Association of Caveolin-1 gene polymorphism with kidney transplant fibrosis and allograft failure

Context: Caveolin-1 (CAV1) is an inhibitor of tissue fibrosis.
Objective: To study the association of CAV1 gene variation with kidney transplant outcome, using kidney transplantation as a model of accelerated fibrosis.
Design, Setting, and Patients: Candidate gene association and validation study. Genomic DNA from 785 white kidney transplant donors and their respective recipients (transplantations in Birmingham, England, between 1996 and 2006; median followup, 81 months) were analyzed for common variation in CAV1 using a singlenucleotide polymorphism (SNP) tagging approach. Validation of positive findings was sought in an independent kidney transplant donor-recipient cohort (transplantations in Belfast, Northern Ireland, between 1986 and 2005; n=697; median follow-up, 69 months). Association between genotype and allograft failure was initially assessed by Kaplan-Meier analysis, then in an adjusted Cox model.
Main Outcome Measure: Death-censored allograft failure, defined as a return to dialysis or retransplantation.
Results: The presence of donor AA genotype for the CAV1 rs4730751 SNP was associated with increased risk of allograft failure in the Birmingham group (donor AA vs non-AA genotype in adjusted Cox model, hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.29-3.16; P=.002). No other tag SNPs showed a significant association. This finding was validated in the Belfast cohort (in adjusted Cox model, HR, 1.56; 95% CI, 1.07-2.27; P=.02). Overall graft failure rates were as follows: for the Birmingham cohort, donor genotype AA, 22 of 57 (38.6%); genotype CC, 96 of 431 (22.3%); and genotype AC, 66 of 297 (22.2%); and for the Belfast cohort, donor genotype AA, 32 of 48 (67%); genotype CC, 150 of 358 (42%); and genotype AC, 119 of 273 (44%).
Conclusion: Among kidney transplant donors, the CAV1 rs4730751 SNP was significantly associated with allograft failure in 2 independent cohorts.

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Randomized Controlled Trial Evaluation of Time to Read, a Volunteer Tutoring Program for 8-9 Year Olds

Tutoring is commonly employed to prevent early reading failure, and evidence suggests that it can have a positive effect. This article presents findings from a large-scale (n = 734) randomized controlled trial evaluation of the effect of Time to Read—a volunteer tutoring program aimed at children aged 8 to 9 years—on reading comprehension, self-esteem, locus of control, enjoyment of learning, and future aspirations. The study found that the program had only a relatively small effect on children’s aspirations (effect size +0.17, 95% confidence interval [0.015, 0.328]) and no other outcomes. It is suggested that this lack of evidence found may be due to misspecification of the program logic model and outcomes identified and program-related factors, particularly the low dosage of the program.

A progressive failure model for composite laminates subjected to low velocity impact damage

This paper presents a 3-D failure model for predicting the dynamic material response of composite laminates under impact loading. The formulation is based on the Continuum Damage Mechanics (CDM) approach and enables the control of the energy dissipation associated with each failure mode regardless of mesh refinement and fracture plane orientation. Internal thermodynamically irreversible damage variables were defined in order to quantify damage concentration associated with each possible failure mode and predict the gradual stiffness reduction during the impact damage process. The material model has been implemented into LS-DYNA explicit finite element code within solid elements and it has proven to be capable of reproducing experimental results with good accuracy in terms of static/dynamic responses, absorbed energy and extent of damage.

Macroscopic limit of time-dependent density-functional theory for adiabatic local approximations of the exchange-correlation kernel

Time-dependent density-functional theory is a rather accurate and efficient way to compute electronic excitations for finite systems. However, in the macroscopic limit (systems of increasing size), for the usual adiabatic random-phase, local-density, or generalized-gradient approximations, one recovers the Kohn-Sham independent-particle picture, and thus the incorrect band gap. To clarify this trend, we investigate the macroscopic limit of the exchange-correlation kernel in such approximations by means of an algebraical analysis complemented with numerical studies of a one-dimensional tight-binding model. We link the failure to shift the Kohn-Sham spectrum of these approximate kernels to the fact that the corresponding operators in the transition space act only on a finite subspace.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Beyond the standard work model? Varieties of flexible working time in European economies

Working time has been among the first aspect of the employment relation to be the object of intense regulation at the national and supra-national level. This standard regulation of working time comprised a number of elements: full-time hours, rigid working schedules, strong employers’ control and clear boundaries around working time In spite of general claims about the erosion of this model, few studies have investigated this process in a comparative and empirical perspective. The aim of this paper is to investigate the diversity of working time arrangements in European economies by applying latent class analysis to data
from the European Working Conditions Survey (EWCS). This analysis shows the existence of six different types of working time organization highlighting five cross-national patterns: multiple flexibilities, extended flexibility, standard, rigid and fragmented time.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Strength model for end cover separation failure in RC beams strengthened with near-surface mounted (NSM) FRP strips

As an alternative to externally bonded FRP reinforcement, near-surface mounted (NSM) FRP reinforcement can be used to effectively improve the flexural performance of RC beams. In such FRP strengthened RC beams, end cover separation failure is one of the common failure modes. This failuremode involves the detachment of the NSM FRP reinforcement together with the concrete cover along the level of the tension steel reinforcement. This paper presents a new strength model for end cover separation failure in RC beams strengthened in flexure with NSM FRP strips (i.e. rectangular FRP bars with asectional height-to-thickness ratio not less than 5), which was formulated on the basis of extensive numerical results from a parametric study undertaken using an efficient finite element approach. The proposed strength model consists of an approximate equation for the debonding strain of the FRP reinforcement at the critical cracked section and a conventional section analysis to relate this debondingstrain to the moment acting on the same section (i.e. the debonding strain). Once the debonding strain is known, the load level at end cover separation of an FRP-strengthened RC beam can be easily determined for a given load distribution. Predictions from the proposed strength model are compared with those of two existing strength models of the same type and available test results, which shows that the proposed strength model is in close agreement with test results and is far more accurate than the existing strength models.