dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Grid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity and improve the speed of computations. This work starts by examining suitable geographical grids, and how epidemiological data is projected to such a grid. Based on this framework, data-mining algorithms are introduced which enable both a data-driven approach and a hypothesis-driven approach. These algorithms enable rapid discovery of clusters of cases as well the testing of specific environmental factors. A new open-source software tool (FGBASE) implementing these algorithms, is presented together with a case study of its use on the "Isis-Diab" cohort of French T1D cases. We hope that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.
Acknowledgements This research was supported by grant  MRES-PRNPE-1-CVS-014  from the  (French Ministry of Environment , and the  Programme Hospitalier de Recherche Clinique of the French Ministry of Health  ( AOM08049 ),  Inserm, NovoNordisk Laboratory. We acknowledge the participation of the pediatricians of the diabetes centers contributing to the Isis-Diab cohort (see list of participating centers in Additional file 1: Table S1). We gratefully thank  Sofia Meurisse  for the geocoding of patients. We thank the patients and parents who participated in the study. Finally we would like to thank the two anonymous reviewers for their helpful comments and suggestions.
PARTICIPANTS AND EXPERIMENTAL DESIGN Four healthy right-handed adult volunteers aged 24-38 years old participated in the study (1 female). Written informed consent was obtained from all participants, with all procedures carried out in accordance with the Swinburne University Human Research Ethics Committee. Participants underwent 20 sessions of neurofeedback over the course of 7 weeks. In addition, three assessment sessions were completed across the course of the neurofeedback period, one at baseline, one after 10 sessions, and a final assessment after the 20 sessions. Beyond the data from the baseline assessment session used to isolate individual sources for neurofeedback, these assessment sessions will not be further discussed in the present report.
As part of the neurofeedback protocol, each participant required individually determined weights corresponding to the BSS component showing the strongest correlation with the group MTL-parietal theta source identified in  White et al. (2012)  during completion of this same task. Using identical EEG acquisition and pre-processing routines as that used in  White et al. (2012) , individual participants' EEG data during spatial navigation was decomposed using the identical BSS method (AJDC using the same parameters previously reported, using ICoN software, Version 3. 1 1 ). Source time-series derived from the individual BSS decomposition were then correlated with the group MTL/parietal theta source time-series described in  White et al. (2012) , with the individual source showing the strongest correlation selected as the feedback source (for all four participants, r ≥ ± 0.
Neurofeedback protocol All participants underwent 20 neurofeedback sessions across 7 weeks. Each neurofeedback session involved a resting eyes open baseline, then five blocks of training each lasting 4 min. As this study represented a preliminary investigation exploring the feasibility of BSS-based training, no control neurofeedback group was included. Instead, a series of trials were conducted at a follow-up session upon completion of the 20 sessions in which participants were asked to increase or decrease the feedback signal. As the focus of this experiment was the feasibility of learned regulation of BSS-derived source activity, these trials were included to probe for evidence of learned volitional regulation of the target signal. SynAmps 2 amplifiers and Acquire 4. 3 software were used to acquire the EEG data as part of the neurofeedback sessions (Neuroscan Inc.
, Abbotsford, VIC, Australia). Data acquisition for neurofeedback sessions employed a band-pass filter from 1-50 Hz, with a linked mastoid reference. This was done to ensure consistency with the reference used in the off-line analysis of the previous experiment. A second computer running the Open-ViBE software platform  (Renard et al. , 2010)  provided the on-line processing and feedback required by the neurofeedback paradigm. The set-up made use of the built-in client/server operations available in Scan Acquire 4. 3 software; whereby the acquisition system acted as the server which sent acquired data on to the client system (Open-ViBE) via a Local Area Network. An acquisition driver written in C++ facilitated this process within the Open-ViBE platform. A "Scenario" was developed for each participant within the Open-ViBE software which applied a processing chain to generate the feedback signal, before providing visual feedback to the participant with minimal delay.
Learning within and across sessions involve desired changes emerging over the course of training, and thus can be plotted as a learning curve. In each of these cases, the presence of learning is demonstrated by the desired increase or decrease in the signal of interest across sessions or over blocks of time within sessions. Within and across sessions learning was assessed by ordinary least squares regression, with the time within training as the predictor (session number for across sessions, and minute from beginning of feedback for within sessions; both 1-20). For both learning analyses, data was normalized with respect to the mean and standard deviation of source pθ /totθ ratio during a baseline period. For within sessions learning, mean data was extracted for each minute of neurofeedback at each session, and normalized to the corresponding baseline data for that session.
, 2004). As part of the present exploration, participants completed a follow-up session upon completion of the 20 neurofeedback sessions in which they were asked to increase or decrease the feedback signal in eight randomized blocks of 3-min each (total of four "up" and four "down" trials). A randomization t-test  (Edgington, 1987)  comparing the mean pθ/totθ ratio feedback signal obtained in each up trial vs. the mean of each down trial exploits the design of the trials, providing an appropriate assessment of volitional self-regulation of the feedback signal post-training. A designated exploration of the relationship between ocular artifact and the feedback signal was undertaken offline, using data from the first neurofeedback session for each participant. Power in the delta range (1-3 Hz), averaged across frontal electrode sites, formed a surrogate measure of EOG artifact.
The field of astroparticle physics is currently the focus of prolific scientific activity. In the last decade, this field has undergone significant developments thanks to several experimental results from CREAM, PAMELA, Fermi, and H. E. S. S. Moreover, the next generation of instruments, such as AMS-02 (launched on 16 May 2011) and CTA, will undoubtedly facilitate more sensitive and precise measurements of the cosmic-ray and γ -ray fluxes. To fully exploit the wealth of high precision data generated by these experiments, robust and efficient statistical tools such as Markov Chain Monte Carlo algorithms or evolutionary algorithms, able to handle the complexity of joint parameter spaces and datasets, are necessary for a phenomenological interpretation. The Grenoble Analysis Toolkit (GreAT) is an user-friendly and modular object orientated framework in C++, which samples the user-defined parameter space with a pre-or userdefined algorithm.
Introduction A rapidly increasing proportion of modern physical and astrophysical analyses are characterised by the application of extensive experimental datasets to constrain the parameter spaces of complex multi-dimensional models. Such analyses are necessarily highly computationally intensive; so much so that even for moderate increases in the dimensionality of the model parameter spaces, or the extent of the plausible hyper-regions under exploration, naïve scanning approaches rapidly become unfeasible. These difficulties are particularly relevant in the context of increasingly prevalent analyses involving sophisticated modelling and global fitting of diverse, multi-messenger data. Fortunately, the computational expense of such analyses can be substantially reduced by the application of more discriminatory scanning algorithms such as Markov Chain Monte Carlo (MCMC).
The Grenoble Analysis Toolkit (GreAT) 1 provides a flexible framework which allows researchers to rapidly implement advanced tools for statistical data analysis. In particular, the highly customisable, modular design of GreAT facilitates straightforward development of utilities for parameter estimation, the derivation of confidence intervals, goodness-of-fit quantification and hypothesis testing. In order to provide a fast and efficient handling and plotting of the data, GreAT is interfaced with the ROOT package 2  developed at CERN. In addition, the Function Parser library fparser4. 5. 1  3  is included in the GreAT software for parsing and evaluating mathematical function from a string. In Section 2 we outline the GreAT package content before describing an implementation of an Markov Chain Monte Carlo algorithm in Section 3.
Markov Chain Monte Carlo The Bayesian approach aims to assess the extent to which an experimental dataset improves our knowledge of a given theoretical model. The technically difficult point of Bayesian parameter estimation lies in the determination of the individual posterior probability-density function (PDF), which requires an (highdimensional) integration of the overall posterior density. Thus an efficient sampling method for the posterior PDF is mandatory. We have implemented a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian parameter inference  [1, 2]. In general, MCMC methods attempt to study any target distribution of a vector of parameters θ, by generating a sequence of n points (hereafter a chain) {θ i } i=1,. ,n = {θ 1 , θ 2 ,. , θ n }. The chain is Markovian in the sense that the sampling distribution of θ n+1 is influenced entirely by the value of θ n.
How to use the GreAT software: a simple example To illustrate the use of the GreAT software, we consider a simple problem, implemented in the MCMCSegmentedLine. cc example, where data are adjusted to a segmented line with a breakpoint (BP) at x BP : f (x) :=  a 1 • x + b if x < x BP a 1 • x BP + b + a 2 • (x -x BP ) if x ≥ x BP (1) where a 1,2 and b are the slopes and the y-axis intercept of the line segments, respectively. An example code for the above defined function is given in listings 1. Assuming that the uncertainties σ y i of the n measured data points y i are independent and normally distributed, the least square and the maximum likelihood are equivalent. The logarithmic likelihood function can hence be defined as: log L = - 1 2 n  i=1 (y i -f (x i )) 2 σ 2 y i = - 1 2 χ 2 , (2) where the additive terms that do not depend on the parameters have been dropped.
3. Without any need of a trial list analyser the estimator can be used to visualise one or more trial lists for all parameter and logarithmic likelihood values with the help of the  PlotTrialList(const int &i, const int &color = 1, TCanvas *c = NULL) function. An example code for trial lists with index 0 and 9 is given in listing 6. A zoom of the resulting plot for the breakpoint parameter x BP along with their burn-in and correlation length is shown in Fig. 4. A clear improvement in the sampling efficiency through an updated covariance matrix (as described in Section 3) is visible.
Application to cosmic-ray physics One outstanding issue in the field cosmic-ray (CR) physics is the determination of the transport parameters in the Galaxy. During the journey of Galactic cosmic rays (GCRs), from the acceleration sites to the solar neighbourhood, secondary CR species are produced due to nuclear interactions of heavier primary species with the interstellar medium. Hence, they are tracers of the CR transport in the Galaxy. Secondary-to-primary ratios, such as e. g. , B/C, sub-Fe/Fe, are therefore suitable quantities to constrain the transport parameters for species Z ≤ 30. We show here some constraints obtained for the B/C ratio in the framework of a diffusion model using the MCMC algorithm described above, interfaced with the USINE propagation package  [2]. We consider the 1D thin disc diffusion model  [5]  with constant Galactic wind V c and minimal reacceleration.
The diffusion coefficient is given by K (R) = K 0 βR δ. The free parameters of this model are the constant Galactic wind speed V c , the normalisation K 0 and the slope δ of the diffusion coefficient K (R), and finally the Alfvén velocity V a related to the reacceleration. Here we used the B/C data only and we fixed the halo size of the Galaxy L to 4 kpc. The B/C dataset consists of (i) low-energy data taken by the IMP7-8  [6] , the Voyager 1&2  [7] , and the ACE-CRIS  [8]  spacecrafts; (ii) intermediate energies acquired by HEA0-3 data  [9] ; and    (iii) higher energy data from Spacelab  [10]  and the published CREAM data  [11]. The marginalised PDFs for the model parameters and their correlations are shown in Fig. 5. Taking advantage of the knowledge of the posterior distribution, sampled by the MCMC, we can estimate credible intervals for each parameter as well as a credible region in the whole parameter space.
Conclusion Whereas most statistical analysis packages focus on one specific algorithm and method, the Grenoble Analysis Toolkit (GreAT) is an user-friendly and modular object orientated framework in C++, which allows scientists to easily apply many kinds of statistical analysis to their data. In addition to the delivered algorithms, the user can effortlessly implement or interface their favoured analysis from other packages. It will soon be available for download from the GreAT website (http://lpsc. in2p3. fr/great). Here we have briefly described an implementation of the Metropolis-Hastings MCMC algorithm and its subsequent application to address an outstanding question in cosmic-ray physics. Future developments of the GreAT software will include multi-threading of the base code, and additional scanning algorithms such as genetic and differential evolution algorithms.
Background: Different methods have been proposed for analyzing differentially expressed (DE) genes in microarray data. Methods based on statistical tests that incorporate expression level variability are used more commonly than those based on fold change (FC). However, FC based results are more reproducible and biologically relevant. Results: We propose a new method based on fold change rank ordering statistics (FCROS). We exploit the variation in calculated FC levels using combinatorial pairs of biological conditions in the datasets. A statistic is associated with the ranks of the FC values for each gene, and the resulting probability is used to identify the DE genes within an error level. The FCROS method is deterministic, requires a low computational runtime and also solves the problem of multiple tests which usually arises with microarray datasets.
Background To select the differentially expressed (DE) genes in a microarray dataset with two biological conditions, the Fold Change (FC) which is calculated as a ratio of averages from control and test sample values was initially used  [1, 2]. Levels of change or cutoffs, (e. g. 0. 5 for downand 2 for up-regulated) are used and genes under/above thresholds are selected. Then, other statistical methods were introduced. Many of these methods use three steps. First, a statistical test (e. g. Student's t-test or similar) is performed to obtain a p-value for each gene in the study. Second, these p-values are compared to a threshold which is chosen to have an acceptable False Discovery Rate (FDR), and a list of genes is obtained. Third, a selection is done from the above list using FC level thresholds for down-and up-regulated genes  [3, 4].
New Full list of author information is available at the end of the article statistical methods more adapted to microarray data were proposed  [5] [6] [7] [8] [9]. The significance analysis of microarrays (SAM) method  [6]  provides an improvement to the ordinary Student t-test, as it imposes limits on the variability of genes, to exclude genes that do not change and which are associated with very low p-values. The performances of several methods were compared in  [10] [11] [12] [13]  using two classes microarray datasets. It has been shown that the FC based selection of genes leads to more reproducible results irrespective of the technology that is used  [14] [15] [16]. Kadota et al. [12]  proposed a FC based method, weighted average difference (WAD), which promotes highly expressed genes.
Some of these methods include the FC information to avoid the three step selection procedure mentioned above. McCarthy et al. [18]  directly include a threshold for the gap between the averages in the Student t-test: t-test relative to a threshold (TREAT). The TREAT method is based on the linear model in  [9]. In  [19] , the FC is combined with the hypothesis testing for assessing prediction error in the selection of the DE genes. More recently, Xiao et al. [20]  combined the FC with a two samples statistical test p-value to obtain a score they called π-value. In all methods that can calculate a probability for each gene independently of the others, the problem of multiple tests arises. To avoid this problem, Qi et al. [21]  used a mixture model. This model has four components corresponding to the expression status (yes or no) for the two biological conditions for each gene in the dataset.
To select downregulated genes the sorting is done in increasing order and all subsequent steps are modified accordingly. The quality of the selection of the DE genes using the rank products method will depend mainly on the quality of the approximation of the expectation of the RP values using a permutation analysis. More recently, an exact statistic was proposed for the RP method  [23]. However, for data with a large number of samples, the computational load is very heavy and this method is thus not recommended. Here, we propose another method based only on FC ranks. This method is very fast in comparison to the RP method, even for large numbers of samples in the dataset. In our method it is not necessary to search for up-and down-regulated genes in two separate steps. The statistic we obtain for each gene gives direct information on its status: down-regulated, up-regulated or not changed.
Preliminaries We consider a two conditions microarray experiment where n probes (genes) are used with m 1 control and m 2 test samples. The number n of probes is generally greater than 10, 000 except for few species like yeast. Values for m 1 and m 2 are however small, most often lower than 100. We note x i = (x c i , x t i ) = (x c i1 x c i2. x c im 1 x t i1 x t i2. x t im 2 ) the values for the gene i (i = 1, 2,. , n) for the control samples (x c ij , j = 1, 2,. , m 1 ) and the test samples (x t ij , j = 1, 2,. , m 2 ), respectively. For a single color microarray, values (x c ij , x t ij ) are log 2 levels, while they are log 2 ratios for a two-color microarray. Here are examples of log 2 transformed data for two genes (MACF1 and TREM2) taken from an experiment using Agilent microarrays (SurePrint, design 028004_D_F_20101102), with one color hybridization.
4. Use R and σ 2 R as parameters of a normal distribution and associate probabilities to genes through their ri values. Since a p-value refers to the probability associated with a hypothesis testing statistic, we call probabilities associated to fold change ranks ordering statistics f-values. A f-value close to 0. 5 corresponds to an equally expressed (EE) gene, while down-and up-regulated genes have f-values close to 0 and 1, respectively. 5. Set error levels, α 1 and α 2 , for down-and up-regulated genes to select the DE genes. Parameter k The size of the integer k allows to fulfill the conditions to apply the central limit theorem, higher values for k being optimal. The maximum value m 1 m 2 for k is determined by the number of control and test samples in the dataset. Parameter δ Parameter δ takes its value in the interval [0, 1].
The ideal value δ = 1 is unlikely to be obtained. A small value of the parameter δ leads to a small variance σ 2 R i. This will happen when the difference between upper and lower bounds of the ordered a. o. r ri becomes smaller, i. e. , if the observed changes in the ranks associated with genes are large, so that the a. o. r will tend to move away from the ideal bounds 1 and n. We can consider the parameter δ as a fraction of the dataset size range: δ = b-a n-1 = n n-1 (β -α) where β = b/n and α = a/n. From this point of view, a value of δ equal to 0. 98 can correspond to (b = 0. 99n, a = 0. 01n) and is better than a value for δ equal to 0. 66 which can correspond to the bounds (b = 5  6 n, a = 1 6 n) which are more distant from n and 1. We provide numerical values for δ in Additional file 1: Figures S3 and S5 using synthetic and real microarray datasets.
Results and discussion To evaluate the performance of the FCROS method, we used synthetic and real microarray datasets. We compared the results obtained with our method to those obtained using six other methods: the simple fold change (FC), the weighted average difference (WAD), the rank product (RP), the Student t-test (Ttest), the significant analysis of microarray (SAM) and the t-test relative to a threshold (TREAT) methods. All calculations were performed on the same computer (Personnal Computer equiped with i7-2640M processor, 8GB of RAM, under Microsoft Windows Professional 7) and R version 3. 0. 1. We implemented a new R package, fcros, which is available from the comprehensive R archive network (http://cran. r-project. org/web/packages/fcros/)  [25]. For all results presented, the trim parameter was set to 0.
3. We also used three other R packages, samr  [26] , RankProd  [27]  and limma  [9]  with their default settings, but the parameter huge in the RankProd package was set to TRUE. We used the ROC (receiver operating characteristics) R package  [28]  to obtain an area under a ROC curve (AUC) for methods when true DE genes are available. For real microarray datasets, no prefiltering was performed before searching for the DE genes except for one dataset.
Synthetic datasets 1 To evaluate the behavior of the FCROS method in the presence of noise, we used three different values for the parameter σ n of MADSIM. 100 simulations corresponding to different initializations (rseed = 10, 20, 30,. , 1000. ) were used. All other parameters of MADSIM were set to their default settings. More precisely, m 1 = m 2 = 7, n = 10, 000 and the proportion of DE genes was set to 0. 02. These settings lead to an expected number of 200 DE genes. Additional file 1: Figure  S1  shows the M-A plot  [31]  for 3 datasets which correspond to 3 setting values for parameter σ n of MAD-SIM. For each dataset, i. e. corresponding to a given value for parameters rseed and σ n , we used the FCROS and the six other methods to determine the DE genes, the number of which was set to that of true DE genes.
Synthetic dataset 2 We used MADSIM to generate a dataset with m 1 = m 2 = 15 and default settings for all other parameters. This dataset has 198 true DE genes. Additional file 1: Figure  S2  shows the M-A plot  [31]  of this synthetic dataset. Synthetic dataset2 was used in different scenarios where we specified different values (m 1 xm 2 ) for the control and test samples, and performed the following steps: a) random selection of m 1 control and m 2 test samples from their respective sets, b) running the FCROS and the six other methods, c) selection of the top 198 DE genes for each method, and assignment of a value of 1 to true DE genes and of 0 to all other genes. These 3 steps are repeated 100 times and the total occurences of 1 for each method and m 1 xm 2 combination is calculated as its score, which is thus comprised between 0 and 100.