dataesr/scientific-paragraphs-categorization huggingface dataset:
Total RNA was isolated from sorted cell subsets using RNeasy Mini kit (Qiagen) and assessed RNA quality using Agilent 2100 Bioanalyzer (Agilent Technologies). 3′ mRNA-Seq libraries for all cell subsets were prepared from 100 ng total RNA using QuantSeq kit (Lexogen) according to the manufacturer's instructions and sequenced 50-bp single-end on the HiSeq 4000 (Illumina). Sequence reads were mapped to the human genome reference (GRCh38) with Gencode annotation (r26) using STAR aligner  50. Reads were normalized by median of ratios using the DEseq2 package  51. The R function featureCounts was used to obtain gene-level read counts  52. We selected overlapping genes between RNA-Seq gene counts and PRE scores, and Pearson's correlation test was performed using the cor. test function in R. The significance of the correlation was confirmed by permutation testing (n = 1000).
Acknowledgements This investigation was supported in part by the following sources:  NIH/NINDS  awards  R01NS088155  and  1R01NS099240 , the  Valhalla Charitable Foundation , and the  Heidrich Family and Friends Foundation (Sergio E. Baranzini). US National Multiple Sclerosis Society  ( TA 3056-A-2 ), the  Harvard NeuroDiscovery Center  and an  Intel Parallel Computing Center award (Nikolaos A. Patsopoulos). Swedish Medical Research Council; Swedish Research Council for Health, Working Life and Welfare, Knut and Alice Wallenberg Foundation ,  AFA insurance, Swedish Brain Foundation , the  Swedish Association for Persons with Neurological Disabilities. Cambridge NIHR Biomedical Research Centre, UK Medical Research Council  ( G1100125 ) and the  UK MS society  ( 861/07 ). NIH/NINDS :  R01 NS049477 ,  NIH/NIAID :  R01 AI059829 ,  NIH/NIEHS :  R01 ES0495103.
Research Council of Norway  grant  196776  and  240102. NINDS/NIH   R01NS088155. Oslo MS association. Research Council KU Leuven ,  Research Foundation Flanders. AFM, AFM-Généthon ,  CIC ,  ARSEP ,  ANR-10-INBS-01  and  ANR-10-IAIHU-06. Research Council KU Leuven ,  Research Foundation Flanders. Inserm ATIP-Avenir Fellowship and Connect-Talents Award. German Ministry for Education and Research, German Competence Network MS (BMBF KKNMS). Oslo MS association, Research Council of Norway  grant  196776  and  240102. Dutch MS Research Foundation. TwinsUK is funded by the  Wellcome Trust, Medical Research Council, European Union , the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust  in partnership with King's  College London.
Methods Predicted regulatory effects (PRE). Genome-wide regulatory elements from ENCODE and REP were collected from regulomeDB  46  (which contains more than 400 million genomic regulatory features collected from 400 cell and tissues; Supplementary Table  3 ) for all non-MHC independent effects (SNPs)  17. Specifically, single nucleotide polymorphisms (SNP) corresponding to all non-MHC GW (n = 200; Supplementary Data 33), SR (n = 416; Supplementary Data 34) and NR (n = 3695; Supplementary Data 35) were extracted for analysis GW, SR, and NR as defined previously  17.
Purpose or Objective The ongoing phase III POLCA trial (NCT02444000) was designed to determine whether treating patients with newly diagnosed 1p/19q-codeleted anaplastic gliomas with PCV alone can increase overall survival without neurocognitive deterioration as primary endpoint. The control group treatment is intensity-modulated radiation therapy (IMRT) followed by 6 cycles of PCV chemotherapy and the experimental group treatment 6 cycles of PCV chemotherapy (radiotherapy being deferred at the time of progression) (October 2018: 60 patients in each arm). We report the results of retrospective individual case reviews (ICRs) for the first patients randomized to assess the study protocol compliance.
01) and smaller planning target volume (p<0. 05) were significant favorable prognostic factors (Table  1 ). Eight patients (12. 5%) developed severe, hospitalization required, acute toxicity and five cases (7. 7%) of radionecrosis were reported in the UMC Utrecht dataset (n=65). The ReRT risk score showed good calibration by corresponding results of the Cox regression and Kaplan-Meier estimators (mOS of the prognostic groups in our cohort: 14. 6, 9. 76, 5. 32 months versus Niyazi's development cohort 14. 2, 9. 1 and 5. 3 versus Niyazi's validation cohort 13. 8, 8. 8 and 3. 8 months). Discrimination performance of the score is modest (cindex: 0. 65).
The coding finally selected is that associated with the smallest p-value. This practice implies multiple testing, and an adjustment of the p-value is therefore required. The CPMCGLM package  [2]  can be used to adjust the p-value in the context of generalized linear models (GLM). We present here the statistical context, and the various codings available in this R package. We then briefly present the available methods for type-I error correction, before presenting an example based on the PAQUID cohort dataset.
Generalized linear model Let us consider a generalized linear model with q explanatory variables  [3] , in which Y = (Y 1 ,. , Y n ) is observed and the Y i 's are all identically and independently distributed with a probability density function in the exponential family, defined as follows: f Y i (Y i , θ i , φ) = exp Y i θ i -b(θ i ) a(φ) + c(Y i , φ) ; with E[ Y i ] = μ i = b (θ i ), Var[ Y i ] = b (θ i )a(φ) (θ i , φ) belong to ⊂ R 2 , where θ i is the canonical parameter and φ is the dispersion parameter. The CPMCGLM package allows the use of linear, Poisson, logit and probit models. The specifications of the model are defined with formula, family and link arguments, as a glm() function. In this context, the main goal is evaluating the association between the outcome Y i and an explanatory variable of interest X i , adjusted on a vector of explanatory variables Z i.
Permutation test procedure The first resampling-based method is a permutation test procedure. This procedure is used to build the reference distribution of statistical tests based on permutations. From a theoretical point of view, the statistical test procedures are developed by considering the null hypothesis to be true, i. e. in our context, under the null hypothesis, X i has no impact on Y. Under the null hypothesis, if the exchangeability assumption is satisfied  [15] [16] [17] [18] [19] [20] , then resampling can be performed based on the permutation of X i the variable of interest in our dataset. The procedure proposed by Liquet and Riou could be summarized by the following algorithm  [6] : 1 Apply the minimum p-value procedure to the original data for the K transformations considered.
Dichotomous coding Dichotomous coding is often used in clinical and psychological research, either to facilitate interpretation, or because a threshold effect is suspected. In regression models with multiple explanatory variables, it may be seen as easier to interpret the regression coefficient for a binary variable than to understand a one-unit change in the continuous variable. In this context, dichotomous transformations of the variable of interest X are defined as: X(k) = 1 if X ≥ c k ; 0 if X < c k , where c k denotes the cutoff value for the transformation k (1 ≤ k ≤ K). In this R package, the dicho argument of the CPMCGLM() function allows the definition of desired cutoff points based on quantiles in a vector. An example of the dicho argument is provided below: Code 1 : Definition of 3 dichotomous transformations dicho <-c( 0.
PAQUID database PAQUID is a longitudinal, prospective study of individuals aged at least 65 years on December 31, 1987 living in the community in France. These residents live in two administrative areas in southwestern France. This elderly population-based cohort of 3111 community residents aimed to identify the risk factors for cognitive decline, dementia, and Alzheimer's disease. The data were obtained in a nested case-control study of 311 subjects from this cohort (33 subject with dementia and 278 controls).
Generate B new datasets s * b , b = {1,. , B} by repeating step 2 B times; 4 For each new dataset, apply the minimum p-value procedure for the transformation considered. We note p * b min the smallest p-value for each new dataset. 5 The p-value is then approximated by: p minP = 1 B B b=1 I p * b min <p min , where I {•} is an indicator function.
Conclusion We present here CPMCGLM, an R package providing efficient methods for the correction of type-I error rate in the context of generalized linear models. This is the only available package in R providing such methods applied to this context. We are currently working on the generalization of these methods to proportional hazard models, which we will make available as soon as possible in the CPMCGLM package. In practice, it is important to correct the multiplicity on all the codings that have been tested. Indeed, if this is not done, the type-I error is not controlled, and then it is possible to obtain some false positive results. To conclude, this package is designed to help researchers who work principally in epidemiology to analyze with riguor their data in the context of optimal cutoff point determination.
1 ; 3 13 Generate B new datasets s * b , b = {1,. , B} by repeating step 2 B times; 4 For each new dataset, apply the minimum p-value procedure for the transformation considered. We note p * b min the smallest p-value for each new dataset. 5 The p-value is then approximated by: p minP = 1 B B b=1 I p * b min <p min , where I {•} is an indicator function.
This method provides a good compromise between accuracy and robustness. This method has been developed to overcome the di©culties of this kind of two-phase §ow simulations relying on the presence of strong gradients and high density ratios due to the simultaneous presence of liquid and gas. Then, in section 4, it is described how the 4-equation LHF model and the sectional approach are combined following and improving the strategy previously proposed in  [2123]. Moreover, a formulation for the atomization model with new source terms between the dense LOx and the spray composed to oxygen droplets is proposed. The fully Eulerian coupling strategy between ¤separated¥ and ¤dispersed¥ two-phase §ows solvers has been implemented in the ONERA£s CEDRE code  [1]. Finally, in section 5, this tool has been applied to the numerical simulation of the MASCOTTE test facility  [24]  on the 10-bar operating point corresponding to cryogenic rocket engines under transient operating conditions.
). This type of computation opens new perspectives in terms of flexibility and accuracy. The contribution of this paper is threefold. First, we investigated to what extent symbolic regression allow an improvement of existing calibration techniques accuracy. Secondly, we validated this technique on ground truth data collected during a user study which aimed to compare smooth pursuit and vestibulo-ocular calibration methods. After that, we assessed the effect of symbolic regression on calibrations based on marker and finger stimulus. The two studies delved empirically into how the type of calibration procedure impacts gaze estimation accuracy. We completed 48 (12 × 4) trials with 12 participants, and investigated 4 different calibration procedures (SP using Marker, SP using Finger, VOR using Marker and VOR using Finger).
The visual marker is shown on the screen and moves from a position P1 to a position P2 onward and backward. The user is asked to fixate on the marker during the whole movement, making a smooth pursuit eye movement. More recently,  [19]  attempted to find the best terms that can be used to construct the mapping model, however, they stated that for polynomials up to 3 degrees, it may become challenging to check all possible sets. Therefore, they provide an open, extendable software (ETCAL) in which they implemented a heuristic algorithm that served to reduce the number of models to explore based on genetic programming, however, finding the best solution was not guaranteed, the computational time required to find a good model was not provided by the authors and they did not consider recent methods based on vestibulo-ocular calibration.
Definitions Symbolic regression is the automated searching of a function model, along with its intrinsic parameters, that infers estimation of output values from input values  [31]. This method relies on Genetic algorithms (GA), more specifically genetic programming  [27]  which searches and modifies, the best solutions that define a problem among a population of individuals. For each solution, multiple individuals are randomly picked, modified and mutated to represent the new population to explore. Theoretically, given a set of n collected observations of the form {(x 1 , y 1 ),. , (x n , y n )} such that x i represents the eye features vector (pupil center coordinates in eye camera frame) of the i-th k-dimensional observation and y i is its corresponding target (i. e the center of the marker in the world camera image), the algorithm seeks to find a set of models F = f 1 , f 2 , ‥that best fit the dataset: f p : X À ! Y x 7 À ! f p ðxÞ � � � � � � ð1Þ where X is the set of the pupil positions observed during the calibration and Y the output observations, namely, the center of the target in the world camera frame.
Examples This section presents two different examples of how symbolic regression detects models based on the operators, population of basic functions given as input and the data obtained during a calibration procedure. We could assume we are trying to find the cubic model of the form f(x) = x 3 + 8x 2 + 10x -427. From this model, 30 pairs (x i , y i ) are retrieved where y i = f(x) and x i are normally distributed in [0, 100]. This dataset is given as input to the algorithm to detect the model f(x) without knowing its form beforehand. The population used consists of a list of individuals I = {x, cos, sin, log, exp, constant c 2 Rg and a list of operators O = {-, +, � , /}. At the beginning, the algorithm starts with a random solution f 0 (x) = 42 comprising one leaf. After 200 milliseconds, the best polynomial found f 200 (x) gave a mean absolute error (MAE) of 0.
078 and is similar to f(x): f 200 ðxÞ ¼ 9:97x þ 8x 2 þ x 3 À 427 ð4Þ To investigating the effect of the algorithm on the data size, we provide the results for 3 different samples of size n1 = 75, n2 = 150, n3 = 200. The results are given in Table  1  and simulate the size of respectively 2. 5 seconds, 5 seconds and 6. 65 seconds calibration data with a 30 frames/second camera. The second example, similar to the one shown in  [31] , used the data generated by the function f(x) = sin(x) + 8. We collected 30 pairs (x i , y i ). x i were selected randomly from an interval between 0 and 100, and y i were obtained by projecting x i through the function f. In addition to the dataset, we gave the following population to the algorithm as input: a list of individuals I ¼ fx; cos; log; exp; c 2 Rg and a list of operators O = {-, +, � , /}.
A note on outliers and noise in the dataset While this method enables the detection of models of previous examples, in real-world scenarios, the collected data can be corrupted by noise and outliers. The noisy data present in realworld come from various aspects altering the measurements. In VOG-based eye tracking systems, eye features detection is prone to error due to the occlusion of eyelashes, eyelids, the presence of mascara, contact lenses, internal defects of cameras or the computer vision algorithm employed  [35]. These factors yield false data when collecting calibration points. Considering this fact, we modified 20% (6 observations) of y i dataset in example 1 in order to simulate noise. The MAE was 1. 71 after approximately 200 milliseconds giving a model of the form f 200 (x) = 9.
Extracted models using eye tracking calibration data Using Symbolic regression, different models are extracted. Some models are less complex than others. Table  2  and Fig 2 present some samples of captured models over time-one participant's calibration data was tested in this example. The data was smoothed beforehand using 1€ Filter  [36] , a first-order low-pass filter with an adaptive cutoff frequency. We obtained the models from the training points, as explained in section Training versus validation points, and illustrated the examples with validation points. Models on x and y axes are given. Notice that, initially (at t 0 ), the algorithm starts with either the input values (P y on yaxis) or a random number lying in the interval of the input values (346 on x-axis). Thereafter, improved models are produced.
Pearson's product-moment correlation coefficient The Pearson's correlation coefficient is a measurement of the strength of the linear correlation between two variables. There is a positive correlation if both datasets fall or rise together in the same direction. [37]  applied Pearson's product-moment correlation on eye tracking data. They used it to select among many targets, the one at which the user is looking. Esteves et al. [38]  designed a method of selecting widgets on a smartwatch based on the correlation between moving targets and the user's eye positions. Those approaches inspired many subsequent studies and Pearson's correlation proved to work well for eye tracking data  [39, 40]. To validate our statement in section Extracted models using eye tracking calibration data, that is, there are more.
Data collection The data were recorded as (x,y) position coordinates for all participants. The pupil center positions were detected in the eye camera frame and the target positions in the world camera frame. Lighting conditions were controlled to be the same throughout the two experiments. The experimenter started and ended the data collection by pressing a button. No filtering was applied during the collection. We recorded the data at an average rate of 114 frames per second. Each frame allowed the detection of one or zero points (zero if there is no detection). For each calibration, we excluded the duplicated pupil and marker centers entries and their corresponding pairs. We intentionally left a few milliseconds before the marker actually starts moving to ascertain that the data samples are effectively recorded from this initial position.
Training versus validation points We used a cross-validation method to evaluate the accuracy of the models  [19]. The collected data obtained during the calibration procedure were divided into two separate datasets, the training (60%) and validation (40%) sets. The first set was used to find a mapping function through regression and the second set served to compute the gaze estimation. In other words, it allowed the calculation of the reprojection errors corresponding to the offset between the estimated and the measured value  [42]. The reprojection errors were used to compute the calibration accuracy, i. e. , the closeness with which the measurement of the indicated gaze estimation is related to the actual target position. High accuracy means that the estimation is on or very close to the target position.
Study 1: Marker-as-a-target calibration The first study dealt with calibration procedures using a marker. Through a series of tasks, we explored the difference in results between the calibration procedures described in section and, both using a marker as a target, and the effect of Symbolic regression on the mean absolute error (MAE). The participants performed the calibration procedures and all analyses were made after the experiment. To this end, only calibration data were collected during the experiment which spanned two days.
Apparatus and analysis Fig 7B depicts the eye tracking system. A Pupil Labs Eye tracker was used during the two experiments. The device is equipped with one world camera (Sampling rate: @120Hz, 1920X1080 pixels) and one eye camera (Sampling rate: @120Hz, 640X480 pixels). The data collection was processed at 114 Hz. A C# desktop software was built with EmguCv 3. 1 for the computer vision technique implementation. The equipment setup was a XPS 15 9530 Dell Laptop ×64, Intel Core I7-4712HQ CPU@2. 30GHz, 2. 3 GHz, 16 GB of Random Access Memory, 2GB swapping Memory. We used a 24 inch Dell 2408 WFP monitor (L x W x H Dimensions: 22 x 8. 17 x 15. 62 inches) which has a resolution of 1920x1200 pixels and 24 Milliseconds response time. The marker was displayed in the monitor which is placed 75 cm from the participants.
SP vs VOR calibrations using standard regression: The data of all participants were included in the analysis. A paired T-test showed that there was no significant difference in accuracy between SP (M = 0. 81, SD = 0. 21) and VOR (M = 1. 4, SD = 1. 23) calibration on x axis, t(5) = 1. 10, p = 0. 3. However, SP calibration resulted in better results compared to the VOR calibration on y axis (t(5) = 3. 161, p = 0. 025). The Mean of the differences observed was 0. 49˚. These results suggested that SP calibration can help reduce gaze estimation accuracy on at least one component of Cartesian coordinates plane, that is, y axis in this example (Table  3 ). SP using standard regression vs SP using symbolic regression: Although the results shown in Table  4  bear witness to the success of the symbolic regression algorithm in reducing estimation errors and increasing accuracy, it was still valuable to statistically evaluate whether the differences were significant.
Background: With the advent of high-throughput sequencing, microbiology is becoming increasingly data-intensive. Because of its low cost, robust databases, and established bioinformatic workflows, sequencing of 16S/18S/ITS ribosomal RNA (rRNA) gene amplicons, which provides a marker of choice for phylogenetic studies, has become ubiquitous. Many established end-to-end bioinformatic pipelines are available to perform short amplicon sequence data analysis. These pipelines suit a general audience, but few options exist for more specialized users who are experienced in code scripting, Linux-based systems, and high-performance computing (HPC) environments. For such an audience, existing pipelines can be limiting to fully leverage modern HPC capabilities and perform tweaking and optimization operations.
Moreover, a wealth of stand-alone software packages that perform specific targeted bioinformatic tasks are increasingly accessible, and finding a way to easily integrate these applications in a pipeline is critical to the evolution of bioinformatic methodologies. Results: Here we describe AmpliconTagger, a short rRNA marker gene amplicon pipeline coded in a Python framework that enables fine tuning and integration of virtually any potential rRNA gene amplicon bioinformatic procedure. It is designed to work within an HPC environment, supporting a complex network of job dependencies with a smart-restart mechanism in case of job failure or parameter modifications. As proof of concept, we present end results obtained with AmpliconTagger using 16S, 18S, ITS rRNA short gene amplicons and Pacific Biosciences long-read amplicon data types as input.
e. , mainly amplicons and shotgun) in order to increase their adoption in the aforementioned settings. The Gen-Pipes workflow management system, including an implementation of a QIIME-based 16S rRNA amplicon pipeline, was recently published  [21]. Here, as a proof of concept, we leveraged Gen-Pipes's capabilities to build AmpliconTagger, a versatile bioinformatic pipeline managing job generation, submission, dependency, and smart restart that can process any type of gene amplicon sequencing data (16S, 18S, ITS rRNA genes and other custom marker or functional genes) of various sequencing configurations integrating multiple bioinformatic packages. We validated our pipeline with 3 QIIME2 workflows (VSEARCH, Deblur, and DADA2) using 2 mock communities datasets for which we know the exact community composition.