A reference haplotype panel for genome-wide imputation of short tandem repeats.

Short tandem repeats (STRs) are involved in dozens of Mendelian disorders and have been implicated in complex traits. However, genotyping arrays used in genome-wide association studies focus on single nucleotide polymorphisms (SNPs) and do not readily allow identification of STR associations. We leverage next-generation sequencing (NGS) from 479 families to create a SNP + STR reference haplotype panel. Our panel enables imputing STR genotypes into SNP array data when NGS is not available for directly genotyping STRs. Imputed genotypes achieve mean concordance of 97% with observed genotypes in an external dataset compared to 71% expected under a naive model. Performance varies widely across STRs, with near perfect concordance at bi-allelic STRs vs. 70% at highly polymorphic repeats. Imputation increases power over individual SNPs to detect STR associations with gene expression. Imputing STRs into existing SNP datasets will enable the first large-scale STR association studies across a range of complex traits

Algorithmic Problems in Access Control

Access control is used to provide regulated access to resources by principals. It is an important and foundational aspect of information security. Role-Based Access Control (RBAC) is a popular and widely-used access control model, that, as prior work argues, is ideally suited for enterprise settings. In this dissertation, we address two problems in the context of RBAC. One is the User Authorization Query (UAQ) problem, which relates to sessions that a user creates to exercise permissions. UAQ's objective is the identification of a set of roles that a user needs to activate such that the session is authorized to all permissions that the user wants to exercise in that session. The roles that are activated must respect a set of Separation of Duty constraints. Such constraints restrict the roles that can be activated together in a session. UAQ is known to be intractable (NP-hard). In this dissertation, we give a precise formulation of UAQ as a joint-optimization problem, and analyze it. We examine the manner in which each input parameter contributes to its intractability. We then propose an approach to mitigate its intractability based on our observation that a corresponding decision version of the problem is in NP. We efficiently reduce UAQ to Boolean satisfiability in conjunctive normal form (CNF-SAT), a well-known NP-complete problem for which solvers exist that are efficient for large classes of instances. We also present results for UAQ posed as an approximation problem; our results suggest that efficient approximation is not promising for UAQ. We discuss an open-source implementation of our approach and a corresponding empirical assessment that we have conducted. The other problem we consider in this dissertation regards an efficient data structure for distributed access enforcement. Access enforcement is the process of validating an access request to a resource. Distributed access enforcement has become important with the proliferation of data, which requires access control systems to scale to tens of thousands of resources and permissions. Prior work has shown the effectiveness of a data structure called the Cascade Bloom Filter (CBF) for this problem. In this dissertation, we study the construction of instances of the CBF. We formulate the problem of finding an optimal instance of a CBF, where optimality refers to the number of false positives incurred and the number of hash functions used. We prove that this problem is NP-hard, and a meaningful decision version is in NP. We then propose an approach to mitigate the intractability of the problem by reducing it to CNF-SAT, that allows us to use a SAT solver for instances that arise in practice. We discuss an open-source implementation of our approach and an empirical assessment based on it.4 month

Constructing cascade bloom filters for efficient access enforcement

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.cose.2018.09.015 © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/We address access enforcement — the process of determining whether a request for access to a resource by a principal should be granted. While access enforcement is essential to security, it must not unduly impact performance. Consequently, we address the issue of time- and space-efficient access enforcement, and in particular, study a particular data structure, the Cascade Bloom filter, in this context. The Cascade Bloom filter is a generalization of the well-known Bloom filter, which is used for time- and space-efficient membership-checking in a set, while allowing for a non-zero probability of false positives. We consider the problems, in practice, of constructing Bloom, and Cascade Bloom filters, with our particular application, access enforcement, in mind. We identify the computational complexity of the underlying problems, and propose concrete algorithms to construct instances of the data structures. We have implemented our algorithms, and conducted empirical assessments, which also we discuss in this paper. Our code is available for public download. As such, our work is a contribution to efficient access enforcement

Phase-field modelling of fluid driven fracture propagation in poroelastic materials considering the impact of inertial flow within the fractures

This paper presents a computational framework for modelling of fluid pressurised fracture propagation in saturated porous media. The framework rests on the principle of the variational phase-field theory to predict the fracture propagation pathway. The paper sets out the variational formulations and associated weak forms of the partial differential equations describing the pressure-deformation interplays of the fracturing domain, which are solved in the context of the Updated Lagrangian Finite Element method. The proposed formulation reflects the impact of the temporal evolution of the porous media attributes such as porosity, compressibility, permeability, and mechanical stiffness, on the nonlinear hydro-mechanical behaviour of the porous media during the fracture propagation. The inertial effect of the nonlinear flow inside the fracture is resolved using Forchheimer equation. Robustness of the modelling framework is examined by simulating benchmark examples. The effects of poroelastic characteristics of porous media such as the compressibility of solid skeleton and drained bulk modulus on the hydro-mechanical and cracking behaviour of porous rocks and on the total energy of the system are addressed. The nonlinearity of the fluid flow is found to be influential on the length of the leak-off and flow-back regions across the fractured zones, and on the amount of the fluid to be exchanged between the fractures and the porous zone, which is important in the prediction of the productivity of the fracking process in engineering applications

Midazolam-induced learning and memory impairment is modulated by cannabinoid CB1 receptor agonist and antagonist

Background: Memory impairment is a well-known effect of many benzodiazepine compounds which is mediated through their action on gamma-aminobutyric acid type A (GABAA) receptors. On the other hand, cannabinoids can affect learning and memory process through presynaptic modulation of the release of both excitatory glutamate and inhibitory GABA transmitters in brain regions involved in learning and memory. The aim of the present study was to investigate the effect of cannabinoids on memory impairment and long-term potentiation (LTP) reduction properties of the short acting benzodiazepine midazolam.Materials and Methods: One week after insertion of guide cannula by stereotaxic surgery, cannabinoid compounds or midazolam were administered by intracerebroventricular (i.c.v.) injection into lateral ventricle of male rats. Spatial memory task was evaluated using Morris water maze (MWM) test. Electrophysiological evaluation was done by field potential recording of hippocampal neurons in unconscious rats.Results: In MWM test, while i.c.v. administration of AM251 (200 and 500 ng) per se could not change learning and memory function in rats, pretreatment of rats with AM251 (500 ng; i.c.v.) attenuated midazolam-induced memory impairment. In field potential recording, while i.c.v. administration of AM251 (500 ng) and WIN55212-2 (10 μg) did not have any effect on population spike amplitude, pretreatment of rats with both AM251 and WIN55212-2 significantly diminished midazolam-induced PS amplitude reduction in hippocampal neurons.Conclusion: OurOur results suggest the involvement of cannabinoid CB1 receptors in both memory impairment and LTP reduction in hippocampal neurons which was produced by midazolam. This interaction is likely through their effect on both GABAergic and glutamatergic receptors in hippocampus

Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate

This study compares different methods to predict the simultaneous effects of conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA) sample. PMMA is a kind of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to get data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For Deep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM) method was introduced to find accurate results in the least processing time than the numerical method. A recent study derived the combined heat transfers and their temperature profiles for the PMMA sample. Furthermore, the transient temperature profile is validated by another study. A comparison proves a perfect agreement. It shows the temperature gradient in the primary positions that makes a spectral amount of conductive heat transfer from a PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution it can be understood that the conductive and radiative heat flux is similar in the case of gradient behavior, but it is also twice in its amount approximately. Hence, total heat flux has a constant value in an approximated steady state condition. In addition to analyzing their composition, ROC curve and confusion matrix were implemented to evaluate the algorithm performance.Comment: 15 pages, 11 figure

Investigation of Scale-Forming and Corrosiveness Potential of Drinking Water (Case Study of Shiraz Drinking Water Distribution System)

This research was conducted to investigate the potential of scale forming and corrosiveness of drinking water in the reservoirs and drinking water distribution network in Shiraz, Iran.The area under study was divided into 17 zones. During winter, spring, and summer 2011, 144 water samples were collected from the water reservoirs and the various sites of water distribution system. The chemical parameters were measured. Then, values of the Langelier (LI), Rayznar (RI), Larson (LI) and aggressive (AI) indices were calculated for each sample. In this research, 41 samples of home pipes were collected from different zones of Shiraz and the rate of scale formation was calculated for each sample. The scale composition of 33 home pipe samples and 8 network pipe samples were analyzed by X-ray diffraction method. Results showed that the mean values of LI, RI, LS, and AI were 0.07 (considered as slightly scale forming), 7.1 (non-scale forming), 1.2 (corrosive), and 14 (non-corrosive) respectively. The average rate of scale formation and their values for the drinking water of Shiraz pipes is 0.26 mm/yr. The research found that the main compositions in the scale samples were calcium carbonate, calcium sulfate, magnesium carbonate, magnesium sulfate, hematite, maghemite, magnetite, goethite, zinc oxide, gypsum, vivianite, dolomite, hydroxyapatite, and troilite. The main elements in the scale samples were magnesium, silicon, phosphorus, sulfur, zinc, copper, and lead. According to the results of this research, zones located in the east, south, and southeast of Shiraz, because of high levels of hardness and sulfate exhibit more scale formation as a problem

EVALUATION OF HEAVY METALS CONCENTRATION IN JAJARM BAUXITE DEPOSIT IN NORTHEAST OF IRAN USING ENVIRONMENTAL POLLUTION INDICES

Heavy metals are known as an important group of pollutants in soil. Major sources of heavy metals are modern industries such as mining. In this study, spatial distribution and environmental behavior of heavy metals in the Jajarm bauxite mine have been investigated. The study area is one of the most important deposits in Iran, which includes about 22 million tons of reserve. Contamination factor (CF), the average concentration (AV), the enrichment factor (EF) and geoaccumulation index (GI) were factors used to assess the risk of pollution from heavy metals in the study area. Robust principal component analysis of compositional data (RPCA) was also applied as a multivariate method to find the relationship among metals. According to the compositional bi-plots, the RPC1 and RPC2 account for 57.55% and 33.79% of the total variation, respectively. The RPC1 showed positive loadings for Pb and Ni. Also, the RPC2 showed positive loadings for Cu and Zn. In general, the results indicated that mining activities in the bauxite mine have not created serious environmental hazards in the study area except for lead and nickel. Finding potential relations between mining work and elevated heavy metals concentrations in the Jajarm bauxite mine area necessitates developing and implementing holistic monitoring activities

New metal organic framework (MOF) nanoparticle for gas separation by matrix membranes

{[Dy(BTC)(H2O)]•DMF}n metal organic framework nanoparticles was synthezed through solvthermal method. The product was characterized by XRD, TG, BET, and SEM techniques. SEM images showed that the synthesized sample has semi-cubic particles with average size of 70 nm in length.For improve the gas separation performance,the MOF nano particles were dispersed in polydimethylsiloxane (PDMS) for preparation of mixed matrix membrane (MMM) on support of polyethersulphone (PES). The performance of obtained MMM in separation of NO, N2 and O2 gas were investigated, and the effect of MOF nanoparticles (5, 10, and 15% wt)and feed pressure (100-250 kPa) on permeability and selectivity were studied. It was found that the membrane performance is evaluated by addition of MOF nano particles in membrane (polymeric matrix), and the feed pressure have not important effect on separation. The performance (NO/N2 and NO/O2 selectivity) increased as the loading of MOF particles (up to 15% wt) being dispersed within the polymer matrices