Bat pollination of kapok tree, Ceiba pentandra

Two species of pteropodid bats Cynopterus sphinx and Pteropus giganteus visited inflorescences of kapok trees, Ceiba pentandra through the night. A third species, Rousettus leschenaulti infrequently visited the inflorescences. Both C. sphinx and P. giganteus foraged in groups and there were temporal variations in their visits to the trees. The ventral body surfaces of the bats were covered with pollen grains when they landed on the inflorescences to lap up the nectar. In addition to bats, moths also visited the inflorescences. Bat and insectexclusion experiments were performed to study their pollination efficiency. Bats were more efficient in pollinating flowers of C. pentandra than other pollinators like insects

Distress call-induced gene expression in the brain of the Indian short-nosed fruit bat, Cynopterus sphinx

Individuals in distress emit audible vocalizations to either warn or inform conspecifics. The Indian short-nosed fruit bat, Cynopterus sphinx, emits distress calls soon after becoming entangled in mist nets, which appear to attract conspecifics. Phase I of these distress calls is longer and louder, and includes a secondary peak, compared to phase II. Activity-dependent expression of egr-1 was examined in free-ranging C. sphinx following the emissions and responses to a distress call. We found that the level of expression of egr-1 was higher in bats that emitted a distress call, in adults that responded, and in pups than in silent bats. Up-regulated cDNA was amplified to identify the target gene (TOE1) of the protein Egr-1. The observed expression pattern Toe1 was similar to that of egr-1. These findings suggest that the neuronal activity related to recognition of a distress call and an auditory feedback mechanism induces the expression of Egr-1. Co-expression of egr-1 with Toe1 may play a role in initial triggering of the genetic mechanism that could be involved in the consolidation or stabilization of distress call memories

Process for the preparation of (R)-3-(4-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-1H-pyrazol-1-yl)-3-cyclopentylpropanenitrile fumarate

Abstract: Process for the preparation of crystal modification 1 of (R)-3-(4-(7Hpyrrolo[2,3-d] pyrimidin-4-yl)-1H-pyrazol-1-yl)-3-cyclopentylpropanenitrile fumarate of formula-1a, which is represented by the following structural formula: Formula-1

Comparative analysis of methods for detecting interacting loci

<p>Abstract</p> <p>Background</p> <p>Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.</p> <p>Results</p> <p>We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding <it>multiple </it>sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.</p> <p>Conclusion</p> <p>This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: <url>http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list</url>.</p

The feasibility of genome-scale biological network inference using Graphics Processing Units

Abstract Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.https://deepblue.lib.umich.edu/bitstream/2027.42/136186/1/13015_2017_Article_100.pd

Synthetic Nitrogen Fertiliser in South Asia: Production, Import, Export, and Use for Crops, South Asia Nitrogen Hub (SANH) Policy Brief

This policy brief is produced by the UKRI GCRF South Asian Nitrogen Hub (SANH). It provides an overview of the patterns and trends in synthetic nitrogen (N) fertiliser use in crop production, import, export and emission in the South Asian Region (SAR) and its member countries; Afghanistan, Bangladesh, Bhutan, Nepal, India, Maldives, Pakistan, and Sri Lanka. In summary, reactive nitrogen (Nr) in fertilisers is essential for meeting global food and animal feed demands, but Nr pollution has become a major environmental issue across all scales. For SAR, inefficient use of synthetic N fertiliser is a key factor contributing to water pollution, air pollution, climate change, biodiversity loss and soil degradation. Further insights are provided on major fertiliser products, as well as in crop production, import and export. These data are essential for informing and promoting sustainable nitrogen management. Evidence based policy is more important than ever. The SANH is supported by UK Research and Innovation (UKRI) through its Global Challenge Research Fund (GCRF) to gather evidence on nitrogen issues to support countries in the South Asian Region (SAR) comprising eight countries (Nepal, Bangladesh, Pakistan, India, Bhutan, Sri Lanka, Afghanistan, and Maldives) to identify solutions and reduce nitrogen waste. SANH is pioneering a UK-SAR research partnership to catalyse transformational change in SAR to tackle the nitrogen challenge, benefi ting the economy, people’s health and the environment. SANH brings together 32 leading research organisations with governments and other partners. This policy brief provides key insights into national fertiliser trends for all eight SAR countries