Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing soccer match sprint distances: A comparison
of GPS-based absolute and relative thresholds

: This study compared the most common absolute sprint threshold (> 25.2 km/h) with relative and individualized thresholds (> 70%, > 75%, > 80%, > 85% and > 90% of peak match speed). Twenty elite soccer players, competing in the first division of the Portuguese League, were monitored using GNSS equipment during thirty-four official matches. Peak match speed was retrieved as the individual maximal speed reached during the full season. Distances were registered when speed overcame the absolute and the relative thresholds. Mean ± SD of peak speeds and distances covered were calculated, and Pearson correlation ( r ) and mean paired differences were performed to analyze relationships and differences between thresholds. The peak match speed was 32.9 ± 1.4 km/h. Correlations between distances covered using the absolute and relative thresholds varied from very strong (> 70%: r = 0.84, p < .001; > 75%: r = 0.89, p < .001; and > 80%: r = 0.88, p < .001), strong (> 85%: r = 0.79, p < .001), to moderate (> 90%: r = 0.59, p < .001). Overall, the > 75% (ES: 0.23 [95% CI: 0.16, 0.31]) and the > 90% (ES: -1.65 [95%CI: -1.85, -1.48]) relative thresholds presented the smallest and largest differences, respectively, with the absolute threshold. Differences were also found when considering the playing positions. While the distances covered by central midfielders were similar between the absolute and > 80% thresholds (-0.03 [-0.16, 0.10]), fullbacks covered largely more distance -1.88 [-2.42 -1.50]) in the absolute threshold than in the > 80% threshold. The distances covered by players varied based on the selected threshold, affecting the distances covered by different playing positions. Being the highest speed threshold within displacements thresholds, the absolute sprint threshold showed greater similarity to lower rather than higher relative thresholds.
Developments in Brain Tumor Segmentation Using MRI: Deep Learning Insights and Future Perspectives

The human brain is an incredible and wonderful organ that governs all body actions. Due to its great importance, any defect in the shape of its regions should be reported quickly to reduce the death rate. The abnormal region segmentation helps to plan and monitor the treatment. The most critical procedure is isolating normal and abnormal tissues from each other. So far, remarkable imaging modalities are being used to diagnose abnormalities at their early stages, and magnetic resonance imaging (MRI) is renowned and noninvasive among those modalities. This paper investigates the current landscape of brain tumor segmentation (BTS) by exploring emerging deep learning (DL) methods for brain MRI analysis. The findings offer a comprehensive comparison of recent DL approaches, emphasizing their effectiveness in handling diverse tumor types while addressing limitations associated with data scarcity and robust validation. DL has shown a vital improvement for BTS, so our primary focus is to include significant DL robust models to analyze the brain MRI. However, DL outperforms traditional methods; still, there are several limitations, especially related to the diverse tumor types, lack of datasets, and weak validations. The future perspectives of DL-based BTS present significant potential for revolutionizing the diagnosis and treatment of brain tumors.
Impacts of sulfamethoxazole stress on vegetable growth and rhizosphere bacteria and the corresponding mitigation mechanism

Antibiotics are an important pharmaceutical class excessively used by humans. Its presence in the soil can impact plant growth and induce antibiotic resistance. This research studies the effect of sulfamethoxazole (SMX) on plant growth, rhizosphere bacteria composition, and resistance genes. Two sets of vegetables (basil, cilantro, and spinach) were treated separately with water and SMX solution. The plant growth data and soil samples were collected and analyzed. The results revealed that SMX increased spinach leaf length (34.0%) while having no significant impacts on basil and cilantro. On the other hand, SMX improved the bacterial diversity in all samples. The shifts in the abundance of plant growth-promoting bacteria could indirectly affect vegetable stem and leaf length. SMX also significantly increased the abundance of resistance genes Sul1 and Sul2. A further study into the correlation between bacteria highlights the importance of Shingomonas and Alfipia for inhibiting the spread of key resistance gene hosts, namely, Pseudomonas, Stenotrophomonas, and Agrobacterium. This research provides insight into SMX’s impact on vegetable growth and microbial diversity. It also points out important microbial interactions that could potentially be utilized to mitigate ARG proliferation.
Effect of Nitrogen Management on Severely Deficit Subsurface Drip-Irrigated Corn

Abstract. Deficit or limited irrigation is becoming a reality in many areas of the Great Plains. A study was conducted at the KSU Northwest Research-Extension Center at Colby, Kansas from 2004 to 2006 to examine the effect of nitrogen application method for severely deficit subsurface drip-irrigated corn. There were no significant differences in corn yields or water productivity between preplant-applied nitrogen and in-season fertigation for severely deficit-irrigated corn. Fertigation tended to establish more kernels per unit area in the drier years (2004 and 2006) when irrigation was limited to 75 mm and also in the wetter year (2005) when increased irrigation (150 mm) allowed for an appreciable increase in overall kernel numbers. Greater kernels/area tended to also increase corn grain yield. Keywords. Microirrigation, corn, nutrient management, irrigation, fertigation, nitrogen management, deficit irrigation
Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.
CMIP5 earth system models with biogeochemistry: An assessment for the southwest Pacific Ocean

An assessment is made of the ability of CMIP5 models to represent the seasonal biogeochemical cycles over the late twentieth century in the southwest Pacific Ocean. In particular, sea surface temperature (SST), surface chlorophyll a, nitrate, phosphate, silicate, and the depth of the seasonal thermocline, are examined to quantify the physical-biogeochemical capabilities of each model; the result is a “ranking” estimate enabling model ensemble generation. The better/less ranked ensembles we refer to as inner/outer, respectively. The ensembles then allow less well observed variables such as iron and vertically integrated primary production to be assessed. The assessment establishes model output confidence limits for setting bounds on future model scenario ecosystem change projections. By the end of the twenty first century under Representative Concentration Pathways (RCP) RCP4.5 and/or RCP8.5 our best estimates suggest that there will be average domain wide increases in SST and surface iron, but average decreases in surface chlorophyll a, nitrate, and phosphate, accompanied by relatively large decreases in the depth of the seasonal thermocline (all changes realised by both ensembles). On the other hand, for surface silicate the inner ensemble suggests general declines, and vice versa for the outer ensemble. For integrated primary production the ensembles predict declines in subtropical water, but elsewhere generally less significant changes. This article is protected by copyright. All rights reserved.
Human Adenovirus Entry and Early Events during Infection of Primary Murine Neurons: Immunofluorescence Studies In Vitro

Human adenovirus (HAdV) is a common pathogen, which can lead to various clinical symptoms and—in some cases—central nervous system (CNS) dysfunctions, such as encephalitis and meningitis. Although the initial events of virus entry have already been identified in various cell types, the mechanism of neuronal uptake of adenoviruses is relatively little understood. The aim of this study was to investigate early events during adenoviral infection, in particular to determine the connection between cellular coxsackievirus and adenovirus receptor (CAR), clathrin, caveolin, and early endosomal proteins (EEA1 and Rab5) with the entry of HAdVs into primary murine neurons in vitro. An immunofluorescence assay and confocal microscopy analysis were carried out to determine HAdV4, 5, and 7 correlation with CAR, clathrin, caveolin, and early endosomal proteins in neurons. The quantification of Pearson’s coefficient between CAR and HAdVs indicated that the HAdV4 and HAdV5 types correlated with CAR and that the correlation was more substantial for HAdV5. Inhibition of clathrin-mediated endocytosis using chlorpromazine limited the infection with HAdV, whereas inhibition of caveolin-mediated endocytosis did not affect virus entry. Thus, the entry of tested HAdV types into neurons was most likely associated with clathrin but not caveolin. It was also demonstrated that HAdVs correlate with the Rab proteins (EEA1, Rab5) present in early vesicles, and the observed differences in the manner of correlation depended on the serotype of the virus. With our research, we strove to expand knowledge regarding the mechanism of HAdV entry into neurons, which may be beneficial for developing potential therapeutics in the future.
Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence

Introduction Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from end-stage renal disease among diabetics. Cellular senescence plays a paramount role, profoundly affecting the onset and progression of chronic kidney disease (CKD) and acute kidney injuries. This study was designed to delve deeply into the pathological mechanisms between glomerulus-associated DN and cellular senescence. Methods Glomerulus-associated DN datasets and cellular senescence-related genes were acquired from the Gene Expression Omnibus (GEO) and CellAge database respectively. By integrating bioinformatics and machine learning methodologies including the LASSO regression analysis and Random Forest, we screened out four signature genes. The receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic performance of the selected genes. Rigorous experimental validations were subsequently conducted in the mouse model to corroborate the identification of three signature genes, namely LOX, FOXD1 and GJA1. Molecular docking with chlorogenic acids (CGA) was further established not only to validate LOX, FOXD1 and GJA1 as diagnostic markers but also reveal their potential therapeutic effects. Results and discussion In conclusion, our findings pinpointed three diagnostic markers of glomerulus-associated DN on the basis of cellular senescence. These markers could not only predict an increased risk of DN progression but also present promising therapeutic targets, potentially ushering in innovative treatments for DN in the elderly population.
Carbon Instrumentation in Patients with Metastatic Spinal Cord Compression

Simple Summary Patients with metastatic spinal cord compression (MSCC) are usually treated with stabilizing surgery with implants and subsequent radiotherapy. Recently, spinal implants consisting of carbon (CI) instead of titanium (TI) has been introduced. This is expected to decrease the deflection of radiation and thereby improve diagnostic imaging. However, we do not know whether it is equally safe and effective to use CI instead of TI in MSCC patients. The aim of this study was to examine the safety and effectiveness of CI in patients with MSCC. We compared 80 patients stabilized with CI versus 83 with TI. The peri-operative blood loss in the CI-group was significantly lower than in the TI-group. There were no significant differences between the groups with regard to mean survival, mean BMI, mean ASA-score, or the number of patients with revisions. Surgical treatment with CI for MSCC is safe and an equally sufficient treatment when compared to TI. Abstract Recently carbon spinal implants have been introduced in the treatment of patients with metastatic spinal cord compression (MSCC). This is expected to decrease the deflection of radiation and improve diagnostic imaging and radiotherapy when compared to titanium implants. The aim of this study was to determine the safety and effectiveness of spinal carbon instrumentation (CI) in patients with MSCC in a large cohort study. A total of 163 patients received instrumentation between 1 January 2017 and 31 December 2021. A total of 80 were stabilized with CI and 83 with TI. The outcome measures were surgical revision, postsurgical survival, peri-operative bleeding, and surgery time. The peri-operative blood loss in the CI-group was significantly lower than that in the TI-group: 450mL vs. 630mL, (p = 0.02). There were no significant differences between the groups in mean survival (CI 9.9) vs. (TI 12.9) months (p = 0.39), or the number of patients needing a revision (CI 6) vs. (TI 10), (p = 0.39). The median duration of surgery was 121 min, (p = 0.99) with no significant difference between the two groups. Surgical treatment with CI for MSCC is safe and an equally sufficient treatment when compared to TI.
OpenH: A Novel Programming Model and API for Developing Portable Parallel Programs on Heterogeneous Hybrid Servers

Heterogeneous nodes composed of a multicore CPU and accelerators are today’s norm in high-performance computing (HPC) platforms due to their superior performance and energy efficiency. Tools such as OpenCL and hybrid combinations such as OpenMP plus OpenACC are used for developing portable parallel programs for such nodes. However, these tools have some drawbacks, including a lack of compiler support for nested parallelism, performance portability, automatic heterogeneous workload distribution, user-friendly thread placement, and processor affinity essential to the portable performance of hybrid programs executing on such nodes. In this paper, we propose OpenH, a novel programming model and library API for developing portable parallel programs on heterogeneous hybrid servers composed of a multicore CPU and one or more different types of accelerators. OpenH integrates Pthreads, OpenMP, and OpenACC seamlessly to facilitate the development of hybrid parallel programs. An OpenH hybrid parallel program starts as a single main thread, creating a group of Pthreads called hosting Pthreads. A hosting Pthread then leads the execution of a software component of the program, either an OpenMP multithreaded component running on the CPU cores or an OpenACC (or OpenMP) component running on one of the accelerators of the server. The OpenH library provides API functions that allow programmers to get the configuration of the executing environment and bind the hosting Pthreads (and hence the execution of components) of the program to the CPU cores of the hybrid server to get the best performance. We illustrate the OpenH programming model and library API using two hybrid parallel applications based on matrix multiplication and 2D fast Fourier transform for the most general case of a hybrid hyperthreaded server comprising $p$ computing devices. Finally, we demonstrate the practical performance and energy consumption of OpenH for the hybrid parallel matrix multiplication application on a server comprising an Intel Icelake multicore CPU and two Nvidia A40 GPUs.
How scatterings from small-scale near-surface heterogeneities affecting seismic data and the quality of depth image, analysis based on seismic resolution

Strong near surface scattering in land acquisition often prevents us from obtaining high-quality seismic data and satisfactory depth images. How small-scale heterogeneities in the near surface layer can affect the seismic data and the quality of seismic image is highly complicated. Based on the seismic resolution theory, we propose a method to analyze their relationship. We use the parameterized random media to characterize the near surface small-scale heterogeneities. Adopt the point spreading function of the image process as the generalized image, and use its amplitude and phase errors to judge the image quality. The migration process is used to relate the statistical parameters of the model and the characteristics of the resolution. Together they form a highly flexible method for investigating the shallow scattering problem. The result shows that the thickness of the random layer, the root mean square fluctuation and the correlation length all affect the quality of the image. The main purpose of this research is developing the analysis tool which can be used for investigating the shallow scattering problem and providing objective judgment for various methods proposed to mitigate the problem.
A survey of mental health literacy in parents and guardians of teenagers

Introduction Parents and guardians (hereafter caregivers) of teenagers need high levels of mental health literacy (MHL) to manage mental health problems arising in teenagers in their care. Previous studies assessing MHL levels in caregivers of teenagers have reported mixed results, making it difficult to clearly estimate caregiver MHL levels. This study aimed to investigate MHL levels in Japanese caregivers of regular teenagers. Methods Responses from caregivers (n = 1,397) of students entering junior and senior high schools to a self-administered online questionnaire were analyzed. The questionnaire assessed (a) knowledge about mental health/illnesses and (b) attitudes towards mental health problems in teens in their care (e.g., recognition of depression as a medical illness and intention to engage in helping behaviors). Results The average proportion of correct answers to the knowledge questions (n = 7) was 55.4%; about one tenth (9.2%) of caregivers correctly answered only one or none of the questions. Few caregivers correctly answered about the life-time prevalence of any mental illnesses (46.1%) and appropriate sleep duration for teenagers’ health (16.5%). The proportions of caregivers who had the intention to listen to the teen in their care, consult another person, and seek professional medical help if the teen suffered from depression were 99.5%, 91.5% and 72.7%, respectively. Conclusions Many teenagers’ caregivers appeared to be willing to help the teens in their care if they were suffering from mental health problems. However, there was much room for improvement in knowledge on mental health/illnesses and intention to seek help from medical professionals. Efforts toward better education should be made.
Accelerator Mass Spectrometry at High Voltage Engineering Europa (HVEE)

In recent years, High Voltage Engineering Europa (HVEE) has demonstrated its capability of developing and installing turnkey accelerator mass spectrometry (AMS) equipment for the analysis of, among others, 14C and 129I. Five 3MV systems using sequential and/or simultaneous injection are operational in the field today, and they have shown excellent long-term stability providing high-precision analyses on a routine basis. Another three AMS systems are in production. It is projected that for the time being, AMS will continue to broaden its field of applications; a saturation of the market of AMS facilities is not expected in the near future. To meet the specific demands of the biomedical research community, we have developed an extreme compact 14C AMS system comprising a hybrid ion source capable of handling both graphite as well as CO2 samples. The source is optimized for easy maintenance, accommodates up to 200 samples, and can be implemented in any other AMS system. More recently, HVEE has initiated the development of an AMS system capable of detecting, among others, 36Cl and 41Ca. The design will include a HVEE 5MV Tandetron™. The accelerator is currently under construction as part of an ion beam analysis system for the Universidad Autonoma de Madrid (Spain).
Active Compensation Technology for the Target Measurement Error of Two-Axis Electro-Optical Measurement Equipment

For two-axis electro-optical measurement equipment, there are many error sources in parts manufacturing, assembly, sensors, calibration, and so on, which cause some random errors in the final measurement results of the target. In order to eliminate the random measurement error as much as possible and improve the measurement accuracy, an active compensation technique for target measurement error is proposed in this paper. Firstly, the error formation mechanism and error transfer model establishment of the two-axis electro-optical measurement equipment were studied, and based on that, three error compensation and correction methods were proposed: the least square (LS)-based error compensation method, adaptive Kalman filter(AKF)-based error correction method, and radial basis function neural network (RBFNN)-based error compensation method. According to the theoretical analysis and numerical simulation comparison, the proposed RBFNN-based error compensation method was identified as the optimal error compensation method, which can approximate the random error space surface more precisely, so that a more accurate error compensation value can be obtained, and in order to improve the measurement accuracy with higher precision. Finally, the experimental results proved that the proposed active compensation technology was valid in engineering applicability and could efficiently enhance the measurement accuracy of the two-axis electro-optical measurement equipment.