A Content-Based Pricing Model for Municipal and Community Wireless Networks

The escalation of municipal and community wireless networks (CWNs) has raised many questions about the most suitable business model, funding instrument, and service pricing policy for a specific community. Unlike traditional Internet service providers, these networks provide wireless Internet access for the purpose of boosting the social and economic development of the community at large. Therefore, such projects need customized business models and pricing policies in order to achieve these objectives. We propose a content-based pricing model where the price of wireless applications is an increasing function of the used bandwidth and a decreasing function of the provided packet delay. We used the Opnet simulation tool to validate the proposed pricing model. The simulation results show that network operators may charge users only for audio and video applications because of the high bandwidth they use compared to data applications. The proposed pricing solution considers the social and economic objectives of CWNs

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

BACKGROUND: Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. RESULTS: Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. CONCLUSIONS: Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies

A Dynamic Bayesian Network Model for Hierarchial Classification and its Application in Predicting Yeast Genes Functions

In this paper, we propose a Dynamic Naive Bayesian (DNB) network model for classifying data sets with hierarchical labels. The DNB model is built upon a Naive Bayesian (NB) network, a successful classifier for data with flattened (nonhierarchical) class labels. The problems using flattened class labels for hierarchical classification are addressed in this paper. The DNB has a top-down structure with each level of the class hierarchy modeled as a random variable. We defined augmenting operations to transform class hierarchy into a form that satisfies the probability law. We present algorithms for efficient learning and inference with the DNB model. The learning algorithm can be used to estimate the parameters of the network. The inference algorithm is designed to find the optimal classification path in the class hierarchy. The methods are tested on yeast gene expression data sets, and the classification accuracy with DNB classifier is significantly higher than it is with previous approaches– flattened classification using NB classifier

Message Passing Clustering with Stochastic Merging Based on Kernel Functions

In this paper, we propose a new Stochastic Message Passing Clustering (SMPC) algorithm for clustering biological data based on the Message Passing Clustering (MPC) algorithm, which we introduced in earlier work. MPC has shown its advantage when applied to describing parallel and spontaneous biological processes. SMPC, as a generalized version of MPC, extends the clustering algorithm from a deterministic process to a stochastic process, adding three major advantages. First, in deciding the merging cluster pair, the influences of all clusters are quantified by probabilities, estimated by kernel functions based on their relative distances. Second, the proposed algorithm property resolve the “tie” problem, which often occurs for integer distances as in the case of protein interaction data. Third, clustering can be undone to improve the clustering performance when the algorithm detects objects which don’t have good probabilities inside the cluster and moves them outside. The test results on colon cancer gene-expression data show that SMPC performs better than the deterministic MPC

Isolation of filter passing bacteria from a range of dental clinic surfaces

Filter passing bacteria have been isolated from a variety of natural environments, appearing as a mixture of Gram-positive and Gram-negative, as well as nano-forms and wall-free species. In this study, filter passing bacteria were isolated from surfaces located in various dental departments at the College of Dentistry, King Saud University Hospital. Surface samples were obtained by using Q-tip swabs, with ten different surfaces being sampled in each clinic during pre-patient and post-patient visits. Filterable bacteria (using 0.4 and 0.2 micron filters, but not 0.1 micron filter) were isolated, being mainly Gram-positive cocci. Isolation results of filterable bacteria were compared before and after patient treatment in the clinic. More frequently, filter passing bacteria were isolated on clinic surfaces after patient treatment. The results show that dental settings are contaminated with filterable bacteria which may act as a reservoir for the wider contamination of hospital environments

Pengaruh tahap stres terhadap kepuasan kerja dalam Kalangan pengajar kolej vokasional di Negeri Pahang

Kajian ini dijalankan bertujuan untuk mengkaji mengenai pengaruh stres terhadap kepuasan kerja dalam kalangan pengajar KV di negeri Pahang dengan memberikan tumpuan kepada tiga aspek iaitu aspek beban kerja, aspek karenah pelajar serta aspek penghargaan dan sokongan. Seramai 240 orang responden yang terdiri daripada kalangan pengajar di lapan buah KV di negeri Pahang telah dipilih secara rawak mudah. Nilai kebolehpercayaan Alpha Cronbach bagi keseluruhan soal selidik ini ialah 0.898. Soal selidik berkaitan pengaruh stres terhadap kepuasan kerja dibina sendiri dan selebihnya diubahsuai berpandukan instrumen yang digunakan oleh penyelidik terdahulu bagi menyediakan pelbagai jenis soalan berdasarkan objektif kajian. Kajian sebenar dijalankan dengan mengedarkan borang soal selidik mengandungi 58 item soalan kepada 240 responden. Data yang diperolehi dianalisis menggunakan Statistical Packages for Social Sciences (SPSS) versi 22. Analisis statistik deskriptif iaitu skor min dan sisihan piawai digunakan bagi mengenal pasti tahap stres bagi aspek beban tugas, karenah pelajar serta penghargaan dan sokongan dalam kalangan pengajar. Manakala analisis ujian regrasi pelbagai digunakan bagi mengesan pengaruh stres terhadap kepuasan kerja. Dapatan kajian mendapati min keseluruhan tahap stres bagi aspek beban tugas dan karenah pelajar adalah sederhana dengan nilai skor min 3.49. Manakala hasil dapatan keseluruhan nilai min bagi konstruk tahap stres aspek penghargaan dan sokongan berada pada tahap yang tinggi iaitu 3.81. Dapatan analisis ujian regrasi pelbagai pula menunjukkan tahap stres bagi aspek beban tugas, aspek karenah pelajar dan aspek penghargaan dan sokongan mempengaruhi kepuasan kerja. Oleh itu, beberapa cadangan telah dikemukakan dalam kajian ini dalam usaha menangani stres yang berterusan serta boleh mempengaruhi tahap kepuasan kerja. Antara cadangan pengkaji adalah tenaga pengajar diberi lebih banyak pendedahan berkaitan perubahan sistem pendidikan vokasional yang dialami sekarang agar mereka lebih bersedia dalam menggalas tugas yang baharu seterusmya akan memberi kepuasan kerja dalam kalangan pengajar KV

Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus

The Middle East respiratory syndrome coronavirus (MERS-CoV) was first documented in the Kingdom of Saudi Arabia (KSA) in 2012 and, to date, has been identified in 180 cases with 43% mortality. In this study, we have determined the MERS-CoV evolutionary rate, documented genetic variants of the virus and their distribution throughout the Arabian peninsula, and identified the genome positions under positive selection, important features for monitoring adaptation of MERS-CoV to human transmission and for identifying the source of infections. Respiratory samples from confirmed KSA MERS cases from May to September 2013 were subjected to whole-genome deep sequencing, and 32 complete or partial sequences (20 were ≥99% complete, 7 were 50 to 94% complete, and 5 were 27 to 50% complete) were obtained, bringing the total available MERS-CoV genomic sequences to 65. An evolutionary rate of 1.12 × 10−3 substitutions per site per year (95% credible interval [95% CI], 8.76 × 10−4; 1.37 × 10−3) was estimated, bringing the time to most recent common ancestor to March 2012 (95% CI, December 2011; June 2012). Only one MERS-CoV codon, spike 1020, located in a domain required for cell entry, is under strong positive selection. Four KSA MERS-CoV phylogenetic clades were found, with 3 clades apparently no longer contributing to current cases. The size of the population infected with MERS-CoV showed a gradual increase to June 2013, followed by a decline, possibly due to increased surveillance and infection control measures combined with a basic reproduction number (R0) for the virus that is less than 1