bluuebunny/crossref_metadata_2025_split huggingface dataset:
BERTPhylo: An Efffcient Method to Accelerate Phylogenetic Inference Using a Pretrained DNA Language Model
Authors: Xu Wuqin , Deng Danruo , Wu Bian , Comes Hans , Feng Yu , Li Pan , Zheng Jinfang , Chen Guangyong , Heng Pheng Ann 
Published: July 2024

Abstract

Understanding the phylogenetic relationships among species is crucial for comprehending major evolutionary transitions, serving as the foundation for many biological studies. Despite the ever-growing volume of sequence data providing a significant opportunity for biological research, constructing reliable phylogenetic trees effectively becomes more challenging for current analytical methods. In this study, we introduce a novel solution to accelerate phylogeny inference using a pretrained DNA language model. Our approach identifies the taxonomic unit of a newly collected sequence using existing taxonomic classification systems and updates the corresponding subtree, akin to surgical corrections on a given phylogenetic tree. Specifically, we leverage a pretrained BERT network to obtain high-dimensional sequence representations, which are used not only to determine the subtree to be updated but also identify potentially valuable regions for subtree construction. We demonstrate the effectiveness of our method, named BERTPhylo, through experiments on our established PlantSeqs dataset, focusing on Embryophyta. Our findings provide the first evidence that phylogenetic trees can be constructed by automatically selecting the most informative regions of sequences, without manual selection of molecular markers. This discovery offers a robust guide for further research into the functional aspects of different regions of DNA sequences, enriching our understanding of biology.
Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography
Authors: Yagis Ekin , Aslani Shahab , Jain Yashvardhan , Zhou Yang , Rahmani Shahrokh , Brunet Joseph , Bellier Alexandre , Werlein Christopher , Ackermann Maximilian , Jonigk Danny , Tafforeau Paul , Walsh Claire , Lee Peter D 
Published: July 2024

Abstract

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT).
We begin with an extensive review of current machine learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20µm/voxel, and enabling highly detailed localized zooms up to 1µm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging.
Our three experiments yielded Dice scores of 0.9523 and 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores—such as centerline Dice values ranging from 0.82 to 0.88, certain errors persisted.
Specifically, large vessels that collapsed due to the lack of hydro-static pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP-CT imaging database.
Human tau promotes Warburg effect-like glycolytic metabolism under acute hyperglycemia conditions
Authors: Li Nan , Yao Jinyi , Fu Zhenli , Li Keying , Zheng Jingjing , Chen Zicong , Xu Jiahao , Lai Guoqing , Huang Yaomin , Huang Jinsheng , You Guanying , Han Shuangxue , He Zhijun , Liu Qiong 
Published: July 2024

Abstract

The neurofilaments formed by hyperphosphorylated tau is a hallmark of tau-related neurodegenerative disease, including Alzheimer’s disease, tau related FTDP-17, Pick’s disease, et al. However, the biological functions of tau and the physiological significance of its phosphorylation are still not fully understood. By using human tau (441 a.a.) transgenic (hTau) mice in which murine tau has been deleted simultaneously, murine tau knockout (Tau KO) mice and C57BL/6J (C57) mice, unexpectedly, we found that under acute hyperglycemia conditions, JNK but not previously reported GSK3beta mediated tau phosphorylation. Moreover, Akt, the inhibitory kinase upstream of GSK3beta, was activated in a tau dependent manner. By comparing the membrane-associated proteome, we found that human tau influenced the homeostasis of protein-membrane association under acute hyperglycemia conditions. Of note, with respect to C57 and Tau KO mice, the membrane-association of Krts, TFAM, TRAP1, mTOR et al, were strengthened by human tau. Whereas, the membrane-association of ribosomal proteins Rpls, proteasome proteins Psmds, and mitochondrial proteins, such as COXs, Ndufa1, Mtnt4, et al, were impeded by human tau. In vitro study showed that aerobic glycolysis was promoted in the presence of human tau, which maintained NAD+/NADH ratio. On the other hand, it restricted oxidative phosphorylation level, modulated the activity of SDH, and reduced ROS production upon high glucose challenging. Furthermore, under acute high glucose conditions, the presence of human tau significantly augmented Akt activation, but inhibited 4E-BP1 phosphorylation simultaneously, indicating that human tau is also involved in regulating the alternative activation of mTORC1/2. In summary, the current study revealed that human tau played an important role in regulating glycolytic metabolism under acute high hyperglycemia conditions, which is similar with the Warburg-effect, through influencing the homeostasis of protein-membrane association.
GML-YOLO: A lightweight infrared small object detection algorithm
Authors: Da mei , Jiang lin , Tao you feng , Zhang zhi jian 
Published: July 2024

Abstract

Infrared imaging technology captures the thermal radiation characteristics of targets, enabling object monitoring and filtering redundant road information in complex road scenarios. This paper proposes a lightweight infrared small object detection algorithm GML-YOLO to address issues such as low accuracy, large parameter volume, dependency on high-performance GPU resources, and slow detection speed in infrared pedestrian and vehicle object detection models. Firstly, we designed a lightweight backbone network, G_HGNet, to improve feature extraction efficiency, enabling accurate and real-time feature extraction. Additionally, we incorporated adaptive downsampling and attention mechanisms in the network fusion part, replacing the simple concatenation used in traditional detectors. This design effectively integrates shallow and deep information. Subsequently, the WIOUv3 loss function was employed to enhance model convergence speed and reduce losses, thereby increasing model robustness. Finally, comparative experiments were conducted on our dataset as well as the public FLIR and Pascal VOC datasets. The results demonstrate that GML-YOLO achieves a mean Average Precision (mAP@0.5) of 89.7% on our dataset (ISTD), 86.5% on the FLIR dataset, and 82.0% on the Pascal VOC dataset. Additionally, computational load and parameter volume were reduced by 20% and 27%, respectively. The improved GML-YOLO algorithm outperforms YOLOv3, YOLOv5, YOLOv6, YOLOv8s, and YOLOv8n, validating the feasibility of our proposed algorithm.
Effect of Minimally Invasive Posterior Veneer Preparation Designs on Failure Load and Mode After Fatigue (in-vitro Study)
Authors: Mady Islam Mohamed , Kady Ahmed Safwat El , Aboushelib Moustafa Nabil , Hussein Mohamed Hussein Abdelrahman 
Published: July 2024

Abstract

Introduction:
Ceramic veneers are considered one of the most essential demands in dental aesthetics. It provides high accuracy and efficiency in restoring tooth shape, shade, form, and function. Preparation design is considered a critical factor in the success of dental veneers, as it influences the failure load and fatigue survival of posterior minimally invasive ceramic veneer restorations.
Objective
To assess how various preparation designs affect the minimally invasive posterior ceramic veneer restorations' fatigue survival and failure load.
Materials and methods
64 human premolars were divided into four groups of equal size. Group 1 had a buccal preparation depth of 0.7 mm; group 2 had an additional reduction of the buccal cusp, group 3 had a mesio-occluso distal box. The occlusal box preparation has a vertical dimension of three mm and a horizontal dimension of 2 mm, while sound teeth serve as control. Monolithic lithium disilicate restorations were cemented in adhesive cement and then exposed to dynamic fatigue while thermocycling. Each specimen underwent a single load-to-failure test, and the discrepancy in failure load before and after fatigue was computed.
Results
no significant differences in initial fracture resistance were observed among the four groups evaluated (p < .075). Nevertheless, after fatigue, significant differences were detected (p < .013) as fatigue caused a substantial drop in fracture resistance among all groups.
Conclusion
Within the limitations of this study, minimally invasive preparation designs are recommended compared to full coverage aggressive preparation designs.
Variable Bandwidth Technique-Based Current Control for Permanent Magnet Synchronous Motors
Authors: Choi Hyoseo , Park Jae Kyung , Kim Seok-Kyoon , Kim Yonghoon 
Published: July 2024

Abstract

This paper exhibits a robust current controller incorporating the variable bandwidth technique with the disturbance observer (DOB) for permanent magnet synchronous motors (PMSMs). There are two contributions: (a) the variable bandwidth algorithm, called the auto-tuner, is systematically designed to boost the transient current tracking performance and (b) it is proved that the proposed controller renders the closed-loop system to recover the target tracking performance without steady-state errors. The experimental study is conducted to verify the efficacy of proposed controller using a 700-W prototype PMSM.
Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil
Authors: Francesca Francesca Venturini , Michelucci Umberto 
Published: July 2024

Abstract

Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, widely used for applications such as environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, alongside a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO) during ageing, showing its effectiveness in predicting quality indicators and identifying the spectral bands, and thus the molecules involved in the process. This work describes a significantly innovative approach in the use of deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes.
Emergency department triaging using ChatGPT Based on Emergency Severity Index principles: A cross-sectional study
Authors: COLAKCA Cansu , ERGIN Mehmet , OZENSOY Habibe Selmin , SENER Alp , GURU Selahattin , OZHASENEKLER Ayhan 
Published: July 2024

Abstract

Background
Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI® and is being trained for use in natural language processing tasks. Our study aims to determine the accuracy of patient triage using ChatGPT according to ESI for triage in EDs.
Materials and methods
In our cross-sectional study, 18 years and over patients who consecutively presented to our ED within 24 hours were included. Age, gender, admission method, chief complaint, state of consciousness, and comorbidities were recorded on the case form, and the vital signs were detected at the triage desk. A five-member Expert Committee (EC) was formed from the fourth-year resident physicians. The investigators converted real-time patient information into a standardized case format. The urgency status of the patients was evaluated simultaneously by EC and ChatGPT according to ESI criteria. The median value of the EC decision was accepted as the gold standard.
Results
There was a statistically significant moderate agreement between EC and ChatGPT assessments regarding urgency status (Cohen's Kappa = 0.659; P < 0.001). The accuracy between these two assessments was calculated as 76.6%. There was a high degree of agreement between EC and ChatGPT for the prediction of ESI-1 and 2, indicating high acuity (Cohen's Kappa = 0.828). The diagnostic specificity, NPV, and accuracy of ChatGPT were determined as 95.63%, 98.17%, and 94.90%, respectively, for ESI high acuity categories.
Conclusions
Our study shows that ChatGPT can successfully differentiate patients with high urgency. The findings are promising for integrating artificial intelligence-based applications such as ChatGPT into triage processes in EDs.
Temporal patterns in gut microbiome and resistome of broilers: diversity and function analysis
Authors: Meng Jin-Xin , Li Ming-Han , Elsheikha Hany M , Li Xiao-Man , Wang Xiang-Yu , Yan Wei-Lan , Li Shenghui , Zhang Yue , Ni Hong-Bo , Ma He , Liu Rui , Sun Yu-Zhe , Yang Xing , Zhang Xiao-Xuan 
Published: July 2024

Abstract

Understanding the dynamics and stability of gut microbiota throughout the production cycle of broiler chickens can help identify microbial features associated with better health and productivity. In the present study, we profile changes in the composition and stability of gut microbiota of commercially raised broilers at nine distinct time points using shotgun metagenomics and culturomics approaches. We demonstrate that within the first week post-hatching, there is a rapid decline in pioneer microbial species, accompanied by a substantial decrease in both microbial richness and diversity. This is followed by a gradual increase and stabilization in microbial diversity and population structure, persisting until the broilers reach marketing age. Throughout the production cycle, key bacterial families such as Lachnospiraceae, Bacteroidaceae, and Ruminococcaceae were identified. However, significant shifts at lower taxonomic levels occur at different production stages, influencing the functional capacities and resistance profiles of the microbiota. During the rapid growth phase, enzymes crucial to vitamin and amino acid metabolism dominate, whereas enzymes associated with carbohydrate and energy metabolism are notably more abundant during the fattening stage. Many predicted antibiotic resistance genes are detected in association with typical commensal bacterial species in the gut microbiota, indicating sustained resistance to antibiotic classes such as aminoglycosides and tetracyclines, which persists even in the absence of antibiotic selection pressure. Our research has important implications for the management and health surveillance of broiler production.
The protective effect of docosahexaenoic acid on mitochondria in a SH-SY5Y model of rotenone-induced toxicity
Authors: Eggers Britta , Stepien Jennifer , Reker Ann-Katrin , Esser Svenja , Pfeiffer Kathy , Pawlas Magdalena , Barkovits Katalin , Marcus Katrin 
Published: July 2024

Abstract

Polyunsaturated fatty acids in particular omega-3 fatty acids, such as docosahexaenoic acid (DHA) are essential nutrients and components of the plasma membrane. They are involved in various processes, among them synaptic development, functionality, integrity and plasticity and are therefore thought to have general neuroprotective properties. Considerable research evidence further supports beneficial effects of omega-3 fatty acids specifically on mitochondria through their antioxidant and anti-apoptotic properties, which makes it an attractive addition in treatment options for neurodegenerative disorders, in which mitochondrial alterations are commonly observed. However, precise information on the underlying protective mechanisms are still lacking. Thus, we utilized the most common neuronal cell line (SH-SY5Y) and induced mitochondrial oxidative stress through the addition of rotenone. To study the potential protective effect of DHA, cells were additionally pre-treated with DHA prior rotenone administration. By combining SILAC labelling, mitochondria enrichment, and subsequent proteomic analyses, we aimed to determine the capacity of DHA to alleviate mitochondrial oxidative stress in vitro and further shed light on molecular mechanisms contributing to the proposed neuroprotective effect. Indeed, we confirmed an increased expression of mitochondrial proteins in DHA pre-treated cells. Furthermore, we were able to define a compensatory mechanism by which the inhibition of complex I is overcome by an increased activity of the fatty acid beta oxidation in response to DHA.
Internet Addiction and Sleep Disturbance Among Adolescents in Jordan
Authors: Abu-Awad Malak , Gharaibeh Muntaha K. , Allari Rabia S. 
Published: July 2024

Abstract

Background
This study assesses the prevalence of internet addiction, and characteristics of sleep quality and identifies the association between internet addiction and sleep quality and its associated factors.
Methods
The sample consisted of 1293 adolescents enrolled in schools aged 12 to 17 from Jordan. Participants completed the Internet Addiction Test IAT and the Pittsburgh Sleep Quality Index PSQI.
Results
The results of the study revealed a moderate IAT total average score (53.88 ± 16.2). The PSQI total score of 6.83 ± 3.83, reflecting poor sleep quality among 68.2% of adolescents. A significant positive correlation was found between IAT and all PSQI subscales, with Sleep quality and daytime dysfunction as the strongest correlates. Multiple linear regression results showed that GPA, sleep quality, and age were significant predictors of internet addiction.
Conclusion
It can be concluded that raising awareness among students, parents, teachers, and schools about internet addiction and its impact on adolescents’ sleep quality which affects their performance is essential.
The U-shaped Transformer architecture for precise retinal vessel segmentation
Authors: liu yu , Wen Tingxi 
Published: July 2024

Abstract

Background: Retinal blood vessels serve as crucial biomarkers for many ophthalmic and cardiovascular diseases. Therefore, the development of automatic segmentation models for computer-aided diagnosis holds significant importance. However, existing CNN-based methods, such as U-Net, face challenges in capturing long-range dependencies due to limited receptive fields and inherent biases of convolutional operations. Recently, numerous Transformer-based techniques have been integrated into the U-Net architecture to overcome this limitation by efficiently capturing global feature correlations. However, the self-attention mechanism of Transformers requires substantial computational resources, and integrating Transformers with U-Net may result in a significant increase in model complexity. Many resource-constrained devices may not be capable of handling precise image segmentation tasks, and the substantial increase in computational complexity also brings the risk of overfitting. To address these challenges, we propose a model based on lightweight Transformers and residual networks (MobileViTv2-ResUNet) for accurately segmenting retinal blood vessels.
Methods: Firstly, the algorithm preprocesses images using photometric distortion, simulating various lighting conditions and camera settings that may occur during retinal fundus image acquisition. This enhances the model's adaptability and robustness to real-world scenarios. Secondly, lightweight Transformer modules (MobileViTv2 blocks) are integrated into the encoding stage to replace convolutional modules for feature extraction. Next, Inverted Residuals are introduced in the encoder section to not only perform downsampling operations and reduce computational complexity but also enhance the network's representation and generalization capabilities. Subsequently, ASPP modules are inserted between the encoder and decoder to effectively fuse feature information from different scales. Lastly, our designed LeakyRes modules are incorporated into the decoding stage to prevent the occurrence of "neuron death" phenomena and improve the accuracy of retinal blood vessel segmentation.
Results: Validated on the public datasets HRF、STARE and DRIVE, our MobileViTv2-ResUNet achieves segmentation accuracies of 77.20% and 79.67% for retinal blood vessels, with Intersection over Union (IoU) scores of 66.99% and 69.57%, and recall rates of 77.20% and 79.67%, respectively.
Conclusion：Experimental results indicate that our method excels in improving existing vascular segmentation techniques, particularly in handling images with lesions, branches, crossings, and tiny vessels. It provides more accurate and detailed information on retinal vascular distribution. This advancement aids doctors in formulating personalized treatment plans for patients, thereby significantly enhancing treatment efficacy and patient experience.