dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, 83 healthy controls (41 female, age 60. 2 [50. 3, 70. 9] years) were selected as age-matched control group from a normative database  12. During the experiment, the VPIT recorded kinematic information, specifically the endpoint position, and kinetic information, such as the grip forces applied to the handle (Figs. 1  and SM1 ). From these data, we derived the endpoint velocity and the grip force rate profiles for our analysis based on the focus of previous research  21, 29. These data were temporally segmented to identify four task phases that were defined in prior work and have specific physiological requirements  12  : peg transport (ballistic movement between peg pick-up and insertion into a hole; active grip force modulation required), peg return (ballistic movement between the insertion of a peg and the next peg pick-up; grip force modulation not required), force buildup (phase of maximal grip force production) and force release (phase of maximal grip force reduction).
Shape and consistency of the fPCs. Regarding the kinematic aspects (endpoint velocity) of the peg transport, it is evident that the raw data (Fig. 2A ) as well as the first fPC were bell-shaped whereas higher order fPCs had sinusoidal-like shape (Fig. 2B ). In the shape of raw velocity profiles, clear and continuous significant differences between the control and post-stroke population could be observed (P < 0. 05 and P < 0. 0001, |t|> 2. 98, Cohen's d = 1. 59). In the shape of the fPCs, only few periods with slight, significant differences between the populations could be observed in fPC 1-3. To quantify the similarity of the fPC shape across participants, their variability was evaluated using the average interquartile range (IQR) along the trajectory. We observed a strong coherence across control participants for the first (IQR = 0.
To ease the perception of the virtual reality environment, the haptic end-effector provides a vertical force rendering the virtual pegboard. At the position of the holes, no vertical force is provided by the end-effector. During task execution, kinematic (3D Cartesian translation) and kinetic (grasping force) data are recorded with a sampling rate of 1 kHz. Previously, a signal processing pipeline has been introduced to interpolate, filter, and segment these data (details in  12  ): The first step linearly interpolates temporal gaps of at least 50 samples. The second step combines the 3D kinematic data into a single 1D distance trajectory by summing up their absolute first time-derivatives. Similarly, a 1D grasping force signal was generated by averaging across data from the three sensors.
The third step involved the low-pass filtering of all sensor data using a zero-phase Butterworth filter (4th order, cut-off frequency 8 Hz). Subsequently, the distance and grasping force signals were temporally derived to obtain a velocity and force rate signal, respectively. Lastly, the data were segmented into different phases of interest that are expected to inform on varying aspects of sensorimotor control. This included the transport phase (gross movement from peg pickup until peg insertion) that involves the coordination of arm movements and grip forces. Further, the data was segmented in the return phase (gross movement from peg insertion until peg pickup). This led to nine and eight datapoints per task repetition for the transport and return phase, respectively. The start and end of these phases were defined upon passing and falling below a threshold (10% of maximum) in the velocity domain, respectively.
The core goal is the identification of a basis of R functions � = [ξ 1 , ξ 2 ,. , ξ R ] , which proper combination can reconstruct each entry of the dataset. Fol- lowing the philosophy of classic PCA, basis elements are ordered such that the first functional principal component ξ 1 (t) is the function for which the principal component scores f i1 = ∫ ξ 1 (t)x i (t)dt maximize i f 2 i1 subject to ∫ ξ 2 1 (t)dt = |ξ 1 | = 1 ; the second functional principal component ξ 2 (t) maximizes i f 2 i2 subject to |ξ 2 | = 1 and ∫ ξ 1 (t)ξ 2 (t)dt = 0 (i. e. the second fPC is orthogonal to the first one) and so on. The practical implementation of fPCA, which bypass the solution of the optimization problem, is presented in previous work  15. In a nutshell, for a given dataset, each fPC explains a certain percentage of variance, which is inversely proportional to the order of the fPC considered.
The participants performed the FMA-UE and ARAT under instructions by the same trained healthcare practitioner. Clinical assessments were done specifically for this study and not as part of clinical routine. In addition, all subjects completed an initial familiarization with the VPIT followed by five repetitions (i. e. , insertion of all nine pegs five times) of the test, starting with the mostaffected body side. The interested reader is also invited to refer to  55  , in which we present and release this dataset in a larger framework of multiple multi-modal and multi-center data of healthy and stroke affected human upper limb movements. In order to provide normative reference performance values for the VPIT, an age-matched population of unaffected individuals were selected from a previously recorded normative database  12.
Data analysis. Statistical comparison of the shape of the behavioural time-series. The aim was to compare the shape of the behavioural time-series for each task phase between the control and stroke population. For this purpose, the time-series were visualized on a population level. Given that the task phases can be of different length for each subject and task repetition, we visualized the time-series after they were rescaled to the same length, as also required for the fPCA. The mean and standard deviation was used to aggregate the time-series across participants and enable a smooth visualization. Further, to quantify the similarity of the fPCs shape across subjects, their variability was evaluated using the average interquartile range (IQR) along the trajectory. To provide a quantitative description of the population-level differences in each time-series, we applied 1D statistical parametric mapping  56.
The following intervals were used to interpret correlation coefficients: very high: ρ ≥ 0. 9; high: 0. 7 ≤ ρ < 0. 9; moderate: 0. 5 ≤ ρ < 0. 7; low: 0. 3 ≤ ρ < 0. 5; very low: ρ < 0. 3. All data analysis procedures were implemented using MATLAB (MathWorks, MA, US) and scripts were written for a custom-made implementation of dynamic time-warping and fPCA.
Funding This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 688857  (SoftPro), from the  Swiss State Secretariat for Education, Research and Innovation  ( 15. 0283-1 ), from the  ERC Synergy  Grant No. 810346  ( Natural BionicS ), from the  Italian Ministry of Education and Research (MIUR)  in the framework of the  CrossLab project (Department of Excellence ), and the  P&K Pühringer Foundation. This research is supported by the  National Research Foundation ,  Prime Minister's Office, Singapore  under its  Campus for Research Excellence and Technological Enterprise (CREATE)  programme. The authors declare that the funding bodies did not influence the design of the study, the collection, analysis, and interpretation of data, and the writing of the manuscript.
Helen Bacon isole ainsi plusieurs exemples d'emprunts et de réécritures : le poème-seuil de l'oeuvre frostienne, "The Pasture", se perçoit ainsi comme une tendre vignette de tâches agricoles. Elle voit dans "Hyla Brook", un poème de quinze vers en pentamètres consacré au devenir-livre d'un ruisseau du New Hampshire (où les feuilles mortes du lit de la rivière se conjuguent pour former les feuilles du livre en une palimpsestique transition de "brook" vers "book") une interaction claire avec le léger poème d'Horace, en seize vers, "La Fontaine de Bandusie" ("Fons Bandusiae", treizième ode du troisième livre). Le cristal de l'eau, prêt à recevoir un chevreau en sacrifice, se fait l'écho de murmures et de conversations. La source d'Horace devient, sous les chaudes journées de Nouvelle-Angleterre (les "dog days" placés sous le signe de Sirius et la Constellation du Grand Chien que Frost explore par ailleurs dans l'un de ses "constellation poems", "Canis Major"), une variation sur le thème de la perte transmutée en art, à l'image du mythe de Pan ou l'hamadryade Syrinx transformée en roseau pour échapper aux ardeurs de l'homme-bouc se fait flûte que le vent fera bruire.
Material and methods This study used a large existing dataset describing contextual cueing effects in baboons. In their study, Goujon & Fagot  [31]  studied how 21 baboons differentially learn to find a target among a set of distractors that were either predictive of the target location or non-predictive. Here, we used the response of the baboons when they were in the presence of exactly one partner to study the effect of social comparison on performance (a total of 147 387 trials). This very large sample size, both in terms of the number of primates studied and in terms of the number of trials performed, allows for the study of complex interactions between predictor variables. In the following, we present only the most relevant aspects of the contextual cueing experiment; more details can be found in the original study of Goujon & Fagot  [31].
Identification signals from the microchip are used by the computer to trigger the presentation of the stimulus and to assign behavioural measures to each participant. The equipment is controlled by a test program written with Eprime (Psychology Software Tools, Pittsburgh, PA). The test program allows an independent test regimen for each baboon, irrespective of the test chamber it is using  [33]. Grains of dry wheat are used as a reward (for more details, see  [32, 33] ). During this experiment, the monkeys could see their partners working in adjacent workstations but were unable to see their motor responses on the screen; observational learning was thus impossible.
(c) Experimental procedure The contextual cueing task consisted in finding a target on a touchscreen containing several distractors (stimuli and data have been posted in an open access repository: https://osf. io/8ct3r; doi:10. 17605/OSF. IO/8CT3R). Testing occurred during one full month. During that period, the baboons continually received blocks of 12 test trials. Two levels of difficulty were used. Each block included six predictive trials and six shuffled trials. Six configurations never used in training were assigned to the predictive condition and six to the shuffled condition. The configurations were counterbalanced among subjects. Each predictive configuration was associated with a constant target location. Predictive trials were therefore easy trials because the visual search could be guided by the predictive background.
Data Set and Sample Selection The data used stem from the last three years  (1997) (1998) (1999)  of the Labor Market Monitor Sachsen-Anhalt (LMM-SA). The LMM-SA is a survey of the working-age population living in the new federal state of Sachsen-Anhalt with around 6. 000 survey participants each year. 8 Similar to other surveys, the LMM-SA provides individual information on socio-economic characteristics like age and professional education. As an important innovation, the LMM-SA introduced in the years 1997-1999 a retrospective monthly employment calendar that goes back until 1990, enabling us to analyze JCSs over a long time period after the reunification. Recall data over such a long time span can suffer from recall errors. Paull (2002)  documents the international evidence. Due to recall error she notes a tendency to report too many labor market transitions and at the same time to underreport short unemployment spells.
This allows us to analyze the effect of JCSs for individuals who belonged to the active labor force of the GDR and who were hence fully affected by the transformation process and subsequent introduction of ALMP programs. 10 At the same time, this sampling criteria allows us to exclude individuals who are close to retirement and might use ALMP programs as a bridge to retirement. Table  A. 2 in the Appendix presents an overview of the variables used in this study. Based on these data we construct a sample of inflows into unemployment based on individuals whose labor market history is observable until at least September 1997 without interruption. We consider unemployment spells starting in January 1991 or later only if there exists a prior employment spell of at least one month. 11 his analysis exploits information on 2,235 individuals who experience at least one unemployment spell between January 1991 and the end of the observation period, which can be September 1997, October 1998 or December 1999.
12 In total, the data include 3,864 unemployment spells. Thus, several individuals experience multiple spells and the average number of spells per individual amounts to 1. 7. Transitions to other destinations than to employment are treated as right censored. Thus, if an individual enters an alternative ALMP program like training before finding regular employment, the spell is also considered as censored at the point in time the individual enters the alternative program. As a sensitivity analysis, we estimate a model with endogenous right-censoring. In this specification, we model transitions to other states than employment as a competing risk. We additionally estimate specifications in which we define periods of training participation as unemployment and models in which we consider participation in training programs as an alternative treatment.
Multiple Treatment Effects In this subsection we present estimation results of a model specification which allows for multiple treatment effects: we investigate the impacts of two treatments, participation in a JCS and participation in training. To be precise, we estimate three transition rates: the transition rate from unemployment to employment, the transition rate from unemployment to a JCS and the transition rate from unemployment to training. We allow for correlations between these three transition processes. With this specification we are able to test whether our previous results change when we take into account that some unemployed might participate in a training program before or after participation in a JCS. Our data suggests that 8% of all unemployment spells include treatment only in terms of participation in a JCS and 19% only in terms of participation in training.
Background: Gene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted. Results: We introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR.
Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity. Conclusions: Our experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.
In addition, these tools often use complex models such as expectation-maximization or quadratic programming which demand significant computational resources and therefore are hardly scalable to large-scale datasets, for instance the Genotype-Tissue Expression (GTEx) project  [6, 7]  which contains more than 8500 profiles in its current (6 th ) version. To allow efficient and sensitive detection of tissue heterogeneity, we introduce the software package BioQC. It takes gene expression data as input, performs statistical tests with tissue-enriched gene signatures that come with the package, and reports enrichment scores of more than 150 tissue signatures for each sample. High scores of tissues other than the target tissue of profiling indicate heterogeneity and consequently possible infiltration or contamination events.
Tissue-enriched genes We derived 155 sets of tissue-enriched genes (tissue signatures hereafter) from four datasets: the Neurocrine Biosciences (NB) CNS dataset  [8] , the GNF Gene Expression Atlas  [9] , both based on the Affymetrix microarray technology, and sequencing-based RNASeq Atlas  [10]  and Illumina BodyMap 2. 0 (GEO Accession Number GSE30611  [11] ). For preprocessing we normalised microarray signals with the MAS5 method  [12]  and converted sequencing read counts into the unit of copies per million reads  [13]. Expression signals are averaged in case more than one samples are available for each tissue. Both sequencing-based datasets are merged by removing batch effects using a linear model in order to achieve a wide coverage of tissues comparable with microarray-based datasets.
Gini index  [14]  was used to identify tissue-enriched genes. Given an m×n expression matrix with m genes and samples of n tissues, Gini index for gene i is defined as G i = 1 n n + 1 -2 n j=1 (n + 1 -i)x ij n j=1 x ij , (1) where x ij is the jth value in the non-descending ordered vector of x i• (i = 1,. , m, j = 1,. , n). Gini index ranges between 0 and 1, depending on whether the gene is ubiquitously and uniformly present or absent in all tissues (G = 0) or is exclusively expressed in one tissue (G = 1) or in between (0 < G < 1). We consider gene i enriched in tissue j if G i 0. 7 and x ij ranks among the top three in x i•. Signatures of identical tissues derived from both microarray datasets are merged, while microarray-based and sequencing-based signatures are kept separate so that users can benefit from the high sensitivity offered by the sequencing technology.
Validation of tissue-enriched genes We took a three-tier approach to validate the tissue signatures. First, in order to assess their robustness against batch effects, we applied surrogate variable analysis  [15]  to both GNF and NB datasets to detect uncaptured batch effects (not applicable to sequencing-based datasets where only one sample is available for each tissue). Based on batch-effect-corrected data, we generated a new set of signatures using the procedure described above. We applied the BioQC algorithm using both signature sets to the GTEx gene expression database  [7]  and found that the results are highly similar (Additional file 1: Figure  S1 ), which suggests that the BioQC tissue signatures are robust with regard to potential batch effects in the source data. Next, we used biological knowledge to test the validity of the tissue signatures using Roche Controlled Vocabulary (RCV hereafter), a recently developed, simplified ontology system of annotating human genes with biological processes defined by the Gene Ontology consortium  [16].
We applied the BioQC algorithm, using both tissueenriched gene sets and RCV gene sets, to the GTEx dataset, and found that for many tissues that are included in GTEx, expected tissue signatures are co-enriched with relevant biological processes. For instance, most tissue signatures associated with the nervous system are coclustered with genes associated with neuronal biological processes (Additional file 1: Figure  S2 ). The observation suggests that the tissue signatures are biologically relevant. Last but not least, to test the validity of using Gini index to identify tissue signatures, we applied an independent algorithm, limma  [17] , to the GTEx dataset (limma was prefered to negative-binomial-model based methods such as DESeq2 and edgeR due to computational efficiency and the compatibility of results).
The algorithm of BioQC BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test (Wilcoxon test hereafter)  [18]. The algorithm is accelerated by (a) an approximate test procedure, (b) implementation of the core algorithm in C programming language, and (c) elimination of futile sorting operations. Improvement that we made over standard implementations is detailed in Additional file 2: Document 1. Given a gene expression profile and a tissue signature, BioQC tests by default whether expression of genes in the signature ranks higher than expression of genes not in the signature. Users can test negative enrichment or two-sided (either positive or negative) enrichment, too. BioQC reports an enrichment score of each tissue signature for each sample in the form of |log 10 p| (absolute log 10-transformed p-value of Wilcoxon test).
Given expression profiles of n samples as input and s tissue signatures, BioQC outputs an s × n matrix with scores ranging between 0 (no enrichment) and theoretically positive infinity (strong enrichment). By examining BioQC results and comparing them with target tissues of profiling users can inspect heterogeneity and generate hypotheses about the causes. Although this work focuses on its application to detect tissue heterogeneity, we note that BioQC can be used as a generic gene-set enrichment analysis tool (manuscript in preparation).
Simulations with model-generated data The first simulation study probes the speed of BioQC. We propose a simple model to generate random gene expression profiles of approximately 22,000 genes with each gene following the normal distribution N (0, 1). We let the model generate five datasets with varying sample sizes (n = 1, 5, 10, 50, 100), applied BioQC and timed the runs. For benchmarking we applied the native implementation of Wilcoxon test in R (function wilcox. test) to the same dataset. Both implementations delivered identical numeric results and their memory use is comparable. However, while it takes the native implementation 20 minutes to analyze 100 profiles, it takes BioQC just about one second (Fig. 1a  left panel). Quantitatively BioQC accelerates the Wilcoxon test by a factor between 500 and 1000 for gene expression data (Fig.
1a  right panel). The results suggest that BioQC is computationally efficient and applicable for large-scale datasets even on laptop computers. Next, we investigated the sensitivity of BioQC towards expression changes of tissue-enriched genes. To this end, we adapted the simple model used above: while keeping random-number generators of other genes unchanged, expression levels of genes in a randomly chosen signature (ovary with 43 genes) were drawn from a series of distributions N (μ, 1) with μ varying from 0 to 3. We generated ten samples for each distribution and applied BioQC. We observed that the average enrichment score of ovary increases steadily as μ increases (Fig. 1b  left panel), and as soon as μ reaches or exceeds 1, the ovary signature ranks first among all signatures in all simulated samples (Fig.
1b  right panel). Repeated experiments with other tissue signatures produced consistent and comparable results (data not shown). In other words it is sufficient for a tissue signature to rank first if average expression of its genes increases by one standard deviation. It suggests that BioQC is sensitive to even mild changes in expression of tissue-enriched genes. We note nevertheless that the sensitivity test above suffers from the limitations that (a) the distributions of gene expression are not physiological and (b) elevated expression of tissue-enriched genes is not quantitatively associated with severity of contamination. To corroborate our findings we next performed simulations with real-world data.