dataesr/scientific-paragraphs-categorization huggingface dataset:
ChIP-seq data analysis Raw reads from ChIP-seq experiments and the corresponding control samples (INPUT) were preprocessed by removing potential adaptor sequences using cutadapt  (Martin, 2011)  and trimming low-quality ends using Trimmomatic  (Bolger et al. , 2014). Processed reads were mapped to the Arabidopsis thaliana genome version TAIR10 using Bowtie 2  (Langmead and Salzberg, 2012)  with default settings that result in only the best alignment per read being reported (see Data S3). Only those alignments were kept that had a mapping quality of 30 or larger, which is indicative of a high probability that the read has been mapped to the correct region of the genome. To compare the ChIP-seq replicate samples, we determined the read coverage across the genome using the bamCoverage program of the deepTools suite  (Ramı ´rez et al.
Peak annotation The Bioconductor R package chipPeakAnno  (Zhu et al. , 2010)  was used to relate peaks to Arabidopsis thaliana genes version TAIR10. Peaks were related to genes when they were located within 3 kb upstream and 1 kb downstream of the gene. To determine the most favored peak-center position relative to gene bodies, peaks within 3 kb upstream and 1 kb downstream of the closest gene were classified based on whether the center resided in the (1) promotor, (2) downstream region and (3) open reading frame. For promoter regions, the distance was determined between the peak center to the transcription start site. For downstream regions, the distance was taken between the transcription termination site and the peak center. For those peaks with centers residing within the open reading frame, the distance was calculated between the transcription start site and the peak center and was then normalized by gene length.
Motif discovery The sequences corresponding to identified peaks were extracted and analyzed for enriched motifs using MEME-ChIP  (Ma et al. , 2014)  with the following adapted parameters: minimum motif width (-meme-minw 4);maximum motif width (-meme-minw) 12; motif database (-db ArabidopsisDAPv1. meme). The occurrences of predicted motifs were extracted from the corresponding FIMO output produced by the MEME-ChIP suite. Several motif positions reported by FIMO overlapped but were predicted on the opposite strand. In these specific cases the overlapping motif locations were merged. The positional distribution relative to the peak centers were derived by determining the distance between the peak center and the predicted motif centers.
RNA-seq data analysis Raw RNA-seq single-end reads were pre-processed in the same way as the ChIP-seq and whole-genome resequencing reads. Processed RNA-seq reads were mapped to the genome of A. thaliana with TopHat2  (Kim et al. , 2013)  using the TAIR10 annotation as a guide. The following additional parameter settings were used for the mapping: maximum edit distance 5; maximum mismatches 5; minimum intron length 50; maximum intron length 10000; read gap length 2. Read counts per gene were summarized into an initial count table using HTseq-count  (Anders et al. , 2015). All genes in the count table with a rowSums of less than 10 were removed. The count table was used for further analysis with the DESeq2  (Love et al. , 2014 ) R package using the simple design formula ~mutant where mutant indicated whether the samples were from WT, fd or fdp mutants.
As a quality control, samples were clustered based on inter-sample distances defined as 0. 5-0. 5 * cor(A,B), where cor(A,B) corresponds to the pairwise correlation between the regularized log expression of the 200 most frequently expressed genes in samples A and B. Clustering of the distances was performed using the hclust function in R. Differential expression analysis was performed using the DESeq function of DESeq2 and the contrasts between mutants versus the WT were extracted using the results function. GO ontology analysis and data visualization GO analysis was performed with the BINGO tool using the CYTOSCAPE platform for visualization. The cluster named ''flowering and flower development'' was generated using the list obtained from: https://www. mpipz. mpg. de/14637/Arabidopsis_flowering_genes, which contains related flowering genes, and the list generated using the website: https://bar.
utoronto. ca/thalemine/template. do? name=Keyword_GO_genes&scope=all, which allows associated GO terms and genes to be queried by entering keywords: In this case,:'' flower development'' was used. Both lists were combined and the subset of genes for FD and FDP were extracted when compared with the whole FD and FDP targets. To define the rest of GO defined in the Supplemental datasets, a similar procedure was followed using the ARAPORT tool and entering different keywords with the precise GO terms names obtained in the CYTO-SCAPE tool.
, 2009)  in the automated1 mode. The cleaned protein alignment was transformed into a CDS alignment by replacing amino acids by their corresponding codon triplet and each gap by e5 Cell Reports 31, 107717, June 2, 2020 Article three gaps. MODELTEST  (Posada and Crandall, 1998)  was used to select the best nucleotide substitution model that could be used for maximum likelihood tree estimation with the program PhyML  (Guindon et al. , 2009). Branch support for the maximum likelihood tree was derived from 100 bootstrap rounds.
QUANTIFICATION AND STATISTICAL ANALYSIS All the standard statistical analyses were conducted using SigmaPlot software. When several data points were compared, an ANOVA and Kruskal-Wallis One Way Analysis of Variance on Ranks was used. For simpler two-way comparisons, t test. Most of the graphs were represented using the boxplots of SigmaPlot. The box depicts the distance between the 1st and 3rd quartiles, the Inter Quartile Range (IQR = Q3-Q1). The whiskers are 1. 5*IQR which corresponds to the highest / lowest point within the range. The dotted lines represent the outliers, which are the points greater than 1. 5*IQR. Flowering time was quantified by counting total leaf number (including both cauline and rosette leaves) of at least 10 plants per genotype. Experiments for flowering time and qRT-PCR were performed at least twice independently.
In general, data are represented by mean ± SD, except in the greening cotyledon assay, where the mean ± SE is represented. The statistical analysis of hexamer overrepresentation was done as follows. There are in total 4096 (4 6 ) possible hexamers which can be reduced to 2080 accounting for palindromic reverse complement hexamers. For each of the 2080 remaining hexamers we determined the number of experimental peaks in which it occurs at least once. Next it was determined how many standard deviations (Z-score) this number was away from the mean number of peaks having at least one motif instance in 1000 random sets. Each of these random peak sets was generated as follows: Each experimental peak was re-assigned to a random gene and placed at the same distance from the transcription start site as the original associated gene.
(A) Venn diagram illustrating common peaks (top panel) and genes (bottom panel) in the FD and FDP ChIP-seq datasets. (B) FD and FDP binding profiles to flowering genes. The three panels display FD and FDP and the control (INPUT) peaks at SOC1, LFY, and AP1 visualized with the Integrated Genome Browser (IGB). (C) qRT-PCR analysis of SOC1, LFY, and AP1 mRNA abundance in apices of fd-3 and fdp-CRP2 mutants in 11-and 15-day-old plants under LDs. (D) FDP-binding profiles to flowering genes when expressed from the 35S promoter. (E) Proportion of DEGs (adj. p % 0. 05) that are up-or downregulated in the RNA-seq of 17 LD apices of fd-3 and fdp-CRP2. The number of DEGs directly bound by FD or FDP is illustrated in digits. (F) GO terms enriched within the 109 genes differentially expressed and bound by FD using apices of 17-day-old plants.
ACKNOWLEDGMENTS We thank  John Chandler ,  Diarmuid O'Maoileidigh ,  Lucio Conti ,  Silvio Collani , and  Alice Pajoro  for their useful comments on the paper and on the interpretation of experiments. We thank  BREECON GmbH (Potsdam)  for carrying out the TILLING analysis. We thank  Bruno Huettel  from  Max Planck-Genome Centre Cologne  for all the technical support with the ChIP-seq and  Samson Simon  for helping with the phylogenetic analysis of FD and FDP. P. M. was supported by the  EU Marie Curie Initial Training Network SYSFLO  (agreement number  237909 ). G. C. was a recipient of the  ERC Advanced Grant HyLife  and his laboratory is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany's Excellence Strategy  ( EXC 2048/1  Project ID:  390686111 ) and a core grant from the  Max Planck Society.
Materials Availability All materials generated in this study are available from the Lead Contact. Continued REAGENT or RESOURCE SOURCE IDENTIFIER HTseq-count Anders et al. , 2015 https://htseq. readthedocs. io/en/release_0. 11. 1/count. html DESeq2 Love et al. , 2014 https://bioconductor. org/packages/ release/bioc/html/DESeq2. html Cytoscape Shannon et al. , 2003 https://cytoscape. org BINGO Maere et al. , 2005 Available through the cytoscape program Exonerate Slater and Birney, 2005 https://www. ebi. ac. uk/about/ vertebrate-genomics/software/exonerate muscle Edgar, 2004 https://www. drive5. com/muscle/ downloads. htm trimAl Capella-Gutierrez et al. , 2009 http://trimal. cgenomics. org/downloads MODELTEST Posada and Crandall, 1998 http://evomics. org/resources/software/ molecular-evolution-software/modeltest/ PhyML Guindon et al.
Last, measurement reliability of MRC sum-score has mainly been assessed in cooperative patients and could less accurate in patients still asleep or with delirium  [40]. Strengths of the study include a prospective collection of data with a weaning protocol to assess readiness for extubation and systematic assessment of limb weakness by a physiotherapist before extubation. Moreover, the focus of the study on a homogeneous population of patients at high risk of reintubation may help to better generalize these results to other ICUs.
The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user's and producer's accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.
In thermal-based fire detection, satellites have relied on images received from the fire-sensitive Mid-Infrared (MIR) spectral window (i. e. , 3-5 µm spectral range) to distinguish fire and non-fire pixels. This spectral region is highly sensitive to temperatures of vegetation fires-covering even a subpixel scale-and is less affected by solar reflections than the Shortwave Infrared (SWIR) spectral window. A pixel in a satellite image is directly or implicitly flagged as a fire pixel when the pixel's observed temperature is significantly higher than its expected background temperature-the latter defined as the expected temperature of a pixel under non-fire condition. The first satellite-based remote sensing techniques for hotspot detection were developed using data from the LEO systems. Given the LEO system's high spatial resolution advantage, the expected background temperature was extracted from spatial-contextual information (for example, see  [2] [3] [4] [5] [6] [7] ).
The background temperature can also be derived from information extracted from both multitemporal and multispectral information. For example, the method proposed by  [41]  predicts MIR background temperature from the earth sensitive LWIR temperature using a linear relation derived from the principal components of a temporal sequence of non-fire MIR and LWIR brightness temperatures. The current multitemporal fire detection methods based on recursive Bayesian estimation, first derive the expected DTC of a day (i. e. , the model of a day) online before the start of the day and data are assimilated at observation time into the expected DTC. The derivation of the expected DTC of a day allows only for the inclusion of recent observed DTCs with a short period of invalid data (cloud or fire-contaminated data or missing data) into the training set but observed DTCs with a long period of invalid data are not included.
Two types of data are assimilated into the model of the DTC parameters at each data assimilation cycle, namely, the observed TOA brightness temperatures derived from the MIR window data acquired from the METEOSAT Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and the observed offsets of the thermal sunrise (i. e. , the start time of a DTC) of a day to the sunrise of the same day. The latter is assimilated to transition from one DTC to the subsequent DTC. To produce a smooth continuous data assimilation result in the temporal dimension, three data assimilation methods-each offering different assimilation advantages-were separately used. A variational data assimilation method, which emulates the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) and is implemented only in the time dimension (one-dimensional analysis), performs a point estimation of the posterior.
Fire Detection Algorithm Observed data are assimilated into the background DTC model. In this study, the data assimilation is implemented at each time a new observation is flagged as a non-fire observation (i. e. , fire-contaminated samples are not assimilated). The data assimilation aims to derive better initial conditions of forecasts. The data assimilation and the forecast model of the background temperatures are presented in Section S1 of the Supplementary Materials to this article. The expected background temperature of a pixel is derived from the forecasts initiated at the time of the assimilation of data. The forecast is achieved using the ensemble forecasting scheme, described in Section S1 of the Supplementary Materials of this article. The shortest-range ensemble forecasts of brightness temperature verifying at the inspection time k in a pixel location represent the distribution of the background (non-fire ) temperature of the pixel location at time step k.
The mean value of this ensemble is the expected value of the background (non-fire ) temperature of the pixel location at a time step k. The initial condition of the forecast is derived using the weak constraint 4D-Var with a data assimilation window of length N w = 6, the EnKF with number of ensemble N e = 51, and the SIR particle filter with number of particles N p = 51. The input to the fire detector comprises the observations (brightness temperatures in IR 3. 9 MSG spectral channel) at the inspection time, not yet assimilated. It also comprises the expected distribution of the background temperature (non-fire temperature) of each pixel location at the inspection time. A pixel is classified as belonging to either the fire class or the non-fire class using an anomaly detection (one-class classification) mechanism.
e. , CFAR). The CFAR detector sets an adaptive threshold on the feature data r k based on the allowed level of false alarm probability (i. e. , FAR). We assume that the non-fire prediction residual r k in one-pixel location over a lapse of one day long is independent and identically distributed (i. i. d) (a range of one day is taken to satisfy a compromise between the time before the land surface change and sufficient samples to characterize a time step k). Then, the CFAR window, R * ,k , under H 0 , contains 96 consecutive fire-free samples (in a temporal CFAR mechanism considering every single pixel independently) generated up to time step k -1. Since in a given moving window of 96 samples, some samples can be fire contaminated, samples are selected from time step k -1 down to the launch time of the detector, skipping fire-contaminated samples.
Experimental Data and Detection Algorithm Setup For our experiments, we used the MSG-SEVIRI IR 3. 9 images covering a region of Southern Africa spanning from (20 S, 23 E) to (33 S, 38 E) (precisely, from pixel (778, 822) to pixel (1153, 1094); i. e. , a 376 × 273 MSG Level 1. 5 image data) recorded late July 2007 to early August 2007 (i. e. , during the winter fire episode of 2007). Each image of the region counts 22, 934 water pixels that fall in water bodies, identified through MODerate Resolution Imaging Spectroradiometer (MODIS) land/sea mask product (MOD03) and manual visual inspection. Estimation of background temperatures and detection algorithms run on all pixels of the study area, and water-body pixels are masked out after detection to report fires exclusively on land pixels. A fire that is detected in a single pixel at a given inspection time k is counted as one fire event, then instants of fire events are scrutinized.
Fire detection results are reported per FAR and per analysis method. Moreover, no cloud screening is implemented (but noting that a very cloudy DTC or very fired DTC or water pixel DTC is more likely removed from the training set based on conditions set on DTC parameters highlighted in Table  S2  in the Supplementary Materials), and no sunglint-contaminated sample screening is implemented. MATLAB is used to implement and test the proposed fire detection method (The source codes are publicly available online at https://github. com/satfiretech/fire-dataAssimilation).
Performance Evaluation Metrics Results of fire detection are assessed and compared using two performance accuracies, user's accuracy (a measure of the commission error) and producer's accuracy (a measure of the omission error), given by user's accuracy(U) = #correctly reported fire pixels #reported fire pixels (5) producer's accuracy(P) = #correctly reported fire pixels #fire pixels (6) where the #S stands for the cardinality of a set S, '#fire pixels' is the number of fire pixels reported by the reference data product, namely MODIS MOD14/MYD14 and EUMETSAT FIR , 'reported fire pixels' is the number of pixels reported by the designed (proposed) fire detection algorithm (per analysis method), and 'correctly reported fire pixels' is the number of fire pixels according to the reference data products correctly reported (classified) by the designed algorithms.
The pixel reported as fire pixel by the validation fire products but is reported by the proposed fire detection method as a non-fire pixel or or has NaN (expected) background brightness temperature or has NaN observation (i. e. , instant of completely missing data [missed pixels]), is counted as a missed detection. If an MSG-SEVIRI pixel containing more than one MODIS fire pixel is not reported as a fire pixel by a proposed fire detection algorithm, then all MODIS MOD14/MYD14 fire pixels belonging to the MSG pixel are missed (i. e. , counted only under 'fire pixels'), while if reported as a fire pixel, all MODIS fire pixels belonging to the MSG pixel are detected (i. e. , counted under 'correctly reported fire pixels' and 'fire pixels'). The process of deriving the best admissible CFAR at which a fire detection system can operate can be conducted during a phase that can be referred to as the 'detection calibration phase' using an available validation set [reference data] (in this study, the validation set is made solely of MODIS MOD14/MYD14 and EUMETSAT FIR reports).
Fire Detection Performance User's accuracy (U) and producer's accuracy (P) computed per estimated FAR are shown in Figure  1a  and Figure  1b , respectively. The two accuracies are derived from the fire results of the proposed fire detection algorithms in the study area for the period of 31 July 2007 to 4 August 2007. The validation reference data are made of a combination of EUMETSAT FIR , and MODIS MOD14/MYD14 reports. MODIS detection time is rounded off to the nearest MSG-SEVIRI detection time (detection time is 12 min ahead of Level 1. 5 observation time). 4D-Var-CD (N w =6) performs better than other detection methods in terms of U (gives less commission errors) except at the low estimated FARs. However, the low estimated FAR region was not considered during the design of the CFAR detector (the estimation of FAR starts from 10 -6 to 0.
SIR A single operational FAR, when the analysis is implemented with any of the three methods considered in this study, is derived using the error (cost) function J(•) given by Equation  (7). This error function considers both commission and omission errors and is a function of the estimated FAR. It is assumed in this study that c(non-fire |fire )p(fire ) = c(fire |non-fire )p(non-fire ) = 0. 5 and the error function simplifies then to J = 1 2 [(1 -P) + FAR m ]. Figure  2c  depicts a resulting J(•) for a single test set made of data spanning the period 31 July 2007-4 August 2007 with a reference dataset consisting of a combination of MODIS MOD14/MYD14 and EUMETSAT FIR products, and MODIS detection time is rounded to the nearest SEVIRI detection time. The optimal design FAR point is found among discrete points ranging from 10 -8 to 10 -1 (with a base-10 logarithmic step of 0.
EnKF-CD (N e =51) and SIR-CD (N p =51) at 10 -3. 5 FAR achieve a true positive rate of 78. 64% and 79. 34%, respectively, of fire events reported in MODIS MOD14/MYD14 product. A moderate portion of MODIS MOD14/MYD14 reports is detected by EnKF-CD (N e =51) and SIR-CD (N p =51) methods, and the accuracy is restricted given that small and cool fires might not be detected due to the low spatial resolution of the SEVIRI compared to the MODIS resolution. Table  4  summarizes results for 4D-Var-CD (N w =6) (under '4D-Var-CD' columns), EnKF-CD (N e =51) (under 'EnKF-CD' columns) and SIR-CD (N p =51) (under 'SIR-CD' columns) at 10 -3. 5 design FAR with reference data being both EUMETSAT FIR and MODIS MOD14/MYD14 products ('FIR' column presents the number of EUMETSAT FIR reports and 'FIR -(MOD)' column portrays the number of MODIS MOD14/MYD14 reports not reported by EUMETSAT FIR ).
For each of 4D-Var-CD (N w =6) , EnKF-CD (N e =51) , and SIR-CD (N p =51) detection methods, Table  4  lists the number of true positives, according to EUMETSAT FIR product (in 'FIR + ' column). Among the remaining false positives of the proposed detection method, according to EUMETSAT FIR product, true positives according to MODIS MOD14/MYD14 are extracted (listed in 'FIR -, MOD + ' column), and false positives according to both EUMETSAT FIR and MODIS MOD14/MYD14 are presented in 'FIR -, MOD -' column. A significant portion of false alarms, according to EUMETSAT FIR , are reduced when MODIS MOD14/MYD14 product is introduced in the validation reference dataset. Comparing true positive results presented in Table  3  for MODIS MOD14/MYD14 and true positive results presented in Table  4 , the proposed fire detection algorithms detect MODIS MOD14/MYD14 fire reports at a lesser extent than they detect EUMETSAT FIR fire reports.
10%, and detects the combination of EUMETSAT FIR fire events and MODIS MOD14/MYD14 fire events not reported by EUMETSAT FIR at 86. 09%. SIR-CD (N p =51) detects EUMETSAT FIR reports at 96. 48%, and detects the combination of EUMETSAT FIR fire events and MODIS MOD14/MYD14 fire events not reported by EUMETSAT FIR at 87. 67%. All proposed detection methods report a significant number of false alarms according to MODIS MOD14/MYD14 and EUMETSAT FIR , and detect EUMETSAT FIR reports relatively well. Figure  4a ,b present number of fire events at each time of a day (where times are expressed in terms of local solar time) for MODIS MOD14/MYD14 product and EUMETSAT FIR product, respectively. The values of producer's accuracy (or true positive rate) of the proposed detection algorithms at each time of a day are presented in Figure  4c ,d for cases the validation reference dataset is made of MODIS MOD14/MYD14 product only and EUMETSAT FIR product only, respectively.