Noninvasive in vivo imaging of diabetes-induced renal oxidative stress and response to therapy using hyperpolarized 13C dehydroascorbate magnetic resonance.

Oxidative stress has been proposed to be a unifying cause for diabetic nephropathy and a target for novel therapies. Here we apply a new endogenous reduction-oxidation (redox) sensor, hyperpolarized (HP) (13)C dehydroascorbate (DHA), in conjunction with MRI to noninvasively interrogate the renal redox capacity in a mouse diabetes model. The diabetic mice demonstrate an early decrease in renal redox capacity, as shown by the lower in vivo HP (13)C DHA reduction to the antioxidant vitamin C (VitC), prior to histological evidence of nephropathy. This correlates with lower tissue reduced glutathione (GSH) concentration and higher NADPH oxidase 4 (Nox4) expression, consistent with increased superoxide generation and oxidative stress. ACE inhibition restores the HP (13)C DHA reduction to VitC with concomitant normalization of GSH concentration and Nox4 expression in diabetic mice. HP (13)C DHA enables rapid in vivo assessment of altered redox capacity in diabetic renal injury and after successful treatment

A survey on test suite reduction frameworks and tools

Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks

UML models consistency management: guidelines for software quality manager

Unified Modeling Language (UML) has become the de-facto standard to design today’s large-size object-oriented systems. However, focusing on multiple UML diagrams is a main cause of breaching the consistency problem, which ultimately reduces the overall software model’s quality. Consistency management techniques are widely used to ensure the model consistency by correct model-to-model and model-to-code transformation. Consistency management becomes a promising area of research especially for model-driven architecture. In this paper, we extensively review UML consistency management techniques. The proposed techniques have been classified based on the parameters identified from the research literature. Moreover, we performed a qualitative comparison of consistency management techniques in order to identify current research trends, challenges and research gaps in this field of study. Based on the results, we concluded that researchers have not provided more attention on exploring inter-model and semantic consistency problems. Furthermore, state-of-the-art consistency management techniques mostly focus only on three UML diagrams (i.e., class, sequence and state chart) and the remaining UML diagrams have been overlooked. Consequently, due to this incomplete body of knowledge, researchers are unable to take full advantage of overlooked UML diagrams, which may be otherwise useful to handle the consistency management challenge in an efficient manner

Hyperpolarized 13C Spectroscopic Evaluation of Oxidative Stress in a Rodent Model of Steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) has become highly prevalent, now considered the most common liver disease in the western world. Approximately one-third of patients with NASH develop non-alchoholic steatohepatitis (NASH), histologically defined by lobular and portal inflammation, and accompanied by marked oxidative stress. Patients with NASH are at increased risk for cirrhosis and hepatocellular carcinoma, and diagnosis currently requires invasive biopsy. In animal models of NASH, particularly the methionine-choline deficient (MCD) model, profound changes are seen in redox enzymes and key intracellular antioxidants. To study antioxidant status in NASH non-invasively, we applied the redox probe hyperpolarized [1-13C] dehydroascorbic acid (HP DHA), which is reduced to Vitamin C (VitC) rapidly in the normal liver. In MCD mice, we observed a significant decrease in HP DHA to VitC conversion that accompanied hepatic fat deposition. When these animals were subsequently placed on a normal diet, resonance ratios reverted to those seen in control mice. These findings suggest that HP DHA, a potentially clinically translatable imaging agent, holds special promise in imaging NASH and other metabolic syndromes, to monitor disease progression and response to targeted therapies

Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data

Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches

Retinal Angiogenesis Suppression through Small Molecule Activation of p53

Neovascular age-related macular degeneration is a leading cause of irreversible vision loss in the Western world. Cytokine-targeted therapies (such as anti-vascular endothelial growth factor) are effective in treating pathologic ocular angiogenesis, but have not led to a durable effect and often require indefinite treatment. Here, we show that Nutlin-3, a small molecule antagonist of the E3 ubiquitin protein ligase MDM2, inhibited angiogenesis in several model systems. We found that a functional p53 pathway was essential for Nutlin-3-mediated retinal antiangiogenesis and disruption of the p53 transcriptional network abolished the antiangiogenic activity of Nutlin-3. Nutlin-3 did not inhibit established, mature blood vessels in the adult mouse retina, suggesting that only proliferating retinal vessels are sensitive to Nutlin-3. Furthermore, Nutlin-3 inhibited angiogenesis in nonretinal models such as the hind limb ischemia model. Our work demonstrates that Nutlin-3 functions through an antiproliferative pathway with conceivable advantages over existing cytokine-targeted antiangiogenesis therapies

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

Adaptive immune repertoires are composed by the ensemble of B and T cell receptors (BCR, TCR) within an individual and reflect both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Such high-dimensional datasets enable the molecular quantification of clonal selection of B and T cells across a wide variety of conditions such as infection and disease. Due to costs, time required for the analysis and current practices of academic publishing, small-scale sequencing studies are often not made publicly available, despite having informative potential to elucidate immunological principles and guide future-studies. Here, we performed single-cell sequencing of B and T cells to profile clonal selection across murine models of viral infection and autoimmune disease. Specifically, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following acute and chronic viral infection, CD8+ T cells with binding specificity restricted to two distinct peptides of lymphocytic choriomeningitis virus, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate repertoire features such as clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T cell inflation, and regulation. Together, this dataset provides a resource for experimental and computational immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets