Analgesic and anti-inflammatory activity of hydroalcoholic extract of Piper betle leaves in experimental animals

Background: Piper betle leaf, commonly known as ‘paan’ has long been known for its various medicinal properties in traditional medicine but certain properties have remained less explored. We tried to assess the analgesic and anti-inflammatory activities of Piper betle leaves.Methods: Hydroalcoholic extract of Piper betle leaves (HEPBL) was extracted using soxhlet apparatus and its phytochemical analysis was performed. Wistar rats and Albino mice were used for all the experiments. Acute toxicity study was also done according to OECD guideline no.425 and the test doses were decided accordingly. The experimental models of tail-flick method and acetic acid induced writhing were used to study the analgesic activity whereas carrageenan induced paw edema and cotton pellet granuloma models were used for anti-inflammatory action. Statistical analysis was performed using one-way analysis of variance (ANOVA) followed by Dunnett's test.Results: HEPBL showed significant analgesic activity at the doses of 100 mg/kg and 200 mg/kg, and showed significant anti-inflammatory activity at the doses of 50 mg/kg, 100 mg/kg and 200 mg/kg. The sub-therapeutic dose of HEPBL at 50 mg/kg also potentiated the analgesic effect of sub-therapeutic doses of standard analgesics. The analgesic and anti-inflammatory activity of P.betle may be attributed to the presence of various phyto constituents’ viz. flavonoids, tannins, phenols and glycosides.Conclusions: HEPBL has significant analgesic and anti-inflammatory activity in experimental animals in our study

Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.Comment: Accepted in SmartComp 202

Prediction of IL4 Inducing Peptides

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response

Influence of Al doping in LaCoO3 on structural, electrical and magnetic properties

We report investigations on polycrystalline LaCo1-xAlxO3 (x = 0-0.9) bulk samples. The solid state synthesized samples showed a coexistence of rhombohedral and monoclinic phases in the intermediate concentrations (0.2 <= x <= 0.5) and pure rhombohedral phase otherwise. The observed effect of Al doping on dc transport has been analysed on the basis of small polaron hopping mechanism. The magnetisation results presented give evidence of weak ferromagnetism and anomalous temperature dependence of coercivity which we associate to the canting of the localised high-spin Co(III) and anti-symmetric exchange interactions at low temperatures

Towards defining heterotic gene pools using SSR markers in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is a climate resilient crop and the most widely grown millet worldwide. In a maiden attempt to identify potential heterotic groups for grain yield in pearl millet, a total of 88 polymorphic SSR markers were used to genotype 343 hybrid parental lines of pearl millet. The SSR markers generated a total of 532 alleles with a mean value of 6.05 alleles per locus, mean gene diversity of 0.55, and an average PIC of 0.50. Out of 532 alleles, 443 (83.27%) alleles were contributed by B- lines with a mean of 5.03 alleles per locus. R- lines contributed 476 alleles (89.47%) with a mean of 5.41, while 441 (82.89%) alleles were shared commonly between B- and R- lines. The gene diversity and PIC were high among R- lines (0.55 and 0.50) than B- lines (0.49 and 0.44) revealed that R- lines were more diverse than B- lines. The unweighted neighbor-joining tree based on simple matching dissimilarity matrix obtained from SSR data clearly differentiated B- lines into 10 sub-clusters (B1, B2, B3, B4, B5, B6, B7, B8, B9 and B10), and Rlines into 11 sub-clusters (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10 and R11). The parents, three checks and 99 hybrids generated by crossing between representative lines of each of the B- cluster with that of each of the R- cluster were evaluated in line ? tester design over three environments. Based on pooled mean performance, the cross combinations generated between clusters B1 and R3, B2 and R4, B3 and R5, B4 and undetermined cluster, B5 and 11R, B6 and R3, B8 and R4, B9 and R7 and B10 and R5 had shown higher grain yield per plant compared to their counterparts. Based on per se performance, high sca effects and standard heterosis over superior check, F1s generated from crosses between representatives of groups B3 and B10 with representative of group R5 resulted in best heterotic combinations for grain yield. These represent putative heterotic gene pools in pearl millet.publishersversionPeer reviewe

Simulating a base population in honey bee for molecular genetic studies

<p>Abstract</p> <p>Background</p> <p>Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (<it>Varroa destructor</it>) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding value estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has a distinct genetic and reproductive biology. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee.</p> <p>Results</p> <p>Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) χ² statistics for Hardy-Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r² values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r² value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency.</p> <p>Conclusion</p> <p>We developed a software program that takes into account the genetic and reproductive biology specific to the honey bee and that can be used to constitute a genomic dataset compatible with the simulation studies necessary to optimize breeding programs. The source code together with an instruction file is freely accessible at <url>http://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html</url></p

Qualitative analysis of how patients decide that they want risk-reducing mastectomy, and the implications for surgeons in responding to emotionally-motivated patient requests

Objective Contemporary approaches to medical decision-making advise that clinicians should respect patients’ decisions. However, patients’ decisions are often shaped by heuristics, such as being guided by emotion, rather than by objective risk and benefit. Risk-reducing mastectomy (RRM) decisions focus this dilemma sharply. RRM reduces breast cancer (BC) risk, but is invasive and can have iatrogenic consequences. Previous evidence suggests that emotion guides patients’ decision-making about RRM. We interviewed patients to better understand how they made decisions about RRM, using findings to consider how clinicians could ethically respond to their decisions. Methods Qualitative face-to-face interviews with 34 patients listed for RRM surgery and two who had decided against RRM. Results Patients generally did not use objective risk estimates or, indeed, consider risks and benefits of RRM. Instead emotions guided their decisions: they chose RRM because they feared BC and wanted to do ‘all they could’ to prevent it. Most therefore perceived RRM to be the ‘obvious’ option and made the decision easily. However, many recounted extensive post-decisional deliberation, generally directed towards justifying the original decision. A few patients deliberated before the decision because fears of surgery counterbalanced those of BC. Conclusion Patients seeking RRM were motivated by fear of BC, and the need to avoid potential regret for not doing all they could to prevent it. We suggest that choices such as that for RRM, which are made emotionally, can be respected as autonomous decisions, provided patients have considered risks and benefits. Drawing on psychological theory about how people do make decisions, as well as normative views of how they should, we propose that practitioners can guide consideration of risks and benefits even, where necessary, after patients have opted for surgery. This model of practice could be extended to other medical decisions that are influenced by patients’ emotions

Nitrogen Challenges and Opportunities for Agricultural and Environmental Science in India

In the last six decades, the consumption of reactive nitrogen (Nr) in the form of fertilizer in India has been growing rapidly, whilst the nitrogen use efficiency (NUE) of cropping systems has been decreasing. These trends have led to increasing environmental losses of Nr, threatening the quality of air, soils, and fresh waters, and thereby endangering climate-stability, ecosystems, and human-health. Since it has been suggested that the fertilizer consumption of India may double by 2050, there is an urgent need for scientific research to support better nitrogen management in Indian agriculture. In order to share knowledge and to develop a joint vision, experts from the UK and India came together for a conference and workshop on “Challenges and Opportunities for Agricultural Nitrogen Science in India.” The meeting concluded with three core messages: (1) Soil stewardship is essential and legumes need to be planted in rotation with cereals to increase nitrogen fixation in areas of limited Nr availability. Synthetic symbioses and plastidic nitrogen fixation are possibly disruptive technologies, but their potential and implications must be considered. (2) Genetic diversity of crops and new technologies need to be shared and exploited to reduce N losses and support productive, sustainable agriculture livelihoods. Móring et al. Nitrogen Challenges and Opportunities (3) The use of leaf color sensing shows great potential to reduce nitrogen fertilizer use (by 10–15%). This, together with the usage of urease inhibitors in neem-coated urea, and better management of manure, urine, and crop residues, could result in a 20–25% improvement in NUE of India by 2030