Covid19 Identification from Chest X-ray Images using Machine Learning Classifiers with GLCM Features

From staying quarantined at home, practicing work from home to moving outside wearing masks and carrying sanitizers, every individual has now become so adaptive to so called 'New Normal' post series of lockdowns across the countries. The situation triggered by novel Coronavirus has changed the behaviour of every individual towards every other living as well as non-living entity. In the Wuhan city of China, multiple cases were reported of pneumonia caused due to unknown reasons. The concerned medical authorities confirmed the cause to be Coronavirus. The symptoms seen in these cases were not much different than those seen in case of pneumonia. Earlier the research has been carried out in the field of pneumonia identification and classification through X-ray images of chest. The difficulty in identifying Covid19 infection at initial stage is due to high resemblance of its symptoms with the infection caused due to pneumonia. Hence it is trivial to well distinguish cases of coronavirus from pneumonia that may help in saving life of patients. The paper uses chest X-ray images to identify Covid19 infection in lungs using machine learning classifiers and ensembles with Gray-Level Cooccurrence Matrix (GLCM) features. The advocated methodology extracts statistical texture features from X-ray images by computing a GLCM for each image. The matrix is computed by considering various stride combinations. These GLCM features are used to train the machine learning classifiers and ensembles. The paper explores both the multiclass classification (X-ray images are classified into one of the three classes namely Covid19 affected, Pneumonia affected and normal lungs) and binary classification (Covid19 affected and other). The dataset used for evaluating performance of the method is open sourced and can be accessed easily. Proposed method being simple and computationally effective achieves noteworthy performance in terms of Accuracy, F-Measure, MCC, PPV and Sensitivity. In sum, the best stride combination of GLCM and ensemble of machine learning classifiers is suggested as vital outcome of the proposed method for effective Covid19 identification from chest X-ray images

Mathematical Modeling of coupled tank interacting system for controlling water level using GWO and PSO optimization

In bulk drug production industries water level control at a precise point is a major dispute. Loss in production at the initial stage is observed until the water level reaches desired level. Pharmaceutical industries can however earn more profit if they could maintain precise water level control at the initial stage of production. To synchronize the water level precisely having best performance parameters, this work introduces Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). By determining the mathematical model, the method for water proportion (level) control in the bridged tanks for the MIMO system may be accomplished. The prior step for system identification is by observing the open-loop response of the system. This can be processed by analyzing the actual parameters of the coupled tank. State-space analysis of coupled tanks is explained in detail along with its conversion into transfer function. In this paper, the inherent parameters required for the calculation are discussed. MATLAB is used as the platform for observing the responses. Observations from the PID controller articulate that, there is a need for a better controller to enhance the performance. Performance analysis along with its discussion is conferred in this paper

Effects of methylphenidate on cognition and behaviour in children with neurofibromatosis type 1:a study protocol for a randomised placebo-controlled crossover trial

INTRODUCTION: Dopamine dysregulation has been identified as a key modulator of behavioural impairment in neurofibromatosis type 1 (NF1) and a potential therapeutic target. Preclinical research demonstrates reduced dopamine in the brains of genetically engineered NF1 mouse strains is associated with reduced spatial-learning and attentional dysfunction. Methylphenidate, a stimulant medication that increases dopaminergic and noradrenergic neurotransmission, rescued the behavioural and dopamine abnormalities. Although preliminary clinical trials have demonstrated that methylphenidate is effective in treating attention deficit hyperactivity disorder (ADHD) symptoms in children with NF1, its therapeutic effect on cognitive performance is unclear. The primary aim of this clinical trial is to assess the efficacy of methylphenidate for reducing attention deficits, spatial working memory impairments and ADHD symptoms in children with NF1.METHODS AND ANALYSIS: A randomised, double-blind, placebo-controlled trial of methylphenidate with a two period crossover design. Thirty-six participants with NF1 aged 7-16 years will be randomised to one of two treatment sequences: 6 weeks of methylphenidate followed by 6 weeks of placebo or; 6 weeks of placebo followed by 6 weeks of methylphenidate. Neurocognitive and behavioural outcomes as well as neuroimaging measures will be completed at baseline and repeated at the end of each treatment condition (week 6, week 12). Primary outcome measures are omission errors on the Conners Continuous Performance Test-II (attention), between-search errors on the Spatial Working Memory task from the Cambridge Neuropsychological Test Automated Battery (spatial working memory) and the Inattentive and Hyperactivity/Impulsivity Symptom Scales on the Conners 3-Parent. Secondary outcomes will examine the effect of methylphenidate on executive functions, attention, visuospatial skills, behaviour, fine-motor skills, language, social skills and quality of life.ETHICS AND DISSEMINATION: This trial has hospital ethics approval and the results will be disseminated through peer-reviewed publications and international conferences.TRIAL REGISTRATION NUMBER: ACTRN12611000765921.</p

IL-13 is a driver of COVID-19 severity

Immune dysregulation is characteristic of the more severe stages of SARS-CoV-2 infection. Understanding the mechanisms by which the immune system contributes to COVID-19 severity may open new avenues to treatment. Here, we report that elevated IL-13 was associated with the need for mechanical ventilation in 2 independent patient cohorts. In addition, patients who acquired COVID-19 while prescribed Dupilumab, a mAb that blocks IL-13 and IL-4 signaling, had less severe disease. In SARS-CoV-2–infected mice, IL-13 neutralization reduced death and disease severity without affecting viral load, demonstrating an immunopathogenic role for this cytokine. Following anti–IL-13 treatment in infected mice, hyaluronan synthase 1 (Has1) was the most downregulated gene, and accumulation of the hyaluronan (HA) polysaccharide was decreased in the lung. In patients with COVID-19, HA was increased in the lungs and plasma. Blockade of the HA receptor, CD44, reduced mortality in infected mice, supporting the importance of HA as a pathogenic mediator. Finally, HA was directly induced in the lungs of mice by administration of IL-13, indicating a new role for IL-13 in lung disease. Understanding the role of IL-13 and HA has important implications for therapy of COVID-19 and, potentially, other pulmonary diseases. IL-13 levels were elevated in patients with severe COVID-19. In a mouse model of the disease, IL-13 neutralization reduced the disease and decreased lung HA deposition. Administration of IL-13–induced HA in the lung. Blockade of the HA receptor CD44 prevented mortality, highlighting a potentially novel mechanism for IL-13–mediated HA synthesis in pulmonary pathology

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

Peak Power Plays in Database Engines

Database engines often consume significant power during query processing activities, motivating researchers to investigate the redesign of their internals to minimize these overheads. While the prior literature has dealt exclusively with average power considerations, our focus here is on peak power consumption. We begin by profiling the peak power behavior of a representative suite of popular commercial database engines in benchmark query processing environments, and demonstrate that their consumption can often be substantial. Then, we develop a pipeline-based model of query execution plans that lends itself to accurately estimating peak power consumption, suggesting its gainful employment in server design and capacity planning. More potently, given a space of competing plan choices, it could help identify plans with attractive tradeoffs between peak-power and time-efficiency considerations, and we present sample instances of such tradeoffs. Finally, we discuss extensions of our modeling approach to inductive pipelines and multi-query workloads