dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging Imaging was performed a median of 2 weeks (4-week interquartile range) before surgery with no treatment changes between imaging and surgery. Pathologists and radiologists annotated the location of 61 histological samples on the associated MRE sequences, including 10 samples from the margins and 51 samples from the most stenosed areas of resected specimens. These samples originated from the terminal ileum (n 5 52), proximal The central reader monitored image data quality, judging. 95% of sequences to be evaluable (sequences with a quality score of Fair or better) at each site and across sites, overall. In 5 of 6 sites,. 80% of sequences were assessed as Good or Excellent quality. At one site, approximately 59% of data were assessed as Good or Excellent quality (see Supplemental Table  2 , Supplementary Digital Content 1, http://links.
I have included patients in the study. A. H. N. was involved in collecting and interpreting data and drafting the manuscript. R. H-M. was involved in collecting data and drafting the manuscript. Y. B. was involved in planning and conducting the study, collecting data, and drafting the manuscript. M. Z. was involved in collecting data and drafting the manuscript. D. C-H. was involved in planning and collecting histological data. G. d'H. was involved in study design, data collection and interpretation, and critical review of the manuscript. J. S. was involved in planning and conducting the study, collecting data, and drafting the manuscript. S. M. was involved in planning and collecting histological data and drafting the manuscript. G. R. was involved in planning and conducting the study, collecting data, and drafting the manuscript.
Financial support: Genentech, Inc. , funded this study. Genentech, Inc. , participated in the study design, the collection, analysis, and interpretation of the data and in the writing of the report. Potential competing interests: A. C. : Employee of Genentech, Inc. , and shareholder of F. Hoffmann-La Roche Ltd. J. R. : Research grants from AbbVie, Genentech; speaker fees for lectures or consultancy from Janssen, Gilead, Takeda, Bioclinica, Origo biopharma, Lument, Alimentiv, and TiGenix.
EdU detection, using Click-iT Plus EdU Alexa Fluor 488 or Click-iT Plus Pacific Blue Flow Cytometry Assay Kit, and immunostaining was performed according to the manufacturer's instructions (Thermo Fisher Scientific, Waltham, MA). Fluorophore-coupled primary antibodies and dilutions are listed in Supplementary Table  1. Flow cytometric analysis was carried out using a LSRIIB flow cytometer with FACSDiva software (BD Biosciences). Data was analyzed using FACSDiva or FlowJo v10. 7 software (Ashland, OR; https://www. flowjo. com/solutions/flowjo). Dead-cell stain, EdU, C-peptide (Cpep), Ins and glucagon (GCG) labelled cells were detected using the 405-, 488-, 640-and.
Quantitative RT-PCR To assess SphK knockdown efficiency, SphK1 and SphK2 expression levels were measured by quantitative RT-PCR of infected GFP + cells. Briefly, at the end of treatment, islets were dissociated in Accutase (1 µl/islet; Innovative Cell Technologies Inc. , San Diego, CA) for 10 min at 37°C, washed with cold PBS, resuspended in PBS+1% BSA and passed through a 50µm filter prior to flow cytometric sorting using a FACSAria II flow cytometer with FACSDiva software (BD Biosciences, San Jose, CA). To assess ELOVL1 and ACBP knockdown efficiency gene expression was measured by quantitative RT-PCR in whole islets. Total RNA was extracted from 150 to 200 whole islets or at least 50,000 sorted β-cells using the RNeasy Micro kit (Qiagen, Valencia, CA). RNA was quantified by spectrophotometry using a NanoDrop 2000 (Life Technologies Inc.
) maintained at 40°C using an 83 min chromatographic gradient of solvent A (0. 2% formic acid and 10 mM ammonium formate in water) and B (0. 2% formic acid and 5 mM ammonium formate in methanol/acetonitrile/methyl tert-butyl ether [MTBE], 55:35:10 [v/v/v]). A list of MS features, characterized by mass, retention time and signal intensity (area under the curve), was extracted using Mass Hunter B. 06. 00 (Agilent Technologies Inc. ). Subsequent data mining was achieved using an in-house script that applies alignment of the chromatographic runs, frequency filtering (80 % in one condition), signal intensity normalization using cyclic loess algorithm and imputation of missing values with k-nearest neighbor on scaled data. MS signals for 28 sphingolipids including their acyl side chains (20 sphingomyelins (SM), 6 ceramides (Cer), 4 hexoceramides (GlcCer)) were extracted using an in-house database.
Fiji (National Institutes of Health). Image analysis. To quantify vesicles or puncta, the images were cropped to a uniform size and converted to 8-bit TIF files. Ilastik pixel-classifiers were trained separately using 6-15 images covering all genotypes for all markers. Separate classifiers were necessary since the nature of different 'spots' is different. The Batch Processing function in Ilastik was used to process all the images and Simple Segmentation results were exported. Custom Python script was then used to identify the 'spots' and to measure the number of spots in an area of 10000 µm 2. The codes are available at https://github. com/sourabh-bhide/Analyze_Vesicles. In the FLIP experiment, image intensities were measured in the bleached area and the sum of intensities in the area plotted over the time.
All measurements were normalised to the pre-bleached value in the ROI. Data analysis. The plots were generated using the Python 3. 7 library Seaborn (https://seaborn. pydata. org/generated/seaborn. boxplot. html). For statistical hypothesis testing, anindependent and non-parametric (Kruskal-Wallis) t-tests were performed for the mean number of ‚spots' in control and experimental conditions in the graphs in Fig. 1d , 3c, 8d, S1b and S7c. We assumed unequal sample size and unequal variances and calculations were performed using the Scipy library from Python 3. 7 and GraphPad Prism version 8. 1. For all other graphs (Fig. S5f , S5g and S9b) ordinary one-way ANOVA statistical tests were performed after we confirmed normality using the Shapiro-Wilk test (alpha = 0. 05). Values in this paper are presented as box plots.
Acknowledgements. We are grateful to  A. J. Garcia-Saéz ,  F. Papagiannouli ,  M. Rembold ,  M. Graef ,  N. L. Kononenko  and  S. Roth  for critical reading of the manuscript, comments and helpful discussions. We thank  A. Wodarz ,  M. J. Galko ,  L. Partridge  and.
All statistical analyses were performed with IBM SPSS Statistics 25. The following methods were used:  descriptive statistics for qualitative and quantitative variables  nonparametric chi-square test with Fischer's Exact Test for hypothesis evaluation  the Kolmogorov-Smirnov test and the Shapiro-Wilk test in samples with a volume of less than 60 for assessing the normality of the distribution  the nonparametric Mann-Whitney test to compare independent samples for variables with distributions other than standard and Student's t-test for variables with normal distribution. A signifi cance level P < 0. 05 was used to accept statistical signifi cance in all analyses.
Many studies link rosacea with a high level of LDL  [10-16, 21, 42-44]. Given the lack of clarity and overlooking the diff erence between rosacea and CD, we decided to investigate LDL and total cholesterol levels in our group of 141 patients with CD. In the patients with CD, the average values for total cholesterol and LDL were lower both in the control group/CG individuals with similar demographic characteristics and the patients with rosacea. The only diff erences in values for LDL between CD and control group (CG) were not statistically signifi cant. This observation contradicts the established data in the scientifi c literature, and its signifi cance and accuracy are to be further clarifi ed  [45]. In the analysis of dyslipidemia and glucose tolerance in patients with CD, a control group of 50 individuals with similar demographic characteristics was investigated by Belli AA, God SO, Akbaba G, Etgu F, Dogan G.
| Generating the optimal dichotomous/binary HN cutpoint Initially, patients with HBV A and D genotypes within each cohort were combined to identify the optimal HN threshold which could best separate HBsAg loss and HBsAg-positive patients. Then we also analysed patients with the different genotypes A and D separately. For finding the optimal HN threshold, we used the R package 'cutpointr'  27   Note: In the G110 cohort, 8/659 patients were HBeAg negative and were removed from analysis to ensure a complete HBeAg-positive validation cohort. For numerical clinical parameters, the mean value, together with the min-max values and standard deviations (SD) are shown. Only haplotypes with a minimum abundance of 1% were included in the analysis. Abbreviations: ALT, alanine transaminase; HBsAg, hepatitis B virus surface antigen; SD, standard deviation; TAF, tenofovir alafenamide; TDF, tenofovir disoproxil fumarate.
This value will always be between 0 and 1, with a larger area representing a model with better classification potential. In some instances, Prism could not fit a logistic regression. This could be due to perfect separation, or because one or more of the predictors were linearly dependent. This was the case for genotype D patients in the G103 cohort for BCP mutations A1762T and G1764A. In this dataset all six HBsAg loss patients had no mutation for A1762T and G1764A.
F I G U R E 2 for determining the optimal binary HN cutpoint (Table  S3 ). In the 'G110' validation cohort, the optimal binary HN cutpoint for loss of HBsAg on therapy for genotypes A and D at baseline, was also   S3 ). To further demonstrate the robustness of the identified optimal binary HN cutpoints, cohorts G103 and G110 were combined, en-   S3 ). The OR for losing HBsAg with the larger combined dataset was 44 if a patient had ≤2 haplotypes at baseline. abling The probability of remaining HBsAg positive (specificity) and having >2 haplotypes was 96% and was in a similar range compared to the G103 or G110 specificity value (88% and 99% respectively).
These values are mathematically equivalent to eβ0 and eβ1. The ROC data were obtained from the same 'simple logistic regression' modelling used for generating the odds ratio estimates from Prism v 9. 2 for macOS. In some instances, Prism could not fit a logistic regression. This could be due to perfect separation, or because one or more predictors were linearly dependent. This was the case for genotype D patients in the G103 cohort for BCP mutations A1762T and G1764A. In this dataset all six HBsAg loss patients had no mutation at A1762 and G1764. noted using this approach. For example, analysis of HBV quasispecies in the pre-S/S gene in occult HBV infection (OBI)-related hepatocellular carcinoma (HCC) patients showed that mutations within the pre-S2 region and human serum albumin-binding domain were significantly higher in OBI-related HCC than in HBsAg-positive HCC.
The association of NFRP classes with event-free survival (EFS) is of borderline significance in our adult T-ALL patients A total of 86 newly diagnosed adult T-cell acute lymphoblastic leukemia (T-ALL) were included in the present study (Table  1  and supp. Table  S1 ). For 54 of these patients, RNA-seq data were available from our previous work  [7]. The present study included an additional 32 patients, which without RNA-seq data but with detailed clinical information. The N/F mutational status as well as the N/F combined with RAS and PTEN (NFRP) mutation status were reported to impact adult T-ALL patients  [11] [12] [13] [14]. Therefore, NOTCH1, FBXW7, RAS and PTEN mutation status were also assessed for all our T-ALL adult patients. Clinical and biological features of patients with T-ALL were analyzed according to the mutational status of N/F or NOTCH1/FBXW7/RAS/PTEN (NFRP classes) as summarized in Supplementary Table  S1.
A combination of ectopically expressed genes can be used to reliably predict prognosis of T-ALL patients at diagnosis These observations prompted us to seek for new biomarkers which could reliably stratify patients before treatment. We applied a strategy specifically designed to identify the aberrant expression of genes which are normally silent in non-germline adult tissues and to test the association of these ectopic expressions with survival probabilities. By using available RNA-seq data in large series of normal human tissues, we identified 3195 transcripts with an expression restricted to testis, placenta or embryonic stem cells, of which 448 were found ectopically expressed in at least 10% and not in more than 90% T-ALLs samples. We then used a first cohort of T-ALL patients for whom RNA-seq as well as survival data were available.
In addition to the 54 T-ALL adult patients, in order to strengthen the power of the approach, RNA-seq data obtained from 55 samples of children with T-ALL were also included in the training cohort (described in Supp. Table  S1 ). Since our main objective was to identify a common molecular background related to aggressiveness in both children and adult T-ALL, our approach was designed to identify a subset of genes whose expression is associated with poor prognosis considering either the whole population of T-ALL, including children and adult patients, or sub-groups of children or adult patients. Considering each of the 448 genes ectopically expressed in a subgroup of T-ALL, we compared survival probabilities of the two groups of patients, whose malignant cells respectively did or did not express the gene.
Gene expression profile of 5-GEC positive T-ALL is significantly depleted in genes involved in basic cellular activities and identifies specific characteristics in MRD negative / 5-GEC positive T-ALL. Differential expression analyses (Fig. 3  and Supp. Fig. S5 ) and the corresponding GSEA (Fig. 4 , Supp. Fig S6. For all the genesets, the depletion was significant with a nominal p < 0. 01 and FDR < 0. 2. The geneset "histone genes" corresponds to the human histone encoding genes identified in El Kennani et al. 2018  ([24] ). The geneset "mitochondria encoded genes" is constituted of the 13 mitochondrial genes of the human genome. neg: negative; pos: positive. All other genesets were selected from the MSIG database of the Broad Institute (categories C2, C5, C7 or H of the MsigDB). neg: negative; pos: positive The volcano plots and heatmaps shown Fig.
Gene expression datasets RNA-Seq data from 13 normal samples were used as control from the dataset PRJEB4337 available on NCBI Bio-Project portal (https:// www. ncbi. nlm. nih. gov/ biopr oject/? term= PRJEB 4337) including 4 bone marrow samples (SAMEA2162823, SAMEA2149004, SAMEA2154529, SAMEA2163105), 5 lymph node samples (SAMEA2149876, SAMEA2150385, SAMEA1965299, SAMEA2155628, SAMEA2152719) and 4 spleen samples (SAMEA2153031, SAMEA2155751, SAMEA2159764, SAMEA2146236).
RNA-Seq data analysis Raw RNA-seq data obtained from bone marrow samples of 109 pediatric (n = 55) and adult T-ALL (n = 54) enrolled in our center  [7]  as well as from 13 normal samples from the dataset PRJEB4337 available on NCBI BioProject portal (https:// www. ncbi. nlm. nih. gov/ biopr oject/? term= PRJEB 4337) were used for the detection of aberrant expression of genes and correlation with prognosis. Reads from fastq files were aligned using STAR 2. 5. 2b software for UCSC hg19 reference genome. The aligned reads were counted using HTSeq framework (version 0. 9. 1). RPKM (reads per kilobase million) values were obtained by dividing the RPM (reads per million) values by a cumulated length of exons in kilobases and log-transformed by computing log2(1 + RPKM).
Analysis of mutational profiles Mutation calling from RNA-seq data of training cohort has been reported previously  [7]. Mutational hotspot regions of NOTCH1, FBXW7, PTEN, NRAS and KRAS were sequenced using Sanger sequencing in the 32 additional patients of the test cohort. The primer sequences used for NOTCH1 and PTEN were the same as previously described  [43, 44].
Funding This work was supported by the  National Natural Science Foundation of China  ( 81670147 ,  81570178  and  Antrag M-0377 ),  Shanghai Municipal Education Commission-Major Project for Scientific Research and Innovation Plan of Natural Science  ( 2021-01-07-00-02-E00091 ) and the  International Cooperation Projects of Shanghai Science and Technology Committee  ( 21430711800 ). SK laboratory is supported by a grant from  ARC   PGA1RF2019208471  as well as by  ANR   Episperm4 program. Additional supports were from:  Fondation ARC 'Canc'air' project  ( RAC16042CLA ),  Plan Cancer  ( CH7-INS15B66 ,  C7H-KIC19N30-IAB  and  ASC16012CSA ),  INCa-IReSP  ( R19051CC ), the ' University Grenoble Alpes '  ANR-15-IDEX-02  ( SYMER and LIFE)  and the  Cancer ITMO (Multi-Organisation Thematic Institute) of the French Alliance for Life Sciences and Health (AVIESAN) MIC   2021-2024 program.
Data acquisition and processing Data were acquired with a Tobii R Pro Fusion eye tracker (Tobii, Stockholm, Sweden; sampling rate of 250 Hz), with an accuracy of 0. 3 • and a precision of 0. 04 • in optimal conditions. The protocol was run with Tobii R Pro Lab. Each participant was installed in a comfortable armchair in front of the monitor at a distance of about 70 cm (distance calculated by the eye tracker: 57. 3-78. 2 cm). Before each block of videos, a nine-point calibration procedure was performed using animated circles to attract the gaze. The ocular exploration of the videos was analyzed through several parameters depending on Areas of Interest (AOIs). We created several AOIs: first the whole screen, to check that all the videos were equally explored, then two different sets of AOIs (Figure  1C ).
Within each set, all AOIs had the same surface (rectangles in Set 1: 320,000 px; ellipses in Set 2: 30,278 px). Set 1 divided the face into two large parts: the upper part (containing the eyes), and the lower part (containing the whole surgical mask). Set 2 consisted of four elliptical AOIs located on the mouth, the right eye, the left eye, and the space between the eyebrows including the tip of the nose. We mainly analyzed the time spent (in s) within those AOIs, relatively to the total time spent on the screen, as computed by Tobii Pro Lab (every valid eye tracking sample). We also analyzed the latency of the first entry (in ms) in each AOI of Set 2. For pupil analysis, we extracted raw data from Tobii and processed the signal using in-house MATLAB scripts. Some data loss was observed for some participants.
Except in one subject, data loss always lasted less than the duration of a blink (200-300 ms) and was interpolated to its nearest values. For the subject with longer data loss, trials (n = 2) with lost data were removed. Blinks and signal artifacts were identified thanks to a velocity threshold and pupil diameter values were replaced by the median values of a pre-and post-blink 120 ms interval. Then, we applied a median filter to remove signal artifacts and smooth the signal. Residual blinks were visually identified and manually interpolated. For each trial (starting at video onset), a baseline pupil size was calculated by taking the median value of the pupil diameter recorded over the last 200 ms before the video onset. This baseline value was subtracted from the pupillary diameter recorded during the 4 s after the start of the video presentation (2 s of video and 2 s of inter-trial).
Statistical analyses Statistical analyses were carried out using the software Statistica13 R. For all parameters, normality of the distribution and homogeneity of variance were verified with the Kolmogorov-Smirnov and Levene tests. The influence of the accessory (×4: no accessory, sunglasses, scarf, and mask) on the different parameters according to the emotion (×3: neutral, happy, and sad) and the AOIs for ocular exploration (×2 for Set 1 and ×4 for Set 2), was evaluated with a repeated measure ANOVA within the General Linear Model (GLM) Framework, corrected by Greenhouse-Geisser and completed by post-hoc corrected planned multiple comparisons. Pupil time courses were also analyzed with a GLM, adding the effect of time (×8 time points: one mean value for each 500 ms time interval), with Bonferroni multiple comparisons.
Execution of the Simulation Study The simulation study is conducted using the open source software R (R Core Team, 2020). The underlying R code is available at GitHub 1. For each of the 1296 predefined scenarios, an original data set is generated and clustered with the k-prototypes algorithm using the validation_kproto function  (Aschenbruck & Szepannek, 2020)  of the R package clustMixType  (Szepannek, 2018; Szepannek & Aschenbruck, 2021). The resulting so-called original cluster prototypes and cluster assignments are used in the following for benchmarking as described in Section 4. 3. Afterwards the missing values are generated as described in Section 4. 2 with the function ampute of the R package mice (van Buuren & Groothuis-Oudshoorn, 2021). For conducting cluster analysis and imputation on the incomplete data, the following four presented methods are compared.
The first three named variants are based on the approach derived in Section 2 and differences between those are presented in more detail in Section 2. 3. The last approach is based on Section 3. i) kproto(…, na. rm = "imp. internal") The function kproto from the R package clustMixType is executed, and within the algorithm the missing values are imputed with the current prototypes after each assignment of new prototypes. ii) kproto_imp. external(…) In this variation, the function kproto is executed as well, but the missing values are ignored during the execution of the cluster algorithm. After a partition is found, the (former) missing values are imputed with the values of the corresponding prototypes. Afterwards, the kproto function is executed again on the imputed data set, as long as the imputed values are changing (the maximum number of iterations with an imputation after each execution is 100).
iii) kproto(…, na. rm = "imp. onestep") Again, the function kproto is applied ignoring the missing values during the execution of the cluster algorithm, but here it is applied only once (meaning the just presented strategy ii) is stopped after the first step). So finally, after determining the partition, the missing values are imputed with the values of the corresponding cluster prototypes. iv) kproto_mi. pool(…) In this approach, multiple imputation is used to create n imp = 5 data sets by executing R function mice of R package mice (van Buuren & Groothuis-Oudshoorn, 2021). Hereof, internally the functions mice. impute. pmm (set size of 5 closest as recommended by van Buuren 2018) and mice. impute. logreg (van Buuren & Groothuis-Oudshoorn, 2011;  White et al. , 2010)  are used for imputation depending on the variable type.
MATERIALS AND METHODS 2. 1. System Preparation. S pro consists of three identical polypeptide chains and is divided into the S1 (residues 1-1146 per chain) and S2 (residues 1146-1273 per chain) subunits. The S1 subunit is the head of the protein that is exposed to the exterior and is responsible for contact with metal surfaces. However, the S2 subunit links the protein to the virion. Fully glycosylated structures of the S1 subunit of S pro were taken from the CHARMM-GUI archive  75  (PDB IDs: 6VSB and 6VXX for S up and S down , respectively, Figure  S1 ). The difference between the S up and S down is based on the orientation of their receptor binding domain (RBD). The downloaded structures are based on cryo-EM resolved crystal structures, reported by Walls et al. ,  72  and have the predicted missing residues and the linked glycans reported by Woo et al.