dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present study, bioinformatics evaluation of 566 primary tumors resected from colon cancer patients (GSE 39582 dataset gathering transcriptomic analyses) revealed that the cumulative effects of High-YAP and High-CREB transcript levels correlated with a shorter overall survival (p = 0. 00225). Interestingly, High-TAZ combined with High-CREB is associated with a detrimental impact on the survival of colon cancer patients (p = 0. 00141), suggesting that TAZ is connected with CREB signaling pathways. As expected, a synergetic effect between YAP and WWTR1 gene (encoding TAZ protein) expression on survival outcome was effective (p = 0. 0032). In contrast, the YAP and CCNE1 genes pair displayed no cumulative effect according SynTarget. In summary, our study indicates that potential synergies between YAP/TAZ and CREB are effective in primary colorectal tumors and liver metastases.
Western blotting and phospho-kinase array Total extracts of cells were obtained with a RIPAbased buffer containing protease and phosphatase inhibitors (Roche). Subcellular fractionation was realized as previously described  [1]. Western blot were carried out using 20 µg of protein lysates with the NuPage Electrophoresis and Iblot transfert systems (Life Technologies). The list of primary antibodies is described in the Supplementary Material and Methods and HRPlabelled secondary antibodies (Thermoscientific) were used accordingly to the primary antibody species. β-Actin was used as loading control for total extracts, SP-1 was used for labeling nuclear fractions. Western blot pictures were taken with the LAS 4000 camera (Fujifilm) and quantification were done with ImageJ software. Phosphokinase array (ARY003b) was done accordingly to manufacturer's protocol with 0.
Flow cytometry Cell viability and toxicity was analysed by exclusion of Propidium Iodide and flow cytometry. For cell cycle analysis, cell suspensions (200,000 cells per condition) were fixed with 70% ethanol at -20°C and incubated with 0. 4 µg of anti-human Alexa 488-conjugated Ki67 antibody or matching 488-conjugated isotype (BD Biosciences) for 30 minutes on ice and for 30 minutes with 50 µg/ml propidium iodide (Sigma Aldrich) and 5 μg/ml RNase (Ambion) at room temperature. Reaction was stopped on ice and cells were analyzed on the Cyan ADP analyzer (Beckman Coulter). Cytometry data were analyzed with Kaluza software (Beckman Coulter).
ACKNOWLEDGMENTS We thank  M. H. Gevaert  and  R. M Siminsky  ( Department of Histology, University of Lille 2 ),  N. Jouy  ( cytometry platform, University of Lille 2 ),  M Tardivel  (microscopy platform,  University of Lille 2 ) and  A. S Drucbert  (luciferase measurement experiences,  University of Lille 2 ) for their technical help. We thank the  Tumor Bank of the Reference Center in Cancerology (Laurence Wicquart and Sylvie Janas)  for the samples from human tumors. Matthieu Corvaisier  is a recipient of a  doctoral fellowship of region "Nord-Pas-de-Calais-Picardie " and  CHRU of Lille and SIRIC ONCOLille  grants.
Case Study on European Mammals Mammals are a fairly well known group of species regarding their ecology, distributions as well as phylogeny. Nevertheless, their phylogenies are not fully resolved, but contain polytomies. Resolving the polytomies randomly results in variation among different trees, but having good spatial distribution data provides a good opportunity for investigating the influence of such uncertainty on spatial conservation prioritization. Mammals are also considered to be of high conservation interest due to their public appeal  (Smith et al. 2012). They were the first focal taxon of the EDGE programme  (Isaac et al. 2007) , which was a pioneering endeavor to bring highly threatened and evolutionarily unique species to the limelight and to improve their conservation. We conducted spatial prioritizations for European mammal conservation with the Zonation conservation planning software.
Material and Methods European Mammal Distributions We used data on the spatial distribution of european terrestrial mammals described in  Maiorano et al. (2013). The primary data were extents of occurrence (EOOs) of the species occurring in Europe and Turkey obtained from the Global Mammal Assessment (http://www. iucnredlist. org/initiatives/mammals; accessed 15 August 2013 (IUCN 2012)). To refine EOOs and remove potential false presences, habitat requirements were used in an expert-based modelling approach. More specifically, for each species, habitat requirement was defined by experts (G. Amori, D. Russo and L. Boitani) and published literature (see  Maiorano et al. 2013  for the full list of references) based on three environmental variables: land cover, elevation and distance to water.
For each species, data collected were used to assign a suitability score (0, unsuitable; 1, secondary habitat and 2, primary habitat) to each of the 46 GlobCover land-use/land-cover classes. Elevation and distance to water were then combined to the habitat suitability score to refine the available EOOs and obtain current distribution with a cell size of 300 m resolution. The models were validated with help of field data (see  Maiorano et al. 2013  for more details). From these 288 species we used 275 for which phylogenies were available. As running the phylogenetic analyses and Zonation prioritization at 300 m resolution would have been too demanding for the equipment available at the time, we scaled up the species distributions following a regular grid of 10′. As a value for each 10′ cell, we kept the percentage of 300 m cells considered as either 1 (primary habitat) or 2 (secondary habitat), and we refer to this value as "the proportion of suitable area" hereafter.
For aesthetic reasons, all the maps presented hereafter have been projected using the Lambert conformal conic projection (UTM zone 34). Mammal Phylogenies Phylogenetic data for mammals were based on the supertree of  Bininda-Emonds et al. (2007)  updated by  Fritz et al. (2009). We used 100 fully resolved phylogenetic trees, where polytomies were randomly resolved applying a birth-death model to simulate branch lengths  (Kuhn et al. 2011). Protected Areas We used the WDPA dataset on protected areas (UNEP 2010) categories I-IV (I: Strict nature reserve or wilderness area, II: National park, III: Natural monument or feature and IV: Habitat/Species management area) excluding the categories that are generally considered less beneficial for biodiversity conservation (categories V and VI), and areas where the category was either 'not reported' or 'not applicable'.
The Zonation Approach Zonation is a spatial prioritization software meant to be used as a decision support tool  (Moilanen et al. 2009). While other approaches typically select a fraction of the landscape according to a pre-determined target, e. g. 10 % of species distributions, or maximize what is achieved with a pre-determined budget, Zonation instead ranks all cells in the entire landscape in the order of conservation value. A Zonation solution can be used to identify any best (or worst) fraction of the landscape. The ranking is based on the evaluation of range size normalized richness of biodiversity features in each cell  (Moilanen et al. 2005 (Moilanen et al. , 2011)). In plain words, this means that features (e. g. species) with broad distributions contribute very little to the conservation value of a single cell, whereas narrowly distributed species substantially increase the conservation value of the cells they occupy.
The remaining analyses were dedicated for the evaluation of the current protected areas network. We used only one cost-layer (corresponding to the equivalent number of Rao's quadratic entropy extracted from tree 1 and a medium weighting) to evaluate the protected area network at the (c) European scale and (f) the national scale. Finally we ran Zonation without any phylogenetic diversity data to assess the representation of species within the protected areas network at (d) European scale and (f) national scale. Abbreviations: med medium, phyl. div phylogenetic diversity measured as the equivalent number of Rao's quadratic entropy The three phylogenetic weightings give very similar results. The pair-wise Spearman rank correlations between these differently weighted Zonation analyses were very high (low-medium: 0.
Objective: To update an environmental scan of training programs in SDM for health professionals. Methods: We searched two systematic reviews for SDM training programs targeting health professionals produced from 2011 to 2015, and also in Google and social networks. With a standardized data extraction sheet, one reviewer extracted program characteristics. All completed extraction forms were validated by a second reviewer. Results: We found 94 new eligible programs in four new countries and two new languages, for a total of 148 programs produced from 1996 to 2015-an increase of 174% in four years. The largest percentage appeared since 2012 (45. 27%). Of the 94 newprograms, 42. 55% targeted licensed health professionals (n = 40), 8. 51% targeted pre-licensure (n = 8), 28. 72% targeted both (n = 27), 20.
Data sources and searches We identified SDM training programs in all fields of healthcare through three main sources: (i) systematic reviews in the field of SDM, (ii) structured Google TM searches, and (iii) requests and periodic searches of social media networks (Facebook and Twitter). Briefly, we reviewed the reference lists of the two recently published systematic reviews on SDM and its implementation in clinical practice  [11, 17]. Our team is also currently updating the Cochrane review of SDM interventions in healthcare  [11] , which allowed us to identify training programs included in studies published since 2012. From November 2011 to November 2015, trained research assistants performed a weekly search in Google TM using the following keywords: ("shared decision making" OR "patient engagement" OR "patient involvement") AND (embedding OR training OR education).
Trained reviewers screened data sources for eligible programs using a standardized form detailing eligibility criteria. PubMed and Google TM were also searched to retrieve published or unpublished evaluations of the identified programs. Any type of evaluation was considered (e. g. satisfaction with the training, knowledge test, self-reported competence, etc. ). Programs cited by their developers as evaluated but not published were also included. One reviewer screened and assessed each program retrieved for its eligibility, and two different reviewers (NTD, HR) confirmed eligibility. In case of disagreement a senior team member (FL) reviewed the program. Reviewers then extracted the following characteristics from eligible programs: program name, author information, date of creation or publication, country of origin, language used, level of care (primary or specialty), clinical domain, health profession(s) targeted, interprofessional focus or not, educational format and duration of training sessions.
Combined scan The flow scheme of the environmental scan is presented in Fig. 1. Between November 2011 and November 2015, we identified 119 new potentially eligible programs with our search strategy. Of these, 94 were considered eligible and were thus added to the 54 programs identified by our initial scan, representing a 174% increase over four years. The two research periods together produced a total of 148 programs developed in 18 countries and in 12 different languages. A small majority (54. 05%) targeted more than one kind of healthcare professional and nearly three-quarters (71. 62%) were generic, i. e. for use in any clinical context. While 43 of the programs have been evaluated, only 37 program evaluations have been published in articles available in a database (e. g. PubMed). Main characteristics of the programs are summarized in Table  1 , while full program details are provided in Supplementary file 1.
Conflict of interest The Canada Research Chair in Shared Decision Making and Knowledge Translation mission is to provide health professionals and their patients with the necessary skills to promote shared decision-making throughout the healthcare continuum. Therefore, it has produced a number of shared decision making training programs that are included in this scan. Supplementary data associated with this article can be found, in the online version, at http://dx. doi. org/10. 1016/j. pec. 2016. 06. 008.
Introduction High dimensionality is increasingly ubiquitous in numerous scientific fields incuding genetics, economics and physics. Reducing the dimensionality is a challenge that most statistical methodologies must meet not only to remain interpretable but also to achieve reliable predictions. In linear regression models, dimension reduction techniques often refer to variables selection. Approaches for variables selection are implemented in publicly available software, that involve the well-known R packages glmnet and spikeslab. The R package glmnet implements the Elastic net methodology  [Zou and Hastie (2005) ], which is a generalization of both the LASSO  [Tibshirani (1996) ] and the ridge regression (RR)  [Hoerl and Kennard (1970) ]. The R package spikeslab in turn, implements the Spike and Slab methodology  [Ishwaran and Rao (2005) ], which is a Bayesian approach for variables selection.
Dimension reduction can not however, be restricted to variables selection. Indeed, the field can be extended to include approaches which aim is to create surrogate covariates that summarizes the information carried in initial covariates. Since the emblematic Principal Component Regression (PCR)  [Jolliffe (1982) ], many of the latter methods spread in the recent literature. As specific examples, we may refer to the OSCAR methodology  [Bondell and Reich (2008) ], or the PACS methodology  [Sharma, Bondell, and Zhang (2013) ] which is a generalization of the latter approach. Those methods mainly proposed variables clustering within a regression model as a way to reduce the dimensionality. Despite their theoretical and practical appeal, implementations of those methods were often proposed only through Matlab or R scripts, limiting thus the flexibility and the computational efficiency of their use.
e. b 1 = 0. This enhancement mainly aimed at facilitating the interpretation of the model. Indeed when b 1 is set to 0, variables assigned to the cluster associated with b 1 might be considered less relevant than other variables. Those two new features were implemented in a C++ program available through the R package clere. The outline of the present paper is the following. In Section 2, the definition of the model is recalled and the strategy to estimate the model parameter is presented. In Section 3 are described both the MCEM and SEM algorithms. This section also presents how the number of clusters is chosen and how the constraint on parameter b 1 can be interpretated. Section 4 presents the main functionalities of the R package clere. In Section 5, numerical experiments are presented aiming at illustrating the computational gain of the SEM algorithm over our former strategy and the good predictive performances of CLERE compared to standard dimension reduction methods.
Secondary functions summary(), plot(), ggPlot(), clusters() and predict() Examples of calls for the functions presented in this section are given in Section 5. 3. The summary() function prints an overview of the estimated parameters and returns the estimated likelihood and information based model selection criteria (AIC, BIC and ICL). The call of functions plot() and ggPlot() are similar to the one of function summary(). The latter function function produces graphs such as ones presented in Figure  1. The function ggPlot() requires a prior installation of the R package ggplot2. However, there is no dependancies with the latter package since the R package clere can be installed without ggplot2. When ggplot2 is not installed, the user can still make use of the function plot(). The function clusters(), takes one argument of class Clere and a threshold argument.
Mean squared prediction error (MSPE) defined as M SP E a = E (y v -X v θ a ) (y v -X v θ a ) , where y v and X v are respectively a vector of responses and a design matrix from a validation dataset. 4. Maximum log-likelihood (ML) reached. This quantity was approximated using 1,000 samples from p(Z|y; θ). Three versions of the MCEM algorithm were proposed for comparison with the SEM algorithm, depending on the number M (or nsamp) of Gibbs iterations used to approximate the E step. That number was varied between 5, 25 and 125. Those versions were respectively denoted MCEM 5 , MCEM 25 and MCEM 125. The comparison was performed using 200 simulated datasets. Each training dataset consisted of n = 25 individuals and p = 50 variables. Validation datasets used to calculate MSPE consisted of 1,000 individuals each.
All covariates were simulated independently according to the standard Gaussian distribution: ∀(i, j) x ij ∼ N (0, 1). Both training and validation datasets were simulated according to model (1) using β 0 = 0, b = (0, 3, 15) , π = (0. 64, 0. 20, 0. 16) , σ 2 = 1 and γ 2 = 0. This is equivalent to simulate data according to the standard linear regression model defined by: y i ∼ N   32 j=1 0 × x ij + 42 j=33 3 × x ij + 50 j=43 15 × x ij , 1   All algorithms were run using 10 different random starting points. Estimates yielding the largest likelihood were then used for the comparison.
Description of the methods In this section, we compare our model to standard dimension reduction approaches in terms of MSPE. Although a more detailed comparison was proposed in  [Yengo et al. (2013) ], we propose here a quick illustration of the relative predictive performance of our model. The comparison is achieved using data simulated according to the scenario described above in Section 5. 1. The methods selected for comparison are the ridge regression  [Hoerl and Kennard (1970) ], the elastic net  [Zou and Hastie (2005) ], the LASSO  [Tibshirani (1996) ], PACS  [Sharma et al. (2013) ], the method of Park and colleagues  [Park, Hastie, and Tibshirani (2007) ] (subsequently denoted AVG) and the spike and slab model  [Ishwaran and Rao (2005) ] (subsequently denoted SS). The first three methods are implemented in the freely available R package glmnet.
The latter package was used with default options regarding the choice of tuning parameters. PACS methodology proposes to estimate the regression coefficients by solving a penalized least squares problem. It imposes a constraint on β that is a weighted combination of the L 1  norm and the pairwise L ∞ norm. Upper-bounding the pairwise L ∞ norm enforces the covariates to have close coefficients. When the constraint is strong enough, closeness translates into equality achieving thus a grouping property. For PACS, no software was available. Only an R script was released on Bondell's webpage 1. Since this R script was running very slowly, we decided to reimplement it in C++ and observed a 30-fold speed-up of computational time.
% of times Similarly to Bondell's R script, our implementation uses two parameters lambda and betawt. In  [Sharma et al. (2013) ], the authors suggest assigning betawt with the coefficients obtained from a ridge regression model after the tuning parameter was selected using AIC. In this simulation study we used the same strategy; however the ridge parameter was selected via 5fold cross validation. 5-fold CV was preferred to AIC since selecting the ridge parameter using AIC always led to estimated coefficients equal to zero. Once betawt was selected, lambda was chosen via 5-fold cross validation among the following values: 0. 01, 0. 02, 0. 05, 0. 1, 0. 2, 0. 5,  1, 2, 5, 10, 20, 50, 100, 200 and 500. All other default parameters of their script were unchanged. The AVG method is a two-step approach.
Variables assigned to the spike are dropped from the model. We used the R package spikeslab to run the spike and slab models. Especially, we used the function spikeslab from that package to detect influential variables. The number of iterations used to run the function spikeslab was 2,000 (1,000 discarded). When running fit. clere(), the number nItEM of SEM iterations was set to 2,000. The number g of groups for CLERE was chosen between 1 and 5 using AIC (option analysis="aic"). Two versions of CLERE were considered: the one with all parameters estimated and the one with b 1 set to 0. The latter approach is subsequently denoted CLERE 0 (option sparse=TRUE).
Description of the datasets We used in this section the real datasets Prostate and eyedata from the R packages lasso2 and flare respectively. The Prostate dataset comes from a study that examined the correlation between the level of prostate specific antigen and a number of clinical measures in n = 97 men who were about to receive a radical prostatectomy. This dataset was used in multiple publications including  [Tibshirani (1996) ]. We used the prostate specific antigen (variable lpsa) as reponse variable and the p = 8 other measurements as covariates. The eyedata dataset is extracted from the published study of  [Scheetz (2006) ]. This dataset consists in gene expression levels measured at p = 200 probes in n = 120 rats. The response variable utilized was the expression of the TRIM32 gene which is a biomarker of the Bardet-R> x <-as.
Results of the analysis Table  4  summarizes the prediction errors and the number of parameters obtained for all the methods. CLERE 0 had the lowest prediction error in the analysis of the Prostate dataset and the second best performance with the eyedata dataset. The AVG method was also very competitive compared to variable selection approaches stressing thus the relevance of creating groups of variables to reduce the dimensionality. It is worth noting that in both datasets, imposing the constraint b 1 = 0 improved the predictive performance of CLERE. In the Prostate dataset, CLERE robustly identified two groups of variables representing influcial (b 2 > 0) and not relevant variables (b 1 = 0). In the eyedata dataset in turn, AIC led to select only one group of variables. However, this did not lessened the predictive performance of the model since CLERE 0 was second best after AVG, while needing significantly less parameters.
PACS low performed in both datasets. The Elastic net showed good predictive performances compared to the variable selection methods like LASSO or Spike and slab model. Ridge regression and Elastic net had comparable results in both datasets. 4 : Real data analysis. Out-of-sample prediction error (averaged CV-statistic) was estimated using cross-validation in 100 splitted datasets. The number of parameters reported for CLERE/CLERE 0 was selected using AIC.
CUORE is a 741 kg array of TeO 2 bolometers for the search of neutrinoless double beta decay of 130 Te. The detector is being constructed at the Laboratori Nazionali del Gran Sasso, Italy, where it will start taking data in 2015. If the target background of 0. 01 counts/(keV•kg•y) will be reached, in five years of data taking CUORE will have a 1σ half life sensitivity of 10 26 y. CUORE-0 is a smaller experiment constructed to test and demonstrate the performances expected for CUORE. The detector is a single tower of 52 CUORE-like bolometers that started taking data in spring 2013. The status and perspectives of CUORE will be discussed, and the first CUORE-0 data will be presented.