Study on anisotropies and momentum densities in AlN, GaN and InN by positron annihilation

The independent particle model (IPM) coupled with empirical pseudopotential method (EPM) was used to compute the thermalized positron charge densities in specific family of binary tetrahedrally coordinated crystals of formula ANB8-N. Initial results show a clear asymmetrical positron charge distribution relative to the bond center. It is observed that the positron density is maximum in the open interstices and is excluded not only, from the ion cores but also to a considerable degree from the valence bonds. Electron-positron momentum densities are calculated for the (001,110) planes. The results are used to analyze the positron effects in AlN, GaN and InN compounds. Our computational technique provides the theoretical means of interpreting the k-space densities obtained experimentally using the twodimensional angular correlation of annihilation radiation (2D-ACAR)

Fabrication and characterization of cellulose acetate-based nanofibers and nanofilms for H2S gas sensing application

Electrospun nanofibers and solution-casting nanofilms were produced from an environmentally friendly cellulose acetate (CA) blended with glycerol (as an ionic liquid (IL)), mixed with polypyrrole (PPy, a conducting polymer) and doped with tungsten oxide (WO3) nanoparticles. The sensing membranes fabricated were used to detect H2S gas at room temperature and shown to exhibit high performance. The results revealed that the lowest operating temperature of both nanofiber and nanofilm sensors was 20oC, with a minimum gas detection limit of 1 ppm. Moreover, the sensor exhibits a reasonably fast response, with a minimum average response time of 22.8 and 31.7 s for the proposed nanofiber and nanofilm based sensors, respectively. Furthermore, the results obtained indicated an excellent reproducibility, long-term stability, and low humidity dependence. Such distinctive properties coupled with an easy fabrication technique provide a promising potential to achieve a precise monitoring of harmful H2S gas in both indoor and outdoor atmospheres

Protective effect of berberine chloride on Plasmodium chabaudi-induced hepatic tissue injury in mice

AbstractThe present study aimed to investigate the protective role of berberine (BER) against Plasmodium chabaudi-induced infection in mice. Animals were divided into three groups. Group I served as a vehicle control. Group II and group III were infected with 1000 P. chabaudi infected erythrocytes. Group III was gavaged with 100μl of 10mg/kg berberine chloride for 10days. All mice were sacrificed at day 10 post-infection. The percentage of parasitemia was significantly reduced more than 30%, after treatment of mice with BER. Infection caused marked hepatic injuries as indicated by histopathological alterations as evidenced by the presence of hepatic lobular inflammatory cellular infiltrations, dilated sinusoids, vacuolated hepatocytes, increased number of Kupffer cells and the malaria pigment, hemozoin. These changes in livers led to the increased histological score. Also, infection induced a significant increase in liver alanine aminotransferase and aspartate aminotransferase and a significant increase in the total leucocytic count. Moreover, mice became anemic as proved by the significant decrease in erythrocyte number and haemoglobin content. BER showed a significant protective potential by improving the above mentioned parameters. Based on these results, it is concluded that berberine could offer protection against hepatic tissue damage

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

The Effect of Mycotoxins on Human Health

The aim of the current study is, what are mycotoxins, what are the risks of mycotoxins on human health, where are mycotoxins found in nature, and what are the health effects of mycotoxins on humans. the questionnaire was created electronically via the Google Drive program, and then it was distributed via mobile phone on the social networking program (WhatsApp)? Using e-mail for all participants to respond to the questionnaire. 650 questionnaires were distributed to all mobile groups, and 600 questionnaires were received on the researcher’s e-mail. (The target group is residents of the holy city of Mecca, aged 25-55 years)

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Male Oxidative Stress Infertility (MOSI): Proposed Terminology and Clinical Practice Guidelines for Management of Idiopathic Male Infertility

Despite advances in the field of male reproductive health, idiopathic male infertility, in which a man has altered semen characteristics without an identifiable cause and there is no female factor infertility, remains a challenging condition to diagnose and manage. Increasing evidence suggests that oxidative stress (OS) plays an independent role in the etiology of male infertility, with 30% to 80% of infertile men having elevated seminal reactive oxygen species levels. OS can negatively affect fertility via a number of pathways, including interference with capacitation and possible damage to sperm membrane and DNA, which may impair the sperm’s potential to fertilize an egg and develop into a healthy embryo. Adequate evaluation of male reproductive potential should therefore include an assessment of sperm OS. We propose the term Male Oxidative Stress Infertility, or MOSI, as a novel descriptor for infertile men with abnormal semen characteristics and OS, including many patients who were previously classified as having idiopathic male infertility. Oxidation-reduction potential (ORP) can be a useful clinical biomarker for the classification of MOSI, as it takes into account the levels of both oxidants and reductants (antioxidants). Current treatment protocols for OS, including the use of antioxidants, are not evidence-based and have the potential for complications and increased healthcare-related expenditures. Utilizing an easy, reproducible, and cost-effective test to measure ORP may provide a more targeted, reliable approach for administering antioxidant therapy while minimizing the risk of antioxidant overdose. With the increasing awareness and understanding of MOSI as a distinct male infertility diagnosis, future research endeavors can facilitate the development of evidence-based treatments that target its underlying cause

The upgrade of the ALICE TPC with GEMs and continuous readout

The upgrade of the ALICE TPC will allow the experiment to cope with the high interaction rates foreseen for the forthcoming Run 3 and Run 4 at the CERN LHC. In this article, we describe the design of new readout chambers and front-end electronics, which are driven by the goals of the experiment. Gas Electron Multiplier (GEM) detectors arranged in stacks containing four GEMs each, and continuous readout electronics based on the SAMPA chip, an ALICE development, are replacing the previous elements. The construction of these new elements, together with their associated quality control procedures, is explained in detail. Finally, the readout chamber and front-end electronics cards replacement, together with the commissioning of the detector prior to installation in the experimental cavern, are presented. After a nine-year period of R&D, construction, and assembly, the upgrade of the TPC was completed in 2020.publishedVersio

Global, regional, and national burden of Alzheimer's disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND: The number of individuals living with dementia is increasing, negatively affecting families, communities, and health-care systems around the world. A successful response to these challenges requires an accurate understanding of the dementia disease burden. We aimed to present the first detailed analysis of the global prevalence, mortality, and overall burden of dementia as captured by the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, and highlight the most important messages for clinicians and neurologists. METHODS: GBD 2016 obtained data on dementia from vital registration systems, published scientific literature and surveys, and data from health-service encounters on deaths, excess mortality, prevalence, and incidence from 195 countries and territories from 1990 to 2016, through systematic review and additional data-seeking efforts. To correct for differences in cause of death coding across time and locations, we modelled mortality due to dementia using prevalence data and estimates of excess mortality derived from countries that were most likely to code deaths to dementia relative to prevalence. Data were analysed by standardised methods to estimate deaths, prevalence, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs; computed as the sum of YLLs and YLDs), and the fractions of these metrics that were attributable to four risk factors that met GBD criteria for assessment (high body-mass index [BMI], high fasting plasma glucose, smoking, and a diet high in sugar-sweetened beverages). FINDINGS: In 2016, the global number of individuals who lived with dementia was 43·8 million (95% uncertainty interval [UI] 37·8-51·0), increased from 20.2 million (17·4-23·5) in 1990. This increase of 117% (95% UI 114-121) contrasted with a minor increase in age-standardised prevalence of 1·7% (1·0-2·4), from 701 cases (95% UI 602-815) per 100 000 population in 1990 to 712 cases (614-828) per 100 000 population in 2016. More women than men had dementia in 2016 (27·0 million, 95% UI 23·3-31·4, vs 16.8 million, 14.4-19.6), and dementia was the fifth leading cause of death globally, accounting for 2·4 million (95% UI 2·1-2·8) deaths. Overall, 28·8 million (95% UI 24·5-34·0) DALYs were attributed to dementia; 6·4 million (95% UI 3·4-10·5) of these could be attributed to the modifiable GBD risk factors of high BMI, high fasting plasma glucose, smoking, and a high intake of sugar-sweetened beverages. INTERPRETATION: The global number of people living with dementia more than doubled from 1990 to 2016, mainly due to increases in population ageing and growth. Although differences in coding for causes of death and the heterogeneity in case-ascertainment methods constitute major challenges to the estimation of the burden of dementia, future analyses should improve on the methods for the correction of these biases. Until breakthroughs are made in prevention or curative treatment, dementia will constitute an increasing challenge to health-care systems worldwide