Regulation of Gene Expression by Small RNAs Rajesh K.GaurJohn J.Rossi2009CRC Press431 pp., $149.95 hardcover

Unknow

Autism genetic database (AGD): a comprehensive database including autism susceptibility gene-CNVs integrated with known noncoding RNAs and fragile sites

Background: Autism is a highly heritable complex neurodevelopmental disorder, therefore identifying its genetic basis has been challenging. To date, numerous susceptibility genes and chromosomal abnormalities have been reported in association with autism, but most discoveries either fail to be replicated or account for a small effect. Thus, in most cases the underlying causative genetic mechanisms are not fully understood. In the present work, the Autism Genetic Database (AGD) was developed as a literature-driven, web-based, and easy to access database designed with the aim of creating a comprehensive repository for all the currently reported genes and genomic copy number variations (CNVs) associated with autism in order to further facilitate the assessment of these autism susceptibility genetic factors. Description: AGD is a relational database that organizes data resulting from exhaustive literature searches for reported susceptibility genes and CNVs associated with autism. Furthermore, genomic information about human fragile sites and noncoding RNAs was also downloaded and parsed from miRBase, snoRNA-LBME-db, piRNABank, and the MIT/ICBP siRNA database. A web client genome browser enables viewing of the features while a web client query tool provides access to more specific information for the features. When applicable, links to external databases including GenBank, PubMed, miRBase, snoRNA-LBME-db, piRNABank, and the MIT siRNA database are provided. Conclusion: AGD comprises a comprehensive list of susceptibility genes and copy number variations reported to-date in association with autism, as well as all known human noncoding RNA genes and fragile sites. Such a unique and inclusive autism genetic database will facilitate the evaluation of autism susceptibility factors in relation to known human noncoding RNAs and fragile sites, impacting on human diseases. As a result, this new autism database offers a valuable tool for the research community to evaluate genetic findings for this complex multifactorial disorder in an integrated format. AGD provides a genome browser and a web based query client for conveniently selecting features of interest. Access to AGD is freely available at http://wren.bcf.ku.edu/ webcite

Intersecting geodesics on the modular surface

We introduce the \textit{modular intersection kernel}, and we use it to study how geodesics intersect on the full modular surface $\mathbb{X}=PSL_2\left(\mathbb{Z}\right) \backslash \mathbb{H}$. Let $C_d$ be the union of closed geodesics with discriminant $d$ and let $\beta\subset \mathbb{X}$ be a compact geodesic segment. As an application of Duke's theorem to the modular intersection kernel, we prove that $\{\left(p,\theta_p\right)~:~p\in \beta \cap C_d\}$ becomes equidistributed with respect to $\sin \theta ds d\theta$ on $\beta \times [0,\pi]$ with a power saving rate as $d \to +\infty$. Here $\theta_p$ is the angle of intersection between $\beta$ and $C_d$ at $p$. This settles the main conjectures introduced by Rickards \cite{rick}. We prove a similar result for the distribution of angles of intersections between $C_{d_1}$ and $C_{d_2}$ with a power-saving rate in $d_1$ and $d_2$ as $d_1+d_2 \to \infty$. Previous works on the corresponding problem for compact surfaces do not apply to $\mathbb{X}$, because of the singular behavior of the modular intersection kernel near the cusp. We analyze the singular behavior of the modular intersection kernel by approximating it by general (not necessarily spherical) point-pair invariants on $PSL_2\left(\mathbb{Z}\right) \backslash PSL_2\left(\mathbb{R}\right)$ and then by studying their full spectral expansion.Comment: Fixed a few errors in the proof. Revised to improve the readability. 25 page

FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach.

The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming. RESULTS: We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases. CONCLUSIONS: FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance

FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach

<p>Abstract</p> <p>Background</p> <p>The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.</p> <p>Results</p> <p>We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.</p> <p>Conclusions</p> <p>FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.</p

Genetic Studies of Complex Human Diseases: Characterizing SNP-Disease Associations Using Bayesian Networks

Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype. RESULTS: To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data. CONCLUSIONS: We propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism

Пределы векторных мер в пространствах Фреше

(uk) Розв’язане питання про те, які міри в просторах Фреше можуть бути зображені як границі аналітичних векторних мір в топологіях збіжності за варіацією, відносно напівваріації та збіжності на системі вимірних множин.(en) It is solved a problem: Which measures in Frechet spaces could be represented as limits of analytic vector measures in topologies of variational convergence, semi-variational convergence and convergence on every measurable set

Nano-particle deposition in the presence of electric field

The dispersion and deposition of nano-particles in laminar flows in the presence of an electric field were studied. The Eulerian-Lagrangian particle tracking method was used to simulate nano-particle motions under the one-way coupling assumption. For nano-particles in the size range of 5–200 nm, in addition to the Brownian excitation, the electrostatic and gravitational forces were included in the analysis. Different charging mechanisms including field and diffusion charging as well as the Boltzmann charge distributions were investigated. The simulation methodology was first validated for Brownian and electrostatic forces. For the combined field and diffusion charging, the simulation results showed that in the presence of an electric field of 10 kV/m, the electrostatic force dominates the Brownian effects. However, when the electric field was 1 kV/m, the Brownian motion strongly affected the particle dispersion and deposition processes. For the electric field intensity of 1 kV/m, for 10 nm and 100 nm particles, the deposition efficiencies for the combined effects of electrostatic and Brownian motion were, respectively, about 27% and 161.2% higher than the case in the absence of electric field. Furthermore, particles with the Boltzmann charge distribution had the maximum deposition for 20 nm particles

Impact of variable fluid properties on forced convection of Fe3O4/CNT/water hybrid nanofluid in a double-pipe mini-channel heat exchanger

The objective of this study is to assess the hydrothermal performance of a non-Newtonian hybrid nanofluid with temperature-dependent thermal conductivity and viscosity compared with a Newtonian hybrid nanofluid with constant thermophysical properties. A counter-current double-pipe mini-channel heat exchanger is studied to analyze the effects of the hybrid nanofluid. The nanofluid is employed as the coolant in the tube side, while the hot water flows in the annulus side. Two different nanoparticles including tetramethylammonium hydroxide-coated Fe3O4 (magnetite) nanoparticles and gum arabic-coated carbon nanotubes are used to prepare the water-based hybrid nanofluid. The results demonstrated that the non-Newtonian hybrid nanofluid always has a higher heat transfer rate, overall heat transfer coefficient, and effectiveness than those of the Newtonian hybrid nanofluid, while the opposite is true for the pressure drop, pumping power, and performance evaluation criterion. Supposing that the Fe3O4-carbon nanotube/water hybrid nanofluid is a Newtonian fluid with constant thermal conductivity and viscosity, there leads to large error in the computation of pressure drop (1.5–9.71%), pumping power (1.5–9.71%), and performance evaluation criterion (18.24–19.60%), whereas the errors in the computation of heat transfer rate, overall heat transfer coefficient, and effectiveness are not considerable (less than 2.91%)

Melting and solidification characteristics of a double-pipe latent heat storage system with sinusoidal wavy channels embedded in a porous medium

The aim of this investigation is to explore the combined effects of porous medium and surface waviness on the melting and solidification of PCM inside a vertical double-pipe latent heat storage (LHTES) system. The results are compared with the cases of smooth channels and pure PCM. In the system, water is passed through the inner tube while composite PCM is placed in the annulus side. Different effective parameters including wavelength and wave amplitude of the sinusoidal wavy channels, porosity and pore size of the porous structure, Reynolds number and inlet temperature of water are examined to find the optimum geometric as well as operating conditions in both melting/solidification processes. The results show that utilizing both the high conductive porous structure and wavy channel reduces the melting/solidification times significantly. For the best case, the melting and solidification times of PCM reduce by 91.4% and 96.7%, respectively, compared with the smooth channels pure PCM system. The average rate of transferred heat for the wavy channel composite PCM are 10.4 and 18.9 times that for the smooth channel pure PCM case. Comparing with the pure PCM system, the presence of copper foam reduces the effect of channel waviness significantly for both melting/solidification processes