In vivo Evaluation Of Antidiarrhoeal Activity Of Rhus semialata Fruit Extract In Rats

Rhus semialata Murr. (Anacardiaceae) is a deciduous tree of north eastern India. The fruit of this plant is traditionally used to control diarrhoea and dysentery. The Present study was undertaken to evaluate anti-diarrhoeal potency of methanol extract of fruits of Rhus semialata using Wister albino rats to substantiate folklore claims. The extract at graded doses (100, 200, 400 and 600 mg/kg body weight) was investigated for anti-diarrhoeal activity in term of reduction in the rate of defecation in castor oil induced diarrhoea. To understand the mechanism of its antidiarrhoeal activity, the gastrointestinal transit and PGE2-induced intestinal fluid accumulation (enteropooling) were further evaluated. At graded doses, the extract showed a remarkable anti-diarrhoeal activity evidenced by the reduction in the rate of defecation up to 80.70 % of control diarrhoeal animals at the dose of 600 mg/kg body weight. Results are comparable to that of standard drug diphenoxylate (50 mg/kg body weight). Extract produced profound decrease in intestinal transit (8.02 – 47.05 %) at selected doses comparable to that of single intraperitoneal injection of standard drug atropine sulphate at doses of 0.1 mg/kg body weight. It significantly inhibited PGE2 - induced enteropooling (21.98 – 56.03 %). The results indicated that the methanol extract of the fruits of R. semialata possesses significant anti-diarrhoeal effect and substantiated the use of this herbal remedy as a non-specific treatment for diarrhoea in folk medicine. Keywords: Atropin sulphate, Castor oil, Diarrhoea, Diphenoxylate, Rhus semialata. African Journal of Traditional and Complementary Medicine Vol. 5 (1) 2008: pp. 97-10

Transcriptomic and connectomic correlates of differential spatial patterning among gliomas.

Unravelling the complex events driving grade-specific spatial distribution of brain tumour occurrence requires rich datasets from both healthy individuals and patients. Here, we combined open-access data from The Cancer Genome Atlas, the UKBiobank and the Allen Brain Human Atlas to disentangle how the different spatial occurrences of Glioblastoma Multiforme (GBM) and Low-Grade Gliomas (LGG) are linked to brain network features and the normative transcriptional profiles of brain regions. From MRI of brain tumour patients we first constructed a grade-related frequency map of the regional occurrence of LGG and the more aggressive GBM. Using associated mRNA transcription data, we derived a set of differential gene expressions from GBM and LGG tissues of the same patients. By combining the resulting values with normative gene expressions from postmortem brain tissue, we constructed a grade-related expression map indicating which brain regions express genes dysregulated in aggressive gliomas. Additionally, we derived an expression map of genes previously associated with tumour subtypes in a GWAS study (tumour-related genes). There were significant associations between grade-related frequency, grade-related expression, and tumour-related expression maps, as well as functional brain network features (specifically, nodal strength and participation coefficient) that are implicated in neurological and psychiatric disorders. These findings identify brain network dynamics and transcriptomic signatures as key factors in regional vulnerability for GBM and LGG occurrence, placing primary brain tumours within a well-established framework of neurological and psychiatric cortical alterations

Brucellosis remains a neglected disease inthe developing world: a call forinterdisciplinary action

Brucellosis places significant burdens on the human healthcare system and limits the economic growth of individuals, communities, and nations where such development is especially important to diminish the prevalence of poverty. The implementation of public policy focused on mitigating the socioeconomic effects of brucellosis in human and animal populations is desperately needed. When developing a plan to mitigate the associated consequences, it is vital to consider both the abstract and quantifiable effects. This requires an interdisciplinary and collaborative, or One Health, approach that consists of public education, the development of an infrastructure for disease surveillance and reporting in both veterinary and medical fields, and campaigns for control in livestock and wildlife species

MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways

Abstract Background Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases. Results This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes. Conclusion Our experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research

All solutions of the localization equations for N=2 quantum black hole entropy

We find the most general bosonic solution to the localization equations describing the contributions to the quantum entropy of supersymmetric black holes in four-dimensional N=2 supergravity coupled to n_v vector multiplets. This requires the analysis of the BPS equations of the corresponding off-shell supergravity (including fluctuations of the auxiliary fields) with AdS2 \times S2 attractor boundary conditions. Our work completes and extends the results of arXiv:1012.0265 that were obtained for the vector multiplet sector, to include the fluctuations of all the fields of the off-shell supergravity. We find that, when the auxiliary SU(2) gauge field strength vanishes, the most general supersymmetric configuration preserving four supercharges is labelled by n_v+1 real parameters corresponding to the excitations of the conformal mode of the graviton and the scalars of the n_v vector multiplets. In the general case, the localization manifold is labelled by an additional SU(2) triplet of one-forms and a scalar function.Comment: 27 page

DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to SAE Level-3. In this paper, we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which includes real and simulated driving scenarios: distraction, gaze allocation, drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A comparison with existing similar datasets is included, which shows the DMD is more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated by extracting a subset of it, the dBehaviourMD dataset, containing 13 distraction activities, prepared to be used in DL training processes. Furthermore, we propose a robust and real-time driver behaviour recognition system targeting a real-world application that can run on cost-efficient CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated with different types of fusion strategies, which all reach enhanced accuracy still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and Robotic

Handling linkage disequilibrium in qualitative trait linkage analysis using dense SNPs: a two-step strategy

<p>Abstract</p> <p>Background</p> <p>In affected sibling pair linkage analysis, the presence of linkage disequilibrium (LD) has been shown to lead to overestimation of the number of alleles shared identity-by-descent (IBD) among sibling pairs when parents are ungenotyped. This inflation results in spurious evidence for linkage even when the markers and the disease locus are not linked. In our study, we first theoretically evaluate how inflation in IBD probabilities leads to overestimation of a nonparametric linkage (NPL) statistic under the assumption of linkage equilibrium. Next, we propose a two-step processing strategy in order to systematically evaluate approaches to handle LD. Based on the observed inflation of expected logarithm of the odds ratio (LOD) from our theoretical exploration, we implemented our proposed two-step processing strategy. Step 1 involves three techniques to filter a dense set of markers. In step 2, we use the selected subset of markers from step 1 and apply four different methods of handling LD among dense markers: 1) marker thinning (MT); 2) recursive elimination; 3) SNPLINK; and 4) LD modeling approach in MERLIN. We evaluate relative performance of each method through simulation.</p> <p>Results</p> <p>We observed LOD score inflation only when the parents were ungenotyped. For a given number of markers, all approaches evaluated for each type of LD threshold performed similarly; however, RE approach was the only one that eliminated the LOD score bias. Our simulation results indicate a reduction of approximately 75% to complete elimination of the LOD score inflation while maintaining the information content (IC) when setting a tolerable squared correlation coefficient LD threshold (r²) above 0.3 for or 2 SNPs per cM using MT.</p> <p>Conclusion</p> <p>We have established a theoretical basis of how inflated IBD information among dense markers overestimates a NPL statistic. The two-step processing strategy serves as a useful framework to systematically evaluate relative performance of different methods to handle LD.</p

Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation

Domestication and selective breeding has resulted in over 1000 extant cattle breeds. Many of these breeds do not excel in important traits but are adapted to local environments. These adaptations are a valuable source of genetic material for efforts to improve commercial breeds. As a step toward this goal we identified candidate regions to be under selection in genomes of nine Russian native cattle breeds adapted to survive in harsh climates. After comparing our data to other breeds of European and Asian origins we found known and novel candidate genes that could potentially be related to domestication, economically important traits and environmental adaptations in cattle. The Russian cattle breed genomes contained regions under putative selection with genes that may be related to adaptations to harsh environments (e.g., AQP5, RAD50, and RETREG1). We found genomic signatures of selective sweeps near key genes related to economically important traits, such as the milk production (e.g., DGAT1, ABCG2), growth (e.g., XKR4), and reproduction (e.g., CSF2). Our data point to candidate genes which should be included in future studies attempting to identify genes to improve the extant breeds and facilitate generation of commercial breeds that fit better into the environments of Russia and other countries with similar climates

Approaches to Understanding COVID-19 and its Neurological Associations

There is an accumulating volume of research into neurological manifestations of COVID-19. However, inconsistent study designs, inadequate controls, poorly-validated tests, and differing settings, interventions, and cultural norms weaken study quality, comparability, and thus the understanding of the spectrum, burden and pathophysiology of these complications. Therefore, a global COVID-19 Neuro Research Coalition, together with the WHO, has reviewed reports of COVID-19 neurological complications and harmonised clinical measures for future research. This will facilitate well-designed studies using precise, consistent case definitions of SARS-CoV2 infection and neurological complications, with standardised forms for pooled data analyses that non-specialists can use, including in low-income settings

What is the Role of Community Capabilities for Maternal Health? An Exploration of Community Capabilities as Determinants to Institutional Deliveries in Bangladesh, India, and Uganda

Background: While community capabilities are recognized as important factors in developing resilient health systems and communities, appropriate metrics for these have not yet been developed. Furthermore, the role of community capabilities on access to maternal health services has been underexplored. In this paper, we summarize the development of a community capability score based on the Future Health System (FHS) project’s experience in Bangladesh, India, and Uganda, and, examine the role of community capabilities as determinants of institutional delivery in these three contexts. Methods: We developed a community capability score using a pooled dataset containing cross-sectional household survey data from Bangladesh, India, and Uganda. Our main outcome of interest was whether the woman delivered in an institution. Our predictor variables included the community capability score, as well as a series of previously identified determinants of maternal health. We calculate both population-averaged effects (using GEE logistic regression), as well as sub-national level effects (using a mixed effects model). Results: Our final sample for analysis included 2775 women, of which 1238 were from Bangladesh, 1199 from India, and 338 from Uganda. We found that individual-level determinants of institutional deliveries, such as maternal education, parity, and ante-natal care access were significant in our analysis and had a strong impact on a woman’s odds of delivering in an institution. We also found that, in addition to individual-level determinants, greater community capability was significantly associated with higher odds of institutional delivery. For every additional capability, the odds of institutional delivery would increase by up to almost 6 %. Conclusion: Individual-level characteristics are strong determinants of whether a woman delivered in an institution. However, we found that community capability also plays an important role, and should be taken into account when designing programs and interventions to support institutional deliveries. Consideration of individual factors and the capabilities of the communities in which people live would contribute to the vision of supporting people-centered approaches to health