dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis We computed winter and summer seasonal mean temperatures and deaths in 39 cities and examined whether there were associations between winter/summer mortality ratios and seasonal temperature differences using graphical displays and ordinary linear regression. We compared winter season excess deaths (the ratio of December, January and February deaths to June, July and August deaths) to the difference in mean temperature between these two seasons and to mean winter season temperature across the 39 cities. To examine the importance of controlling for season when testing for temperature effects on mortality over time, we fit a penalized spline function to the relation between same day temperature and mortality in four representative cities: two with substantial seasonal temperature swings (Paris and New York City), one with a hot, humid climate (Miami), and one with intermediate climate (Marseille).
To examine temporal associations between temperature and mortality with or without control for season, first we aggregated data into time series of daily deaths and daily weather for each of the four cities noted above. To compare the effects of average daily mean temperature (T mean ) on mortality (Y) with different levels of control for season, we fit the Poisson regression model: Where k was set to 0, 6, or 8 respectively. For the smooth function of time, natural splines with k*(years of study) degrees of freedom were used. Yt is the time series of daily death counts for a city. Temperature was modelled using a 10 df spline function. The objective of these analyses was to explore the sensitivity of the fitted temperature function to differing amounts of seasonal adjustment (i. e. , using different of freedom on the spline of time).
Introduction Air pollution has become the biggest environmental health risk, killing about 7 million people in 2012, according to a recent World Health Organization study  (WHO, 2014). It is therefore important to provide air quality forecasts on global, regional and local scales to enable vulnerable people to take preventative action during pollution episodes. The Monitoring Atmospheric Composition and Climate (MACC) project (www. copernicus-atmosphere. eu) is the pre-operational atmospheric service of the European Copernicus programme funded by the European Commission's Framework Program 7 (FP7). MACC will evolve into the Copernicus Atmospheric Monitoring Service in 2015. MACC combines state-of-the art chemistry and transport models with satellite data from various sensors to provide consistent global analyses and forecasts of 3-dimensional fields of the atmospheric composition, including ozone (O 3 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), formaldehyde (HCHO), as well as methane (CH 4 ), carbon dioxide (CO 2 ) and aerosols  (Flemming et al.
, 2015; Huijnen et al. , 2014). It has also run routinely as a CTM without data assimilation since November 2012 producing daily 5-day forecasts. A more detailed description of C-IFS (CB05) and the differences between it and the previously used coupled IFS-MOZART system is given in  Flemming et al. (2015). The anthropogenic emissions used in the C-IFS runs described in this paper come from the MACCity emission data base  (Granier et al. , 2011) , with increased winter-time road traffic CO emissions over North America and Europe according to an early version of the emission correction described by  Stein et al. (2014). Biomass-burning emissions are provided by MACC's Global Fire Assimilation System (GFAS v1. 0,  Kaiser et al. , 2012) , and biogenic emissions are taken from the POET database for the year 2000  (Granier et al.
The AMS was operated during three experiments. The instrument was switched between two modalities: a singlereflectron configuration (V-mode) which offers a higher sensitivity and lower resolving power (up to ∼ 2100 at m/z 200) and a double-reflectron configuration (W-mode) which provides a higher resolving power (up to ∼ 4300 at m/z 200) but lower sensitivity  (De Carlo et al. , 2006). Data were taken with a time resolution of 8 min. In V-mode, data were collected in the mass spectrum (MS) mode for 5 min, for aerosol quantification, and in the particle time-of-flight (PToF) mode for 1 min for size distribution measurement  (De Carlo et al. , 2006 , Canagaratna et al. , 2007). Only MS data were recorded in the W-mode (2 min). The AMS data were analysed using Squirrel (ToF-AMS Analysis 1. 51B) and Pika (ToF-AMS high-resolution (HR) Analysis 1.
Acknowledgements. This research work has been supported by the  European Community  within the  seventh Framework Programme: Eurochamp-2  ( EU-FP7  grant agreement no. 228335 ). We also acknowledge the  National Research Agency ANR  (project  CU-MULUS , no. ANR-10-BLAN-0617 ). We thank  Frank Siekmann  and  Sylvain Ravier  ( Aix-Marseille Université, France ) for their help in calibrating the AMS during the measurement campaign. We thank  Michel Maille  ( LISA, France ) for the analysis with electron microscopy. We gratefully acknowledge the  MASSALYA instrumental platform (Aix Marseille Université , lce. univ-amu. fr)for the analysis and measurements used in this publication. We thank  Brendan Mahon  and  Steven J. Campbell  ( University of Cambridge, UK ) for helpful revision of the manuscript.
Data collection Data collection was done by 8 teams (teams of 2 physicians and one local attendant or facilitator as appropriate). Investigators were medical physicians attending a master's course with special lectures in epidemiology, field research and public health at the "Institute pour la Francophonie de Medecine Tropicale" (IFMT). They received specific training and conducted three pre-testing investigations in Vientiane markets and surrounding villages from January to March 2010. They were assisted by 6 supervisors and a team leader. Among them, some could speak other Lao ethnic languages such as Khmu (1) and Hmong  (3). A 56-main item structured questionnaire was used. The questionnaire addressed the following issues: socioeconomic characteristics of interviewees, name of insects consumed, reasons for consumption, frequency of consumption by day, week or months, and trends in consumption in the past 10 years, names of insects collected and sold, reports of side effects and toxicity.
Data entry and analysis Data was entered in EpiData freeware (S1 Dataset). All records were cross-checked with the original data sheets. Analyses were carried out using STATA, Version 8 (Stata Corporation, College Station, TX, USA). Data is presented as number and frequency (%), mean and 95% Confidence interval (95% CI). Chi Square and Fisher's exact tests, Students't-test and Wilcoxon test were used to compare categorical variables and continuous data, respectively. P<0. 05 was considered as statistically significant.
Acknowledgments We thank the  Institut pour la Francophonie de Médecine Tropicale (IFMT)  medical students of P11 class IFMT ( P Khattignavong ,  HV Nguyen ,  M Lao ,  KH Nguyen ,  K Sayteng ,  K Vilivong ,  GT Hoang ,  S Xayaheuang ,  L Ravolanjarasoa ,  C Lorgnihoua ,  E Sayavong ,  M Loungnilanh ,  OS Andrianarivo ,  L Chunmei ,  X Yang ,  V Gnothlysack ) and their supervisors ( Y. Buisson ,  A. Harimanana ,  F Rasoanandrasana ,  Phetvongsinh Chivorakul ,  Maniphet Phimmasane  and  C Rajaonarivo ). We thank  Dr. Philaysak Naphayvong  for facilitating the relationship with Lao Authorities,  G Empis  and  IFMT administrative Staff. We thank the Lao national and regional health authorities. We thank all participating families. We thank the  FAO team  for kind support. We thank  M Gripenberg  and  P Aaron  for editing the manuscript.
Nevertheless, using a regional, well-studied insect group makes it possible to evaluate the stability of biodiversity in the face of one or more global stressors (i. e. , global change). The Plecoptera (also called stonefly) is one such group. Plecoptera is a minor order of insect fauna but plays important roles in the running waters  7. Plecoptera is widely distributed over all continents except Antarctica, and currently, more than 3,497 species are described  8. As an ancient insect group, the extant Plecoptera assemblages provide insights into geographical history. For example, the breakup of Pangaea into Laurasia and Gondwanaland seems to have caused the separation of Plecoptera into distinct southern and northern hemisphere suborders, i. e. , Antarctoperlaria and Arctoperlaria  9. Plecopteran nymphs mainly inhabit cold and clean running waters, and their basic physiological functions are extensively influenced by external thermal conditions  [9] [10] [11] [12].
In our study, we compiled survey data of Plecoptera from the western Mediterranean region. We then extracted 18 sub-regions from these data to evaluate the stabilities of Plecoptera assemblages in the context of the effects of land uplift (continental drift) and long-term climatic variations (from the last inter-glacial [LIG, 120-140 thousand years (Ka) ago] to the future 100 years) (Supplementary Fig. S1 ). Based on the differences in Plecoptera diversity at both spatial and temporal scales, we particularly aimed to test the following two hypotheses: (1) in contrast to the broadly acknowledged biogeographical isolation theory, at the borders of continents there is less similarity of Plecoptera assemblages within rather than between continents (i. e. , Africa and Europe) due to the effect of continental drift, and (2) Plecoptera richness is higher in the last glacial maximum than in the last inter-glacial because plecopteran nymphs prefer low temperatures.
Methods Data collection. We compiled a set of geographical qualitative records (i. e. , presence/absence data) of Plecoptera across the western Mediterranean region, including North Africa, South Europe, and several large islands in the Mediterranean Sea. Data of France were obtained from the Protection of Insect Environment (http://www. opie-benthos. fr), and all other data were obtained from the University of Granada in Spain. In total, 306 species of Plecoptera were observed in the western Mediterranean region. Taxa of Plecoptera observed in each sub-region is available in Supplementary Table  S2. We extracted 18 sub-regions from the study region according to the similarity of geographical characters (Supplementary Table  S1 , Fig. S1 ). The geographical coordinates of each sub-region were the approximate geographical center and a buffer zone with a diameter of 50 km was used to extract the average elevation and climatic variables in each sub-region.
5 arc-minutes for the last inter-glacial and the last glacial maximum, respectively) and the current period (30 arc-seconds) were obtained from WorldClim (http://www. worldclim. org). Climatic variables for the 2080s were obtained from the CIAT database (http://www. ccafs-climate. org; resolution: 30 arc-seconds) based on A1B emission scenario (720 ppm atmospheric CO 2 in the 2100s). We selected the A1B emission scenario because it includes a balanced mix of all energy sources and closely matches the most likely future status of national energy sources  12, 39. The paleogeographical maps of North Africa, Europe, and the Mediterranean Sea were obtained from Colorado Plateau Geosystems, Inc. (http://cpgeosystems. com). Three paleoclimatic periods were incorporated: Eocene (50 million years, Ma), Oligocene (25 Ma), and Miocene (13 Ma).
The map size of the output nodes was chosen according to a heuristic equation ( ) n 5  42. After the learning process of SOM, the SOM units were classified into groups based on the dendrogram of a hierarchical cluster analysis with Ward's linkage method using Euclidian distance  40, 41. The above process was performed by the SOM toolbox in Matlab (version 6. 1)  43. NMDS was performed using PC-ORD version 5. 3. A two dimensional solution was used for all analyses in deriving stress values. A Monte Carlo test with 50 iterations was used to evaluate whether NMDS extracted stronger axes than expected by chance. To test our second hypothesis that the Plecoptera richness is higher in the last glacial maximum than in the last inter-glacial, a generalized additive model (GAM) was used to predict the Plecoptera richness in the last inter-glacial, last glacial maximum, and the 2080s  2, 44.
GAM quantifies the relationships between six climatic variables and Plecoptera richness in the current period. The results of this model are used to estimate Plecoptera richness in paleoclimatic and future climatic periods by comparing the climatic variations between the current and predicted periods 2. This formula is shown in eq. (  1 ): ∑ μ α ( ) = + ( ) ( ) = g f x 1 i n i i 1 where g(μ) is the response (i. e. , the predicted species richness), f i is the smoothing function, x i is the explanatory variables (i. e. , six climatic variables) at site i, n is the number of explanatory variables and a is the residual error. GAMs were conducted by using the gam package in R (R project, Vienna, Austria; http://cran. r-project. org). Paired t-tests were used to determine the difference between the climatic variables and predicted Plecoptera richness for each of the four climatic periods.
(2012 Hultmark et al. ( , 2013)). Below we refer to this data as NSTAP Superpipe data. We start by fitting the data with (8) in the range y * < y ≪ δ and 1 2 u ′2 (y)/u 2 τ ≈ C s0 -C s1 ln(δ/y) -C s2 (y/δ) d1 Re d2 τ ( 12 ) instead of (6) in the range δ ν ≪ y < y * where y * = δRe -d2/d1 τ. This is a model where we ignore the various matching conditions which led to (6) with the specific relations between C s0 , C s1 and C s2 and the parameter C 0 , m, p, q, A, α and Re τ. It is also a model where we just set A = 1, d 1 = p(1 -m) and d 2 = q(1 -m) so that y * = δRe -d2/d1 τ. In figure  1  we show the result of this fit against the NSTAP Superpipe data and in figure  2  we show the fitting values of C s0 , C s1 , C s2 and d 1 and d 2 and their dependence on Re τ in a lin-log plot. First note in figure  1  the clear presence when Re τ is larger than about 20 000 of a logarithmic region at the higher y-values in agreement with the Townsend-Perry equation (  8 ) which fits it quite well (the fit is much better if we allow C ∞ to be different from C 0 as in equation (  2 )).
Acknowledgements We are very grateful to  Dr M. Vallikivi  and  Professor A. J. Smits  for kindly providing us with their NSTAP Superpipe data (first published in Hultmark et al. (2012Hultmark et al. ( , 2013))) and with energy spectra from the same measurements. This work was supported by  Campus International pour la Sécurité et l'Intermodalité des Transports, la Région Nord-Pas-de-Calais, l'Union Européenne, la Direction de la Recherche, Enseignement Supérieur, Santé et Technologies de l'Information et de la Communication et le Centre National de la Recherche Scientifique. JCV acknowledges the support of an  ERC Advanced Grant  ( 2013-2018 ).
Studies comparing and evaluating the temporal (on annual, seasonal and daily basis) and spatial variability of desert dust load and deposition simulated by different models, contribute to determine the degree of uncertainty in estimates of dust emission and transport. They highlight the sources of uncertainty in these estimates, and point to the key foci for future research in order to constrain them (e. g. Tegen, 2003; Kinne et al. , 2006; Textor et al. , 2006; Huneeus et al. , 2011). Dust-related products, such as horizontal visibility, particulate matter concentration, aerosol optical depth (AOD), and extinction vertical profiles will likely be incorporated as added value information in future climate services databases. Long-term dust-related observations and model reanalysis may contribute to understand assessments and plan activities of health and energy communities and to other economic sectors in many regions of the world.
Sources for the other aerosol types which are linked to emissions from domestic, industrial, power generation, transport and shipping activities, are taken from the SPEW (Speciated Particulate Emission Wizard), and EDGAR (Emission Database for Global Atmospheric Research) annual or monthly mean climatologies. More details on the sources of these aerosols are given in  Dentener et al. (2006). Emissions of organic matter (OM), black-carbon (BC) and SO 2 linked to fire emissions are obtained using the Global Fire Assimilation System (GFAS) system based on Moderate Resolution Imaging Spectrometer (MODIS) satellite observations of fire radiative power, as described in  Kaiser et al. (2012). The 10-year reanalysis employs GFAS-based fire emissions for 2009-2012 and the GFED3 inventory for  2003 -2008 (van der Werf et al.
We have used the PM 10 daily means available in the AMMA database. This data set was filtered by wind direction in order to select geographical sectors mainly affected by desert mineral dust during sampling, rejecting the contribution of biomass burning aerosols, and sea-salt aerosols in M'Bour. A detailed description of the PM 10 AMMA stations, the measurement program, data screening and filtering, and the main spatiotemporal dust distribution is provided by  Marticorena et al. (2010).
3. 2) over the selected AERONET stations is also included in the analysis. A set of standard skill scores defined within the MACC-II project (see Appendix A) have been computed using data from those days of the period 2007-2008 when there are simultaneous data of MACC, OMI, MODIS and AERONET. They have been computed on a monthly and seasonal basis for each AERONET station and for the eight sub-regions defined in Fig. 2  and Table 1. To represent the results we have used seasonal (Fig. 5 ) and monthly (Fig. 6 ) series as well as Taylor diagrams (Fig. 7 ), where daily AOD from MACC II, MODIS and OMI are compared each other using AERONET as the reference. We show the six most relevant regions for the sake of brevity: Sahara, Sahel, the Middle East, eastern Mediterranean, western Mediterranean and subtropical North Atlantic.
Dust content is difficult to verify because bulk optical observations are not specific for dust. Since AOD is the degree to which a mixture of atmospheric aerosols prevents the transmission of light by absorption or scattering, a criteria is needed for filtering data to ensure that most of the AOD is influenced by mineral dust, i. e. the dust optical depth (DOD). Nevertheless, the criteria should not be as restrictive as to reduce drastically the number of observations because it would preclude proper assessments of this evaluation in dust transport regions where mineral dust concentrations are significantly lower than in near-source regions. For the present AERONET comparison, we used AE < 0. 6 as a first approach to discriminate when mineral dust is the main aerosol component in the direct-sun AERONET data set according to previous studies  (Duvobik et al.
Subtropical North Atlantic Ocean The subtropical region is a well-known Saharan dust transport corridor, mainly in summertime  (Prospero et al. , 1995; Engelstaedter et al. , 2006) , so it is a good testing bench to evaluate the performance of MACC-II in situations of dust transport. Santa Cruz de Tenerife (SCO), is a station located in Tenerife, Canary Islands, it monitors marine aerosols within the marine boundary layer (MBL) and mineral dust during Saharan outbreaks. The AOD annual course from MACC-II tracks well that observed by AERONET and MODIS. A slight overestimation in MACC-II and MODIS is observed in July and August when the maximum AOD is recorded as a result of a higher dust intrusions frequency. The agreement between MACC-II and MODIS is excellent (Fig. 6p ), most likely be-cause the data assimilation from MODIS is successful over the ocean, once the dust cloud is accurately identified by MODIS.
Furthermore, our CALIOP data correspond to a circular area of 1. 5 • radius around the ground-based lidar, and therefore dust and cloudy conditions might be totally different in some events for the lidar and CALIOP at the time of comparison. A well-defined MBL is observed from lidar extinction profiles in all seasons with a high extinction (> 0. 2 km -1 ) and a rather constant depth (∼ 1 km altitude) in both stations. Similar results are found with CALIOP vertical profiles. MACC-II does not match the observed extinction within the MBL. However, we must bear in mind that lidars provide no credible data in the first hundred metres due to overlap and after pulse limitations, and that extinction overestimation might be caused by the use of a relatively high lidar ratio in the MBL. Surface dust daily mean concentrations from MACC-II reanalysis has been evaluated with daily averaged PM 10 at three monitoring stations of the Sahelian Dust Transect during 2007 and 2008.
An important limitation of the validation is the uncertainty associated with dust observation itself. In addition, the assessments at ground stations have the added difficulty of comparing an observed value at a point with a value simulated in a relatively large grid size. A second limitation is the scarcity of observations in desert dust source regions such as northern Africa and the Middle East. Reanalysis data correspond to regions where there are very few ground based observations, and where satellite sensors have major problems to obtain accurate information due to high ground reflectivity. For this reason dust reanalysis data become unique information in this study domain. The results highlighted in the present study will help not only the climate-weather scientific community but also enduser communities to prevent the impact of severe events over desert source regions where dust is considered to be a harmful air pollutant.
Quantification of vibrios Whole tissue samples of a pool of 3 to 8 oysters per batch, diluted in sterilized seawater (1:100), were spread on marine broth medium and thiosulphate citrate bile salts-sucrose (TCBS) in Petri dishes to quantify cultivable bacteria, and more specifically, vibrios  (Petton et al. 2013). The bacteriological media used in this study were obtained from Difco Laboratories. The plates were incubated at 22. 5°C for 6 d in marine broth, and for 2 d in TCBS before counting the number of colony forming units (CFU, Table  1 ). Data are expressed as % Vibrio spp. (100 × CFU of vibrios / CFU of total cultivable bacteria).
Lifetest Nonparametric estimates of the survivor function were computed by the Kaplan-Meier method  (Kaplan & Meier 1958). Survival time was measured as days from the onset of the experiment, when oysters were transferred back to the Ifremer facilities in Argenton at 21°C. The data were read as the number of dead animals within each experimental unit at each time interval. Survival curves of oysters were compared among treatments (Expt B: duration of field exposure at each season; Expt C: age and/or size, duration of field exposure; Expt D: water renewal and biomass of infected oysters) and status (Expt D: previously fieldexposed or laboratory-challenged). The resulting survival estimates were compared by using the log-rank test of homogeneity of strata.
, the GIGASSAT project funded by ANR-AGROBIOSPHERE N° ANR-12-AGRO-0001-01 and the French Ministry of Food, Agriculture and Fisheries(Convention DPMA 2014 -Ifremer). LITERATURE CITEDAnderson RM, MayRM (1979)  Population biology of infectious diseases: part I. Nature 280: 361-367 Anderson JA, Senthilselvan A (1982) A two-step regression model for hazard functions. J R Stat Soc Ser C Appl Stat 31: 44-51 Arzul I, Renault T, Thébault A, Gérard A (2002) Detection of oyster herpesvirus DNA and proteins in asymptomatic Crassostrea gigas adults. Virus Res 84: 151-160 Ben-Horin T, Lenihan HS, Lafferty KD (2013) Variable intertidal temperature explains why disease endangers black abalone. Ecology 94: 161-168 Bingham P, Brangenberg N, Williams R, van Andel M (2013) Investigation into the first diagnosis of ostreid herpesvirus type 1 in Pacific oysters.