dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the following people for their important assistance with this manuscript: The clinicians and researchers behind the Irish and Italian ALS registers and the pharmaceutical companies which provided data to the  PRO-ACT  dataset, that enabled this entire endeavor, the hundreds of participants on the crowdfunding effort that provided this challenge's award. Prof. David Schoenfeld  for his assistance with statistical considerations, and, of course, the solvers who participated in the challenge and the patients who inspired this effort. Data used in the preparation of this article were obtained from the  Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT ) Database. As such, the following organizations and individuals within the  PRO-ACT Consortium  contributed to the design and implementation of the PRO-ACT Database and/or provided data, but did not participate in the analysis of the data or the writing of this report:  Neurological Clinical Research Institute at MGH, Northeast ALS Consortium, Novartis, Prize4Life Israel, Regeneron Pharmaceuticals Inc.
In vivo imaging In vivo imaging was performed using the Ami-x platform (Spectral Instruments Imaging, AZ). Greyscale reference images taken under low illumination were collected and overlaid with images capturing the emission of photons from the lux-expressing p19A-lux using AMIView software. Live mice were anesthetized and images were taken. Thereafter, the mice were euthanized, followed by imaging of the intestinal tissues (before and after washing with PBS).
Sections were viewed at 350, 488, and 594 nm on a Zeiss AxioImager microscope. Images were obtained using a ZeissAxio Imager microscope equipped with an AxioCam HRm camera operating through AxioVision software (Version 4. 4). Electron microscopy (EM) fixation and imaging. Sigirr -/-mice were pre-infected with p19A for 1 day, and then exposed to 2. 5% DSS in their drinking water. After 2 days of DSS exposure, cecal and colonic tissues of p19A infected mice were removed and processed. EM studies of tissue samples and overnight p19A cultures (37°C in LB) were performed as previously described  66.
Real time qPCR analysis Total RNA from cecal and colonic tissues was extracted using the Qiagen RNeasy kit following the manufacturer's instructions, and quantified using a NanoDrop Spectrophotometer (ND1000). 1 µg of RNA was reverse-transcribed using a Qiagen OmniscriptRT kit (Qiagen), followed by dilution of the cDNA at 1:5 in RNase/DNasefree H 2 O. The qPCR was carried out using a Bio-Rad CFX connect Real-time PCR detection system, with the specificity for each of the PCR reactions confirmed by melting point analysis. Quantitation was performed using CFX Maestro TM software (Bio-Rad). The expression of genes was normalized to that of Rplp0. Primer sequences and reaction conditions for all genes analyzed are summarized in Table  1.
Acknowledgements This work was supported by grants from  Crohn's and Colitis Canada (CCC)  and the  Canadian Institutes of Health Research (CIHR)  to B. A. V. B. A. V. is the  Children with Intestinal and Liver Disorders (CH. I. L. D. ) Foundation Chair in Pediatric Gastroenterology. K. J. is a Senior Clinician Scientist supported by the  BCHRI Clinician Scientists Award Program ,  University of British Columbia , and by the  CH. I. L. D. Foundation. H. C. M-L. was supported by the  University of Copenhagen , the  Statens Serum Institute , and the  Torben and Alice Frimodts Foundation. E. S. B. was supported by a studentship from  BC Children's Hospital Research Institute (BCHRI).
Spectra preprocessing All preprocessing steps were performed using the TopSpin 3. 1 Bruker software (Bruker BioSpin, Karlsruhe, Germany). Spectra were aligned to the TSP signal at 0 ppm and phase-corrected. A five-order polynomial was systematically applied for baseline correction. All spectra from 4. 678 to 0. 60 ppm were divided into intervals or buckets equal to 10 -3 ppm using the AMIX software (Bruker). Each bucket was normalized to total spectrum amplitude. Because this procedure can mask minor peak variations when very intense peaks are present in the spectra, e. g. , lipid peaks (L2 peak; Fig. 4A ), we also performed bucketing on a reduced spectral region starting from 1. 825 ppm (Fig. 4A , into brackets). Resonance assignment was performed as previously described [for metabolites:  (74, 75) ; for lipids:  (76, 77) ].
Multivariate statistical analysis Buckets were then imported into SIMCA v13 software (Umetrics AB, Umea, Sweden) for multivariate statistical analysis. Buckets were mean-centered, scaled to unit variance (i. e. , weighted by 1/SD for a given variable), and submitted to a principal components analysis (PCA) to ensure good homogeneity of data and possibly to exclude outliers. For this aim, data were visualized by score plots, where each point represents an NMR spectrum and thus a sample. Supervised analyses like orthogonal partial least square discriminatory analyses (OPLS-DA) were thus performed using the group belonging (control or exposed) as Y matrix. The number of components was determined using the cross-validation procedure that produces R 2 Y and Q 2 factors (>0. 5). Moreover, the reliability of our OPLS-DA model was assessed by a CV-ANOVA (cross-validated analysis of variance) test.
Acknowledgments: We are grateful to  E. Fontaine  and  K. Padmanabhan  for helpful comments about the results. We thank  J. Courchet ,  B. Viollet , and  J. Maurer  for sharing cells and reagents. We thank  S. Michallet ,  B. Sefrin , and  P. Vernet  from the animal facility of the  Institute for Advanced Biosciences  and  F. Blanquet  and  C. Colomb  from the animal facility PHTA of Grenoble. We are grateful to  D. Bouvard  for giving us the R26R mice and to  G. Chevallier  for help with mouse breeding and genotyping. We thank  V. Blanc-Marquis  for technical help. S. Michallet  and  M. Schweitzer  were very helpful with excellent technical assistance. C. Caron  helped us with human sample studies. This work benefited from the help of  T. Dufourd  and  M. Aguilera  during their internships in the laboratory.
METHODS or stillbirth were excluded from this study. This study was approved by the hospital Ethics Committee. The prevalence of intact perineum was investigated and a multivariate analysis with logistic regression was conducted to identify the predictive factors. A p value less than 0. 05 was considered significant, with a confidence interval of 95 percent. Data were analyzed using SPSS version 22. 0 Study criteria for inclusion were fulfilled by 1748 women with singleton pregnancy and cephalic presentation. The primary outcome was defined as intact perineum: qualitative dichotomous (yes or no). Independent variables included parity, previous caesarean section (without a previous vaginal birth), standing up during the first stage of labour, position during the second stage of labour, birth weight, gestational age, attending a prenatal childbirth preparation course, onset of labour, use of hydrotherapy, use of the pilates ball and administration of epidural analgesia.
The use of unnecessary interventions should maybe improve the need of more techniques for perineal protection. A Cochrane systematic review conducted by Aasheim (2017) had concluded that there is moderate-quality evidence suggesting that the use of warm compresses, and the use of massage, may reduce the occurrence of third and fourth-degree perineal tears but evidence of the benefits of these techniques on other outcomes was nuclear or inconsistent. There are insufficient data showing results of other perineal techniques in improved outcomes for women and babies  (45). Our study included variables which were investigated in other studies such as previous caesarean section, hydrotherapy and use of pilates ball during the first stage of labour therefore findings could be compared. Findings from our study provide information on how midwifery practice could be improved.
Data Extraction and Definitions In both the design and validation cohorts, data were extracted without using patient identifiers. For the validation cohort, we queried all three local databases for patients who underwent DP-CAR for all indications (premalignant or malignant) in adult patients (C 18). We updated survival on existing cases and added new cases until 1 June 2017. The preoperative variables included baseline characteristics (age, sex, BMI, Charlson comorbidity index, surgical history), performance status (ASA classification), vascular and/or organ involvement, and tumor etiology. Procedures were considered multivisceral if additional organ resections besides those for pancreas, gallbladder, or spleen were performed. The annual DP-CAR case volume per center was based on the average number of reported procedures between 1 January 2014 and 31 December 2016.
21 We determined the model's ability to identify high-versus low-risk patients (discrimination) by comparing the area under the receiver operating curve (AUC)  22  in both cohorts using the DeLong test. 23 We assessed the accuracy of the risk model predictions per risk quantile using calibration plots. 24 ategorical variables are reported as counts and proportions and continuous variables as means ± standard deviations and/or as medians (interquartile ranges) based on normality. To determine statistical significance (alpha 0. 05), we used Fisher's exact test for categorical variables and the Mann-Whitney U test for continuous variables. Kaplan-Meier estimation was used to assess overall survival. All confidence intervals are 95%. The data were analyzed using STATA version 15. 0 (StataCorp LP, College Station, TX, USA).
ACKNOWLEDGMENT We acknowledge the office of the  European-African Hepato-Pancreato-Biliary Association  for its support and the  Living With Hope (LWH) foundation and Cancer Center Amsterdam  for providing travel grants. We are grateful to  Sarah L. Gerritsen ,  Mustapha Adham ,  M. Teresa Albiol Quer ,  Frederik Berrevoet ,  Manuela Cesaretti ,  Raffaele Dalla Valle ,  Benjamin Darnis ,  Markus K. Diener ,  Marco Del Chiaro ,  Thilo H. Hackert ,  Robert Gru ¨tzmann ,  Traian Dumitrascu ,  Helmut Friess ,  Seiko Hirono ,  Arpad Ivanecz ,  Anastasios Karayiannakis ,  Giuseppe K. Fusai ,  Knut J. Labori ,  Santiago Lopez-Ben ,  Jean-Yves Mabrut ,  Motoki Miyazawa ,  Willem Niesen ,  Fernando Pardo ,  Julie Perinel , and  Geert Roeyen  for data collection/provision and/or local study coordination.
In this work, we use the engagement index proposed by Pope and colleagues to provide both a proxy for the level of sustained attention during task completion and as input for the BCI. The BCI integrates a novel computational classifier which is trained initially during a brief calibration task, and then continuously adapts to changes in threshold values as the task is performed in order to reduce the previously reported volitivity of the measure. Brain activity was recorded continuously using fNIRS from frontal and occipital areas and utilized in the analysis to determine significant brain activity during each task cycle. This data was then coupled with the engagement index data to produce a wavelet coherence analysis  (Verdière et al. , 2018) , to provide analysis and visualizations of coherence activity between the BCI and measured fNIRS response.
Engagement Index We utilized an engagement index  (Pope et al. , 1995; Mikulka et al. , 2002)  to provide real-time assessment of a user's level of sustained attention and to drive the neurofeedback mechanism (countermeasure) of the simulation task interface. EEG signals were recorded and utilized in real-time using NeuroRT (Mensia Technologies, Paris). Band power in the α, β and θ bands for all sites (see section Data Acquisition) was derived and divided by the total power across all electrodes to create a ratio value for each frequency band. The engagement index was then calculated as the ratio β/(α + θ). The index value was then passed to a custom controller to adapt the interface as described above, where the gradient color change is tied to both the current value of the engagement index and the adaptive thresholds; this allows the gradient to "breath"; slowly increasing and decreasing with the current level of sustained attention without changing colors sharply.
Data Acquisition A 32-electrode EEG montage (Brainvision, Morrisville, NC, United States) was utilized to measure variations in brainwave activity in the θ (4-7 Hz), α (8-12 Hz) and β (13-21 Hz) frequency bands, from frontal and occipital cortical regions at the F3, F4, O1, O2 sites on the international 10-20 system  (Jasper, 1958). These data were captured at 500 Hz and down sampled to 250 Hz in real-time. Data were filtered in real-time using a low pass filter of 1 hz, a high pass filter of 50 z, notch filter set to 60 hz. Artifacts were detected utilizing a Riemannian Potato  (Barachant et al. , 2013)  automatic and adaptive detection method, and artifacts were flagged within the BCI architecture; if long-duration artifacts were detected the BCI shut down, data segments for short-duration artifacts were imputed to allow the BCI to continue operation.
, 2012). For the current study, WTC is used to assess the relationship (as significant coherent activity) between the NIRS signal and engagement index generated by each participant in each group (CCM, ECM, NOCM). That is, do changes in the engagement index driven by the feedback mechanism -as part of a closed-loop -show concomitant changes in the hemodynamic response indicative of increased brain activity for a given period? Wavelet coherence was performed in MATLAB (2017) (Wavelet Toolbox 4. 1. 9, MathWorks Inc, Natick, MA, United States) using custom scripts to derive data sets for each participant and group. Each dataset consisted of the per-one-second engagement index value and the channel aggregate average per one second of NIRS (HbO) data for each participant for the duration of phase 2 of the experiment, to obtain the wavelet coherence as a function of time and frequency.
To further increase the rigor of the wavelet coherence analysis, further statistical tests were performed to these data. To perform the wavelet coherence analysis, the first 10 min (600 s) of business task (phase 2) data -corresponding to the task preamble-was removed. Then for each participant within each group, NIRs data and the EEG data (consisting of engagement index values) were aggregated to create a dataset and a wavelet coherence analysis performed for each participant. The result from this analysis is 133 frequency bands of magnitudesquared coherence data every second between the two signal types for a total of 4500 s, which corresponds to the length of the task. Coherence data were then averaged for every 15 s of data to coincide with a typical hemodynamic response function  (Scholkmann et al.
, 2014). To reduce the number of frequency bands for analysis, data were then separated into task types (decision and monitoring), and the 133 frequency bands averaged every 22 bands to produce a total of 6 frequency intervals: I: 0 -0. 06, II: 0. 06 -0. 13, III: 0. 13 -0. 4, IV: 0. 2 -0. 26, V: 0. 26 -0. 33, VI: 0. 33 -0. 4 Hz which represents the mean magnitude squared coherence between the two signal types EEG and fNIRS. Typically, coherence analysis is performed between two signals of the same type; i. e. , NIRS signal data, which would then indicate the level of coherence as the similarity between the two signals in both the frequency and time domain. However, in this case, WTC is used to indicate how changes in one signal (EEG) drive changes in the other (fNIRS) -i. e. , how the BCI and interface countermeasure influences EEG signal, which in turn influences changes in the NIRS signal, which in this case is the proxy for brain activity and energy utilization as HbO and HbR.
Wavelet Coherence The results (Figure  7 ) show significant differences between the three groups with respect to the coherent activity within the defined frequency intervals, for the duration of the business task. However, this analysis does not reflect any significant differences between task cycles and removes the temporal aspect from the WTC. Due to the density of the reported results and the high number of interactions, the results report in tabular form are listed in their entirety in Supplementary Dataset S1. Within interval I, significant differences were found in coherent activity between the three groups. The NOCM group was shown to have the highest coherent activity (p < 0. 001) when compared to both the CCM and ECM groups. This is followed by the CCM group who had significantly more coherent activity compared with the ECM group (p < 0.
Pairwise Comparisons CCM Coherence Results To characterize the relationship of coherence between the BCI and NIRS response on a within-task, between-group basis, pairwise comparisons were performed as part of the GLMM. As previously described, the experimental timeline was partitioned into decision and monitoring cycles lasting 4 and 16 min, respectively, such that there are four decision and four monitoring cycles. The full tabulated results of the pairwise comparisons are supplied in Supplementary Dataset S1. Figure  8  shows the level of magnitude-squared coherence in both the frequency and time domain for the duration of the experimental session. In this figure, there can be seen a high degree of magnitude-squared coherence for significant portions of the experimental task (shown as lighter shading within the figure).
General Linear Model fNIRS Analysis Hemodynamic activity was recorded concurrently throughout the experiment with a twofold purpose, firstly to ascertain if fNIRS would prove a suitable measure as input for a hybrid BCI and secondly to allow a more direct measurement of brain activity during the completion of the task. As the recording of fNIRS is decoupled from the operation of BCI, in that its output is not used as input for the BCI, the data remains unaffected except as a measure of "driven" brain activity from which energy utilization can be inferred. As discussed previously, the GLM analysis was performed using the NIRS AnalyzIR Toolbox on a per task block basis comparing group by task type. Data were partitioned into one decision cycle followed by one monitoring cycle for each task block.
However, we do understand that NIRS, as with all technologies, is undergoing an accelerated evolution in terms of size, comfort and reliability and that potentially within 5 years the technology will be unrecognizable by today's standards and ready to be applied in contexts requiring realtime feedback. Another limiting factor within the current study pertains to the design of the NIRS probe. The probe was inspired by the current literature relating to the engagement index, and thus we set out to measure hemodynamic responses from those sites. Another limitation is that of sample size, with the previously discussed issues with data reduction due to discomfort, signal loss or artifacts, our eventual sample size reduces the positive impact of the results reported here. However, we believe that the results we report here give a good indication of how much stronger the effect sizes would have been, given larger sample sizes.
The Red Pitaya ADC and DAC had a maximum sample rate of 125 MSPS and it was set a 64 prescaler, resulting in effective sample rate of 1953125 SPS. Drive signals, from the Red Pitaya DAC, are amplified by a 5 W Class-B Push-Pull symmetric voltage amplifier with ±10V of output signal. The signal processing was performed by MATLAB in a PC, which was connected to the Red Pitaya through an Ethernet cable. To evaluate the transducer performance in the field and behavior of the available bandwidth, 10 chirp signals (from f1 to f2); 10→20, 50→60, 100→200, 200→300, 300→400, 400→500, 500→600, 600→700, 700→800 and 800→900 kHz were selected, with power spectrum as represented in Figure  10. Chirp signals were used, to avoid possible interference that could arise from surrounding noise when using single-frequency signals.
To evaluate the transducer performance in the field and behavior of the available bandwidth, 10 chirp signals (from f 1 to f 2 ); 10→20, 50→60, 100→200, 200→300, 300→400, 400→500, 500→600, 600→700, 700→800 and 800→900 kHz were selected, with power spectrum as represented in Figure  10. Chirp signals were used, to avoid possible interference that could arise from surrounding noise when using single-frequency signals. Each chirp signal was transmitted in burst mode, with a signal duration of 512 µs and a repetition period of 1024 µs, during a total time of 8 ms (8 equal chirp bursts were sent). On the receiver side, the signal was cross-correlated using MATLAB, with 10 selected chirp signals samples to detect which chirp was received. The test was carried out 3 times, for 1 and for 15 meters, for each of the 10 selected chirp frequency ranges (resulting in a total of 60 tests), and each test was registered.
Drive signals, from the Red Pitaya DAC, are amplified by a 5 W Class-B Push-Pull symmetric voltage amplifier with ±10V of output signal. The signal processing was performed by MATLAB in a PC, which was connected to the Red Pitaya through an Ethernet cable. To evaluate the transducer performance in the field and behavior of the available bandwidth, 10 chirp signals (from f 1 to f 2 ); 10→20, 50→60, 100→200, 200→300, 300→400, 400→500, 500→600, 600→700, 700→800 and 800→900 kHz were selected, with power spectrum as represented in Figure10. Chirp signals were used, to avoid possible interference that could arise from surrounding noise when using single-frequency signals.
Woodborers are affected by temperature, similar to other insects with stage-based niche changes  [23] , and their tolerance of extreme temperatures are significantly varies among their different life stages. In a previous study on the potential geographical range of S. noctilio, in Ireland, Bulman  [10]  identified the temperature thresholds of this species mainly according to data provided by Madden  [12]  and Nahrung  [14]. The upper-temperature threshold for S. noctilio was also determined based on unpublished data regarding symbiotic fungi, while the temperature on which cold stress occurs was determined according to known S. noctilio distribution. We support the use of data on symbiotic fungi and the known distribution of S. noctilio to predict the potential areas of distribution for this species, however, the impact of some climatic factors, such as higher or colder temperatures, on S.
Statistical analyses To determine the lethal temperatures (50% and 99%), the probability model (and its 95% confidence interval) was fitted to the survival data using the SPSS statistical software version 23. 0 (probit; SPSS Inc. , Cary, NC). Differences in temperature tolerance between adults and larvae were analyzed with t-test, and differences acquisition periods were analyzed with Fisher's LSD test (SPSS 23. 0). Simple descriptive statistics calculations (mean ± SEM), linear model analysis and the identification of the frequency distribution of the CTmax were conducted with Graph-Pad Prism version 7 (GraphPad Software Inc. , La Jolla, CA, USA).
Experimental section The synthesis of our material is reported elsewhere. 13  where R is the light yield, and V is the channel of the Compton edge position. In order to assess its neutron/gamma discrimination ability, the scintillator was coupled using RTV141A optical grease to a Hamamatsu H11284MOD photomultiplier tube fed with a CAEN N1470 high voltage operating in negative mode. The {scintillator + PMT} system was placed 15 cm away from a 252 Cf source (≈ 1. 8 MBq activity), the latter being partially thermalized with a 10 cm thick polyethylene brick. To reduce the gamma-ray incident flux on the sample, a 5 cm denal ® brick (a tungsten-based alloy) was added between the 252 Cf source and the polyethylene brick. The anode signal fed a CAEN DT5730B digitizer, running with the DPP-PSD software.