dataesr/scientific-paragraphs-categorization huggingface dataset:
tification of these 2 caseinolytic proteases, effects of calpain inhibitors (leupeptin and E-64 both purchased from Sigma-Aldrich, Saint-Quentin Fallavier, France) were assessed before electrophoresis. As expected, the presence of these calpain inhibitors completely blocked the activity of these neutral cysteine proteases (data not shown). The μ-calpain activity was unaffected by force feeding in both breeds (Table  2 ). On the other hand, the activity of the m-calpain was found to be lower in FF than in NFF Muscovy and Pekin ducks. Whereas the activity of the μ-calpain was significantly lower in Muscovy ducks than in Pekin ducks regardless of the diet; that of the m-calpain was similar for the 2 breeds. Finally, total calpain activity was not influenced by breeds or diets.
Cows and experimental procedures The data used to test the real-time EB body method came from an already completed feeding experiment using 88 cows  (Weisbjerg and Munksgaard, 2008). Therefore, it was not necessary to seek additional ethical committee approval to conduct the present study. This particular data set was well suited to test the ability of the method to detect short-term changes in EB, because the cows were given an increased concentrate allowance for a period during early or midlactation (see below). For the purpose of the present study, 11 cows were removed from data because of incomplete weight data over the lactation, leaving 77 cows in the present data set, calving from October 2005 to May 2006. All cows were loose-housed throughout the year in a barn at the Danish Cattle Research Centre (Tjele, Denmark) with an automatic milking system (Voluntary Milking System (VMS), DeLaval, Tumba, Sweden) and automatic feeding stations (Roughage Intake System, Insentec BV, Marknesse, The Netherlands).
BCS was assessed every 2 weeks by one of two trained observers to the nearest quarter unit on a scale from 1 (thin) to 5 (obese)  (Ferguson et al. , 1994). Table  1  The distribution of the 77 cows used to test the real-time EB body method according to breed (HOL 5 Holstein; JER 5 Jersey; RED 5 Red), parity and main treatment (LH: low-energy TMR and high amount of concentrate a ; MM: medium-energy TMR and medium amount of concentrate a ; ML: medium TMR and low amount of concentrate a ; HL: high-energy TMR and low amount of concentrate a )  Processing of cow BW data to allow calculation of EB body Adjusting BW for milk and meal-related gutfill. In the VMS, the cow stands on a weighing platform and the BW is recorded automatically during the entire milking. For each milking the end-weight, or so-called milk-free BW, was derived from the weighing platform data, described in detail by  Thorup et al.
(2012). An example of milk-free BW and the meal-related gutfill-free BW curve for one cow relative to DIM has been shown in Figure  1a. The lactations of the cows in the present study spanned a period from October 2005 to January 2007. Unfortunately, owing to loss of the raw weighing files from a database for a period of 4 months (February to May 2006), all lactations had a period of missing observations. However, BWs calculated as an average of the highest 100 weights during milking  (Bossen et al. , 2009)  were available for all milkings, which allowed milk-free BW to be estimated for the missing periods by subtracting milk yield plus 9. 2 kg from this 'high' BW. A total of 9. 2 kg was the observed average difference between (high BW -milk yield) milk-free BW when both BW measures were available.
This permits DBL to be estimated from DEBW as follows: DLipid À free EBW std ¼ DBP std=k ð7Þ DBL ¼ DEBW À DBP std=k ð8Þ It then remains to estimate DBP std. Data on directly measured DBP are scarce and usually come from serial slaughter experiments with limited number of animals. In the present study, we chose to examine the feasibility of using equations (  5 ) and (  6 ) in the present paper to provide the standard curves through lactation. To avoid bias in estimation of DBP std curves, we first excluded the sensitivity of equation (  5 Here 56 MJ/kg is the amount of energy required to create 1 kg lipid (see equation 4) and 0. 77 is the lipid proportion of live weight gain  (Wright and Russel, 1984). Cows of third parity and older were used for this comparison, on the assumption that these animals were mature, and therefore there was no overall change in body protein.
Statistical analysis All processing of data and statistical analysis was performed in R (R Development Core Team, 2011). The testing of the real-time method of calculating EB body to detect short-term changes in EB was carried out using data from two periods: 70 to 90 DIM and 130 to 145 DIM. In the first period, half of the cows experienced an increase in concentrate intake starting on day 75. In the second period, half of the cows experienced a decrease in concentrate intake starting on day 135. In both periods, average EB body was calculated for 5-day intervals starting 5 days before the change in concentrate intake (at 75 and 135 DIM for increased and decreased concentrate intake, respectively). Before-after EB body differences were then calculated by subtracting the before 5-day average from either the 0 to 5, 5 to 10 or 10 to 15 days after change average, yielding three EB body differences (MJ EE/day): DEB5, DEB10 and DEB15 for the 70 to 90 DIM period containing the concentrate decrease and two EB body differences (MJ EE/day): DEB5, and DEB10 for the 130 to 145 DIM period containing the concentrate decrease.
For each period separately, the effects of breed, parity, main treatment and concentrate change on the EB body differences were tested in a linear model (M2) using the lme4 package in R: Y ijkl ¼ m þ breed i þ parity j þ treatment k þ change l þ ijk l ; ðM2Þ where the response variable Y was the difference in EB body of the ijklth observation, m was the overall mean, the explanatory effects were: breed (i 5 Holstein, Jersey, Red), parity (j 5 1, 21), main treatment (k 5 LH, ML, MM, HL), change (l 5 changed, unchanged (to distinguish the cows for which concentrate intake was changed from those not changed)). e ijkl was the residual error term associated with the ijklth observation. Further, the six possible two-way interactions were included in the initial model. The insignificant interactions were removed stepwise for each response variable.
The residual gutfill was assumed constant over time in accordance with  Martin and Sauvant (2010b) , being affected only by animal size, because differences in feed composition and differences owing to prior feed intake both tend towards zero with time. It would be desirable to validate the EB body presented here on larger, independent data sets to confirm the robustness of the method, specifically in relation to different diet types. Here, differences in forages and especially grazing v. indoor feeding will be important tests. It will also be important to validate the BP std curves proposed in this study on a larger population. Finally, the present study estimates EB body for lactating cows, because the weights are measured in connection with milking; however, if dry cows were weighed on a daily basis, EB body could be equally well calculated for the dry period.
Transcriptome Analysis Digital gene expression (DGE) tag profiling was performed using an Illumina Genome Analyzer II ('t  Hoen et al. , 2008). A total of 1 mg RNA per sample was analyzed using Illumina's DGE Tag Profiling Kit according to the manufacturer's protocol (version 2. 1B). Tags were filtered and alignment against the mouse genome performed using SOAP  (Li et al. , 2008) , and filtered tags were normalized to 1M tags and functional annotation was performed with DAVID Tools (http://david. abcc. ncifcrf. gov/summary. jsp). Gene set enrichment analysis was performed using the Broad Institute software (http:// www. broadinstitute. org/gsea/index. jsp). Quantitative RT-PCR Quantitative RT-PCR was performed  (Clement et al. , 2009a)  with the primers listed in Table  S2. All samples were run in duplicates and normalized to the.
ACKNOWLEDGMENTS This work was supported by grants from the  Lundbeck Foundation , the  Danish Heart Association , the  Novo Nordisk Foundation , the  Danish Council for Independent Research, Natural Sciences  (grants  10-085373  and  09-070398 ),  Nordforsk , the  Dagmar Marshalls Foundation ,  Aase og Ejnar Danielsens Fond, Fonden til Laegevidenskabens Fremme, Arvid Nilssons Foundation , and  Reinholdt W Jorck og Hustrus Fond. The  Wilhelm Johannsen Centre  for Functional Genome Research is established by the  Danish National Research Foundation. We thank  Prof. Harald Stenmark  ( Institute for Cancer Research, Norway ) for the GFP-2xFYVE plasmid,  Lillian Rasmussen  and  Anni Nielsen  for excellent technical assistance, and  Prof. Karsten Kristiansen  and the  Beijing Genomics Institute at Shenzhen  for assistance with transcriptomic analysis.
4 ). Similarly, greater than 97% of 240 microsatellite markers and 96% of the 319,996 ESTs were aligned to the duck assembly. We further aligned the duck and chicken assemblies to the human using Narcisse. This effort showed that the coverage of the two avian assemblies of the human genome (GRCh37) was similar, indicating that the quality of the duck and chicken assemblies was comparable. In addition, in aligning the duck assembly onto the chicken genome sequence using GLINT software with its default parameters  18  , only 41 of the 78,847 duck scaffolds were identified as possible chimeras when applying the criterion that they contain sequences that partially map to two different chicken chromosomes. These data indicate that the duck assembly has good coverage and generally provides a reasonable substrate for the analysis presented in this study.
We defined reference gene sets from the duck assembly using the BGI and Ensembl pipelines (Supplementary Note). For the BGI reference set, we aligned the duck transcriptome data and human (22,232)  and chicken (16,701) genes to the duck genome assembly, yielding 15,065 predicted protein-coding duck genes. We applied GENSCAN  19  and Augustus  20  with the default parameters and predicted 32,383 and 22,739 protein-coding genes in the duck draft genome for the respective reference sets (Supplementary Table  6 ). Finally, we integrated all gene sources and created a reference set containing 19,144 genes, constituting approximately 2. 3% of the total duck assembly. Of these 19,144 genes, 9,678 were mapped to categories established by the Gene Ontology (GO) Project, 14,725 had orthologs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and 18,012 were supported by the duck ESTs (Supplementary Figs.
Expansion and contraction of immune gene families We identified the predicted duck, chicken and zebra finch genes that were homologs of 3,542 human and 1,415 mouse immune genes (comprising 4,344 unique immune genes), which were derived from analyses of Import, IRIS, Septic Shock Group, MAPK-NF-κB network and immunome databases using TreeFam  24. In total, 6,044 human and 5,715 mouse genes were clustered into 3,726 immunerelated gene families, all of which included at least one of the 4,344 unique immune genes. However, only 3,116 duck genes, 3,294 chicken genes and 3,355 zebra finch genes were clustered into the immunerelated gene families, indicating that avian immune gene repertoires were contractive. We used cytokines as an example to compare immune gene repertoires in mammals and birds.
After detecting cytokines in the above five species with TreeFam 24 , we manually queried their repertoires against the non-redundant database in NCBI and examined their assemblies in Ensembl (version 57). Using the combined duck transcriptome and assembly data, we identified 150 duck cytokine genes; although this number resembles the numbers of such genes identified in chicken (149 genes) and zebra finch (150 genes), it is substantially lower than the numbers of mammalian cytokine genes (230 in humans and 218 in mice) (Table  2 ). We found that the duck genome contains 16 defensins distributed over 3 scaffolds, a number that was slightly higher than that of the 14 defensins found in chicken  25. Closing the sequence gaps within the three scaffolds via Sanger sequencing, we identified three additional duck defensin genes (including one pseudogene).
Evidence for positive selection We performed likelihood ratio tests (LRTs) for positive selection with the codeml program under a branch-site model 29 using 8,409 avian quaternions (Supplementary Note). These LRTs (false discovery rate (FDR) <0. 05) predicted that 2. 7%, 5. 2%, 6. 2% and 10. 9% of genes showed evidence of positive selection in chicken, duck, zebra finch and turkey, respectively. These proportions are significantly lower than the previously reported values (10. 7% in chicken and 11. 3% in zebra finch)  30. This large variation in the proportion of genes showing evidence of positive selection within a particular lineage is partly attributed to alignment problems and poor sequence quality  31, 32. A comparison across species suggested that the proportion of A r t i c l e s positively selected genes in duck was in the range of the proportions detected in high-quality genomes, such as those of the chicken (2.
DISCUSSION We draw four noteworthy conclusions from our results. First, using nextgeneration sequencing technology, we generated the first draft sequence for a waterfowl, one which is a natural host of avian influenza viruses. Second, we showed that immune-related gene repertoires in three bird lineages (including 3,116-3,355 genes) seemed to be contractive compared to equivalent repertories in mammals (with 5,715-6,044 genes). Further efforts identified about 150 cytokines in three bird species using assemblies in Ensembl57 and the NCBI non-redundant database (as of November 2012), whereas there were about 220 cytokines in 2 mammal species (Table  2 ). Third, we performed deep transcriptome analysis to characterize gene expression profiles and to identify genes that are responsive to avian influence viruses (for example, AvIFT, AvDB7-AvDB10, IFITM5 and IFITM10).
We predicted the positively selected genes using the codeml program under the branch-site model  29. We constructed maximum-likelihood trees with protein sequences using PHYML version 2. 4. 4 under the JTT model with four substitution rate classes  42. Facility. Studies of the H5N1 viruses (DK/49 and GS/65) were conducted in a biosecurity level 3+ laboratory that was approved by the Chinese Ministry of Agriculture. All animal studies were approved by the Review Board of the Harbin Veterinary Research Institute, Chinese Academy of Agriculture Sciences. Viruses, duck infection and RNA extraction. The DK/49 and GS/65 H5N1 viruses were isolated from a duck and a goose, respectively, during the avian influenza outbreak of 2005 in China  43. The viruses were propagated in 10-d-old fertilized chicken eggs.
Subsequently, cDNA was subjected to end repair and phosphorylation using Klenow polymerase (Enzymatics), T4 DNA polymerase (Enzymatics) and T4 polynucleotide kinase (to blunt end the DNA fragments) (Enzymatics). End-repaired cDNA fragments were 3′ adenylated using Klenow (exo-) DNA polymerase (Enzymatics). Then, Illumina paired-end adapters were ligated to the ends of these 3′-adenylated cDNA fragments. Gel electrophoresis was used to separate the cDNA fragments from any unligated adapters. cDNA fragments from 180-220 bp in size were selected. cDNA libraries were amplified using 12 cycles of PCR with Phusion polymerase (NEB), and 75-cycle paired-end sequencing was performed on the Illumina Genome Analyzer. Transcriptome analysis. We clustered the BGI and Ensembl reference gene sets (Supplementary Note) and the duck transcripts deposited in the NCBI database to create a merged reference gene set consisting of 20,647 proteincoding genes.
ACKNoWLEDGMENTS We would like to thank  S. Hu  and  Q. Li  for duck sample collection. We also thank  S. Zou  and  X. Liu  for assistance in mRNA purification. The sequencing of the duck genome was funded by the  National High Technology Research and Development Program of China  ( 2010AA10A109 ), the  National Key Technology Support Program of China  ( 2008BADB2B08 ) and the  Recommend International Advanced Agricultural Science and Technology 948 Program  ( 2012-G1  and  2013-G1(2) ) of the  Ministry of Agriculture of China.
World Health Organization, http://www. who. int/influenza/human_animal_interface/H5N1_ cumulative_table_archives/en/index. html; influenza activity in the United States and worldwide, 2004-2005 season, http://www. cdc. gov/mmwr/preview/mmwrhtml/mm5425a3. htm; Narcisse, http:// narcisse. toulouse. inra. fr/multi/cgi-bin/narcisse. cgi; InnateDB, http:// www. innatedb. com/index. jsp; Ingenuity Systems Pathway Analysis (IPA), http://www. ingenuity. com/. Supplementary information is available in the online version of the paper. METHODS Methods and any associated references are available in the online version of the paper. Accession codes. Duck genome assembly and reads have been depos- ited under GenBank accession PRJNA46621. Transcript sequencing data have been deposited under GenBank Gene Expression Omnibus (GEO) accession GSE22967 and Short Read Archive (SRA) accession PRJNA194464.
nursa. org/10. 1621/datasets. 02001. reinforcing the notion that PPAR is required for the control of the adipose inflammation process  [63]. Another study has also examined the effects of fibrates on the inflammatory changes induced by the interaction between adipocytes and macrophages in obese adipose tissue. Systemic administration of Wy or fenofibrate to genetically obese ob/ob mice significantly reduced Tnf- and Mcp-1 mRNA expression in WAT  [64]. Similar observation was also reported using adipose tissue explants from ob/ob mice suggesting a direct effect of PPAR agonists. To check for the definitive involvement of PPAR in the effects of Wy-mediated reduction in the production of proinflammatory cytokines by white fat pads, adipose tissue explants obtained from PPAR-deficient mice were also used  [64].
Acknowledgments This work was supported by the  Institut National de la Santé et de la Recherche Médicale  ( INSERM, Centre de Recherches U866 ), the  Conseil Régional de Bourgogne , the  Université de Bourgogne , and a  French Government Grant  managed by the  French National Research Agency  under the program " Investissements d' Avenir " with reference  ANR-11-LABX-0021. D. Patsouris  is supported by l'  Agence Nationale pour la Recherche (ANR ;  ANR-09-RPDOC-018-01 ).
The deep ocean is the largest and least known ecosystem on Earth. It hosts numerous pelagic organisms, most of which are able to emit light. Here we present a unique data set consisting of a 2. 5-year long record of light emission by deep-sea pelagic organisms, measured from December 2007 to June 2010 at the ANTARES underwater neutrino telescope in the deep NW Mediterranean Sea, jointly with synchronous hydrological records. This is the longest continuous time-series of deep-sea bioluminescence ever recorded. Our record reveals several weeks long, seasonal bioluminescence blooms with light intensity up to two orders of magnitude higher than background values, which correlate to changes in the properties of deep waters. Such changes are triggered by the winter cooling and evaporation experienced by the upper ocean layer in the Gulf of Lion that leads to the formation and subsequent sinking of dense water through a process known as ''open-sea convection''.
This work was partially funded by the  ANR -POTES program  ( ANR-05-BLAN-0161-01 ),  ANTARES-Bioluminescence  project ( INSU-IN2P3 ),  AAMIS  project ( Univ. Mediterranee) ,  EC2CO   Biolux  project ( CNRS INSU ),  Excellence Research Groups  ( 2009-SGR-1305 ,  Generalitat de Catalunya) ,  EuroSITES  ( FP7-ENV-2007-1-202955 ),  MARINERA-REDECO  ( CTM2008-04973-E/MAR) ,  HERMIONE  ( FP7-ENV-2008-1-226354 ),  KM3NeT-PP (212525 ),  ESONET NoE  ( FP6-GOCE-036851 ),  DOS MARES  ( CTM2010-21810-C03-01 ) and  CONSOLIDER-INGENIO GRACCIE  ( CSD2007-00067 ) projects. SM was granted a  MERNT fellowship (Ministry of Education, Research and Technology, France). LH acknowledges the support of the  Direction Generale de l'Armement  (supervisor:  Elisabeth Gibert-Brunet ). The authors also acknowledge the financial support of the funding agencies:  CNRS ,  CEA ,  ANR ,  FEDER  fund and  Marie Curie Program, Regions Alsace and Provence-Alpes-Cote d'Azur, Departement du Var and Ville de La Seyne-sur-Mer, France; BMBF, Germany ;  INFN, Italy ;  FOM  and  NWO , the  Netherlands; Council of the President of the Russian Federation  for young scientists and leading scientific schools supporting grants, Russia; ANCS, Romania; MICINN ( FPA2009-13983-C02-01 ),  PROMETEO  ( 2009/026 ) and  MultiDark  ( CSD2009-00064 ).
ACKNOWLEDGMENTS We thank  A. Mansoor ,  P. Denozi , and their team of the  Centre Hospitalier Paul Ardier in Issoire, France , for their valuable help in collecting feces samples. This work was supported by two PhD scholarship supports, one from the  French Ministère de l'Enseignement Supérieur et de la Recherche  to N. G. , and the other from the  European Union (EU)  and the Auvergne.
Automated Enumeration of Circulating Tumor Cells The recorded image sets from the patients were copied to central hard drives and analyzed by an algorithm developed in Matlab 2009a, using the DipImage toolbox (Delft University, Delft, The Netherlands). The algorithm was optimized using survival of 185 castration resistant prostate cancer patients as a training parameter and is described in more detail elsewhere  [35]. In brief the optimal CTC definition was selected based on 1. High Cox Hazard ratio for borh baseline and follow-up samples; 2 Higher HR for folllow-up than baseline samples; 3 Low relative and absolute number of CTC in 68 control samples. The algorithm was then applied on 144-180 images from each patient. First objects in the CK-PE channel are selected by dynamic thresholding and their outline is saved  [44].
Statistical Analysis SPSS 16 (IBM, New York, USA) was used for survival analysis. Least-squares linear regression analysis was performed to compare aCTC and mCTC, baseline and follow-up samples, and to compare different studies of the same type of cancer. Kaplan-Meier analysis was performed by dichotomizing the patient population on the clinically used cut-off value of five CTC for breast and prostate cancer, and three CTC for colorectal cancer. Beyond these cut-offs, other cut-off values of 1, 5, 10, and 100 for aCTC or mCTC were tested to dichotomize the patient population per study for univariate Cox hazard regression. Logrank tests were used to compare survival between groups. Cox hazard ratios (HRs) with 95% confidence intervals (CI) were determined and collected in a table. Survival was defined as the time between baseline sample and death from any cause.
The cause of this discrepancy was traced back to the CD45 staining of objects, as the CD45 staining in the IMMC-01 study was much higher as compared to the IC2006-04 study. This resulted in the rejection of many objects in the IMMC-01 study. Lowering of the CD45 threshold eliminated the discrepancy between the number of aCTC in the IMMC-01 and other studies (data not shown). IMMC-01 was the first of all studies in this report, potential causes for the higher CD45-APC signal in IMMC-01 are a higher concentration of the CD45-APC fluorochrome, differences in sample preparation or differences in filter cubes giving rise to more crosstalk of CK-PE signal into the CD45-APC channel. Most image sets of IMMC-01, 06 and 38 were scanned on the CellSpotter Analyzer instead of the CellTracks Analyzer II used for the other studies, therefore differences between CellTracks and CellSpotter do not offer an explanation for the differences in CD45-APC signal.