Miracle-dz/UniFAR-FTUs huggingface dataset:
Shallow Net for COVID-19 Classification Based on Biomarkers

In many cases, especially at the beginning of epidemic disaster, it is very important to be able to determine the severity of illness of a given patient. Picking up the severe status will help in directing the effort in a proper way. At the beginning, the number of classified status and the available data are limited, so, in such situation, one needs a system that can be trained based on limited data to give a trusted result. The current work focuses on the importance of the bioscience in differentiation between recovered patients and mortalities. Even with limited data, the decision trees (DT) was able to distinguish between recovered patients and mortalities with accuracy of 94%. Shallow dense network achieved accuracy of 75%. However, when a 10-fold technique was followed with the same data, the net achieved 99% of accuracy. The used data in this work was collected from King Faisal hospital in Taif city under a formal permission from the health ministry. PCA analysis confirmed that there are two parameters that have the greatest ability to differentiate between recovered patients and mortalities. ROC curve reveals that the parameters that can differentiate between recovered patients and mortalities are calcium and hemoglobin. The shallow net gives an accuracy of 92% when trained using calcium and hemoglobin only. This paper shows that with a suitable choosing of the parameters a small decision tree or shallow net can be trained quickly to decide which patient needs more attention so as to use the hospitals resources in a more reasonable way during the pandemic. All codes and data can be accessed from the following link “codes and data” © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.
Phosphate in the Context of Cognitive Impairment and Other Neurological Disorders Occurrence in Chronic Kidney Disease

The nervous system and the kidneys are linked under physiological states to maintain normal body homeostasis. In chronic kidney disease (CKD), damaged kidneys can impair the central nervous system, including cerebrovascular disease and cognitive impairment (CI). Recently, kidney disease has been proposed as a new modifiable risk factor for dementia. It is reported that uremic toxins may have direct neurotoxic (astrocyte activation and neuronal death) and/or indirect action through vascular effects (cerebral endothelial dysfunction, calcification, and inflammation). This review summarizes the evidence from research investigating the pathophysiological effects of phosphate toxicity in the nervous system, raising the question of whether the control of hyperphosphatemia in CKD would lower patients’ risk of developing cognitive impairment and dementia.
OsMATE6 gene putatively involved in host defense response toward susceptibility against Rhizoctonia solani in rice

ABSTRACT Sheath blight caused by Rhizoctonia solani AG1-IA is the second most serious disease of rice worldwide. Elucidating the role of multi-drug and toxic compound extrusion (MATE) gene family in host-pathogens interactions may uncover a new possible way to comprehend the mechanism of sheath blight resistance in rice. We foremost explored the role of OsMATE genes against R. solani resistance through comparative transcriptomics in PR114 (susceptible) and ShB-8 (moderately resistant) at 24 and 48 hpi (hours post-inoculation) of R. solani infection, respectively. Six OsMATE genes were differentially expressed and further validated through qRT-PCR. OsMATE6 gene was identified as a potential candidate for sheath blight susceptibility as it was significantly up-regulated in PR114. OsMATE6 is conserved within the wild relatives and might be translocated from Oryza nivara during the domestication of rice. Further studies are focused to verify its role by overexpression and protein interactions to understand the molecular mechanism of sheath blight resistance.
Streptomyces: Still the Biggest Producer of New Natural Secondary Metabolites, A Current Perspective

There is a real consensus that new antibiotics are urgently needed and are the best chance for combating antibiotic resistance. The phylum Actinobacteria is one of the main producers of new antibiotics, with a recent paradigm shift whereby rare actinomycetes have been increasingly targeted as a source of new secondary metabolites for the discovery of new antibiotics. However, this review shows that the genus Streptomyces is still the largest current producer of new and innovative secondary metabolites. Between January 2015 and December 2020, a significantly high number of novel Streptomyces spp. have been isolated from different environments, including extreme environments, symbionts, terrestrial soils, sediments and also from marine environments, mainly from marine invertebrates and marine sediments. This review highlights 135 new species of Streptomyces during this 6-year period with 108 new species of Streptomyces from the terrestrial environment and 27 new species from marine sources. A brief summary of the different pre-treatment methods used for the successful isolation of some of the new species of Streptomyces is also discussed, as well as the biological activities of the isolated secondary metabolites. A total of 279 new secondary metabolites have been recorded from 121 species of Streptomyces which exhibit diverse biological activity. The greatest number of new secondary metabolites originated from the terrestrial-sourced Streptomyces spp.
Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning

With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neurons with complex dynamics and learning paradigms with biologically plausible plasticity principles. Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. The population coding at the network scale is integrated with the dynamic neurons coding (containing 2nd-order neuronal dynamics) at the neuron scale towards a powerful spatial-temporal state representation. Extensive experimental results show that our MDC-SAN performs better than its counterpart deep actor network (based on DNNs) on four continuous control tasks from OpenAI gym. We think this is a significant attempt to improve SNNs from the perspective of efficient coding towards effective decision-making, just like that in biological networks.
An Efficient and Optimal Deep Learning Architecture using Custom U-Net and Mask R-CNN Models for Kidney Tumor Semantic Segmentation

Today, kidney medical imaging has become the backbone for health professionals in diagnosing kidney disease and determining its severity. Physicians commonly use Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) scan models to obtain kidney disease information. The significance and impact of kidney tumor analysis drew researchers to semantic segmentation of kidney tumors. Traditional image processing methodologies, in general, require more computational power and manual assistance to analyze kidney medical images for tumor segmentation. Deep Learning advances are enabling less computational and automated models for kidney medical image analysis and tumor lineation. Blobs (regions of interest) detection from medical images is gaining popularity in kidney disease diagnosis and is used widely in detecting tumors, glomeruli, and cell nuclei, among other things. Kidney Tumor segmentation is challenging compared to other segmentation models due to morphological diversity, object overlapping, intensity variance, and integrated noise. In this paper, It have proposed a kidney tumor semantic segmentation model based on CU-Net and Mask R-CNN to extract kidney tumor information from abdominal MR images. Initially, It trained the Custom U-Net architecture on abdominal MR images with kidney masks for kidney image segmentation. The Mask R-CNN model is then used to lineate tumors from kidney images. Experiments on abdominal MR images using Python image processing libraries revealed that the proposed deep learning architecture segmented the kidney images and lined up the tumors with high accuracy. Keywords—Kidney tumor (Blob) detection; custom U-Net; mask R-CNN; semantic segmentation; deep learning; medical image processing
Sena TLS-Parser: A Software Testing Tool for Generating Test Cases

—Currently, software complexity and size has been steadily growing, while the variety of testing has also been increased as well. The quality of software testing must be improved to meet deadlines and reduce development testing costs. Testing software manually is time consuming, while automation saves time and money as well as increasing test coverage and accuracy. Over the last several years, many approaches to automate test case creation have been proposed. Model-based testing (MBT) is a test design technique that supports the automation of software testing processes by generating test artefacts based on a system model that represents the system under test's (SUT) behavioral aspects. The optimization technique for automatically generating test cases using Sena TLS-Parser is discussed in this paper. Sena TLSParser is developed as a Plug-in Tool to generate test cases automatically and reduce the time spent manually creating test cases. The process of generating test cases automatically by Sena TLS-Parser is be presented through several case studies. Experimental results on six publicly available java applications show that the proposed framework for Sena TLS-Parser outperforms other automated test case generation frameworks. Sena TLS-Parser has been shown to solve the problem of software testers manually creating test cases, while able to complete optimization in a shorter period of time.
EasySED: Trusted Sound Event Detection with Self-Distillation

Sound event detection aims to identify the sound events in the audio recordings, whose applications seem to be evident in our daily life, such as the surveillance and monitoring applications. In this paper, we present a novel framework for the detection task, by combining using several improvements. To compress the model efficiently while retaining the detection accuracy, the self-distillation paradigm is employed to improve offline training. To empower the machines with the ability of uncertainty estimation, the Monte Carlo dropout is used in our framework. Moreover, the inference data augmentation strategy is utilized to improve the robustness of the detection task. Lastly, we present an interactive interface, which can be used to visualize the detection and the uncertainty for the prediction. We hope our tool can be helpful for practical machine listening.
Changes in academic efficacy beliefs in the first year of lower secondary school. Is it related to changes in social and emotional competencies?

Abstract During the first year of lower secondary school, the student’s academic efficacy beliefs (AEB) could be challenged. Social and emotional competencies (SECs) could support AEB. The primary aim of this longitudinal study was to investigate links between intra-individual changes in the SECs relationship skills, and emotional regulation with AEB among a sample of 1142 students in the first year of lower secondary school. Intra-individual changes in perceived emotional support from teachers and collaboration with peers were included as intermediate variables. A latent change score approach with structural equation modelling (SEM) was applied. A strong positive direct link between changes in emotional regulation and AEB was found, whereas results yielded a more modest link between changes in relationship skills and AEB. Parts of the associations were indirect via emotional support from teachers and collaborative peer relations. Finally, these relations and AEB showed average negative changes during the first year of lower secondary school.
Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education

Computerized Adaptive Testing (CAT) refers to an efficient and personalized test mode in online education, aiming to accurately measure student proficiency level on the required subject/domain. The key component of CAT is the "adaptive" question selection algorithm, which automatically selects the best suited question for student based on his/her current estimated proficiency, reducing test length. Existing algorithms rely on some manually designed and pre-fixed informativeness/uncertainty metrics of question for selections, which is labor-intensive and not sufficient for capturing complex relations between students and questions. In this paper, we propose a fully adaptive framework named Neural Computerized Adaptive Testing (NCAT), which formally redefines CAT as a reinforcement learning problem and directly learns selection algorithm from real-world data. Specifically, a bilevel optimization is defined and simplified under CAT's application scenarios to make the algorithm learnable. Furthermore, to address the CAT task effectively, we tackle it as an equivalent reinforcement learning problem and propose an attentive neural policy to model complex non-linear interactions. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of NCAT compared with several state-of-the-art methods.
Domain Adaptation in Predicting Turbocharger Failures Using Vehicle’s Sensor Measurements

The discrepancy in the distribution of source and target domains is usually referred to as a domain shift. It is one of the reasons for the inferior performance of machine learning solutions at deployment. We illustrate that the domain shift issue is pertinent to the readings of the vehicles’ operational sensors. This is due to the fact that these measurements are collected over a period of time and are susceptible to various changes that happen in the meantime. Examples of these changes are usage pattern variations, aging of the vehicles, seasonal shifts, and driver changes. However, domain adversarial neural networks (DANN) have shown promising results to reduce the negative impact of the domain shift. The present study investigates domain adaptation (DA) in the predictive maintenance field by estimating the remaining useful life (RUL) of turbochargers. The devices are operating on a fleet of VOLVO trucks, and the information about their services is collected over four years between 2016 and 2019. The input features to the model are a set of bi-weekly collected measurements called logged vehicle data (LVD). The contributions of this paper are two-fold. First, we propose a new approach for detecting domain (covariate) shift using an autoencoder. Second, we adapt domain adversarial neural networks to the specific application of predicting turbocharger failures. Finally, we deploy a recurrent feature extraction layer in the DANN architecture to incorporate temporal aspect of the data. The experimental results demonstrate the superiority of the proposed method over the traditional approach.
HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)

Large-scale transformer-based methods are gaining popularity as a tool for predicting the properties of chemical compounds, which is of central importance to the drug discovery process. To accelerate their development and dissemination among the community, we are releasing HuggingMolecules -- an open-source library, with a simple and unified API, that provides the implementation of several state-of-the-art transformers for molecular property prediction. In addition, we add a comparison of these methods on several regression and classification datasets. HuggingMolecules package is available at: github.com/gmum/huggingmolecules.
A Multi-Factor Classification Framework for Completing Users' Fuzzy Queries (Student Abstract)

Intent identification is the key technology in dialogue system. However, not all online queries are clear or complete. To identify users' intents from those fuzzy queries accurately, this paper proposes a multi-factor classification framework on the query level. Experimental results on our online serving system JIMI demonstrate the effectiveness of our proposed framework.
Non-Contact HR Extraction from Different Color Spaces Using RGB Camera

Nowadays, non-contact vital sign measurement from facial videos using an RGB camera has gained popularity among researchers as it is a feasible and convenient method suitable for personalized and clinical health monitoring. This paper proposes a simple but cogent technique for heart rate (HR) estimation from the facial RGB videos. It is suggested that the integration of color channels from different color spaces derived from the RGB model can provide a better estimation of the pulsating component of arterial blood synchronous with the cardiac cycle. The shared pulse signal related to blood volumetric changes underneath the skin existing in these color signals is separated using the principal component analysis, and the resultant signal is used to determine the HR value using the short-time Fourier transform. The experiments are performed using three publicly available datasets including PURE, UBFC-rPPG, and Cohface. In the experimental analysis, the proposed technique yields lower values of the mean absolute error (MAE) and root mean square error (RMSE) for the three datasets as, PURE: MAE = 1.65 beats per minute (bpm) and RMSE = 2.9 bpm, UBFC-rPPG: MAE = 2.57 and RMSE = 5.57 bpm, and Cohface: MAE = 4.51bpm and RMSE = 6.5 bpm. These performance measures for the proposed technique are found to be lower than those obtained from some of the state-of-art methods. This study suggests that color channels from the alternative color spaces can be used for non-contact vital sign monitoring.
Disintegrating Involvement in Second Life

The following paper is a theoretical perspective on identity and embodiment disintegration in designed virtual sex involvements. The study data, derived from avatar sex participant interviews and virtual world field observations demonstrates the variance of enacted cognitive processes in the progressions of anticipatory desire and realized interactions. It is suggested that we design our selves and our worlds in anticipation of involvements and, reciprocally, in the acts of involving with these designed worlds and selves we, in turn, are progressively defined and modified. To designate worlds of potential involvement is to engage in a relentless struggle with incongruity. The empirical disintegration of self and world is an ongoing fact, emergent with ongoing re-designation and re-integration of self and world. This essay highlights the role of emphatic congruence assessments in the disintegrating involvements of avatar sex and intimacy.
Perceived Stress and Interest in Non-Invasive Aesthetic Procedures During the COVID-19 Pandemic

Purpose Multiple effects of the COVID-19 pandemic have been described, including an increase in concerns about one’s facial appearance resulting in greater interest in cosmetic procedures. However, additional research is required to examine the impact of the pandemic on patients’ overall stress levels and whether this reported stress is associated with an interest in facial aesthetic procedures. We aimed to describe perceived stress and to identify factors associated with increased stress among patients seeking aesthetic treatments during the pandemic. Patients and Methods Patients coming to a Singapore-based clinic and who completed a questionnaire over a one-month period were included. Stress was evaluated using the Perceived Stress Scale (PSS); additional questions sought to understand factors associated with perceived stress and future interest in aesthetic procedures. Results Two hundred and thirteen respondents participated in the study. Separation from family and inability to travel for leisure were major stressors in 54% and 55%; getting COVID-19 was less frequently a stressor (11%). Patients reported greater concern with appearance (increased worry in 32%, comparison to others in 41%). Ninety-one percent of patients continued aesthetic procedures despite the pandemic and 75% reported interest in proceeding with treatments not previously tried before. PSS scores were higher than published norms. Factors associated with increased scores included residency status, job insecurity and markers of increased concern about appearance. Finally, higher stress scores were observed in patients with greater likelihood of receiving future aesthetic treatments, although this was not statistically significant after applying the Bonferroni correction for multiple comparisons. Conclusion Significantly higher stress scores were seen in patients seeking aesthetic treatments. We identified factors associated with increased stress with a suggested association between increased likelihood of proceeding with any aesthetic procedure and higher levels of stress. These highlight the importance of caution in treating patients given their increased stress levels and potential vulnerability.
Comparative Analysis of Gene Expression in Normal and Degenerative Human Tendon Cells

Background: Tendinopathy is a clinical problem for which treatment shows mixed results and treatment options are limited. Gene expression signatures early in the mechanotransduction pathway can accurately predict risk and correlate with different clinical outcomes. Studies aimed at elucidating the molecular mechanisms of tendinopathy have focused on small cohorts of genes that show an incomplete picture of the degeneration process. This study compared the effect of cyclic strain on gene expression in tendon cells from normal tendon and chronically painful areas of tendinopathy in 3 patients. Methods: We measured a panel of mechanotransduction genes and cytoskeletal tensional balance with and without cyclic strain, which disrupts connective tissue synthetic-degradative equilibrium. Normal and degenerative tendons were obtained from patients undergoing surgery to treat chronic painful tendinopathy. A cyclic strain model was established to measure cytoskeletal tensional homeostasis. Results: Prior to cyclic strain, the normal tendon cells exhibited varying patterns of elevated expression of 7 genes compared with degenerative tendon cells. In response to cyclic strain, gene expression of COL1A1, ITGA6, CTNNA1, and CLEC3B was up-regulated in normal tendon cells. Cyclic strain had no effect on degenerative tendon cells. Cyclic strain exacerbated the inhibition of protein synthesis in both cell types, especially in the degenerative tendon cells. Conclusion: Alterations in the pattern of gene expression are suggestive of a dynamic equilibrium between synthesis and degradation, whereby cell adhesion molecules are predominantly up-regulated to facilitate cellular reorientation in response to their altered functional environment. Clinical Relevance: These data might have future applications, including the identification of markers for early diagnosis, targets for drug design, and indicators for treatment responsiveness and prognosis.