bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Burden of Musculoskeletal Disorders in The Middle East and North Africa (MENA) Region: A Longitudinal Analysis from The Global Burden of Disease Dataset 1990 - 2019
Authors: Al-Ajlouni Yazan , Al-Ta'ani Omar , Mushasha Rand , Lee Justin , Capoor Jaishree , Kapadia Mitul , Alejandro Ruth 
Published: March 2023

AbstractBackground Musculoskeletal (MSK) disorders are one of the main causes of disability among adults globally. The burden of MSK disorders varies greatly between different regions and is the highest in low- and middle income- countries. This study sought to investigate trends in the burden of MSK disorders across the MENA region, utilizing the GBD 2019 dataset. Methods This study utilized data from the Global Burden of Disease (GBD). Using age standardized rates of prevalence and disability-adjusted life-years (DALYs), we report trends in the burden of MSK disorders in The Middle East and North Africa (MENA) region between 1990 and 2019. Furthermore, we analyze trends in risk factors contributing to MSK disorders. Results In 2019, the age-standardized rate prevalence and DALYs for all MSK disorders among both genders was 17706 [95% UI = 16641, 18794] and 1782 [95% UI = 1278, 2366], respectively. Low back pain continued to be the most prevalent MSK condition in 2019. Afghanistan had the lowest age standardized DALYs rate attributed to MSK disorders (1408 per 100,000; 95% UI = 980–1899), while Iran had the highest burden of 2007 per 100,000 (95% UI = 1440–2655). In 2019, occupational risks, high body mass index, and tobacco smoking were the main risk factors for DALYs of MSK disorders. Conclusion MSK disorders carry a large burden of disease in the MENA region, and multiple risk factors contribute to this increased burden in the past decades. Interventions that address risk factors have the potential to improve health outcomes among the population. Future research should continue to explore the burden of MSK disorders and better understand how to intervene.
Deletion of Tet1 in Mice with Impaired Prefrontal Cortex Functions by Activating the NGF/TrkA Signaling Pathway
Authors: Bi Yanhua , Gao Hui , Bi Yanhua 
Published: March 2023

Abstract

Tet1 (ten-eleven translocation methylcytosine dioxygenase 1) is the enzyme methylcytosine dioxygenase of DNA demethylation in the nervous system. Tet1 controls and mediates gene transcription, memory formation, and extinction. However, little is known about Tet1 in prefrontal cortex (PFC) functions especially in the medial prefrontal cortex (mPFC), which controls cortex flexibility and emotional reactivity in the CNS. This study conducted behavioral tests including an open field test, sociability and social novelty preference tests, social dominance, and prepulse inhibition test to examine brain functions, especially PFC functions after the deletion of Tet1. The mPFC from Tet1 KO mice and WT adult mice was analyzed using qRT-PCR to assess neuron growth-related genes, including Gsk3β, Pi3k, Crx4, Fgfr1, Fgfr2, Egfr, Dbn1, Akt2, Vegf, Vegfr, and Akt3. Subsequently, primary PFC neuronal cells were administered shTet1 to knockdown the Tet1 gene and function. We found that the deletion of Tet1 in the mouse brains impaired social interaction, novelty, and prepulse inhibition (PPI) in the mice. Knockdown of the Tet1 gene influenced the growth and complexity of neurons. The increase in Ngf and Bdnf by Western blotting were found in Tet1 deficient mice. The results support and complement the view that Tet1 deficiency may be related to schizophrenia.
Diagnosis and Screening of Velocardiofacial Syndrome by Evaluating Facial Photographs Using a Deep Learning-Based Algorithm
Authors: Baek Rong-Min , Cho Anna , Chung Yoon Gi , Jeon Yonghoon , Kim Hunmin , Hwang Hee , Myung Yujin 
Published: March 2023

Abstract
Purpose: The early detection and diagnosis of rare genetic diseases are crucial for maintaining patient health and well-being. However, the diagnosis of said diseases can be challenging owing to their rarity, limited clinical experience of physicians, and specialized facilities required for diagnosis. In recent years, deep learning algorithms have been investigated as a potential strategy for the efficient and accurate diagnosis of these diseases. Herein, we used the deep learning algorithm of multitask cascaded convolutional neural networks (MTCNN) to develop a face recognition model for the diagnosis of velocardiofacial syndrome (VCFS).
Methods: We trained the model on a publicly available labeled face dataset and evaluated its performance. Subsequently, we analyzed the binary classification performance of diagnosing VCFS using the most efficient face recognition model. The facial images of 98 patients with VCFS and 91 non-VCFS controls who visited Seoul National University were used to create training and test sets. Moreover, we analyzed whether the classification results matched the known facial phenotype of patients with VCFS.
Results: The facial recognition model showed high accuracy, ranging from 94.03% to 99.78%, depending on the training dataset. In contrast, the accuracy of the binary classification model for the diagnosis of VCFS varied from 80.82% to 88.02% when evaluating with photographs taken at various angles of the patient depending on the structure. When only evaluating frontal photographs, the accuracy was 95.00%. Moreover, the importance level analyzed through the gradient-weighted class activation mapping heatmap showed the characteristic parts of perinasal and periorbital area to be consistent with the conventional facial phenotypes of VCFS.
Conclusion: We attempted to diagnose the patients' genetic syndrome using MTCNN-based deep learning algorithms only with the photos of the faces of patients with VCFS. We obtained high accuracy, and deep learning-based diagnosis has been conformed to be greatly helpful for medical staff in the early detection and diagnosis of children with rare genetic diseases, enabling them to provide treatment in a timely manner.
Responses of Yield, Quality and Water Use Efficiency of Potato Grown Under Different Drip Irrigation and Nitrogen Levels
Authors: AKKAMIS Mustafa , CALISKAN Sevgi 
Published: March 2023

Abstract
Proper irrigation and fertilization are essential to achieve high tuber yield and quality in potato production. Water and nitrogen are required to be improved due to high costs of these agricultural inputs. This study aimed to optimize the use of water and nitrogen in potato to ensure high water use efficiencies as well as achieve optimum yield and tuber quality. Potato plants were subjected to three irrigation treatments, 100% (S100), 66% (S66) and 33% (S33) of field capacity and six N levels, 0 (N0), 100 (N1), 200 (N2), 300 (N3), 400 (N4) and 500 (N5) kg N ha− 1. Yield, growth parameters and tuber quality were generally more sensitive to irrigation than nitrogen. The highest total tuber yield was obtained under full irrigation with an application of 300 kg N ha− 1. Plants indicated higher values for yield and growth parameters in full irrigation applications, and significantly lower values were obtained at low irrigations. On the contrary, the full irrigation (S100) application caused a decrease in quality characteristics compared to low irrigations (S66 and S33). Water levels treatments indicated that S66 had the highest mean value of WUE in both years. These results indicated that potato can be produced with acceptable yields while saving irrigation water and certain nitrogen level.
A chimeric vaccine protects saltwater crocodiles (Crocodylus porosus) from skin lesions caused by West Nile virus
Authors: Hall Roy , Habarugira Gervais , Harrison Jessica , Moran Jasmin , Colmant Agathe , Hobson-Peters Jody , Isberg Sally , Bielefeldt-Ohmann Helle , Suen Willy 
Published: March 2023

Abstract
West Nile virus (WNV) causes skin lesions in farmed crocodiles leading to depreciation of the value of their hides and significant economic losses. However, there is no commercially available vaccine designed for use in crocodilians against WNV. We tested chimeric virus vaccines composed of the non-structural genes of the insect-specific flavivirus Binjari virus (BinJV) and genes encoding the structural proteins of WNV. The BinJV/WNV chimera, is antigenically similar to wild-type WNV but replication-defective in vertebrates. Subcutaneous application of two doses of BinJV/WNV in hatchling saltwater crocodiles (Crocodylus porosus) elicited a robust neutralising antibody response and conferred protection against viremia and skin lesions after challenge with WNV. In contrast, mock-vaccinated crocodiles became viraemic and 22.2% exhibited WNV-induced lesions. This suggests that the BinJV/WNV chimera is a safe and efficacious vaccine preventing WNV-induced skin lesions in farmed crocodilians. This is the first report of a vaccine that protects reptiles against viral infection.
CXCL5 and CXCL14, but not CXCL16 as potential non-invasive biomarkers of colorectal cancer?
Authors: Zajkowska Monika , Dulewicz Maciej , Kulczyńska-Przybik Agnieszka , Safiejko Kamil , Juchimiuk Marcin , Konopko Marzena , Kozłowski Leszek , Mroczko Barbara 
Published: March 2023

Abstract
Purpose
Experts emphasize that incidence and mortality of colorectal cancer (CRC) is increasing. That is why, its early detection is of utmost importance. Patients with cancer diagnosed in earlier stages have better prognosis and chance for faster implementation of treatment. Consequently, it is vital to search for new parameters that could be useful in its diagnosis.
Methods
Therefore, we evaluated the usefulness of CXCL5, CXCL14 and CXCL16 in serum of 115 subjects (75 CRC patients and 40 healthy volunteers). Concentrations of all parameters were measured using Luminex. CRP levels were determined by immunoturbidimetry, while levels of classical tumor markers were measured using CMIA.
Results
Concentrations of CXCL5 were statistically higher in CRC group when compared to healthy controls. The diagnostic sensitivity, specificity, positive and negative predictive value, and area under the ROC curve (AUC) of CXCL5 and CXCL14 were higher than those of CA 19 − 9.
Conclusions
Obtained results suggest the usefulness of CXCL5 and CXCL16 in the determination of distant metastases and differentiation between TNM stages, as well as usefulness of CXCL14 and CRP combination in CRC detection. However, further studies concerning their role in CRC progression are crucial to confirm and explain their diagnostic utility and clinical application as biomarkers.
Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat
Authors: Jain Swati , Varghese Nebu , Joshi Kimee , Kathiria Preeti , Garg Sanjay , Putrevu Deepak , Pandey Dharmendra 
Published: March 2023

Abstract
Both spatial and non-spatial data are critical to a country's social and economic development. Land use and land cover (LULC) maps are data that can be used to retrieve land information and detect environmental changes. Several recent studies have used deep learning approaches to generate LULC maps from various remote-sensing image formats. They have correctly recognized several land cover elements such as built-up areas, forests, water, etc. On the other hand, spaceborne radar images must be used to test deep learning methods because radar sensors operate in all weather and lighting circumstances and have diverse image characteristics depending on the wavelength. Although many researchers have applied deep learning techniques to classify the land cover features, most of them need to do more by taking less or more several land cover features and taking proper evaluation metrics to justify the classification techniques. The current research shows segmentation on Sentinel-1 C-band synthetic aperture radar (SAR) images of Gujarat's Ahmedabad, Anand, and Kheda regions using four deep-learning models. U-Net, SegNet, FCN, and DeepLabV3+ have primarily been employed in prior research to classify SAR and other satellite image formats and showed excellent results in identifying different land cover features. Training samples of five primary classes for ground-truth data were obtained using Google Earth and Sentinel-2 images as reference maps. For segmenting the Sentinel-1 image, above mentioned four deep-learning semantic segmentation models were chosen and fine-tuned. All models did exceptionally well, with overall accuracy ranging from 76% to 90%. U-Net fared well, followed by DeepLabV3+ in classifying all five land cover features, with mean intersection over union (IoU) of 0.67 and 0.55, respectively. The results reveal that using a self-generated reference map, the Sentinel-1 dual-pol images can categorize the primary land cover features.
Impact of topography and land cover on air temperature space-time variability in an urban environment with contrasted topography (Dijon, France, 2014–2021)
Authors: Crétat Julien , Richard Yves , Pohl Benjamin , Emery Justin , Dudek Julita , Roy Damien , Pergaud Julien , Rega Mario , Poupelin Mélissa , Joly Daniel , Thévenin Thomas , Marquès Eva , Masson Valéry 
Published: March 2023

Abstract
The influence of topography and land cover on air temperature space-time variability is examined in an urban environment with contrasted topography through simple and multiple linear regression (SLR and MLR) models ran for each hour of the period 2014–202 to explain air temperature spatial patterns observed by a dense in-situ network. The SLR models reveal a complementary influence of topography and land cover, with largest influence during daytime and nighttime, respectively. The MLR significantly improves upon the SLR models despite persistent intensity errors at night and spatial errors in the early morning. Topography influences air temperatures all year round, with an adiabatic gradient during the day and frequent thermal inversions at night (up to 30% of the time). Impervious surfaces are more influential in summer and early fall, especially during the late afternoon for the fraction covered by buildings, and during the early night for distance from the city centre. They contribute to warm air temperature close to the city centre and where the fraction covered by buildings increases. On the other hand, vegetation contributes to cool air temperature during the night, especially in spring and early summer for field crops, summer and early fall for forests and water, and late fall and winter for low vegetation. Our framework proves to be a low-cost and efficient way to understand the static drivers of air temperature along the annual and diurnal cycles, and is easily transposable to other areas and study fields, such as viticultural environments to further understand spring frost events.
Exploring the prognostic efficacy of machine learning models in predicting adenocarcinoma of the esophagogastric junction
Authors: Gao Kaiji , Wang Yihao , Cao Haikun , Xiang Zheng , Zhang Xinxin , Jia Jianguang 
Published: March 2023

Abstract
This study investigated the predictive performance of machine learning models for adenocarcinoma of esophagogastric union (AEG), based on 287 AEG patient data collected clinically. After grouping, Cox proportional hazards regression model (Cox-PH) and four machine learning models were constructed and internally validated. The AUC values of 3-year survival rate in validation set of Cox-PH, extreme gradient boosting (XGBoost), Random Forest (RF), support vector machines (SVM), and Multi-layer Perceptron (MLP) were 0.870, 0.901, 0.791, 0.832 and 0.725, respectively. The AUC values of 5-year survival rate in validation set of each model were 0.915, 0.916, 0.758, 0.905 and 0.737. The internal validation AUC values of the four machine learning models, XGBoost, RF, SVM and MLP were 0.818, 0.772, 0.804 and 0.745, respectively. In conclusion, compared with Cox-PH, machine learning models do not need to meet proportional assumption or linear regression model, and can include more influencing variables, which has good predictive performance for the 3-year and 5-year survival rate of AEG patients, among which XGBoost model is the most stable and has obvious superiority in prediction performance than other machine learning methods, practical and reliable.
Deep Learnıng-Based Sustaınable Subsurface Anomalıes Detectıon In Barker-Coded Thermal Wave Imagıng
Authors: Parvez Muzammil , Mohammad Ali Baig , Ghali Venkata Subba Rao , Yadav Chandra Sekhar , Vesala Gopi Tilak , Lakshmi Atala Vijaya , Alagarsamy Aravindhan , Palanisamy Sivasubramanian , Kechagias John , Santulli Carlo 
Published: March 2023

Abstract
Deep learning-based sustainable subsurface anomaly detection is the perceiving of thermographic research. Subsurface detection of an anomaly in various materials using deep learning increases reliability. This article aims to describe a method that uses thermal wave imaging to identify subsurface anomalies in materials. The proposed method is based on the experiments that were carried out with different kinds of samples and have been compared to other modern techniques for detecting subsurface anomalies. Subsurface anomalies visualized using the proposed deep learning method give better visualization, and the results were compared to that of contemporary approaches. In addition, region-based active contour segmentation-based detection is also proposed for the GFRP sample.
Development of WAAM process: slicer, robot trajectories, and examples of parts
Authors: BOLOT Rodolphe , MATHIEU Alexandre , SIMON Maxime 
Published: March 2023

Abstract
This work is devoted to Wire Arc Additive Manufacturing processing. The question of automatic generation of robot trajectories is mainly addressed. An in-house slicer was first developed. Once the part to be manufactured has been designed and converted into STL format, the slicer allows automatic generation of successive vertices defining the part contours and corresponding robot trajectories. Automatic generation of the robot program is then performed including displacement instructions written in KRL language (case of KUKA robots), as well as commands related to the CMT welding device. The different steps in the offline programming pre-treatment process are first described. In a second step, two distinct parts with increasing degrees of complexity were printed using the corresponding offline programming tool. Finally, the question of the scanning strategy is discussed, the manufactured parts are shown, and required further improvements are explained.
The Safety and Feasibility of Mediterranean-Ketogenic Dietary Interventions on Gut Health in Parkinson’s Disease: A Protocol for an Open-label, Randomized, Crossover Design Clinical Trial (KIM Trial)
Authors: Keymanesh Shayan , Tosefsky Kira , Lam Joyce S. T. , Metcalfe-Roach Avril , Uzelman Petra , Sacheli Matthew , Tetzlaff Wolfram , Cohen Tamara R. , Wellington Cheryl , Finlay B. Brett , Appel-Cresswell Silke 
Published: March 2023

Abstract
Background
Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, characterized by a constellation of motor and non-motor symptoms. The etiology of PD is not fully understood, however, the early presence of gastrointestinal symptoms and alterations in the gut microbiome suggest a possible intestinal origin. Another pathophysiological feature of PD is an inefficient utilization of glucose by neuronal cells as the main energy source leading to bioenergetic deficits of the brain. Dietary interventions such as the Mediterranean (MeDi) diet and the ketogenic diet (KD) have shown promise in alleviating the gastrointestinal symptoms and bioenergetics deficits of PD, respectively. Nonetheless, classical KDs may unfavorably alter the gut microbiome, e.g., by decreasing short-chain fatty (SCFA) acid levels. Hence, combining the principles of the MeDi and KD may allow us to harness the potential benefits of both these dietary interventions, while maintaining gut health.
Methods
This study will utilize an open-label, randomized, cross-over design to investigate the safety and feasibility of the Mediterranean-ketogenic diet (MeDi-KD) and MeDi diet supplemented with medium-chain triglycerides (MeDi-MCT) in 50 participants diagnosed with PD. Participants will be randomized to start with either the MeDi-KD or the MeDi-MCT intervention. They will adhere to the respective dietary regimens for 8 weeks followed by an 8-week washout period wherein they will return to their pre-study dietary habits. Following the washout period, the participants will start the other dietary intervention for another 8 weeks. Fecal and blood samples will be collected before and after each intervention to examine the biomarkers associated with gut health. The primary outcome measure of this study will be changes from baseline in fecal SCFA levels, particularly butyrate.
Discussion
The primary objective of this study is to investigate the safety of two Mediterranean-ketogenic interventions with respect to gut microbiome health in patients with PD. This study will provide preliminary evidence and guidance for subsequent large-scale clinical trials investigating multi-pronged dietary interventions to treat PD. If successful, it will de-risk future studies on ketogenic interventions by providing vital information about the safety, tolerability, adherence, and feasibility of the MeDi-KD and MeDi-MCT.
Trial Registration
ClinicalTrials.gov Identifier: NCT05469997
C1ql1-Bai3 signaling is necessary for climbing fiber synapse formation in mature Purkinje cells in coordination with neuronal activity
Authors: Aimi Takahiro , Yuzaki Michisuke 
Published: March 2023

Abstract
Changes in neural activity induced by learning and novel environments have been reported to lead to the formation of new synapses in the adult brain. However, the underlying molecular mechanism is not well understood. Here, we show that Purkinje cells (PCs), which have established adult-type monosynaptic innervation by climbing fibers (CFs) after elimination of weak CFs during development, can be reinnervated by multiple CFs by increased expression of the synaptic organizer C1ql1 in CFs or Bai3, a receptor for C1ql1, in PCs. In the adult cerebellum, CFs are known to have transverse branches that run in a mediolateral direction without forming synapses with PCs. Electrophysiological, Ca2+-imaging and immunohistochemical studies showed that overexpression of C1ql1 or Bai3 caused these CF transverse branches to elongate and synapse on the distal dendrites of mature PCs. Mature PCs were also reinnervated by multiple CFs when the glutamate receptor GluD2, which is essential for the maintenance of synapses between granule cells and PCs, was deleted. Interestingly, the effect of GluD2 knockout was not observed in Bai3 knockout PCs. In addition, C1ql1 levels were significantly upregulated in CFs of GluD2 knockout mice, suggesting that endogenous, not overexpressed, C1ql1-Bai3 signaling could regulate the reinnervation of mature PCs by CFs. Furthermore, the effects of C1ql1 and Bai3 overexpression required neuronal activity in the PC and CF, respectively. C1ql1 immunoreactivity at CF-PC synapses was reduced when the neuronal activity of CFs was suppressed. These results indicate that C1ql1-Bai3 signaling mediates CF synaptogenesis in mature PCs in concert with neuronal activity.