dataesr/scientific-paragraphs-categorization huggingface dataset:
The medical research was run through phone calls survey targeting houses and the results presented herein are qualitative and quantitative (percentage estimates). The only personal data about the responder and/or allergic person are his age and sex. Based on previously established prevalence rate of 3% through literature review and an estimated 95% confidence interval of 1. 5-4. 5%, a sample size of 506 subjects was needed for the study. A pilot study with a sample of 100 subjects was taken from digitalized white pages to assess the response rate in order to decide on the final sample of phone numbers needed. Based on a response rate of 27. 6%, the final sample of 1832 subjects was chosen from the digitalized white pages database. The preliminary target was 1500 call attempts, which were increased to 1832 to obtain 506 responses.
Algorithm 1 briefly outlines the cross validation procedure for the selection of the optimal solution. In this work, we have used Lasso modified LARS implementation of SPAMS software  [21]  for 1 -regularization. Algorithm 1 Algorithm to determine optimal LARS solution using leave-one-out cross validation. Input: sample vector z ∈ R Q and matrix Φ ∈ R Q×P Output: vector of coefficients v ∈ R P 1: Run the Lasso modified LARS procedure to obtain N r solutions v 1 ,. , v Nr of the regularization path, with corresponding sets of non zeros coefficients A 1 ,. , A Nr. 2: for j = 1,. , N r do 3: Recompute the non zero coefficients v j Aj of v j using ordinary least-squares: v j Aj = arg min v∈R #A j z -Φ Aj v 2 2 4: Compute h q = (Φ Aj (Φ T Aj Φ Aj ) -1 Φ T Aj ) qq. 5: Compute relative leave-one-out error j = 1 Q Q q=1 (z)q-(Φ A j v j A j )q (1-hq)σ(z) 2 , where σ(z) is the empirical standard deviation of z.
2. 6 An ABM was designed to assess the impact of this hunting activity on the population of blue duikers. The CORMAS platform  (Bousquet et al. 1998; Le Page et al. 2012 ) was used to implement the model called "Frotembo" from the local name of the blue duiker. The computer code and the full documentation (including ODD) are available from the CoMSES Net Computational Model Library.
8 ppm for Sr. An in-house coral standard was repeatedly measured (n = 92) achieving a standard deviation of ±0. 05 mmol mol -1. We obtained a 22-year coral record spanning from 1982 to 2004. The chronology is based on maxima and minima peak matching between Sr / Ca and the OISST (NOAA optimum interpolation (OI) sea surface temperature) monthly product (version 2,  Reynolds et al. , 2002). The monthly Sr / Ca data were linearly interpolated (12 times per year) using the AnalySeries software  (Paillard et al. , 1996).
0607 × SST + 10. 553; hereafter C06). However, the intercept of the C06 equation is representative of a mean SST of 25 • C, which is not the mean SST at all coral sites of our database. In order to minimize the site-specific mean SST effect, we elected to work with SST anomalies rather than absolute SST, using only the slope of C06  (SST = Sr / Ca anom / -0. 0607). We also use the rescaled bio-smoothing Sr / Ca-SST slope of  Gagan et al. (2012)  (-0. 084 mmol mol -1 • C -1 ) for a comparison. Moreover, in order to have a consistent instrumental reference, we chose to perform the Sr / Ca-SST calibrations using the OISST monthly product (version 2,  Reynolds et al. , 2002)  for all Sr / Ca records. The OISST product blends instrumental data from different sources and is regularly updated. Additionally, this product is spatially complete due to the use of satellitederived SST estimates  (Deser et al.
"ns" means not significant (p > 0. 01). The significant correlations are significant at the 99 % confidence level. n is the number of values for each record at both resolutions. lution (Madagascar, Mayotte, Ogasawara, Ha'afera, Aqaba) and eight times per year (Rarotonga) have been resampled at the same resolution by calculating corresponding monthly average. The relationships between SST and the Simple Ocean Data Assimilation (SODA) SSS product (version 2. 2. 4, Carton and Giese, 2008; Fig. 1a  and b   value at seasonal and interannual resolution (Fig. 2a  and Table  2 ; Fig. 2b  and Table  3 , respectively). As with SST, we used the closest SSS grid point for each location (Table  S1 ). The SODA SSS is a reanalysis product involving modelling and data assimilation, but unfortunately, it lacks salinity data in several oceanic regions.
However, the SODA SSS product is the only global ocean gridded SSS database currently available, making it the only possible choice for this study. To test the potential limitation of the seasonal and interannual SSS variability in the SODA SSS product at our sites, we investigated the correlation between T and SSS using the 1 • × 1 • gridded instrumental IRD SSS product (covering 120 • E-70 • W, 30 • N-30 • S;  Delcroix et al. , 2011)  at 12 tropical Pacific sites of our database (Fig. 1c  and Table  S2  and 3 ). The IRD SSS product data set is made freely available by the French Sea Surface Salinity Observation Service (http://www. legos. obs-mip. fr/observations/sss/). As for SODA SSS, we resampled the corresponding IRD SSS monthly average for the five at a 2-month resolution and the eight-times-per-year coral records.
Here, we used data from RCTs focusing on core ID symptoms (behavioral or cognitive developmental functions) to investigate if patients with genetically determined ID display placebo responses. To address the critical question of report bias, we compared the placebo response to subjective (a third person's evaluation of the patient, e. g. , clinicians' or parents' ratings) with more objective outcome measures (direct evaluation of the patient's abilities, e. g. , motor or memory task). By limiting our investigation to genetically determined syndromes we controlled for the possibility that acquired ID (e. g. from childhood disease) could be an advantage due normal brain development before ID onset. There is always a possibility that the placebo response is partially represented by spontaneous remission, or the natural history of the condition being treated.
Data extraction and quality assessment Eligibility judgment of studies was performed in consensus meeting before extraction of data. Studies were excluded if (i) the studies did not report any inference test or enough descriptive information to compute an effect size (for instance, percentages or means and a variability measure), (ii) less than five patients were included, (iii) no separate reports for drug/placebo were available, (iv) not a randomized trial (placebo group added from previous study), (v) only healthy controls were treated and no patients. All data was extracted independently by two separate reviewers (KY, KJ). Once all data was extracted, the values were compared and inconsistencies were resolved in consensus meetings and confirmed with a third reviewer (AC). For each study, we extracted outcome data for the placebo and drug treatment arms separately, both at baseline and at study end.
Data synthesis and analysis Data management, and calculation of bias-corrected standardized mean difference (Hedges's g) were performed using the Comprehensive Meta-Analysis software version 3. 0 (www. metaanalysis. com). Since considerable heterogeneity was expected, all analyses were performed with randomeffects rather than a fixed-effects model  [24]. The reported Q statistic reflects the distance of each study from the mean effect (weighted, squared, and summed overall studies). Additional sensitivity analyses explored the effect of bias on the results. First, we assessed the presence of publication bias visually by Funnel plot and formally by its statistical analogues, the Begg and Mazumdar-rank correlation test. Sensitivity to the estimate of publication bias was assessed by the Trim and Fill method.
Two types of analyses for placebo and drug effects were performed: one assessing the effect size of pre-post treatment within each treatment arm (drug and placebo separately), the other assessing the effect size between drug and placebo within each study. When a study provided several outcome measures, all of them were combined to provide one value per study. A priori defined subgroups of data were analyzed by mixed-effects analyses, using the weighted mean effect size for each group (based on random-effect weights), to test the difference between these values (Q statistic). The subgroups were: patients with co-morbid dementia/patients with no dementia, subjective/objective outcomes, type of diagnosis (FX, DS, PW) and drug category (Ampakine, anorexic drug, GABA agonist, Growth Hormone, Inhibitor of acetylcholine esterase, NMDA antagonist, tetracycline, thyroxine, aminoacid, vitamins).
Microarray protocols including the hybridization and post-hybridization procedures were performed as previously reported  (20). Hybridized slides were scanned with the GenePix 4000A scanner (Axon Instruments, Union City, CA), and the acquired images were analyzed with the software GenePix Pro 3. 0. A two-step global lowess normalization was performed using the background-subtracted median intensity of each spot and the resultant Cy5/Cy3 log 2 (ratio) were obtained for the 12 pairs of OPLs. In order to identify genes differentially methylated in patients with OPLs who develop OSCC, we filtered out probes with no SmaI fragment ID, on chromosome X and Y, with no CpG island, and with an absolute minimal distance to transcription starting site > 1kb. After filtering out those probes to identify genes differentially methylated in OL with malignant transformation, it remained 17,098 probes corresponding to 4,441 unique genes.
In silico validation of candidate genes For in silico validation of candidate genes, we used the following 3 assumptions: i-genes associated with OSCC development should be unmethylated in normal tissues, including normal head and neck mucosa, but methylated in HNSCC; ii-promoter methylation of genes associated with OSCC development may be downregulated in HNSCC when compared to normal mucosa; and iii-genes associated with OC development may also be methylated in lung cancer when compared to normal lung as oral cavity has been reported as a surrogate tissue for lung smoking-induced molecular alterations, i. e. field effect, in particular for promoter methylation markers  (21). We tested those assumptions using the following datasets: 1-10 oral tongue SCC, blood and normal mucosa from the same patients with available genome wide DNA methylation profiles by Digital Restriction Enzyme Analysis of Methylation (DREAM), a method based on next generation sequencing analysis of methylation-specific signatures created by sequential digestion of genomic DNA with SmaI and XmaI enzymes, that has the ability to absolutely quantitate methylation of 13,000 unique genes (30,000 CpG sites) at transcription start sites individual genes  (22) ; 2-publicly available DNA methylation and gene expression profiles of the following studies were downloaded from Gene Expression Omnibus (GEO): GSE19434 (  23 ), GSE25083  (24) , GSE13601  (25) , GSE27902  (26) , GSE9844  (27) , and GSE46802  (28).
A detailed description of the samples and platforms used in those studies is provided in Supplementary Table  1. DNA methylation studies used the Illumina Methylation Assay and BeadChip technology (GSE19434, GSE25083 and GSE46802). Normalized data, presented as beta values, and representing the degree of methylation at each CpG site (with 0 being unmethylated and 1 being fully methylated), was used to evaluate the degree of methylation of our candidate genes overlapping with genes analyzed on the Illumina platform. All probes were preserved irrespective of the number of probes per gene symbol. For the gene expression study (GSE13601, GSE6791, and GSE9844), raw microarray data were processed using quantile normalization and robust multi-array average algorithm. Using an Affymetrix platform, a unique gene can be represented by more than one probesets that were all preserved.
Validation of 5 candidate genes and of global DNA methylation in 37 patients with OPLs, using pyrosequencing The validation set consisted of 37 OPLs collected in patients from the previously mentioned chemoprevention trial, including 23 from patients who did not develop OSCC and 14 from patients who did develop OSCC. Comprehensive data including demographics, smoking and alcohol history, histology, OPLs anatomical site, treatment arm and loss of heterozygocity (LOH) at various sites (d9s171, d3s1285, tp53, d951747, d1751176 and d85254) were available. The degree of promoter methylation of AGTR1, FOXI2, homeobox A9 (HOXA9), PENK, and Zic family member 1 (ZIC1) and the degree of methylation of the repetitive sequence Long Interspersed Elements (LINE1) was evaluated using pyrosequencing-based methylation analysis in the validation set of OPLs.
All primers for pyrosequencing were designed using the Pyrosequencing Assay Design 2. 0 software (Qiagen/Biotage, Uppsala, Sweden). To validate our pyrosequencing assays, the degree of AGTR1, FOXI2, HOXA9, PENK, and ZIC1 promoter methylation obtained by pyrosequencing in 61 head and neck cell lines described in Supplementary Table  2  was correlated with the results obtained in the same set of cell lines and the appropriate controls [HCT116-DKO (colon cancer cell line double knockdown for DNMT1 and DNMT3B), C42 (primary skin fibroblast cell line), HBEC16 (normal bronchial epithelial cell line), and HMVEC (primary dermal lymphatic endothelial cell line] using the Illumina HumanMethylation27 Beadchip (Illumina, Inc. , San Diego, CA). This platform analyzes 27,579 CpG sites around promoters of 14,475 consensus coding sequences.
Statistical analysis Data analysis was performed using GraphPad Prism version 5. 00 (San Diego, CA). Summary statistics, including median and range values, were used to describe the distribution of candidate genes in different datasets. Cancer and normal samples were compared using a log2 fold-change and a paired or non-paired two-sided Student's t-test. Correlations were computed using the Spearman rank correlation coefficient. The Pearson correlation coefficient and Ward linkage method were used for hierarchical clustering of samples. The Kaplan-Meier method was used to construct oral cancer-free survival curves, and the log-rank test was used to test the difference by covariate levels. Univariate Cox proportional hazards model was fitted for all available variables and multi-covariable Cox model analysis including smoking status and significant variables in univariate analysis were performed.
0001) (Supplementary Fig. 5A ). Our third assumption was based on the fact that oral cavity has been reported as a surrogate tissue for lung smoking-induced molecular alterations. Genes methylated early during oral tumorigenesis may therefore be methylated as well in lung cancer when compared to normal lung. To test this, we computed a methylation index in 47 matched NSCLC and normal lung using 29 probes corresponding to 12/86 (14%) candidate genes overlapping with the platform used (GSE27902). Fig. 3B  shows that the methylation index was significantly higher in NSCLC as compared to normal lung. Hierarchical clustering of the 94 samples showed 2 clusters of samples, the first one including 38/47 normal lung and 1/47 NSCLC, and the second one including 9/47 normal lung and 46/47 NSCLC (Fisher's exact test P < 0.
6 ). The percent methylation of AGTR1, FOXI2, HOXA9, PENK and ZIC1 was then evaluated in a validation set of 37 OPLs. Fig. 5A-C  shows that the percent methylation of AGTR1, FOXI2 and PENK in patients who developed OSCC (group B) was significantly higher compared to patients who did not develop OC (group A). The difference between group A and B was not as pronounced for ZIC1 and was not significant for HOXA9 (data not shown). We then computed a Methylation Index (MI) by averaging the % methylation of AGTR1, FOXI2, and PENK. The group with high MI as defined by the median had a significantly worse oral cancer-free survival compared with the group with low MI (log-rank P=0. 0031) (Fig. 5E ). We then tested whether the degree of methylation of LINE1, a group of genetic elements that composes 17% of the human genome and is being used as a surrogate of global DNA methylation, was associated with the development of OSCC.
We also report the value of global DNA methylation to identify patients at high risk to develop OSCC. We identified a set of 86 candidate genes with high levels of simultaneous gene promoter methylation associated with the development of OSCC in patients with OPLs. A majority were consistently found to be methylated in OSCC but not in normal tissues. Although our approach may have been biased by the use of a restriction enzyme inherent to the MCA method and limited to 8,369 unique genes analyzed on the Agilent 4×44k custom CGH microarray, we validated our findings in multiple independent datasets either found in the public domain and using a different array-based technology (GEO), as well as in samples profiled by DREAM, a method based on next generation sequencing. We believe that consistent observations from independent studies using various platforms strengthen our conclusion.
In conclusion, AGTR1, FOXI2 and PENK promoter methylation may be associated with an increased risk of OSCC development in patients with OPLs. They may be associated with an early CIMP during oral tumorigenesis. LINE1 hypomethylation may also be associated with OSCC risk. Further studies are needed to validate these new biomarkers. The role of ARBs in head and neck and lung cancer warrants further investigation. In each pair, one OPL from a patient who developed OCSCC was labeled with Cy5 and cohybridized with one OPL from a patient who did not develop OCSCC labeled with Cy3. Probes on chromosome X and Y, with no SmaI fragment ID, with no CpG island, and with an absolute minimal distance to transcription starting site > 1 kb were removed. (24). ANOVA was used to compare both groups. Promoter methylation was given by the average beta values of AGTR1, FOXI2, HOXA9, PENK and ZIC1 in a set of 146 blood samples, 18 head and neck normal mucosa, and 91 HNSCC (GSE25083)  (24).
Acknowledgments Grant Support:  University of Texas SPORE in Head and Neck   P50 CA97007 ,  University of Texas SPORE in Head (Pickering CR, Papadimitrakopoulou V, William WN Jr, Mitchell FT, Wang J, Lang W, Feng L, Fan YH, Hong WK, El-Naggar AK ,  Lee JJ ,  Myers JN ,  Lippman SM , Mao L, Saintigny P) and  Neck Career Development Award   2011-2012  ( 5 P50 CA097007 09 ) (Saintigny P),  Fondation pour le Recherche Médicale (Foy JP) , and  Cancéropôle Lyon Auvergne Rhône-Alpes  (CLARA) 201-2016 Structured Program -Grant  N°CVPPRCAN000153  ( International Head  and  Neck Prevention Act-IHNPACT) (Saintigny P).
Data, sources of information, and conservation objectives We obtained spatial data for existing MPAs, resource uses, threats, habitats, and fisheries from CI-Philippines. These datasets were used to develop the suitability layers for additional MPAs in the expansion scenarios. We supplemented habitat maps from CI-Philippines with new maps of coral reefs from the Millennium Coral Reef Mapping Project  [41]. The habitats considered in the analyses were coral reefs, mangrove forests, seagrass beds, and "other benthic substrata", including rocky and soft-sediment seabed. For other benthic substrata, we used five depth classes from bathymetric maps (0-10 m, 10-20 m, 20-30 m, 30-40 m and >40 m) to reflect expected changes in species composition with depth  [42] [43] [44]. We based our conservation objectives on the CTI-National Plan of Action  [45]  which recommended that 20% of the extent of each major coastal and marine habitat should be set aside in permanent no-take zones.
The uncoordinated scenario was based on the MPA efforts initiated by individual municipalities and their corresponding communities prior to coordination (before 2008). The partially and fully coordinated scenarios were based on the existing institutional arrangements by the provincial and local-government alliances in the Verde Island Passage (2008 onwards). These scenarios were programmed using MatLab (R2011b) software (see text on expansion rules, below). The four systematic scenarios were designed to reflect MPA establishment guided by explicit objectives in conservation planning software, but varied in the spatial contexts considered for objectives and selection of areas (Table  1 ). The spatial contexts for three systematic scenarios (4-6) matched those of the non-systematic scenarios (1-3) to allow direct comparisons, with the final scenario applying conservation planning across the whole Verde Island Passage.
Key informants were selected based on their understanding and experience working on MPAs in the region and in the Philippines. The informants included managers of community-based local MPAs, local-government officials, and MPA experts in universities and bridging organizations. Informants were asked to identify the criteria used for MPA establishment. We used Maxent to model the two suitability layers based on the spatial predictors (Table  2 ) identified by informants. Maxent was developed to predict the suitability of areas for species  [47] , but has characteristics that make it appropriate for modelling suitability for establishment of different kinds of MPAs  [38]. Maxent uses presence-only data to predict areas of interest based on observed characteristics, and the input data on existing MPAs were presence-only.
Acknowledgments We thank  Vanessa Adams  and  Johnathan Kool  for their suggestions in developing the expansion scenarios,  April Reside  and  Jeremy van der Wal  for their advice during the development and analysis of the Maxent models,  Rebecca Weeks  for the advice during the Marxan analysis, and  Karen Chong-Seng  and  Melanie Hamel  for their comments and suggestions on the maps and figures that helped improved this paper. We also thank the editor,  Sebastian Ferse , and the three reviewers for their comments and suggestions that improved our work. We thank  Conservation International-Philippines  for allowing us to use their data. VH and RLP acknowledge the support of the  Australian Research Council.
The data in the Supporting Information were used to develop the models described in the paper. The data used to develop the models, model results and GIS files (SHP format) can be downloaded from Dryad: http:// dx. doi. org/10. 5061/dryad. 3dp44. Third-party data was also obtained from Conservation International-Philippines. The first author gained access to the data by contacting the use of the data. All other interested parties may request to obtain the same data used in this paper, in the same manner.
governments independently, without local governments MPAs established determine the location individually guidance or with only (within 15 km from the before coordination of potential MPAs. The minimal guidance from shore of each began in 2008, and from MPA sizes from the bridging organizations municipality) based on key informant interviews. database (prior to 2008) and without the Local Government Factors used to were used to inform the consideration of Code. determine suitability of simulation for assigning ecological processes planning units for MPA sizes of MPAs. across areas larger establishment include than municipalities. This habitat types, depicted the situation accessibility, and prior to efforts to distance to another establish the Verde MPA. Island Passage MPA network. This situation could recur if efforts to sustain collaborative partnerships diminish.