PHYTOCHEMICAL SCREENING OF SAPTAPARNA (ALSTONIA SCHOLARIS R. Br.) BARK

The Ayurvedic Pharmaceutical Industries use barks of many herbal drugs like Neema, Vata, Udumbara, Parisha, Saptaparna, Plaksha, Baboola etc. for the manufacturing of different herbal medicines. The stem bark of Alstonia scholaris R. Br. commonly known as Saptaparna in Ayurved, is in demand, due to its antipyretic, galactogogue, cardiotonic, anticancer, anti-helminthic etc. activities. But, the identification and separation of these barks drugs from each other is very difficult. Hence, a preliminary study has been done to ensure the basic phytochemical profile of A. scholaris R. Br. stem bark for identification of herbal drug. Physicochemical parameters, preliminary phytochemical screening, quantitative estimation of alkaloid and Thin Layer Chromatography (TLC) were carried out in the present study. Physicochemical data revealed, there is more amount of water soluble extractive value (27.10% w/w) than alcohol soluble extractive value (7.40% w/w). Facts of phytochemical screening showed presents of alkaloid, carbohydrate, tannin, saponin, flavanoids and cardiotonic glycosides in the sample. Result of TLC identification showed 7, 8, and 6 spots under short UV, long UV and after spray reagent respectively. The present study on phytochemical investigation of A. scholaris R. Br. bark will be helpful in developing standards for quality, purity and sample identification of this plant

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

The first recorded outbreak of cryptosporidiosis due to Cryptosporidium cuniculus (formerly rabbit genotype), following a water quality incident

Background: We report the first identified outbreak of cryptosporidiosis with Cryptosporidium cuniculus following a water quality incident in Northamptonshire, UK. Methods: A standardised, enhanced Cryptosporidium exposure questionnaire was administered to all cases of cryptosporidiosis after the incident. Stool samples, water testing, microscopy slides and rabbit gut contents positive for Cryptosporidium were typed at the Cryptosporidium Reference Unit, Singleton Hospital, Swansea. Results: Twenty-three people were microbiologically linked to the incident although other evidence suggests an excess of 422 cases of cryptosporidiosis above baseline. Most were adult females; unusually for cryptosporidiosis there were no affected children identified under the age of 5 years. Water consumption was possibly higher than in national drinking water consumption patterns. Diarrhoea duration was negatively correlated to distance from the water treatment works where the contamination occurred. Oocyst counts were highest in water storage facilities. Conclusions: This outbreak is the first caused by C. cuniculus infection to have been noted and it has conclusively demonstrated that this species can be a human pathogen. Although symptomatically similar to cryptosporidiosis from C. parvum or C. hominis, this outbreak has revealed some differences, in particular no children under 5 were identified and females were over-represented. These dissimilarities are unexplained although we postulate possible explanations

Comparative gene expression study between two turmeric (Curcuma longa L.) cultivars

Two turmeric (Curcuma longa L.) cultivars differing in curcumin content viz GNT-2 (4.6 % curcumin) and NDH-98 (1.6% curcumin) were selected for comparative gene expression study in association with total curcumin contents. Sampling was done at six months after planting in open field condition. Differential gene expression patterns were observed between two cultivars by reverse transcription quantitative real time polymerase chain reaction (RT-qPCR), and total curcumin contents were quantified using high performance liquid chromatography (HPLC). Low curcumin yielding cultivar, NDH-98, exhibited higher expression of DCS and CURS3 whereas lower expression of CURS1 and CURS2. However, opposite pattern was observed in a high curcumin yielding cultivar, GNT-2, where DCS and CURS3 expressions were lower but CURS1 and CURS2 expressions were higher. CURS3 showed similar expression between both cultivars. CURS1 and CURS2 expression patterns showed more closer association than DCS and CURS3 gene expression patterns with each other. Differential gene expression patterns could be predictively associated with differential curcuminoids concentrations in turmeric cultivars

New approaches to measuring anthelminthic drug efficacy: parasitological responses of childhood schistosome infections to treatment with praziquantel

By 2020, the global health community aims to control and eliminate human helminthiases, including schistosomiasis in selected African countries, principally by preventive chemotherapy (PCT) through mass drug administration (MDA) of anthelminthics. Quantitative monitoring of anthelminthic responses is crucial for promptly detecting changes in efficacy, potentially indicative of emerging drug resistance. Statistical models offer a powerful means to delineate and compare efficacy among individuals, among groups of individuals and among populations.; We illustrate a variety of statistical frameworks that offer different levels of inference by analysing data from nine previous studies on egg counts collected from African children before and after administration of praziquantel.; We quantify responses to praziquantel as egg reduction rates (ERRs), using different frameworks to estimate ERRs among population strata, as average responses, and within strata, as individual responses. We compare our model-based average ERRs to corresponding model-free estimates, using as reference the World Health Organization (WHO) 90 % threshold of optimal efficacy. We estimate distributions of individual responses and summarize the variation among these responses as the fraction of ERRs falling below the WHO threshold.; Generic models for evaluating responses to anthelminthics deepen our understanding of variation among populations, sub-populations and individuals. We discuss the future application of statistical modelling approaches for monitoring and evaluation of PCT programmes targeting human helminthiases in the context of the WHO 2020 control and elimination goals

Influence of wiring cost on the large-scale architecture of human cortical connectivity

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio