Development of a Computationally Efficient Fabric Model for Optimization of Gripper Trajectories in Automated Composite Draping

An automated prepreg fabric draping system is being developed which consists of an array of actuated grippers. It has the ability to pick up a fabric ply and place it onto a double-curved mold surface. A previous research effort based on a nonlinear Finite Element model showed that the movements of the grippers should be chosen carefully to avoid misplacement and induce of wrinkles in the draped configuration. Thus, the present study seeks to develop a computationally efficient model of the mechanical behavior of a fabric based on 2D catenaries which can be used for optimization of the gripper trajectories. The model includes bending stiffness, large deflections, large ply shear and a simple contact formulation. The model is found to be quick to evaluate and gives very reasonable predictions of the displacement field

A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies

Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders. © 2014 Chang et al

HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values

We propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning step to it. The size of the complete mining results is typically prohibitively large, despite the fact that only a small percentage of high utility patterns are interesting. Thus it is inefficient to wait for the mining algorithm to complete and then apply feature selection to post-process the large number of resulting patterns. Instead of generating the complete set of frequent patterns we are able to directly mine patterns with high utility values in an incremental manner. In this paper we propose a novel utility measure called an influence factor using the concepts of external utility and internal utility of an item. The influence factor for an item takes into consideration its connectivity with its neighborhood as well as its importance within a transaction. The measure is especially useful in problem domains utilizing network or interaction characteristics amongst items such as in a social network or web click-stream data. We compared our technique against state of the art incremental mining techniques and show that our technique has better rule generation and runtime performance

Communication style and exercise compliance in physiotherapy (CONNECT). A cluster randomized controlled trial to test a theory-based intervention to increase chronic low back pain patients’ adherence to physiotherapists’ recommendations: study rationale, design, and methods

Physical activity and exercise therapy are among the accepted clinical rehabilitation guidelines and are recommended self-management strategies for chronic low back pain. However, many back pain sufferers do not adhere to their physiotherapist’s recommendations. Poor patient adherence may decrease the effectiveness of advice and home-based rehabilitation exercises. According to self-determination theory, support from health care practitioners can promote patients’ autonomous motivation and greater long-term behavioral persistence (e.g., adherence to physiotherapists’ recommendations). The aim of this trial is to assess the effect of an intervention designed to increase physiotherapists’ autonomy-supportive communication on low back pain patients’ adherence to physical activity and exercise therapy recommendations. \ud \ud This study will be a single-blinded cluster randomized controlled trial. Outpatient physiotherapy centers (N =12) in Dublin, Ireland (population = 1.25 million) will be randomly assigned using a computer-generated algorithm to either the experimental or control arm. Physiotherapists in the experimental arm (two hospitals and four primary care clinics) will attend eight hours of communication skills training. Training will include handouts, workbooks, video examples, role-play, and discussion designed to teach physiotherapists how to communicate in a manner that promotes autonomous patient motivation. Physiotherapists in the waitlist control arm (two hospitals and four primary care clinics) will not receive this training. Participants (N = 292) with chronic low back pain will complete assessments at baseline, as well as 1 week, 4 weeks, 12 weeks, and 24 weeks after their first physiotherapy appointment. Primary outcomes will include adherence to physiotherapy recommendations, as well as low back pain, function, and well-being. Participants will be blinded to treatment allocation, as they will not be told if their physiotherapist has received the communication skills training. Outcome assessors will also be blinded. \ud \ud We will use linear mixed modeling to test between arm differences both in the mean levels and the rates of change of the outcome variables. We will employ structural equation modeling to examine the process of change, including hypothesized mediation effects. \ud \ud This trial will be the first to test the effect of a self-determination theory-based communication skills training program for physiotherapists on their low back pain patients’ adherence to rehabilitation recommendations. Current Controlled Trials ISRCTN63723433\u

Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline

Background: As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.Results: We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HSA: DE genes with non-zero effect sizes in all studies, (2) HSB: DE genes with non-zero effect sizes in one or more studies and (3) HSr: DE gene with non-zero effect in "majority"of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.Conclusions: The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HSA, HSB, and HSr). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website. © 2013 Chang et al.; licensee BioMed Central Ltd

Discovering monotonic stemness marker genes from time-series stem cell microarray data

© 2015 Wang et al.; licensee BioMed Central Ltd. Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/

Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle

Field Emission Properties and Fabrication of CdS Nanotube Arrays

A large area arrays (ca. 40 cm2) of CdS nanotube on silicon wafer are successfully fabricated by the method of layer-by-layer deposition cycle. The wall thicknesses of CdS nanotubes are tuned by controlling the times of layer-by-layer deposition cycle. The field emission (FE) properties of CdS nanotube arrays are investigated for the first time. The arrays of CdS nanotube with thin wall exhibit better FE properties, a lower turn-on field, and a higher field enhancement factor than that of the arrays of CdS nanotube with thick wall, for which the ratio of length to the wall thickness of the CdS nanotubes have played an important role. With increasing the wall thickness of CdS nanotube, the enhancement factorβdecreases and the values of turn-on field and threshold field increase

Bifurcation Boundary Conditions for Switching DC-DC Converters Under Constant On-Time Control

Sampled-data analysis and harmonic balance analysis are applied to analyze switching DC-DC converters under constant on-time control. Design-oriented boundary conditions for the period-doubling bifurcation and the saddle-node bifurcation are derived. The required ramp slope to avoid the bifurcations and the assigned pole locations associated with the ramp are also derived. The derived boundary conditions are more general and accurate than those recently obtained. Those recently obtained boundary conditions become special cases under the general modeling approach presented in this paper. Different analyses give different perspectives on the system dynamics and complement each other. Under the sampled-data analysis, the boundary conditions are expressed in terms of signal slopes and the ramp slope. Under the harmonic balance analysis, the boundary conditions are expressed in terms of signal harmonics. The derived boundary conditions are useful for a designer to design a converter to avoid the occurrence of the period-doubling bifurcation and the saddle-node bifurcation.Comment: Submitted to International Journal of Circuit Theory and Applications on August 10, 2011; Manuscript ID: CTA-11-016

Incense smoke: clinical, structural and molecular effects on airway disease

In Asian countries where the Buddhism and Taoism are mainstream religions, incense burning is a daily practice. A typical composition of stick incense consists of 21% (by weight) of herbal and wood powder, 35% of fragrance material, 11% of adhesive powder, and 33% of bamboo stick. Incense smoke (fumes) contains particulate matter (PM), gas products and many organic compounds. On average, incense burning produces particulates greater than 45 mg/g burned as compared to 10 mg/g burned for cigarettes. The gas products from burning incense include CO, CO2, NO2, SO2, and others. Incense burning also produces volatile organic compounds, such as benzene, toluene, and xylenes, as well as aldehydes and polycyclic aromatic hydrocarbons (PAHs). The air pollution in and around various temples has been documented to have harmful effects on health. When incense smoke pollutants are inhaled, they cause respiratory system dysfunction. Incense smoke is a risk factor for elevated cord blood IgE levels and has been indicated to cause allergic contact dermatitis. Incense smoke also has been associated with neoplasm and extracts of particulate matter from incense smoke are found to be mutagenic in the Ames Salmonella test with TA98 and activation. In order to prevent airway disease and other health problem, it is advisable that people should reduce the exposure time when they worship at the temple with heavy incense smokes, and ventilate their house when they burn incense at home