Miracle-dz/UniFAR-FTUs huggingface dataset:
On the reliability of motor evoked potentials in hand muscles of healthy adults: a systematic review

Aims Motor evoked potentials (MEP) elicited by transcranial magnetic stimulation (TMS) over the primary motor cortex are used as a neurophysiological marker of cortical excitability in clinical and scientific practice. Though, the reliability of this outcome parameter has not been clarified. Using a systematic approach, this work reviews and critically appraises studies on the reliability of MEP outcome parameters derived from hand muscles of healthy subjects and gives a proposal for most reliable TMS practice. Methods A systematic literature research was performed in PubMed, according to the PRISMA guidelines. Articles published up to March 2023 that were written in English, conducted repeated measurements from hand muscles of healthy subjects and reliability analysis were included. The risk of publication bias was determined. Two authors conducted the literature search and rated the articles in terms of eligibility and methodological criteria with standardized instruments. Frequencies of the checklist criteria were calculated and inter-rater reliability of the rating procedure was determined. Reliability and stimulation parameters were extracted and summarized in a structured way to conclude best-practice recommendation for reliable measurements. Results A total of 28 articles were included in the systematic review. Critical appraisal of the studies revealed methodological heterogeneity and partly contradictory results regarding the reliability of outcome parameters. Inter-rater reliability of the rating procedure was almost perfect nor was there indication of publication bias. Identified studies were grouped based on the parameter investigated: number of applied stimuli, stimulation intensity, reliability of input-output curve parameters, target muscle or hemisphere, inter-trial interval, coil type or navigation and waveform. Conclusion The methodology of studies on TMS is still subject to heterogeneity, which could contribute to the partly contradictory results. According to the current knowledge, reliability of the outcome parameters can be increased by adjusting the experimental setup. Reliability of single pulse MEP measurement could be optimized by using (1) at least five stimuli per session, (2) a minimum of 110% resting motor threshold as stimulation intensity, (3) a minimum of 4 s inter-trial interval and increasing the interval up to 20 s, (4) a figure-of-eight coil and (5) a monophasic waveform. MEPs can be reliably operationalized.
Soybean Growth and Management Quick Guide

JUNE 1999 Reviewed and Reprinted August 2004 D.A. McWilliams D.R. Berglund G.J. Endres North Dakota State University University of Minnesota Growth, development and yield of soybeans are a result of a variety’s genetic potential interacting with environment and farming practices. Correct production decisions using plant growth staging and timing are important for successful soybean production. Minimizing environmental stress will optimize seed yield. Farmers who understand how a soybean plant grows and develops can establish their field practices to maximize the genetic potential of the varieties grown. Management practices that may influence crop growth include seedbed preparation, variety selection, planting rate, planting depth, row width, pest management (diseases, insects and weeds), fertilization and harvesting. Soybeans are classified as indeterminate, semi-determinate or determinate in growth in the United States. Many southern varieties are determinate in growth and cease vegetative growth when the main stem terminates in a cluster of mature pods. Most northern varieties are indeterminate in growth habit. Indeterminate varieties develop leaves and flowers simultaneously throughout a portion of their reproductive period, with one to three pods at the terminal apex. With soybean development being driven by photoperiod,
Privacy-Preserving Multi-Label Propagation Based on Federated Learning

Multi-label propagation algorithms (MLPAs) aim to find vertex communities in a complex network or a cloud system by propagating and updating vertex labels, which have been widely applied in customer recommendation, protein molecule discovery, and criminal tracking. As more and more people are concerned about the leakage of their sensitive information, detecting communities without disclosing personal privacy has become a hot topic in complex network analysis. The existing anonymization-based community detection methods have to modify the network structure to protect the sensitive vertices or links, which complicates the recognition of true communities and incurs substantial accuracy loss. In this article, we first propose a federated graph learning model (FGLM) for distributed privacy-preserving network data mining. Second, a federated MLPA for distributed and attributed networks is implemented by adapting a standalone MLPA to FGLM to verify the model's effectiveness. We develop a label perturbation strategy to conceal vertex degrees in distributed label updating and employ a homomorphic encryption system to protect label weights exchanged between the participants. The experiments on real-world and synthetic datasets demonstrate that the new algorithm achieves zero accuracy loss and more than 200% higher accuracy than the simple distributed MLPA without federated learning.
Biochar Enhances Soil Resource Availability and Suppresses Microbial Metabolism Genes in the Rhizosphere of Wheat

Despite the well-documented role of biochar in promoting soil quality and crop productivity, the underlying biological mechanisms remain poorly understood. Here, we explored the effects of straw biochar on soil microbiome in the rhizosphere from wheat using metagenomic sequencing. Our results showed that straw return decreased the yields of wheat, while the straw biochar return increased the wheat yields. Further, both the richness and community composition confirmed different effects of the straw return and straw biochar return. The straw biochar return also resulted in greater rhizosphere effects from wheat, represented by resource availability, including soil organic carbon, soil total nitrogen, available phosphorus, and available potassium. The rhizosphere effects from wheat, represented by microbial metabolism genes involved in carbon, nitrogen, phosphorus, and potassium cycling, however, were decreased by straw biochar returning. In addition, the rhizosphere effects from nitrogen content and the nitrogen cycling genes showed negative relationships with wheat yields. Together, these results revealed that straw biochar enhanced soil resource availability but suppressed microbial metabolism genes in the rhizosphere from wheat, supporting the idea that straw biochar serves as a nutrient pool for crops.
Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data

Introduction Myxomatous mitral valve disease (MMVD) is the most common cause of heart failure in dogs, and assessing the risk of heart failure in dogs with MMVD is often challenging. Machine learning applied to electronic health records (EHRs) is an effective tool for predicting prognosis in the medical field. This study aimed to develop machine learning-based heart failure risk prediction models for dogs with MMVD using a dataset of EHRs. Methods A total of 143 dogs with MMVD between May 2018 and May 2022. Complete medical records were reviewed for all patients. Demographic data, radiographic measurements, echocardiographic values, and laboratory results were obtained from the clinical database. Four machine-learning algorithms (random forest, K-nearest neighbors, naïve Bayes, support vector machine) were used to develop risk prediction models. Model performance was represented by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best-performing model was chosen for the feature-ranking process. Results The random forest model showed superior performance to the other models (AUC = 0.88), while the performance of the K-nearest neighbors model showed the lowest performance (AUC = 0.69). The top three models showed excellent performance (AUC ≥ 0.8). According to the random forest algorithm’s feature ranking, echocardiographic and radiographic variables had the highest predictive values for heart failure, followed by packed cell volume (PCV) and respiratory rates. Among the electrolyte variables, chloride had the highest predictive value for heart failure. Discussion These machine-learning models will enable clinicians to support decision-making in estimating the prognosis of patients with MMVD.
Bayesian Modelling of the effects of nitrogen doses on the morphological characteristics of braquiaria grass

The Bayesian approach in regression models has shown good results in parameter estimations, where it can increase accuracy and precision. The objective of the current study was to analyze the application of Bayesian statistics to the modeling yield for leaf dry matter (LM) and stem (SM), in kg ha-1, leaf ratio (LR), crude protein content for leaves (CPL) and stem (CPS) (%) of Brachiaria grass as a function of varying N doses (0; 100; 200 and 300 kg ha-1 yr-1). Simple and two degree polynomial linear regression models were analyzed. Information for a priori distributions was obtained from the literature. A posteriori distribution was generated using a Monte Carlo method via Markov chains. Parameters significance was assyed with HPD (Highest Posteriori Density) with a 95% interval. Model selections was performed using DIC (Deviance Information Criterion); and adjustment quality estimated with means and 95% HPD for Bayesian R2 distribution ranges. The models selected for the variables LM, SM and CPS were linear, while for LR and CPL, they were second level polynomial. The lowest doses that maximize response variables were: LM: 274 ha-1yr-1, SM: 280 ha-1yr-1, LR: 113 ha-1yr-1, CPL: 265 ha-1yr-1, CPS: 289 ha-1yr-1. The Bayesian approach allowed the inclusion of literatureverified a priori information, and the identification of evidence optimization range intervals.
Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

Facial expressions are one of the communication ways between humans and their behavior can be determined through their facial expressions. Recently computer technology has been used to identify the facial expressions of people in order to predict their intentions. It remains a challenge for facial expression recognition to extract discriminative features from training sets with few labels because most deep learning-based algorithms primarily rely on spatial information and large labels. In this paper, we propose a system to classify seven types of facial expressions (Angry, Sadness, Surprise, Happiness, Fear, Neutral, and Disgust) instead of six, as in most previous research. In the proposed system, machine learning algorithms with deep learning are used to increase classification accuracy based on removing some unimportant facial regions. The support vector machine (SVM) algorithm is trained to detect the eyes and mouth regions from the face depending on histogram-oriented gradient (HOG) which is used as a features extractor. Then, merge the eyes and mouth regions for each image to create a new form of an image. After that, five different types of images are generated from the merged image named (RGB, HSV, Gray, Binary, and YCbCr). The images are fed one by one into the convolution neural network (CNN) algorithm. Finally, the voting process is used to select the most predictive class. The proposed system has been tested on three different types of datasets (KDEF, JAFFE, and FER2013) and the prediction accuracy in the system has reached more than 98% in all used datasets. The conclusion is that eliminating unimportant regions impacts the results of the classification accuracy.
High resolution anisotropic imaging of the Central European crust from phase and group velocities using ambient noise surface waves

The morphology of Europe results from the convergence of the African plate towards the European plate via micro-plates such as the Adriatic micro-plate. The direct result of the convergence are numerous subductions in the Mediterranean basin as well as many mountain ranges like the Alps, or the Apennines. During the last decade several efforts have been directed to image the structure of the European lithosphere using many methods such as, controlled source seismology (CSS), receiver functions, or local earthquake tomography (LET). Many tomographic models using teleseismic events have illuminated the structure of the European mantle. Despite these significant efforts, there is no consensus regarding an European lithosphere model. Hence it remains necessary, first to successfully image the European crust via independent techniques to access the P and S structure, and then merge the information and compile a comprehensive and coherent picture of the lithosphere/asthenosphere European system. The work presented here is a first step towards these objectives. The continued increase in the usage of ambient noise method, combined with the significant increase in the number of broadbands seismic networks in Europe allow to access the S structure of the European’s lithosphere at high-resolution. The S structure is key to understanding the structure and evolution of the European lithosphere and presently remains poorly resolved. Using stations from the Swiss Seismic Network (SED), the German Regional Seismological Network (GRSN) and the Italian broadband stations managed by Italian Instituto Nazionale di Geofisicae Vulcanologia (INGV) and the European Orfeus network a database of continuous signals from 196 stations was assembled with inter-station distances from 40km to 2000 km. The cross-correlations between each pair of stations are processed to provide coherent surface waves signals. Group and phase velocities dispersion curves are then measured to infer the structure of the European crust. Using a linear tomographic inversion algorithm, a set of 2D maps of isotropic velocities were obtained for periods between 5s and 35s from the group and phase velocity measurements. Comparison of our group velocity tomographic results with those obtained from previous studies using ambient noise technique or using combined information from CSS and LET to image the Moho topography, validates our database and methodology, including the pseudo-Green’s functions obtained by cross-correlation of continuous signals. Phase velocities, of the same period, are sensitive to greater depths than the group ve-
Olfactory Dysfunction Is Associated with Cerebral Amyloid Deposition and Cognitive Function in the Trajectory of Alzheimer’s Disease

Olfactory dysfunction is consistently observed in individuals with Alzheimer’s disease (AD), but its association with beta-amyloid (Aβ) deposition remains unclear. This study aimed to investigate the relationship among olfactory function, cerebral Aβ deposition, and neuropsychological profiles in individuals with no cognitive impairment (NCI), mild cognitive impairment (MCI), and AD dementia. A total of 164 participants were included, and olfactory function was assessed using the brief smell identification test (B-SIT). Cerebral Aβ deposition was measured using [18F]-flutemetamol PET imaging (A-PET). The results show a significant group difference in olfactory function, with the highest impairment observed in the Aβ-positive MCI and AD dementia groups, and the impairment was the lowest in Aβ-negative NCI. Olfactory dysfunction was positively associated with cognitive impairments across multiple domains. Furthermore, individuals with Aβ deposition had lower olfactory function compared to those without Aβ, even within the same neuropsychological stage. The association between olfactory dysfunction and Aβ deposition was observed globally and in specific cortical regions. These findings suggest that olfactory dysfunction is associated with both cognitive function and cerebral Aβ pathology in the trajectory of AD. Olfactory deficits may serve as an additional marker for disease progression and contribute to understanding the underlying mechanisms of AD.
Envelope Recombination: A Major Driver in Shaping Retroviral Diversification and Evolution within the Host Genome

Endogenous retroviruses (ERVs) are integrated into host DNA as the result of ancient germ line infections, primarily by extinct exogenous retroviruses. Thus, vertebrates’ genomes contain thousands of ERV copies, providing a “fossil” record for ancestral retroviral diversity and its evolution within the host genome. Like other retroviruses, the ERV proviral sequence consists of gag, pro, pol, and env genes flanked by long terminal repeats (LTRs). Particularly, the env gene encodes for the envelope proteins that initiate the infection process by binding to the host cellular receptor(s), causing membrane fusion. For this reason, a major element in understanding ERVs’ evolutionary trajectory is the characterization of env changes over time. Most of the studies dedicated to ERVs’ env have been aimed at finding an “actual” physiological or pathological function, while few of them have focused on how these genes were once acquired and modified within the host. Once acquired into the organism, genome ERVs undergo common cellular events, including recombination. Indeed, genome recombination plays a role in ERV evolutionary dynamics. Retroviral recombination events that might have been involved in env divergence include the acquisition of env genes from distantly related retroviruses, env swapping facilitating multiple cross-species transmission over millions of years, ectopic recombination between the homologous sequences present in different positions in the chromosomes, and template switching during transcriptional events. The occurrence of these recombinational events might have aided in shaping retroviral diversification and evolution until the present day. Hence, this review describes and discusses in detail the reported recombination events involving ERV env to provide the basis for further studies in the field.
Transcriptomic analysis reveals upregulated host metabolisms and downregulated immune responses or cell death induced by acute African swine fever virus infection

The African swine fever virus is a virulent and communicable viral disease that can be transmitted by infected swine, contaminated pork products, or soft tick vectors. Nonstructural proteins encoded by ASFV regulate viral replication, transcription, and evasion. However, the mechanisms underlying the host response to ASFV infection remain incompletely understood. In order to enhance comprehension of the biology and molecular mechanisms at distinct time intervals (6, 12, 24 h) post infection, transcriptome analyses were executed to discern differentially expressed genes (DEGs) between ASFV and mock-infected PAMs. The transcriptomic analysis unveiled a total of 1,677, 2,122, and 2,945 upregulated DEGs and 933, 1,148, and 1,422 downregulated DEGs in ASFV- and mock-infected groups at 6, 12, and 24 h.p.i. The results of the transcriptomic analysis demonstrated that the infection of ASFV significantly stimulated host metabolism pathways while concurrently inhibiting the expression of various immune responses and cell death pathways. Our study offers crucial mechanistic insights into the comprehension of ASFV viral pathogenesis and the multifaceted host immune responses. The genes that were dysregulated may serve as potential candidates for further exploration of anti-ASFV strategies.
Propietats pragmàtiques i gramaticals en el desenvolupament de la coreferència pronominal

Summary: In this paper, we examine the referential properties of null and overt subject pronouns in Catalan in the semi-spontaneous production of oral and written narrative texts by three groups of speakers/writers: 9–10 year olds, 12–13 year olds, and 16–17 year olds. We aim at determining whether these two types of pronouns play the same role and to what extent they contribute to anaphora resolution and discourse cohesion, both at an intra- and an inter-sentential level. We concentrate on anaphoric relations expressed by 3rd person pronouns in subject position. Our results show that, at an intra-sentential level, null pronouns normally refer to subject antecedents, mainly in the spoken modality, while overt pronouns refer back to either a subject or a non-subject antecedent. This entails that once a discourse element becomes a discourse topic and is the subject of a sentence, a null pronoun is used. Moreover, a parallel tendency is observed at an inter-sentential and discourse level. [Keywords: Reference; pronouns; Catalan; anaphora resolution; written development; oral development]
DEVELOPMENT OF PERFORMANCE APPRAISALS AS AN ALTERNATIVE TO MEASURING CRITICAL THINKING SKILLS IN ELEMENTARY MATHEMATICS LEARNING

This study aims to produce a performance assessment instrument as an alternative to measure critical thinking skills in elementary mathematics learning. This study describes the process of developing assessment instruments and rubrics used to measure critical thinking skills in grade IV elementary school mathematics learning. This research was conducted in elementary schools in DKI Jakarta. The development of performance appraisal instruments as an alternative to measuring critical thinking skills in elementary mathematics learning is research and development (Research and Development / R &D) using mixed methods research. The model used in this study is the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). This research has just been carried out with the stages carried out in this study Analyze, Design, Development, and Implementation. Data collection using non-test techniques, namely interviews and product assessments. Data was analyzed using descriptive analysis techniques including expert judgment conducted by material experts (elementary mathematics learning), learning evaluation, and language. The calculation of validation results using Aiken's statistics and instrument reliability is inter-rater reliability calculated using the Hoyt variance technique. VProduct feasibility alidation in terms of material, construction, and language aspects. The results of the analysis show that all instrument items are valid with a reliability coefficient of 0.98. The feasibility test results showed that the average feasibility of material aspects was 86.7%; construction aspect by 92%; and the language aspect of 90.7% with an overall average of 89.8%. These results show that the performance assessment instrument as an alternative to measuring critical thinking skills in grade IV elementary mathematics learning is categorized as very feasible. Thus, this instrument can be used in grade IV elementary mathematics learning.