Miracle-dz/UniFAR-FTUs huggingface dataset:
The Branching and Innervation Pattern of the Radial Nerve in the Forearm: Clarifying the Literature and Understanding Variations and Their Clinical Implications

Background: This study attempted to clarify the innervation pattern of the muscles of the distal arm and posterior forearm through cadaveric dissection. Methods: Thirty-five cadavers were dissected to expose the radial nerve in the forearm. Each muscular branch of the nerve was identified and their length and distance along the nerve were recorded. These values were used to determine the typical branching and motor entry orders. Results: The typical branching order was brachialis, brachioradialis, extensor carpi radialis longus, extensor carpi radialis brevis, supinator, extensor digitorum, extensor carpi ulnaris, abductor pollicis longus, extensor digiti minimi, extensor pollicis brevis, extensor pollicis longus and extensor indicis. Notably, the radial nerve often innervated brachialis (60%), and its superficial branch often innervated extensor carpi radialis brevis (25.7%). Conclusions: The radial nerve exhibits significant variability in the posterior forearm. However, there is enough consistency to identify an archetypal pattern and order of innervation. These findings may also need to be considered when planning surgical approaches to the distal arm, elbow and proximal forearm to prevent an undue loss of motor function. The review of the literature yielded multiple studies employing inconsistent metrics and terminology to define order or innervation.
Network Traffic Characterization in the Control Network of OpenStack based on Virtual Machines State Changes

: The adoption of private clouds is an option for optimizing the use and organization of computing resources. Although the cloud beneﬁts are been known for some time, there are still questions on how to plan the cloud infrastructure in order to avoid basic network performance issues. OpenStack is one of the most widely used open source solutions for building private Infrastructure as a Service (IaaS) clouds. OpenStack distributes network trafﬁc across multiple interfaces and virtualized networks, which connect hosts to its cloud services, divided into the domains: control, public, or guest. The present work aims to characterize network trafﬁc in the OpenStack control domain produced by changing the state of virtual machines (VMs). There are few works related to the network infrastructure scenario on private clouds, research in this area is generally focused on public domain or guest domain operations. In this sense, we performed a characterization in the OpenStack administrative network. In order to perform OpenStack network characterization, experimentation methods were used to identify operating services as well as to measure network trafﬁc in the OpenStack control domain during a set of common operations in VMs.
Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response.
Development of a competition index for young conifer plantations established on boreal mixedwood sites

A competition index was developed, and then used to examine the relationships between perennial competition and plantation performance. Species included were white spruce, black spruce, and jack pine for both over-wintered containers and bare root stock. A total of fifteen four-year-old plantations were used in this study representing a range of soil textures located on boreal mixedwood sites in northern Ontario.Effective canopy cover, derived from vertical hemispherical photographs taken at the base of seedling leaders, provided an accurate estimate of the perennial woody competition affecting seedling diameter growth. Initial seedling size, defined by the stem volume at time of planting, proved to be an important covariate in the analysis.Using the relationship between effective canopy cover and seedling root collar diameter, the following questions regarding vegetation control decision-making were addressed: 1) jack pine was more sensitive to increased levels of competition, 2) over-wintered container .
F-actin flashes on phagosomes mechanically deform contents for efficient digestion in macrophages

ABSTRACT The mechanism and role of transient F-actin recruitment, or F-actin ‘flashes’, on phagosomes remains enigmatic. Here we provide a comprehensive characterization of F-actin flashing dynamics on phagosomes, including receptor and signaling involvement. F-actin flashes predominate during the integrin-driven complement receptor (CR)-mediated phagocytosis. F-actin flashes begin shortly after internalization and persist on phagosomes for approximately 3 minutes before disassembling and reassembling several times within the first hour. Strikingly, the appearance of F-actin flashes on phagosomes coincides with morphological deformation, lysis and occasional fission of internalized red blood cells. The cadence of flashes depends on particle stiffness, and the F-actin networks on phagosomes are enriched in mechanosensitive components including focal adhesion proteins, RhoA and actomyosin. Inhibiting Arp2/3 and myosin IIA activity significantly reduces the frequency at which phagosome cargo becomes deformed during transient F-actin accumulation. At later time points, post-F-actin flashing, enhanced degradation of phagosome contents is observed, compared with non-flashing phagosomes. Taken together, these data suggest that actomyosin-driven phagosome contractions serve to disrupt malleable particles physically, a process akin to mastication, to enhance later enzymatic digestion. Summary: We find that internalized particles are mechanically deformed in macrophages, suggesting that the process of phagocytosis also employs a ‘chewing’ step to help destroy unwanted targets in macrophages.
Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that 'NC versus MCI' showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; 'NC versus AD' showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and 'MCI versus AD' showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.
Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions (p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics.
Impact of Developers Sentiments on Practices and Artifacts in Open Source Software Projects: A Systematic Literature Review

: Context: Sentiment Analysis proposes the use of Software Engineering techniques for automated identiﬁcation of human behavior. There is a growing interest in the use of Sentiment Analysis in topics related to Computing, more speciﬁcally in Software Engineering itself. Objective: Analyze the impact of developers sentiments on software practices and artifacts in open source software projects. Methods: We conducted a Systematic Review to collect evidence from the literature regarding the impacts of developers sentiments on software practices and artifacts. Results: We have found that the growing number of studies in this area provides greater visibility of the direct inﬂuence of developers sentiments on software practices. Practices associated with developers productivity and collaboration, along with source code, are the most vulnerable to sentiments variation. Conclusions: With the results presented, we hope to contribute to the discussion about the potential of improvement the social environment quality of software projects, as the sentiments of developers can positively or negatively impact software practices and artifacts.
Reproductive effort of male hooded seals (Cystophora cristata): estimates from mass loss

This study investigated mass loss, body composition, and behaviour patterns of male hooded seals during the reproductive season. During the 6 years of study (between 1989 and 1995), 139 records of male mass were obtained that involved 115 individuals. Body masses of males ranged from 147 to 434 kg. Mean mass at first capture was 312.5 ± 53.0 kg (N = 119). Year, date of sampling, and age all significantly influenced mass. Nineteen males were recaptured at least twice during a single season. Mean rate of mass loss among these individuals was 2.5 ± 1.1 kg/day (range 0.7–4.6 kg/day). Body composition early in the breeding season, measured using tritiated water (N = 6), produced mean estimates of 51.6 ± 1.6% water, 29.3 ± 2.4% fat, 16.9 ± 0.7% protein, and 1.9 ± 0.2% ash. Time–depth recorders attached to three males indicated that they spent 84.7 ± 15.4% of their time hauled out on the surface of the ice during the breeding season. Each of these males was on the ice for a few days; they then spent a few hours .
Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
Influence of Drainage on Peat Organic Matter: Implications for Development, Stability, and Transformation

The agricultural use of peatlands, the stabilization of the substrate for building or road construction or for increasing the capacity of soil to support heavy machinery for industrial activities (peat and petroleum extraction), harvesting to provide peat for energy, and the growing media and initiation of chemical processes must be preceded by drainage. As a consequence of drainage, peat underwent an irreversible conversion into moorsh (secondary transformation of the peat). The object of the study was to investigate comparatively the organic matter composition and molecular structure of humic acids (HAs) in the raised bog, fen, and peat-moorsh soils developed in various compositions of botanical cover, peat-forming species, and oxic and anoxic conditions as a result of the oscillation of ground water during drainage as well as to evaluate the vulnerability of soil organic matter (SOM) to decomposition. Drainage was shown to be the principal factor causing the various chemical compositions and physicochemical properties of HAs. Large and significant differences in chemical composition of peat and the properties of HAs were found to be related to the degree of decomposition. The HAs from drained peatlands were less chemically mature. In contrast, the HAs from fen and raised bog were found to be more mature than that of the corresponding drained peatlands. The above findings showed the distinguishable structure of HAs within the soil profile created by the plant residue biodegradation and formed in both oxic and anoxic conditions. The analytical methods of thermal analysis together with the optical densities and paramagnetic behaviour are suitable and effective tools for studying structure–property relationships characterizing the origin and formation process of HAs in various environmental conditions.
Testing methods and physical qualities of male age grade rugby union players: A systematic review

Background Rugby union match demands are complex, requiring the development of multiple physical qualities concurrently. Quantifying the physical qualities of age grade rugby union players is vital for practitioners to support athlete preparation and long-term development. Aim This systematic review aimed to identify the methods used to quantify the physical qualities of male age grade (≤ Under-20) rugby union players, present the normative values for physical qualities, and compare physical qualities between age grades and positions. Methods Electronic databases were systematically reviewed from the earliest record to November 2019 using key words relating to sex, age, sport and physical testing. Results Forty-two studies evaluated the physical qualities of age grade rugby union players. Seventy-five tests were used to quantify body composition, muscular strength, muscular power, linear speed, change of direction ability, aerobic capacity and anaerobic endurance. Thirty-one studies met the eligibility criteria to present the physical qualities. Physical qualities differentiate between age groups below Under-16, while differences in older age groups (Under-16 to Under-20) are not clear. Positional differences are present with forwards possessing greater height, body mass, body fat percentage and strength while backs are faster and have greater aerobic capacities. Conclusions A wide variety of tests are used to assess physical qualities limiting between study comparisons. Although differences in older age grades are unclear, older age groups (Under-19-20) generally performed better in physical tests. Positional differences are associated with match demands where forwards are exposed to less running but a greater number of collisions. Practitioners can use the results from this review to evaluate the physical qualities of age grade rugby union players to enhance training prescription, goal setting and player development. Future research should consider the use of national standardised testing batteries due to the inconsistency in testing methods and small samples limiting the reporting of positional differences.
Evaluation of Walleye Embryo Survival and Larval Viability after Iodine Treatment

Abstract Securing health and avoiding viral disease transmission are of paramount importance for stocking and management of walleyes Sander vitreus. Iodine compounds are widely used as nonselective antiviral substances; however, their adverse effects on embryo survival are less known. Our experiments investigated how maternal attributes influence the relationship between toxicity of iodine (expressed as concentrations and duration of treatment) and survival of walleyes after fertilization and during early larval growth. Eleven female and four male walleyes were collected in April 2007 from the Maumee River, Perrysburg, Ohio, and the gametes were transported unfertilized (2 h) to the laboratory. After fertilization and tannic acid treatment, embryos were exposed for 15, 30, and 180 min to iodine concentrations of 0 (control), 50, 100, 200, 400, and 800 mg/L. The calculated survival of 50% of the embryos at the eyed stage corresponded to an iodine concentration of 380 mg/L for the exposure duration of 15 mi.
Insight Into the Effects of Nisin and Cecropin on the Oral Microbial Community of Rats by High-Throughput Sequencing

The oral microbiome has major impacts on oral health and disease. Antimicrobial peptides (AMPs), such as nisin and cecropin, have been widely used as food preservatives or feed additives, and are thus inevitably ingested by consumers through their oral cavity. However, as broad-spectrum antimicrobial reagents, the effect of AMPs on the oral microbiome of consumer’s remains poorly characterized. In this study, we performed 16S rDNA high-throughput sequencing to investigate the effect of nisin and cecropin on the oral microbiomes of rats. Our results suggest that although nisin and cecropin have different effects on the oral microbiome of rats, both AMPs impact the composition of oral microbial communities at the phylum and genus levels. Cecropin significantly reduced the diversity and richness of rat oral microbial communities. Notably, the relative abundance of the pathogen Acinetobacter baumannii increased in the oral microbial community of rats fed cecropin-containing feed. In addition, nisin significantly reduced the amount of secretory immunoglobulin A in the saliva of rats.
Validation of a Bioanalytical Method for the Determination of Synthetic and Natural Cannabinoids (New Psychoactive Substances) in Oral Fluid Samples by Means of HPLC-MS/MS

New psychoactive substances (NPS) represent an important focus nowadays and are continually produced with minimal structural modifications in order to circumvent the law and increase the difficulty of identifying them. Moreover, since there are a high number of different compounds, it is arduous to develop analytical screening and/or confirmation methods that allow the identification and quantification of these compounds. The aim of this work is to develop and validate a bioanalytical method for detecting new synthetic drugs in biological samples, specifically oral fluid, using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) with minimal sample pretreatment. Oral fluid samples were simply centrifuged and denaturized with different rapid procedures before injection into the LC-MS/MS system. Calibration curves covered a linear concentration range from LOQ to 100 ng/mL. Validation parameters such as linearity, precision, accuracy, selectivity, matrix effect and thermal stability were evaluated and showed satisfactory results, in accordance with US Food & Drug Administration guidelines. The inter-day analytical bias and imprecision at two levels of quality control (QC) were within ±15% for most compounds. This method was able to identify and calculate the concentration of 10 NPS validated in this biological sample, even in the presence of matrix effect.