dataesr/scientific-paragraphs-categorization huggingface dataset:
Microscopy images were acquired with a Nikon Ti-E inverted fluorescence microscope, equipped with a Â 100 plan-apochromat oil-immersion objective and a Princeton Instruments Pixis 1024 CCD camera controlled by MetaMorph software (Molecular Devices, Downington, PA, USA). Exact three-dimensional positions of smFISH fluorescent spots in each animal were detected using a custom MATLAB (The Mathworks) script, based on a previously published algorithm  9. In brief, we first convolved smFISH microscopy images with a Gaussian filter to increase the brightness of spots of the correct size and suppress the background signal. Next, we select candidate spots by thresholding, using a manually determined threshold. We further refined the candidate spots by finding regional intensity maxima within each candidate spot, to separate smFISH spots whose fluorescence signals are partially overlapping.
Timelapse imaging. Individual animals were staged on an agar pad with a small patch of E. coli as a food source  43. In brief, a single L1 animal was transferred to a small drop of M9 on a 5% agar pad. A cover slip was coated with a small amount of OP50 bacteria transferred from a petri dish with a worm pick and then gently placed on top of the agar pad. To prevent the sample from drying out, the coverslip was sealed with VALAP, an equal-weight mixture of vaseline, lanolin and paraffin wax. Staged animals were grown at 20 °C and briefly imaged at B1 h intervals at room temperature, using a Nikon Ti-E inverted fluorescence microscope, equipped with a plan-fluor Â 40 objective and a Photometrics HQ2 CoolSnap camera, controlled by mManager software (http://www. micro-manager. org). Fit of gonad length extension.
We generated 5 Â 10 4 random parameter combinations of the parameters y, b, f and l lagÀ2 2. For each parameter the value was given by 10 E , with the exponent E uniformly distributed in a particular interval, so that the parameter values span many orders of magnitudes (Supplementary Fig. 7 ). For each time point, we obtained the average VPC body length as the mean distance between nuclei of adjacent VPCs for P4. p-P8. p, averaged over all animals in the time point. This yielded L C ¼ {15, 15,17,19,19,18}mm for each time point ordered by increasing time of induction. For each random parameter combination and for each time point, we then obtained a LIN-3 decay length and amount of activator A that formed the best fit to the measured spatial expression profiles in Fig. 4a, b. Specifically, we obtained the values for l and a lag À 2 that minimized the SSE between the experimental data and the spatial expression profile calculated for Model B3 using equation 3.
Acknowledgements This work was supported by the  US NIH/National Cancer Institute Physical Sciences Oncology Center at MIT  ( U54CA143874 ), a  US NIH   Pioneer award  ( 8 DP1 CA174420-05 ) and a  European Research Council   Advanced grant  ( ERC-AdG 294325-GeneNoiseControl ) to A. v. O. as well as a  Human Frontiers Science Program Long-Term Fellowship  ( LT00142/2008-L ) to J. S. v. Z. We thank  David Lubensky  and  Pieter Rein ten Wolde  for discussions and critical reading of the manuscript. Some nematode strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ).
Fluorescence-activated cell sorting (FACS) analysis Cells were washed once with PBS and harvested with 0. 05% trypsin/0. 025% EDTA. Detached cells were washed with EDTA, re-suspended in EDTA (4 × 10 4 /ml), and Fcblocked for 15 minutes at room temperature. Anti-human αv integrin conjugated to Phycoerythrin (PE) (R&D systems, Abingdon, UK) was added and cells were incubated for 45 minutes at 4°C. PE-conjugated mouse IgG anti body (R&D systems, Abingdon, UK) was used as a negative control. Cells were washed three times in PBS and PE fluorescence was measured on a BD LSR II Cytometer. Results were analyzed with BD FACS Diva 6. 1 software.
Apoptosis assay The apoptotic effects of αv integrin were examined using propidium iodide (PI) flow cytometry as previously described  [26]. Detached and adherent cells were collected and labeled for 15 minutes at room temperature with PI (40 μg/ml) and immediately analyzed on a BD LSRII flow cytometer (BD Biosciences, Breda, Netherlands) using BD FACS Diva6. 1 software.
RNA isolation and real-time quantitative PCR (RT-PCR) RNA was extracted with a NucleoSpin RNA II kit (BIOKE, Leiden, Netherlands) according to the supplier's manual. For RT-PCR a RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Leusden, Netherlands) was used. RT-PCR was performed on a CFX connect real-time PCR system (Bio-Rad, Veenendaal, Netherlands) and analyzed with CFX Manager software version 2. 0 (Bio-Rad, Veenendaal, Netherlands). The sequences of the primers are given in the supplemental information. All samples were analyzed in triplicate and normalized to GAPDH. The sequences of the primers were as follows: Integrin αv forward: 5′-CTTCTTGGTGGTCCTGGTAGG-3′; Integrin αv reverse: 5′-TTTCTGCCACTTGATCCGA AA-3′; GAPDH forward: 5′-AGCCACATCGCTCAG ACA C-3′; GAPDH reverse: 5′-GCCCAATACGACCA AATC C-3′; N-cadherin forward: 5′-CAGACCGACCC AAACAGCAAC-3′; N-cadherin reverse: 5′-GCAGCA ACAGTAAGGACAAACATC-3′;Snail forward: 5′-AC CACTATGCCGCGCTCTT-3′; Snail reverse: 5′-GGT CGTAGGGCTGCTGGAA-3′; Slug forward: 5′-ATG AGGAATCTGGCTGCTGT-3′; Slug reverse: 5′-GAGGA GAAAATGCCTTTGGA-3′; Vimentin forward: 5′-CCA AACTTTTCCTCCCTGAACC-3′; Vimentin reverse: 5′-C GTGATGCTGAGAAGTTTCGTTGA-3′; CTGF forward: 5′-TGCGAAGCTGACCTGGAAGAGAA-3′; CTGF reverse: 5′-AGCTCGGTATGTCTTCATGCTGGT-3′; IL-11 forward: 5′-ACTGCTGCTGCTGAAGACTC-3′; IL-11 reverse: 5′-CCACCCCTGCTCCTGAAATA-3′; PAI-1 forward: 5′-GCAGGACATCCGGGAGAGA-3′; PAI-1 reverse: 5′-CCTGAGAACCTCCCTTGACCTT-3′.
Establishment of stable αv integrin knockdown in MDA-MB-231 cells Analysis of Oncomine™ gene expression data  [29]  revealed that αv integrin is significantly upregulated in ductal breast carcinoma in situ epithelia or invasive ductal breast carcinoma epithelia (Figure  1A ). Using publicly available databases we also found that αv integrin is highly expressed in breast cancer and that its expression was found to correlate with poor prognosis in 2,960 patients (Figure  1B )  [30]. To further explore the expression of αv integrin in breast cancer cells, we analyzed αv integrin mRNA levels in breast cancer cell lines of different molecular subtypes by using a published dataset  [31]. Integrin αv expression was higher in basal-like breast cancer cells, including MDA-MB-231 and MCF10A cell lines, than in luminal cell lines (Figure  1C ).
siRNA-mediated depletion of αv integrin caused reduced expression of mesenchymal markers as well (Additional file 4: Figure  S4A ). Knockdown of αv integrin in MCF10A-M4 also significantly decreased Snail expression (Additional file 4: Figure  S4B ). Depletion of αv integrin in MDA-MB-231 cells did not affect E-Cadherin protein levels (data not shown). These data indicate that in MDA-MB-231 cells αv integrin is required for the expression of various mesenchymal markers that are important for acquisition of an aggressive and metastatic phenotype. By analyzing the mRNA expression profiles of a publicly available dataset of a panel of breast cancer cell lines, we found that high expression of αv integrin positively correlates with expression of the mesenchymal markers Slug, N-Cadherin and Vimentin (Figure  3C )  [31].
Moreover, to investigate the relationship between αv integrin and mesenchymal marker expression in breast cancer patients, we analyzed the expression of αv integrin, Slug and Vimentin in the Gene Expression Omnibus (GEO) database [GEO: GSE9309] and observed that αv integrin expression positively has been found to correlate with Slug and Vimentin expression in 142 breast cancer patients (Figure  3D ). Analysis of Oncomine™ gene expression data (TCGA database  [38] ) showed that both αv integrin and Snail mRNA are upregulated in invasive breast carcinoma (Additional file 4: Figure  S4C ). Together, these results illustrate that αv integrin expression correlates with mesenchymal characteristics in metastatic breast cancer cells. αv integrin-TGF-β interplay in MDA-MB-231 breast cancer cells TGF-β plays a critical role during breast cancer metastasis, and also has been reported to stimulate αv integrin expression in certain cell types  [15, 19].
GLPG0187 inhibits progression of established bone metastasis According to the previous zebrafish results, GLPG0187 shows potent anti-tumor progression activities. A phase-I trial in healthy volunteers with GLPG0187 has started (ClinicalTrials. gov Identifier: NCT01313598, NCT01580644, NCT00928343). To further confirm antimetastatic activity of GLPG0187 we performed xenograft experiments in a mouse model of female nude mice that were intravenously injected with MDA-MB-231/BO2 cells, a subclone of MDA-MB-231 that stably expresses luciferase, and which selectively metastasizes to bone  [23]. First detection of metastatic cells in bone by luminescence was possible at day 13, which allowed randomization. At the day following randomization, mice were treated with vehicle or GLPG0187 weekly to evaluate whether GLPG0187 can inhibit established metastases.
For example, αvβ3 has high affinity binding with the bone marrow stroma ligands, osteopontin and vitronectin, to promote prostate bone metastasis  [49]. Interestingly, analysis of a previously published dataset, in which 52 breast cancer cell lines were transcriptionally profiled  [31] , revealed significant correlation between expression of αv integrin and the mesenchymal markers N-Cadherin, Slug and Vimentin. Moreover, αv integrin was found to be highly expressed in mesenchymal breast cancer, with an invasive and metastatic phenotype. A previous study revealed that BMP7 may inhibit TGF-β-induced EMT and bone metastasis in both breast and prostate cancer by decreasing αvβ3 integrin expression  [19, 50]. Moreover, antibody-mediated blockade of αv integrin function, in particular of αvβ6, downregulates EMT and inflammation associated with fibrosis, metastasis and cancer  [51] [52] [53] [54].
Acknowledgements We thank  Dr B Ewa Snaar-Jagalska  and  Dr Chao Cui  for valuable discussion, and  Midory Thorikay  and  Martijn Rabelink  for excellent technical assistance. This study was supported by the  Cancer Genomics Centre Netherlands, Swedish Cancerfonden  ( 09 0773 ),  LeDucq foundation Transatlantic Network of Excellence , and  Swedish Cancerfonden 090773  (to PTD). This project was in part supported by  Zhejiang Provincial Natural Science Foundation of China  (grant number  R14C070002 ) and a grant from  The Chinese National Natural Science Funds  (grant number  31471315 ).
Study participants Case-control data were collected by the NECSS from 1994 to 1997 from participants residing in eight Canadian provinces: British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Nova Scotia, Newfoundland, and Prince Edward Island. Mailed, self-administered questionnaires were used to collect detailed information for a number of potential risk factors, for 19 cancer sites, from a national sample of 20,755 incident cancer cases  [13]. Controls were randomly sampled from the general population of the eight provinces from which cases were selected, using either random digit dialing (Newfoundland and Alberta) or random sampling from provincial health insurance plan databases (British Columbia, Manitoba, Saskatchewan, Nova Scotia, Prince Edward Island). The health insurance plan databases cover more than 95% of the population in the province.
The initial dataset for this analysis included 670 histologically confirmed male bladder cancer cases and 2547 controls. After applying the aforementioned exclusion criteria, 658 incident bladder cancer cases and 1360 controls remained for analyses. Approximately 90% (n = 593) of bladder cancer cases were of the urothelial (transitional cell) carcinoma type and 522 of 658 (79%) cases were coded as ICD-10 code C67. 9 (bladder cancer, unspecified). No information was available on cancer stage or grade. Ethics review board approval for the NECSS study protocol was obtained by all participating provincial cancer registries. All participants provided informed consent. Ethics approval for the exposure assessment work and this analysis was granted by both the University of Toronto and Health Canada Research Ethics Boards.
Assignment of occupational exposures The NECSS participants were asked to provide information for each job held, in Canada, for at least 12 months from the time they were 18 years old. This included job title, main tasks, industry, location, and period of employment, as well as information on part-time, full-time, and/ or seasonal job status. All jobs were assigned a 7-digit Canadian Classification and Dictionary of Occupation code  (1971) (1972) (1973) (1974) (1975) (1976) (1977) (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989). Assignment of exposure to diesel engine emissions, gasoline engine emissions, aromatic amines, asbestos, and silica for the control series had recently been carried out by a team of chemists and hygienists based on job descriptions for previous work with the NECSS data  [17].
To maximize the consistency in exposure assessment across all subjects, a JEM was developed using occupational information from controls as a basis for assigning exposures for bladder cancer cases. The same team of chemists and hygienists who assigned exposure to the control series then reviewed job descriptions for bladder cancer cases, adjusting exposure based on details provided by subjects for each job. The few jobs (11. 8%) not covered by the JEM were assessed for exposure manually using the same principles as those applied to the controls. Lastly, for cases and controls, a subset of jobs that were considered to be representative of occupations with exposure to diesel and gasoline emissions were individually reviewed to ensure consistency in exposure assessment. The expert review involved confirming or changing any of the three exposure dimensions assigned by the JEMs, based on specific information in the NECSS database such as job tasks, company or industry name, and era of exposure.
The expert review relied on the scientific and technical literature, consultation with external experts, and exposure databases constructed in previous studies. The expert-based assessment approach has been previously described in detail by Parent et al. [18]. In brief, while the participants' job title was a factor in attributing exposure, the idiosyncrasies of the job activities were taken into account, and there were many examples of participants with the same job title having different exposure profiles; conversely, similar exposures were attributed to many participants with different job titles. Nonexposure was defined as a level that could be encountered in the general environment. Exposure was characterized across three dimensions: reliability, concentration, and frequency according to a semiquantitative three-point scale of low, medium, and high.
Odds ratios (OR) and their 95% confidence intervals were determined by entering each exposure metric into the model one at a time while controlling for confounders. A linear test for trend was conducted by entering each ordinal exposure metric in the model as a continuous variable. All significance tests were two-tailed, and all analyses were carried out using SAS Version 9. 4 (Cary, NC, USA). CE = k ∑ i=1 C i F i D i L. Latifovic et al.
The results of the analysis with occupational exposure to gasoline emissions are presented in Table  4. Ever exposure, highest attained concentration of exposure and duration of exposure at high concentrations of gasoline emissions were not related to bladder cancer in this dataset. The observed OR for highest attained frequency of exposure (OR = 1. 44, 1. 08-1. 91), duration of exposure from six to less than 22 years at any concentration of gasoline (OR = 1. 36, 1. 03-1. 79), five to less than 22 years of exposure at low concentrations of gasoline emissions (OR = 1. 34, 1. 02-1. 78), and the middle tertile of CE to gasoline emissions (OR = 1. 31,1. 00-1. 72) suggest an association with bladder cancer in the minimal model adjusted for proxy respondent, province of residence, and age at interview but these associations disappeared after additional adjustment for cigarette pack-years and cumulative occupational exposure to asbestos and silica (full model).
Acknowledgments This Project was funded by a research grant provided by the  Workplace Safety and Insurance Board  (Ontario)-WSIB# 10011. We thank the chemists/industrial hygienists at  INRS Institute Armand-Frappier ,  Louise Nadon ,  Benoit Latreille ,  Ramzan Lakhani , and  Mounia Rhazi  for their contribution to this work. We also acknowledge the support of the  Ontario Occupational Cancer Research Center (OCRC)  and  Health Canada. Marie-Élise Parent  is the recipient of career awards from  Fonds de recherche du Québec-Santé (FRQS).
Nuclei were stained for 15 minutes at RT with DRAQ5 (DR500200, Biostatus limited, Shepshed, UK) at 1:1,000. Finally, slides were mounted with Prolong Gold (Invitrogen, P36934). To quantify GFP fluorescence, muscle sections were scanned using a Digital slide scanner (Nanozoomer 2. 0, Hamamtsu, Japan) using FITC and TRITC filters to detect GFP and laminin respectively. For each group, exposure times were set up so that no GFP fluorescence would be detected on dPBSinjected sections. Myofibers were individualized using laminin fluorescence. The total number of fibers including GFP negative fibers and the mean GFP intensity of each cell were determined using ImageJ software on a gray scale ranging from 0 to 255 a. u. (Rasband, W. S) (ImageJ, US National Institutes of Health, Bethesda, MD, http://imagej.
Characteristics of targets and oligonucleotides are listed in Supplementary Table  S1. Primers and probes were provided by Sigma after purification by a desalting procedure, except those already included in kits (Reference Gene Panel Mouse, TATAA Biocenter, Gothenburg, Sweden). Quantitative PCR reactions were performed in a final volume of 20 µl from either 50 ng of DNA or 2. 5 µl of 10-fold diluted cDNA. All samples were analyzed in duplicates by StepOne Plus thermal cycler (Applied Biosystem). The predicted specificity of qPCR reactions was verified by UCSC in silico PCR (http://genome. ucsc. edu/cgi-bin/hgPcr). For SYBR green-based qPCR we also systematically validated the specificity by melting curve analysis. Quantification cycle (C q ) values were calculated with StepOne software v2.
RNA immunoprecipitation The different parameters analyzed in this study were obtained as follows: (i) for the determination of vector genome copy number (vg) per host diploid genome (dg) the mouse Albumin (mAlb) was selected as reference gene (vg/ dg: 2 × GFP copy number/mAlb copy number); (ii) for RT-qPCR analyses of GFP (RQ and RQ'), the reference gene was determined using the Reference Gene Panel Mouse (TATAA Biocenter). Twelve murine reference transcripts were analyzed; data were compared with NormFinder and GeNorm algorithms and Hypoxanthine-guanine phosphoribosyltransferase (Hprt) was selected as presenting the lowest variations between the analyzed tissues. RQ and RQ' were calculated with the 2-ΔCq method (RQ = 2-(C q target -C q Hprt)) in the case of similar efficiencies between target and HPRT qPCR reactions.
If the efficiencies were different, this calculation was adapted as follows: RQ = ((EHprt) × (C q Hprt))/((Etarget) × (C q target)), as previously described  35  ; (iii) ChIP qPCR data were normalized using the Satellite-5 control target, which is comprised in a single copy LINE/LTR located in a murine gene and which was found particularly enriched in H3K9me3. ChIP relative enrichment was calculated from qPCR data as follows: Bound/(Bound+Unbound) for target sequences (RSV or GFP) divided by B/(B+U) obtained for Satellite-5; (iv) percentages of INPUT obtained after RNA IP were calculated from qPCR data by dividing the copy number of the desired target in the isolated fraction by the copy number of the same target in the INPUT. In each case, sensitivity thresholds were calculated using the smallest amount of plasmid template detected in calibration curves and the average copy number of reference gene measured by the appropriate qPCR.
ACKNOWleDGMeNTS This work was supported by the Region Pays de la Loire and the Association Francaise contre les Myopathies (AFM). The authors would like to thank Marine Allais, the Atlantic Gene Therapies institute Vector Core, GENETHON Bioexperimentation department and Biogenouest Genomics core facility for their technical assistance. We are also grateful to the AFM sponsored-DMD/µDys network which includes GENETHON, l'Institut de Myologie, Atlantic Gene Therapies and the Royal Holloway (UCL) for providing samples from Golden Retriever muscular dystrophy dogs. This work was performed under a Cooperative Agreement between INSERM, AFM, and the University of Florida Center of Excellence for Regenerative Health Biotechnology. R. S. owns equity in a gene therapy company that is commercializing AAV for gene therapy applications.
4d, e ). We found in cKO-CT mice significantly increased levels of very long-chain ceramides (Fig. 4f ) compared with WT-CT mice. Interestingly, we observed that HFD treatment does not affect long-chain ceramide levels, as there is no exacerbation of the cKO condition under HFD. Furthermore, cKO-CT mice exhibit a drop in the content of several phospholipids in the WAT compared with WT-CT mice (Supplementary Table  1 ). Altogether, these data confirm a strong impact of Napepld deletion on lipid metabolism in the basal state. Napepld deletion modifies inflammation and browning process. To better explain the increased fat mass and altered lipid profile observed following the deletion of Napepld in adipose tissue, we analyzed the gene expression profiles in the WAT by microarray. These revealed an increased expression of a large number of genes involved in inflammatory pathways and a decreased expression of numerous genes involved in adipose tissue metabolism in cKO-CT, WT-HFD and cKO-HFD mice compared with WT-CT mice (Fig.
5a ). Genes were assigned to relevant pathways by analyzing gene ontology using the bioinformatics tool DAVID (Supplementary Fig. 4 ). Figure  5a  represents relative ratios in our experimental groups of selected genes that were down or upregulated by at least 1. 5-fold in cKO-CT versus WT-CT mice. The entire list of genes is available in Supplementary Data 1. To confirm the results obtained from the microarray analyses, we performed quantitative PCR (qPCR) measurements on key inflammatory markers as well as on lipid metabolism and browning markers. All of the qPCR results supported the results from the microarrays and confirmed that cKO mice exhibit a markedly increased inflammatory tone in adipose tissue (Fig. 5b ). It is worth noting that the levels of inflammatory markers in cKO-CT mice are similar to those of WT-HFD and cKO-HFD mice or are even more pronounced than under HFD conditions, emphasizing the effect of Napepld deletion on adipose tissue inflammation under physiological conditions.
The hybridization, wash and scan were done according to the Affymetrix kits and procedures specific to the mouse gene ST chips. After the scan, the quality controls of the hybridization were checked using the Affymetrix Gene Expression Console software. Using the Affymetrix APT suite tools, we normalized the data by the RMA-Sketch procedure and computed the signal detection P values using the DABG algorithm. All the probe sets that have the DABG P value 40. 05 in all conditions were discarded from the analysis. The rest of probes sets were kept for fold-change analysis. Functional annotation and pathway analysis was done using the DAVID web tool  58. Both tools were fed with the list of selected official gene names as input, and the threshold of significance was set by default to P values o0.