dataesr/scientific-paragraphs-categorization huggingface dataset:
mRNA Abundance and Coordination of Mitochondrial Biogenesis in Drought We examined the abundance of selected nuclear transcripts coding the investigated drought-responsive proteins. RT-qPCR assays were carried out with application of primers specific for the dedicated cDNA fragments (Table  S7 ). As an internal calibrator of gene expression, cauliflower actin1 (ACT1) mRNA was applied (the partial sequence was previously cloned and deposited in GenBank under accession. version KC631780. 1; [13] ). The cauliflower AOX1a partial sequence was previously cloned and deposited in GenBank (accession. version KC631778. 1; [13] ). Accumulation of AOX1a mRNAs was significantly increased under mild drought in "C"; on the contrary, they were strongly decreased in abundance under mild and severe drought in "A" and severe drought in "C" (Figure  4B ).
We used antisera that were able to immunodetect respective cauliflower PUMP isoforms. An observed decrease in abundance prevailed the pattern, and only in severe drought was the increase in PUMP 40 kDa abundance in "P" noted (Figure  3 ). Also, the abundance of selected PUMP isoforms in pea mitochondria was increased in drought  [19]. To extend our proteomic data on further components of energy dissipating systems, we also investigated AOX protein accumulation. This enzyme controls respiration rate and photosynthetic efficiency in drought, and often increases in abundance during severe water deficit and re-watering  [22, 76, 77]. Three polypeptides of 29-36 kDa in cauliflower mitochondria  [13]  likely represent AOX isoforms resulting from the expression of AOX gene family and diverse posttranslational modifications of this protein (Figure  4A ).
Growing evidence supports the presence of miRNAs (nuclear-encoded and processed before import to mitochondria) or components of miRNA biogenesis machinery within mitochondria  [84] [85] [86] [87]. We focused on in silico miRNA candidates that may putatively target B. oleracea AOX1a by psRNATarget (Available omline: http://plantgrn. noble. org/psRNATarget/analysis?function=2;  [88] ) search (Table  S8 ). Arabidopsis and Brassica miRNAs were taken into account, because of the relative high similarity of AOX1a nucleotide sequence (GenBank accession. version KC631778. 1) between those genera. According to our data, most of the predicted miRNAs resulted rather in messenger degradation, than in affected translation (Table  S8 ). This does not obviously exclude the possibility that multiple non-coding RNAs could influence AOX1a mRNA and protein abundance.
Proteome Analysis Gels were scanned and evaluated using ImageMaster 2D Platinum v7. 0 software (GE Healthcare). After spot detection, 2D gels (three gels from three independent biological samples) were aligned and matched, and normalized spot volumes were determined quantitatively. For each matched spot, the percent volume was calculated as the volume divided by the total volume of matched spots. Spots with variations in abundance were subjected to ANOVA, Tukey-Kramer HSD tests and contrast analysis (JMP software, SAS Institute, Cary, NC, USA) to assign spots that significantly varied (p < 0. 05) in abundance for two factors: drought and cultivar, and their interactions (Table  S2 ). An unpaired two-tailed Student's t-test was used to assign significant variations in abundance (a given drought treatment vs.
05% formic acid with a flow rate of 150 nL min -1. The column outlet was directly coupled to the electrospray ionization (ESI) ion source of an Orbitrap Velos type mass spectrometer (Thermo Electron Corp. , San Jose, CA, USA), working in the regime of data dependent MS to MS/MS switch. An electrospray voltage of 1. 5 kV was used. A blank run preventing cross-contamination from previous samples preceded each analysis. Proteins were identified using the Mascot search algorithm (Available online: www. matrixscience. com) against the NCBInr (Available online: www. ncbi. nig. gov) databases. Protein identification was performed using the Mascot search probability-based molecular weight search (MOWSE) score. The ion score was -10 × log(P), where P was the probability that the observed match was a random event.
Statistical Analysis All experiments were conducted in triplicate, unless otherwise indicated. Results of densitometric analyses (Sections 2. 4, 2. 5, and 2. 7), and 2D PAGE spot pattern alterations based on the spot volume (Sections 2. 2 and 2. 3) are presented as means ± SE. An unpaired two-tailed Student's t-test was used to identify significant differences; in particular, differences were considered to be statistically significant if p < 0. 05 (*), p < 0. 01 (**), or p < 0. 001 (***). Significant correlations (Table  S3 ) among variables were calculated using Spearman's correlation coefficient (p < 0. 05) with the help of STATISTICA 13. 1 (StatSoft Poland, Kraków, Poland) software.
Acknowledgments: This work was supported by  OPUS grant of National Science Centre, Poland  (grant No. 2011/03/B/NZ9/05237 ) as well as by  KNOW Poznan RNA Centre  (grant No. 01/KNOW2/2014 ). We would like also to thank  Mikołaj Knaflewski ,  Alina Kału  żewicz and  Anna Zaworska  ( Poznan University of Life Sciences, Poland ) for additional help during plant material cultivation and physiological analyses. We gratefully thank  Grzegorz Pietkiewicz  and  Jakub Kosicki  ( Adam Mickiewicz University, Pozna ń ) for technical assistance and valuable participation in data statistical analysis, respectively. We are grateful to  Michał Dadlez  and the staff of the  Mass Spectrometry Laboratory  ( Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland ) for MS analyses and the help with protein identification.
2013). Our ability to predict where and when wildlife-vehicle collisions most likely occur may actually be hampered by specific habitat choice behaviours and its ecological correlates in the landscape. Based on data recorded at the management unit (MU) scale over 9 departments between 1990 to 2006 in France, we first describe the spatial distribution of vehicle-wildlife collisions of the sympatric red deer, roe deer and wild boar. At this scale of observation, we aim at explicting what factors affect the number of roadkills in a given MU. To do so, we compare the relative effects of a set of environmental, biological and anthropogenic variables on the number of wildlife-vehicle collisions among species, using a Bayesian statistical framework to test the following predictions (Table  1):  1.
Explanatory variables of observation Three types of explanatory variables were used to describe the road characteristics (anthropogenic variables), the landscape patchiness based on habitat composition (environmental variables), and the large herbivore populations (biological variables). Anthropogenic variables: We described the road network using the Routes 500 database from the Institut Géographique National  (IGN 2001)  to derive the road density of MUs. We classified roads in four categories based on the importance of road sections for the traffic (see Supporting information 1): local roads, regional roads, national roads and motorways. For each road type, we assigned one of the three possible road density classes (low, medium and high) to the MU to explore non-linearity in the effect of traffic on wildlife-vehicle collisions.
Environmental variables: We extracted landscape variables with a GIS by mapping all MUs and calculating 10 descriptors of the environment. We used the CORINE Land cover (  2006 ) database to derive a categorical variable "Habitat type" (HT) describing the land cover type in the MUs. For each MU, HT returns the proportion of the area covered by forest, agricultural crops, natural open areas, and urban and anthropogenic habitats (4 habitat types). Moreover, we indexed the fragmentation of the forested habitat with the number of connected forest patches in the MU. Let F f be the number of connected forest patches. To account for the larger number of forested patches in larger MUs we scaled F f by the area of the corresponding MU, yielding a density of connected forest patches D f. However, because the relationship between D f and the proportion of forest in the MU was non-linear (i.
e. D was redundant with the land cover variable "Forest"), we used a nonparametric loess regression (degree = 2, span = 75%, Cleveland, 1993) to predict the logarithm of D as a function of the proportion of forest in a MU. Ultimately, we used the residuals of this regression as an index of the forest fragmentation (noted R f ), whereby a positive values meant more forest patches in the MU for a given forest cover, and conversely for negative values. Following the same procedure, we calculated a fragmentation index of urban patches (R u ), using the density of urban patches F u instead of the density of forest patches. Finally, we used the IGN geographic database to calculate a 3-classes categorical variable of elevation defined as the proportion of the MU area found <600 m, between 600 m and 1 500 m, and > 1 500 m a.
s. l. Biological variables: For the three species of large herbivores, we assessed population abundance with the number of harvested animals per km 2 for each MU (referred to "hunting bag"; see  Seiler (2005) ,  Morelle et al. (2013)  for a similar approach). We used the number of harvested animals per km 2 during the previous hunting season, spanning from September of year t -1 to the end of February of year t, to characterise the abundance of a species during year t and to use it as a predictor of the density of wildlife-vehicle collisions. We hence make the assumption that annual hunting bag is positively associated with the population abundance of large herbivores. These data were provided by the local hunting associations in the 9 departments, for every hunting season for which we had collisions data.
Bayesian model fit and variable selection For each species, we first modelled the number of collisions in a MU with a log-linear model with mixed effects. We assumed a Poisson distribution for the number of collisions, and we classically modelled the logarithm of the mean of this distribution as a linear combination of the characteristics described in the previous section (the usual log-link was used to ensure a positive predicted mean number of collisions). We accounted for the overdispersion in the response variable by including Gaussian residuals in our linear predictor, following the approach of  Hadfield (2010). We entered the department (9 levels factor) as a random effect on the intercept. We fitted this model in a Bayesian context with the JAGS software  (Plummer 2016). We used a Bayesian variable selection approach to identify the variables affecting the density of vehicle-wildlife collisions in a MU, that is the number of collisions per surface unit.
5 ) in four contrasting environments: (i) a MU (labeled "Forest"), characterized by 91% of forest and 3% of the three other habitat types, (ii) a MU with a high land-cover of agricultural areas (labeled "Agri"), characterized by 91% of agricultural areas and 3% of the three other habitat types, (iii) a MU (labeled "Open"), characterized by 37% of open areas, 30% of forests, 30% of agricultural areas, and 3% of urban areas, and (iv) an urbanized MU (labelled "Urban") characterized by 16% of urban area, 14% of open area, 35% of forests and 35% of agricultural areas. A careful examination of our dataset revealed that these particular sets of environmental conditions are typical of those encountered in France: thus, the habitat composition in the "Forest" MU is typical of the more forested management units observed in our dataset; the habitat composition in the "Urban" MU is typical of the highly urbanized management units, etc.
Data and code accessibility We have bundled the code used for the analysis as well as the complete data set in a R package named "ungulateCollisions". This package contains a vignette named "modelfit" which describes how the analyses carried out in this paper were performed. To install it, the reader should first install the software JAGS on their computer (see http://mcmc-jags. sourceforge. net/ for instructions). The reader can easily install the package and access the vignette by copying the following R code in the R console:    interpreted as a proxy of population abundance, the density of national roads (increasing from top to bottom, this variable was already discrete as for figure  1 ), and the landcover by four habitat types. Wildlife-vehicles collision data were collected in 9 departments (administrative boundaries) of France from 1990 to 2006.
None of these diagnostics showed any evidence of nonconvergence of the MCMC. We then examined the fit of the model using the approach recommended by  Gelman and Meng (1996). For every iteration of the MCMC, i. e. for every value θ r = β 0. of the vector of parameters of this second model sampled by MCMC, we simulated a hypothetical replication of the dataset using equation (1), i. e. we simulated a number of collisions in each MU. We then compared the observed number of collisions with the statistical distribution of simulated numbers of collisions. We calculated that 99% (red deer), 100% (roe deer) and 100% (wild boar) of the 90% of the credible intervals contained the observed number of collisions, which indicates that the fit was correct for the three species. Note that it is difficult to compare coefficients β j of a variable j across models containing different variables and different sampling units  (Becker and Wu 2007) , which precluded the comparison of the models across species.
Changes to ZGA caused by the single or combined LOF of Wnt, Nodal and/or BMP were then determined at the late blastula stage on a transcriptome-wide scale using deep RNA sequencing (RNA-Seq). Analysis was limited to the 3,315 zygotic genes for which spatiotemporal expression data is available  (Blitz et al. , 2017; Owens et al. , 2016)  and where reduced expression (≥50% loss of exonic and/or intronic transcript counts, FDR ≤10%) could be detected in α-amanitin-injected embryos (Figure  2C  and Table  S3 )  (Gentsch et al. , 2018b). α-Amanitin-mediated inhibition of RNAPII elongation impedes the morphogenetic tissue movements of gastrulation and ultimately leads to early embryonic death  (Gentsch et al. , 2018b). Spatial gene expression patterns were inferred from experiments comparing the transcriptomes of embryos dissected along their animal-vegetal and dorso-ventral axes  (Blitz et al.
Gap coordinates were obtained from the Francis Crick mirror site of the UCSC genome browser (http://genomes. crick. ac. uk). Simple repeats were masked with RepeatMasker v4. 0. 6  (Smit et al. )  using the crossmatch search engine v1. 090518 (Phil Green) and the following settings: RepeatMasker -species "xenopus silurana tropicalis" -s -xsmall. Tandem repeats were masked with Jim Kent's trfBig wrapper script of the Tandem Repeat Finder v4. 09  (Benson, 1999)  using the following settings: weight for match, 2; weight for mismatch, 7; delta, 7; matching probability, 80; indel probability, 10; minimal alignment score, 50; maximum period size, 2,000; and longest tandem repeat array (-l), 2 [million bp]. The enrichment and occurrence of predetermined DNA binding motifs was calculated using 100 bp centred across the top 2,000 peaks per chromatin feature and developmental stage: findMotifsGenome.
Differential Gene Expression Analysis Differential expression analysis was performed with both raw exon and intron counts excluding those belonging to ribosomal and mitochondrial RNA using the Bioconductor/R package DESeq2 v1. 22. 1  (Love et al. , 2014). In an effort to find genes with consistent fold changes over time, p-values were generated according to a likelihood ratio test reflecting the probability of rejecting the reduced (~ developmental stage) over the full (~ developmental stage + condition) model. Resulting p-values were adjusted to obtain false discovery rates (FDR) according to the Benjamini-Hochburg procedure with thresholds on Cook's distances and independent filtering being switched off. Equally, combinatorial LOF profiling and regional expression datasets  (Blitz et al.
, 2017)  without time series were subjected to likelihood ratio tests with reduced (~ 1) and full (~ condition) models for statistical analysis. Fold changes of intronic and exonic transcript levels were calculated for each developmental stage and condition using the mean of DESeq2normalized read counts from biological replicates. Both intronic and exonic datasets were filtered for ≥10 DESeq2-normalized read counts that were detected at least at one developmental stage in all uninjected or DMSO-treated samples. Gene-specific fold changes were removed at developmental stages that yielded <10 normalized read counts in corresponding control samples. Next, the means of intronic and exonic fold changes were calculated across developmental stages. The whole dataset was confined to 3,318 genes for which at least 50% reductions (FDR ≤10%) in exonic (default) or intronic counts could be detected in α-amanitin-injected embryos.
Analysis of Enriched Gene Ontology (GO) Terms Over-represented GO terms were found by applying hypergeometric tests of the Bioconductor/R package GOstats v2. 42. 0  (Falcon and Gentleman, 2007)  on gene lists. The process was also supported by the Bioconductor/R packages GSEABase v1. 44. 0  (Morgan et al. , 2017)  and GO. db v3. 4. 1  (Carlson et al. , 2007). The gene universe was associated with GO terms by means of BLAST2GO  (Conesa et al. , 2005)  as previously outlined  (Collart et al. , 2014; Gentsch et al. , 2015).
ACKNOWLEDGEMENTS We thank  Abdul Sesay ,  Leena Bhaw ,  Harsha Jani ,  Deborah Jackson  and  Meena Anissi  for deep sequencing;  Mareike Thompson  for critical reading of the manuscript; and the  Smith lab  for discussions and advice. G. E. G and J. C. S. were supported by the  Medical Research Council  (program number  U117597140 ) and are now supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001-157 ), the  UK Medical Research Council  ( FC001-157 ), and the  Wellcome Trust  ( FC001-157 ).
DATA AND SOFTWARE AVAILABILITY Sequencing reads (FASTQ files) and raw RNA-Seq read counts reported in this paper are available in the GEO database (www. ncbi. nlm. nih. gov/geo) under the accession numbers GSE113186 and GSE122551. All analyses were performed in R v3. 5. 1 (Bioconductor v3. 8), Perl v5. 18. 2 (https://www. perl. org) and Python v2. 7. 12 (http://www. python. org) as detailed above. The R code, genome annotation, intermediate datasets and graphs are available on GitHub at https://github. com/gegentsch/SpatioTemporalControlZGA. Original datasets are also available on Mendeley Data at http://dx. doi. org/10. 17632/jn466b4n8v. 1.
Processing of External Datasets High-time (30-min intervals) resolution of total and poly(A) RNA-Seq (GSE65785) was processed as reported in the original publication  (Owens et al. , 2016). In addition, intron read counts were corrected by spike RNA-derived normalization factors. For visualization normalized exon and intron counts were scaled to the maximal count detected across the time course and fitted using cubic smoothing splines from 0 to 23. 5 hpf: smooth. spline(1:48, x, spar=0. 6). Other RNA-Seq (GSE81458) and ChIP-Seq (GSE67974, GSE30146, GSE53654 and GSE72657) were processed as described in detail above except for H3K4me3 and H3K36me3 whose enriched regions were detected as follows: findPeaks -style histone -fragLength 175 -inputFragLength 175 -fdr 0. 001 -gsize 1. 435e9 -F 2 -C 1 -region -size 350 -minDist 500.
Identification of Variable Protein Spots Twenty two spots (3. 2% on the silver-stained master gel) were significantly variable (verified by the analysis of variance [ANOVA] and Tukey's honest significant difference [HSD] test) as detected using Image Master 7 Platinum software. Spot positions depicted in Figure  1 , Figure  S1  and S2 were calculated from three biological replicates. All spots were successfully identified by LC-MS/MS. The obtained data were used for searching Mascot against the National Centre for Biotechnology Information (NCBI) database (version 20100203). Cauliflower stress-responsive mitochondrial proteins were identified by using the Viridiplantae section of the database (with the aid of Arabidopsis and B. oleracea nuclear and mitochondrial  [53]  genomes). To avoid possible misidentifications resulting from large datasets, as pointed out by Schmidt et al.
Functional Categorization of Identified Proteins Based on Arabidopsis protein orthologues, we used a functional categorization (FunCat) scheme at the Munich Information Center for Protein Sequences database (Available online: Immunoblotting of ATP1 on 2D blots containing cauliflower curd mitochondrial proteins from control grown plants (K) and from heat-recovered plants (HA). 100 µg of mitochondrial proteins were loaded onto all gels. 24-cm immobilized pH gradient strips (linear pH 3-10) for the first dimension and precast Ettan DALT 12. 5% SDS-polyacrylamide gels for the second dimension were used. For protein transfer onto Immobilone membrane, a semidry system was applied. Blots were probed with polyclonal antibodies raised against mitochondrial ATP synthase subunit α (ATP1). Detection was carried with chemiluminescence assays after incubation with HRP-conjugated secondary antibody.
The next ones were represented by respiratory chain (RC) components and protein synthesis apparatus (each of ca. 9%). Interestingly, eight spots (36%) representing six proteins were already annotated as stress responsive in the MIPS database. Spots linked to RC components increased in abundance after heat stress, as well as after heat recovery, however, the ones linked to amino acid metabolism were upregulated after cold and heat stress. In contrast, spots linked with carbohydrate metabolism decreased in abundance after cold and heat, but markedly upregulated in cold-and heat-recovered plants (Figure  3 , panel: protein spots by abundance), Interestingly, the total abundance of spots related to the protein fate showed some increase after cold, but neither after heat stress (where it was decreased), nor after recovery phase.
The next ones were represented by respiratory chain (RC) components and protein synthesis apparatus (each of approximately 9%). Interestingly, eight spots (36%) representing six proteins were already annotated as stress responsive in the MIPS database. Spots linked to RC components increased in abundance after heat stress, as well as after heat recovery, however, the ones linked to amino acid metabolism were upregulated after cold and heat stress. In contrast, spots linked with carbohydrate metabolism decreased in abundance after cold and heat, but markedly upregulated in cold-and heat-recovered plants (Figure  3 , panel: protein spots by abundance), Interestingly, the total abundance of spots related to the protein fate showed some increase after cold, but neither after heat stress (where it was decreased), nor after recovery phase.
However, after heat recovery, leaf transpiration was slightly elevated (Figure  6 ). respiration and (PhR) photorespiration (all expressed in µmol CO2 m -2 •s -1 ) at 200 (RT200 and PhR200), 400 (RT400 and PhR400) and 600 (RT600 and PhR600) µmol m -2 •s -1 illumination rate in control grown (K), heat-stressed (H), heat-recovered (HA), cold-stressed (C) and cold-recovered (CA) plants. All parameters were measured on 3-month-old plants with fully developed leaves with the application of an infrared gas analyser. Data were recorded after at least 2 h of illumination. During the experiment, each of the analysed leaves were placed into a 6-cm 2 chamber of the analyser. Results were recorded after initial leaf acclimation to the desired light and CO2 concentration, relative humidity and temperature.