dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods The baseline food web model of the Mediterranean Sea. We used a previously developed food web model  24  constructed with the Ecopath with Ecosim approach (EwE) using the Ecopath mass-balance module  25  representing the whole Mediterranean ecosystem in the 1950 decade. We used the Ecopath model as a baseline to run temporal hind-cast (1950-2011) analyses, assessing the response of the Mediterranean marine ecosystem to changes in primary productivity and fishing effort. The baseline Ecopath model consisted of 103 functional groups, ranging from phytoplankton and invertebrates to top predator species and it was divided in four sub-models representing the four main areas of the Mediterranean basin: 1) Western Mediterranean Sea (W); 2) Adriatic Sea (A); 3) Ionian and Central Mediterranean Sea (I); and 4) Aegean Sea and Levantine Sea (E).
GT was extrapolated, for the missing years, as the average ratio of GT in the observed time periods, while number of days spent fishing were kept as the ratio of days at sea observed respectively in Sacchi et al. 28  for the majority of the countries, Piroddi et al. 24  for Italy, and Moutopoulos et al. 30  for Greece. For Spanish and Italian trawlers, we complemented our trends with data from EVOMED  32  , a European project that assessed the evolution and technological improvement of fishing capacity for the major countries of the Mediterranean Sea for the early 1900-2010 period. Fishing effort (kW*days -1 ) was calculated as the product of the number of fishing vessels, kW per vessel (inferred from their GT), and the number of days spent fishing. To account for improvements in technology (e.
g. , mobile phone, GPS, sonar, radio) that were not captured by kW as a measure of effort (Figure  S5 ), a conservative technological "creep factor" of 1% as observed by Damalas et al. 33  and EVOMED  32  was applied from 1980 to 1995 while for the remaining periods (1950-1979 and 1996-2010) a 0. 5% and ~ 1. 9% (this value varied with gear type; see S6 Table ) were respectively used following the work of Pauly and Palomares  34. Temporal dynamic modelling and model derived indicators. The dynamic module of the EwE software, Ecosim  25, 35  , uses a set of differential equations to estimate biomass fluxes for each species and/or functional group of the ecosystem as follows: ∑ ∑ = - + - + + dB dt g Qji Qij I M F e B / ( ) (1) i i j j i i i i i where dB i /dt is the biomass growth rate of group (i) during the interval dt, g i is the net growth efficiency (production/consumption ratio), I i is the immigration rate, M i and F i are natural and fishing mortality rates of group (i), e i is emigration rate and B i the biomass  25.
Low values of vulnerability (close to 1) indicate that prey production determines the predation mortality (phenomenon also known as 'bottom-up' control) and that the predator is close to carrying capacity, while high values of vulnerability (e. g. , 100) indicate that predator biomass determines how much prey is consumed (top-down control) and that predators are far away from carrying capacity  25. Mixed effect (vulnerability = 2) is set as the default value in Ecosim. The Ecosim approach was used here to fit the model to observed time-series of data using the sum of squares (SS) ratio between predicted and observed data as a metric for assessing model performance  37. We used survey biomasses and catches for those functional groups with available information to compare predicted and observed data (S1).
In particular, biomass time series for sea turtles, pinnipeds, benthic invertebrates and deep sea fish were taken from scientific literature, whereas for demersal species (functional groups n° 12-14; 16-19 and 21 in Fig. 2 ), European anchovy, European pilchard and large pelagic fishes, we used scientific surveys (e. g. , MEDITS trawl survey and MEDIAS acoustic survey) and stock assessments data (S2). Catch data was taken from the United Nation's Food and Agriculture Organization (FAO) database (FishStat: http://data. fao. org/database) available from 1950 to 2010. These time series were complemented with data (available per country) from the Sea Around Us Project (www. searoundus. org) to assign species to fishing fleet. When applying the fitting procedure, we noticed that the baseline fishing mortality (Ecopath baseline in 1950 s) for the most commercially important target species (European pilchard, anchovy and hake) was relatively low (between 0.
02 and 0. 05) compared to the reference levels reported in the literature  [38] [39] [40]. This initially caused a very low reaction of these species to changes in historical fishing effort and primary productivity. To correct these estimates and reflect a more appropriate fishing mortality for these three species, we used the reconstruction of the catches of the Sea Around Us Project and, in particular, for each country of the Mediterranean Sea, we considered the proportion of catch of these species relative to the total catch and applied it respectively in each of our sub-areas. To fit the temporal dynamic model accounting for data quality/reliability in available time series, we weighted the time series using a factor either of 0. 5 or 1 (0 indicating that time series are not considered in the calculation of SS and 1 indicating that they are fully considered  37  ).
As for fishing effort, since our reconstruction was done up to 2010, but the majority of our biomass time series were available until 2011, we decided to keep fishing efforts observed in 2010 constant until 2011. The fitting procedure consisted of seven general steps (Table  1 ) following the same approach as described and applied by Mackinson  50. This method uses the Akaike Information criterion (AIC)  51, 52  : = + AIC nlog minSS n k ( / ) 2 (3) where n is number of observations, minSS is the minimum sum of squares resulting from the comparison of predicted with observed datasets, and k is the number of parameters, to test statistical hypotheses related to changes in predator-prey dynamics (also called vulnerabilities: Vs); changes in primary production (PP anomaly, considering the number of PP spline points (sPP) for smoothing the time series); impact of fishing and possible combinations of the above-mentioned factors (Table  1 ).
The AIC is a tool used for model selection that penalizes for fitting too many parameters, and which is used to choose the "best" model (the one yielding the lowest AIC) considering a good fit and the least number of estimated parameters to do so. In this study, we used the second-order Akaike Information Criterion (AICc) calculated as follow: = + - -- AIC AIC k k n k 2 ( 1)/( 1) (4) c to account for small sample sizes (n of observations) in the dataset. In our case, the fitting procedure was conducted five times: individually for the four sub-models (Western, Adriatic, Ionian and Eastern Mediterranean) as the majority of the functional groups are restricted to one sub-area only, and one extra time for the model representing the whole basin to fit highly migratory species ('large pelagics' and 'sea turtles' groups) that are allowed to move and feed in all four areas.
Community biomass Cm Index calculated at community level as the sum of the biomass only for those groups fitted to time series data (Unit: t/km 2 )  89. The Cm biomasses assessed were: Forage fish (European pilchard and European anchovy); Demersal fish (European hake, medium and small demersals); Sharks and rays (sharks, rays/ skates); and Invertebrates (benthic and benthopelagic cephalopods, crustaceans and benthos).
Regarding catches, the fitting procedure enabled us to detect issues related to landings data at the beginning of our survey period. Low fishing mortalities were observed in the '50 s, in each sub-area, for three very important commercial species (European pilchard, anchovy and hake). Mortality rates for these species were between 5 and 10 times lower than the average reference values reported for these fish stocks in the Mediterranean Sea  38, 39, 56. This supports the hypothesis, already highlighted by several studies  30, 41, 72, 73  , of poor quality of fisheries statistics, particularly in historical times (1950-1970). Part of this problem could be related to the different way fisheries data were collected and aggregated by the different countries and regional institutions  74. Poor data quality and availability could also be explained by the presence of illegal, unreported and/or unregulated (IUU) fishing activities occurring in the region  [41] [42] [43] [44]  especially with regard to illegal nets and mesh sizes, the landing and marketing of undersized fish, and compliance with restrictions on fishing season and areas  39.
This highlights the need to utilize better catch data in modelling exercises in the Mediterranean Sea in order to account for more realistic fishing mortality estimates and trends, and guide proper management decisions. Recent catch reconstruction efforts, which aim at considering all types of fisheries removals (from reported and unreported landings to recreational landings and discards), have been constructed and are now available (www. seaaroundus. org) for the different countries of the Mediterranean Sea  75. In this study, the catch reconstruction has been partly used to fill up gaps encountered in the FAO catch database (e. g. , contribution of specific species/functional groups to fishing fleets and the proportion of the reconstructed catches of European pilchard/anchovy/hake relative to the total catch).
Acknowledgements M. C. acknowledges support by the  European Commission  through the  Marie Curie Career Integration Grant Fellowships -PCIG10-GA-2011-303534 - to the  BIOWEB  project. RD, MC and JS were partially funded by the  European Union 's  Horizon research and innovation programme  grant agreement No  689518  for the  MERCES  project. VC acknowledges support through  NSERC   Discovery Grant   RGPIN-2014-05782.
Something similar is observed at Ta=276. 7, and the new structure with m=32 is obtained up to Ta=318. 1, the limit Ta value in our study. By using image processing with Matlab, we were able to extract spatio-temporal diagrams from the videos available for some of the flow states (sequences of 100 to 200 images are chosen, covering time intervals of 9 to 13 seconds). For each image of the videos, we have registered the grey levels along a given generatrix of the cone (280 pixels along a generatrix). If the generatrix determined by the vertical plane perpendicular to the image is taken as a reference (this generatrix corresponds to the vertical line in the middle of the image), the generatrix chosen for the spatio-temporal diagrams presented in Fig. 12  is the generatrix at -/6 (on the left part of the image) and for the diagram in Fig.
, β T d X, ). When this model holds, the projection of X onto the space spanned by the vectors {β i , i = 1,. , d} captures all the information about Y. In addition,  [3]  shows that a basis of this space can be recovered using an inverse regression strategy provided that the so called linearity condition holds. It has been shown that the linearity condition is satisfied as soon as X is elliptically distributed. Moreover, this condition approximately holds in high-dimensional datasets, see  [4]. However, solutions have been proposed to deal with non elliptical distributed predictors and to overcome the linearity condition limitation  [5, 6, 7]. The inverse regression approach to dimensionality reduction gained then rapid attention  [8]  and was generalized in  [9]  which shows the link between the axes spanning the central subspace and an inverse regression problem with Gaussian distributed errors.
Not surprisingly, Student SIR provides better results for small degrees of freedom but the difference with SIR is reduced as the degree of freedom increases and the multivariate Student gets closer to a Gaussian. The stan-dard deviation follows the same trend. In case III -(ii) the convergence of SIR becomes extremely slow. Regarding computational time, results are reported in  Table 3 (b). Student SIR has multiple iterations, which increases computational time compared to SIR. It is interesting that, in the cases in which SIR fails (I -(ii), II -(ii), III -(ii) see Table  1 ), the convergence of Student SIR is fast, requiring less than a second on a standard laptop (Our Matlab code is available at https://hal. inria. fr/hal-01294982). All reported results have been obtained using a threshold of 0.
Evaluation setting The number of samples n is very large and the proportion of outliers is very small compared to the whole dataset. The following strategy is adopted: 1000 random subsets of X of size n a = 3, 000, X a i , i = 1,. , 1000 and size n b = 30, 000, X b i , i = 1,. , 1000 are considered to compare the performance of SIR and Student SIR. First a reference result BSIR , Bst-SIR is obtained using the whole dataset X, using respectively SIR and Student SIR with the dimension of the e. d. r. space set to d = 3 and the number of slices to h = 1000. The value d = 3 was selected via BIC computed for d = 1,. , 20, which is reliable for such a large sample size. The proximity measure r (26) between the two reference spaces is r( BSIR , Bst-SIR ) = 0. 95. SIR and st-SIR are identifying approximately the same same e.
2% for SIR). In the second test, the sample size of the subsets is increased to n b = 30, 000. Accordingly, the number of slices is increased to h = 100. Not surprisingly, the means (and standard deviations) of r SIR i and r st-SIR i are increasing to 0. 97(0. 04) and 0. 99(0. 00). Student SIR however still performs better than SIR (Figure  1  (b)) with some low values of the proximity measure for SIR while Student SIR has almost all the values (93. 4% of random subsets) concentrated around r = 0. 98. The difference between the two approaches is then further emphasized in Figure  2  where the cloud of points in the upper left corner of the plot corresponds to datasets for which SIR was not able to estimate a correct basis of the e. d. r space while Student SIR shows good performance. Even if the true e.
Student SIR as well as SIR still suffer from the need to inverse large covariance matrices. A regularization, to overcome this problem, has been proposed in  [10]  and could be extended to our Student setting. Another practical issue is how to set the dimension d of the central subspace. We have proposed the use of BIC as a natural tool in our maximum likelihood setting. It provided good results but may be not suited when the sample size is too small. A more complete study and comparison with other solutions would be interesting. To conclude, Student SIR shows good performance in the presence of outliers and is performing equally well in case of Gaussian errors. In our experiments, the algorithm has shown fast convergence being a promising alternative to SIR since nowadays most datasets include outliers.
In all, 79 people were interviewed, including six ministers of health, 20 senior health officials, development partners at national and regional levels and other national and sub-national health workers in the three countries visited. Interviews were recorded manually and then entered into NVivo software (QSR International), where data were coded according to the initial questionnaire, then further coded to capture emerging themes and concepts. Participantsincluded people from all three Pacific groupings: Melanesia, Polynesia and Micronesia. The quantitative review consisted of examining secondary data from regional and global data collections. Indicators were sought according to the following criteria: data available to assess trends over a 20-year time frame; data covering both the subregion and individual countries; and data reflecting progress on one of the original Healthy Island descriptors.
Acknowledgements This article uses some of the findings of The First 20 Years of the Journey towards the  Vision of Healthy Islands in the Pacific , which was supported financially by theWorldHealthOrganization (WHO)andis available at http:// iris. wpro. who. int/bitstream/ 10665. 1/10928/3/9789290617150  _eng. pdf,verified 14 October 2016], and  The State of HumanDevelopmentin the Pacific ;  A Report on Vulnerability and Exclusion in a Time of Rapid Change ', available at: http://www. unescap. org/resources/state-human-developmentpacific-report-vulnerability-and-exclusion-time-rapid-change[verified 2 May 2015]. The authors thank  Vivian Lin , who provided valuable commentary at the drafting stage, and  Junsoo Ro , who assisted with the compilation of indicators. The views expressed in this paper are those of the authors and do not necessarily reflect the official policy or position of the  World Health Organization or Pacific Community.
Comparators In the UK, the initial recommended treatment is a generic selective serotonin reuptake inhibitor (SSRI). If a patient is required to switch treatment due to lack of efficacy or intolerability, an alternative SSRI or newer generation, better tolerated antidepressant is suggested. At third-line of treatment CG90 guidelines recommend patients switch to an antidepressant of a different class (i. e. serotoninnorepinephrine reuptake inhibitors [SNRIs]), with tolerability issues including discontinuation symptoms a key decision maker in treatment  (9). The comparators considered in this analysis were the SNRIs (duloxetine and venlafaxine). Additionally, agomelatine was included due to the availability of head-to-head data with vortioxetine. The results compared to the SSRIs (escitalopram, citalopram and sertraline) were also provided.
All patients underwent a clinical investigation, including a detailed patient history analysis and physical examination. Variables were collected for each patient on the basis of the interviews, physical examination, and on data drawn from clinical records at the time of visits. These included basic sociodemographic data (sex, age, and BMI); duration of hypertension; and known cardiovascular risk factors, such as smoking, diabetes mellitus (abnormal fasting plasma glucose levels or the current use of insulin or an oral hypoglycemic agent), and dyslipidemia (abnormal fasting plasma cholesterol levels or the current use of lipid-lowering agents). Current antihypertensive medication use (dosing, administration timing, total count of antihypertensive drugs, and pills taken each day), number of antihypertensive fixed-dose combination pills, antidiabetics, antiplatelet and lipid-lowering drugs were registered, as well as total count of all drugs and pills taken each day.
Enrolled and final cohort At baseline, out of the 213 patients screened, 10 (4. 7%) were ineligible. After exclusion, 203 patients enrolled in the study. Among the 203 enrolled patients, 179 (88%) had data available from the urinary drug detection. Indeed, we did not include 19 (9%) patients receiving only antihypertensive drugs that we could not detect with our UPLC/MS-MS method, two (2%) patients with difficulty to urinate at the clinical appointment, and three (1%) patients with missed urine samples. Among those 179 patients, 174 completed the MMAS-4 questionnaire. Among those 174 patients, 105 completed a second study visit with a mean follow-up of 11 months [interquartile range (IQR): 4-13)].
Statistical analysis The chi-square or Fisher test was used to compare categorical variables. Continuous variables were examined by using the t test or the Mann-Whitney test. We assessed the impact of nonadherence on BP by using multiple linear regression models with BP as dependent quantitative variable; also age and antihypertensive medication adherence (categorized into binary phenotype) as independent parameters. Statistical analysis was performed using NCSS 2007 [Hintze J. (2009), Kaysville, Utah, USA]. Data are presented as mean AE SD, that is, mean (SD). Two-sided P values less than 0. 05 were considered to be significant.
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved. (diabetes, hyperlipidemia, etc. ) or noncardiovascular nature. This analysis is also consistent with the high proportion of nonadherence observed in patients with apparent resistant hypertension  [5, 6, [8] [9] [10]. The current study shows that information on drug nonadherence obtained with urine detection did not overlap with self-report questionnaire. Indeed MMAS-4 score was not associated with urine drug detection; in addition, 38 out of the 174 tests were nonconcordant (Supplemental data). A smaller number of parameters significantly differed between adherent and nonadherent patients when drug adherence was determined with MMAS-4 score, compared with data observed with urine detection. When drug adherence was determined with MMAS-4 score, patients who did not adhere to their treatment received lower number of total medications than adherent patients.
g. refs 10,11). Our study aims to determine the role of nutrient limitation in the interplay between the structure and dynamics of forests growing on poor tropical soils. We analysed data from French Guiana, a tropical region that has an ancient and nutrient-poor Precambrian geological substrate (Fig. 1 , Table  S1 ) that is particularly low in P content (compared to the generally younger, nutrient-rich soils of western Amazonia  7, 22. We used the Guyafor network dataset, which contains very precise long-term data on forest structure and dynamics at large (4-12 ha) permanent sites where soil texture and the nutrient contents of soil and litter have been monitored. This dataset offers a unique opportunity to analyse the potential effects of nutrient limitation on the structure and dynamics of the tropical forests of the Guiana Shield.
Results General patterns of forest structure and dynamics and nutrient content. Quadratic diameter ranged from 240 to 296 mm, biomass from 310 to 470 t ha -1 , and stem density from 447 to 670 stems ha -1   S1 ). The figure was made by Quantum GIS Geographic Information System v. 2. 16 (QGIS Development Team, 2016. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://www. qgis. org/) by the authors using own data from UMR Ecofog (Kourou). (diameter at breast height (DBH) > 10 cm). Growth and mortality differed two-fold amongst sites, and productivity ranged from 3. 8 to 7 t ha -1 y -1 (Table  S2 ). Geographic distance between sites did not clearly account for the similarities or differences in forest structure and dynamics amongst sites (Figs  1  and 2 ). For example, quadratic diameter, productivity, and mortality were similar at distant (ca.
Materials and Methods Study sites and data collection. French Guiana is located on the Guiana Shield (northeastern Amazonia). This South American region hosts substantial geodiversity  47  and a wide variety of soils  48. The data were collected at nine study sites in natural, unlogged primary forests across the coastal zone (Fig. 1 , Table  S1 ). The study sites belonged to the Guyafor network (www. ecofog. gf/spip. php?article364), which was designed to assess the spatiotemporal variations in forest structure, composition, and dynamics. The network consists of several permanent plots that are monitored over time, with varying census frequencies amongst sites (Table  S1 ). Each site consists of several contiguous 1-ha plots (except for 1. 56-ha plots at Paracou); each plot was used as a replicate within each site in the statistical analyses.
All trees ≥ 10 cm DBH were measured in each census every 2-5 years, and more than two-thirds of the stems were identified with high levels of certainty by our botanical team using vouchers deposited at the regional herbarium in Cayenne. All living and dead trees were mapped, tagged, and inventoried in the database. A total of 34 134 monitored trees have been considered in the analyses. Soil and litter samples were collected between 2008 and 2010 at all study sites. Each soil sample was a composite of 10 mixed soil cores to a depth of 20 cm collected randomly within a 40 × 10 m rectangle. Five rectangles were sampled across each site and thus a total of five composite soil samples were obtained per site. All rectangles were sampled on the dominant topography of each site (either a plateau or a slope; Table  S1 ).