dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the Abide project cohort We analysed the size of the corpus callosum in the Abide c o h o r t (http :// fcon _1000. projects. nitrc. org / indi / abide). W e processed the 1102 subjects with T1-weighted MRI data available using FreeSurfer v5. 1. Statistical analyses were performed using JMP Pro 10. 0. 2 (http :// www. jmp. com), R, iPython (http :// ipython. org) and G*Power. MRI data in Abide is labelled with 20 different "Site" levels, corresponding to 17 different scanning sites. Indeed, some sites contributed with more than 1 subject sample (for example, UCLA_1 and UCLA_2, or UM_1 and UM_2). In all cases, however, subjects were scanned using the same scanner and the same parameters, so we combined these site labels into a single one (for example, UCLA or UM). We developed an open online tool to visually control the accuracy of the segmentations (http :// siphonophore.
The number of uncorrected significant P-values was the expected given the number of multiple tests). Finally, the computational neuroanatomy methods that we used to segment and measure CC, BV and ICV were all standard and well validated. We are making available our data tables and scripts as well as a web interface with our quality control decisions to allow the community to inspect and criticise our analyses. The major, clear, difference between our analysis and the previous ones was statistical power. Using the Abide cohort we achieved 99% power to detect differences of 0. 3 standard deviations, whereas the highest statistical power to detect this effect size among the studies in our meta-analysis was of only 36%. Given the low mean statistical power of the previous reports (20% on average), even if there were a real difference in corpus callosum, there should be about 80% of negative reports.
5%) of human-related MLVA genotypes among the present collection. Eighteen percent of the S. aureus mastitis collection belonged to clonal complexes apparently not associated with other pathological conditions. Some of them displayed a relatively low level of diversity in agreement with a restricted ecological niche. These findings provide arguments suggesting that specific S. aureus lineages particularly adapted to ruminant mammary glands have emerged and that MLVA is a convenient tool to provide a broad overview of the population, owing to the availability via internet of databases compiling published MLVA genotypes.
Genotyping data production and analysis The 16 VNTR loci included in MLVA16 Orsay were amplified in two multiplex PCR using the CeeramTools® Staphylococcus typing kit (Ceeram, La Chapelle sur Erdre, France) as previously described  [22]. The typing data file was imported into BioNumerics version 6. 6 (Applied-Maths, Sint-Martens-Latem, Belgium). A cut-off value of 45% similarity was applied to define clusters according to  [19]. Simpson's diversity index was used  [29]. The MLVA16 Orsay data derived from the 152 strains of this study were compared to published data obtained with the same method  [22]  in order to tentatively assign the new strains to MLST CC  [19, 22, 30]. In a previous study, 251 S. aureus strains isolated from human (n = 106), swine (n = 32), poultry (n = 30), companion animals (n = 17), horse (n = 5), small ruminant (n = 11), rodent (n = 2), cattle (n = 1), food (n = 34) and food poisoning events (n = 13) were characterized by MLVA16 Orsay complemented by MLST and spa typing.
This reference dataset is now used to link new strains. All unclustered strains from the present investigation were characterized by MLST and spa typing. The primers and condition used for the spa tandem repeat amplification and MLST analysis were as previously described  [10, 31, 32]. The amplicons were purified using the QIAquick PCR purification kit (Qiagen, Courtaboeuf, France) and sequenced (Eurofins MWG Operon, Ebersberg, Germany or Beckman-Coulter Genomics, Hertfordshire, UK). The spa repeat nomenclature was that of Shopsin et al. [12]  and spa types were retrieved from  [33]. MLST alleles and sequence types (ST) were identified using the MLST database  [34]. Data were analysed by chi-square or Student's t-tests. Differences were considered significant when p < 0. 05.
Acknowledgements We thank  Fabienne Loisy-Hamon  and  Benoit Lebeau  from the  Ceeram company  for their support to this project. This includes the cost of the MLVA typing of all S. aureus isolates. The contributions of SS and ATF are financially supported by the  German Federal Ministry of Education and Research (BMBF)  through the  German Aerospace Center (DLR) , grant number  01KI1301D  ( MedVet-Staph II ). This study also benefited from the support of the association " Vaincre La Mucoviscidose " (Grant  N°RC0630 ). This work has benefited from the facilities and expertise  of IMAGIF (Centre de Recherche de Gif ). The development of tools for the surveillance of bacterial pathogens is supported by the  French Direction Générale de l'Armement (DGA). We thank  Roswitha Becker ,  Vivian Hensel  and  Marie-Claude Hygonenq  for excellent technical assistance.
Information sought included identifying the involvement of key stakeholders, sources of funding and specific activities undertaken. Furthermore the survey sought to collect data on any existing campaign evaluation, whether an evaluation had been performed, who was responsible, the source of funding for the evaluation, the involvement of stakeholders, the methodology employed and what indicators of success were utilised. If an evaluation had not been performed, the survey explored reasons for this, and whether an evaluation was planned for the future. Data were entered into Microsoft Excel 2010. No statistical analysis was undertaken on the data. Text responses were grouped by keywords in order to identify and group major themes for open ended questions.
Acknowledgements We acknowledge with thanks members of the Coordination group of the  Spanish Hand Hygiene Campaign, Ministry of Health, Social Services and Equality, Spain  (yagra@msssi. es): Coordination group of the  Spanish Hand Hygiene Campaign ,  Yolanda Agra ,  Concepción Barrera ,  Juan Beltrán ,  Antonia Blanco ,  Margarita Cueto ,  Jordi Espuñes ,  Miquel Ezquerra ,  Concepción Fariñas ,  Mar Fernández-Maillo ,  Paloma García ,  Olga Hidalgo ,  Fuencisla Iglesias ,  Juan Limón ,  Rosa Mareca ,  Dolores Martín ,  Carmen Martínez ,  Jesús Molina ,  Cristina Navarro ,  Sonsoles Paniagua ,  Teresea Pi-Sunyer ,  Paz Rodríguez ,  Jorge Taboada ,  Belén Tirapu ,  Rosaura Toribio ,  Antonia Torres ,  Susana Valbuena ,  Pilar Vázquez. 13 Military Faculty  Hospital Prague, Prague, Czech Republic.
Information Data were recorded during a 5-hour in-person visit for a complete nephrological workup comprising a large set of clinical and laboratory measurements, including blood pressure, body mass index (BMI), and treatments received. Diabetes was defined as either fasting glycemia $7 mmol/L or HbA1c $6. 5% or antidiabetic treatment, hypertension by either a systolic or diastolic blood pressure. 140/90 mm Hg or antihypertensive treatment, metabolic acidosis as venous CO 2 ,22 mmol/L or alkaline treatment.
Laboratory measurements At each visit, mGFR was measured concurrently for all patients by 51 Cr-EDTA renal clearance and fractional creatinine clearance. Subjects also provided 24-hour urine collection, which enabled us to measure 24-hour creatinine clearance as well as creatininuria with the Jaffe method. Because 24-hour urine collection may be inaccurate, we primarily used 24-hour urinary creatinine extrapolated from fractional creatinine clearance in this study, on the assumption that creatinine excretion is stable over the 24-hour period. Moreover, the analysis of consistency between the two measures of creatinine clearance and that of mGFR enabled us to exclude outliers and to assess the completeness of the 24-hour urine collection. Thus, in addition to 84 patients with missing urinary creatinine, we excluded 78 patients with missing data for fractional creatinine clearance and 56 outliers for mGFR or for fractional or 24-hour creatinine clearance or both, leaving 1609 patients for the analysis of baseline data (Figure  1 ).
In this analysis, we excluded 99 patients with CKD stage 5 at baseline and 37 with missing urinary urea, leaving 1473 patients with a median follow-up of 1. 43 years (0-3. 34, maximum 5 years). Median interval between visits was 1. 12 year (1. 00-1. 69) for the 855 patients with at least two visits. This analysis was based on a total of 3268 observations. Interactions with time were tested for all covariates. In the final mixed model, we only included interaction terms which were statistically significant using Wald test and improved the model according to Akaike information criteria (AIC). A two-sided P-value ,0. 05 indicated statistical significance. Statistical analyses were performed with SAS software, version 9. 3 (SAS institute, Cary, NC).
Acknowledgments We thank  Jo-Ann Cahn  for the English revision of this manuscript. The NephroTest study group:  Bichat Univ Hospital (Paris): Francois Vrtovsnik, Eric Daugas (Nephrology)  and  Martin Flamant ,  Emmanuelle Vidal-Petiot (Physiology) ;  European Georges Pompidou Univ Hospital (Paris) :  Christian Jacquot ,  Alexandre Karras ,  Eric Thervet ,  Christian d'Auzac (Nephrology)  and  P. Houillier ,  M. Courbebaisse ,  D. Eladari  et  G. Maruani  (Physiology);  Tenon Univ Hospital (Paris ):  Jean-Jacques Boffa ,  Pierre Ronco ,  H. Fessi ,  Eric Rondeau  (Nephrology) and  Emmanuel Letavernier ,  Jean Philippe Haymann (Physiology); Landy Clinic (Saint-Ouen): P. Urena-Torres (Nephrology) ;  Bordeaux Univ Hospital: Christian Combe (Nephrology) , and  Inserm (Villejuif) :  M. Metzger ,  B.
Nuclear magnetic resonance (NMR) is a powerful tool for observing the motion of biomolecules at the atomic level. One technique, the analysis of relaxation dispersion phenomenon, is highly suited for studying the kinetics and thermodynamics of biological processes. Built on top of the relax computational environment for NMR dynamics is a new dispersion analysis designed to be comprehensive, accurate and easy-to-use. The software supports more models, both numeric and analytic, than current solutions. An automated protocol, available for scripting and driving the graphical user interface (GUI), is designed to simplify the analysis of dispersion data for NMR spectroscopists. Decreases in optimization time are granted by parallelization for running on computer clusters and by skipping an initial grid search by using parameters from one solution as the starting point for anotherusing analytic model results for the numeric models, taking advantage of model nesting, and using averaged non-clustered results for the clustered analysis.
(2004)  for MMQ data. For R 1 -type data, analytic equations include the  Meiboom (1961)  2-site fast exchange model for on-resonance data (M61), extended by  Davis et al. (1994)  to off-resonance data (DPL94), and the  Trott and Palmer (2002)  and  Miloushev and Palmer (2005)  2-site models for non-fast and all time scales (TP02, MP05). Different numeric solutions (NS) can be designed for SQ or MMQ data. Diverse software solutions exist for analysing relaxation dispersion data including CPMGFit (http://www. palmer. hs. colum bia. edu/software/cpmgfit. html), cpmg_fit (available on request from Dmitry Korzhnev), CATIA  (Hansen et al. , 2008) , NESSY  (Bieri and Gooley, 2011) , GUARDD  (Kleckner and Foster, 2012) , ShereKhan  (Mazur et al. , 2013)  and GLOVE  (Sugase et al. , 2013). The software relax (d'  Auvergne and Gooley, 2008 ) is a platform for studying molecular dynamics using experimental NMR data, and can be used as a numerical computing environment.
Herein, support for relaxation dispersion within relax is presented. Distributed as part of relax, this is the most comprehensive dispersion package supporting the greatest number of dispersion models and NMR data types. The number of dispersion models supported by relax is extensive (Table  1 ). This allows for detailed comparisons between modern numeric and traditional analytic approaches. Different user interfaces (UIs) can be used to analyse dispersion data including the prompt, scripting and graphical user interface (GUI). The scripting UI enables the greatest flexibility and allows for most analysis protocols to be replicated. By implementing a novel automated analysis and providing an easy-touse GUI based on this auto-analysis, the study of dispersion data is much simplified. The set-up of the auto-analysis includes defining the molecular system, loading the dispersion data directly from peak lists, clustering atoms with the same kinetics, modifying the list of dispersion models and setting up Monte Carlo (MC) simulations for error propagation.
The second is model equivalence-when two models have the same parameters. For example, the CR72 model parameters are used as the starting point for the CPMG numeric models, resulting in a huge computational win. The third is for spin clustering-the analysis starts with the averaged parameter values from a completed non-clustered analysis. The dispersion analysis in relax is implemented in Python using NumPy and the GUI using wxPython. Optimization using the Nelder-Mead simplex and log-barrier constraint algorithms from the minfx library (https://gna. org/projects/minfx/) removes the need for numerical gradient approximations, which add a second numeric layer to the NS models. Data visualization is via the software Grace.
Statistical Analysis Results were expressed as mean 6 S. E. M. Figures and analyses were performed using GraphPad Prism 5. 0 (GraphPad Software, San Diego, USA). Neurological scores were compared using a Kruskal-Wallis analysis followed by a Mann-Whitney test. Whenever suitable, other results were analysed using one-way ANOVA followed by a Dunnett post-hoc test or two-way ANOVA followed by a Student's t-test. Differences were considered statistically significant when P,0. 05.
Study population We retrospectively collected data of all consecutive patients treated with FOLFOX adjuvant chemotherapy after surgical resection of a stage III colon cancer between 06/2003 and 12/2007 in 3 University centers (Ambroise Paré, Georges Pompidou, and Saint-Antoine hospitals) and 1 private hospital (Mutualiste Montsouris Institute). Patients were eligible for this retrospective multicenter study if they met the following criteria: older than 18 years, histologically proven colon cancer, at least 1 pathologically involved lymph node, R0 resection, available tumor specimen in the pathology departments, adjuvant FOLFOX chemotherapy starting within 8 weeks after surgery and no history of sensitive peripheral neuropathy. Fourteen stage III colon cancer patients enrolled in the MOSAIC trial with available tumor and treated in these centers with FOLFOX between 11/1998 and 10/2000 were also included.
The discriminatory capacity was tested using the Harrell's concordance index. Harrell's C statistic was used to estimate the proportion of correct predictions. Harrell's C index ranges from 0. 5 (no discrimination) to 1 (perfect discrimination). P values < 0. 05 were considered as statistically significant. All analyses were performed using Stata V11 software (StataCorp LP, College Station, TX). The cutoff date for this analysis was December 2010.
Exclusion criteria were: • previous musical training for more than 4 years; • psychiatric problems (as assessed by standard clinical investigation); • cognitive impairment or aphasia. Initially, 36 patients were identified that matched the inclusion criteria and provided informed consent to participate. However, six patients (17%) were released from the hospital prior to finishing our therapy program. Furthermore, two patients (6%) dropped out of therapy because they no longer felt therapy was effective. Our final sample consisted of 28 patients. Patients were assigned quasi-randomly to the groups. Patients were included two or three at a time, since insufficient patients were available to include all patients at the same time. A custom designed computer script was used to quasi-randomly assign patients to groups making sure that (1) the number of patients in the two groups were as close as possible, and (2) the two groups were as closely matched as possible for age, gender, Barthel index, and nine-hole pegboard test score.
Each member in the patient pair played on their individual M-Key V2 MIDI controller keyboard that was chosen for its light touch. The two keyboards were connected through the M-Audio Midisport Uno MIDI-to-USB converter to a Linux laptop. The laptop ran a custom made C program that recorded the MIDI events and forwarded them to the software synthesizer Fluidsynth, which generated the sounds using a Steinway sound font. The program additionally changed the MIDI velocity value (loudness) to its maximum value. As a result, all sounds were maximally loud, regardless of how strong patients' keystroke was. This was done to prevent patients' typically very soft keystrokes from being inaudible. The sounds were then played through Creative Inspire T10 speakers (Creative Labs, Inc. ) at a comfortable loudness level.
Both sensors were read out by an Arduino Duemilanove experimentation board running a custom made C program to sample sensors at 3 kHz. The data was then transferred online over USB to a Linux laptop running a custom Python program allowing the therapist to preview the data. We made the blueprints of the device set up as well as the custom programs available online for free for future research groups to use (http://github. com/florisvanvugt/immmotion). In paced thumb-to-index tapping, patients were instructed to tap as regularly as possible in time with a metronome at 69 BPM (i. e. , 1. 15 Hz) during 60 s from the first tap  (Calautti et al. , 2006). The metronome sound was generated using direct digital synthesis (DDS) by the Arduino experimental board as follows. Essentially, we created a wave table (440 Hz, 20 ms) which was written to a PWM pin connected to an audio jack plug.
The fingers were held in a relaxed posture close to maximal extension but slightly bent so that the position could be sustained without muscular effort. No metronome was used in these speed tapping trials. The patients were instructed to tap as fast and as regularly as possible, lifting their finger at least 2 cm above the table on each cycle. The force sensor surface was placed on the table and the patients were instructed to tap on the same spot every time. In middle finger speed tapping, the procedure was the same as with index finger speed tapping but switching to the middle finger. The raw data files containing the force trace over time were preprocessed using a custom developed python script (we do not report the accelerometer data here). The script discarded the first and last 0. 5 s of data from the recordings and then converted the force sensor trace into Newtons using a previously established calibration table.
We then smoothed the signal using a 160-sample Bartlett window (which amounted to approximately 53 ms at our sample rate). The script detected the tap onset landmarks (a sudden impact) when the force exceeded 0. 05 Newton. Tap offsets (a release of contact from the tapping measurement surface) were defined as the time point when the force trace dropped below 0. 05 Newton again at least 40 ms after the last tap onset. Similarly, the next onset was restricted to occur at least 75 ms after the last tap offset. All data files with their landmarks were furthermore visually inspected to ensure our method of analysis did not introduce any artifacts. In a number of cases the 0. 05 Newton onset/offset threshold was adjusted manually to compensate for the fact that some patients tapped too softly or off the sensor surface.
ACKNOWLEDGMENTS This work was supported by the  EBRAMUS, European Brain and Music Grant  ( ITN MC FP7, GA 238157 ). We are indebted to  Britta Westner ,  M. A. , for running a pilot study. We furthermore would like to thank  Mr. Richter  for valuable feedback about music-supported therapy. Also,  Dr. Sabine Schneider  kindly shared her knowledge of the previous implementation of the music-supported therapy that formed the basis for the current therapy program. We thank all the medical staff (doctors and nurses) in the Hessisch Oldendorf clinic for their cooperation and for indicating to us patients who might be suitable for inclusion in the study. Finally we wish to extend our gratitude to all the patients who devoted their time to participation in this study. We hope the therapy may benefit them in their daily lives.
Patient and samples CellSearch® images from blood samples used for the detection of CTCs and characterization of CTC HER2 protein expression by CellSearch® from breast cancer patients were retrieved for this study. The blood samples were processed at one of the three different laboratories; the Institut Jules Bordet, Belgium (IJB), the Institut Curie, France (IC), and the University of Hamburg, Germany (UH), respectively. The IJB dataset included CellSearch® images from women with non-metastatic (M0) and metastatic (M1) breast cancer treated according to the standard of care in Belgium  [17]. The IC dataset consisted of Cell-Search® images from women with inflammatory M0 breast cancer participating in the BEVERLY-I and -II phase-2 trials  [25, 26]. The UH dataset included CellSearch® images from women receiving neoadjuvant chemotherapy in the GeparQuattro and GeparQuinto studies (M0 cohort)  [16, 27]  and women from the Detect Study (M1 cohort)  [18].
If there was <60% agreement about the HER2 positivity or negativity among the Veridex readers for a CTC, this image was not included in any of the subsequent comparisons. For each of the 22 academic readers, percentage (%) agreement with VC was calculated for the CTC detection question (no CTC versus CTC) and for the question of HER2 expression on CTCs (CTC HER2-versus CTC HER2+). In order to understand problems related to Cell-Search® image interpretation, we compared the locations in the distributions of the agreement (%) between VC and the academic readers according to the dataset (IJB versus IC versus UH), disease stage (M0 versus M1), number of detected CTCs in the entire sample (≥5 CTCs versus <5 CTCs) and administration of systemic treatment before blood sampling (yes versus no) using non-parametric tests (Wilcoxon signed rank and Freedman test).
Agreement between academic readers and Veridex consensus for CTC definition For the question of whether an image was a CTC (no CTC versus CTC), the median agreement between the academic readers and VC was 92% (range 69% to 97%), with a median κ statistic of 0. 83 (range 0. 37 to 0. 93) (Table  2 ). We then asked whether agreement between the academic readers and VC was different according to the dataset (IJB versus IC versus UH), disease stage (M0 versus M1), CTC counts (<5 versus ≥5) and administration of systemic treatment before blood sampling (yes versus no). We observed significantly lower agreement between academic readers and VC for CellSearch® images from patients of the IJB dataset compared to the other datasets, from patients with M0 compared to M1 disease, <5 CTCs compared to ≥5 CTCs, and blood samples drawn after the administration of systemic treatment as compared to samples drawn before systemic treatment (Table  3 , Additional file 1: Table  S1  and Additional file 2: Table  S2  analyzed, lower agreement was observed for images from samples with <3 CTCs (median 87%, range 66% to 95%) compared to those from samples where ≥ 3CTCs were detected (median 95%, range 77% to 99%), (P <0.