dataesr/scientific-paragraphs-categorization huggingface dataset:
qRT-PCR RNA was harvested using the Direct-zol RNA MiniPrep kit (Zymo Research, through Cambridge Biosciences, Cambridge UK) as per manufacturer's instructions. Total RNA (100 ng) was analysed using Taqman gene expression assays and RNA-to-CT kit (Life Technologies) on a StepOneplus Cycler (Life Technologies). Data were normalized to the expression of GAPDH and to controls for each time point using the 2 -ΔΔCt method  [50]. The following primer pairs were used: GAPDH (Mm99999915), DRP1 (Dmn1; Mm01342903), Fis1 (Mm00481580), Mitofusin1 (Mm00612599), Mitofusin2 (Mm00500120), OPA1 (Mm01349707), Oma1 (Mm01260328) and Yme1L (Mm00496843) from Life Technologies.
Secondary antibodies: Li-cor IRDye Goat anti-rabbit 800 or Goat anti-mouse 680 secondary antibodies were used at 1:10,000 and incubated for 2 h at room temperature. Membranes were imaged on a Li-Cor Odyssey Infrared Imaging System (Li-Cor Imaging Biotechnology UK Ltd. , Cambridge, UK) using the manufacturer's Image Studio software for band quantification.
Data Analysis Data are expressed as mean ± SD. All primary neuronal culture experiments were performed on 3-6 independent litters as stated in the figure legends. Mitochondrial length was analyzed using Squassh (segmentation and quantification of subcellular shapes) software, part of the Mosaic toolkit in Image J  [51]. All statistical analyses were performed using GraphPad Prism 6 Software (GraphPad Software, San Diego, CA, USA). All data were assessed for normality. Data was assessed either by a Student's t-test or for multiple conditions, with a two-way ANOVA. If significant, a Bonferroni post-hoc analysis was conducted. Analyses used are detailed in table and figure captions. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001.
Data shown are an average of 360 mitochondria per time point, and mitochondria from the first and last time points analyzed by student's t-test in the panel below, *** p < 0. 001; (d) Primary neurons were subjected to 90 min of OGD followed by up to 24 h incubation in normal growth medium. Lysates were assayed for citrate synthase activity at time points shown following the insult. Data is shown ± SD, n = 4-6 independent litters, determined by two-way ANOVA followed by a Bonferroni post-hoc test, ** p < 0. 01 for interaction and treatment.
Acknowledgments We gratefully acknowledge the support of the  Department of Perinatal Imaging and Health  and financial support from  Wellcome Trust  ( WT094823 ), the  Medical Research Council ,  Action Medical Research, Swedish Medical Research Council  ( VR 2012-3500 ),  Brain Foundation (HH) ,  Ahlen Foundation (HH) ,  ALF-GBG (426401 ), ERA-net (EU; VR 529-2014-7551 ) and the  Leducq Foundation  ( DSRRP34404 ) to enable this study to be completed. CH was supported by the  King's Bioscience Institute  and the  Guy's and St Thomas' Charity Prize PhD Programme in Biomedical and Translational Science. In addition , the authors acknowledge financial support from the  Department of Health  via the  National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre Award to Guy's & St Thomas '  NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust.
Membranes were then incubated with primary antibodies to CD147 (clone A-12,  37  1 : 3000, Santa Cruz Biotechnology, Inc. ), MMP9 (1 : 1500, Abcam), PCNA (1 : 3000, Santa Cruz Biotechnology, Inc. ) or GAPDH (1 : 3000, Santa Cruz Biotechnology, Inc. ) in TTBS supplemented with 5% nonfat dry milk overnight at 4 °C, followed by the appropriate secondary antibodies coupled to horseradish peroxidase (1 : 5000, Santa Cruz Biotechnology, Inc. ). Peroxidase activity was detected on HyBlot CL autoradiography film (Denville Scientific, Inc. , Plainfield, NJ, USA) using SuperSignal West Pico Chemiluminiscence substrate (Thermo Scientific). Immunoblots were quantified using ImageJ software. In situ zymography. In situ gelatinase activity was measured using the EnzCheck Gelatinase Assay kit (Molecular Probes, Carlsbad, CA, USA) following the manufacturer's instructions.
Slides were incubated in LipidTOX Green neutral lipid stain (Invitrogen, Grand Island, NY, USA) in a moist chamber for 30 min at room temperature. Slides were then mounted in VectaShield mounting medium containing DAPI (Vector Laboratories), and observed under a fluorescence microscope. Quantification of lipid staining was performed using imageJ software (NIH, Bethesda, MD, USA). For lipid staining in mouse tissue, cryosections (10-μm thick) were air-dried, rinsed in 60% isopropanol, and stained with 0. 3% Oil Red O (Fisher Biotec, Wembley, WA, Australia) for 15 min at room temperature. The sections were rinsed again and mounted in VectaShield mounting medium for light microscopy. Cell Death and Disease is an open-access journal published by Nature Publishing Group. This work is licensed under a Creative Commons Attribution 4.
Acknowledgements. This work has been supported by  NIH  Grants  EY014847  and  EY024031  to PA and an  ARVO Foundation/Vistakon Dry Eye Research Fellowship  to JM. JD was supported through a  MD fellowship  by the  Boehringer Ingelheim Fonds, Foundation for Basic Research in Medicine, Germany , and by the  Sicca Forschungsförderung of the Association of German Ophthalmologists. SJ was supported through an unrestricted grant from  Research to Prevent Blindness  to the  University of Southern California. We would like to thank  Ashley Woodward ,  Christina Kaiser-Marko ,  Ann Tisdale ,  Balaraj Menon , and  Wendy R. Kam  for their skillful assistance and advice. We also thank  David A. Sullivan  for generously providing the hTERT-immortalized human meibomian gland epithelial cell line, and  M.
In addition, a regulatory network analysis was performed in CORNET to get a view on the regulatory interactions between the coexpressed genes. This analysis used the confirmed regulatory interactions from the AGRIS  (Davuluri et al. , 2003; Palaniswamy   , 2006; Yilmaz et al. , 2011)  and microarray genetarget relations databases. In addition, the text-mining database EVEX  (Van Landeghem et al. , 2013)  was used to find extra regulatory interactions described in the literature. Interactions with neighbor genes were included for the regulatory interactions, adding 144 genes to the network, bringing the total to 346 genes. The coexpression and regulatory interaction analyses showed high connectivity between the differentially expressed genes (Fig. 5 ). The 20 genes with the highest number of interactions (both coexpression and regulatory) are given in Supplemental Table  S8.
Statistical Analysis of Phenotypic Measurements All statistical analyses were performed with SAS/STAT software, version 9. 4 of the SAS System for Windows 7 64bit. (SAS Institute; http://www. sas. com). A mixed model was fitted to the leaf 3 area data, both at transition and maturity. The fixed effects part of the model contained the main effects genotype and treatment and their interaction. The replication factor experiment was included in the model as a random factor to account for the correlation between observations belonging to the same experiment. Type 3 F tests were performed to estimate the pairwise comparisons between the ecotypes at control and stress levels as well as the pairwise comparisons between the ecotypes of the decrease in leaf 3 area due to stress. P values were adjusted for multiple comparisons with the false discovery rate (FDR) implemented in the SAS multtest procedure.
Effects were considered significant at an FDR threshold of 0. 05. Cellular measurements were analyzed like the leaf 3 area data. In the case of a significant interaction (at the 0. 05 significance level) between genotype and treatment, simple F tests of effects were performed to estimate differences between the stress and control treatments for each genotype. In the absence of a significant interaction but the presence of significant main effects, all pairwise comparisons were calculated between the genotypes and between the treatments. P values were adjusted with Tukey's method as implemented in SAS. A longitudinal data analysis was conducted in SAS on the daily measured PRA. A mixed model was used including the main effects of genotype, treatment, time, and all two-way and three-way interaction terms.
RNA Sequencing Library preparation was done using the TruSeq RNA Sample Preparation Kit version 2 (Illumina). In brief, poly(A)-containing mRNA molecules are reverse transcribed, double-stranded complementary DNA is generated, and adapters are ligated. After quality control using the 2100 Bioanalyzer (Agilent), clusters are generated through amplification using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) followed by sequencing on the Illumina HiSeq2000 with the TruSeq SBS Kit v3-HS (Illumina). Sequencing was performed in paired-end mode with a read length of 100 bp. The quality of the raw data was verified with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/; version 0. 9. 1). Next, quality filtering was performed using the FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/; version 0.
0. 13): reads were globally filtered in which, for at least 75% of the reads, the quality exceeds Q20, and 39 trimming was performed to remove bases with a quality below Q10, ensuring a minimum length of 90 bp remaining. Repair was performed using a custom Perl script. Reads were subsequently mapped to the Arabidopsis reference genome (The Arabidopsis Information Resource 10) using GSNAP version 2012-07-20  (Wu and Nacu, 2010) , allowing maximally five mismatches. The concordantly paired reads that uniquely map to the genome were used for quantification at the gene level with htseq-count from the HTSeq. py python package  (Anders et al. , 2014). Data were normalized using TMM, implemented in the edgeR package  (version 3. 5. 27) in R (version 3. 1. 0; Robinson et al. , 2010; McCarthy et al.
They showed that the overall FDR in a stepwise multiple comparison procedure can be controlled below the prespecified FDR significance level a when the sum of the FDR significance levels used in each of the different stages is below a. They also showed that it is advantageous to choose a 1 in the first stage larger than the remaining stages, so that more genes can enter stage II. Here, the overall FDR over tests I. a and I. c in the first stage is controlled at 4%, while the FDR in stage II and III is set at 0. 5%. All analyses were conducted with the edgeR package  (version 3. 5. 27) in R (version 3. 1. 0; Robinson et al. , 2010; McCarthy et al. , 2012).
Interactions between query genes and neighbors were included. From the AGRIS database, only confirmed interactions were used. Computationally inferred regulatory interactions from microarray data were added to the network together with regulatory interactions retrieved from the literature using the EVEX text-mining tool  (Van Landeghem et al. , 2012). The network of regulatory interactions was merged with the coexpression network using Cytoscape. Transcriptome (RNA-Sequencing) data from this article can be found in the ArrayExpress data libraries under accession number E-MTAB-3279.
1 This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (grant no. FP7/2007-2013,  European Research Council  grant agreement no. [ 339341-AMAIZE]11 ), by  Ghent University  ( Bijzonder Onderzoeksfonds Methusalem  grant no. BOF08/01M00408  and  Multidisciplinary Research Partnership Biotechnology for a Sustainable Economy  grant no. 01MRB510W ), by the  Interuniversity Attraction Poles Programme  (grant no. IUAP P7/29  " MARS ") initiated by the  Belgian Science Policy Office , by the  Agency for Innovation by Science and Technology in Flanders  (predoctoral fellowship to P. C. ), by the  Multidisciplinary Research Partnership Bioinformatics: From Nucleotide to Networks  (grant no. 01MR0310W  from  Ghent University  to L. C. ), and by the  Research Foundation Flanders (postdoctoral fellowship  to S.
Material and Methods The data presented in this section have been obtained on a set of Paleozoic-Mesozoic Nubia sandstone samples selected from different locations in Egypt which were followed up and sampled through a cross section extending over more than 300 km through the north of the Eastern Desert and Sinai in Egypt. Four areas are studied in this cross section, namely Gebel Somr El-Qaa in the Eastern Desert, and Gebel Abu Durba, Wadi Soal (at Gebel El-Rome) and Wadi Watir (at Bir Sanafir) in Sinai Peninsula. In the studied sites, the Nubia sandstones are represented by Araba, Naqus, Abu Durba, and Malha Formations (collectively called the Nubia sandstones). More details on the geological background and rock composition will be given in a paper in preparation  [Nabawy et al. , under review].
Results As stated above, we report in Figure  1  the water mass intake plotted vs. the square root of time for eight tested samples. The values for the coefficient of capillarity C and the corresponding correlation coefficient for the linear regression (Table  1 ) show that, overall, the linear approximation is very satisfying. The coefficient of capillarity C varies by one order of magnitude (0. 006-0. 07 g/cm 2 /s 1/2 ). Porosity (17. 7-22. 4%), permeability (42-269 mD) and final saturation after the imbibition test (47-61%) are also reported in Table  1. Based on the analysis of a limited number of rocks, David et al. [3]  expected a correlation between the permeability k and the capillary coefficient C, as both parameters involve fluid displacement within the pore space. To verify this assumption on a larger dataset, capillaritypermeability data of the present study along with those of recent studies on oolitic limestones  [11] , dolostones  [12] , sandstones and carbonates  [3]  and a variety of sedimentary rocks  [13]  are reported in Figure  2.
The investigated volume is therefore a truncated cylinder 18. 6 mm in width and 68. 3 mm and 76. 2 mm in height for Saint Maximin and Savonnières, respectively. Each scanning stage lasts 1 s and is repeated every 5 s. Note that during each 3D volume scanning (32 simultaneous slices), the capillary front keeps rising. The pixel size in each reconstructed image is 0. 15 × 0. 15 mm. ImageJ software package has been used to stack the longitudinal images for a selected set of reconstructed volumes. Out of each stacked volume, two slices normal to the sample axis at heights corresponding to the ultrasonic transducer planes are extracted. The mean gray level in a rectangular crop (185 × 125 pixels) of these slices is computed for selected scanning stages to assess, during the imbibition process, the local change in density of the specimen associated with water invasion.
Acknowledgments We thank  Marie Claude Lynch  for her help with the CT scanner at  IFPEN ,  Lionel Esteban  for his help with the CT scanner at  CSIRO  and  Maxim Lebedev  for discussions and technical support. We also acknowledge  Linda Stalker  from the  National Geosequestration Laboratory (NGL)  to authorize the use of the  Dual-Beam CT Scanner. We have benefited from discussions on seismic wave propagation with Christophe Barnes (GIM-labs). The work on Nubia sandstones was possible thanks to  Ville de Paris  who granted a post-doctoral fellowship to BN during his stay at Cergy-Pontoise and ENS Paris. Finally many thanks to the reviewers.
Carbohydrate and Metabolite Analysis Frozen tissues (20-100 mg) were weighed and ground for 30 to 60 s while frozen using a Tissue Lyser II (Qiagen). Sugars (Suc, Fru, and Glc) were then extracted following the method of  Lunn et al. (2006)  using lactose as an internal standard. Starch was extracted from the pellet generated during the extraction of soluble sugars and quantified by the analysis of Glc resulting from hydrolysis. Samples were analyzed with a high-pressure capillary ion chromatograph system (ICS-5000, PA-20 column; Thermo Scientific Dionex) using a 1-mL injection volume and 45 mM KOH as eluent. Sugar peaks were identified in comparison with known sugars and an internal standard (lactose), and data were analyzed using the formula described previously  (Henry et al. , 2014)  with lactose/Glc ratio of 1:10 for leaf and 1:100 for kernel and cob.
(2014). Reverse transcription quality and absence of genomic DNA contamination were then checked by semiquantitative PCR on 5 mL of complementary DNA (1:100 dilution) in a final volume of 25 mL, using ZmEF1-1 alpha primers (forward, 59-AGACTCACATCAACATTGTGGTCAT-39; reverse, 59-GTTGTCACCTTCAAAACCAGAGATT-39) designed around an intronic region and GoTaq DNA Polymerase (Promega) as recommended by the supplier. For each time point and biological replicate, qRT-PCR was repeated three times. Experiments were performed on three biological replicates. Three out of eight reference genes (Supplemental Table  S1 ) were selected for the normalization of gene expression using the Genorm software  (Vandesompele et al. , 2002). Relative gene expression was calculated using the method of  Hellemans et al. (2007).
ACKNOWLEDGMENTS We thank  Regina Feil ,  John E. Lunn , and  Mark Stitt  for performing the measurements of T6P and other phosphorylated metabolites; the  Harkamal Walia ,  George Graef , and  Brian Waters  laboratories for technical support concerning RNA and sugar extraction, qRT-PCR, and protein activity assays, respectively; and the greenhouse facility workers for daily maintenance of the plants. Received May 20, 2015; accepted August 7, 2015; published August 12, 2015.
The morphotaxonomic approach is usually restricted to benthic macrofaunal taxa whereas the sequencing of eDNA/RNA molecules can extend the taxonomic analysis by including the meiofauna. From both morphotaxonomic inventories and normalized eDNA/RNA sequence data, we reconstruct both the ITI and AMBI indices for 10 stations located at different distances from salmon cages. We compared the indices inferred from molecular and morphotaxonomic diversity datasets and evaluate how these two different views on the benthic communities impact the assessment of the quality of environmental samples.
We then dereplicated the filtered set of sequences into Individual Sequence Units (ISUs) and we removed singletons. We considered as singletons every ISU represented by only one read throughout the entire dataset (i. e. we would keep an ISU represented by one read in more than one sample). Then, we extracted all ISUs matching any entry of a subset of the PR2 reference database 28 containing all Metazoa V4 sequences (23,999 records). We performed BLASTn v. 2. 2. 25+ searches  29  as follows: blastn -word_size 20 -max_ target_seqs 50 -perc_identity 70 -strand plus. We then used MOTHUR v. 1. 33. 3  30  to compute pairwise global alignments (Needleman-Wunsch algorithm) and we built Operational Taxonomic Units (OTUs) using a 3% sequence dissimilarity threshold (average linkage clustering). We chose the threshold of 3% in order to avoid inflated diversity estimates, as it has been shown that the 18S rDNA marker reduces the magnitude of diversity estimates, particularly for the meiofauna  31.
Biotic indices. For both morphological and molecular data (but separately for DNA and RNA), we calculated the Infaunal Trophic Index (ITI) as well as three alternative versions of the AMBI  7. This allows comparing the performance of using either the sequence abundance (H-AMBI) or the OTU richness (S-AMBI) information  34  , or both as it is commonly calculated from morphotaxonomic data (M-AMBI). The taxon-specific bioindicator values for ITI and AMBI were extracted from previous works  [35] [36] [37]  and from the AZTI software v. 5. 0, respectively. Because of the uneven distribution of sequences among the samples, we performed a normalization of the OTU-to-sample dataset prior to the indices computations  38. Briefly, we randomly subsampled the OTUs of each sample replicate 100 times (with replacement), picking a number of reads corresponding to the median of the number of reads per sample (n = 4102).
036, Supplementary Fig. 1  and Supplementary Table  5 ). Similarly, the OTU richness is systematically higher in DNA samples than in RNA samples, irrespective of the threshold used for OTUs clustering (Friedman rank sum test excluding samples 1 and 2 because of missing sample pairs, p-value: 1. 54 10 -5 ). We used the sequence dataset of 163 OTUs clustered at 3% dissimilarity for further analyses of metazoan communities. The OTU richness is significantly higher at remote stations than close to the cages, but only for DNA samples (Fig. 1 , Supplementary Table  6 ). For the RNA data, the OTU richness does not show any particular pattern, except for the most distant station 9 appearing less OTU-rich than the stations close to the cages. Interestingly, even in the DNA data the increase of the OTU richness observed in distant stations is much lower than the number of morphologically identified species.
There are also important differences between morphotaxonomic and metabarcoding analyses at lower taxonomic levels. Although the annelids dominate both assemblages, their richness at the family level is much lower in the HTS data (12 families) compared to the morphological inventory (26 families). This difference is even higher for molluscs, with only 7 genera detected with HTS versus 24 genera with morphological examination. Similarly, none of the 11 Malacostraca species identified morphologically are present in the HTS crustacean assemblage dominated by the copepods and ostracods. Yet, the proportions of the taxa that can be found using both techniques are fairly similar. For instance, out of 11 shared genera, only the proportions of Arthropoda and Mollusca genera are highly skewed (Supplementary Fig.
2 ). This is also the case for the 15 shared families, that are represented by a majority of the OTUs (60. 2%) and species (53. 5%) assigned to a family. In spite of these differences, the congruence between morphological and HTS data is high for the most abundant morphotaxa. The genus Capitella by far dominates both morphological and molecular datasets. The next three most abundant morphotaxa (Nemertea, Malacoceros fuliginous, Tubificoides) represented by more than 200 specimens are also present in the HTS data. Nevertheless, the proportion of taxa present in both morphological and HTS datasets decreases rapidly for the rare ones. In total, less than 20% of morphotaxa are found in the HTS data. Metazoan OTUs distribution. We analyzed the distribution of OTUs in different stations, separately for DNA and RNA, and compared it to the morphotaxonomic inventories (Fig.
Both OTUs are absent from distant samples. The nematode OTUs exceptionally reach the abundance of 43. 7% in a sample, but in general never exceed 10% in AZE samples (on average 5. 04 ± 7. 78% SD) and 5% in distant samples. The distant samples are characterized by highly diversified assemblages compared to the AZE samples. The replicates taken at the same distant station rarely present the same taxonomic composition, as evidenced by sample dissimilarities computed based on the presence/absence of OTUs (Supplementary Fig. 3 ). When compared along with the distant samples, the AZE samples all seem similar, but the replicates remain more similar when the sequence abundance information is used (especially for DNA). Moreover, there is a good congruence between the sequence abundance observed in DNA and RNA datasets (Fig.