dataesr/scientific-paragraphs-categorization huggingface dataset:
ChIP-seq data For the sequence of bound factors, we used the enriched genomic regions of 50 chromatin factors measured with ChIP-seq in 14-16 hour D. melanogaster embryos  [54]. Specifically, we downloaded the following factors: BEAF, H3K23ac, H3K79Me1, HP1, POF, CP190, H3K27Ac, H3K79me2, HP1b, Pc, CTCF, H3K27me2, H3K79me3, HP1c, Psc, Chro, H3K27me3, H3K9acS10P, HP2, RNA Pol II, GAF, H3K36me1, H3K9me1, HP4, RPD3, H1, H3K36me2, H3K9me2, JHDM1, SU(HW)-HB, H2AV, H3K36me3, H3K9me3, LSD1, Su(var)3, H2B-ubiq, H3K4me1, H4, MBD-R2, ZW5, H3, H3K4me1, H4K16ac(M), MOF, dMi, H3K18Ac, H3K4me3, H4K20me1, NURF301, dRING. This data is publicly available at http://www. modencode. org/ as part of the modENCODE project. Using the same genomic binning that was used in constructing the contact map (size of 10 kbp), we built M (here M = 50) sequence profiles of length N (here N = 9663) by calculating what fraction of each site was enriched for a given chromatin factor.
Transcription data Gene transcription data was obtained from publicly available RNA tag sequences detected with Illumina GAII with the digital gene expression (DGE) module from duplicate RNA samples from Kc167 cells  [30] (GSE22069). We assigned a transcription score to each genomic bin by multiplying RNA counts by the fraction of the genomic bin that is occupied by the the gene in question. Next, we classified each bin into either active of inactive by tresholding transcription scores at one (inactive: transcription score < 1, active: transcription score > 1).
Both W n k and β i are trainable parameters. The cost function to be minimized during the fitting procedure was taken to be the mean squared error between experimental and predicted distance-normalized contact maps, along with L2 regularization of the filter weights. The technique of dropout regularization, that consists on setting the output of randomly selected neurons to zero with a given probability was used to control for over-fitting  [56]. Optimization was done using stochastic gradient descent. We use the Python package Keras (https://github. com/keras-team/ keras) to code our model, running on top of TensorFlow (https://www. tensorflow. org/). For the particular example used in the Results the following network was built and fit. The output from the convolutional filter is fed to four ReLu-activated layers of exponentially increasing size, where the last layer is the output layer with same size as the output chromatin map data (Layer 1: 460 neurons, Layer 2: 881 neurons, Layer 3: 1690 neurons, Output Layer: 3240 neurons).
Datasets for training, validating and testing For training and validating the models, we have used sequence and contact map data from the D. melanogaster chromosomes 2L, 2R, 3L and the first half of chromosome 3R (from 1 to 12. 95 Mbp). From these regions we obtained 13814 pairs of local sequences and structures using w = 80. We randomly subsampled 80% of this data as a training set (11052 pairs) and 20% was set aside as a validation set that was not used in the parameter fitting procedure (2768 pairs). For testing the predictions of the model, we used the remaining second half of chromosome 3R (from 14. 95 to 26. 91 Mbp) as a test set, which contained 2112 pairs of sequences and structures. (It should be noted that our datasets included left-right inverted versions of the data, as the directionality of the genome should not influence the relationship between chromatin contacts and sequence.
Gradient analysis of DNNs We calculated gradients of the network models using a method known as sensitivity analysis  [57, 58]. In particular, we followed the DeepTaylor tutorial in www. heatmapping. org to calculate for a given output neuron the gradient of the output function with respect to the input variables. First, we exported the values of every layer of our trained neural network to text files. Then, for each neuron in the last layer that we wanted to calculate the gradient of, we rebuilt the trained neural network and only included the neuron of interest in the last layer. These neural networks were built using a minimal neural network implementation that can be found in the script "modules. py" from www. heatmapping. org/tutorial. The backpropagation of the gradient of the neuron in the last layer with respect to the input data was done using the methods in "utils.
g. , DIAP1. A first piece of evidence emerged from the comparison of in vivo chromatin immuno-precipitation (ChIP-seq) datasets between Svb 14 and Yki  59. We found that Svb and Yki share >1. 300 common genomic binding sites (Supplementary Figure  7a  and Supplementary Table  1 ) and statistical tests established the significance of this overlap (Supplementary Figure  7b ). Interestingly, co-binding of Yki was rare for the direct target genes of Svb identified in the epidermis  [12] [13] [14]  , as illustrated by shavenoid or dusky-like that both lack Yki binding (Supplementary Figure  7c, d ). In contrast, Svb was often bound to known Yki target genes, such as bantam, piwi, fat, or nanos 60 (Supplementary Figure  7e, f ). Importantly, ChIP-seq also revealed that Svb binds in vivo to an enhancer of DIAP1 (Fig.
Tubules were then washed 3 times in WB and incubated 30 min at 65 °C with 99 µl of 100 mM Na-Citrate + 1 µl of 10% Triton X-100. Following quick washes with WB, tubules were incubated in 45 µl of labelling solution (in situ cell death detection TMR Red kit, Roche), for 30 min at 37 °C. 5 µl of the enzyme solution was added for 2 h at 37 °C. Tubules were finally washed in PBT and mounted in Vectashield. Microscopy, image, and statistical analysis. Images of whole Malpighian tubules were acquired on a LSM710 confocal scanning microscope (×20 objective), using automated multi-position scan. After stitching, tiled images of individual pairs of tubules were analyzed with IMARIS 8. 0 to quantify the number of GFP-positive cells. Data of at least three independent experiments (approx. 20 tubules) were combined.
Acknowledgements We are grateful to  FlyBase, Bloomington and Vienna stock centers, Developmental Studies Hybridoma Bank, Drosophila Genomics Resource Center , supported by  NIH  grant  2P40OD010949 , as well as  N. Tapon ,  J. Colombani ,  K. F. Harvey ,  D. J. Pan ,  J. Dow ,  H. Skaer ,  M. Crozatier ,  K. Irvine ,  A. Bergmann , and  M. Dominguez , for providing flies, antibodies, and molecular reagents. We thank  B. Ronsin  ( Toulouse RIO Imaging ) for help with microscopy,  A. Alsawadi ,  A. Dib , and  M. Soulard  for experimental support. We also thank all members of the  Payre lab  for critical reading of the manuscript. This work was supported by  ANR (Chrononet) ,  Fondation pour le Recherche Médicale  (Equipe FRM  DEQ20170336739 ),  Agence Universitaire de la Francophonie  ( PCSI AUF-BMO ) for D.
Data availability The ChIP-seq datasets analyzed during the current study are available in the EMBL-EBI repository: https://www. ebi. ac. uk/biosamples/samples/ SAMEA2439952, https://www. ebi. ac. uk/biosamples/samples/SAMN01041482, https://www. ebi. ac. uk/biosamples/samples/SAMN02231205, https://www. ebi. ac. uk/biosamples/samples/SAMN02231213, https://www. ebi. ac. uk/biosamples/ samples/SAMN02231206. The data that support the findings of this study are given in Supplementary Table  1. Detailed scripts and computer code used for ChIP-seq analysis are available from the corresponding author on request.
Conclusions: The higher initial inflammatory cytokine production was, the more innate immunity was altered, while evaluated by low mHLA-DR expression. Children with decreased mHLA-DR expression developed more secondary acquired infections. Upon confirmation in multicenter cohorts, these results pave the way for immunostimulation for the most immunosuppressed children in order to prevent nosocomial infections in PICU. Trial registration PedIRIS study NCT02848144.
Clinical data and definitions Clinical data were collected prospectively and obtained from the electronic medical record. At admission (day 1), severity of illness was evaluated using the pediatric index of mortality 2 (PIM2)  [20]. Organ failure was assessed by the measurement of PEdiatric Logistic Organ Dysfunction Score, version 2 (PELOD-2) at day 1, 3, and 7  [21]. Vasoactive treatments during hospitalization in PICU were determined by the cumulative vasopressor index (CVI)  [22]. We also collected incidence of secondary acquired infection, mortality (death occurring within 28 days after the onset of shock), number of mechanical ventilation-free days, and number of PICU-free days in the first 30 days. Secondary acquired infection was defined by Center for Disease Control criteria, included any new bacterial or fungal infection, distinct from initial infection, occurring more than 48 h after sepsis onset, during the first 30 days after sepsis onset  [23].
Statistical analysis Results are expressed as median and interquartile range  [IQR]. Comparisons between groups were analyzed using the Man-Whitney U test for continuous nonparametric variables; the independent paired t test for continuous parametric variables and the Chi-square test for categorical data. Correlation analyses were performed using the Pearson test for variables following a normal distribution, and Spearman for nonparametric variables. Kaplan-Meier analyses were performed using Youden's index to stratify groups of patients. A p value < 0. 05 was considered to represent significant statistical difference. Data were analyzed by using Prism6 software (GraphPad Inc. , La Jolla, CA, USA).
Subjects Between September 2014 and July 2016, 73 children were screened for septic shock. Among these, 47 were excluded (toxic shock syndrome, non-infectious shock, known immunosuppression, or chronic inflammatory disease). A total of 26 children were included and analyzed (Fig. 1 ). Demographic data are presented in Table  1. Nine children (35%) presented a complex chronic condition. The most frequent was prematurity (n = 4), but all children presented a corrected age greater than 1 month. Others presented psychomotor delay (n = 2), polymalformative disease (n = 2), and sickle cell disease (n = 1). Two children died: one precociously at day 2, due to refractory shock with multi-organ failure; and the second one at day 32, with withdrawal treatment for major brain damage after meningitis.
Taken together, these data reinforce the idea that most immunosuppressed septic children might benefit from immunostimulation as an adjunctive therapy  [43]. Clinical trials are in progress with GM-CSF (NCT02361528) or IL-7 (NCT02640807/NCT02797431) in adults, and standardized tools are currently used to stratify patients in those trials. This progressively paves the way for this kind of approach in pediatrics, especially for innate immunity that seems to be more affected. The study does, however, have some limitations. First, the relatively small number of included children precluded multivariate analyses to be performed. Second, some children were discharged from PICU before all samples were obtained, and therefore the time course of different immune parameters in less severe children was not determined.
The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor.
These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q2Vu15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms.
Satellite Products and Dataset Generation The passive microwave (PMW) precipitation products within the EUMETSAT H-SAF (  [21] ) are based on the development and refinement of retrieval techniques exploiting all available radiometers in the GPM constellation. In this context, operational PMW precipitation products for the different radiometers are being released within H-SAF. They are based on two approaches (  [22] ): the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD) algorithm (  [23, 24] ) for conically scanning radiometers and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (  [25, 26] ). Three PMW H-SAF products were considered in this study: H01 (CDRD approach applied to SSMIS), H02 (PNPR developed for AMSU/MHS), and H18 (PNPR adapted to ATMS).
The algorithms are based on the use of a cloud-radiation database made up of thousands of microphysical-meteorological profiles derived from cloud-resolving model simulations of different precipitation events including 60 simulations over the European/Mediterranean area (  [23] ) and 34 simulations over Africa and Southern Atlantic. The main features of each product can be accessed in detail in the respective references cited above. To perform the validation over Brazil, we had to acquire all the input data (brightness temperatures in TB) for the the CHUVA experiments timeframe, and then, to process the H-SAF products to extend the coverage of the retrievals in order to include the whole country (extended to 75 • N-60 • S and 80 • W-80 • E). The inputs for the H01 were the SSMI/S orbits of the DMSP F16, F17 and F18 satellites.
For the H02 algorithm, the input files were the AMSU-A, AMSU-B and MHS orbits from NOAA-18, NOAA-19, MetOp-A and MetOp-B. In addition, lastly, we used the ATMS orbits from the Suomi-NPP satellite as input for the H18 algorithm. Though we have analyzed the three products: H01, H02 and H18 for the Manaus campaign, we only analyzed the H01 and H02 products for the Vale do Paraíba campaign because the ATMS data was not available in 2011.
Validation of H-SAF Precipitation Products The second part, which is the main focus of this study, is to identify the performance of the H-SAF products in order to provide the algorithm developers with information on the limitations and issues of the retrievals over specific regions in Brazil. The case studies under analysis are the same 14 cases (for both campaigns) that were considered previously on the radar data quality analysis. As mentioned before, three satellite rainfall products were analyzed: H01 (CDRD approach applied to SSMIS), H02b (PNPR v1 developed for AMSU/MHS), and H18 (PNPR v2 adapted to ATMS). We present a statistical analysis based on continuous and dichotomous statistical scores computed within the CVC, along with an in-depth analysis of the selected cases, followed by a pixel by pixel analysis.
Statistical Evaluation Because of its best estimation performance, the R q2Vu15 algorithm was chosen for the statistical evaluation. In order to investigate the impact of different quality indexes in the radar retrievals, we performed a sensitivity analysis (not shown here). According to this analysis, we found that a quality index value equal or greater than 0. 7 could be considered a good compromise between the desired performance of the statistical scores and the size of the dataset (that guarantees the reliability of the results), i. e. , the number of pixels in the sample. The statistical scores used in the validation procedure are presented in Tables  2  and 3. The continuous statistical scores were computed for the pixels in which both radar and satellite give rainfall estimates larger than 0.
On the other hand, in the area with the most intense convective cores, the H18 tends to produce a lower overestimation rate when compared to H02. This aspect is related to the difference in the two retrieval algorithms (PNPR for AMSU/MHS and PNPR for ATMS) (as pointed out by  [26] , where H18 is based on just one neural network for all surface types, trained with a unique database with the additional channels in the water vapor absorption band at TB 183±3 (  [26] ). It is noticeable that the three algorithms have a tendency to overestimate the larger rain rates. The study in reference  [27]  evaluated the ability of two GPM rainfall algorithms (GPROF2014 and IMERG) in reproducing the main characteristics and the diurnal cycle of precipitation as observed by the S-band SIPAM radar, in the Manaus region during the CHUVA campaign.
Discussion We will discuss the main features of the H01 algorithm that mostly influence the results in Brazilian areas: the precipitation screening process, the surface classification and the representativeness of the cloud model simulations in the a priori database. The first aspect, the precipitation screening process, selects the potentially precipitating pixels before the retrieval process. The screening used in the algorithms is based on the method described by  [28] , which uses the comparison of the TBs, which are the water vapor absorption band at TB 183±3 and TB 183±7 GHz and the TB 53 GHz channel in the oxygen absorption band around 50 GHz. A detailed description of the implementation of the H-SAF products screening process can be accessed in  [21, 25]. Figure  20  present the radiative signatures from the TB 183±3 , TB 183±7 and TB 53 GHz channels for Manaus for the case study on 21 February 2014.
Differently from Manaus, we can see that the retrieval does not generate precipitation (gray pixels in Figure  21e ) in the entire area provided by the screening as potentially precipitating (indicated by red in Figure  21d ). In the Amazon region we have noticed that the surface characteristics were not properly classified. This region is dominated by large inland water bodies (Amazonas and Rio Negro rivers) and surrounded by vegetated land. Figure  22a  shows that, in the radar area, the algorithm classifies the surface as coastal pixels, which is the least populated database, therefore, the least representative and usually affected by larger uncertainties. In the Vale do Paraíba region (Figure  22b ) the algorithm considers large part of the radar area as vegetated land and the Bayesian approach look for land profiles, which leads to the choice of more appropriate rain rate profiles.
In the Bayesian approach, the surface classification impacts the choice of channels used in retrieval processing, which influences the selection of hydrometeor profiles in the a priori database and, ultimately, the rainfall rate retrieval. Finally, the representativeness of the cloud model simulations in the a priori database, which is currently optimized for Europe and Africa, does not properly represent the typical microphysical and rainfall profiles of brazilian regions. Improving the database including simulations that represent the brazilian rainfall regimes could solve the issue.
Regarding the results from the satellite algorithms validation, for the Manaus region, the CDRD algorithm (H01) tends to overestimate all rain rates classes (light to heavy). The PNPR algorithm for AMSU-A/AMSU-B/MHS sensors (H02) presents better POD than H01, but it also presents high FAR values. The PNPR for the ATMS sensor (H18) presents lower overestimation of heavy rain rates when compared to H02, probably due to the different neural network used on H18. It is worth considering that the sample size for this verification study (14 cases) was quite small, and a more extensive validation with a larger ground-based dataset would be suggested to perform a more comprehensive quality assessment. All analyzed rainfall retrieval algorithms for the Amazon region showed high FAR values and larger precipitation patterns which are deeply related to the precipitation screening scheme.
The screening seems to be substantially affected by the high water vapor content in this region. The H01 (CDRD for SSMIS-Bayesian approach), in specific, it was affected by highly variable surface emissivities, impacting the selection of hydrometeor profiles in the a priori database and, ultimately, the rainfall rate retrieval. Moreover, we must take into account the uncertainties on precipitation estimates, which in the Bayesian approach are represented by the coefficients of error covariance matrices (for TBs, and ancillary and meteorological parameters) as analyzed in  [24]  ) and in  [29]. For the Vale do Paraíba region, both algorithms, H01 and H02, produced ME values that were quite close to zero (or negative) and lower FAR values (from 0. 21 to 0. 49) than Manaus. Unlike the Manaus case, the precipitation patterns were well detected and the estimations were in good agreement with the reference as indicated by the low ME values.
As for future plans, we intend to perform new verifications in a denser and more comprehensive sample for other Brazilian regions. Since we identified limitations in the precipitation screening scheme over the Amazon region, we plan on developing a new scheme that is able to handle the water vapour content in this region. That would require, furthermore, work on the development of a new surface classification capable of characterizing the surface diversity properly. The next steps must be made using the pluviometric precipitation dataset provided by the National Institute for Space Research (CPTEC/INPE) and also the dataset from new radars acquired by the National Center for Natural Disaster Monitoring and Alerts (CEMADEN). Lastly, for future work we intend to extend the cloud-radiation database used as a priori information in the algorithms in order to obtain radiative and microphysical profiles representative of brazilian rainfall regimes.
Genome-wide replication timing of repetitive DNA elements correlates with GC content, gene density and chromatin state First, we asked whether the replication timing of repetitive DNA elements may depend on the genomic distribution and, thus, the chromatin state of such elements. To characterize such relation, we retrieved publicly available genomic data of 12 different human repetitive elements, covering all different types: direct, inverted, mirror, tandem (microsatellite/SSRs), low complexity (AT and GC) and interspersed elements. The latter were dissected further into SINEs (Alu and MIR), LINEs (L1 and L2) and LTR/DNA transposons (MER) without further subdivision into subfamilies (Table  1 ;  [29, 30] ). These classifications utilize the reference genome assembly and are not ploidy-adjusted.