Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases

DeTraC: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

Due to the high availability of large-scale annotated image datasets, paramount progress has been made in deep convolutional neural networks (CNNs) for image classification tasks. CNNs enable learning highly representative and hierarchical local image features directly from data. However, the availability of annotated data, especially in the medical imaging domain, remains the biggest challenge in the field. Transfer learning can provide a promising and effective solution by transferring knowledge from generic image recognition tasks to the medical image classification. However, due to irregularities in the dataset distribution, transfer learning usually fails to provide a robust solution. Class decomposition facilitates easier to learn class boundaries of a dataset, and consequently can deal with any irregularities in the data distribution. Motivated by this challenging problem, the paper presents Decompose, Transfer, and Compose (DeTraC) approach, a novel CNN architecture based on class decomposition to improve the performance of medical image classification using transfer learning and class decomposition approach. DeTraC enables learning at the subclass level that can be more separable with a prospect to faster convergence.We validated our proposed approach with three different cohorts of chest X-ray images, histological images of human colorectal cancer, and digital mammograms. We compared DeTraC with the state-of-the-art CNN models to demonstrate its high performance in terms of accuracy, sensitivity, and specificity

Prevalence of occult hepatitis B infection in Diyala province, Iraq

Background: Occult HBV infection (OBI) is the absence of hepatitis surface antigens (HBsAg) that is not apparent during detection by serological tests despite the presence of virus DNA. This study aimed to explore the prevalence of OBI infection among various populations in Diyala province, Iraq. Methods: A prospective cross-sectional study was conducted from 1st January to 30th September, 2021, at Ibn Sina Dialysis Center, Baquba Teaching Hospital, Iraq. Three hundred and sixty participants were equally involved (90 individuals for each) from the dialysis department, the thalassemia department, blood bank donation Centre, and the control group. Study populations were screened for HBV Ag, HBV c IgG, HBV c IgM, abusing the enzyme-linked immunosorbent assay (ELISA) test, and detecting HB core gene. Demographic data of the study group were recorded. Descriptive analysis was done using SPSS Version 25, and the P-value was considered significant wherever it was below 0.05. Results: The positivity rate of serological markers of OBI among the study population was (6.7%) of the participants were HBs Ag positive. Whereas 22 (6.1%) were anti-HBc IgG positive and 3 (0.8%) were anti-HBc IgM positive. The detection rates of the PCR products of 76 participants after amplification using specific primers for (core-gene) have been presented to the gel electrophoresis, which showed none of the 76 participants were positive for the HBc gene. Conclusion: The current study showed a medium percentage of anti-HBc IgG in the serum of the study groups without the presence of HBs Ag, which indicates the presence of a previous infection that was resolved or the occurrence of occult hepatitis B infection. The current study results also showed that the serum of any of the study groups was not positive for the core gene, which confirms the possibility of infection with OBI

Nanoparticles Titanium Dioxide with Thymus vulgaris extract in preservation and prolong the shelf life of cheese

Cheese is considered a perishable food that is affected by microorganisms, and due to the properties of nanomaterials that have antimicrobial activity, they have been used synergistically with plant extracts in inhibiting the action of microorganisms that cause cheese spoilage. In this study, TiO2 (Titanium dioxide) nanoparticles were synthesized using Thymus vulgaris leaves extract (TVLE) . Atomic Force Microscopywas used to investigate Titanium dioxide/ TVLE nanoparticles characterize, which improved the regular spherical shape and granular distribution of nanoparticles with a particle size of 13 nm . The results showed that the minimum inhibitory concentration (MIC) of Titanium dioxide was at a concentration of 4 mg/ml and 80 mg/ml for TVLE, while it was 2 + 20 mg /ml for Titanium dioxide and TVLE .The inhibitory effect increased against Brucella melitensis recorded 12 mm when mixing Titanium dioxide and TVLE, compared with the inhibitory effect of Titanium dioxide, which recorded 10.5 mm and TVLE, with an inhibition diameter 8.1 mm. The effect of using titanium particles and thyme leaves’ extract was studied alone at a concentration of 4 mg/ml and  80 mg/ml and also when mixed in the microbial properties and pH of white soft cheese samples, which were prepared in the laboratory and contaminated with Brucella melitensis at refrigerated storage conditions (5Cº) for 21days. The effect of the synergism relationship between TiO2 / TVLE significantly reduced the total number of microorganisms in samples contaminated and uncontaminated with B. melitensis. Adding titanium dioxide and TVLE at concentrations of 4 and 80 mg/ml contributed significantly to maintaining the pH level during the storage period compared with the control group

Pedestrian Friendly Environment in Residential Complexes Case Study Erbil

This paper studies the concept of Pedestrian Friendly Environment in Residential complexes. It's defined it as (an environment that posses number of design and social characteristics, such as safety, vitality, connected and accessible. Also interesting, comfortable, through good design of pedestrian axes, mixed use and sustainable transportation. The research problem is that (there is a knowledge gap about the characteristics of this environment, and its treatment in residential complexes locally .The research hypothesis is that (The Pedestrian Friendly Environment in Residential complexes materialized through, planning dimensions, design and environmental characteristics and treatments). The paper found that the aim of pedestrian friendly environment is to improve the body activities, increase the quality of space and the social interaction, materialized the sustainable transportation. The planning dimensions were the intense mixed use, and the design of pedestrian axes. The main design characteristics were accessibility, vitality, permeability and safety. And finally providing shads as environmental treatment. Key Words: planning dimensions, pedestrian friendly environment characteristics, pedestrian friendly environment treatments, residential complexes

Prevalence and Virulence Genes Profile of Zoonotic Campylobacter species in Chickens and Human in Aswan Governorate

This study evaluated the mutual function of chickens in the transmission of Campylobacter jejuni and Campylobacter coli to patients in Aswan Governorate, Egypt. Samples from fresh chickens (no= 108) and frozen chickens (no= 100), were collected randomly from supermarkets in Aswan Province, Egypt as well as 60 diarrheal samples were assembled from hospitalized patients. Biochemical and molecular techniques were employed through duplex polymerase chain reaction objecting the 23S rRNA, mapA, and ceuE genes specific to genus Campylobacter, C. jejuni, and C. Coli, respectively, after that virulence genes (flaA and cadF genes) were detected. By using conventional and duplex PCR , the overall incidence of Campylobacter was 29% and 25.4 %, respectively. C. jejuni and C. coli by conventional and PCR were identified as 18.1, 5.1%, and 12.3, 7.2%, respectively, while 5.8% mixed infection was discovered by both techniques. Campylobacter species isolated from 66.7, 25, 17.5 and 18.3% of fresh chickens, frozen chickens, frozen liver and gizzard, and human, respectively with statistically significant difference. Epidemiologically, the insignificant age risk factor was statistically reported in this study among patients although Campylobacter was dominant in the 21-35 and 36-50 age groups. Campylobacter incidence was higher among females (33.3%) than males (11.9%). FlaA virulence gene was detected in 10.3% of both C. jejuni and C. coli isolated from chickens but not be detected in human . cadF virulence gene isolated in 20.5, 23.1, 36.4, and 9.1% of C. jejuni and C. coli of chickens′ and human , respectively

A Polyphasic Approach to Compare the Genomic Profiles of Aflatoxigenic and Non-Aflatoxigenic Isolates of Aspergillus Section Flavi

Aflatoxins (AF) are highly toxic compounds produced by Aspergillus section Flavi. Theyspoil food crops and present a serious global health hazard to humans and livestock. The aim ofthis study was to examine the phylogenetic relationships among aflatoxigenic and non-aflatoxigenicAspergillus isolates. A polyphasic approach combining phylogenetic, sequence, and toxin analyses wasapplied to 40 Aspergillus section Flavi isolates collected from eight countries around the world (USA,Philippines, Egypt, India, Australia, Indonesia, China, and Uganda). This allows one to pinpoint thekey genomic features that distinguish AF producing and non-producing isolates. Based on molecularidentification, 32 (80%) were identified as A. flavus, three (7.5%) as A. parasiticus, three (7.5%) asA. nomius and one (2.5%) as A. tamarii. Toxin analysis showed that 22 (55%) Aspergillus isolateswere aflatoxigenic. The majority of the toxic isolates (62.5%) originated from Egypt. The highestaflatoxin production potential was observed in an A. nomius isolate which is originally isolatedfrom the Philippines. DNA-based molecular markers such as random amplified polymorphic DNA(RAPD) and inter-simple sequence repeats (ISSR) were used to evaluate the genetic diversity andphylogenetic relationships among these 40 Aspergillus isolates, which were originally selected from80 isolates. The percentage of polymorphic bands in three RAPD and three ISSR primers was 81.9%and 79.37%, respectively. Analysis of molecular variance showed significant diversity within thepopulations, 92% for RAPD and 85% for ISSR primers. The average of Polymorphism InformationContent (PIC), Marker Index (MI), Nei’s gene diversity (H) and Shannon’s diversity index (I) in ISSRmarkers are higher than those in RAPD markers. Based on banding patterns and gene diversitiesvalues, we observed that the ISSR-PCR provides clearer data and is more successful in geneticdiversity analyses than RAPD-PCR. Dendrograms generated from UPGMA (Unweighted Pair GroupMethod with Arithmetic Mean) cluster analyses for RAPD and ISSR markers were related to thegeographic origin.</p

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network,which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50 000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected

Comparison of biomass and deoxynivalenol production of northern European and southern European Fusarium graminearum isolates in the infection of wheat and oat grains

The 3ADON chemotype of Fusarium graminearum predominates in northern Europe, whereas the 15ADON chemotype is predominant in central and southern Europe. Therefore, it has been suggested that there are two F. graminearum populations in Europe, which may have been specialized to different host plants. The aim of the present work was to test this hypothesis by comparing southern European isolates (15ADON chemotype) from southern Russia and northern European isolates (3ADON chemotype) from Finland in the infection of grains in wheat cultivar Wellamo and oat cultivar Venla. F. graminearum biomass levels were measured by TaqMan (2018) and SYBR Green (2019) qPCR, while DON levels were measured by chromatographic methods. Most of the qPCR and DON results are supporting the hypothesis that in F. graminearum the 15ADON isolates from southern Russia are more specialized to wheat than the 3ADON isolates from Finland. In oat, there were not as clear differences between the 15ADON and 3ADON isolates, but in 2018 higher F. graminearum DNA levels and in 2019 higher DON and F. graminearum DNA levels were found in oat samples inoculated with 3ADON isolates. Our results are in line with literature according to which F. graminearum DNA and DON levels are also highest in oat in northern Europe, while in southern Europe they are highest in wheat and maize