bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-dimensional single-cell analysis of human natural killer cell heterogeneity
Authors: Rebuffet Lucas , Melsen Janine E. , Escalière Bertrand , Basurto-Lozada Daniela , Bhandoola Avinash , Björkström Niklas K. , Bryceson Yenan T. , Castriconi Roberta , Cichocki Frank , Colonna Marco , Davis Daniel M. , Diefenbach Andreas , Ding Yi , Haniffa Muzlifah , Horowitz Amir , Lanier Lewis L. , Malmberg Karl-Johan , Miller Jeffrey S. , Moretta Lorenzo , Narni-Mancinelli Emilie , O’Neill Luke A. J. , Romagnani Chiara , Ryan Dylan G. , Sivori Simona , Sun Dan , Vagne Constance , Vivier Eric 
Published: July 2024

AbstractNatural killer (NK) cells are innate lymphoid cells (ILCs) contributing to immune responses to microbes and tumors. Historically, their classification hinged on a limited array of surface protein markers. Here, we used single-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to dissect the heterogeneity of NK cells. We identified three prominent NK cell subsets in healthy human blood: NK1, NK2 and NK3, further differentiated into six distinct subgroups. Our findings delineate the molecular characteristics, key transcription factors, biological functions, metabolic traits and cytokine responses of each subgroup. These data also suggest two separate ontogenetic origins for NK cells, leading to divergent transcriptional trajectories. Furthermore, we analyzed the distribution of NK cell subsets in the lung, tonsils and intraepithelial lymphocytes isolated from healthy individuals and in 22 tumor types. This standardized terminology aims at fostering clarity and consistency in future research, thereby improving cross-study comparisons.
Direct neuronal reprogramming of mouse astrocytes is associated with multiscale epigenome remodeling and requires Yy1
Authors: Pereira Allwyn , Diwakar Jeisimhan , Masserdotti Giacomo , Beşkardeş Sude , Simon Tatiana , So Younju , Martín-Loarte Lucía , Bergemann Franziska , Vasan Lakshmy , Schauer Tamas , Danese Anna , Bocchi Riccardo , Colomé-Tatché Maria , Schuurmans Carol , Philpott Anna , Straub Tobias , Bonev Boyan , Götz Magdalena 
Published: July 2024

AbstractDirect neuronal reprogramming is a promising approach to regenerate neurons from local glial cells. However, mechanisms of epigenome remodeling and co-factors facilitating this process are unclear. In this study, we combined single-cell multiomics with genome-wide profiling of three-dimensional nuclear architecture and DNA methylation in mouse astrocyte-to-neuron reprogramming mediated by Neurogenin2 (Ngn2) and its phosphorylation-resistant form (PmutNgn2), respectively. We show that Ngn2 drives multilayered chromatin remodeling at dynamic enhancer–gene interaction sites. PmutNgn2 leads to higher reprogramming efficiency and enhances epigenetic remodeling associated with neuronal maturation. However, the differences in binding sites or downstream gene activation cannot fully explain this effect. Instead, we identified Yy1, a transcriptional co-factor recruited by direct interaction with Ngn2 to its target sites. Upon deletion of Yy1, activation of neuronal enhancers, genes and ultimately reprogramming are impaired without affecting Ngn2 binding. Thus, our work highlights the key role of interactors of proneural factors in direct neuronal reprogramming.
LMBiS-Net: A lightweight bidirectional skip connection based multipath CNN for retinal blood vessel segmentation
Authors: Matloob Abbasi Mufassir , Iqbal Shahzaib , Aurangzeb Khursheed , Alhussein Musaed , Khan Tariq M. 
Published: July 2024

AbstractBlinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net’s robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.
Model test study of bridge pile horizontal bearing behavior under landslide deformation
Authors: Zhao Yan-yan , Fu Hai-ying , Yang Tao , Zhou Ming-zhe , Li Huan 
Published: July 2024

AbstractPile is a common foundation on the slope, which poses a serious threat to the construction and operation if the slope deformation and causes landslide. In this study, a model device of pile foundation on landslide was independently developed by relative displacement loading between pile and soil to explore the influence of landslide deformation on pile and analysis the soil failure rule and the deformation characteristics of pile in different stages of landslide deformation, a few model tests were completed including the relative displacement between soil and pile from 1 to 17 cm, and the pile diameter and the modulus of slide bed were also considered. The results indicated that: the evolution process of landslide deformation with pile foundation on could be divided into four stages including soil compaction, cracks growth, yield stage, and failure stage; ratios of the maximum soil pressure and bending moment growth from the soil compaction stage to the cracks growth stage to the total growth in these four stages are both exceeding 60%; the soil pressure increases with the increase of pile diameter and sliding bed modulus. Therefore, it is best to effectively monitor and control the landslide in the initial soil compression stage that in soil compaction stage and methods such as increasing pile foundations or reinforcing the sliding bed can be used for protection.
Heavy metal concentrations and clinical symptoms in patients diagnosed with schizophrenia related to cigarette smoking
Authors: Ghaderi Amir , Khoshakhlagh Amir Hossein , Gruszecka-Kosowska Agnieszka , Askari-Lemjiri Fatemeh , Alemi Fatemeh , Molavi Nader , Hazegh Pooya , Farokhi Bahareh , Dehkohneh Somayeh Ghadami , Ghoreishi Fatemeh Sadat 
Published: July 2024

AbstractIn our study, blood concentrations of lead (Pb), arsenic (As), and cadmium (Cd) and urine concentrations of thallium (Tl) were measured together with related symptoms of heavy metal poisoning in cigarette smoking volunteers diagnosed with schizophrenia, in cigarette smokers not diagnosed with schizophrenia, and in the control group of non-smokers and not diagnosed with schizophrenia volunteers. Our study was performed on 171 volunteers divided into the following subgroups: patients diagnosed with schizophrenia with at least 1 year of continuous cigarette smoking experience (56 participants), cigarette smokers not diagnosed with schizophrenia with at least one year of continuous smoking experience (58), and control group (not diagnosed with schizophrenia and non-smoking volunteers) (57). Smoking durations of cigarette smokers diagnosed with schizophrenia and cigarette smokers not diagnosed with schizophrenia are not similar (p = 0.431). Blood Pb, As, and Cd concentrations and urine Tl concentrations were the highest in the subgroup of cigarette smokers not diagnosed with schizophrenia, followed by the subgroup of cigarette smokers diagnosed with schizophrenia, and the control group. Only blood Pb concentrations were significantly higher (probability value p < 0.05) in the group of cigarette smokers not diagnosed with schizophrenia (5.16 μg/dL), comparing to the group of cigarette smokers diagnosed with schizophrenia (3.83 μg/dL) and to the control group (3.43 μg/dL). Blood Cd and As concentrations and urine Tl concentrations were significantly higher (p < 0.05) in cigarette smokers not diagnosed with schizophrenia than in the control group. The results revealed a statistically significant positive correlation (p < 0.001) in the cigarette smokers in the schizophrenia diagnosed group between blood Pb, blood As, and urine Tl concentrations and the duration of cigarette smoking.
Epidemiological exploration of fleas and molecular identification of flea-borne viruses in Egyptian small ruminants
Authors: Barghash Safaa M. , Yassin Samah E. , Sadek Al-Shaimaa M. , Mahmoud Dalia M. , Salama Mohamed S. 
Published: July 2024

AbstractThe study aimed to investigate molecularly the presence of flea-borne viruses in infested small ruminants with fleas. It was carried out in Egypt’s Northern West Coast (NWC) and South Sinai Governorate (SSG). Three specific primers were used targeting genes, ORF103 (for Capripoxvirus and Lumpy skin disease virus), NS3 (for Bluetongue virus), and Rdrp (for Coronavirus), followed by gene sequencing and phylogenetic analyses. The results revealed that 78.94% of sheep and 65.63% of goats were infested in the NWC area, whereas 49.76% of sheep and 77.8% of goats were infested in the SSG region. Sheep were preferable hosts for flea infestations (58.9%) to goats (41.1%) in the two studied areas. Sex and age of the animals had no effects on the infestation rate (p > 0.05). The season and site of infestation on animals were significantly different between the two areas (p < 0.05). Ctenocephalides felis predominated in NWC and Ctenocephalides canis in SSG, and males of both flea species were more prevalent than females. Molecular analysis of flea DNA revealed the presence of Capripoxvirus in all tested samples, while other viral infections were absent. Gene sequencing identified three isolates as sheeppox viruses, and one as goatpox virus. The findings suggest that Capripoxvirus is adapted to fleas and may be transmitted to animals through infestation. This underscores the need for ongoing surveillance of other pathogens in different regions of Egypt.
Automated eyeball volume measurement based on CT images using neural network-based segmentation and simple estimation
Authors: Han Sujeong , Lee Jeong Kyu , Lee Daewon , Lee Jaesung 
Published: July 2024

AbstractWith the increase in the dependency on digital devices, the incidence of myopia, a precursor of various ocular diseases, has risen significantly. Because myopia and eyeball volume are related, myopia progression can be monitored through eyeball volume estimation. However, existing methods are limited because the eyeball shape is disregarded during estimation. We propose an automated eyeball volume estimation method from computed tomography images that incorporates prior knowledge of the actual eyeball shape. This study involves data preprocessing, image segmentation, and volume estimation steps, which include the truncated cone formula and integral equation. We obtained eyeball image masks using U-Net, HFCN, DeepLab v3 +, SegNet, and HardNet-MSEG. Data from 200 subjects were used for volume estimation, and manually extracted eyeball volumes were used for validation. U-Net outperformed among the segmentation models, and the proposed volume estimation method outperformed comparative methods on all evaluation metrics, with a correlation coefficient of 0.819, mean absolute error of 0.640, and mean squared error of 0.554. The proposed method surpasses existing methods, provides an accurate eyeball volume estimation for monitoring the progression of myopia, and could potentially aid in the diagnosis of ocular diseases. It could be extended to volume estimation of other ocular structures.
Multi-branch CNN and grouping cascade attention for medical image classification
Authors: Liu Shiwei , Yue Wenwen , Guo Zhiqing , Wang Liejun 
Published: July 2024

AbstractVisual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.
The severity of dry eye symptoms and risk factors among university students in Saudi Arabia: a cross-sectional study
Authors: Alqurashi Anas , Almaghrabi Hatim , Alahmadi Muath , Alotaibi Abdulaziz , Alotaibi Bandar , Jastaniah Abdulaziz , Bukhari Ameera , Binhussein Mohammad , Othman Basant , Khojah Amer 
Published: July 2024

AbstractDry eye syndrome (DES) is a tear film disorder caused by increased tear evaporation or decreased production. The heavy workload on the eye and the increased usage of digital screens may decrease blink frequency, leading to an increased evaporation rate and an upsurge in the incidence and severity of DES. This study aims to assess the severity of DES symptoms and the risk factors among university students. A cross-sectional study was conducted at Umm AlQura University to evaluate the severity of DES among students and explore its potential association with digital screen use. Validated questionnaires were used to assess the severity of DES and digital screen usage. The study included 457 participants, of which 13% had symptoms suggestive of severe DES. Furthermore, multiple risk factors had a significant association with the severity of DES, including gender, use of monitor filters, monitor and room brightness, and smoking habits. DES symptoms were prevalent among university students, particularly female students. Although there was no significant association with the duration of screen usage and collage distribution. Other factors however, such as the usage of screen monitors and the brightness of both the monitor and the room, were significantly associated with the severity of DES symptoms.
The influence mechanism analysis on the farmers’ intention to adopt Internet of Things based on UTAUT-TOE model
Authors: Li Lianying , Min Xin , Guo Jinyong , Wu Feng 
Published: July 2024

AbstractInternet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers’ intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers’ intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers’ adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers’ intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.
Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial
Authors: Yilmaz Recai , Bakhaidar Mohamad , Alsayegh Ahmad , Abou Hamdan Nour , Fazlollahi Ali M. , Tee Trisha , Langleben Ian , Winkler-Schwartz Alexander , Laroche Denis , Santaguida Carlo , Del Maestro Rolando F. 
Published: July 2024

AbstractTrainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer. This randomized controlled trial compared real-time AI feedback to in-person expert instruction. Ninety-seven medical trainees completed a 90-min simulation training with five practice tumor resections followed by a realistic brain tumor resection. They were randomly assigned into 1-real-time AI feedback, 2-in-person expert instruction, and 3-no real-time feedback. Performance was assessed using a composite-score and Objective Structured Assessment of Technical Skills rating, rated by blinded experts. Training with real-time AI feedback (n = 33) resulted in significantly better performance outcomes compared to no real-time feedback (n = 32) and in-person instruction (n = 32), .266, [95% CI .107 .425], p < .001; .332, [95% CI .173 .491], p = .005, respectively. Learning from AI resulted in similar OSATS ratings (4.30 vs 4.11, p = 1) compared to in-person training with expert instruction. Intelligent systems may refine the way operating skills are taught, providing tailored, quantifiable feedback and actionable instructions in real-time.
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Authors: Karbasi Masoud , Ali Mumtaz , Bateni Sayed M. , Jun Changhyun , Jamei Mehdi , Farooque Aitazaz Ahsan , Yaseen Zaher Mundher 
Published: July 2024

AbstractElectrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.