breeding through historical data optimization
Authors: Fernández-González Javier , Haquin Bertrand , Combes Eliette , Bernard Karine , Allard Alix , Isidro y Sánchez Julio 
Published: March 2024

AbstractGenomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.
AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings
Authors: Lee Jae Hoon , Lee Unseok , Yoo Ji Hye , Lee Taek Sung , Jung Je Hyeong , Kim Hyoung Seok 
Published: March 2024

Abstract
Background
Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.

Results
Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.

Conclusions
AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example
Authors: Li Peiyuan , shen Tao , Li Li , Wang Yuanzhong 
Published: March 2024

Abstract
Background
Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today’s medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period.

Results
The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55.

Conclusions
These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.
Infrared: a declarative tree decomposition-powered framework for bioinformatics
Authors: Yao Hua-Ting , Marchand Bertrand , Berkemer Sarah J. , Ponty Yann , Will Sebastian 
Published: March 2024

Abstract
Motivation
Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations.

Methods
We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency.

Results
Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework—together with our novel results—underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations.

Availability
Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.
The influence of anterior cervical discectomy and fusion surgery on cervical muscles and the correlation between related muscle changes and surgical efficacy
Authors: Sun Chong , Xiang Hongfei , Wu Xiaolin , Chen Bohua , Guo Zhu 
Published: March 2024

Abstract
Background
Anterior cervical discectomy and fusion surgery (ACDF) is a common technique in treating degenerative cervical spondylosis. This study is to evaluate the changes of cervical muscles after ACDF and analyze the correlation between related muscle changes and clinical efficacy.

Methods
Sixty-five postoperative patients (single-level ACDF) with cervical spondylotic myelopathy from January 2013 to December 2022 were analyzed. The measured parameters include: the axial section of longus colli cross-sectional area (AxCSA), the volume of cervical longus, the ratio of long and short diameter line (RLS), the cervical extensor cross-sectional area (CESA), the vertebral body area (VBA), and the CESA/VBA. The visual analog scale (VAS), modified Japanese Orthopedic Association score (mJOA), and neck disability index (NDI) were evaluated. The changes in muscle morphology were analyzed, and the correlation analysis was conducted between morphological changes and function scores.

Results
The postoperative AxCSA of surgical segment (3rd month, 12th month, and the last follow-up) was decreased compared to preoperative (141.62 ± 19.78), and the differences were significant (P < 0.05). The corresponding data reduced to (119.42 ± 20.08) mm2, (117.59 ± 19.69) mm2, and (117.41 ± 19.19) mm2, respectively (P < 0.05). The RLS increased, and the volume of cervical longus decreased significantly after surgery (P < 0.05). Negative correlation was found between postoperative volume of cervical longus and VAS at the 3rd month (r = − 0.412), 12th month (r = − 0.272), and last follow-up (r = − 0.391) (P < 0.05). Negative correlation existed between postoperative volume of cervical longus and NDI at the 3rd month (r = − 0.552), 12th month (r = − 0.293), and last follow-up (r = − 0.459) (P < 0.05).

Conclusion
The volume of cervical longus decreased and its morphology changed after ACDF surgery. The mainly affected muscle was the cervical longus closing to the surgical segment. Negative correlation was found between the postoperative volume of cervical longus and function scores (VAS and NDI).
Exosomes derived from umbilical cord-mesenchymal stem cells inhibit the NF-κB/MAPK signaling pathway and reduce the inflammatory response to promote recovery from spinal cord injury
Authors: Luan Zhiwei , Liu Jingsong , Li Mi , Wang Yangyang , Wang Yansong 
Published: March 2024

AbstractSpinal cord injury (SCI) is a serious traumatic disease of the central nervous system and leads to incomplete or complete loss of the body’s autonomous motor and sensory functions, seriously endangering human health. Recently, exosomes have been proposed as important substances in cell-to-cell interactions. Mesenchymal stem cell (MSC)-derived exosomes exert good therapeutic effects and play a crucial role in neurological damage repair. However, the detailed mechanisms underlying their effects remain unknown. Herein, we found that compared to SCI rats, those subjected to umbilical cord MSC (UC-MSC)-derived exosomes injection showed an improved motor ability. Nevertheless, the transcriptome of BV2 microglia in different treatment groups indicated that the action pathway of exosomes might be the NF-κB/MAPK pathway. Additionally, exosomes from UC-MSCs could inhibit P38, JNK, ERK, and P65 phosphorylation in BV2 microglia and SCI rat tissues. Moreover, exosomes could inhibit apoptosis and inflammatory reaction and reactive oxygen species (ROS) production of BV2 microglia in vitro and in vivo. In conclusion, UC-MSCs-derived exosomes might protect SCI in rats by inhibiting inflammatory response via the NF-κB/MAPK signaling pathway, representing novel treatment targets or approaches for SCI.
Rejuvenating aged microglia by p16ink4a-siRNA-loaded nanoparticles increases amyloid-β clearance in animal models of Alzheimer’s disease
Authors: Shin Hyo Jung , Kim In Soo , Choi Seung Gyu , Lee Kayoung , Park Hyewon , Shin Juhee , Kim Dayoung , Beom Jaewon , Yi Yoon Young , Gupta Deepak Prasad , Song Gyun Jee , Chung Won-Suk , Lee C. Justin , Kim Dong Woon 
Published: March 2024

AbstractAge-dependent accumulation of amyloid plaques in patients with sporadic Alzheimer’s disease (AD) is associated with reduced amyloid clearance. Older microglia have a reduced ability to phagocytose amyloid, so phagocytosis of amyloid plaques by microglia could be regulated to prevent amyloid accumulation. Furthermore, considering the aging-related disruption of cell cycle machinery in old microglia, we hypothesize that regulating their cell cycle could rejuvenate them and enhance their ability to promote more efficient amyloid clearance. First, we used gene ontology analysis of microglia from young and old mice to identify differential expression of cyclin-dependent kinase inhibitor 2A (p16ink4a), a cell cycle factor related to aging. We found that p16ink4a expression was increased in microglia near amyloid plaques in brain tissue from patients with AD and 5XFAD mice, a model of AD. In BV2 microglia, small interfering RNA (siRNA)-mediated p16ink4a downregulation transformed microglia with enhanced amyloid phagocytic capacity through regulated the cell cycle and increased cell proliferation. To regulate microglial phagocytosis by gene transduction, we used poly (D,L-lactic-co-glycolic acid) (PLGA) nanoparticles, which predominantly target microglia, to deliver the siRNA and to control microglial reactivity. Nanoparticle-based delivery of p16ink4a siRNA reduced amyloid plaque formation and the number of aged microglia surrounding the plaque and reversed learning deterioration and spatial memory deficits. We propose that downregulation of p16ink4a in microglia is a promising strategy for the treatment of Alzheimer’s disease.
Gli1-mediated tumor cell-derived bFGF promotes tumor angiogenesis and pericyte coverage in non-small cell lung cancer
Authors: Lei Xueping , Li Zhan , Huang Manting , Huang Lijuan , Huang Yong , Lv Sha , Zhang Weisong , Chen Zhuowen , Ke Yuanyu , Li Songpei , Chen Jingfei , Yang Xiangyu , Deng Qiudi , Liu Junshan , Yu Xiyong 
Published: March 2024

Abstract
Background
Tumor angiogenesis inhibitors have been applied for non-small cell lung cancer (NSCLC) therapy. However, the drug resistance hinders their further development. Intercellular crosstalk between lung cancer cells and vascular cells was crucial for anti-angiogenenic resistance (AAD). However, the understanding of this crosstalk is still rudimentary. Our previous study showed that Glioma-associated oncogene 1 (Gli1) is a driver of NSCLC metastasis, but its role in lung cancer cell-vascular cell crosstalk remains unclear.

Methods
Conditioned medium (CM) from Gli1-overexpressing or Gli1-knockdown NSCLC cells was used to educate endothelia cells and pericytes, and the effects of these media on angiogenesis and the maturation of new blood vessels were evaluated via wound healing assays, Transwell migration and invasion assays, tube formation assays and 3D coculture assays. The xenograft model was conducted to establish the effect of Gli1 on tumor angiogenesis and growth. Angiogenic antibody microarray analysis, ELISA, luciferase reporte, chromatin immunoprecipitation (ChIP), bFGF protein stability and ubiquitination assay were performed to explore how Gli1 regulate bFGF expression.

Results
Gli1 overexpression in NSCLC cells enhanced the endothelial cell and pericyte motility required for angiogenesis required for angiogenesis. However, Gli1 knockout in NSCLC cells had opposite effect on this process. bFGF was critical for the enhancement effect on tumor angiogenesis. bFGF treatment reversed the Gli1 knockdown-mediated inhibition of angiogenesis. Mechanistically, Gli1 increased the bFGF protein level by promoting bFGF transcriptional activity and protein stability. Importantly, suppressing Gli1 with GANT-61 obviously inhibited angiogenesis.

Conclusion
The Gli1-bFGF axis is crucial for the crosstalk between lung cancer cells and vascular cells. Targeting Gli1 is a potential therapeutic approach for NSCLC angiogenesis.
Trauma systems in high socioeconomic index countries in 2050
Authors: Gauss Tobias , de Jongh Mariska , Maegele Marc , Cole Elaine , Bouzat Pierre 
Published: March 2024

AbstractConsiderable political, structural, environmental and epidemiological change will affect high socioeconomic index (SDI) countries over the next 25 years. These changes will impact healthcare provision and consequently trauma systems. This review attempts to anticipate the potential impact on trauma systems and how they could adapt to meet the changing priorities. The first section describes possible epidemiological trajectories. A second section exposes existing governance and funding challenges, how these can be met, and the need to incorporate data and information science into a learning and adaptive trauma system. The last section suggests an international harmonization of trauma education to improve care standards, optimize immediate and long-term patient needs and enhance disaster preparedness and crisis resilience. By demonstrating their capacity for adaptation, trauma systems can play a leading role in the transformation of care systems to tackle future health challenges.