Promising Technologies for dry land Agriculture

Not AvailableNatural Resource Management has important research agenda in view of the climate change, degradation of land and declining productivity in greenrevolution areas. Efficient methods of soil and rain water conservation and water harvesting become important areas of dryland agriculture research to achieve sustainability. Variation in crop yields is more in dry lands due to non receipt of timely rainfall and prolonged dry spells during crop periods. Adoption of soil and moisture conservation measures and improved management practices will help in getting higher yields.A large number of location specific practices for insitu moisture conservation, water harvesting and supplemental irrigation have been developed and tested successfully at All India Co-ordinated Research Project for Dryland Agriculture (AICRPDA), Agricultural Research Station, Ananthapuramu. Dryland Agriculture occupies a prominent place in rural livelihoods of Andhra Pradesh. In Andhra Pradesh out of 92.04 lakh ha of cultivable land an area of 34.56 lakh ha is under rainfed agriculture, mainly in scarce rainfall and southern agro climatic zones. AICRPDA, ARS, Ananthapuramu is continuing efforts to generate location specific technologies in the areas of rain water management, integrated nutrient management, cropping systems, farming systems, alternate land use and energy management.Not Availabl

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

RNAseq Analyses Identify Tumor Necrosis Factor-Mediated Inflammation as a Major Abnormality in ALS Spinal Cord

ALS is a rapidly progressive, devastating neurodegenerative illness of adults that produces disabling weakness and spasticity arising from death of lower and upper motor neurons. No meaningful therapies exist to slow ALS progression, and molecular insights into pathogenesis and progression are sorely needed. In that context, we used high-depth, next generation RNA sequencing (RNAseq, Illumina) to define gene network abnormalities in RNA samples depleted of rRNA and isolated from cervical spinal cord sections of 7 ALS and 8 CTL samples. We aligned \u3e50 million 2X150 bp paired-end sequences/sample to the hg19 human genome and applied three different algorithms (Cuffdiff2, DEseq2, EdgeR) for identification of differentially expressed genes (DEG’s). Ingenuity Pathways Analysis (IPA) and Weighted Gene Co-expression Network Analysis (WGCNA) identified inflammatory processes as significantly elevated in our ALS samples, with tumor necrosis factor (TNF) found to be a major pathway regulator (IPA) and TNFα-induced protein 2 (TNFAIP2) as a major network “hub” gene (WGCNA). Using the oPOSSUM algorithm, we analyzed transcription factors (TF) controlling expression of the nine DEG/hub genes in the ALS samples and identified TF’s involved in inflammation (NFkB, REL, NFkB1) and macrophage function (NR1H2::RXRA heterodimer). Transient expression in human iPSC-derived motor neurons of TNFAIP2 (also a DEG identified by all three algorithms) reduced cell viability and induced caspase 3/7 activation. Using high-density RNAseq, multiple algorithms for DEG identification, and an unsupervised gene co-expression network approach, we identified significant elevation of inflammatory processes in ALS spinal cord with TNF as a major regulatory molecule. Overexpression of the DEG TNFAIP2 in human motor neurons, the population most vulnerable to die in ALS, increased cell death and caspase 3/7 activation. We propose that therapies targeted to reduce inflammatory TNFα signaling may be helpful in ALS patients

Life Quality Impairment Caused by Hookworm-Related Cutaneous Larva Migrans in Resource-Poor Communities in Manaus, Brazil

Hookworm-related cutaneous larva migrans (CLM) is a parasitic skin disease common in developing countries with hot climates. In resource-poor settings, CLM is associated with considerable morbidity. The disease is caused by animal hookworm larvae that penetrate the skin and migrate aimlessly in the epidermis as they cannot penetrate the basal membrane. Particularly in the rainy season, the intensity of infection is high with up to 40 larval tracks in an affected individual. Tracks are very itchy and are surrounded by a significant inflammation of the skin. Bacterial superinfection is common and intensifies the inflammation. The psychosocial consequences caused by CLM have never been investigated. We showed that CLM causes skin disease-associated life quality impairment in 91 patients with CLM. Skin disease-associated life quality was significantly impaired. The degree of impairment correlated to the intensity of infection and the number of body areas affected. After treatment with ivermectin, life quality was rapidly restored

Physiological Roles of ArcA, Crp, and EtrA and Their Interactive Control on Aerobic and Anaerobic Respiration in Shewanella oneidensis

In the genome of Shewanella oneidensis, genes encoding the global regulators ArcA, Crp, and EtrA have been identified. All these proteins deviate from their counterparts in E. coli significantly in terms of functionality and regulon. It is worth investigating the involvement and relationship of these global regulators in aerobic and anaerobic respiration in S. oneidensis. In this study, the impact of the transcriptional factors ArcA, Crp, and EtrA on aerobic and anaerobic respiration in S. oneidensis were assessed. While all these proteins appeared to be functional in vivo, the importance of individual proteins in these two major biological processes differed. The ArcA transcriptional factor was critical in aerobic respiration while the Crp protein was indispensible in anaerobic respiration. Using a newly developed reporter system, it was found that expression of arcA and etrA was not influenced by growth conditions but transcription of crp was induced by removal of oxygen. An analysis of the impact of each protein on transcription of the others revealed that Crp expression was independent of the other factors whereas ArcA repressed both etrA and its own transcription while EtrA also repressed arcA transcription. Transcriptional levels of arcA in the wild type, crp, and etrA strains under either aerobic or anaerobic conditions were further validated by quantitative immunoblotting with a polyclonal antibody against ArcA. This extensive survey demonstrated that all these three global regulators are functional in S. oneidensis. In addition, the reporter system constructed in this study will facilitate in vivo transcriptional analysis of targeted promoters

Peccei-Quinn extended gauge-mediation model with vector-like matter

We construct a gauge-mediated SUSY breaking model with vector-like matters combined with the Peccei-Quinn mechanism to solve the strong CP problem. The Peccei-Quinn symmetry plays an essential role for generating sizable masses for the vector-like matters and the $\mu$-term without introducing dangerous CP angle. The model naturally explains both the 125GeV Higgs mass and the muon anomalous magnetic moment. The stabilization of the Peccei-Quinn scalar and the cosmology of the saxion and axino are also discussed.Comment: 33 pages, 5 figures; version to be published (JHEP

Postpartum psychiatric disorders

Pregnancy is a complex and vulnerable period that presents a number of challenges to women, including the development of postpartum psychiatric disorders (PPDs). These disorders can include postpartum depression and anxiety, which are relatively common, and the rare but more severe postpartum psychosis. In addition, other PPDs can include obsessive–compulsive disorder, post-traumatic stress disorder and eating disorders. The aetiology of PPDs is a complex interaction of psychological, social and biological factors, in addition to genetic and environmental factors. The goals of treating postpartum mental illness are reducing maternal symptoms and supporting maternal–child and family functioning. Women and their families should receive psychoeducation about the illness, including evidence-based discussions about the risks and benefits of each treatment option. Developing effective strategies in global settings that allow the delivery of targeted therapies to women with different clinical phenotypes and severities of PPDs is essential

Phytoremediation of heavy metal-contaminated sites: Eco-environmental concerns, field studies, sustainability issues and future prospects

Environmental contamination due to heavy metals (HMs) is of serious ecotoxicological concern worldwide because of their increasing use at industries. Due to non-biodegradable and persistent nature, HMs cause serious soil/water pollution and severe health hazards in living beings upon exposure. HMs can be genotoxic, carcinogenic, mutagenic, and teratogenic in nature even at low concentration. They may also act as endocrine disruptors and induce developmental as well as neurological disorders and thus, their removal from our natural environment is crucial for the rehabilitation of contaminated sites. To cope with HM pollution, phytoremediation has emerged as a low-cost and eco-sustainable solution to conventional physico-chemical cleanup methods that require high capital investment and labor alter soil properties and disturb soil microflora. Phytoremediation is a green technology wherein plants and associated microbes are used to remediate HM-contaminated sites to safeguard the environment and protect public health. Hence, in view of the above, the present paper aims to examine the feasibility of phytoremediation as a sustainable remediation technology for the management of metals-contaminated sites. Therefore, this paper provides an in-depth review on both the conventional and novel phytoremediation approaches, evaluate their efficacy to remove toxic metals from our natural environment, explore current scientific progresses, field experiences and sustainability issues and revise world over trends in phytoremediation research for its wider recognition and public acceptance as a sustainable remediation technology for the management of contaminated sites in 21st century