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.

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.

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.

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.

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.

Brain tumor classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison with AlexNet.

Aim: This research work aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from a magnetic resonance imaging (MRI) dataset. We developed a new MIDNet18 CNN architecture in comparison with the AlexNet CNN architecture for classifying normal brain images from brain tumor images.

Materials And Methods: The novel MIDNet18 CNN architecture comprises 14 convolutional layers, seven pooling layers, four dense layers, and one classification layer.

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.

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.

Ultrastructures and Mechanics of Annealed 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.

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.

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.