dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, there was no association between rMS and risk of breast cancer. Nevertheless, tightening the focus to key functional domain rMS resulted in a significant association with an OR of approximately 3. 0. In this sense, the MRN genes behave as the homologous recombination repair genes BRCA1, BRCA2, and ATM -genes in which rare missense substitutions that are pathogenic because of missense dysfunction per se are largely confined to key functional domains. Combining MRN T + SJV and key functional domain rMS, we observed an OR of 2. 88 with a P value of 0. 0090. That P value meets the threshold of P <0. 01 that Hollestelle et al. suggested for establishing intermediaterisk susceptibility genes that were already strong candidates based on their biochemical function  [3]. Thus with a mutation screening and data analysis approach that considered MRE11A, RAD50, and Nibrin as a unique functional entity and focused the analysis of rMS to those that fall in the key functional domains of the MRN complex, we overcame the limitation of previous suggestive studies that were based on a small number of founder mutations  [13, 15, 18] , and confirmed the hypothesis that MRE11A, RAD50, and NBN are intermediate-risk susceptibility genes in a general sense.
In contrast, the MRE11A rMS that described from a breast cancer case (p. Thr481Ile), as well as the rMS observed at the same position in a pair of ataxia-telangiectasia-like disease cases (p. Thr481Lys), score as severe C65 substitutions when evaluated with the mammals-only alignment but as likely innocuous C0 substitutions when evaluated with the evolutionarily deep alignment. Since the observation of a nonconservative rMS at MRE11A position Thr481 in a pair of ataxia-telangiectasia-like disease cases increases the odds that substitutions at this position are in fact pathogenic, it appears that using the evolutionarily deeper alignments is, for the MRN genes, counterproductive. On the other hand, the empirically determined allele frequency thresholds derived by combining older ATM, BRCA1, BRCA2, and CHEK2 case-control mutation screening data with EVS and 1000G data -found to be 0.
Conclusions Results reported here establish that MRE11A, RAD50, and NBN are intermediate-risk breast cancer susceptibility genes and help to justify their inclusion on panelbased cancer susceptibility gene tests. Protein-truncating variants and rare missense substitutions falling in the key functional domains of these proteins appear to confer two-to three-fold increased risk of breast cancer. Like ATM and CHEK2, the spectrum of pathogenic variants in the MRN genes includes a relatively high proportion of missense substitutions. However, the data neither establish whether variants in each of the three genes are best evaluated under the same analysis model nor achieve clinically actionable classification of individual variants observed in this study. Given the relatively low frequency of likely pathogenic variants in the MRN genes, development of clinically applicable rare missense substitution classification models for these genes will require data from very large observational studies supplemented, in all likelihood, by carefully calibrated functional assays.
0 6. 0 1 0 1 [51][52][53][54][55][56][57][58][59][60][61][62][63]3852del4, which falls within the C-terminal ATPase domain (a key domain), which we consider analytically equivalent to a key domain rMS graded C65. rMS, rare missense substitution. Figure1MRE11, RAD50, and nibrin key functional domains. The protein domain diagrams are updated from Williams et al. [4]and include information from the InterPro protein sequence analysis and classification database, the Uniprot Protein Knowledgebase, and the NCBI Conserved Domains Database[47][48][49][50]plus structural studies of the MRN proteins[51][52][53][54][55][56][57][58][59][60][61][62][63].
Settings For the evaluation, we use anechoic speech samples taken from the TIMIT database. We use a subset of the database with 10 female and 10 male speakers, each one pronouncing one sentence. These signals are then convolved with two different sets of Room Impulse Responses (RIR). The first set is intended to evaluate the algorithm in realistic situations and contains measured RIRs taken from the AIR database  [17]. The selected impulse responses correspond to a hands free use of a mock up mobile telephone in 10 different rooms. For the second set, we use the Fast Image-Source Method  [18]  to simulate the RIRs of a room with dimensions [3x4x5]m and T60 ranging from 100ms to 1. 2s. This set will be used to evaluate the performance of the method as a function of the T60. The reverberated signals x(t), sampled at 16 kHz, are processed with the proposed algorithm to produce the dereverberated signal y(t).
The octave filterbank is obtained by recursively performing a diadic partition of the available frequency bins. We performed a grid search on each parameter introduced in Section 3 and selected the value yielding the maximum SRMR on y(t). From this analysis, the dictionary length is set to L = 10 and the delay is set to δ = 5 frames. This delay corresponds to 40 ms of speech which is sufficient to remove the direct signal from the dictionary. For the block processing we use an observation length of N = 8 frames, corresponding to 64 ms long segments of speech signal. We solve problem (10) using the MATLAB's mexLasso function from the SPAMS optimization toolbox  1. The estimated late reverberation is smoothed with a single pole low-pass filter with time constant τ = 32 ms to compensate the discontinuities introduced by the block-wise processing.
Choice of the subsampling scheme In a first experiment, we evaluate the influence of the two subsampling strategies presented in Section 3 when used individually and together. In addition, we run 100 iterations of each approach on the whole database and we evaluate the average CPU time needed for the execution. We use a computer with an Intel Core i7-640M processor at 2. 8GHz and 4 GB RAM. We analyze the average Real Time Factor (RTF) defined as the ratio between the processing time and the total length of the speech samples. The results of the evaluation are summarized in Table  1. When we do not apply any subsampling, the proposed method yields the best results in terms of reverberation suppression but it is also very  slow and impractical for real-time applications. We also observe that subsampling along the frequencies states for the major reduction of the complexity of the method.
Bagging (bootstrap aggregation) can be used to fit many models to bootstrapped replicates of the dataset (with and without random subsetting of predictors used across trees as in random forests). In contrast, boosting uses a forward stagewise method to build an ensemble, at each step modeling the residuals of the models fitted to date. Taking ensembles of relatively simple models usually increases complexity because combinations of simple models will not necessarily be simple. In contrast, ensembles of more complex models can average over idiosyncrasies of individual models to produce smoother response curves  (Elder 2003).
Recommendations If presence-absence data are available, one should assess the degree of spatial autocorrelation in the residuals and implement methods to control for spatial autocorrelation. Methods include spatially-explicit models that separate the spatial pattern from the environmental response  (Latimer et al. 2006 , Dormann et al. 2007 , Beale et al. 2010) , using spatial eigenvectors as predictors (Diniz-Filho and Bini 2005), or stratified sub-sampling of the data to minimize autocorrelation  (Hijmans 2012). Complex models should be used cautiously in the presence of spatial autocorrelation, because their flexibility may lead to them confounding aggregation in geographic space with complexity in environmental space. For example, if a large number of presences are recorded in a small region of environmental space due to social behavior in geographic space, it is more likely that a complex model can find some feature in environmental space that correlates with this clustering.
Attributes of the available data may be less important with process-based models when relevant test datasets are well understood. However, data considerations are important when mechanisms or parameters are inferred from data or when assessing the spatiotemporal resolution over which particular degrees of abstraction and parameter values are relevant  (Evans et al. 2013 , Lonergan 2014 , Snell et al. 2014). In any case, we expect that progress towards improved process-based models lies in challenging occurrence-based SDMs with stronger biological justifications and interpretations that aim to shed light on the mechanisms that drive process-based models. based hypotheses about it. The end result is a model that adds complexity only to the extent necessary to reproduce observed patterns. Uncertainty analysis is a relatively untapped resource for understanding appropriate model complexity.
Biological Despite the valuable insights we can gain from occurrence models, it is worth acknowledging that fundamental limitations to biological inference may emerge from these studies  (Tyre et al. 2001 , Araújo and Guisan 2006 , Araujo and Peterson 2012 , Merow et al. 2013). Balancing complex and simple models in such a way as to discover and discuss these limits may be as important as the actual patterns identified with some datasets. More broadly, it is important to keep in mind that we are ultimately performing exploratory analyses of occurrence-environment relationships. Occurrence records are not the ideal data to predict attributes of populations,  Thuiller et al. (2014)  provide an interesting cautionary note by showing weak relationships between occurrence probability and various demographic parameters for 108 tree species in temperate forests.
Acknowledgements -This study arose from two workshops entitled 'Advancing concepts and models of species range dynamics: understanding and disentangling processes across scales'. Funding was provided by the  Danish Council for Independent Research | Natural Sciences  (grant no. 10-085056  to SN). CM acknowledges funding from  NSF  grant  1046328  and  NSF  grant  1137366. WT acknowledges support from the  European Research Council  under the European Community's  Seven Framework Programme FP7 /2007-2013 Grant Agreement no. 281422  (TEEMBIO). RW acknowledges support from the  Swiss National Science Foundation  ( Synergia  Project  CRS113-125240 ,  Early Postdoc Mobility Grant   PBZHP3_147226 ). JE acknowledges funding from the  Australian Research Council  (grant  FT0991640 ). TE states that mention any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
Collaborative updates of the knowledge base It is important for the SHOM knowledge base to be kept constantly up-to-date. To achieve this goal, INAUT will be used as a tool for collaborative update. Indeed, INAUT has been designed as the optimal compromise between easiness of use (since contributors have a priori no KM proficiency) and formality (as the knowledge base will be fed directly by the incoming data). To make the system more robust, we validate on two levels. First, the lexical and syntactic level: a Web interface segments written in INAUT and validates them. In case of errors it provides correction hints. Second, the semantic level: a human controller monitors incoming INAUT data which, depending on the contributors trust level are automatically fed into the knowledge base (with the possibility of making the modification retroactive) or are stored in a waiting list until manual validation.
The visual word form area (VWFA), a region systematically involved in the identification of written words, occupies a reproducible location in the left occipitotemporal sulcus in expert readers of all cultures. Such a reproducible localization is paradoxical, given that reading is a recent invention that could not have influenced the genetic evolution of the cortex. Here, we test the hypothesis that the VWFA recycles a region of the ventral visual cortex that shows a high degree of anatomical connectivity to perisylvian language areas, thus providing an efficient circuit for both grapheme-phoneme conversion and lexical access. In two distinct experiments, using highresolution diffusion-weighted data from 75 human subjects, we show that (1) the VWFA, compared with the fusiform face area, shows higher connectivity to left-hemispheric perisylvian superior temporal, anterior temporal and inferior frontal areas; (2) on a posteriorto-anterior axis, its localization within the left occipitotemporal sulcus maps onto a peak of connectivity with language areas, with slightly distinct subregions showing preferential projections to areas respectively involved in grapheme-phoneme conversion and lexical access.
Participants Seventy-nine French subjects gave written informed consent to participate in the "Archi Database Part 1" project (Neurospin, CEA, Saclay, France). All participants indicated French as their first language, had normal or corrected-to-normal visual acuity, and had no history of neurological, learning, or reading disorders. Four subjects were excluded because of defects in fMRI acquisition (one subject) or handedness as assessed with the Edinburgh handedness inventory  (Oldfield, 1971 ; three left-handed subjects), because lefthanded subjects are more likely to display atypical lateralization of both language networks and the VWFA  (Cai et al. , 2008; Van der Haegen et al. , 2012). The current study was thus based on a database of 75 subjects (46 males/29 females, mean age 24 years, range 19 -50, SD 5.
One checkerboard trial consisted in eight alternating black/white checkerboards, each displayed for 200 ms. To isolate the FFA, we used a subpart of the experimental conditions included in another short protocol designed to investigate social cognition, contrasting activations by faces minus scrambled images. In the face condition, subjects had to judge the gender or the trustworthiness of a human character according to his/her face. Before the trial, a sentence shortly displayed in the center of the video screen (1200 ms) reminded the task to be done ("face of a man or of a woman?" or "trustworthy or untrustworthy?"). Stimuli consisted in a series of 32 black and white images of standardized faces with direct gaze and neutral expressions, originally extracted from the Karolinska database  (Lundqvist et al.
For each acquisition, the first four volumes were discarded to reach equilibrium. T1-weighted images were also acquired for anatomical localization. Data processing. Data were preprocessed using SPM8 software as follows: slice timing, motion correction, normalization to the MNI template (resampled voxel size ϭ 3 ϫ 3 ϫ 3 mm), and smoothing (5 mm FWHM). Each voxel time series was fitted with a linear combination of functions derived by convolving a standard hemodynamic response function with the time series of the stimulus categories, plus their temporal derivatives. Individual contrast images were computed for the two contrasts of interest, then smoothed (5 mm FWHM), and eventually entered in a second level random effect group analysis. We used functional and anatomical data to create two special types of images for use in Experiments 1 and 2 (Fig.
, 2007). Fiber tracts were reconstructed using the diffusion model estimation and a streamline deterministic tractography algorithm provided in Connectomist software. The tracts were calculated as the trajectories of particles with inertia, leading to regularized curvature  (Perrin et al. , 2005). Streamline deterministic tractography was initiated from 27 (3 ϫ 3 ϫ 3) seeds in each voxel of the propagation mask (with T1 resolution), in both retrograde and anterograde directions, according to the maximal direction of the underlying ODF. Retrograde and anterograde tracking were merged into one single tract. The tracking step was 0. 4 mm. Track-ing was stopped either when the particle exited the propagation mask, or when the angle between the two last moves exceeded 30°, or when the tract length exceeded 200 mm.
Tracking of fibers from ROIs. In Experiments 1 and 2, we defined ROIs that we used as seeds for tracking fibers. To this end, we selected all streamlines intersecting the given ROI on a minimal distance of 0. 2 mm. Individual connectivity maps, indicating in each voxel of the diffusion space the number of fibers passing through, were derived from those tractograms. Individual connectivity maps were then binarized and registered to an optimal FA template in MNI space drawn from the present dataset using Advance Normalization Tools (http://www. picsl. upenn. edu/ANTS/). Affine and diffeomorphic deformations used for the registration were computed from individual FA maps to the obtained template  (Avants et al. , 2008; Klein et al. , 2009). Similar methods are reported in Thiebaut de  Schotten et al.
(2011). Normalized connectivity maps were finally smoothed using a Gaussian kernel of 4 mm FWHM, and were used for statistical analyses as described below. 3D fusion images of connectivity or statistical maps with a mesh of the average gray-white interface from the MNI database were produced using the BrainVisa package (http://brainvisa. info). Except for the images of maximal projections in Figure  4 , which use point-to-point fusion, each point of the mesh is labeled with the maximum value in a surrounding 5 mm sphere.
To this end, we entered individual normalized smoothed connectivity maps in a nonparametric repeated-measures ANOVA using the permutation method, as implemented in FSL software  (Nichols and Holmes, 2002). This analysis included one within-subject factor (the 3 ROIs), one regressor of noninterest (the individual volume of the ROI), and subjects were treated as a random factor. 5000 permutations were run and the thresholdfree cluster enhancement (TFCE) method was applied  (Smith and Nichols, 2009). We report results significant at a threshold of 0. 05, familywise error (FWE) corrected, based on the TFCE statistic image. Within this model, we contrasted the projections of the VWFA ROI and of the FFA ROI minus the other two ROIs (Fig. 2C ). This showed that the VWFA connected significantly more to most perisylvian language-related areas, including Broca's area and insula, the superior and lateral temporal lobe, and the posterior inferior parietal lobule.
49, by the  HIPPIP Grant  and the European CONNECT project (http://www. brain-connect. eu). Acquisitions were performed by the scientists involved in the  Multiscale Brain Architecture research program  of NeuroSpin and by the staff of the  BioMedical Laboratory of NeuroSpin  (headed by  Dr Lucie Hertz-Pannier ), under the ethical approval  CPP100002/CPP100022  (principal investigator  Dr Denis Le Bihan ). Access to the database.
Data processing Data for the period from 1999 to 2010 were used in this analysis. Precipitation and throughfall data sampled during more than 330 days per year, and with concentration values for more than 300 days per year were included. Sampling periods with mean precipitation below 0. 1 mm day À1 were counted even if no chemical analyses could be performed. Data from each sampling period were interpolated to regular monthly and annual data by: (i) splitting each sampling period overlapping two consecutive months by distributing precipitation quantity in proportion to the duration of the new sampling periods; (ii) setting deposition ¼ 0 for periods with missing concentrations and mean precipitation <0. 1 mm day À1 ; (iii) calculating TF and BD (Q$c$10 À2 , in kg ha À1 ) by multiplication of the precipitation quantity (Q, in L m À2 ), the concentrations (c, in mg L À1 ) and the unity conversion factor 10 À2 ; (iv) summing up to fluxes by month and year, respectively.
Linear regression and Kendall tests were performed using the 'rkt' package  (Marchetto, 2013)  in the R software  (R Development Core Team, 2009). For the Kendall tests (MK, SMK, PMK), trend slopes b (kg ha À1 yr À2 ) were estimated following  Sen (1968). For each time series, we calculated a relative slope rslope (yr À1 ), as an estimated mean relative change per year, with rslope ¼ b=meanðyÞ; (1) where b (kg ha À1 yr À2 ) is the estimator for the absolute trend resulting from the trend analyses and mean (y) (kg ha À1 yr À1 ) the mean value of the time series.
Minimum detectable trends Minimum detectable trends rslope emp min were derived empirically from the p-values and the rslope results of the individual trend analyses for each combination of parameter, time series length and trend analysis technique. The rslope emp min value above which the majority of tests identify a trend as significant, with p < 0. 05 (at significance level 95%), was determined by fitting a Gauss shaped function through the band of p-to rslope values of the test results (see Annex). Secondly, minimum detectable trends were modelled based on the temporal variability of the overall dataset with rslope mod min ¼ c 7 2 CV n years T crit n 2 ffiffi ffi n 2 q (2) where n years is the duration of time series in years, n is the number of observations (n ¼ n years for annual and n ¼ 12$n years for monthly data), CV the coefficient of variation of the temporal variability (see Table  1 ) and T crit the test statistic of the T-test (e.
Comparison of minimum detectable trend to sources of uncertainty of the measurements This study suggests that the data quality objective to 'detect a change of 30% in 10 years' which is defined in the ICP Forests manual (ICP Forests, 2010) seems realistic. It has to be mentioned that this study only investigated the uncertainty related to the statistical methods. However, uncertainties related to the steps prior to the trend analyses  (Thimonier, 1998; Houston et al. , 2002; Bleeker et al. , 2003; Erisman et al. , 2003; Staelens et al. , 2006; Marchetto et al. , 2011; Zlindra et al. , 2011b)  were on average lower in magnitude than the uncertainty resulting from the temporal variability of the deposition.
L€ ovblad, E. Ulrich, N. Clarke and K. Hansen. Applying these methods involved numerous technicians for the installation and maintenance of about 4000 samplers, collection of roughly a million samples, and chemical analyses of approximately 200,000 pooled samples, as well as on-going supervision by some 40þ scientists. Data transmission involved national focal centres and the data centre of ICP Forests that was subsequently at Alterra, Wageningen (FIMCI), at the Joint Research Centre in Ispra and at the Programme Coordination Centre of ICP Forests in Hamburg. Data transmission included sophisticated conformity and plausibility checks developed by the involved database specialists. The comparability of the laboratory analyses has been improved by the activities of the Working Group on QA/QC in laboratories initiated and supported by R.
Mosello, N. K€ onig, K. Derome, the late John Derome, A. Kowalska, A. Marchetto and others. For field installations, similar activities were coordinated by G. Draaijers, A. Bleeker, J. Erisman, E. Ulrich, and D. Zlindra. Data quality objectives and other methodological improvements were the results of activities initiated by the QA/QC committee of ICP Forests chaired by M. Ferretti. The atmospheric deposition measurements typically involved access being granted by the land owners, financial support from the participating countries and the EU, as well as support from subordinated governmental organisations such as communal or forest services. The EU partially funded the deposition network under the Council Regulation (EEC) 3528/86 on the 'Protection of Forests against Atmospheric Pollution' and the Regulation (EC) No 2152/2003 concerning monitoring of forests and environmental interactions in the community (Forest Focus) and by the project LIFE 07 ENV/D/000218 "Further Development and Implementation of an EU-level Forest Monitoring System (FutMon)".