dataesr/scientific-paragraphs-categorization huggingface dataset:
The MRR estimates were based on a local reflectivity-to-snowfall rate relation, obtained for summer snowfall data only. An important assumption, which will need to be verified or improved, is that we considered this relation representative for the entire year of MRR measurements. Precipitation occurrence was generally overestimated by ERA-Interim with respect to the MRR, especially in the summer period, and was underestimated by the visual reports collected by Météo France. The overestimation of occurrence by ERA-Interim could be due to its microphysical parameterization or to a spatial resolution that is very different from the one of the point measurements used as a reference. On the contrary, the underestimation of occurrence by visual reports is probably due to their discontinuous nature and the difficulties in discriminating, at the ground, pure precipitation and blowing snow.
Future work should also focus on better discriminating between snowfall and blowing snow, on the validation of satellite-based snowfall retrievals since it is of great interest to monitor the entire Antarctic continent, and in further validating ERA-Interim reanalyses and other weather and climate models. Data availability. All the relevant observations collected in the framework of the APRES3 project will be made available as soon as possible on the website of the project (http://apres3. osug. fr), or upon direct request to the authors. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. The authors are thankful to  Météo France , and in particular to the team of  Dumont d'Urville  who provided the access to their in situ measurements and observations. We thank the  French Polar Institute (IPEV) , in particular Gregory Tran, www. the-cryosphere. net/11/1797/2017/The Cryosphere, 11, 1797-1811, 2017.
To date, 1,336 sheep QTL influencing 212 different traits have been reported in a total of 119 publications (http://www. animalgenome. org/cgibin/QTLdb/index; accessed at 24 November 2016)  [5]. In relation to milk traits, 242 QTL have been reported  [5]. However, the traditional methodology used for QTL mapping with genome-wide sparse microsatellite markers or with low/middle density Single Nucleotide Polymorphism (SNP) genotyping platforms makes it difficult to identify the true causal mutations underlying these complex traits. Over the last few years, the constant improvement of high-throughput sequencing platforms and the availability of genome sequencing data have facilitated the detection of a substantial number of genetic variants in livestock  [6, 7]. The identification of this genomic variation is crucial to the rapid identification of mutations that compromise animal health and productivity but also to build a database of polymorphisms that could be used as molecular markers for more accurate genomic predictions and genome-wide association studies  [6].
33 million non-duplicated paired-end reads per animal mapped to the Oarv3. 1 genome assembly. General RNA-Seq metrics obtained with the RSeQC software  [14]  that consider the annotation bed file of the reference sheep genome are summarized in Table  1. In our dataset of the sheep MSCs transcriptome, an average of 120. 47 million tags per animal were defined. The term "tag" accounted for the number of times one read is spliced. The RSeQC program assigned an average of 110. 08 million tags per merged sample to the annotated sheep genome regions. Therefore, approximately 10. 39 million tags were not assigned to annotated regions, suggesting that approximately 10 million tags per sample mapped to intergenic regions. The comparative analysis performed in a previous study of the assembled transcripts of this RNA-Seq dataset with the ovine genome assembly Oar_v3.
The high transcription rate of the casein cluster region, with an average of 3. 48 million of tags per kb of exon, refers to the transcription of both exons and the surrounding intronic regions. Hence, it is remarkable that a very high number of tags per kb of intron was found in the casein cluster region (7011. 22 tags per kb of intron) when compared with the average across the whole sheep genome (16. 86 tags per kb of intron). Previous RNA-Seq analysis suggest that the pattern of the intronic sequence read coverage in RNA-Seq could be explained by an inefficient poly(A) + purification  [22] , the presence of intronic reads flanked by poly(A) + stretches  [23]  or by transcripts undertaking splicing after polyadenylation  [23]. The annotation analyses performed with SnpEff  [24]  and Variant Effect Predictor (VEP)  [25]  are summarized in Table  2.
The number of variants processed with SnpEff was higher (216,637) when compared to the variants processed with the VEP software (212,742) because SnpEff performs the annotation of the variants present in the whole domestic sheep genome (Oar_v3. 1), chromosomes and scaffolds, whereas VEP only annotates variants within ovine chromosomes. Variants were assigned to four types of biological impact based on the significance of the effect of the variant: high (e. g. , frame shift, stop gain/loss, start loss, etc. ); moderate (e. g. , nonsynonymous coding changes, codon insertion/deletion, etc. ); low (e. g. , synonymous changes etc. ); or modifier (used for terms with hard-to-predict effects and markers) (Table  2 ). The number of functional effects assigned was larger than the number of loci because the categories were not mutually exclusive.
Among the total number of effects detected, the vast majority of the variants were predicted to have modifier impacts by both software programs (312,170 with SnpEff and 232,768 with VEP) (Table  2 ). This is because most of the variants detected were located in downstream gene regions (Table  2 ). Among the distribution of the variants by type of effect, the results of the two annotation tools were generally consistent (Table  2 ). Only two non-coding categories show marked discrepancies as follows: the variants annotated as intergenic regions and the variants annotated as non-coding transcript variants (Table  2 ). A higher number of variants were found by SnpEff than by VEP in intergenic regions (96,639 and 16,991, respectively), which could be due to the different performances of the annotation algorithms.
The VEP software found a greater number of non-coding transcript variants than SnpEff (9,492 and 10 variants, respectively) because VEP annotates regulatory region variants without providing additional datasets to the software  [25]. Among the results described in Table  2 , it is remarkable the large proportion of variants identified within non-coding regions (e. g. downstream, intergenic, intronic variants) which could indicate the presence of variants in unannotated exons and/or noncoding but functionally transcribed genomic regions. As we have pointed above, the 62% of the transcripts detected within the ovine MSCs transcriptome were intergenic and moreover, the 11% were classified as potentially novel isoforms  [15]. Therefore, the detection of variants out of known protein coding regions can be expected.
Approximately 82. 15% of the identified novel variants were considered sequence modifiers; the remaining (~17,85%) were inferred to produce high impact (2. 52%), moderate impact (7. 42%) or low impact (7. 91%) changes in the corresponding transcriptional unit (Fig. 2 ). Considering that the variants found within QTL regions may have been a consequence of selective pressures related to dairy production traits, we performed a functional enrichment analysis of the genes containing the variants with high and moderate functional impacts. For this analysis, we considered the variants that were classified as high and moderate impact variants (Fig. 2 ) by the two annotation software programs used, SnpEff  [24]  and VEP  [25]. However, based on the large number of moderate missense variants identified by both programs (Fig.
2 ), we performed additional filtering to consider only the missense mutations predicted to be deleterious by SIFT  [28] , an external tool implemented in the VEP software that predicts the effects of an amino acid substitution on protein function. Hence, after discarding those variants predicted to be tolerated, a final total of 371 unique genes containing relevant functional variants (Additional file 2) were used to perform a functional enrichment analysis using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt)  [29]. These genes were categorized by 14 enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms (p adj < 0. 05) (Additional file 3). The highest enriched KEGG pathway was "protein processing in endoplasmic reticulum" with a p adj of 2. 60e-05. Metabolic processes in endoplasmic reticulum (ER) are associated with the synthesis and folding of membrane and secretory proteins as well as lipid synthesis.
The large number of tags mapped to introns within the casein cluster, which was pointed above, together with the higher variability generally expected in non-coding regions may explain the high level of genetic variation identified in this region. Among the variants detected in the coding regions by both software programs (SnpEff and VEP), we found one splice donor variant, which was classified as a high impact effect mutation, and ten missense variants. These mutations found within protein genes are summarized in Table  3. The splice donor variant found in the CSN1S2 gene is a novel variant that was detected in the two studied breeds (allele frequency of 0. 625). This variant affects a putative splice donor site at the third intron of the CSN1S2 gene (GCA_000298735. 1:6:85186875:G:A). Thus, this SNP could cause intron retention resulting in a novel isoform of CSN1S2, which should be confirmed by further research.
Animals and sampling For this study, a MSCs transcriptome dataset from Assaf and Spanish Churra dairy sheep breeds was used. The dataset is available in the Gene Expression Omnibus (GEO) database under the accession number GSE74825. The source of the animals and the sampling process protocol are described in detail in the related data descriptor manuscript  [66]. The milk samples of eight healthy sheep (four Churra and four Assaf ewes) belonging to the commercial farm of the University of León were collected on days 10 (D10), 50 (D50), 120 (D120) and 150 (D150) after lambing. At each sampling timepoint, we collected 50 ml of milk from each ewe one hour after the routine milking at 8 a. m. and ten minutes after the administration of five IUs of Oxytocin Facilpart (Syva, León, Spain). The time-point for milk collection was chosen to maximize the concentration of MSCs.
Alignment, variant identification and annotation The read qualities of the RNA-Seq libraries were evaluated using FastQC  [69]. Using the STAR aligner [70] the reads were mapped against the ovine genome assembly v. 3. 1. (Oar_v3. 1 [71] ). After the alignment, Samtools [72] was used to convert sam files to bam files and then to sort and merge the bam files from the same animal at different time-points. Metrics from the bam files were obtained with RSeQC software  [14]  based on the annotation bed file of the Oar_v3. 1 sheep assembly obtained from the UCSC Genome Browser  [73]. Then, Picard [74] was used to add read groups and mark duplicated reads on the merged bam files. SNP and Indel calling was performed using the Genome Analysis Toolkit (GATK, version 3. 4. 46) software package following GATK best practices  [75].
To obtain high-quality variants, strict filter conditions were applied using vcffilter [76] and SnpSift [77] (Variation Quality (QUAL) >30, Mapping Quality (MQ) >40, Quality By Depth. (QD) >5, Fisher Strand (FS) <60 and a minimum Depth of coverage (DP) >5 in all the samples). The bcftools "annotate -c ID" option [72] and the ovine reference vcf file downloaded from the Ensembl database (SNPdb-version 143) were used to annotate the known variants detected in our study. Two software programs, SnpEff  [24]  and Variant Effect Predictor  [25] , were used to predict the functional consequences of the detected variants. SnpEff allows users to define specific intervals and customize the annotation of the variants. Considering that the final aim of this study is the characterization of the transcriptome variants that may be of special interest for the dairy industry, we used SnpEff to select (i) the variants included within previously reported sheep QTL studies for milk protein percentage, milk fat percentage and milk yield  [5]  and (ii) the variants included within candidate genes related to milk protein and fat content.
Filtering variants in QTL regions affecting milk production traits The coordinates of the genomic regions containing the QTL related to milk protein percentage, milk fat percentage and milk yield, based on the annotation of the SheepQTLdb  [5] , were downloaded from the Ensembl database [71]. This information, provided as a bed file (Additional file 5), was used by the SnpEff software (-fi option) to retain only the variants matching the target QTL intervals from the total number of variants identified through the GATK protocol. Due to the high number of variants detected in the selected QTL regions (57,795), those variants already described in the Ensembl database were filtered out using vcftools  [78]. Among the novel variants, we selected those which were predicted by the two annotation analyses (SnpEff and VEP) to have relevant functional consequences.
Thus, we retained those variants that were classified in terms of their functional consequences as "high" and "moderate" by the two different software programs. Due to the large number of variants classified as "moderate", within the moderate missense variants, we selected those predicted to be "deleterious" by the VEP option "-sift b"  [25]. This option allows the use of the SIFT tool  [28]  for any of the variants annotated as missense. SIFT is an algorithm that predicts whether an amino acid substitution will have a deleterious effect on the protein function  [28]. Finally, we extracted the names of the genes containing these functionally relevant mutations and used them to perform a functional enrichment analysis with the Webbased Gene Set Analysis Toolkit (WebGestalt)  [29].
Filtering variants on protein and fat candidate genes The candidate genes selected for a detailed analysis of their genetic variability in the studied dataset included those codifying for major milk constituent proteins (CSN1S1, CSN1S2, CSN2, CSN3, PAEP, LALBA) and 17 genes related to mammary gland lipid metabolism (Table  4 ). These genes were selected based on a previous study by our research group that evaluated the gene expression of candidate milk genes in the milk sheep transcriptome that affect cheese-related traits  [51]. To obtain the variants within the target genes selected for the study, we used the -fi option from SnpEff followed by a bed file with the coordinates of the selected genes (Additional files 6 and 7) and the -onlyTr option followed by a file with an ID list with the Ensembl transcripts name of the selected genes.
By utilizing this methodology, we have generated proteomic profiles that reveal consistent molecular fingerprints for synaptic and nonsynaptic mitochondria. Quantitative fluorescent western blotting was used to confirm these proteomic and fractionation data in a range of species, including rat and ovine tissues. These results were consistent with the existence of distinct mitochondrial sub-populations containing patterns of relative protein abundances that are conserved between different mammalian species. To determine if the molecular differences between these mitochondrial subpopulations may be capable of influencing the vulnerability of synapses, we manipulated the expression of mitochondrial candidates in vivo to assess synaptic stability in the Drosophila neuromuscular system. Our data demonstrate that selective knock-down of intrinsic mitochondrial proteins identified in this manner have the potential to alter synaptic morphology and the area of the presynaptic active zone in vivo.
The 15 most intense peptide ions from the preview scan in the Orbitrap were fragmented by collision-induced dissociation (normalized collision energy, 35%; activation Q, 0. 250; and activation time, 10 ms) in the LTQ after the accumulation of 10,000 ions. Dynamic exclusion parameters were set as follows: repeat count, 1; repeat duration, 30 s; exclusion list size, 500; exclusion duration, 45 s; exclusion mass width, plus/minus 10 ppm (relative to reference mass). Maximal filling times were 1000 ms for the full scans and 150 ms for the MS/ MS scans. Precursor ion charge state screening was enabled, and all unassigned charge states as well as singly charged species were rejected. The lock mass (445. 120024), option was enabled for survey scans to improve mass accuracy. Data were acquired using the Xcalibur software (for all raw data see PRIDE project accession: PXD005537).
Raw proteomic data were imported into Progenesis for characterization and analysis of relative ion abundance. 2D representations of MS/MS output were created for each sample and these were aligned to determine similar features (average alignment score > 90%). Following alignment, data was filtered by retention time with features detected below 5 min and above 110 min discarded to correct for elution variability and peptides with charge state between 2 and 5 only included in the search. The runs were grouped according to subcellular localization (synaptic and non-synaptic) and Statistical P values were automatically generated in Progenesis software through a 1 way ANOVA on the ArcSinh transform of the normalized data. Peptides were filtered by the following criteria: power < 0. 8, fold change >2, p<0.
Ingenuity pathway analysis IPA was performed as previously described  [20]  with the interaction data limited as follows: direct and indirect interactions; experimentally observed data only; 35 molecules per network; 10 networks per dataset. Prediction activation scores (z-score) were calculated in IPA. The z-score is a statistical measure of the match between an expected relationship direction and the observed protein expression. Positive z-scores indicate activation (orange) and negative z-scores indicate inhibition (blue)  [21].
Quantitative fluorescent western blotting Quantitative fluorescent western blotting was performed as previously described  [22]. Samples were diluted to provide desired protein concentration. 10-15 μg protein was loaded per well into Nu-PAGE® Novex® 4-12% Bis Tris mini-gels (Life Technologies) and transferred to PVDF membranes using an iBlot® and Invitrogen gel transfer stacks. Membranes were incubated in primary antibodies at 4 °C and secondary antibodies at room temperature (concentrations according to manufacturers instructions) before imaging on Li-COR Odyssey infrared scanner. Protein expression wasquantified utilising ImageStudio Lite software (Li-COR Biosciences). Primary antibodies: citrate synthase (OriGene, Cat. TA310356), VDAC1 (Abcam, Cat. ab14734), telomerase (Abcam, Cat. ab32020), HIBCH (Abcam Cat.
Drosophila stocks Flies were raised on standard cornmeal food at room temperature. Homology of rat gene of interest and Drosophila orthologue was determined by input into DIOPT (DRSC Integrative Ortholog Prediction Tool  [24] ). Offtarget effects of candidate lines were assessed using E-RNAi software  [25]. Candidate lines exhibited no off-target effects based on genetic mapping data and all displayed 97. 21-100% efficiency for targeting the intended gene (Ndufb8 = 97. 75%; mitofilin = 97. 21%; Vdac1 = 100%; Aldh = 100%). The elav-Gal4 driver strain (Bloomington Drosophila stock center) was used for all experiments. Stocks were obtained from the VDRC (IDs: 21,707, 47,615, 101,336, 30,413) and Bloomington Drosophila stock center (Canton-S). Crosses were maintained at 22 °C for 24 h before removal of adults and embryos were incubated in a water bath at 30 °C to increase levels and activity of the Gal4 proteins.
To establish whether size differences existed between synaptic and non-synaptic mitochondria, we examined transmission electron microscopy images from human and mouse cortical tissue. Mitochondria were classified as 'synaptic' if they were <1 μm from the post-synaptic density and vesicles were clearly present within the presynaptic terminal. Mitochondria outside of the pre-and postsynaptic boutons were classed as non-synaptic; anything ambiguous was excluded from the analysis. The results indicate that in both mouse and human cortex, non-synaptic mitochondria appear significantly larger than the organelles present within synaptic terminals ((Fig. 1 ) mouse: p<0. 0001; human: p=0. 0099, respectively). Non-synaptic populations in both species demonstrated elongation versus that of synaptic mitochondria, which displayed a spherical morphology (data not shown).
Label-free proteomics reveals significant mitochondrial heterogeneity To identify molecular differences between mitochondria derived from synaptic and non-synaptic neuronal compartments, we performed label-free proteomics on mitochondrial-enriched fractions isolated from rat forebrain. Quantitative label-free proteomic analyses identified >1500 proteins associated with both synaptic and non-synaptic mitochondria, revealing dynamic variations in protein expression dependent upon subcellular localisation (Fig. 2a  and b ). Strikingly, over 400 proteins were altered by greater than 2-fold between mitochondrial subpopulations (Fig. 2c ), demonstrating significant compartment-dependent biochemical adaptations. Relative purity of mitochondrial isolates was verified using bioinformatics (Table  1 ), quantitative enrichment analyses utilizing the raw proteomic data and quantitative fluorescent western blotting (QFWB) on ovine forebrain.
3c ). In addition to demonstrating purification of mitochondrial subpopulations, we again employed QFWB to determine the veracity of the proteomic data. We observed corresponding protein expression trends in each tissue preparation, as indicated by the proteomics, for multiple proteins (Fig. 3d-f ). Taken together, these results indicate the relative purity of the mitochondrial isolates and suggest the proteomic data is representative of the molecular alterations occurring in the tissue samples. Furthermore, the data indicate that the observed heterogeneity in mitochondrial protein expression may be conserved between mammalian species.
Expression profiling highlights bioenergetic alterations between synaptic and non-synaptic mitochondrial proteomes Having confirmed the veracity of the proteomic data, we next sought to further dissect the proteomic data using Biolayout Express 3D  [31]. The software applies unbiased Markov clustering algorithms to the input data and groups proteins displaying similar expression trends. This allows visualisation of spatial profiles promoting the identification of physiological cascades altered within the dataset. The nodes, shown as spheres with identical dimensions, signify individual proteins and the edges, represented by connecting lines, are indicative of the correlation of protein expression within the data. Graphs were constructed in 3D space utilizing the 1511 identified proteins from the mitochondrial proteomic analysis (see Fig.