dataesr/scientific-paragraphs-categorization huggingface dataset:
LCA also allows the identification of the main environmental burdens, so that improvement actions can be more effectively defined and put into practice. However, when evaluating risk to human health and/or the environmental impacts of food systems in a specific geographic area, the LCA methodology needs to increase the granularity of its analysis to regionalize these risks and impacts. To do so, LCA has to move from its global perspective to the concrete site-specific focus of RA. Nowadays, this can be achieved by using site-dependent characterization factors  (Saouter et al. , 2017) , national databases (Environmental Protection Agency, 2021; NREL, 2012), and grid cell-based inventories  (Hill et al. , 2019; Thakrar, Goodkind, Tessum, Marshall, & Hill, 2018). In this context, when LCA moves in the direction of RA, the integration of both methodologies becomes more harmonious.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovative programme  under the  MSC-ITN  grant agreement No. 813329  (PROTECT) (http://www. protect-itn. eu/). Paola Guzmán-Luna ,  Miguel Mauricio-Iglesias  and  Almudena Hospido  belong to a Galician Competitive Research Group (GRC), co-funded by  FEDER.
food. dtu. dk/ Database interface/database) OECD QSAR Tool (online X X X https://www. oecd. org/chemicalsafety/risk-assessment toolbox interface/database) /oecd-qsar-toolbox. htm Euromix Tool (online X X X X https://mcra. rivm. nl/Select Toolbox (Nl) interface/database) EUSES 2. 2. 0 Computer program X X X https://echa. europa. eu/support/dossier-submission-too ls/euses Foodrisk. org Online resource X X X X https://www. foodrisk. org/ center QMRAwiki Online resource X X X X http://qmrawiki. canr. msu. edu/index. ph center p/Quantitative_Microbial_Risk_Assessment_(QMRA)_Wiki EFSA knowledge Online resource X X X X https://zenodo. org/communities/efsa-kj/?pag=1&size junction center =20 ICPS manual Reference material X X X X https://www. who. int/publications/i/item/9789 241572408 Red book Reference material X X X X https://www.
fao. org/3/ba0092e/ba0092e00. pdf performing RAs during material emergency situations Risk ranger Tool (MS X http://www. fao. org/food-safety/resources/tools/ Excel-based) details/en/c/1191489/ Microhibro Tool (online X http://www. microhibro. com/# interface) sQMRA 2. 0 Tool (MS X https://www. foodrisk. org/resources/display/56 Excel-based) iRisk-FDA 4. 0 Tool (online X X https://irisk. foodrisk. org/ interface) Foodrisk. org Online X X X X https://www. foodrisk. org/ resource center QMRAwiki Online X X X X http://qmrawiki. canr. msu. edu/index. ph resource p/Quantitative_Microbial_Risk_Assessment_(QMRA) center _Wiki EFSA knowledge junction Online X X X X https://zenodo. org/communities/efsa-kj/?page=1 resource &size=20 center ECDC burden of Database X X https://www. ecdc. europa. eu/en/burden-commun Communicable disease icable-diseases EU RASFF Database X X https://ec.
europa. eu/food/safety/rasff_en US FDA Import detention Database X X https://www. fda. gov/industry/actions-enforcement /import-alerts Badbug book-USFDA Reference X X https://www. fda. gov/food/foodborne-pathogen material s/bad-bug-book-second-edition ANSES-Microbiological Reference X X https://www. anses. fr/en/content/microbiologica hazard files and data material l-hazards-files sheet on foodborne biological hazards de Kamp, Temme, & van.
Landslides and flash floods are geomorphic hazards (GH) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data is insufficient. Here we present a new method to accurately estimate GH event timing which requires no prior knowledge of the GH event timing, using Synthetic Aperture Radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (~10-15 m) and a high repeat time (6-12 days) from 2016 to 2020.
, 2000). Additionally, a change in atmosphere (precipitation events, ionospheric disturbances) can dynamically influence the coherence values  (Rocca et al. , 2000 , Jacquemart & Tiampo, 2021). To better assess the ability to detect GH timing, it is essential to understand the dynamic factors controlling the behavior of the signal. We derive precipitation estimates from the GPM Level 3 IMERG Final Daily (10km spatial resolution) dataset that has been validated through rain gauge data within the area  (Nakulopa et al. 2022). General vegetation patterns per GH event are visualized using the Normalized Difference Vegetation Index (NDVI;  Tucker, 1979). NDVI time series are derived from the Landsat-8 (30m spatial resolution) archive and processed within the GEE environment We use the Landsat 8 atmospherically corrected surface reflectance images provided within the GEE environment.
Perspectives 1. We have studied, for the first time in a GH event timing detection approach, both landslides and flash floods in a combined methodology. Since these GH often co-occur and interact  (Marengo & Alves, 2012 , Jacobs et al. , 2016a , Rengers et al. , 2016)  they should be analyzed in a multi-hazard approach. Our method can be well applied within a multi-hazard methodology. For example, multihazard inventories can serve as an input for our methodology to improve event timing accuracy. Regional results can subsequently be used in hazard assessment, early warning, and disaster risk reduction strategies. 2. Our study shows that there is a clear advantage to analyzing different S1 SAR data products when estimating GH event timing. The fact that  Burrows et al. (2022)  shows better results for amplitude compared to coherence data is in contrast with our results but reinforces the idea of investigating both data products when applying the method to new regions.
Additionally, in its current form, the methodology does not account for the GH events that occur within a time span that is longer than the acquisition time (> 6-12 days) of S1 images (i. e. multi-temporal GH events). In that case one would require a time window of occurrence, rather than a specific date. The methodology can be adapted to allow it to derive a time window of GH occurrence. This could mainly be done following timing workflow 1 (the GH event scale). The start and the end date of the GH event inducing change within the SAR time series (applicable for all data products) should be indicative of the time window of GH event occurrence. However, this remains to be investigated. 4. The open-access S1 satellite with its high resolution, high repeat time and global coverage proves to be an excellent data product for estimating GH event timing and allows for our developed method to be applied on every region of the world.
Conclusion We established a new method to automatically determine GH event timing from SAR images, that can be applied without prior knowledge of the GH event. Our method is original as it is the first time that landslides and flash floods are studied together. By showing that these two processes can be detected and therefore studied together, we open new perspectives in the study of multi-hazards, which can aid in hazard assessment, early warning, and disaster risk reduction strategies. Our methodology has the potential to be combined with existing spatial detection methods to support inventory creation and boost GH event research in remote inaccessible areas such as the African cloud-covered tropics. From a data processing point of view, the method is established around an unprecedented analysis of various SAR products coming from Sentinel-1 (S1) images.
We show that there is a need to investigate different SAR data products when estimating GH event timing (amplitude, spatial amplitude correlation, and coherence) since the signal response can be different and sometimes contradictory when looking at one single event. The implementation of our method on a cloud computing platform can be beneficial in terms of scalability, data availability and processing time. However, the main limitations in this context are: (1) no control in pre-processing of S1 imagery and, (2) S1 coherence data is so far not available within these platforms. With a focus on four events containing a total of about 2500 landslides and flash flood features in contrasting landscapes, we propose a method that is adapted to be applied to other regions. Here, we focused on tropical environments where climate conditions and land use dynamics are rather specific.
Acknowledgement This study was supported by the  Belgium Science Policy (BELSPO)  through the  PAStECA  project ( BR/165/A3/PASTECA ) entitled " Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa " (http://pasteca. africamuseum. be/, last access  09 June 2022 ). The compilation of the inventory data benefited from field-based insight and discussion with  Arthur Depicker ,  Josué Mugisho Bachinyaga ,  John Sekajugo  and  Judith Uwihirwe. PlanetScope data provided by the  European Space Agency. Small, D. : Flattening gamma: Radiometric terrain correction for  SAR imagery. IEEE Trans. Geosci. Remote Sens. , 49(8),  3081-3093 , https://doi. org/10. 1109/TGRS. 2011. 2120616,2011 Srivastava, H. S. , Patel, P. and Navalgund,  R. R. : How  far  SAR  has fulfilled its expectation for soil moisture retrieval.
While AP-1, GRE, TEAD, and p53 motifs were found to be overrepresented in both CO and MR EKO ChIPseq datasets, there were statistically significant differences in signal intensity between genotypes. Along these lines, there was a decrease in GRE motifs in GR-bound sequences (GBS) in MR EKO versus CO cells. Immunofluorescence and nuclear fractionation experiments indicated that GR showed a decreased nuclear localization in response to Dex in MR EKO versus CO. Also, using surface plasmon resonance (SPR), we assessed GR-and MR-protein-protein interactions and estimated the affinity and kinetics of their physical associations. Data suggest that MR homodimers have the highest affinity but the shortest lifetime, while GR homodimers and GR/MR heterodimers show similar intermediate affinity and extended dimer half-life.
i2bc. paris -saclay. fr/). DNA fragments were end repaired and dA-tailed with the NEBNext® Ultra™ II End Repair/dA-Tailing Module and Illumina TruSeq adapters were ligated with the NEBNext® Quick Ligation Module (New England Biolabs Inc. ). Libraries were amplified with Kapa Biosystems Hifi polymerase (Sigma). Library quality was verified on an Agilent Bioanalyzer 2100, using an Agilent High Sensitivity DNA Kit. Libraries were pooled in equimolar proportions and sequenced on a Paired-End 2 × 43 bp run, on the Illumina NextSeq500. Raw sequence reads were quality checked, adapter trimmed, and filtered using Cutadapt1. 15 and FastQC v0. 11. 5 and only reads longer than 10 bp were kept for analysis. Trimmed sequences were mapped to the Mus musculus genome (assembly: GRCm38/mm10) using the bwa-mem algorithm.
Only reads mapped with a mapping quality of 60 (maximum) were kept for further analyses. Peak calling and search for enrichment of TF-binding motifs in identified GR-bound sequences (GBS) were performed using HOMER,  39  with the findPeaks and find-MotifsGenome functions, using respective input controls for each genotype. Peaks were assigned an identification code based on genomic location for comparative purposes. Overlapping intervals were grouped into "active regions," defined by the start coordinate of the most upstream interval and the end coordinate of the most downstream interval. 40 The criterion for overlapping represents at least 50% coincidence among peak sequences. Peaks were visualized using Integrated Genomics Viewer (version 2. 11. 9) Software. 41 We used CREMA (Cis-Regulatory Element Motif Activities; https://crema.
unibas. ch/cruma ra/) to identify statistically significant differences in TF motif enrichment in identified CO and MR EKO GBS. Comprehensive motif analysis with XTREME  42  determined consensus GRE sequences and the positional distribution in GBS, as well as the percentage of GBS in datasets containing one or more identified motifs (parameters: motif width between 6 and 30 bp and any number of repetitions of motif within peaks). Gene ontology was determined using DAVID functional clustering with an EASE score less than 0. 05 and medium stringency. 43,44.
FASTQ files were obtained from applying the bcl2fastq2 Conversion Software (V2. 18. 12) to the BCL files generated by the sequencer. Adapters were removed with Cutadapt 1. 15; only reads longer than 10 bp were kept for analysis. Filtered FASTQ files were mapped against the Mus musculus reference genome (assembly: GRCm38/mm10) with HISAT2, with the -rna-strandness RF argument. Absolute values ranging from 117 to 151 million paired-end 51-35 bp reads were obtained from samples. Reads mapping on each feature were counted with the HTSeq-count script, with the "union" mode of overlapping and with the-stranded option for detecting reads falling in the reverse strand. A count table was created with the information derived from HTSeq-count for each sample. Later, the DESeq2 package in R was used to analyze differential gene expression using this count table as an input.
As a reference, one of the channels was also amine-activated and blocked in the absence of protein. The same protein samples were run over the ancGR2-LBD and MR-LBD immobilized partners in a running buffer including 50 mM HEPES, pH 7. 2, 50 mM NaCl, 3% glycerol, 1 mM dithiothreitol (DTT), 50 μM Dex. Nr3c1 (5′-GGATACACTGTGTACT-3′; 5′-AGTACACAGTGTATCC-3′), Nfil3 (5′-TAGAACATTCT GTACT-3′; 5′-AGTACAGAATGTTCTA-3′), and Tsc22d3 (5′-AAAAACAGAATGTTCA-3′; 5′-TGAACATTCTGTTT TT-3′) oligonucleotides were run over the ancGR2-DBA-LBD immobilized partner in a pH 3 buffer consisting of 10 mM HEPES, 150 mM NaCl, 0. 005% Tween-20, 10 mM Na molybdate, and 5 mM Zn citrate. Sensorgrams were analyzed with the BIAcore T200 Evaluation software 3. 0 and fitted according to the Langmuir 1:1 model.
| Statistics and data analysis Unless otherwise mentioned, statistical significance of experimental data was calculated using the GraphPad Prism software (version 5. 2; GraphPad Software Inc. , San Diego, CA) and IBM SPSS software. Data were obtained in at least three independent experiments. Data with normal distribution are presented as mean ± SD; non-normal data were reported as median ± interquartile range (Figure  2D ) or mean ± SEM (Figure  3D ). Means of two and three or more groups of normally distributed variables were compared by Student's t test or ANOVA with a Tukey post hoc test, respectively. The Mann-Whitney U test or the Kruskal-Wallis with a post hoc Dunn test was used to compare means of two and three or more groups, respectively, of non-normally distributed data. p-values <.
Indeed, 47% of GRE-containing peaks were unique to CO (Figure  2C ). Moreover, GBS with GREs identified in both genotypes showed a 14% decrease in tag count in MR  EKO  versus CO (Figure  2D ; p-value <10 -8 ). Ontological analysis of GRE-containing peak-associated genes unique to CO showed enrichment in transcriptional regulation and cell differentiation annotation clusters (Figure  S4 ); notable genes within these categories included Nfil3, Nr3c1, Dusp10, Nfkb1, and Pparg. Comprehensive motif analysis of GBS by XTREME showed that the composition and positional distribution of palindromic GREs did not vary between genotypes. However, the number of GREs present in GR-bound genomic regions was decreased in MR  EKO  versus CO datasets (Figure  2E ). This suggests that MR is involved in cooperative interactions between multiple GRE within a GBS.
performed RNA-seq. Bioinformatics analyses identified differentially expressed genes (DEGs) with statistical significance in both genotypes (FDR <0. 05) (Table  S3 ). A fold-change plot illustrates Dex versus vehicle gene expression in CO and MR EKO cells (Figure  5A ). The overall number of DEGs was almost identical in CO and MR EKO cells (Figure  5B ; 457 and 451 DEGs, respectively), with similar percentages of up-and downregulated genes (Figure  5B ,C; 70% and 30% vs 64%, and 36%, respectively). Gene expression profiles between CO and MR EKO were very similar, with high overlap between datasets and similar degrees of induction or repression among commonly regulated genes, suggesting a relatively small contribution of MR to the transcriptional response to Dex at this time point in keratinocytes.
Upon evaluation of upregulated genes in both genotypes, we found that 16% of CO genes required MR for their regulation, while 8% of genes of the MR EKO dataset were uniquely induced in the absence of this TF (Figure  5C ). For downregulated genes, 22% of CO genes were dependent on MR for regulation, while a striking 33% of MR EKO genes were uniquely repressed, suggesting that MR loss has greater impact on GR-mediated gene repression (Figure  5C ). The expression of selected candidate genes was validated in independent experiments by RT-qPCR. Differences between CO and MR EKO reached statistical significance for genes such as Tsc22d3/Gilz, Per1, and Sgk1 (Figure  5D ). However, relative gene induction or repression by Dex was of similar magnitude in both genotypes for Nfil3 and Dusp10 (Figure  5D ).
The differences in GR binding were less marked for Lmnb2, however, the extent of GR recruitment and delayed kinetics was decreased in CO cells (Figure  S7 ). Altogether, these data indicate that MR modulates timing of GR genomic binding in keratinocytes in response to Dex. However, Dex did not elicit changes in Hlf or Lmnb2 gene expression either in the RNA-seq data or the time points assessed (not shown) suggesting that GR binding to these regulatory sequences is unproductive. Consistent with previous work,  50  comparison of ChIP-seq and RNA-seq datasets indicated that GR binding showed a higher correlation with gene induction; this was true in both genotypes (Figure  S8 ; 11%-13% of induced genes were bound by GR). Altogether, our data indicate that differences in the magnitude of expression between genotypes upon Dex treatment are gene and time dependent.
| DISCUSSION Our previous work demonstrated the central role of keratinocyte GR in mediating the GC transcriptional response in vitro and in vivo while keratinocyte MR was identified as an important modulator of GR-mediated transcription in this tissue in response to Dex. 24, 30  These data prompted us to address the cell-type specific role of MR in GR genomic binding and transcriptional regulation in response to Dex in keratinocytes by integrated ChIPseq and RNA-seq analyses. In this study, we show that the absence of keratinocyte MR correlates with the global reduction of GR genomic binding, and elicits differential GR recruitment to specific TF-binding sites, in particular, a reduction in the number of GREs as well as number of palindromic motifs per GBS (Figure  2 ). Changes in MR EKO versus CO were also in line with a decreased GR nuclear/cytoplasmic ratio (Figure  3D ).
Even in GRE-containing GBS present in both genotypes, we observed a lower tag count in MR EKO (Figure  2D ), suggesting an overall decreased affinity of GR for genomic targets. While these data strongly suggest that Dex induces GR and MR co-binding to DNA, direct proof of concurrent binding is lacking. Also, we cannot rule out that different association patterns of GR in MR EKO versus CO cells after Dex are due to the lack of MR tethering. 16 owever, the fact that CORT elicited robust MR binding to GBSs identified by GR ChIP-seq (Figure  S3 ) indicates that distinct receptor-ligand complexes feature different affinities toward regulatory sequences. 23 ne major finding is the demonstration of physical complex formation between GR-and MR-LBDs by SPR analyses (Figure  4 ). The estimated K D for GR homodimers is similar to previous data on GR and other steroid receptors such as AR and ER.
53 Importantly, to the best of our knowledge, this is the first report demonstrating the measurement of MR-LBD self-association affinity and kinetics constants. Indeed, the estimated K D for MR homodimers was one order of magnitude lower than that obtained for GR homodimer, indicating higher affinity. However, homodimers of MR had the shortest lifetime, followed by those of GR and GR-MR heterodimers (Figure  4 ). Taken together, MR-containing dimers showed higher affinity while GR-containing dimers showed the longest lifetime. Therefore, as MR dimers likely represent a minor fraction due to low protein abundance, data indicate that MR mainly acts as modulator of GR-dependent transcription, placing the GR-MR heterodimer-with similar affinity to the GR homodimer but a 4. 5-fold longer lifetime-as a key player in response to synthetic GCs.
Our data are also consistent with previous evidence demonstrating that GR-MR interactions can be mediated by alternative interfaces including the GR-LBD. 15, 21 Also, while we demonstrated that GR is able to form oligomers,  15, 53, 54  preliminary data indicate that neither MR-MR nor MR-GR form higher order complexes (data not shown). Collectively, our data not only emphasize the importance of the GR:MR balance in keratinocytes but also support the idea that GR-MR heterodimers represent an important mechanism to extend the regulatory potential of GCs in cells that co-express both receptors. 22 Indeed, combinatorial possibilities of GR-MR dimers likely contribute to cooperative or inhibitory transcriptional effects after Dex treatment. The fact that MR loss results in decreased (Tsc22d3/Gilz) or increased (Per1 and Sgk1) gene expression (Figure  5C ) is consistent with previous reports of MR as a cooperative or antagonistic partner for GR in the GC-dependent transcription.
In addition, there was higher co-occurrence of p53 and other TF motifs at GRE-containing GBS in MR EKO versus CO cells (Figure  2F ; 12% vs 6%, respectively), likely due to constitutive increased levels of p63 in MR EKO keratinocytes (Figure  3A, B ). We also identified additional TF motifs for STAT, KLF, and NF-1, although overrepresentation was not significant likely due to the restricted cut-off used (data not shown). Despite the changes in GR genomic binding in MR EKO relative to CO, the transcriptional profiles of both cells upon Dex treatment showed a high overlap (Figure  5  and Figure  S5 ). Nonetheless, several known GC targets with important roles in keratinocytes were differentially expressed upon Dex treatment only in CO cells, including Krt6a, Foxp1, Mt1, Mt2, Cxcl1, Spred2, Axin2, Grhl3, and Sprr2h.
This is likely due to the basal differences in the transcriptomic profile of MR EKO versus CO keratinocytes, with overrepresentation of keratinocyte differentiation process, including p63 targets such as small proline-rich repeat (Sprr)2 genes (data not shown). This is also consistent with more differentiated status of MR-deficient keratinocytes in vivo as shown by patches of increased keratinization in the epidermis of MR EKO relative to CO mice. 36 he choice of time points to perform ChIP-seq and RNA-seq (1 h vs 3 h) was based on previous analyses that showed optimal binding and gene expression changes at these time points. 38 However, genomic data showed poor association between these two parameters in both genotypes (Figure  S5 ), consistent with previous reports in other cell types. This may be also due to the fact that binding of MR and GR to specific genomic sites is cyclic, as reported in renal cells.