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Rotavirus An infection, Illness, and Vaccine Efficiency throughout Malnourished Young children: Overview of the Literature.
This research is the first report of simple, lab-based methods for profiling of CUR based on natural or synthetic origin and identification of SC.A gradient derived base layer extractive phase was synthesized and applied for the determination of volatile organic compounds (VOCs) in saliva samples using the headspace cooled in-tube microextraction (HS-CITME) method. The base layers from three different sols of phenyltriethoxysilane (PTES), octyltrimethoxysilane (OTMS) and methyltrimethoxysilane (MTMS) as nonpolar precursors were individually dip coated on the stainless steel wires (SSW). Then, the hydrolyzed polar precursor aminopropyltriethoxysilane (APTES) reacted with the silanol groups already formed on the surface of SSWs via controlled rate infusion (CRI) method. The presence of polar and non-polar functional groups on the surface of substrate was evaluated by Fourier-transform infrared spectroscopy (FTIR) while the morphology and thickness of the most suitable gradient coating (amine/phenyl) were also investigated by scanning electron microscopy (SEM). Assessment of the gradient extractive phase efficiency was carried out determining a group of V%) (95-108 %) were achieved and low quantities of VOCs were detected.Connexin 43 (Cx43), known to form gap junction transmembrane channels between the cytoplasm of two adjacent cells, plays a key role in physiological functions, such as regulating cell growth, differentiation, and maintaining tissue homeostasis. Cashmere goat is an important farm animal that provides cashmere, which was produced by secondary hair follicles (SHF), for human consumption; however, there is no report about the role of Cx43 on the growth and development of SHF in cashmere goat. In this study, we investigated the effect of Cx43 on proliferation secondary hair follicle dermal papilla cells (SHF-DPCs) in Albas cashmere goat. In SHF-DPCs, Cx43 overexpression promoted cell proliferation and upregulated the expression of IGF-1, whereas Cx43 knockdown was associated with the opposite effects. These results suggested that Cx43 may promote cell proliferation by inducing IGF-1. Overall, our research not only contributes to a better understanding of the mechanism of the growth and development of SHF in cashmere goat, but also shed light on cashmere quality control in the future.Aflatoxin B1 (AFB1) is one of the most common contaminants of poultry feed and has been linked to adverse effects on animal health and productivity. VU0463271 In this study, the degradation of AFB1 was studied with cell-free extracts (CFE) of Trametes versicolor and Bacillus subtilis using High-Performance Liquid chromatography (HPLC). CFE from B. subtilis and T. versicolor gave 60% and 34% of AFB1 degradation respectively, while heat-inactivated extracts showed no degradation. By-products obtained at the end of AFB1 degradation were analyzed by Liquid Chromatography with tandem mass spectrometry (LC-MS/MS). After 96 h of incubation, by-products with lower m/z values were obtained with CFE from B. subtilis as compared to that from T. versicolor, indicating a higher degradation efficiency of the former. Additionally, the detection of a by-product which could correspond to AFB1-8,9 dihydrodiol - a less toxic derivative of AFB1 - after 72 and 96 h of incubation with CFE from B. subtilis, could indicate the simultaneous detoxification along with degradation of AFB1 by B. subtilis CFE.
deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem.

the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection.

four pathological groups of datasets were used for training and evaluation of the model heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45s to 0.34s in an average.

the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.
the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.
Glaucoma, a worldwide eye disease, may cause irreversible vision damage. If not treated properly at an early stage, glaucoma eventually deteriorates into blindness. Various glaucoma screening methods, e.g. Ultrasound Biomicroscopy (UBM), Optical Coherence Tomography (OCT), and Heidelberg Retinal Scanner (HRT), are available. However, retinal fundus image photography examination, because of its low cost, is one of the most common solutions used to diagnose glaucoma. Clinically, the cup-to-disk ratio is an important indicator in glaucoma diagnosis. Therefore, precise fundus image segmentation to calculate the cup-to-disk ratio is the basis for screening glaucoma.

In this paper, we propose a deep neural network that uses anatomical knowledge to guide the segmentation of fundus images, which accurately segments the optic cup and the optic disc in a fundus image to accurately calculate the cup-to-disk ratio. Optic disc and optic cup segmentation are typical small target segmentation problems in biomedical images. We propose to use an attention-based cascade network to effectively accelerate the convergence of small target segmentation during training and accurately reserve detailed contours of small targets.

Our method, which was validated in the MICCAI REFUGE fundus image segmentation competition, achieves 93.31% dice score in optic disc segmentation and 88.04% dice score in optic cup segmentation. Moreover, we win a high CDR evaluation score, which is useful for glaucoma screening.

The proposed method successfully introduce anatomical knowledge into segmentation task, and achieve state-of-the-art performance in fundus image segmentation. It also can be used for both automatic segmentation and semiautomatic segmentation with human interaction.
The proposed method successfully introduce anatomical knowledge into segmentation task, and achieve state-of-the-art performance in fundus image segmentation. It also can be used for both automatic segmentation and semiautomatic segmentation with human interaction.
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