dataesr/scientific-paragraphs-categorization huggingface dataset:
Data quality assessment The obtained data was evaluated based on intensity levels and three antibodies were excluded from further analysis as the median MFI were below negative control antibodies (bare and rabbit IgG beads). Because one stock solution of mixed beads was created and aliquoted into each assay plate, other experimental errors were linked to the procedure for individual samples. Thus, eight samples were flagged that seemingly failed. Such samples were those 1) that had median values of MFIs ± 2 SD or below the median of control measurement without any sample (buffer only), and 2) that were identified as outliers using Robust PCA using 'rrcov' R package (version 1. 4-3). 51 The cutoff probability values in an outlier diagnostic plot were set to 0. 001 for both score and orthogonal distances.
Plasma Proteomics -targeted assays Samples from DIRECT study centers were manually randomization by a mix-shake-distribute procedure and placed into 96-well plates. All samples were analyzed at SciLifeLab in Stockholm using several different immunoassay platforms. A. The input data set var pruned by recursively removing variables that met a cut-off based on Pearson correlation coefficients between 0. 8 -0. 2 in steps of 0. 1. Archetype clustering analysis was then run on each of the data set cuts according to the protocol for the original archetypes. For each cut we evaluated the cosine similarity with the original archetype scores and searched for a cut-off where all four archetypes were reconstructed at a cosine similarity > 0. 8.
ACKNOWLEDGMENTS The work leading to this publication has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  115317  (DIRECT), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in-kind contribution. The  Novo Nordisk Foundation  is acknowledged (grants  NNF17OC0027594  and  NNF14CC0001 ). E. P. holds a  Wellcome Trust New Investigator Award  ( 102820/Z/13/Z ). M. I. C. holds grants from  NIDDK  ( U01-DK105535 ) and the  Wellcome Trust  ( 090532 ,  098381 ,  106130 ,  203141 , and  212259 ). M. I. C. was a Wellcome investigator. K. B. and  S. Brunak  received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement  115881  (RHAPSODY).
was applied to a 1-day case study collected during wintertime in the Arve River valley near Chamonix-Mont-Blanc during the Passy-2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground-based remote sensors  [GBReS] ) combination is compared with high-frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.
| Material Observational data come from the Passy-2015 experiment that took place in the Northern Alps and was focusing on boundary layer meteorology and air quality in a section of the Arve river valley near Chamonix-Mont-Blanc at wintertime  (Paci et al. , 2017; Sabatier et al. , 2018). The case study chosen here to illustrate the main code functionality was February 19, 2015, which is part of the Passy-2015 second Intensive Observation Period (IOP). It was chosen for the high concentration of remote sensors, high-frequency RS, and kilometric scale numerical modeling, which is particularly suited to testing innovative methods (see, e. g. ,  Martinet et al. 2017; Chemel et al. 2016 ). The second IOP was characterized by a short anticyclonic period between two synoptic disturbances with little snow cover.
Meteorological conditions within the valley were marked by pronounced nighttime boundary layer inversions disappearing during daytime due to the sun-driven heating of the ground. Three sources of data are used in this study: AROME model forecasts  (Brousseau et al. , 2016) , high-frequency RS, temperature profiles retrieved from microwave radiometer, and ceilometer backscatter profiles (GBReS). They provide several parameters onto a time-altitude grid, as shown in Figure  1. All data are at the same site. For AROME, 1-h forecasts of absolute temperature and specific humidity profiles were used. The 35 vertical levels of AROME within the ABL start approximately at 3. 28 m above ground level, and they are linearly stretched to decrease the resolution with altitude (Δz = 11. 3 m near the ground and Δz = 115.
6 m near 2000 m altitude). The horizontal grid resolution is 1. 3 km. For RS, values of absolute temperature and relative humidity were used. Twelve launches were done this day; therefore, the average time between two launches is about Δt = 2 h. However, they were irregularly spaced; more launches were done in the afternoon. The spacing between vertical levels of RS is of Δz = 9. 47 m on average. For GBReS, we make a combination between a ceilometer (Vaisala CT25K) and a microwave radiometer (RPG HATPRO G3;  Rose et al. , 2005). Values of rangecorrected backscatter intensity were taken from the ceilometer and retrieved values 2 of absolute temperature were taken from the radiometer. The resolutions of ceilometer data are Δt = 15 s and Δz = 15 m. The resolutions of the microwave radiometer data are Δt = 12 min and a stretched vertical grid (Δz = 50 m close to the surface, Δz = 200 m at z = 2000 m).
All algorithms were operated thanks to the Scikit-learn open-source Python package  (Pedregosa et al. , 2011) , and the code is fully available following this link: https://github. com/ThomasRieutord/blclassification. y new ¼ b C x ð Þ, x X new. 3. Unsupervised classifiers are based on distance between individuals and they automatically gather the closest individuals within the same class. More specifically, we used clustering algorithms  (Friedman et al. , 2001; Jain et al. , 1999) , with setting details given when the results are presented. Note that the unsupervised classification is only a way to lighten the human supervision. The physical consistency of the classes is ensured by human supervision, not by the classification settings. The identification of ABL types is made by examining the characteristics of the formed groups, according to  Stull (1988).
| Comparison between AROME, RS, and remote sensors The classification is now applied to data from all sources in order to compare whether the same objects are visible from model data, RS data, and ground-based remote (a) (b) (c) (d) (e) (f) F I G U R E 3 Result of classification for GBReS data for 2-7 clusters-hierarchical clustering with "cityblock" distance, average linkage, and linear interpolation on the grid (Δt = 30 min, Δz = 40 m) sensing data. For the RS and AROME sources, the backscatter information was replaced by another concentration variable: the relative humidity. Figure  4  shows the cluster labels in a time-altitude grid for the three sources of data. This classification was made with hierarchical clustering with Euclidean distance and Ward linkage and cut at three clusters.
One can see on all classification outputs two clusters in contact with the ground, one in the morning and one in the afternoon, and a third cluster above the two previous. We identify the morning ground-connected cluster as a potential SBL, the afternoon ground-connected cluster as a potential mixing layer (ML), and the aloft cluster as the potential FA. The locations of clusters vary depending on the data source. However, they are very consistent between GBReS observations and RS data, demonstrating the ability of the classification algorithm to run on various types of data. In addition, the benefits of the increased temporal and vertical resolution provided by the remote sensing instruments in GBReS are very clear when compared to the more scarce RS data. Another inference from Figure  4  is that the data preparation, although different between GBReS (require interpolation) and RS (do not require interpolation), does not prevent the classification to give similar results.
This suggests that the necessary data interpolation applied to GBReS data is not disruptive. We can note that the classification from AROME data shows larger discrepancies compared with the two other classifications. With AROME forecasts, the potential SBL is not very realistic as it goes too high within the atmosphere-over 1250 m at 01:00. The potential ML develops sooner (around 08:00) for AROME than for other data sources (around 12:00), and its maximum vertical extension is limited to 1000 m. In Figure  4b, c , the green area near the ground between 18:00 and 00:00 could be a shallow, stable layer not seen by the AROME model. However, many differences are already visible in Figure  1. Therefore, these differences are rather more likely due to model limitations (e. g. , subgrid physics and topography representation) than to erroneous classification by the algorithm used on model data.
| Conclusions The current study presents a new method to perform pixel-based boundary layer classification using machine learning techniques. It assumes relevant atmospheric variables are known onto a time-altitude grid. Pixels of the time-altitude grid are cast into classes that have similar atmospheric values thanks to hierarchical clustering. The method was applied to AROME model forecasts data, high-frequency RS data and a combination of GBReS data for a wintertime valley boundary layer case observed during the Passy-2015 experiment's second IOP on February 19, 2015. The study yields the following results: • Hierarchical clustering is able to facilitate a boundary layer classification with instrument combination. The resulting classification combines the strengths of both instruments and provides better results that could be deduced from applying the method to both instruments individually.
| Prospects The purpose of the present study is to explain a new methodology and give an insight into its potential, but additional work is needed to investigate more deeply the benefits of this method for automatic boundary layer classification. First, the study should be extended to a broader dataset. The interesting objects detected by the hierarchical classification here (like the cloud layer in Figure  3c  or the transition layer in Figure  3f ) may not be detected another day. The ability of the classification to deal with various, including unprecedented, situations must be investigated in order to quantify the information brought by the classification and how reliable it is. The recent field experiment SOFOG3D  (Burnet et al. , 2020 ) could be used, as it operates co-located microwave radiometers and ceilometers for 6 months at 6 different sites as well as AROME simulations with fine vertical resolution and frequent RS during fog events.
ACKNOWLEDGEMENTS The authors would like to thank  Vinciane Unger ,  Diane Tzanos , and  Jean-Marie Donier  for the preparation of the data (MWR, RS, and ceilometer, respectively), the  CNRM teams GMEI/LISA  and  GMEI/4M  for the data collection, and  Météo-France  for funding this study. The Passy-2015 field experiment was supported by the  French Environment and Energy Management Agency (ADEME)  through the  French national program LEFE/INSU  and by  Météo-France. Data are managed by  SEDOO at the Observatoire Midi-Pyrénées  (https://passy. sedoo. fr/).
Here, we integrated different Arabidopsis lincRNAs gene annotations and explored various functional genomics datasets to characterize lincRNA expression in a biologically relevant context. We leveraged large-scale expression datasets and protein-DNA interaction data to study the molecular networks controlling lincRNA gene activity. Combined with evolutionary conservation analysis, we explored the global transcriptional regulatory properties of different evolutionary age categories and, through regulatory network analysis, identified specific TFs controlling lincRNA regulation in roots.
, 2005). This score reports the probability for each nucleotide to evolve neutrally or under negative, or purifying, selection (low or high phastCons score, respectively). In contrast to the age categories, which are based on finding similar homologous sequences in other plant genomes and do not give information about the mode of selection, phastCons works by fitting a two-state hidden Markov model to a genome-wide multiple sequence alignment and predicting, based on the pattern of nucleotide substitutions, conserved elements representing sites under purifying selection  (Siepel et al. , 2005). For lincRNA, pri-miRNA, and protein-coding gene loci, we compared phastCons scores for the gene body and the promoter region (2kb upstream of transcription start site) (Supplemental Data Set S2). In general, for gene bodies, lincRNAs are significantly less conserved than pri-miRNAs and protein-coding genes (Figure  2B , purple, yellow, and green series).
Before we used TF ChIP data to characterize the organization, complexity, and evolution of TF binding for proteincoding genes  (Heyndrickx et al. , 2014) , hence we here re-processed publicly available ChIP-Seq to identify TF binding events potentially controlling lincRNA gene expression. A total of 114 TF ChIP-Seq datasets, covering 45 TFs with at least two biological replicates, were reprocessed (Supplemental Table  S4 ). Starting from our genome annotation containing protein-coding genes, pri-miRNAs and the high-confidence lincRNAs, ChIP-Seq peaks were assigned to the closest genes. A gene was defined as a potential target gene for a profiled TF if it was the closest to at least one ChIP-Seq peak of that TF (see Material and Methods). Based on all 471,078 TF peak-target gene pairs identified from the 114 ChIP-Seq datasets, 329,688 (70%) of the peak-gene pairs were confirmed by two or more replicates and were kept to construct a robust TF-target gene regulatory network (Figure  4B ).
To confirm the root and cell-type specificity of the TF-lincRNA regulatory interactions, we integrated publicly available chromatin accessibility datasets of Arabidopsis roots based on Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq): three bulk datasets  (Maher et al. , 2018; Potter et al. , 2018; Tannenbaum et al. , 2018)  and one single-cell dataset  (Dorrity et al. , 2021). Assessing the tissue specificity of our TF-lincRNA regulatory network revealed that 93% of the TF peaks associated with lincRNAs were detected in root accessible chromatin regions (ACRs), of which 21% were specifically detected in cell-type specific root ACRs. Randomly shuffling the TF peaks associated with lincRNAs showed that the observed overlap of TF-lincRNA regulatory interactions with bulk and single-cell root ACRs was 10-fold and 36-fold higher than expected by chance, respectively, confirming the high specificity of the inferred regulatory interactions.
We strongly focused on using replicated samples when processing high-throughput datasets to obtain high-confidence gene information  (Ponting and Haerty, 2022). Combined with comparative genome analysis yielding information about age categories and selection acting on gene bodies and promoter regions, we found that different subsets of lincRNAs have distinct molecular properties. While the high tissue-specificity and low levels of primary sequence conservation corroborate previous findings about lincRNAs  (Necsulea et al. , 2014; Ma et al. , 2019; Palos et al. , 2022) , the analysis of lincRNA expression using a genomewide gene expression atlas covering 769 samples revealed that lincRNA expression is widespread in different organs and not restricted to stress conditions, which is in agreement with previous studies  (Jha et al.
While most regulatory interactions were confirmed by TF peaks overlapping with root bulk ACRs, for several lincRNAs cell-type specificity was The coding potential of transcripts was assessed using the Coding-Non-Coding Index (CNCI; Version 2)  (Sun et al. , 2013) , CPC2, and Pfam-scan (PFAM)  (Finn et al. , 2016) , and only those transcripts fulfilling the CPC2 (cutoff < 0), CNCI (cutoff < 0) and PFAM (E-value 1e-5) criteria were retained. (6) All candidate intergenic transcripts were assigned to lincRNA loci using the GFFcompare program  (Pertea and Pertea, 2020). The number of transcripts retained after each filtering step is reported in Supplemental Figure  S1B.
Expression analysis of lincRNAs To generate an expression atlas for lincRNAs, pri-miRNAs and protein-coding genes, we used Curse  (Vaneechoutte and Vandepoele, 2019)  to search and curate relevant RNA-seq experiments. Details of the 18 RNA-seq experiments across all 791 samples are shown in Supplemental Table  S2  and Supplemental Data Set S3. Next, we imported the expression metadata to the Prose tool  (Vaneechoutte and Vandepoele, 2019) , which downloads the raw data from SRA by using the SRA toolkit, performs quality control and adapter detection, trimmomatic to perform adapter clipping and quality trimming and finally Kallisto  (Pertea et al. , 2015)  for quantifying transcript expression to normalized transcripts per million (TPM) values. We downloaded the transcript FASTA file for 27,655 protein-coding genes and 325 pri-miRNAs from the Araport11 genome release  (Cheng et al.
05). For visualization purposes, the two enrichment metrics were combined into the π-value, which is the -log10(p-value)*enrichment fold. TF ChIP-Seq peak coordinates were retrieved from the PlantPAN 3. 0 database  (Chow et al. , 2019)  and  (Song et al. , 2016). The original ChIP-Seq of KAN1, MYB44 and PIF4 were derived from these studies  (Merelo et al. , 2013; Pfeiffer et al. , 2014; Song et al. , 2016). For each peak the closest gene was identified and only peaks confirmed in two or more replicates and within a 2kb window of the gene body were retained. Starting from all TF -target gene pairs (either a protein-coding gene or a lincRNAs; Supplemental Data Set S5), enrichment analysis was performed to identify enriched TFs in different expression clusters or age categories. For all enrichment analyses the hypergeometric distribution was applied and the q-value of enrichment was determined using the Benjamini-Hochberg correction for multiple hypotheses testing.
Overlap with accessible chromatin regions ATAC-seq data for Arabidopsis root hair, non-hair and whole roots were collected from three publications  (Maher et al. , 2018; Potter et al. , 2018; Tannenbaum et al. , 2018) , and scATAC-seq data for Arabidopsis root epidermis, endodermis, stele (pericyle, xylem, phloem), and cortex cells were collected from  (Dorrity et al. , 2021). Cell type-specific marker peaks were identified by scATAC-seq data (p<0. 05 and avg_lofFC>0). BEDtools intersect v2. 30. 0  (Quinlan and Hall, 2010)  was used to detect whether TF ChIP-seq peaks associated with lincRNAs overlapped with ACR peaks. Only overlaps between ACRs and TF ChIP peaks in at least two replicates were retained, requiring that at least of 10% of the ChIP peak was covered by the ACR. BEDtools shuffle v2.
Validation of TF-lincRNA regulatory interactions using reverse transcription-quantitative PCR Total RNA from five to eight 14-day roots grown vertically on solid MS ½ media was extracted using TRI Reagent (Sigma-Aldrich) and digested with RNase-free DNase (Fermentas) following the manufacturer's recommendations. cDNA was synthetized using Maxima Reverse Transcriptase (Thermo Scientific). Expression analysis by RT-qPCR was performed using SYBR Green master I (Roche) and the LightCycler® 96 system following a standard protocol (40 cycles, 60°C annealing). Data were analyzed using the ΔΔCt method with PP2A (PROTEIN PHOSPHATASE 2A SUBUNIT A3 (AT1G13320)) as reference transcript for normalization of RT-qPCR data. WT Col-0 plants grown at the same time were used as sample reference. For the dexamethasone inducible KAN1-GR expression lines analysis, after 14 days on MS media, the plants were transferred for one day either on dexamethasone containing plates (10µM) or only DMSO (dexamethasone solvent) as control.
ACKNOWLEDGEMENTS We thank  Mariek Dubois ,  Andrés Ritter ,  Tereza Vavrdova ,  Freya De Winter ,  Rebecca De Clercq  for kindly providing the Arabidopsis lines. We thank  Olivier Martin  and  Federico Ariel  for providing comments on the manuscript. The IPS2 is benefited from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). L. L. is supported by the  China Scholarship Council  for a  PhD fellowship  ( 201808530499 ). M. H. is supported by a  PhD fellowship  from the  Fondation pour la Recherche Médicale  ( ECO202106013730 ).
nr. MRZGP126), RNA Clean & Concentrator-5 (BaseClear, cat. nr. Zymo R1015, Leiden, the Netherlands), and the Baseline-ZERO DNase (Immunosource, cat. nr. Epicentre DB0715K, Schilde, Belgium). The library preparation PCR step used 15 cycles. Libraries were pooled and 1. 3 mL (1. 9 pM) combined with 1% PhiX Control v3 (Illumina, cat. nr FC-110-3001) were sequenced using Illumina NextSeq equipment and the NextSeq 500/550 High Output Kit v2. 5 (cat. nr. 20024906) and 75 cycles. For the second study, RNA-Seq was performed at BaseClear, using their Illuminabased protocols for bacterial RNA-Seq. A total number of 13 samples was used for RNA-Seq. FASTQ raw data were sent to RIVM via file transfer protocol (FTP) for further analysis.
Data Analysis Technical quality control of the RNA sequencing data was performed in a Linux environment using a pipeline developed in-house containing open-source tools such as FastQC (version 0. 11. 5, Babraham Institute, Cambridge, UK) and MultiQC (version 1. 5, Stockholm University, Stockholm, Sweden). This quality control step comprised aspects such as basecalling accuracy, GC content, N content, sequence length distribution, and adapter content. Next, NGS reads were trimmed to the first 50 nucleotides and mapped to the genome of C. tetani using Bowtie2 software (version 2. 2. 4, University of Maryland, College Park, MD, USA) at standard settings. Mapping was done using the published reference genome sequence of the E88 strain [26], which consists of a chromosome (accession number NC_004557.
1) containing 2620 coding genes and a plasmid (accession number NC_004565. 1) containing 86 coding genes, including tetX. All genome data used were downloaded from NCBI. The E88 genome is practically identical to that of the A strain  [27]. Bowtie2 alignments were processed by Samtools (version 1. 1, Sanger Institute, Cambridge, United Kingdom) to make a count table, which was further analyzed using Microsoft Excel (Microsoft, Redmond, WA, USA) and R (version 3. 5. 1, R Foundation for Statistical Computing, Vienna, Austria) statistical software. Normalization and statistical analysis were done in R statistical software using additional R packages "gplots," "seqinr," "UpSetR," and "viridis" from CRAN (https://cran. r-project. org/ accessed on 28 November 2021) and "DESeq2" and "limma" from Bioconductor (https://bioconductor.
2) (EFOSC2;  Buzzoni et al. 1984 ) on the 3. 6 m New Technology Telescope (NTT). The NIR spectrum was taken with the Son of ISAAC infrared spectrograph and imaging camera (SofI;  Moorwood et al. 1998 ) on the NTT. All these spectra were reduced using the PESSTO pipeline as described in  Smartt et al. (2015). In addition, five optical spectra were collected with the Southern African Large Telescope (SALT) using the Robert Stobie Spectrograph (RSS;  Smith et al. 2006 ) with a 1 5 long-slit. We used the PG0300 grating in two tilt angles to cover the optical wavelength range and a custom data reduction pipeline based on the PySALT package  (Crawford et al. 2010). One low-dispersion optical spectrum was obtained on 2016 February 27 by the FLOYDS spectrograph  (Brown et al. 2013). The FLOYDS spectrum was reduced following standard procedures using the FLOYDS pipeline  (Valenti et al.