dataesr/scientific-paragraphs-categorization huggingface dataset:
75 log-e fold change compared to the rest of the cells. Naked mole rat differential uTARs were further filtered based on being in at least 50% of cells in the cluster and more than 0. 5 log-e fold change compared to the rest of the cells. Sea urchin differential uTARs were further filtered based on being in at least 50% of cells in the cluster and more than 1 log-e fold change compared to the rest of the cells. We manually examined the coverage of filtered uTARs, selected regions with coverage peaks, and used BLASTn to find nucleotide sequence homology with the nucleotide collection database to label differential uTAR features. Pseudo-bulk versus single-cell derived uTAR comparison: uTAR pseudo-bulk coverage was calculated using bedtools coverage on the merged alignment files for each experiment.
Spatial transcriptome processing and visualization: We used Seurat spatial transcriptome tools to visualize spatially tagged expression data. Feature expression matrices were log-e normalized before further processing. We identified spatial uTARs that overlap with differentially expressed scRNA-seq uTARs in chromosome position and strandedness. If several spatial uTARs overlap with a scRNA-seq uTAR, we visualized and calculated spatial correlations based on the spatial uTAR with the highest total expression. TAR-scRNA-seq tool and scripts: https://github. com/fw262/TAR-scRNA-seq.
UMAPs for samples labeled with (*) are shown in Extended Data Fig. 1B. Cell labels are defined by gene annotation clustering. C) Correlation between top 5 PC loadings and pseudo-bulk read coverage of uTARs across 11 samples. Horizontal line at uTAR PC loading = 0. 5, vertical line at uTAR pseudo-bulk read coverage = 1e+4, r 2 = 4. 0e-3. Quadrant numbers represent the number of uTARs in respective quadrant. D) Relative percentage of uTARs containing homology to any sequence (blue) and mRNA sequences (light blue) as a function of log-e fold change expression for each cell type in naked mole rat spleen data. BLAST sequence homology results relative to nucleotide collection database thresholds: mean uTAR peak query length = 686±731 bps, uTAR peak percent identity > 71%, evalue < 0. 053, bitscore > 52.
ACKNOWLEDGEMENTS: We thank  P. Schweitzer  and colleagues at the  Cornell Biotechnology Resource Center (BRC)  for help with sequencing assays. We thank  Phillip S. Burnham ,  Hao Shi ,  Alexandre P. Cheng ,  Adrienne Chang ,  Benjamin Grodner ,  Olga Botvinnik ,  Camille Sophie Ezran ,  Angela Wu ,  Mark Krasnow , and the  Lemur Cell Atlas Consortium  for discussions and feedback. This work was supported by  R33CA235302  (to I. D. V. ),  R21AI133331  (to I. D. V. ),  DP2AI138242  (to I. D. V. ), and a  National Sciences and Engineering Research Council of Canada  fellowship  PGS-D2  (to M. F. Z. W).
In parallel, KIR2DL2/3 + /1/S1 -/S2 -/NKG2A -(L3 + others -) NK cell subsets were targeted and the KIR2DL3/S2 (1F12)/2DL3 mAbs combination led us to discriminate L3 + and L2 + NK cell subsets (Figure  6c ). We assessed the inhibition of KIR2DL + NK cell degranulation against a panel of 3 C1 and four C2 transfected 221 cell lines focusing on main KIR2DL1/2/3 allotypes. The C2 specificity of KIR2DL1 + NK cells was confirmed with nonetheless a hierarchy of recognition toward different HLA-C molecules belonging to the C2 group (data not shown  [34] ). In contrast, 2DL2 and 2DL3 did not exclusively recognize C1 ligands since a strong inhibition of 2DL2 + and 2DL3 + NK was observed with the 221-HLA-C*04:01 cell line, being C2 + target (data not shown  [34] ) as we previously reported  [15]. The highest inhibition level of NK cell degranulation was observed for the L1*003 allotype for all C2 + targets and more specifically against the 221-C*02:02 between L1*002 + and L1*003 + NK cells and the 221-C*15:03 target between L1*003 + and L1*004 + NK cells (Figure  6d ).
for each allotype (L1*002 n = 5, L1*003 n = 9, L1*004 n = 5, L2*001 n = 8, L2*003 n = 5, L3*001 n = 5, L3*002 n = 2 and L3*005 n = 2). The mean ± SD is indicated. Statistical differences were analyzed using unpaired t-test or one-way ANOVA followed by Turkey's multiple comparison test. * p < 0. 05, ** p < 0. 01. We assessed the inhibition of KIR2DL + NK cell degranulation against a panel of 3 C1 and four C2 transfected 221 cell lines focusing on main KIR2DL1/2/3 allotypes. The C2 specificity of KIR2DL1 + NK cells was confirmed with nonetheless a hierarchy of recognition toward different HLA-C molecules belonging to the C2 group (data not shown  [34] ). In contrast, 2DL2 and 2DL3 did not exclusively recognize C1 ligands since a strong inhibition of 2DL2 + and 2DL3 + NK was observed with the 221-HLA-C*04:01 cell line, being C2 + target (data not shown  [34] ) as we previously reported  [15].
Cohort of T-Replete Haplo-Identical HSCT Patients This retrospective study has included 81 adult patients with various hematological malignancies who received a T cell-replete haploidentical HSCT with post-transplant cyclophosphamide (haplo-PTCY) in the Hematology Department of Nantes University Hospital. Conditioning regimens of a Baltimore-based RIC regimen with fludarabine (n = 26)  [50]  or clofarabine (Clo-Baltimore, n = 27)  [51]  or a CloB2A1 regimen with clofarabine 30 mg/m 2 /day (d), d-6 to -2, busulfan 3. 4 mg/kg d-3 and d-2, and ATG 2. 5 mg/kg d-1 (n = 28)  [52]. The source of graft was peripheral blood stem cell (PBSC) for all cases. The study complies with the Declaration of Helsinki. All patients provided informed consent for collecting their own data from the PROMISE database of the European Bone Marrow Transplantation (EBMT).
The KIR library preparation was performed using the NGSgo GENDX kit (Bedia Genomics, Chavenay, France) according to the manufacturer's instructions. The final denatured library was subsequently sequenced by using a MiSeq sequencer (HLA laboratory, EFS Nantes, France) with 500 cycles v2 kits which generated 250-base paired-end sequence reads. The quality of raw data sequences was monitored by using the Sequencing Analysis Viewer (SAV) Illumina software. KIR2DL1/2/3/S1/S2 allele assignment was performed by using the Profiler software developed by Dr M. Alizadeh (Research Laboratory, Blood Bank, Rennes, France)  [31]. An updated KIR allele library (v2. 9) was implemented into the Profiler software version 2. 24.
Statistical Analysis Categorical data were analyzed by Chi-square test and univariate comparisons were performed by the Student t test. Statistical differences in KIR2DL + NK cell frequencies between individuals having different KIR2DL1/2/3 alleles were analyzed with unpaired t tests or one-way ANOVA test for multiple comparisons using the GraphPad Prism v6. 0 software (San Diego, CA, USA). Clinical and demographic variables for patients (i. e. , age, gender, status at treatment, disease risk index, conditioning and donor centromeric AA KIR motif) were evaluated for their impact on relapse incidence in univariate and multivariate analyses using LogRank test and Cox proportional hazards models adjusted for significant clinical factors. Multivariate analysis was performed including only variables having a p-value less than 0.
Diego), and then manually optimized. The X-ray crystallographic structure of the KIR2DL1 bound to HLA-Cw4 (PDB code 1IM9) was used to obtain the structural models of the KIR2DL1 allotypes/HLA-Cw4 complexes. The structure was first prepared by adding hydrogen atoms, removing water molecules, and inserting the missing loop region using the Prepare Protein tool within DS. Non-bound KIR2DL1*002 allotype structure was extracted from the prepared structure complex (100% identity) and used to obtain the structural model of the two other non-bound KIRD2DL1 allotypes (*003 and *004) using the "Build Mutant" tool of DS with secondary restraints and a distance cut-off of 5 Å centered on the mutate residues for including neighboring residues in optimization. The generated models were checked using Verify_3D (https://servicesn.
Acknowledgments: We are grateful to all blood donors for participating in this study. We thank  Steven G. E. Marsh ,  James Robinson  ( Anthony Nolan Research Institute, London, UK ),  Tanguy Regnault  ( EFS, Rennes, France ) for their help in updating the IPD-KIR allele database into the Profiler software. We also thank  Alexandre Walencik  ( HLA laboratory, EFS Nantes, France ) for his advices and the access of the MiSeq platform.
Funding: This research was funded by  EFS Centre-Pays de la Loire  and by grants from  International Research Group on unrelated Hematopoietic stem cell Transplantation (IRGHET) ,  Etablissement Français du Sang (EFS) ,  Leucémie Espoir Atlantique Famille (LEAF) ,  La Ligue contre le Cancer and Agence de la Biomédecine (ABM). L. D. and D. R. M. are PhD students supported by la  Région Pays de la Loire/EFS Centre Pays de la Loire  (N •  2018-09766 ) and by  Industrial Agreement for Training through research grants  ( N • 2017-0850 ) respectively. B. M. was a PhD student supported by  Industrial Agreement for Training through research grants  ( N • 481-2015 ).
Context. Large scale solar eruptions significantly impact space weather and damages space-based human infrastructures. It is necessary to predict large scale solar eruptions, which will enable us to protect our vulnerable infrastructures of modern society. Aims. We aim to investigate the difference between flaring and non-flaring active regions. We also want to investigate the forecasting probability of the solar flare. Methods. We use photospheric vector magnetogram data from Solar Dynamic Observatory's Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We build a database of flaring and non-flaring active region observed on the solar surface from the years 2010 to 2017. We train the machine learning algorithm by the time evolution of these active region parameters.
Aggarwal et al. (2018)  used filament metadata for the prediction of eruptive events. However, most of the studies used vector magnetogram data obtained from the Helioseismic Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) for the purpose of flare prediction. Different machine learning classifiers have been used for the solar flare prediction. Some studies also used the time series of the magnetic field data obtained from HMI for flare prediction  (Hamdi et al. 2017; Ma et al. 2017). In this paper, we aim to investigate the importance of the time evolution of magnetic parameters in terms of flare forecasting. We find that there is a significant difference between the eruptive and non-eruptive active region in terms of both strength and time evolution of photospheric magnetic parameters.
In the supervised machine learning scenario, we have to train the computer model based on known well-labeled data. As we already have a well-known database of flaring and non-flaring active regions, we have used supervised learning techniques for our problem. In the supervised machine learning scenario, first, we have to train the computer-based on known flaring and non-flaring active region data. After the training, the computer will be able to tell us the possibility of active region eruption. |∇B tot | = 1 N ( ∂B ∂x ) 2 + ( ∂B ∂y ) 2 MEANGBZ Mean gradient of vertical field G/Mm |∇B z | = 1 N ( ∂B z ∂x ) 2 + ( ∂B z ∂y ) 2 MEANGBH Mean gradient of horizontal field G/Mm |∇B h | = 1 N ( ∂B h ∂x ) 2 + ( ∂B h ∂y ) 2 MEANJZH Mean current helicity G 2 /m Hc ∝ 1 N B z J z MEANJZD Mean vertical current density mA/m 2 J z ∝ 1 N ( ∂B y ∂x -∂B x ∂y ) MEANALP Mean characteristic twist parameter, α 1/Mm α total ∝ J z B z B 2 z 4.
Standard deviation (σ) basically represents the dispersion around the mean. Skewness and kurtosis is basically a statistical measure to describe the distribution. While skewness is the measure of the symmetry of the dataset; kurtosis tells us how the tails of the distribution differ from the tails of the normal distribution. We consider 17 magnetic parameters for our study. So, we have a time series for every seventeen magnetic parameters. As we represent each of the time series by 7 statistical parameters, so we have 119 entries in the resultant matrix.
Results We use the active region vector magnetogram data from June 2010 to December 2017 for this study. After generating the dataset, we perform some preprocessing in our data set due to the presence of some missing values. We simply replace them with the mean of the corresponding features. It is also necessary to normalize the dataset before training as it will transform the ranges of all feature values into a uniform range. In this study, we use the zero-one data transformation technique for normalization. We randomly divide our dataset into a training set (70%) and a testing data set (30%). We maintain the same class ratio (N/P) in both training and testing data set following the prescription of  Bobra & Couvidat (2015)  and  Ahmed et al. (2013). Please note that we do not include C class flares in our positive data set.
Our data set is highly imbalanced as flaring regions are much less compared to non-flaring ones. Oversampling and undersampling are the two well-known strategies to make an imbalanced data set balanced. While oversampling involves the strategy of adding more positive examples in the dataset, undersampling involves the strategy of removing the majority negative examples from the dataset. However, both methods have some limitations. In oversampling, the addition of many replicas of positive examples may cause the model to memorize the pattern and makes the model prone to overfitting. On the other hand, we are removing many negative examples in the undersampling strategy, thus the computer is not learning from the entire data set. In this study, we use the concept of weighted techniques to tackle the issue of class imbalance.
We have provided the means and standard deviations in the table by repeating training and testing phases few times. We use a 24-hour forecasting window for the data sets namely loop24span12, loop24span24, and loop24span0. We use 12 and 24 hours time evolution of magnetic parameters respectively for the data sets loop24span12 and loop24span24 as training purposes (span 12 hours and 24 hours). While we use no time evolution of magnetic parameters for the data set loop24span0 as training purposes. The loop24span0 data set is almost similar like  Bobra & Couvidat (2015)  but with larger time ). We use 12 hours time evolution of magnetic parameters respectively for the data sets namely loop12span12 and loop48span12 as training purpose but we use different forecasting windows. The forecasting window is 12 hours for the dataset loop12span12; while the forecasting window for the dataset loop48span12 is 48 hours.
We tune the hyperparameters of the support vector machine (SVM) algorithm by using a grid search algorithm provided by the Scikit-Learn software package  (Pedregosa et al. 2012). This algorithm finds the best combination of hyperparameters after performing an exhaustive search over a predefined set of hyperparameters. Our SVM hyperparameters namely, regularization parameter (C) varies between 0. 001 and 10 and the RBF parameter γ varies between 0. 001 and 1. We use binary cross-entropy as a loss function and rmsprop as an optimizer for our multilayer perceptron algorithm. We also use Keras l2 regularizer as a kernel regularizer for our MLP algorithm. Table 2 lists the eleven performance metrics found af-ter running two different classifiers, namely support vector machine and multilayer perceptron, on the five different data sets.
We have also listed the results of our previous three classifiers for comparison. The hyperparameters for our other classifiers are the following: Random Forest (n estimators=10, max depth=None, criterion=gini, class weight=balanced), Naive Bayes (Priors=None), KNN (number of neighbors k=1, dis-tance= Eucledian). A comparison between the results obtained from different classifiers confirms the robustness of our model in distinguishing flaring and non-flaring active regions. We also note that our baseline classifier Logistic Regression (which is easily interpretable) have very good performance. The code for this study can be found in our github repository (http://gitub. com/soumitrahazra/Flaring_Region_Prediction).
This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the socalled differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into "sensitive" and "relatively-resistant" groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high.
For the analyses described here a subset of the data consisting of n = 209 gene expression profiles (68:141 placebo:aflibercept ratio), obtained after quality-control of samples for tumor content and quality of RNA-seq profiling (see below) was used. Associated clinical information was available for almost all of the trial subjects. Measured clinical variables for each subject included assigned treatment arm, progression-free survival (PFS) time and censoring status, corresponding values for overall survival (OS) time, and objective response (OR). Overall, significant increase in PFS for aflibercept relative to placebo was observed, with computed hazard ratio hR = 0. 618 [0. 48, 0. 79] 0. 95 and P-value pR = 2. 8 × 10 -4 (log-ranks test) obtained from analysis for all n = 332 patients. The n = 209 subset of these patients with highquality gene expression profiles exhibited a smaller aflibercept to placebo hazard ratio hR = 0.
To implement bootstrap resampling, the 5-fold cross-validation procedure was embedded in an outer computational loop, in which 1000 random resamplings with replacement of the n = 209 samples were generated, with a full cross-validation done on every resampled data set. For each resampled realization, the five folds required for cross-validation were also (randomly) re-generated from scratch. Patient classification into the two response groups was done for each resampling, with fixed decision threshold Δξ c = -0. 815, and the resulting bootstrapped hazard ratio values hR(S) * and hR(R) * and other quantities were recorded under each resampling. Following completion of the outer resampling loop, statistical analyses were conducted on the collected data to generate confidence intervals and distributional plots for the quantities of interest.
815. A signature for triple-negative breast cancer predicts the existence of sensitive and resistant subgroups of patients As an additional application of the general methodology presented above, we considered treatment of triple-negative breast cancer (TNBC) with the small molecule iniparib  [12, 13]. The gene expression data analyzed here was generated by microarray profiling (Affymetrix HuGene1. 0ST microarray) of FFPE samples from phase 2 and phase 3 two-arm studies conducted to test the efficacy of iniparib in combination with standard-of-care chemotherapy in patients with metastatic recurrence of TNBC  [12, 13]. In each of the trials, patients were randomly assigned to one of two treatment arms, one using standardof-care cytotoxic gemcitabine/carboplatin combination therapy alone (the "control" arm), and the other with the same cytotoxic treatment augmented by iniparib (the "iniparib" arm).
Methods Much of the computational work reported here was performed in R. The package 'survival' was used throughout for basic estimation functions such as coxph or Surv. These functions were embedded in custom-built programs written in R and integrated into the Gecko gene expression analysis platform  [40]. These programs and all underlying functions are available under project name 'predSS' from GitHub (https://github. com/joachimt1/predSS). A detailed description of methods used has been incorporated step by step in the Results section above, as it was felt that this would result in a more organic presentation of the methodology. participating countries. A complete list of all Institutional Review Boards and Ethics Committees for both the AFLAME and TNBC studies is provided in Additional file 10.
Acknowledgements The authors wish to thank  Mark Magid ,  Parminder Mankoo ,  Dipen Sangurdekar  and  Joon Lee  for their essential contributions to the NGS processing pipelines used in the early phases of data analysis. Steven Rowley  is thanked for scientific discussions and presentations on closely related topics. All members of  Sanofi Clinical Sciences & Operations  are thanked for their work in sponsorship and operational and scientific management of the AFLAME clinical trial.
Skin site and city as main drivers of microbiome variation in study population Participants living in the two cities were evaluated for their facial signs and exposure to PAHs in 12-cm hair samples (reflecting the extent of exposure during a 1year period)  [10, 11]. An increased severity was observed in almost all facial signs, including wrinkles and pigmentation disorders, in individuals living in Baoding  [10]. [11]. b Annual mean concentrations of PM 10 and PM 2. 5 in outdoor air for cities of selected countries for the years 2013 to 2016. Concentration measurements for Baoding and Dalian are highlighted. PM 10 and PM 2. 5 data acquired from the WHO (http://www. who. int/airpollution/data/cities/en/, accessed 5th November 2018) Furthermore, biomonitoring of PAHs showed quantitative differences in the exposure levels between the two populations  [11].
Genes belonging to pathways related to PAHs degradation such as gyrA (K00015) and fdhA (K00148)  [37]  were significantly associated with exposure levels of acenaphthylene, benz[k]fluoranthene, and fluorene, in addition to KOs of pathways important for the degradation of aromatic and xenobiotic compounds such as benzoate and toluene (Additional file 16: Table  S9 ). KOs related to phenylalanine metabolism (K02610 paaB, K02611 paaC, K02612 paaD, K00074 paaH, K02614 paaI, and K02618 paaZ), reported to be involved in the aerobic strategy of aromatic compounds degradation by bacteria  [38] , were also associated with increased exposure levels to multiple PAHs (Fig. 5d  and Additional file 16: Table  S9 ).
Specifically, whether changes in abundances of these functional genes by PAHs are due to shifts in the microbial community, and/or an adaptive response of the existing microbiota, remain to be elucidated. Following the pilot shotgun metagenomics assessment in this study, future works involving a larger sample size will undoubtedly provide a more in-depth account of the potential roles PAH exposure have on the compositional and functional potential of the cutaneous microbiota. In addition, while a preliminary assessment was attempted in determining the viral community of the dataset, and some reads from the sebaceous cheek and scalp sites were classified as Propionibacterium phages, our DNAbased approach did not allow assessment of the RNAviral populations. Subsequent works with laboratory and analytical methodologies specialized for virome analyses  [53]  will undoubtedly provide a more comprehensive account of the skin viral population, which is a crucial part of the cutaneous microbiota that links to host skin physiology  [4]  and acne  [54].