Non-polio enterovirus association with persistent diarrhea in children as revealed by a follow-up study of an Indian cohort during the first two years of life

Background: We recently reported significant association of non-polio enteroviruses (NPEVs) with acute diarrhea in children. Persistent diarrhea (PD) remains a major cause of morbidity and mortality in infants below two years of age in developing countries. Understanding age-dependent frequency and duration of NPEV infections is important to determine their association with persistent diarrhea and disease burden. Objectives: A cohort of 140 infants was followed for 6 months to 2 years of age to determine the frequency, duration, and association with PD of NPEV infections in comparison with rotavirus and other agents. Study design: Stool samples were collected every 14 days, and diarrheal episodes and their duration were recorded. Enteroviruses were characterized by RT-PCR and VP1 gene sequence analysis, rotavirus by electropherotyping, and other agents by PCR. Results: Of 4545 samples, negative for oral polio vaccine strains, 3907 (85.96%) and 638 (14.04%) were NPEV-negative and NPEV-positive, respectively, representing 403 (8.87%) infection episodes. About 68% of NPEV infections occurred during the first year with every child having at least one episode lasting between four days and four months. Approximately 38% and 22% of total diarrheal episodes were positive for NPEV and RV, respectively. While about 18% of NPEV infection episodes were associated with diarrhea, 6% being persistent, 13% of total diarrheal episodes were persistent involving infections by monotype NPEV strains or sequential infections by multiple strains and other agents. Conclusions: This is the first report revealing NPEVs as the single most frequently and persistently detected viral pathogen in every PD episode. (C) 2014 Elsevier B.V. All rights reserved.

Discovering Math APIs by Mining Unit Tests

In today's API-rich world, programmer productivity depends heavily on the programmer's ability to discover the required APIs. In this paper, we present a technique and tool, called MATHFINDER, to discover APIs for mathematical computations by mining unit tests of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code to compute the expression by mapping its subexpressions to API method calls. For each subexpression, MATHFINDER searches for a method such that there is a mapping between method inputs and variables of the subexpression. The subexpression, when evaluated on the test inputs of the method under this mapping, should produce results that match the method output on a large number of tests. We implemented MATHFINDER as an Eclipse plugin for discovery of third-party Java APIs and performed a user study to evaluate its effectiveness. In the study, the use of MATHFINDER resulted in a 2x improvement in programmer productivity. In 96% of the subexpressions queried for in the study, MATHFINDER retrieved the desired API methods as the top-most result. The top-most pseudo-code snippet to implement the entire expression was correct in 93% of the cases. Since the number of methods and unit tests to mine could be large in practice, we also implement MATHFINDER in a MapReduce framework and evaluate its scalability and response time.

Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.

The Self-Association of Graphane Is Driven by London Dispersion and Enhanced Orbital Interactions

We investigated the nature of the cohesive energy between graphane sheets via multiple CH center dot center dot center dot HC interactions, using density functional theory (DFT) including dispersion correction (Grimmes D3 approach) computations of n]graphane sigma dimers (n = 6-73). For comparison, we also evaluated the binding between graphene sheets that display prototypical pi/pi interactions. The results were analyzed using the block-localized wave function (BLW) method, which is a variant of ab initio valence bond (VB) theory. BLW interprets the intermolecular interactions in terms of frozen interaction energy (Delta E-F) composed of electrostatic and Pauli repulsion interactions, polarization (Delta E-pol), charge-transfer interaction (Delta E-CT), and dispersion effects (Delta E-disp). The BLW analysis reveals that the cohesive energy between graphane sheets is dominated by two stabilizing effects, namely intermolecular London dispersion and two-way charge transfer energy due to the sigma CH -> sigma*(HC) interactions. The shift of the electron density around the nonpolar covalent C-H bonds involved in the intermolecular interaction decreases the C-H bond lengths uniformly by 0.001 angstrom. The Delta E-CT term, which accounts for similar to 15% of the total binding energy, results in the accumulation of electron density in the interface area between two layers. This accumulated electron density thus acts as an electronic glue for the graphane layers and constitutes an important driving force in the self-association and stability of graphane under ambient conditions. Similarly, the double faced adhesive tape style of charge transfer interactions was also observed among graphene sheets in which it accounts for similar to 18% of the total binding energy. The binding energy between graphane sheets is additive and can be expressed as a sum of CH center dot center dot center dot HC interactions, or as a function of the number of C-H bonds.

The 2014 M-w 6.1 Bay of Bengal, Indian Ocean, Earthquake: A Possible Association with the 85 degrees E Ridge

Oceanic intraplate earthquakes are known to occur either on active ridge-transform structures or by reactivation of their inactive counterparts, generally referred to as fossil ridges or transforms. The Indian Ocean, one of the most active oceanic intraplate regions, has generated large earthquakes associated with both these types of structures. The moderate earthquake that occurred on 21 May 2014 (M-w 6.1) in the northern Bay of Bengal followed an alternate mechanism, as it showed no clear association either with active or extinct ridge-transform structures. Its focal depth of >50 km is uncommon but not improbable, given the similar to 90 Ma age of the ocean floor with 12-km-thick overlying sediments. No tectonic features have been mapped in the near vicinity of its epicenter, the closest being the 85 degrees E ridge, located similar to 100 km to its west, hitherto regarded as seismically inactive. The few earthquakes that have occurred here in the past are clustered around its southern or northern limits, and a few are located midway, at around 10 degrees N. The 2014 earthquake, sourced close to the northern cluster, seems to be associated with a northwest-southeast-oriented fracture, located on the eastern flanks of the 85 degrees E ridge. If this causal association is possible, we believe that reactivation of fossil hotspot trails could be considered as another mechanism for oceanic intraplate seismicity.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).