dataesr/scientific-paragraphs-categorization huggingface dataset:
We get an agreement of all satellite XCH 4 products with the TCCON data within 1%. This comparison reveals a good reliability of the TROPOMI and the combined XCH 4 products, because of their independency on the a priori data (the comparison of the IASI data is affected by the a priori data and thus cannot be directly interpreted). We found that the AirCore data are a very good reference for the consistent validation of the CH 4 total column amounts and the CH 4 vertical distribution; however, they are limited to northern hemispheric high and middle latitudes. Concerning total column comparison we get a very low 1σ scatter between the satellite products and the AirCore reference data (within 1%, which is similar to the comparison with TCCON). For the UTLS partial columns the scatter is also within 1% and for the tropospheric partial columns it is 1.
1% -1. 7%. While the comparison to TCCON shows no significant bias, the comparsion to AirCore reveals a significant positive bias in the MUSICA IASI XCH 4 and tropospheric partial column data (significant in the sense that the systematic difference is outside the 1σ scatter and that it can also be not explained by the uncertainty of the AirCore references). For the combined tropospheric partial column product we report a slightly significant positive bias of about +1. 4%. We have only 24 AirCore profiles measured in collocation to satellite observations. A statistically more robust validation of the tropospheric partial column products can be achieved by using continuous CH 4 observations from two nearby GAW There might be a chance to further improve the quality of the combined data product by performing detailed investigations on the inconsistency between the TROPOMI and the MUSICA IASI XCH 4 data.
The availability of additional CH 4 profile reference data for low latitudes (e. g. obtained by the AirCore system) would be very beneficial for such purpose. The proposed method takes benefit from the outputs generated by the dedicated individual TROPOMI and IASI retrievals, it needs no extra retrievals, and is thus computationally very efficient. This makes it ideal for an application at large scale, and allows the combination of operational IASI and TROPOMI products in an efficient and sustained manner. This has a particular attraction, because IASI and TROPOMI successor instruments will be jointly aboard the upcoming Metop (Meteorological operational) Second Generation satellites (guaranteeing observations from the 2020s to the 2040s). There will be several 100,000 globally distributed and perfectly collocated observations (over land) of IASI and TROPOMI successor instruments per day, for which a combined product can be generated in a computationally very efficient way.
(A15) In Eqs. (A11) and (A12) we assume that the two individual retrievals use the same constraint (S a -1 ). This is generally not the case and we can a posteriori modify a constraint and its effect on state vectors and covariances by the formalism as presented in Chapter 10. 4 of  Rodgers (2000)  or Sect. 4. 2 of  Rodgers and Connor (2003). For our problem here this is of secondary importance, because we assume that TROPOMI total column data products are almost independent on the constraint (as long as the constraint is reasonable). In Eqs. (A14) and (A15) we assume the usage of the same a priori for the two individual retrievals. Since generally two indivudually performed retrievals use two different a priori settings we have to perform an a priori adjustment. Using the a priori of retrieval 2 as the reference (x 2,a = x a ) we can adjust the output of retrieval 1 by (see Eq.
(C8) The respective calculations for total column averaged mixing ratios can be made by replacing W * by w * T. For the total column avereraged mixing ratios the covariance is a simple variance (the scalar S * ) and the kernel has the dimension 1 × nol, i. e. it is a row vector a * T. The total column amount kernel (a T T ) provided with the TROPOMI data set can be converted into a total column averaged mixing ratio kernel a * T T by the following calculation: a * T T = w * T A = (w T Zw) -1 a T T Z. (C9) The total column averaged mixing ratio kernel a * T T used in Sects. 2 and 3 is valid for the vertical grid used by the MUSICA IASI retrieval. It is calculated according to Eq. (C9), but using a TROPOMI total column amount kernel (a T ) that is interpolated onto the MUSICA IASI grid (see also Appendix C1).
S2 ), indicating that the prepared ATAC-seq libraries were mainly composed of nucleosome-free reads. Nucleosome-free reads are accessible chromatin regions where TFs might bind, whereas nucleosome-containing reads were relatively less accessible to TF binding  (Sijacic et al. 2018). In short, our ATAC-seq datasets could be used for the identification of accessible chromatin regions in V. amurensis. The called peaks, or THSs, denote the enriched accessible chromatin regions. Contrary to Arabidopsis, in which the majority of THSs were located in the upstream of transcription start site (TSS;  Maher et al. 2018 , Sijacic et al. 2018) , most of THSs in V. amurensis were found to be located within gene body (exon and intron, 34%) or located in the intergenic regions (52%). Around 8% of THSs were located within 3 kb upstream of TSS, and only 6% were located within 1 kb downstream of gene transcription end site (TES; Fig.
S4-S5 ). By searching the plant TFs (motifs) database, we could know the TFs that probably recognize the specific motifs. As expected, the motifs for the key TFs CBF1, 2, 3 and 4 were identified within the 77 motifs, suggesting that CBFs may play important roles in cold response in grape (Supplementary Fig. S4 ). Furthermore, a number of TFs belonging to IDD, GATA factor, zinc finger TF family, MYB TF family and AP2/ERF TF superfamily were also identified as candidates controlling intracellular responses to cold stress (Supplementary Fig. S4 ). Given that the public data of plant TF-binding profiles are mainly collected from the model plant species Arabidopsis, we thus identified grape TFs using BLAST search by taking advantage of conservative property of predicted motifs and homologous TFs proteins between different species.
This analysis led to the selection of 51 grape TFs after removing the redundant and unannotated TFs (Supplementary Dataset 1). Combining the RNA-seq results, we finally obtained 31 TFs genes with altered expression after cold treatment (Fig. 3A ). Although most of these genes could be induced or repressed by cold, only nine TFs genes, including the cold-responsive gene CBF4, exhibited significant changes [an absolute value of log2 fold change (log2FC) >1 and a P value <0. 05] in their expression (Fig. 3B ). All the nine TFs genes were up-regulated by cold treatment except for CRF2 and ESE3. The expression of the two genes was obviously decreased by cold stress (Fig. 3B ). In contrast, CBF4, RAV1 and the ERF gene ERF104 were strongly induced (log2FC >4) by cold (Fig. 3B ).
4B-C ; Supplementary Dataset 2-3). Additionally, specific GO terms were also identified for the two gene modules. The DEGs from the CBF4 and CRF2 module were differentially enriched in cellular response to cytokinin stimulus, glutathione, glucose 6-posphate and glycerolipd metabolic process and transmembrane receptor activity and so on (Fig. 4B, D ; Supplementary Dataset 2). In contrast, the DEGs from RAV1 and ERFs regulatory network were specifically enriched in GO terms involving phytohormones, such as ethylene biosynthetic process, ethylene-activated signaling pathway, salicylic acid and auxin metabolic process (Fig. 4B, D ; Supplementary Dataset 3). Moreover, there is also a number of DEGs involved in the sucrose metabolic process (Fig. 4D ). These results suggest that the identified TFs, which were clustered into two groups, may function in response to cold stress through different pathways.
amurensis after cold treatment, and nine TFs, including CBF4, were identified as the candidates involved in early cold response (2 h) in grapevine (Fig. 3B ). Intriguingly, among the nine identified TFs, eight belong to the AP2/ERF superfamily except MYB108 (Fig. 3B ; Supplementary Dataset 1). AP2/ERF superfamily includes AP2, ERF and RAV families. ERF family can be further divided into two subfamilies, namely ERF and CBF/DREB  (Nakano et al. 2006). The role of CBF/DREB subfamilies in cold acclimation had been widely demonstrated, whereas the function of ERF family in plant cold tolerance remains less studied. Interestingly, the GO analysis of RAV1 and ERFs regulatory network revealed a role for ethylene signaling pathway in response to cold stress (Fig. 4B, D ). Recently, a few reports suggested that ERFs contribute to cold tolerance in plants  (Tian et al.
Genomic distribution of THSs The called peaks or THSs that were found in at least two biological replicates were mapped to genome features to determine their distributions. The distribution of THSs relative to genomic features was assessed with 'upstream' regions set as 3000-bp upstream of the annotated TSS and 'downstream' regions set as 1000-bp downstream of the TES by using the PAVIS web tool  (Huang et al. 2013). Besides, for each ATAC-seq dataset, all the THSs were assigned to the closest transcription start sites regardless of the positions (upstream or downstream) from the THSs to identify THS-proximal genes by using the 'TSS' function of the PeakAnnotator 1. 4 program  (Salmon-Divon et al. 2010).
Differential THSs for motif analysis To identify specific open accessible regions after cold treatment, the differential accessible peaks were screened and analyzed. The peaks that were found in three biological replicates of each sample were calculated and merged as a file using the bedtools software. The counts of the reads were determined for each sample using bedtools multicov. Differential accessible peaks were determined using DESeq2, and those regions identified with a value of log2FC ≥1 (positively enriched) or ≤-1 (negatively enriched) and a P value <0. 05 were considered as specific accessible peaks. These peaks were then used for motif analysis with the HOMER's FindMotifGenome. pl tool. The peak files and grape genome fasta files were adopted as input files. The extracted DNA sequences were compared with known motif database (JASPAR, http://jaspar.
Identification of motifs within gene promoters To identify motifs within promoters of genes of interest, the 2-kb upstream sequences of genes were downloaded as the promoters from the grape genome (http://genomes. cribi. unipd. it/grape/index. php), and the motif profiles used for motif occurrence identification were downloaded from JASPAR and HOMER databases using gene names or IDs as the inputs. The motif occurrences were identified using the function of motif scanning of FIMO (http://memesuite. org/tools/fimo; Grant et al. 2011). Those motif occurrences with a P value <0. 0001 were considered significant.
WGCNA and GO analysis The WGCNA was conducted with RNA-seq data using R package to develop gene co-expression networks. The log2(FPKM of each treatment +1) was used as input, and soft-thresholding was set to 6. The GO analysis was conducted using the agriGO v2. 0 with default parameters  (Tian et al. 2017). The GO terms with a corrected P value of (false discovery rate, FDR) <0. 05 were considered significant. The co-expression networks and enriched GO terms were visualized using the cytoscape software.
Finally, the quality of libraries was assessed on the Agilent Bioanalyzer 2100 system and sequenced with the Illumina Hiseq 4000 (125/150-bp paired-end reads). The raw reads were first processed with FastQC for quality control, and sequences with adaptor and poor-quality reads were removed. High-quality reads were then mapped to grape 12X genome (version 2. 1) using STAR. HTSeq was used to calculate the numbers of unique reads. Differentially expressed genes were analyzed using the DESeq R package (P < 0. 05). RNA-seq and data analysis were conducted by Shanghai Jiayin Biotechnology Co. , Ltd. For qPCR, the cDNA was synthesized from 1 µg of total RNA with the HiScript II Q RT SuperMix Kit (Vazyme). qPCR was performed using the CFX Manager system (BioRad) and SYBR Green Master Mix (Vazyme).
The Actin (GenBank accession no. 100232866) and GAPDH (GenBank accession no. 100233024) genes were used as internal controls. Primers used for qPCR are available in Supplementary Table  S1. The obtained data were calculated using 2 -∆∆CT method  (Livak and Schmittgen 2001) , and the test of significance was performed using unpaired Student's t-test.
2016 and 2017 tags were connected to a large titanium anchor (Wildlife Computers) via a 50 cm stainless steel tether. Tether lengths were selected to allow the anchor to be placed 8-10 cm below the skin and leave space to let the tag lie flat against the body surface in the case of the former, and to facilitate breaking the air-surface barrier for transmission during deployment for the latter. Individual sharks were measured (total length, TL) by one of two methods; (1) by using visual observation and comparing the shark to an object of known size, and/or (2) by an intense photogrammetric laser measurement campaign using the methods as described in  Jeffreys et al. (2013). Photo-ID images were also taken of the left and right flanks of tagged individuals and matched with the existing Djibouti database using the public domain pattern-recognition software I 3 S (Interactive Individual Identification System;  Van Tienhoven et al.
Histogram sampling was offset by three hours so that depth, and temperature data were collected for local day (6:00-12:00 and 12:00-18:00) and night (18:00-00:00 and 00:00-6:00) periods. The 2012 tags recorded and archived depth data at five-minute intervals, which were subsequently summarized into bins. A Wilcoxon rank-sum test was used to compare median day-and night-time depths for time-series data from the two 2012 tags. Location data and/or processed archived data were transmitted and retrieved through the Argos satellite system when the tags detached. Detachment of the tag from the shark was identified by a combination of near-continuous high quality Argos transmissions for the first few hours of each day and depth summaries from histograms consistent with surface records  (Hearn et al.
| MiniPAT track reconstruction The two individuals tagged in 2012 were fitted with MiniPAT tags which did not have the capability to record locations during deployment. Track locations were thus estimated by light-levels based on data received via Argos transmission after the tags had detached from the host shark; consequently, these were not contiguous data streams. The transmitted data were first processed through the Data Analysis Program software suite (WC-DAP, Wildlife Computers, Redmond, WA) to extract the dawn/dusk light level as well as the temperature and depth data for each 24-hr period. These data  ). The Iknos Walker routine can shorten tracks before the tag detachment location due to the last GPE location(s) being too far away compared with the speed limit set; this can be mitigated by increasing the acceptable animal speed.
Movements of six whale sharks Rhincodon typus tagged with satellite tags in Djibouti in 2016 and 2017. Points indicate location recorded by Argos or GPS and are colored by month. Red triangles indicate tag deployment location. Individuals are (a) 157783; (b) 157782; (c) 42856; (d) 165698; (e) 165699 and; (f) 42858. Note that scale varies among maps. Bathymetry data were extracted from the ETOPO1 database using marmap in R. Dotted lines indicate connection between consecutive points | 4923 ANDRZEJACZEK Et Al.
Tagged whale sharks were matched with an existing photo-ID database for the Djibouti aggregation site consisting of 1,039 individuals (D. Rowat unpublished data). Shark 104073 was the only individual observed in subsequent years following tag deployment. It was first recorded in 2007, and observed again each year from 2009 to 2015. For all other sharks, observations were either made in the year of tag deployment only (sharks 157783, 165698, and 165699), or additionally, in the one or two years prior to tag deployment (sharks 104072, 42858, and 157782).
RNA-Seq A Ribo-Zero rRNA Removal Bacteria kit (Epicenter, Madison, WI) used with manufacturer's protocol was applied to deplete a maximum of rRNAs and enrich total RNA in mRNA. As it is often the case with Archaea, ribodepletion attempts were poorly efficient and the RNA-seq dataset still contained 73-94% ribosomal sequences. Ion Total RNA-Seq Kit v2 was used to make cDNA, to add barcodes, and to amplify the library. All cleanup steps were performed using Agencourt Ampure XP beads (Thermo Fisher Scientific). Each step was validated by a bioanalyzer quality control on Agilent RNA chips and/or with the "Qubit TM " fluorometer (Life Technologies) to determine the quality, size of fragments and quantity of produced material. RNA libraries were sequenced on P1v2 chips using the Ion S5 TM System (Thermo Fisher Scientific).
Bioinformatic Data Analysis RNA-seq reads were mapped on all genes with Bowtie v2. 3. 1  (Langmead et al. , 2009). Bowtie2 was run with default parameters and -no-unal. The result was converted from SAM to BAM format using Samtools v1. 3. 1  (Li et al. , 2009). The reads counts were normalized to Transcripts Per Kilobase Million (TPM) (Supplementary Table  1 ). Then, TPM were mapped with Anvi'o (v7)  (Eren et al. , 2015)  onto the T. piezophilus genome sequence deposited in the NCBI database under the accession number GCA_001647085. 1 ASM164708v1. First, a contig database was generated (anvi-gen-contigs-database) with the genome sequence available on NCBI and a table containing all gene positions (external-gene-calls and -ignore-internal-stop-codons arguments). Next, BAM files were processed individually with anvi-init-bam to get indexed alignment files.
Differential Gene Expression Analysis Data normalization and differential gene expression analysis were done with R/bioconductor packages edgeR  (Robinson et al. , 2010)  and DEseq2  (Love et al. , 2014). For each pressure condition, the data obtained from three independent biological replicate cDNA libraries were analyzed. Principal component analysis (PCA) was used to detect potential outlier on the samples. No outlier was detected. Regarding the filtration of transcripts, EdgeR's RLE ("Relative Log Expression") standardization was used, with filtering of weakly expressed genes, and then correction of tests with the BH method (Benjamini-Hochberg procedure for False Discovery Rate; alpha threshold = 0. 05). The DEseq2 package was used to investigate the differential expression of genes between the different pressure conditions.
Data are shown as the mean ± SEM (n = 3). P-value is also reported. Quantitative analysis for morphology changes under μG exposure was performed on fluorescence images. Briefly, the cell outline was manually extracted using ImageJ software by selecting the peripheral actin filaments (Figure  6A-i ) and a binary image was created and used for automatic quantification of morphology parameters, such as cell surface area, shape index (SI), and cell perimeter. The SI value is a measurement of cell roundness, from a perfect spherical shape (SI = 1) to an elongated shape (SI = 0). Another parameter, known as tortuosity index (TI), assesses and quantifies whether cell approaches a smooth circular or elliptical profile (TI = 1) or an irregular star shape profile (TI >1). A sketch of the relationship between the shape, the tortuosity index, and the corresponding morphological changes is illustrated in Figure  6A -ii.
Given the macro dimension of the tissue compartment, the evolution of the fluorescence signal was detected by scanning the sample with a 10× magnification objective in x-y plane for a duration of 1. 2 min. The reconstruction of the entire microfluidic device was then obtained, and post processed with ImageJ and Matlab software. To quantify the permeability (P) the authors used the following equation: P = 1 I 0 V S dI t dt ( 2 ) where I t is the average intensity in the tissue compartment, I 0 is the maximum fluorescence intensity of the vascular channel and V/S is the ratio of tissue compartment volume to the lateral diffusion surface area. The permeability of an empty device ( 1 P cell-free ) was then subtracted from the total apparent permeability (1/P) to obtain the actual permeability of the biological barrier, in analogy with parallel resistors in electrical circuits.
No. P1951) for 1 h in the dark. Nuclei were stained with Hoechst. Morphological Analysis: Morphological analysis was performed on Human Glioblastoma A-172 and hCMEC/D3 cells to evaluate parameters such as area (A), shape index (SI), and Tortuosity index (TI). [84, 85] The cell outline was manually extracted using ImageJ software by selecting the peripheral actin filaments in fluorescence images. Cell area, shape index, and perimeter were then measured automatically. Also, the major and minor axes of the equivalent ellipse for the cell outline were determined. The SI is defined as follows: SI = 4𝜋A P 2 ( 4 ) where A is the cell area and P the cell perimeter. The SI is equal to 1 for the perfect circle and approaches zero for highly elongated shape. The TI is defined as follows: TI = P P ′ ( 5 ) where P is the cell perimeter and P ′ the equivalent ellipse perimeter of the cell.
The equivalent ellipse has equal area and equal moment of inertia to the corresponding extracted cell shape. The TI increases from 1 when the cell shape becomes more tortuous, while the TI is 1 when the cell shape approaches the smooth profile of a circle or an ellipse. Morphological parameters were measured from 50 cells in 8 different fluorescence images for each experimental condition. Spheroid Formation Analysis: To capture the complex dynamic of GBM invasion into the surrounding brain tumor environment, a quantitative analysis on GBM spheroids formation has been performed using ImageJ software. After bioprinting procedures, the 96-well plates were placed on the RPM device and monitored over time for 72 h using a fluorescence microscope (Nikon Ti inverted fluorescence microscope). Fluorescence images of cells stained with Hoechst were then post processed using ImageJ software and binary images were created for quantification of spheroids dimension around the tumor by counting the white pixels corresponding to a single spheroid in each binarized image.
Finally, the plates were scanned with the Odyssey CLX system (Li-Cor Biosciences) equipped with a near-infrared light technology for signal detection. Signal intensity was quantified with Image Studio software (Version 4. 0; Li-Cor), according to the manufacturer's instructions. Statistical Analysis: The statistical analysis of the data was performed using one-way analysis of variance (ANOVA) with Turkey's test for multiple comparisons, using GraphPad Prism 7. 04 for Windows. Average values of at least three independent experiments ± SEM are shown for each of the assays, that is, vascular permeability assay, Proliferation assay and In-cell Western analysis. Morphological parameters were measured from 50 cells in 8 different fluorescence images for each experimental condition. P-value is reported for statistical significance.
There is an equilibrium where all countries are either excluded from financial markets because they have defaulted or they all voluntarily contribute to the fund. In this case, there is full risk sharing among countries participating in financial markets, but default still occurs in general equilibrium. We provide a calibration of the model using both data on sovereign default and on "disaster event", to reproduce both the default probability and income fluctuations. The optimal quantity of assets issued by the fund represents around 4. 1% of world GDP. The countries average contributions is equal to 0. 22% of their own GDP, and the subsidy given by the the fund to joining countries is 3. 49% of their GDP. The fund and its asset supply are obviously reminiscent of the IMF Special Drawing Rights (SDRs).