Colon histology slide classification with deep-learning framework using individual and fused features.

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection.

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

Prospects of thermotolerant Kluyveromyces marxianus for high solids ethanol fermentation of lignocellulosic biomass.

Simultaneous saccharification and fermentation (SSF) is effective for minimizing sugar inhibition during high solids fermentation of biomass solids to ethanol. However, fungal enzymes used during SSF are optimal between 50 and 60 °C, whereas most fermentative yeast, such as Saccharomyces cerevisiae, do not tolerate temperatures above 37 °C. Kluyveromyces marxianus variant CBS 6556 is a thermotolerant eukaryote that thrives at 43 °C, thus potentially serving as a promising new host for SSF operation in biorefineries. Here, we attempt to leverage the thermotolerance of the strain to demonstrate the application of CBS 6556 in a high solids (up to 20 wt% insoluble solid loading) SSF configuration to understand its capabilities and limitations as compared to a proven SSF strain, S. cerevisiae D5A. For this study, we first pretreated hardwood poplar chips using Co-Solvent Enhanced Lignocellulosic Fractionation (CELF) to remove lignin and hemicellulose and to produce cellulose-enriched pretreated solids for SSF. Our results demonstrate that although CBS 6556 could not directly outperform D5A, it demonstrated similar tolerance to high gravity sugar solutions, superior growth rates at higher temperatures and higher early stage ethanol productivity. We discovered that CBS 6556's membrane was particularly sensitive to higher ethanol concentrations causing it to suffer earlier fermentation arrest than D5A. Cross-examination of metabolite data between CBS 6556 and D5A and cell surface imaging suggests that the combined stresses of high ethanol concentrations and temperature to CBS 6556's cell membrane was a primary factor limiting its ethanol productivity. Hence, we believe K. marxianus to be an excellent host for future genetic engineering efforts to improve membrane robustness especially at high temperatures in order to achieve higher ethanol productivity and titers, serving as a viable alternative to D5A.

Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm.

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.

Comparison of the proposed DCNN model with standard CNN architectures for retinal diseases classification.

Deep learning in medical image analysis has indicated increasing interest in the classification of signs of abnormalities. In this study, a new convolutional neural network (CNN) architecture (MIDNet18) Medical Image Detection Network was proposed for the classification of retinal diseases using optical coherence tomography (OCT) images. The model consists of 14 convolutional layers, seven Max Pooling layers, four dense layers, and one classification layer. A multi-class classification layer in the MIDNet18 is used to classify the OCT images into either normal or any of the three abnormal types: Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). The dataset consists of 83,484 training images, 41,741 validation images, and 968 test images. According to the experimental results, MIDNet18 obtains an accuracy of 98.86%, and their performances are compared with other standard CNN models; ResNet-50 (83.26%), MobileNet (93.29%) and DenseNet (92.5%). Also, MIDNet18 with a p-value < 0.001 has been proved to be statistically significant than other standard CNN architectures in classifying retinal diseases using OCT images.

Brain tumour classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison to VGG16.

AIM: This study aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from MRI dataset. The study implemented our novel MIDNet18 CNN architecture in comparison with the VGG16 CNN architecture for classifying normal brain images from the brain tumor images. MATERIALS AND METHODS: The novel MIDNet-18 CNN architecture comprises 14 convolutional layers, 7 pooling layers, 4 dense layers and 1 classification layer. The dataset used for this study has two classes: Normal Brain MR Images and Brain Tumor MR Images. This binary MRI brain dataset consists of 2918 images as training set, 1458 images as validation set and 212 images as test set. Independent sample size calculated was 7 for each group, keeping GPower at 80%. RESULT: From the experimental results, the proposed MIDNet18 model obtained 98.7% accuracy. Whereas, the VGG16 model obtained an accuracy of 50%. Hence, the performance of the proposed MIDNet18 model achieved is better than VGG16. Conclusion: The proposed model is proved to be statistically significant with p value <0.001 (Independent sample t-test) than the existing model VGG16.

THF co-solvent pretreatment prevents lignin redeposition from interfering with enzymes yielding prolonged cellulase activity.

BACKGROUND: Conventional aqueous dilute sulfuric acid (DSA) pretreatment of lignocellulosic biomass facilitates hemicellulose solubilization and can improve subsequent enzymatic digestibility of cellulose to fermentable glucose. However, much of the lignin after DSA pretreatment either remains intact within the cell wall or readily redeposits back onto the biomass surface. This redeposited lignin has been shown to reduce enzyme activity and contribute to rapid enzyme deactivation, thus, necessitating significantly higher enzyme loadings than deemed economical for biofuel production from biomass. RESULTS: In this study, we demonstrate how detrimental lignin redeposition on biomass surface after pretreatment can be prevented by employing Co-solvent Enhanced Lignocellulosic Fractionation (CELF) pretreatment that uses THF-water co-solvents with dilute sulfuric acid to solubilize lignin and overcome limitations of DSA pretreatment. We first find that enzymatic hydrolysis of CELF-pretreated switchgrass can sustain a high enzyme activity over incubation periods as long as 5 weeks with enzyme doses as low as 2 mg protein/g glucan to achieve 90% yield to glucose. A modified Ninhydrin-based protein assay revealed that the free-enzyme concentration in the hydrolysate liquor, related to enzyme activity, remained unchanged over long hydrolysis times. DSA-pretreated switchgrass, by contrast, had a 40% drop in free enzymes in solution during incubation, providing evidence of enzyme deactivation. Furthermore, measurements of enzyme adsorption per gram of lignin suggested that CELF prevented lignin redeposition onto the biomass surface, and the little lignin left in the solids was mostly integral to the original lignin-carbohydrate complex (LCC). Scanning electron micrographs and NMR characterization of lignin supported this observation. CONCLUSIONS: Enzymatic hydrolysis of solids from CELF pretreatment of switchgrass at low enzyme loadings was sustained for considerably longer times and reached higher conversions than for DSA solids. Analysis of solids following pretreatment and enzymatic hydrolysis showed that prolonged cellulase activity could be attributed to the limited lignin redeposition on the biomass surface making more enzymes available for hydrolysis of more accessible glucan.

First hybrid complete genome of Aeromonas veronii reveals chromosome-mediated novel structural variant mcr-3.30 from a human clinical sample.

Recent findings demonstrate the origin of the plasmid-mediated colistin resistance gene mcr-3 from aeromonads. The present study aimed to screen for plasmid-mediated colistin resistance among 30 clinical multidrug-resistant (MDR) Aeromonas spp. PCR was used to screen for the presence of mcr-1, mcr-2, mcr-3 and mcr-4, which revealed mcr-3 in a colistin-susceptible isolate (FC951). All other isolates were negative for mcr. Sequencing of FC951 revealed that the mcr-3 (mcr-3.30) identified was different from previously reported variants and had 95.62 and 95.28 % nucleotide similarity with mcr-3.3 and mcr-3.10. Hybrid assembly using IonTorrent and MinION reads revealed structural genetic information for mcr-3.30 with an insertion of ISAs18 within the gene. Due to this, mcr-3.30 was non-expressive, which makes FC951 susceptible to colistin. Further, in silico sequence and protein structural analysis confirmed the new variant. To the best of our knowledge, this is the first report on a novel mcr-3 variant from India. The significant role of mcr-like genes in different Aeromonas species remains unknown and requires additional investigation to obtains insights into the mechanism of colistin resistance.

Ultrastructures and Mechanics of Annealed Nephila clavipes Major Ampullate Silk.

The semicrystalline protein structure and impressive mechanical properties of major ampullate (MA) spider silk make it a promising natural alternative to polyacrylonitrile (PAN) fibers for carbon fiber manufacture. However, when annealed using a similar procedure to carbon fiber production, the tensile strength and Young's modulus of MA silk decrease. Despite this, MA silk fibers annealed at 600 °C remain stronger and tougher than similarly annealed PAN but have a lower Young's modulus. Although MA silk and PAN graphitize to similar extents, annealing disrupts the hydrogen bonding that controls crystal alignment within MA silk. Consequently, unaligned graphite crystals form in annealed MA silk, causing it to weaken, while graphite crystals in PAN maintain alignment along the fiber axis, strengthening the fibers. These shortcomings of spider silk when annealed provide insights into the selection and design of future alternative carbon fiber precursors.

Birth order and the severity of illness in schizophrenia.

A proposed risk factor for schizophrenia is materno-foetal incompatibility. We tested the hypothesis that, in multiply affected families, later born children would exhibit a more severe form of schizophrenia than their older siblings. The effect of birth order on (1) severity of the worst ever episode of illness; (2) deterioration from premorbid level of functioning; (3) age of onset; (4) response to medication; and (5) illness course, was assessed in 150 sibling pairs with schizophrenia and schizoaffective disorder. We found that later birth order reduced the likelihood of regaining the premorbid level of functioning after an acute episode and was also associated with an earlier age of presentation. This study lends some support to the hypothesis that later birth order results in a more severe form of the disorder, although there are other possible explanations for our findings. Further work is needed to explore the possibility of maternal-foetal genotype incompatibility as a risk factor for schizophrenia.

Telomere dysfunction provokes regional amplification and deletion in cancer genomes.

Telomere dysfunction and associated fusion-breakage in the mouse encourages epithelial carcinogenesis and a more humanized genomic profile that includes nonreciprocal translocations (NRTs). Here, array comparative genomic hybridization was used to determine the pathogenic significance of NRTs and to determine whether telomere dysfunction also drives amplifications and deletions of cancer-relevant loci. Compared to tumors arising in mice with intact telomeres, tumors with telomere dysfunction possessed higher levels of genomic instability and showed numerous amplifications and deletions in regions syntenic to human cancer hotspots. These observations suggest that telomere-based crisis provides a mechanism of chromosomal instability, including regional amplifications and deletions, that drives carcinogenesis. This model provides a platform for discovery of genes responsible for the major cancers affecting aged humans.