Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.
Morality and Values in Sports Among Young Athletes: The Role of Sport Type and Parenting Styles – A Pilot Study

Given the great importance of morality and values in modern sports, especially among young athletes, in this pilot study, we sought to broaden the exploration of the factors that may play role in these contexts, which have not been widely researched to date. Accordingly, the study tested the relationships between sport type (team or individual) and parenting styles (authoritative vs. non-authoritative), and moral decision-making in sport and sport values among 110 adolescent athletes whose age ranges from 11 to 22 (M = 16.04, SD = 2.86). The findings indicated that participants with authoritative parents, as compared to those with non-authoritative parents, are significantly less accepting of cheating in sport, while they also tend more to keep winning in proportion and hold significantly stronger moral values toward sports. Moreover, participants whose main sport is a team sport type tend to accept more cheating and gamesmanship than participants whose main sport is an individualistic sport type. While no differences were recorded between these groups in moral values, team athletes tend to value status in sport more than individual athletes, while the latter tend to value competence regarding their sport. The implications of the findings are discussed in light of no interaction between the effects of parenting styles and sport type on moral and sport values.
Staphylococcus aureus ST59: Concurrent but Separate Evolution of North American and East Asian Lineages

Despite initially being described in North America, Staphylococcus aureus (SA) sequence type ST59 is the most commonly isolated sequence type in Eastern Asia. The origins and evolution of this strain type remains unclear and therefore we gathered a collection of ST59 isolates from Canada and mainland China for a detailed genetic analysis of the lineage. Bayesian inference phylogenomic analysis of our isolates, along with previously published ST59 sequences indicated that the lineage could be divided into 6 distinct subgroups (WGS-1 thorough 6), each having distinct molecular characteristics. Analysis also demonstrated the concurrent but separate evolution of North American and East Asian lineages, as well as the extensive diversification of the East Asian lineage. The presence of a mobile element structure (MES) was found to be the major difference between these two continental lineages, absent in all North American isolates, and present in all East Asian ones. Other mobile genetic elements, such as the Immune Evasion Complex (IEC), Panton Valentine Leukocidin (PVL), and Staphylococcal Cassette Chromosome mec (SCCmec), showed significant variability within each sub-group and likely represents local selective pressures rather than major characteristics defining the groups. Our analysis also demonstrated the existence of a more ancient ST59 sub-lineage from North America, which was MES negative and contained some of the earliest reported ST59 isolates. Combined with the existence of a MES negative isolate from Taiwan, predicted to have appeared prior to diversification of the East Asian lineages, these results hint at the possibility of a North American origin for the lineage, which gained hold in Eastern Asia following acquisition of MES, and subsequently diversified.
Case Report: Systemic Small-Vessel Vasculitis in an Adolescent With Active Ulcerative Colitis

Introduction: Small-vessel vasculitis (SVV) is a rare immunological disease that affects arterioles, capillaries and venules. It causes purpura, but can also manifest in other organs, including the gastrointestinal tract. SVV and inflammatory bowel disease (IBD) co-occur more frequently than would be expected by chance. Case description: A 16-year-old girl, who had been diagnosed with ulcerative colitis (UC) 2 years earlier at a general hospital, developed purpura, progressive abdominal pain with frequent bloody diarrhea and frontotemporal headache and swelling while on azathioprine and mesalamine maintenance therapy. Serology was positive for perinuclear antineutrophil cytoplasmic antibodies (p-ANCA) without antiprotease- or myeloperoixidase antibodies. Endoscopy revealed active left-sided UC and atypical ulcerations in the ascending colon. Biopsies of these ulcerations and of affected skin revealed leukocytoclastic vasculitis. Initially this was interpreted as an extraintestinal manifestation of UC that would subside when remission was induced, consequently infliximab was started. Over the next 3 weeks she developed severe burning pain in her right lower leg that progressed to a foot drop with numbness and the purpura progressed to bullous lesions. The diagnosis was adjusted to ANCA-associated vasculitis with involvement of skin, bowel and peripheral nerves. Infliximab was discontinued and induction treatment with high-dose prednisolone and cyclophosphamide was given until remission of SVV and UC was achieved. Subsequently, infliximab induction and maintenance was re-introduced in combination with methotrexate. Remission has been maintained successfully for over 2 years now. The foot drop only partly resolved and necessitated the use of an orthosis. Conclusion: Pediatric patients with IBD who present with purpuric skin lesions and abdominal pain should be evaluated for systemic involvement of SVV, which includes endoscopic evaluation of the gastrointestinal tract. We discuss a practical approach to the diagnosis, evaluation and management of systemic SVV with a focus on prompt recognition and early aggressive therapy to improve outcome.
Epidemic Risk Perception, Perceived Stress, and Mental Health During COVID-19 Pandemic: A Moderated Mediating Model

The aim of the present study was to investigate relationships among epidemic risk perception, perceived stress, mental health (depression and anxiety), future time perspective, and confidence in society during the novel coronavirus disease (COVID-19) pandemic in China. Especially, we wonder that whether perceived stress mediates associations between epidemic risk perception and mental health and that whether future time perspective and confidence in society moderate the link between perceived stress and mental health. This cross-sectional study was conducted among 693 Chinese adults aged 18–60 years. The results showed that epidemic risk perception was positively related to perceived stress, depression, and anxiety. The correlations between epidemic risk perception and depression and anxiety were reduced when perceived stress was included, suggesting that perceived stress mediated these relationships. Moreover, the boundary conditions for the associations among perceived stress, depression, and anxiety were found in the study. Specifically, positive future time perspective could buffer the negative effects of perceived stress on depression, and confidence in society could weaken the negative effects of perceived stress on anxiety. Based on these findings, practical guidance and theoretical implications are provided for the public to maintain mental health during COVID-19 pandemic. Limitations and future directions are also discussed.
Accumulative Competitive Season Training Stress Affects Neuromuscular Function and Increases Injury Risk in Uninjured D1 Female Athletes

Previous research has shown that acute competition training stress negatively affects neuromuscular function which can perpetuate a predisposition to injury. This study's aim was to investigate the effect of accumulated competition training stress effect on neuromuscular function and incidence of increased injury risk in uninjured female D1 soccer players. Neuromuscular function was evaluated in fifteen female division I soccer athletes who played >85% of competitive season competitions who were tested for mobility/stability, leg length symmetry, and vertical power at three different points across the competitive season (pre, mid, and post time blocks). Leg length symmetry was measured from the anterior superior iliac spine to the lateral malleolus prior to Y-balance testing. The Y-balance testing measures unilateral anterior, posteromedial, and posterolateral reach achieved in single leg stance using metrics that include L/R normalized composite reach (NCOMP), L/R normalized antiorior reach (NANT), and L/R NCOMP/NANT segmental differences across time. Injury risk was evaluated using validated objective criteria that included: (NCOMP total reach <94% of limb length*3), (NANT reach distance <84% leg length) along with NCOMP and NANT asymmetries >4.0. Maximal vertical power (MVP) was measured via vertical jump. Multiple repeated measures ANOVAs evaluated NCOMP, NANT, MVP, and leg length symmetry across time with LSD post hoc testing when relevant (X ± SD). A significant main effect was found [F(1, 14) = 62.92, p < 0.001; η2 =0.82] with training stress and neuromuscular function without affecting maximal vertical power. Eighty percent of subject's bilateral NCOMP scores fell below the YBT reach standard at midseason (ES = 0.95, p = 0.02) while all subjects NANT reach distance remained below the reach threshold (ES = 0.74, p = 0.003) indicating a 6.5× and 2.5× greater injury risk, respectively. Competition stress affected neuromuscular function without affecting maximal power, which negatively impacted stability and increased injury risk.
Bioprospecting of Novel Extremozymes From Prokaryotes—The Advent of Culture-Independent Methods

Extremophiles are remarkable organisms that thrive in the harshest environments on Earth, such as hydrothermal vents, hypersaline lakes and pools, alkaline soda lakes, deserts, cold oceans, and volcanic areas. These organisms have developed several strategies to overcome environmental stress and nutrient limitations. Thus, they are among the best model organisms to study adaptive mechanisms that lead to stress tolerance. Genetic and structural information derived from extremophiles and extremozymes can be used for bioengineering other nontolerant enzymes. Furthermore, extremophiles can be a valuable resource for novel biotechnological and biomedical products due to their biosynthetic properties. However, understanding life under extreme conditions is challenging due to the difficulties of in vitro cultivation and observation since > 99% of organisms cannot be cultivated. Consequently, only a minor percentage of the potential extremophiles on Earth have been discovered and characterized. Herein, we present a review of culture-independent methods, sequence-based metagenomics (SBM), and single amplified genomes (SAGs) for studying enzymes from extremophiles, with a focus on prokaryotic (archaea and bacteria) microorganisms. Additionally, we provide a comprehensive list of extremozymes discovered via metagenomics and SAGs.
Disturbances of Shared Intentionality in Schizophrenia and Autism

Schizophrenia and autism are today considered complex spectrum disorders characterized by difficulties in social behavior. Drawing on recent advances in collective or shared intentionality studies, we present a novel theoretical approach to these social difficulties by exploring them from the angle of shared intentionality. We begin by describing two forms of shared intentionality: joint intentionality and we-intentionality. Joint intentionality crucially relies on the agents' mentalizing abilities such as mind reading and the ability to factor in (or “to be moved” by) their partner's intentions in deliberation and action planning. By contrast, we-intentionality relies on the agents' capacity to understand themselves as group members and to adopt the group's perspective. In schizophrenia spectrum disorders, we propose that joint intentionality remains unaffected, but we-intentionality may be impaired. In severe autism spectrum disorder (i.e., infantile autism), we propose that both forms of shared intentionality are impaired. We suggest that the source of the problems affecting we-intentionality in schizophrenia spectrum disorders lies primarily in trait-like, anomalous self-experiences. In severe autism spectrum disorder, we suggest that problems with mind reading, the ability to “be moved” by others' intentions, and with the capacity for perspective-taking impede both forms of shared intentionality.
Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning

The presence of pathogen-specific antibodies in an individual’s blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term “Domain-Scan”. We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant (“domain”) is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.
SARS-CoV-2 Infection in Dogs and Cats: Facts and Speculations

Department of Veterinary Medicine, University of Bari Aldo Moro, Bari, Italy, Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy, Department of Veterinary Science, University of Torino, Turin, Italy, Department of Veterinary Medicine, University of Milano, Milan, Italy, Department of Veterinary Sciences, University of Pisa, Pisa, Italy, 6 Istituto Zooprofilattico Sperimentale della Sicilia “A. Mirri”, Palermo, Italy, Department of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, Naples, Italy
Direct Electrochemistry of Glucose Oxidase at a Gold Electrode Modified with Graphene Nanosheets

In this work, we report the direct electrochemistry of glucose oxidase (GOD) observed at a gold electrode modified with graphene nanosheets. Initially, graphene nanosheets were synthesized and conjugated to the enzyme GOD and immobilized on to a gold electrode surface. Cyclic voltammetry was then performed using Gold-Graphene-GOD modified electrodes in a pH 7.2 phosphate buffered saline (PBS). A pair of well-defined redox peaks was obtained for GOD with the reduction peak centered at +180 mV and a peak separation of 70 mV in PBS under physiological conditions. Moreover, the electron transfer rate of GOD redox reaction was greatly enhanced and the peak potential was found to be pH dependent at the graphene-GOD surface. Further, the performance of the Gold-Graphene-GOD was found to be stable and excellent under physiological conditions indicating the possibility of employing this platform for real time analysis. The observed results indicated that the 2D-graphene holds great promise for conjugation ability with a variety of enzymes. Further, our results also confirmed that graphene is capable of holding the enzyme GOD in a favorable position and retains its original structure and functionality that are essential for biosensing.
Detection and Classification of Radio Frequency Jamming Attacks using Machine learning

Wireless networks are an important aspect of communication technologies that avoid the cost and burden of cable installation. They play a vital role in our everyday lives. However, these wireless networks have some limitations which can be exploited by malicious users to capture transmitted information or cause disruptions in communications. A Radio Frequency Jamming (RF-Jamming) attack is one such type of attack that interferes with authentic wireless signals by reducing the signal-to-noise ratio. These types of attacks pose serious threats to many applications especially the safety-critical ones such as self-driving cars. Hence, it is crucial to institute countermeasures to prevent these attacks and establish a reliable communication system. Furthermore, to take the appropriate steps for the protection against such attacks, it is important to know the type of jamming attack that a network has been exposed to. In other words, in addition to detection, the classiﬁcation of these attacks is also necessary. Therefore, in this paper, we tackle this problem and propose a machine learning-based classiﬁcation technique for different types of jamming attacks. We simulate the jamming scenario in wireless ad-hoc networks using the network simulator ns-3 and use the data collected from the simulation to train and evaluate different algorithms. We compare the accuracy of each algorithm and provide the results that showcase that the classiﬁcation of jamming attacks can be done with very high accuracy using the Gradient Boosting Algorithm.
Fixing Design Inconsistencies of Polymorphic Methods using Swarm Intelligence

Background: Modern industry is heavily dependent on software. The complexity of designing and developing software is a serious engineering issue. With the growing size of software systems and increase in complexity, inconsistencies arise in software design and intelligent techniques are required to detect and ﬁx inconsistencies. Aim: Current industrial practice of manually detecting inconsistencies is time consuming, error prone and incomplete. Inconsistencies arising as a result of polymorphic object interactions are hard to trace. We propose an approach to detect and ﬁx inconsistencies in polymorphic method invocations in sequence models. Method: A novel intelligent approach based on self regulating particle swarm optimization to solve the inconsistency during software system design is presented. Inconsistency handling is modelled as an optimization problem that uses a maximizing ﬁtness function. The proposed approach also identiﬁes the changes required in the design diagrams to ﬁx the inconsistencies. Result: The method is evaluated on diﬀerent software design models involving static and dynamic polymorphism and inconsistencies are detected and resolved. Conclusion: Ensuring consistency of design is highly essential to develop quality software and solves a major design issue for practitioners. In addition, our approach helps to reduce the time and cost of developing software.
Middle eastern and north african english speech corpus (MENAESC): automatic identification of MENA english accents

This study aims to explore the English accents in the Arab world. Although there are limited resources for a speech corpus that attempts to automatically identify the degree of accent patterns of an Arabic speaker of English, there is no speech corpus specialized for Arabic speakers of English in the Middle East and North Africa (MENA). To that end, different samples were collected in order to create the linguistic resource that we called Middle Eastern and North African English Speech Corpus (MENAESC). In addition to the “accent approach” applied in the field of automatic language/dialect recognition; we applied also the “macro-accent approach” -by employing Mel-Frequency Cepstral Coefficients (MFCC), Energy and Shifted Delta Cepstra (SDC) features and Gaussian Mixture Model-Universal Background Model (GMM-UBM) classifieron four accents (Egyptian, Qatari, Syrian, and Tunisian accents) among the eleven accents that were selected based on their high population density in the location where the experiments were carried out. By using the Equal Error Rate percentage (EER%) for the assessment of our system effectiveness in the identification of MENA English accents using the two approaches mentioned above through the employ of the MENAESC, results showed we reached 1.5 to 2%, for “accent approach” and 2 to 3.5% for “macro-accents approach” for identification of MENA English. It also exhibited that the Qatari accent, of the 4 accents included, scored the lowest EER% for all tests performed. Taken together, the system effectiveness is not only affected by the approaches used, but also by the database size MENAESC and its characteristics. Moreover, it is impacted by the proficiency of the Arabic speakers of English and the influence of their mother tongue.
Gold nanoparticles stabilized by an amphiphilic pillar[5]arene: preparation, self-assembly into composite microtubes in water and application in green catalysis

Gold nanoparticles stabilized by an amphiphilic pillar[5]arene (AP5) are prepared. They can be decorated on the surfaces of microtubes prepared from self-assembly of AP5 to form template composite microtubes (TCMTs). They can also be used in the fabrication of self-assembled composite microtubes (SCMTs) in water without any external assistance. A combination of UV-Vis spectra, Fourier transform-infrared spectroscopy, transmission electron microscopy, scanning electron microscopy, X-ray diffraction measurements, and thermogravimetric analysis is used to confirm the formation of these composite microtubes. The SCMTs are stable at high temperature, with strong acid, with strong base, and under sonication. Interestingly, the properties of SCMTs are different from those of TCMTs and SCMTs can be used in green catalysis with a yield loss within 3% for 20 cycles. This work provides a new method to create amphiphilic nanoparticles for the fabrication of novel functional materials and devices by self-assembly.
Neural Transfer Learning with Transformers for Social Science Text Analysis

Transformer-based models for transfer learning have the potential to achieve high prediction accuracies on text-based supervised learning tasks with relatively few training data instances. These models are thus likely to benefit social scientists that seek to have as accurate as possible text-based measures, but only have limited resources for annotating training data. To enable social scientists to leverage these potential benefits for their research, this article explains how these methods work, why they might be advantageous, and what their limitations are. Additionally, three Transformer-based models for transfer learning, BERT, RoBERTa, and the Longformer, are compared to conventional machine learning algorithms on three applications. Across all evaluated tasks, textual styles, and training data set sizes, the conventional models are consistently outperformed by transfer learning with Transformers, thereby demonstrating the benefits these models can bring to text-based social science research.