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. Consequently, the development of innovative strategies to enhance cattle feed efficiency, while curbing environmental and financial burdens, becomes imperative. The advent of omics technologies has yielded fresh insights into metabolic health fluctuations in dairy cattle, consequently enhancing nutritional management practices. The pivotal role of the rumen microbiome in augmenting feeding efficiency by transforming low-quality feedstuffs into energy substrates for the host is underscored. This microbial community assumes focal importance within gut microbiome studies, contributing indispensably to plant fiber digestion, as well as influencing production and health variability in ruminants. Instances of compromised animal welfare can substantially modulate the microbiological composition of the rumen, thereby influencing production rates. A comprehensive global approach that targets both cattle and their rumen microbiota is paramount for enhancing feed efficiency and optimizing rumen fermentation processes. This review article underscores the factors that contribute to the establishment or restoration of the rumen microbiome post perturbations and the intricacies of host-microbiome interactions. We accentuate the elements responsible for responsible host-microbiome interactions and practical applications in the domains of animal health and production. Moreover, meticulous scrutiny of the microbiome and its consequential effects on cattle production systems greatly contributes to forging more sustainable and resilient food production systems, thereby mitigating the adverse environmental impact.

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. A computer-aided method proposed in the present work detects and then segments the exudates in the images of retina acquired using a digital fundus camera by (i) gradient method to trace the contour of exudates, (ii) marking the connected candidate pixels to remove false exudates pixels, and (iii) linking the edge pixels for the boundary extraction of exudates. The method is tested on 1307 retinal fundus images with varying characteristics. Six hundred and forty-nine images were acquired from hospital and the remaining 658 from open-source benchmark databases, namely, STARE, DRIVE MESSIDOR, DiaretDB1, and e-Ophtha. The exudates segmentation method proposed in this research work results in the retinal fundus image-based (i) accuracy of 98.04%, (ii) sensitivity of 95.345%, and (iii) specificity of 98.63%. The segmentation results for a number of exudates-based evaluations depict the average (i) accuracy of 95.68%, (ii) sensitivity of 93.44%, and (iii) specificity of 97.22%. The substantial combined performance at image and exudates-based evaluations proves the contribution of the proposed method in mass screening as well as treatment process of background diabetic retinopathy.

Use of Nanomaterials for Diagnosis and Treatment: The Advancement of Next-Generation Antiviral Therapy.

Globally, viral illness propagation is the leading cause of morbidity and death, causing wreaking havoc on socioeconomic development and health care systems. The rise of infected individuals has outpaced the existing critical care facilities. Early and sophisticated methods are desperately required in this respect to halt the spread of the infection. Therefore, early detection of infectious agents and an early treatment approach may help minimize viral outbreaks. Conventional point-of-care diagnostic techniques such as computed tomography scan, quantitative real time polymerase chain reaction (qRT-PCR), X-ray, and immunoassay are still deemed valuable. However, the labor demanding, low sensitivity, and complex infrastructure needed for these methods preclude their use in distant areas. Nanotechnology has emerged as a potentially transformative technology due to its promise as an effective theranostic platform for diagnosing and treating viral infection, circumventing the limits of traditional techniques. Their unique physical and chemical characteristics make nanoparticles (NPs) advantageous for drug delivery platforms due to their size, encapsulation efficiency, improved bioavailability, effectiveness, immunogenicity, and antiviral response. This study discusses the recent research on nanotechnology-based treatments designed to combat new viruses.

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. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.

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. To address this issue and to establish anatomical correspondence between multi-phase CT images of the liver, a features-based technique is developed in this article. The proposed model uses fixed and moving images as inputs, with both images having the same dimensions. The registered images are the two images that differ in terms of their combinations/colors. In the output registered images, the tumor in the liver portion has classes with viewpoints. There is an appropriate way to view the tumor, and the output registered images should permit concluding that the registered image of the delayed phases, with a longer delay time, contains the most region portion within the output registered image. The detected and matched values are greater than the values of the feature outcomes. Having a large tumor provides valuable information in the presenting form for discussing the variation of the various phases and delayed testing results. And this will aid the radiologist in making an accurate diagnosis.

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. Online investigation by traditional methods is not sufficient for the detection and identification of such compounds because of the high costs and their complexity. Advancement in the field of genetic engineering provides a potential opportunity to use genetically modified microorganisms. In this regard, whole-cell-based microbial biosensors (WCBMB) represent an essential tool that couples genetically engineered organisms with an operator/promoter derived from a heavy metal-resistant operon combined with a regulatory protein in the gene circuit. The plasmid controls the expression of the reporter gene, such as gfp, luc, lux and lacZ, to an inducible gene promoter and has been widely applied to assay toxicity and bioavailability. This review summarises the recent trends in the development and application of microbial biosensors and the use of mobile genes for biomedical and environmental safety concerns.

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. The proposed work is to detect the presence of a tumor region in the liver and classify the different stages of the tumor from CT images. CT images of the liver have been classified between normal and tumor classes. In addition, CT images of the tumor have been classified between Hepato Cellular Carcinoma (HCC) and Metastases (MET). The performance of six different classifiers was evaluated on different parameters. The accuracy achieved for different classifiers varies between 98.39% and 100% for tumor identification and between 76.38% and 87.01% for tumor classification. To further, improve performance, a multi-level ensemble model is developed to detect a tumor (liver cancer) and to classify between HCC and MET using features extracted from CT images. The k-fold cross-validation (CV) is also used to justify the robustness of the classifiers. Compared to the individual classifier, the multi-level ensemble model achieved high accuracy in both the detection and classification of different tumors. This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.

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 T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

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. Worse eye of each patient was selected for placement of CLS. The timing of the highest signal (IOP related peak) was noted in 24 hour CLS graph and if it fell within the time frame of 11 pm to 5 am, it was labeled as 'nocturnal IOP related peak'. Results: Progressors were found to be significantly more prone to night spike than Non Progressors (χ2 = 6.812; n = 40; P = 0.009), thus, showing a definite association between the two. Association between Nocturnal IOP related peak and various other variables like age, gender, mean daytime IOP and systemic illness was studied. A positive correlation was established between female gender and Nocturnal IOP related spike with a significantly higher proportion of females showing night spike than their male counterparts (χ2 = 5.763; n = 40; P = 0.016). Other parameters did not show any significant relationship with Nocturnal IOP related spike. Conclusion: Dynamic 24 hour recording by CLS is beneficial in detecting nocturnal IOP-related peak, and thus, can potentially improve the clinical care of glaucoma patients, especially those showing progression.

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. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

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. Computer-aided methods based on retinal fundus images support quick diagnosis, impart an additional perspective during decision-making, and behave as an efficient means to assess response of treatment on retinal abnormalities. However, in order to evaluate computer-aided systems, a benchmark database of clinical retinal fundus images is required. Therefore, a representative database comprising of 2942 clinical retinal fundus images is developed and presented in this work. This clinical database, having varying attributes such as position, dimensions, shapes, and color, is formed to evaluate the generalization capability of computer-aided systems for diabetic retinopathy diagnosis. A framework for the development of benchmark retinal fundus images database is also proposed. The developed database comprises of medical image annotations for each image from expert ophthalmologists corresponding to anatomical structures, retinal lesions and stage of diabetic retinopathy. In addition, the substantial performance comparison capability of the proposed database aids in analyzing candidature of different methods, and subsequently its usage in medical practice for real-time applications.

Mapping the Salt Stress-Induced Changes in the Root miRNome in Pokkali Rice.

A plant's response to stress conditions is governed by intricately coordinated gene expression. The microRNAs (miRs) have emerged as relatively new players in the genetic network, regulating gene expression at the transcriptional and post-transcriptional level. In this study, we performed comprehensive profiling of miRs in roots of the naturally salt-tolerant Pokkali rice variety to understand their role in regulating plant physiology in the presence of salt. For comparisons, root miR profiles of the salt-sensitive rice variety Pusa Basmati were generated. It was seen that the expression levels of 65 miRs were similar for roots of Pokkali grown in the absence of salt (PKNR) and Pusa Basmati grown in the presence of salt (PBSR). The salt-induced dis-regulations in expression profiles of miRs showed controlled changes in the roots of Pokkali (PKSR) as compared to larger variations seen in the roots of Pusa Basmati. Target analysis of salt-deregulated miRs identified key transcription factors, ion-transporters, and signaling molecules that act to maintain cellular Ca2+ homeostasis and limit ROS production. These miR:mRNA nodes were mapped to the Quantitative trait loci (QTLs) to identify the correlated root traits for understanding their significance in plant physiology. The results obtained indicate that the adaptability of Pokkali to excess salt may be due to the genetic regulation of different cellular components by a variety of miRs.

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. Our proposed research focuses on the classification of medical images with respect to the classification technique artificial neural network (ANN) to classify an image as normal or abnormal. In the pre-processing step, the input image is selected from the database and adaptive median filtering is used for noise removal. For better enhancement, histogram equalization (HE) is done in the noise-removed images. In the pre-processed images, the texture feature such as gray-level co-occurrence matrix (GLCM) and statistical features are extracted. From the extensive feature set, optimal features are selected using the optimal kernel K-means (OKK-means) clustering algorithm along with the oppositional firefly algorithm (OFA). The proposed method obtained 97.5% accuracy in the classification when compared to the existing method.

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix.

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix.

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. MATERIALS AND METHODS: In a prospective, randomized trial 90 POAG patients (180 eyes; age above 45 y) were assigned to a waitlist control or mindfulness meditation group which practiced daily for 21 days. We measured IOP (primary endpoint), quality of life (QOL), stress-related serum biomarkers [cortisol, ß-endorphins, IL6, TNF-α, brain-derived neurotrophic factor (BDNF), reactive oxygen species (ROS), total antioxidant capacity (TAC)], and whole genome expression. RESULTS: Between-group comparisons revealed significantly lowered IOP in meditators (OD: 18.8 to 12.7, OS 19.0 to 13.1 mm Hg) which correlated with significantly lowered stress-biomarker levels including cortisol (497.3 to 392.3 ng/mL), IL6 (2.8 to 1.5 ng/mL), TNF-α (57.1 to 45.4 pg/mL), ROS (1625 to 987 RLU/min/104 neutrophils), and elevated ß-endorphins (38.4 to 52.7 pg/mL), BDNF (56.1 to 83.9 ng/mL), and TAC (5.9 to 9.3) (all P<0.001). These changes correlated well with gene expression profiling. Meditators improved in QOL (P<0.05). CONCLUSIONS: A short course of mindfulness-based stress reduction by meditation in POAG, reduces IOP, improves QOL, normalizes stress biomarkers, and positively modifies gene expression. Mindfulness meditation can be recommended as adjunctive therapy for POAG.

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. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of "handcrafted" texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of "handcrafted" texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

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. Methods: One hundred and twenty eyes (30 healthy and 90 glaucomatous [30 mild, 30 moderate, and 30 advanced glaucoma]) of 65 participants (15 healthy, 50 glaucomatous [15 mild, 15 moderate, and 20 advanced glaucoma]) underwent Cirrus and RTVue OCT scanning on a single visit. Results: Average RNFL thickness and superior RNFL thickness of both the devices and inferior (ganglion cell complex [GCC] of RTVue device best differentiated normals from all stage glaucomatous eyes (P > 0.05). Cirrus average RNFL thickness and superior RNFL thickness performed better than other parameters (P < 0.05) in differentiating early glaucoma from moderate and advanced. In differentiating advanced from early and moderate glaucoma, RTVue average, superior, and inferior RNFL thickness and inferior GCC parameters had the highest discriminating ability (P < 0.05). Conclusion: Overall, average RNFL thickness had the highest ability to distinguish different stages of the disease. No significant difference was found between RTVue and Cirrus OCT device in different severity levels. No significant difference was observed between RNFL and macular parameters in different stages of glaucoma.