Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production.

Ruminant production holds a pivotal position within the global animal production and agricultural sectors. As population growth escalates, posing environmental challenges, a heightened emphasis is directed toward refining ruminant production systems. Recent investigations underscore the connection between the composition and functionality of the rumen microbiome and economically advantageous traits in cattle.

Automated Detection and Segmentation of Exudates for the Screening of Background Retinopathy.

Exudate, an asymptomatic yellow deposit on retina, is among the primary characteristics of background diabetic retinopathy. Background diabetic retinopathy is a retinopathy related to high blood sugar levels which slowly affects all the organs of the body. The early detection of exudates aids doctors in screening the patients suffering from background diabetic retinopathy.

Data management strategy for a collaborative research center.

The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands.

Feature based CT image registration of liver cancer.

Computer-aided diagnostic systems (CADS) assist radiologists in classifying liver cancer using computed tomography (CT) images. To enhance diagnosis performance, image sequences are recorded at various time points in a single/multi-view format. Mutual information (MI) is a widely used medical image registration metric with a high rate of success, but it can result in misregistration due to a lack of spatial details.

In-situ monitoring of xenobiotics using genetically engineered whole-cell-based microbial biosensors: recent advances and outlook.

Industrialisation, directly or indirectly, exposes humans to various xenobiotics. The increased magnitude of chemical pesticides and toxic heavy metals in the environment, as well as their intrusion into the food chain, seriously threatens human health. Therefore, the surveillance of xenobiotics is crucial for social safety and security.

Ensembled liver cancer detection and classification using CT images.

Computed tomography (CT) images are commonly used to diagnose liver disease. It is sometimes very difficult to comment on the type, category and level of the tumor, even for experienced radiologists, directly from the CT image, due to the varying intensities. In recent years, it has been important to design and develop computer-assisted imaging techniques to help doctors/physicians improve their diagnosis.

Relationship between nocturnal intraocular pressure-related peak recorded by contact lens sensor and disease progression in treated glaucomatous eyes.

Purpose: The aim of this study is to study the association between Nocturnal Intraocular Pressure (IOP) related Peak recorded by a Contact Lens Sensor (CLS) and glaucoma progression in treated glaucomatous eyes.

Methods: Institutional study in which forty glaucoma patients were recruited from glaucoma clinic. A total of 19 patients were labeled as progressors on current anti-glaucoma treatment despite controlled day time IOP whereas twenty one patients were clinically stable showing no progression.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score.

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets , and denoting the degree of truth, indeterminacy, and falseness, respectively.

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives.

Construction of benchmark retinal image database for diabetic retinopathy analysis.

Diabetic retinopathy, a symptomless medical condition of diabetes, is one of the significant reasons of vision impairment all over the world. The prior detection and diagnosis can decrease the occurrence of acute vision loss and enhance efficiency of treatment. Fundus imaging, a non-invasive diagnostic technique, is the most frequently used mode for analyzing retinal abnormalities related to diabetic retinopathy.

Effective segmentation and classification of tumor on liver MRI and CT images using multi-kernel K-means clustering.

Our proposed research technique intends to provide an effective liver magnetic resonance imaging (MRI) and computed tomography (CT) scan image classification which would play a significant role in medical dataset especially in feature selection and classification. There are a number of existing research works classifying the liver tumor disease. Early detection of liver tumor will help the patients to get cured rapidly.

Mindfulness Meditation Reduces Intraocular Pressure, Lowers Stress Biomarkers and Modulates Gene Expression in Glaucoma: A Randomized Controlled Trial.

Background: Reducing intraocular pressure (IOP) in primary open-angle glaucoma (POAG) is currently the only approach to prevent further optic nerve head damage. However, other mechanisms such as ischemia, oxidative stress, glutamate excitotoxicity, neurotrophin loss, inflammation/glial activation, and vascular dysregulation are not addressed. Because stress is a key risk factor affecting these mechanisms, we evaluated whether mindfulness-based stress reduction can lower IOP and normalize typical stress biomarkers.

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model.

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality.

Discriminating ability of Cirrus and RTVue optical coherence tomography in different stages of glaucoma.

Purpose: The aim of this study is to determine which parameter of Cirrus and RTVue optical coherence tomography (OCT) has the highest ability to discriminate between early, moderate, and advanced glaucoma. Simultaneously, to compare the performance of the two OCT devices in terms of their ability to differentiate the three stages of glaucoma. Further, to analyze the macular parameters of both devices and compare them with the conventional retinal nerve fiber layer (RNFL) parameters.

Impact of Modified Anisotropic Diffusion-based Enhancement Method in Computer-Aided Classification of Focal Liver Lesions.

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion-based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves.

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities.

A finite element study to determine the occurrence of abfraction and displacement due to various occlusal forces and with different alveolar bone height.

Background: Noncarious cervical lesions (NCCLs) are rarely described in the periodontal literature, perhaps because no direct link between NCCLs and periodontal lesions has been demonstrated.

Aim: The aim of this study is to determine the stress and displacement produced in the tooth at different bone levels under different occlusal load using finite element model (FEM) study.

Materials And Methods: Four FEMs of maxillary incisor were designed consisting of the tooth, pulp, periodontal ligament, and alveolar bone at the various level of bone height (25%, 50%, and 75%).

Structure based virtual screening of ligands to identify cysteinyl leukotriene receptor 1 antagonist.

Montelukast and Zafirlukast are known leukotriene receptor antagonists prescribed in asthma treatment. However, these fall short as mono therapy and are frequently used in combination with inhaled glucocorticosteroids with or without long acting beta 2 agonists. Therefore, it is of interest to apply ligand and structure based virtual screening strategies to identify compounds akin to lead compounds Montelukast and Zafirlukast.