Epidemic Spreading with External Agents

We study epidemic spreading processes in large networks, when the spread is assisted by a small number of external agents: infection sources with bounded spreading power, but whose movement is unrestricted vis-\`a-vis the underlying network topology. For networks which are `spatially constrained', we show that the spread of infection can be significantly speeded up even by a few such external agents infecting randomly. Moreover, for general networks, we derive upper-bounds on the order of the spreading time achieved by certain simple (random/greedy) external-spreading policies. Conversely, for certain common classes of networks such as line graphs, grids and random geometric graphs, we also derive lower bounds on the order of the spreading time over all (potentially network-state aware and adversarial) external-spreading policies; these adversarial lower bounds match (up to logarithmic factors) the spreading time achieved by an external agent with a random spreading policy. This demonstrates that random, state-oblivious infection-spreading by an external agent is in fact order-wise optimal for spreading in such spatially constrained networks

Precoding-Based Network Alignment For Three Unicast Sessions

We consider the problem of network coding across three unicast sessions over a directed acyclic graph, where each sender and the receiver is connected to the network via a single edge of unit capacity. We consider a network model in which the middle of the network only performs random linear network coding, and restrict our approaches to precoding-based linear schemes, where the senders use precoding matrices to encode source symbols. We adapt a precoding-based interference alignment technique, originally developed for the wireless interference channel, to construct a precoding-based linear scheme, which we refer to as as a {\em precoding-based network alignment scheme (PBNA)}. A primary difference between this setting and the wireless interference channel is that the network topology can introduce dependencies between elements of the transfer matrix, which we refer to as coupling relations, and can potentially affect the achievable rate of PBNA. We identify all possible such coupling relations, and interpret these coupling relations in terms of network topology and present polynomial-time algorithms to check the presence of these coupling relations. Finally, we show that, depending on the coupling relations present in the network, the optimal symmetric rate achieved by precoding-based linear scheme can take only three possible values, all of which can be achieved by PBNA.Comment: arXiv admin note: text overlap with arXiv:1202.340

PDBe-KB: a community-driven resource for structural and functional annotations.

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession

An information theoretic approach to structured high-dimensional problems

textA majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values.Electrical and Computer Engineerin

Microarray based gene expression: a novel approach for identification and development of potential drug and effective vaccine against visceral Leishmaniasis

Visceral Leishmaniasis (VL) is the well-recognized infectious disease among the complex of Leishmaniasis (cutaneous, mucocutaneous, visceral) in tropical and subtropical countries. Treatments for VL are unsatisfactory till date and alarming rise of drug resistance is a serious problem. Vaccines to control VL have shown some promise, but none are in current clinical use. Therefore, urgent needs for new and effective anti-leishmanials are pre-requisite. To identify the potential factors, DNA microarray an advance, high throughput technology, has open the possibility of discovering new genes that can contribute to vaccine initiatives or serve as potential drug targets. It has been successfully applied to many of the protozoan parasites and identified some new genes as targets. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose. After the completion of genome sequencing of Leishmania major and L. infantum, advancement in microarray technologies provide new approaches to study the pattern of gene expression profile during differentiation and development of parasite. It has the potential to improve our understanding of pathogenicity, mechanism of drug resistance and virulence factors by identifying up/down regulated gene and characterizing the respective gene expression. Keeping these backgrounds in mind, we reviewed the data obtained from genome-wide wide expression profiling in Leishmania that focuses on applications of microarray in novel vaccine/drug targets discovery for VL and discuss the potential avenues for their future investigation. Ultimately this will be able to translate the findings into the development of novel therapeutic approaches and targets for VL.Keywords: Visceral Leishmaniasis, Microarray, Stage-specific, Gene expression profiling, Gene discovery, Novel vaccine/drug target

CEFTRIAXONE RELATED ADVERSE DRUG REACTIONS IN CHILDREN IN A TERTIARY CARE HOSPITAL, KOLKATA, WEST BENGAL, INDIA

Ceftriaxone is a third-generation cephalosporin antibiotic, which has broad-spectrum activity against Gram-positive and Gram-negative bacteria. It is a frequently used antibiotic in children worldwide. Studies revealed a number of adverse reactions related to this third generation antibiotic. A survey was done where data related with adverse drug reactions (ADRs) were collected for three months from the Department of Pediatrics of a tertiary care hospital, Kolkata, West Bengal, India and then evaluated. In the study, fifteen ADRs were detected. Ceftriaxone itself or its combinations correlated with more than thirty three percent (33.4%) adverse reaction cases in this study. Most common adverse drug reactions in the present study population were different types of rashes like urticaria and maculopapular eruptions

Hydrochemical investigation of groundwater and probabilistic health risk assessment from fluoride and iron intake in a ferruginous Barind tract

Hydrochemical properties of groundwater (n = 32) collected from eight Gram Panchayats (GP) in Kushmandi Block of South Dinajpur District in West Bengal, India and human health risk posed by groundwater fluoride (F−) and iron (Fe) are reported. About 18% and 28.7% of the collected water samples exceeded the permissible limits for F− and Fe, respectively, as per prescribed potable water quality standards of WHO and Bureau of Indian Standards. Total Dissolved Solid in groundwater never exceeded the acceptable limit of 500 mg l −1 (maximum value was 301.6 mg l −1) while NO3- ranged from near zero to 17.8 mg l−1, never exceeding the safe limit of 45 mg l−1. Hydrogeochemical analyses revealed the dominance of Ca/Na-bicarbonate type water throughout the Block and indicated that fluorite (CaF2) could be the most prominent source of F− in groundwater. Non-carcinogenic health risk index due to exposure to F− was more than unity in infants, children and adults in the order of HQChildren>HQInfant>HQAdult. Sensitivity analysis to ascertain non-carcinogenic human health risk of F− through multi-exposure pathways undertaken by Monte Carlo (MC) simulation (Oracle Crystal Ball, version 11.1.2.4.850) indicated that concentration of F− and Fe (Ci), their ingestion rate (IR), and exposure duration (ED) were the most influential governing factors behind non-carcinogenic health risks posed by F− and Fe consumption. Groundwater was rated as having ‘Low’ irrigation water quality (Sodium Adsorption Ratio, SAR −1) and chloride (maximum 30.8 mg l−1), were also well within the recommended safe limits.</p