pinecone/core-2020-05-10-deduplication huggingface dataset:
An inversion in the wiring of an intercellular signal: evolution of Wnt signaling in the nematode vulva.

Signal transduction pathways are largely conserved throughout the animal kingdom. The repertoire of pathways is limited and each pathway is used in different intercellular signaling events during the development of a given animal. For example, Wnt signaling is recruited, sometimes redundantly with other molecular pathways, in four cell specification events during Caenorhabditis elegans vulva development, including the activation of vulval differentiation. Strikingly, a recent study finds that Wnts act to repress vulval differentiation in the nematode Pristionchus pacificus, demonstrating evolutionary flexibility in the use of intercellular signaling pathways
The synaptic Muscle-Specific Kinase (MuSK) complex: new partners, new functions.

The muscle-specific kinase MuSK is part of an agrin receptor complex which stimulates tyrosine phosphorylation and drives clustering of acetylcholine receptors (AChRs) in the postsynaptic membrane at the vertebrate neuromuscular junction. MuSK also regulates synaptic gene transcription in subsynaptic nuclei. Over the past few years decisive progress has been made in the identification of MuSK effectors, helping at understanding its function in the formation of the NMJ. Alike AChR, MuSK and several of its partners are the target of mutations responsible for diseases of the NMJ, such as congenital myasthenic syndromes. This minireview will focus on the multiple MuSK effectors so far identified that place MuSK at the center of a multifunctional signaling complex involved in the organization of the NMJ and associated disorders
N6-methyladenine: the other methylated base of DNA.

International audienceContrary to mammalian DNA, which is thought to contain only 5-methylcytosine (m5C), bacterial DNA contains two additional methylated bases, namely N6-methyladenine (m6A), and N4-methylcytosine (m4C). However, if the main function of m5C and m4C in bacteria is protection against restriction enzymes, the roles of m6A are multiple and include, for example, the regulation of virulence and the control of many bacterial DNA functions such as the replication, repair, expression and transposition of DNA. Interestingly, even if adenine methylation is usually considered a bacterial DNA feature, the presence of m6A has been found in protist and plant DNAs. Furthermore, indirect evidence suggests the presence of m6A in mammal DNA, raising the possibility that this base has remained undetected due to the low sensitivity of the analytical methods used. This highlights the importance of considering m6A as the sixth element of DNA
Ectoplasmic specialization: A friend or a foe of spermatogenesis?

The ectoplasmic specialization (ES) is a testis-specific, actin-based hybrid anchoring and tight junction. It is confined to the interface between Sertoli cells at the blood-testis barrier, known as the basal ES, as well as between Sertoli cells and developing spermatids designated the apical ES. The ES shares features of adherens junctions, tight junctions and focal contacts. By adopting the best features of each junction type, this hybrid nature of ES facilitates the extensive junction-restructuring events in the seminiferous epithelium during spermatogenesis. For instance, the α6β1-integrin- laminin 333 complex, which is usually limited to the cell-matrix interface in other epithelia to facilitate cell movement, is a putative apical ES constituent. Furthermore, JAM-C and CAR, two tight junction integral membrane proteins, are also components of apical ES involving in spermatid orientation. We discuss herein the mechanisms that maintain the cross-talk between ES and blood-testis barrier to facilitate cell movement and orientation in the seminiferous epithelium. © 2006 Wiley Periodicals, Inc.postprin
GTP-binding proteins of the Rho/Rac family: regulation, effectors and functions in vivo

24 páginas, 5 figuras, 1 tabla.-- El pdf del artículo es el manuscrito de autor.Rho/Rac proteins constitute a subgroup of the Ras superfamily of GTP hydrolases. Although originally implicated in the control of cytoskeletal events, it is currently known that these GTPases coordinate diverse cellular functions, including cell polarity, vesicular trafficking, the cell cycle and transcriptomal dynamics. In this review, we will provide an overview on the recent advances in this field regarding the mechanism of regulation and signaling, and the roles in vivo of this important GTPase family.Funding agencies: XRB’s work is supported by grants from the US National Cancer Institute (5R01-CA73735-10),\ud
the Spanish Ministry of Education and Science (SAF2003-00028), the Castilla-León Autonomous Government\ud
(SA053A05), and the Red Temática de Investigación Cooperativa en Cáncer (RD06/0020/0001, Spanish Ministry of\ud
Health). VS was supported by an EMBO long term postdoctoral fellowship. IMB was partially supported by a Spanish\ud
Ministry of Education and Science FPU fellowship (FP2000-6489). The Centro de Investigación del Cáncer is\ud
supported by endowments from the CSIC, University of Salamanca, Castilla-León Autonomous Government, the Red\ud
Temática de Investigacián Cooperativa de Centros de Cáncer (C03/10, Spanish Ministry of Health), and the Foundation\ud
for Cancer Research of Salamanca.Peer reviewe
cAMP-dependent protein kinase A and the dynamics of epithelial cell surface domains: moving membranes to keep in shape

Cyclic adenosine monophosphate (cAMP) and cAMP-dependent protein kinase A (PKA) are evolutionary conserved molecules with a well-established position in the complex network of signal transduction pathways. cAMP/PKA-mediated signaling pathways are implicated in many biological processes that cooperate in organ development including the motility, survival, proliferation and differentiation of epithelial cells. Cell surface polarity, here defined as the anisotropic organisation of cellular membranes, is a critical parameter for most of these processes. Changes in the activity of cAMP/PKA elicit a variety of effects on intracellular membrane dynamics, including membrane sorting and trafficking. One of the most intriguing aspects of cAMP/PKA signaling is its evolutionary conserved abundance on the one hand and its precise spatial-temporal actions on the other. Here, we review recent developments with regard to the role of cAMP/PKA in the regulation of intracellular membrane trafficking in relation to the dynamics of epithelial surface domains
Gene duplications, robustness and evolutionary innovations

Mutational robustness facilitates evolutionary innovations. Gene duplications are unique kinds of mutations, in that they generally increase such robustness. The frequent association of gene duplications in regulatory networks with evolutionary innovation is thus a special case of a general mechanism linking innovation to robustness. The potential power of this mechanism to promote evolutionary innovations on large time scales is illustrated here with several examples. These include the role of gene duplications in the vertebrate radiation, flowering plant evolution and heart development, which encompass some of the most striking innovations in the evolution of life
Estimation of the parameters of two parallel regression lines under uncertain prior information

The problem of parallelism for bi-linear regression lines arises in many real life investigations. For two linear regression models with normal errors, the estimation of the\ud
slope as well as the intercept parameters  is considered  when it is   apriori suspected that the two lines are parallel. Three different estimators  are defined by using both the sample data and the non-sample uncertain prior information. The relative performances of the   unrestricted, restricted and preliminary test estimators are investigated based on the  analysis of the bias, and risk functions under quadratic loss. An example based on a medical study is used to illustrate the method
Maximally selected chi-square statistics and binary splits of nominal variables

We address the problem of maximally selected chi-square statistics in the case of a binary Y variable and a nominal X variable with several categories. The distribution of the maximally selected chi-square statistic has already been derived when the best cutpoint is chosen from a continuous or an ordinal X, but not when the best split is chosen from a nominal X. In this paper, we derive the exact distribution of the maximally selected chi-square statistic in this case using a combinatorial approach. Applications of the derived distribution to variable selection and hypothesis testing are discussed based on simulations. As an illustration, our method is applied to a pregnancy and birth data set.
Spatio-temporal epidemic modelling using additive-multiplicative intensity models

An extension of the stochastic susceptible-infectious-recovered (SIR) model is proposed in order to accommodate a regression context for modelling infectious disease surveillance data. The proposal is based on a multivariate counting process specified by conditional intensities, which contain an additive epidemic component and a multiplicative endemic component. This allows the analysis of endemic infectious diseases by quantifying risk factors for infection by external sources in addition to infective contacts. Simulation from the model is straightforward by Ogata's modified thinning algorithm. Inference can be performed by considering the full likelihood of the stochastic process with additional parameter restrictions to ensure non-negative conditional intensities.
As an illustration we analyse data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of the classical swine fever virus in Germany during 1993-2004.
Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve

International audienceFinding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC -- area under the receiver operating curve -- which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC -- AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with existing ones and assess the effect of model misspecification -- when estimating the conditional absolute risk function-- on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets
Selection models with monotone weight functions in meta analysis

Publication bias, the fact that studies identified for inclusion in a meta
analysis do not represent all studies on the topic of interest, is commonly
recognized as a threat to the validity of the results of a meta analysis. One
way to explicitly model publication bias is via selection models or weighted
probability distributions. We adopt the nonparametric approach initially
introduced by Dear (1992) but impose that the weight function $w$ is monotonely
non-increasing as a function of the $p$-value. Since in meta analysis one
typically only has few studies or "observations", regularization of the
estimation problem seems sensible. In addition, virtually all parametric weight
functions proposed so far in the literature are in fact decreasing. We discuss
how to estimate a decreasing weight function in the above model and illustrate
the new methodology on two well-known examples. The new approach potentially
offers more insight in the selection process than other methods and is more
flexible than parametric approaches. Some basic properties of the
log-likelihood function and computation of a $p$-value quantifying the evidence
against the null hypothesis of a constant weight function are indicated. In
addition, we provide an approximate selection bias adjusted profile likelihood
confidence interval for the treatment effect. The corresponding software and
the datasets used to illustrate it are provided as the R package selectMeta.
This enables full reproducibility of the results in this paper.Comment: 15 pages, 2 figures. Some minor changes according to reviewer
  comment
Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Brazi
Updating risk prediction tools: A case study in prostate cancer

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision‐making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [‐2]proPSA measured on an external case–control study performed in Texas, U.S.. Recent state‐of‐the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network
A length-based hierarchical model of brown trout (Salmo trutta fario) growth and production

We present a hierarchical Bayesian model (HBM) to estimate the growth parameters, production, and production over biomass ratio (P/B) of resident brown trout (Salmo trutta fario) populations. The data which are required to run the model are removal sampling and air temperature data which are conveniently gathered by freshwater biologists. The model is the combination of eight submodels: abundance, weight, biomass, growth, growth rate, time of emergence, water temperature, and production. Abundance is modeled as a mixture of Gaussian cohorts; cohorts centers and standard deviations are related by a von Bertalanffy growth function; time of emergence and growth rate are functions of water temperature; water temperature is predicted from air temperature; biomass, production, and P/B are subsequently computed. We illustrate the capabilities of the model by investigating the growth and production of a brown trout population (Neste d’Oueil, Pyrénées, France) by using data collected in the field from 2005 to 2010
Clustering in linear mixed models with a group fused lasso penalty

A method is proposed that aims at identifying clusters of individuals that show similar patterns when observed repeatedly. We consider linear mixed models which are widely used for the modeling of longitudinal data. In contrast to the classical assumption of a normal distribution for the random effects a finite mixture of normal distributions is assumed. Typically, the number of mixture components is unknown and has to be chosen, ideally by data driven tools. For this purpose an EM algorithm-based approach is considered that uses a penalized normal mixture as random effects distribution. The penalty term shrinks the pairwise distances of cluster centers based on the group lasso and the fused lasso method. The effect is that individuals with similar time trends are merged into the same cluster. The strength of
regularization is determined by one penalization parameter. For finding the optimal penalization parameter a new model choice criterion is proposed.
The exponential-Poisson model for recurrent event data: an application to a set of data on malaria in Brazil

In this paper,we introduce a newmodel for recurrent event data characterized by a baseline rate function\ud
fully parametric, which is based on the exponential-Poisson distribution. The model arises from a latent\ud
competing risk scenario, in the sense that there is no information about which cause was responsible for\ud
the event occurrence. Then, the time of each recurrence is given by theminimum lifetime value among all\ud
latent causes. The new model has a particular case, which is the classical homogeneous Poisson process.\ud
The properties of the proposed model are discussed, including its hazard rate function, survival function,\ud
and ordinary moments. The inferential procedure is based on the maximum likelihood approach. We\ud
consider an important issue of model selection between the proposed model and its particular case\ud
by the likelihood ratio test and score test. Goodness of fit of the recurrent event models is assessed\ud
using Cox-Snell residuals. A simulation study evaluates the performance of the estimation procedure in\ud
the presence of a small and moderate sample sizes. Applications on two real data sets are provided to\ud
illustrate the proposed methodology. One of them, first analyzed by our team of researchers, considers\ud
the data concerning the recurrence of malaria, which is an infectious disease caused by a protozoan\ud
parasite that infects red blood cells.FAPESPCAPESCNP
Evidence synthesis for count distributions based on heterogeneous and
  incomplete aggregated data

The analysis of count data is commonly done using Poisson models. Negative
binomial models are a straightforward and readily motivated generalization for
the case of overdispersed data, i.e., when the observed variance is greater
than expected under a Poissonian model. Rate and overdispersion parameters then
need to be considered jointly, which in general is not trivial. Here we are
concerned with evidence synthesis in the case where the reporting of data is
rather heterogeneous, i.e., events are reported either in terms of mean event
counts, the proportion of event-free patients, or rate estimates and standard
errors. Either figure carries some information about the relevant parameters,
and it is the joint modeling that allows for coherent inference on the
parameters of interest. The methods are motivated and illustrated by a
systematic review in chronic obstructive pulmonary disease.Comment: 17 pages, 3 figures, 2 table
The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data

Multiple imputation can be used as a tool in the process of constructing prediction models in medical and epidemiological studies with missing covariate values. Such models can be used to make predictions for model performance assessment, but the task is made more complicated by the multiple imputation structure. We summarise various predictions constructed from covariates, including multiply imputed covariates, and either the set of imputation-specific prediction model coefficients or the pooled prediction model coefficients. We further describe approaches for using the predictions to assess model performance. We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model’s performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model’s performance in a real-world clinical setting where some individuals have missing predictors. The approaches are compared through an extensive simulation study based on the UK700 trial.  We determine that measures of ideal model performance can be estimated within imputed datasets, and subsequently pooled to give an overall measure of model performance. Alternative methods to evaluate pragmatic model performance are required and we propose constructing predictions either from a second set of covariate imputations which make no use of observed outcomes, or from a set of partial prediction models constructed for each potential observed pattern of covariate. Pragmatic model performance is generally lower than ideal model performance. We focus on model performance within the derivation data, but describe how to extend all the methods to a validation dataset.Angela Wood part supported by MRC grant G0701619. Ian White from MRC _Biostatistics Unit with unit programme number U105260558This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/bimj.201400004/abstract;jsessionid=144424FA52D50041821329D8A7741BFD.f02t0
Covariate-adjusted measures of discrimination for survival data

Motivation: Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data. Objective: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).Method: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. Results: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices. Conclusions: The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.Ian R. White ... D. Lawlor ... Emerging Risk Factors Collaboratio