dataesr/scientific-paragraphs-categorization huggingface dataset:
[46]  to minimize the influence of genotype-calling artifacts in a candidate gene study. The association between individual SNPs and indels within genes and HAT were tested using the Fisher exact test with Plink v1. 9 software. Results were adjusted for multiple testing by Bonferroni correction. To show significant association during multiple tests, a single marker (SNP) must show, after Bonferroni correction, an alpha value (obtained P value before correction/number of SNPs analyzed) below 0. 000746 (0. 05/68). The Bonferroni correction assumes that each of the statistical tests is independent; however, this is not always true due to the possibility of linkage disequilibrium among the SNPs. In instances where the assumption is not true, the correction is often too strict, leading potentially to false negatives.
Genes and SNPs selected and genotyped 96 loci containing 88 SNPs and 8 indels were tested from 17 candidate genes. The number of SNPs and indels analyzed varied considerably (from 1 to 18) between genes (Table  1 ). The highest number of 18 SNPs and indels was observed for HLA-G and the lowest number of one SNP for IL10, IL1B and CFH. However, it is important to point out that for APOL1 (three SNPs), CFH, TNFA, HLA-A and IL10, the SNPs considered here are only those that have been already reported in the literature. Of the 88 SNPs and 8 indels used in this study, 24 SNPs and 4 indels (with 8 removed for MAF 1%, 7 for missing loci !10%, 5 with HWE P-values <1 x 10 -4 and 8 for linkage at r ! 0. 5) of them were excluded during quality control which excluded one gene (HLA-A) completely. Four samples were also excluded during quality control due to missing individual data !4%.
Interestingly, rs8062041-HP (T/C) is located on chromosome 16 (16q22. 2) in the CNV essv41754 that spans both HP and HPR (Fig  2 ). Such genomic structural variants involving HP/HPR duplication have been reported with higher frequency in people of African descent  [53, 7]. For instance, HP and HPR have been reported in 29 independent studies listed in the Database of Genome Variants  [54]. T. b. gambiense protects itself against killing by APOL1 by reducing the abundance and affinity of the receptor for HPR  [55]. If rs8062041, located in the CNV essv41754 spanning HP and HPR is correlated with CNV genotype, then an increase in HPR expression could drive increased uptake of APOL1 and parasite killing. As in other diseases such as heart disease, cancer, malaria and Crohn's disease, polymorphism in HP could also have direct biological significance in HAT.
Acknowledgments We are grateful for the support and participation of the study populations included in this study as well as the technical team of the  MINSANTE, Divisional Center for Diseases, PNLTHA. We also thank the anonymous reviewers for helpful comments on the manuscript. Members of TrypanoGEN group:  Hamidou Ilboudo ,  Harry Noyes ,  Julius Mulindwa ,  Magambo Phillip Kimuda ,  Mathurin Koffi ,  Justin Windingoudi Kabore ,  Bernadin Ahouty ,  Dieudonne Mumba Ngoyi ,  Olivier Fataki ,  Gustave Simo ,  Elvis Ofon ,  John Enyaru ,  John Chisi ,  Kelita Kamoto ,  Martin Simuunza ,  Vincent P. Alibu ,  Veerle Lejon ,  Vincent Jamonneau ,  Annette MacLeod ,  Mamadou Camara ,  Bruno Bucheton ,  Christiane Hertz-Fowler ,  Issa Sidibe ,  Enock Matovu.
Our focus was on models of governance, and particularly where these models had been evaluated. We used keywords (noncommunicable diseases, sugar, salt, sodium, transfats, alcohol, tobacco, combined with governance, commercial determinants, industry, private or public) and our review was limited to papers published in English available in selected databases (JSTOR, PubMed, SCOPUS). While not definitive, the review provides examples within each of the Buse/Naylor models of interaction, and we use these to explore potential forms of effective governance in reaching population health goals in controlling the commercial determinants of NCDs.
ADA industry partners are provided access to key influencers and decision-makers, and outlets for research findings including professional meetings and scientific publications. In 2015, Coca-Cola was exposed as having provided significant technical and financial support to the nonprofit organization Global Energy Balance. The organization was criticized as little more than a front groupemploying scientists to legitimize its message that obesity results from lack of exercise rather than poor diet  [107]. • Strategic use of research, funding academics and public health bodies: An investigation by the British Medical Journal uncovered a "tangled web" of connections between the sugar industry and public health expertsthrough "research grants, consultancy fees and other forms of funding"  [108].
Patient Selection This study was performed with approval from the institutional review boards at the Malawi College of Medicine and Brigham and Women's Hospital. Patients were selected by reviewing pathology reports from the Malawi College of Medicine in Blantyre, Malawi, to identify invasive SCCs of the uterine cervix. Approximately 500 patients with cervical SCC diagnosed from 2009 to 2012 with available tumor blocks were retrieved from Blantyre. When the age and HIV status of the patient was provided in the pathology report, this information was recorded. Stage, clinical outcome, and survival data were not available.
When this standard procedure did not provide enough PCR product for sequencing (larvae, pupae, and some workers of A. cephalotes), touchdown PCR was performed under the following conditions: 1 cycle at 95 • C for 2 min, 20 cycles at 95 • C for 20 s, the annealing temperature ranging from 60 • C to 55 • C (decreasing 0. 3 • C every cycle) for 15 s and 72 • C for 5 min, then 20 cycles with the same conditions but at a constant 55 • C annealing temperature, and finally 1 cycle at 72 • C for 10 min. Data were deposited in GenBank (Bioproject PRJNA383138).
Data Analyses To compare bacterial 16S rRNA gene copy densities across different samples, we used non-parametric tests in PAST3 because data were not normally distributed  (Hammer et al. , 2001). P-values below 0. 05 were considered statistically significant. 16S rRNA gene sequencing data were analyzed using mothur  (version 1. 36. 1, Schloss et al. , 2009)  and sequences were processed and filtered as described in the standard operating procedure (SOP)  (Kozich et al. , 2013). Two sets of reads for each sample were combined into contigs using default parameters in mothur, which allowed most sequencing errors to be removed. All sequences with ambiguous bases and lengths above 275 bp were removed. We subsequently aligned filtered sequences to the SILVA v119 database, removed sequences which did not overlap with the same region of the 16S rRNA gene after alignment, and fixed the maximal allowed homopolymer length at 8.
Overhangs at both ends of the aligned sequences were filtered out after which we pre-clustered sequences allowing for up to two nucleotide difference between them. We subsequently removed all chimeric sequences. The remaining set of filtered sequences was assigned to taxonomic groups using the Bayesian classifier with a confidence threshold of 60% using the SILVA v119 database. This allowed us to remove all sequences corresponding to gene fragments of chloroplasts, mitochondria, Archaea and eukaryotes and to cluster the retained bacterial sequences into OTUs based on 97% identity cutoff. Rarefaction curves were generated with randomizations of 1000 and increments of 1000 sequences using mothur and plotted in Microsoft Excel 2010. The final OTU table was rarefied at 4448 reads, which reduced the number of OTUs from 1086 to 718 and was used for all downstream analyses.
The samples were inspected and photographed using a Zeiss LSM 710 confocal microscope equipped with ZEN 2009 software and a Leica TCS SP2 microscope. We considered a signal as being specific when it was absent from the negative controls and colocalized with blue spots produced by simultaneous DAPI staining of bacterial DNA. For transmission electron microscopy (TEM), larvae, pupae, and large workers of A. echinatior colony Ae507 and media workers of A. cephalotes colony 2012-2 were dissected in 0. 01M phosphate buffer (pH 7. 4), and guts were fixed in 2. 5% glutaraldehyde (Sigma) in 0. 1 M sodium cacodylate buffer (pH 7. 4) for 2. 5 h. This was followed by washings in the same buffer and postfixation in 1% OsO4 for 1 h, after which samples were placed in a 1% aqueous solution of uranyl acetate and left at 4 • C overnight.
ACKNOWLEDGMENTS We thank the  University of Michigan Host Microbiome Initiative (HMI)  for the library preparation and advice about Miseq sequencing,  Sarah Cherasse ,  Ryan Dosselli , and  Madlen Kratz  for help during the fieldwork,  Anders Garm  for access to the confocal microscope,  Øjvind Moestrup  for access to the transmission electron microscope,  Sylvia Mathiasen  for lab assistance, and  Luigi Pontieri  and  David Nash  for help with the statistics. The TEM work was performed at the facilities of the  Center for Advanced Bioimaging (CAB) ,  University of Copenhagen. The  Smithsonian Tropical Research Institute in Panama  provided lab facilities during fieldwork, and the  Autoridad Nacional del Ambiente of Panama  issued collection and export permits.
All data was analysed with Graphpad 4 (GraphPad Software Inc. , La Jolla, CA, USA) and the level of statistical significance was set at P < 0. 05. Lung grafts were only evaluated at the end of the evaluation period and values were compared with a Mann Whitney test. Oxygenation was evaluated based on partial oxygen pressure over fractional inspired oxygen concentration (PaO 2 /FiO 2 ). Tissue samples for histology were fixed in formaldehyde 6%, embedded in paraffin, stained with haematoxylin-eosin and scored based on injury severity by an experienced pathologist. 18 istological sections were scored from 0-3 based on severity of injury (injury severity score, ISS). 19 The bronchovascular, pleuroseptal and alveolar compartment was scored individually for influx of neutrophils, mononuclear cells, presence of necrosis and congestion.
Measurements of the top pair production cross section Our α s determination is performed using seven σ t t inputs, listed in Table  1. The six measurements at the LHC include three updated measurements by the CMS Collaboration at centre-of-mass energies of 7 TeV, 8 TeV  [13]  and 13 TeV  [14]. These measurements were performed in the eµ decay channel, 3 where the W -bosons from the top quark decays each themselves decay into a charged lepton and a neutrino, one of the W decays producing an electron, the other producing a muon. The measurements are based on data collected in the years of 2011, 2012 and 2015 respectively, with integrated luminosities of 5. 0 fb -1 , 19. 7 fb -1 , and 2. 2 fb -1. From the ATLAS Collaboration, three similar measurements performed in the eµ decay channel are included, based on datasets with integrated luminosities of 4.
Acknowledgements TK is supported by grant Nr. 200020 162665 of the  Swiss National Science Foundation. GPS is supported in part by  ERC Advanced Grant Higgs@LHC  (No. 321133 ) and also wishes to thank the  Munich Institute for Astro-and Particle Physics  for hospitality while this work was being completed. We are grateful to  Andreas Jung  regarding discussions of the Tevatron beam-energy uncertainty.
Microarray displays different gene expression profiles from DRG nociceptors and whole DRG tissue after SNI. We next compared the transcriptional changes induced in rats by SNI at 7 day in LCM collected nociceptors with those observed in DRG whole tissue  1, 21. We found that only 138 genes of the 606 regulated genes in the LCM data set overlap with differentially regulated genes in whole DRG tissue analyses (Fig. 3A ). Common neuronal transcripts include those well known for their roles in nociception such as neurotransmitters Vip, Tac1, Npy, as well as neuronal ion channels such as Scn10a and Scn11a (Suppl. Table  3 ). Interestingly, neuronal transcripts regulated in whole tissue analyses but not present in the LCM data set include Nefh, Trk3, Scn8a and Pavlb (Suppl. Table  3 ). These transcripts are characteristically expressed in large sensory neurons  9  highlighting the specificity of our LCM collection of nociceptors.
In general, this data set is associated with survival, proliferation and glucose functions (Fig. 3B ). In contrast, genes that are unique to the LCM data set are mostly characterized with functions associated with oxidative stress (Fig. 3C ). Genes unique to the LCM data set and associated to oxidative stress were confirmed by qRT-PCR (Fig. 3D ). Out of 6 genes tested we were able to validate the expression levels of Sod2, Sgk1, Mt3, App, Casp6 (Fig. 3E ), but not Duox2 (inconsistent standard curve, data not shown). Of particular interest is the regulation of Casp6 because its implications in axonal degeneration of sensory neurons  22  and in pain hypersensitivity  23  , two hallmarks of neuropathic pain. Caspase 6 is expressed in human and rodent DRG neurons and its inhibition decreases SNI-induced mechanical allodynia.
However, we have validated our approach and results confirming the regulation of CASP6 in nociceptors after SNI and demonstrating its functional role in reducing neuropathic pain in animals after SNI and after paclitaxel injection, a well-characterized animal model associated with mitochondrial and oxidative stress. All together these data significantly increase our understanding of nociceptors and should provide a valuable resource for interpreting previous studies and draw new hypothesis for the treatment of neuropathic pain.
A total of amplified and biotinylated cRNA was then processed on Affymetrix GeneChip Rat Genome 230 2. 0 Array (GeneChip Expression Analysis, Technical Manual 701021, Affymetrix) and signal values and detection calls (present or absent) for all transcripts were assigned with the Affymetrix software MAS 5. 0 (Affymetrix). Each cRNA samples processed on the array was obtained by the microdissection of DRG tissues from 6 rats. Nine arrays were processed for the gene profiling and a total of 56 rats were used for this experiment (6 rats, 3 conditions, and 3 duplicates). To identify differentially expressed transcripts, Mann-Whitney pairwise comparison analyses were carried out allowing the ranking of results by concordance of each identified change in gene expression. Genes for which the concordance in the pairwise comparisons exceeded the threshold of 77% (i.
Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA). To generate gene lists specific to the LCM methodology over a whole tissue analysis, we mined data from two studies using Affymetrix Array to profile transcriptional occurring in rat DRGs after SNI  1, 21. Lists of genes unique to the LCM and whole tissue analysis (Suppl. Table  3 ) were questioned for GO term annotations by Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david. abcc. ncifcrf. gov) and exclusive terms plotted as bars by their significance (Fig. 3B, C ).
These blots were incubated further with HRP-conjugated secondary antibody and developed in ECL solution. Specific bands were evaluated by apparent molecular sizes. The intensity of the selected bands was analyzed using NIH ImageJ software. Data analysis and availability. All data were expressed as mean ± SEM. Four to six animals per group were included for data analyses. Differences between groups were compared using Student's t-test or ANOVA, followed by Tukey's posttest or by unpaired, Student's t test. The criterion for statistical significance was P < 0. 05. Heat map and Pearson hierarchal clustering were generated by GENE-E (http://www. broadinstitute. org/cancer/ software/GENE-E/). Data was plotted using Microsoft software (Excel) or Prism software (Graphpad). All data generated or analyzed during this study are included in this published article (and its Supplementary Information files) and raw data are available from the corresponding author on reasonable request.
Seventeen musculoskeletal sonographers (from Denmark, France, Germany, Hungary, Ireland, Italy, Netherlands, Spain, UK and USA) simultaneously but independently scored the images, which were presented randomly presented with 60 s for evaluating each image. No patient information was made available. Participants were asked to score GS and PD using both a binary (presence/absence) and SQ grading from 0 to 3 (normal, minimal, moderate, severe), according to their own daily practice, on a preprinted data collection sheet. Acquiring and reading images (day 2). A practical exercise was then conducted the following day scanning and scoring synovitis. Eight patients with RA  17  were recruited from the same Rheumatology Department each having only mild to moderate hand deformities in order to eliminate possible acquisition difficulties due to severe structural deformities including ankylosis.
Data were analysed in order to retrieve which information produced the highest agreement between participants for describing US-detected synovitis. The different definitions of synovitis, obtained by the combination of each elementary component as proposed by the experts, as well as the possible grading systems, were then distributed to the participants in a Delphi exercise. Each sonographer had to score, on the proposed definitions, using a 5-level rating scale (1=strongly disagreed to 5=strongly agreed). Only definitions of components and severity grade reaching a consensus of at least 75% were accepted.
Acknowledgements the authors thank  Dr anthony Bouffard  and  Dr Søren torp-Pedersen  who participated in the first day of step 1. Pgc is supported in part by the national institute for Health research (niHr) leeds  Musculoskeletal Biomedical research Unit. the views expressed are those of the author(s) and not necessarily those of the nHS, the niHr or the Department of Health. Contributors MaDa designed the study. all authors have contributed in the accumulation of data and have read and corrected the manuscript. Pa and MaDa performed all statistical analyses and the interpretation was done together with MaDa and lt. Funding the project was partly supported by a eUlar research grant. Competing interests none declared. Patient consent Detail has been removed from this case description/these case descriptions to ensure anonymity.
The vast majority of datasets in ecology fall into the category of short-term independent studies  (Mouquet et al. , 2015). Furthermore, datasets are often not collected with the specific purpose of making anticipatory predictions  (Mouquet et al. , 2015). This currently limits our ability to check the predictive success of particular forecasting techniques and to define the baseline of predictive success in ecology. While ecology in general is only beginning to develop the practice of prediction, related fields such as fisheries science that have to provide quantitative predictions to government agencies, may have already developed standardized reporting rules and rigorous means for assessing predictive proficiency from which ecologists can generally learn. We therefore selected fields and phenomena such as fisheries, epidemiology, eutrophication and algal blooms, ecotoxicology, forestry, and marine and terrestrial biogeochemistry and searched for representative case studies.
Stock-recruitment relationships such as the Ricker or Beverton-Holt curve are used to predict the recruitment of a harvested population using information on its spawning biomass. Cury and colleagues (2014)  have found low explanatory power of the stock-recruitment relationship, explaining only 8. 8% of the variation in a global dataset of stock-recruitment curves, even though it is still widely used in fish stock assessments. They suggested that a better consideration of density-independent factors (e. g. species interactions and temperature) may increase the amount of variation explained. Moving beyond simple linear relationships, non-linear time series analysis has gained traction in fishery sciences. One promising technique is empirical dynamic modeling (EDM) as introduced by  Sugihara (1994).
They report that models performed reasonably well (ρ > 0. 5) on only 4 out of 12 fish species; however, presence was predicted with greater success (8 out of 12). These authors also reported an independent assessment, using a different dataset collected later, in which 5 out of 8 species were predicted with a ρ larger than 0. 5. It has been suggested that predictions of fish stocks could be improved by a more ecosystemfocused fishery management that includes interactions between species and human exploitation  (Travis et al. , 2014). Olsen et al. (2016)  performed a rigorous assessment of the Northeast U. S. Atlantic marine ecosystem model using 40-year hindcasts and 10-year forecasts using different metrics of predictive proficiency (including correlation and different error measures). Twenty-two ecosystem indicators were evaluated, including emergent properties of the model (e.
Lowe et al. (2014)  predicted a high risk of acquiring dengue in Brazil, providing probabilistic forecasts of risk based on seasonal climate forecasts. Aguiar et al. (2015)  also analyzed the epidemiological data, taking into consideration population densities in the twelve host cities, and the seasonality of mosquitoes. In contrast, they predicted a low infection rate during the World Cup due to the incorporation of different drivers, which was confirmed later (M. Aguiar et al. , 2015). Another predictive success in epidemiology was the foot-and-mouth-disease outbreak in Great Britain in 2001, which yielded detailed insights into the disease dynamics and a high quality data set  (Grenfell and Keeling, 2008; Woolhouse, 2011). A statistical model (logistic regression) and individual-based models were used in hindsight to check their ability to predict which farms were at risk during the 2001 outbreak  (Woolhouse, 2011).
Epidemiology also benefits from new data sources, such as internet search queries. A correlation between predicted and observed influenza cases of 0. 96 was obtained  (Ginsberg et al. , 2009) , highlighting the potential of these techniques in improving forecasts and decreasing time delays. As these models rely on correlations between search patterns and disease symptoms, input data have to be reviewed carefully to protect against false alerts  (Woolhouse, 2011). However, issues related to data quality and availability are not unique to new predictive approaches in epidemiology, but apply equally to more traditional approaches. Aguiar et al. (2014)  describe a situation where datasets based on different interpretations of official documents created divergent predictions of disease dynamics.
Dynamic Energy Budget (DEB) theory extrapolates the effects of toxicants measured at the individual level to the population level. DEB is based on first principles in bioenergetics and uses a common model structure for all species. Martin et al. (2013)  present the general approach for animals, and give an example using Daphnia magna exposed to an herbicide. The model captured the density dynamics and changes in the size structure without fitting or calibration at the population level. It therefore successfully extrapolated to environmental conditions not included in the parameterization process. Eco-toxicological data are mostly collected in controlled laboratory studies where lethal effects of toxicants on individuals are measured. Detailed physiological models of toxicant concentration in individuals hence exist, but the challenge is to predict the effects on higher levels of organization.