dataesr/scientific-paragraphs-categorization huggingface dataset:
Trend Analysis Although the observation period is rather short (i. e. , 2009-2018), we used ground-based UVR data from the two study sites to investigate changes and trends over a decade. Trend analyses were performed based on daily and monthly values obtained from observations at Saint-Denis and Cape Town sites. All UVR data were integrated into daily erythemal doses (Figure  5 ). There is a larger dispersion of UVR doses at Saint-Denis (Figure  5a ) in comparison with Cape Town distributions (Figure  5b ). There was a moderate correlation at Saint-Denis, 63% (220/365 points), and a high correlation of 90% (289/365 points) at Cape Town. For both study sites, by comparing UVR doses recorded in 2009 and 2018, positive differences are evident. A bias of +15 ± 90% at Saint-Denis and +4 ± 50% at Cape Town was found by comparing 2009 and 2018.
For both sites, UVR trends were investigated in two ways: on a global and a seasonal basis. For each site, total daily erythemal doses (in SED unit) were averaged monthly and used from January 2009 to December 2018. The trend analyses are shown in Figure  6a, b. The relevance of this analysis depends on the total number of daily observations applied. The histograms at the bottom of each plot show the number of days with available data. The two sites had a rate of observational measurement higher than 60%: 62% for Saint-Denis and 92% for Cape Town over the studied period. Figure  6  shows two opposed global daily erythemal UVR trends for the two sites: an increasing trend at Reunion Island (+3. 7%) and a decreasing trend at Cape Town (-3. 6%). Fountoulakis et al. , 2018 found an increase in UVR of about 3% per decade over Europe, Canada and Japan, by using a 25-year ground-based database while zonal trend analysis (20 years) from Total Ozone Mapping Spectrometer (TOMS) also showed an increase of 3% per decade for latitudes similar to our study sites  [28, 29].
The two opposite trends may be explained by the wavelength range of the UV-biometer (280-340nm). Since the spectral response is corrected by coefficient depending only on ozone and solar zenith angle, differences on atmospheric conditions (aerosols, clouds, …) can induce bias in dataset. Moreover, the trend estimates for Saint-Denis site should be interpreted with care since 38% of daily observations were missing, while 8% of daily observations were missing for Cape Town. As UVR depends on seasonal variability and on changes in the forcings that modulate UVR fluxes at the surface, we broke down the trend analysis by season (DJF: December-January-February, MAM: March-April-May, JJA: June-July-August, and SON: September-October-November), as shown in Table  2. The seasonal decomposition showed two positive trends in Saint-Denis, about +5.
The latter may be due to dynamical changes in the Brewer-Dobson circulation, which is a large-scale circulation that takes place in the winter stratosphere and depends on planetary waves propagation in the middle atmosphere  [33] [34] [35]. Moreover, by analyzing radiosonde and satellite datasets, Toihir et al. [32]  found no significant change in stratospheric ozone in the southern tropics over the period 1998-2013. Indeed, the positive change obtained for surface solar UVR in Reunion could not be attributed to the change in stratospheric ozone. It may be associated with a possible change in the troposphere. Climate models predict that the geographic distribution of cloud changes in response to anthropogenic warming, and the expected forced changes are likely to appear in the upper troposphere  [36].
The change in solar UVR levels at the surface may also be due to a change in aerosol loading  [37]  or in tropospheric ozone formation. The two study sites are famous tourist sites and increasing anthropogenic activities may have resulted in an increase in air pollution. A positive trend in tropospheric ozone over Cape Town has been found by using ground-based, satellite-based, and modeling datasets for past decades  [38]. This change in tropospheric ozone content can explain the change in surface UVR. However, the effect of aerosols is different for our study sites. Indeed, Saint-Denis is continuously affected by the trade winds that result in a short residence time of aerosols. The retrieved UVR trends derived for Saint-Denis should be interpreted with caution mainly because 1) there is missing data in the time series and 2) it is a tropical site with extensive cloud cover, especially during the summer season, and because of the possible change in cloud cover.
Acknowledgments: Authors acknowledge the  French South-African PROTEA  programme and the  CNRS-NRF LIA ARSAIO (Atmospheric Research in Southern Africa and Indian Ocean) , for supporting research activities, the  Conseil Régional de la Réunion , for the PhD scholarship of Jean-Maurice Cadet, the  National Research Foundation  and  South African Medical Research Council  for providing support to  Caradee Y. Wright , and the  South African Weather Service  ( SAWS ) for providing UVR data.
The lower part is placed below the connectors/bars set and the liquid nitrogen is completely spilled in. Then, the upper part is positioned over the lower part and a locking system assures the contact of the two parts. Five minutes of contact with the liquid nitrogen assures the specimen temperature close to -195°C. 2. The upper part is removed with the temperature still controlled by heat exchange with the environment. A mechanism is used to discharge the liquid nitrogen so that the connectors/sample set is no more immersed into the liquid nitrogen. 3. When the desired testing temperature is reached, the test is launched and data are collected. The aforementioned three stages are schematically described in Fig. 2 , while images of the experimental apparatus are reported on Fig. 3. From the latter, it can be seen that the chamber is large enough to ensure a constant temperature in the environment of the specimen.
It is supposed that a homogeneous temperature is obtained at the gauge area of the specimen thanks to its reduced dimensions. However, this information cannot be checked since the thermocouple is bonded at the surface of the specimen. It is also worth adding that the actual temperature during the test cannot be measured due to the very short time at break (less than 1ms). Normally, a set of 3 tests was performed for each testing condition to ensure an acceptable repeatability of results and highlight possible material data dispersion at the same time. For sake of comparison, quasi-static tests were carried out on a ElectroPuls TM Instron E 3000 machine with a strain rate of 0. 008 s -1 at room temperature.
True stress-strain curves and strain at fracture In this section, the Ti6Al4V mechanical behavior is presented with respect to different strain rates and temperatures. The flow stress relations are obtained using David TM software  [9]  on the basis on the governing equations mentioned in a previous paper  [10]. For all the conditions, it is assumed a Poisson's coefficient of 0. 34 and no viscoelasticity effect is considered. Fig. 4  reports the effect of strain rate on the flow stress at room temperature. The tensile tests performed under dynamic conditions showed higher values of the fracture strength compared to the flow stress under quasi-static conditions, confirming the strain rate hardening effect at elevated strain rates. It is worth noting that the changes of the tensile tests strain rate do not influence significantly the elastic modulus of the investigated material: Young's modulus is 80 GPa in quasi-static conditions and close to 90 GPa under dynamic loadings.
Particularly in settings with weak health systems this information should be generated by simple approaches. Routine data collected at health facilities may provide a viable option if the relevant information is captured and readily analysed. However, research on how to best apply geospatial analyses of simple routine data to identify heterogeneity at village level and support targeting malaria interventions is scarce  (Kelly et al. , 2012). This study aimed to assess whether the visualizations of health facility data linked to village of residence of patients provides useful evidence for targeting malaria control interventions. It used malaria incidence data linked to the self-reported village of residence of the patients, collected routinely in four sentinel health facilities (SHF) in PNG.
A study nurse at the facility collected a capillary blood sample from all consenting fever patients for 1) diagnosis of malaria by Rapid Diagnostic Test (RDT). Demographic details including village of residence and selfreported mosquito-net use the previous night were recorded on paper case report forms alongside RDT results. Paper forms were then double entered at the PNGIMR. The study team ensured availability of RDTs throughout the surveillance period. Data was collected from 2010 to 2014 to characterize annual incidence variations between villages in the catchment areas of the SHFs and identify patterns that could guide malaria control efforts. Recently, a paper-based "Malaria Register" has been implemented by the PNG National Department of Health. In addition,and an electronic National Health Information System (eNHIS) is being piloted  (Rosewell et al.
[Table  1 here]  The maps in this visualization illustrate variations in incidence in space and time. using open source GIS software. The video format of the v-communication enabledallowed us to visualize the dynamics display over 40 maps in a convenient and in case incidence over time engaging way alongside together with imagesphotographs illustrating the context of the data collection that provide insights to ofin the study sites. and the, often unknown, geographical and cultural diversity of PNG. In addition, tThe audio-visual format is easily accessible to a great range of stake-holders and with the potential to could potentially better communicate complex geospatial relationships in an understandable format  (Krieger et al. , 2012). Aggregated annual malaria incidence was calculated for every village with at least one malaria case reported by the SHFs.
Commented [A11]: Suggesting to move this sentence to after the next one. You may also slightly re-frame the sentence in order to highlight that this is a key aspect of your visualisation was calculated and mapped as a proxy of local transmission since young children are the least immune age group and compared to adults are less likely to travel  (Bousema et al. , 2012). A census conducted by the PNGIMR in the SHF catchment areas at baseline was used as source of village coordinates and population denominator data. The georeferenced year 2000 National Census database was used to complement the PNGIMR census, particularly for villages outside the catchment area. Self-reported mosquito-net use by all fever patients during the last year of surveillance (  2014 ) was mapped by village and reported net use by all fever cases was graphed by year as an indicator of trends over time.
Since 2015, eNHIS has been piloted in 184 health facilities in PNG. The platform includes a georeferenceding feature for mapping malaria cases inat a village level and automated data analysis, reporting and identification of outbreaks  (Rosewell et al. , 2017). If proven successful eNHIS could considerably strengthen malaria surveillance in PNG. A similar Spatial Decision Support System building upon and extending existing data collection systems and exploiting current geo-spatial tools has been validated in nearby Vanuatu and Solomon Islands in areas of very low transmission  (Kelly et al. , 2012). Areas in PNG that have reached a low level of transmission with clear foci may benefit from a similar approach. However, local (sub-national) capacity to further investigate local foci and implement targeted response action would be required as much as sufficient and sustained funding for these activities.
Commented [A6]: Specify age rangeCommented [A7]: Brand name?Commented [A8]: Introduce abbreviation the PNGIMR in the SHF catchment areas at baseline, during which all houses were identified and each household member was listed, was used as source of village coordinates and population denominator data. The geo-referenced year 2000 National Census database was used to complement the PNGIMR census, particularly to identify villages outside the catchment area. During the surveillance period 25,097 fever cases were tested for malaria across all SHF, 38% (95% CI: 37. 6-38. 8) were RDT-positive.
The literature includes several analyses of the behavior of plant-based materials, assemblies, and structures which propose experimental data to validate numerical models of Heat, Air and Moisture (HAM) transfer. Roels et al. [6]  studied the response of several assemblies composed of wood wool cement board. Experimental results focused on water content discussion. They showed that the theory of Philip and de Vries  [7]  gave satisfactory results for simulating wetting-drying. Talukdar et al. [8, 9]  presented a very complete study on a dataset for benchmarking 1D transient heat and moisture transfer models of spruce plywood and cellulose insulation in two parts. The first presented their experimental setup and its calibration, while the second presented experimental, numerical, and analytical results that could be used for benchmarking.
It was found that if the walls were sufficiently airtight the amount of accumulated moisture in the winter season was limited and dried out in the summer for the given configuration. For the same wall type, Langmans et al. [14]  performed exeprimental and numerical study to analyse the hygrothermal consequences of using an exterior air barrier. They highlighted buoyancy effects for high walls (above 2 m). Rafidiarison et al. [15]  presented a dataset for validating 1D heat and mass transfer models within building walls with hygroscopic materials. They showed that moisture accumulation was observed in the layer close to the plaster for the wall without Oriented Strand Board (OSB). Latif et al. [16]  carried out an in situ experiment on a full-scale timber frame test building to study the hygrothermal performance of wood-hemp composite insulation in timber frame wall panels with and without a vapor barrier.
Temperature, relative humidity and differential pressure sensors were installed in the two compartments to continuously measure the loading applied to the wall studied. Thermohygrometers were also installed on the surface and at the core of the wall, at different positions, to obtain the response of the wall to the loading. The data were acquired every minute. Technical details for the whole data acquisition system installed, calibration of the sensors, installation, and qualification of the experimental set-up (airtightness, regulation, test protocol) are presented in  [21, 22]. Table  3  gives a summary of the hygrothermal operating domain of the experimental setup and the uncertainties on both temperature and relative humidity as a function of the type of sensor. The compartments were composed of two elements, an internal box and an external box that served as a thermal guard.
Measurement Domain Uncertainty Temperature, relative humidity and differential pressure sensors were installed in the two compartments to continuously measure the loading applied to the wall studied. Thermo-hygrometers were also installed on the surface and at the core of the wall, at different positions, to obtain the response of the wall to the loading. The data were acquired every minute. Technical details for the whole data acquisition system installed, calibration of the sensors, installation, and qualification of the experimental set-up (airtightness, regulation, test protocol) are presented in  [21, 22]. Table  3  gives a summary of the hygrothermal operating domain of the experimental setup and the uncertainties on both temperature and relative humidity as a function of the type of sensor.
The coupled heat and moisture transfer model obtained takes the form of a series of partial differential equations (PDE) that govern all the transfers. These non-linear and coupled PDE are resolved simultaneously using the COMSOL Multiphysics software based on the Finite Elements methods. We chose this environment since it can be used for modeling and resolving a wide range of engineering and research problems, mainly problems in PDE form. It is particularly well-adapted to solving Multiphysics problems in which several phenomena are studied simultaneously as in the case of coupled HAM transfers. The use of such tools is justified because they are capable of solving this type of problem and allow the implementation of reliable and well-defined input data and the choice of meshing. It comprises several solvers adapted to a series of given problems.
Funding: This research was funded by the  Agence Nationale de Recherche (ANR) , project  HYGRO-BAT ,  ANR-10-HABISOL-005-01  and the  Région Auvergne-Rhône-Alpes. Funding: This research was funded by the  Agence Nationale de Recherche (ANR) , project  HYGRO-BAT ,  ANR-10-HABISOL-005-01  and the  Région Auvergne-Rhône-Alpes.
TlimQ after each exercise test was compared by ANOVA after verification of the normal distribution of the data by a Shapiro-Wilk test. Symptoms, heart rate, SpO 2 and PtcCO 2 were compared using paired Wilcoxon signed-rank test. Unpaired t-tests with Welsh correction were used for subgroup analyses. Correlations were performed using least square regression analysis between exercise capacity and muscle function and TlimQ changes with IPS. Differences were considered significant at the level of 5%. Statistical analysis was performed using PRISM V5 software (Graphpad software, La Jolla, CA, USA).
Perspectives These data bring insight into the complex interplay between respiratory system and quadriceps function in COPD patients during exercise. In those patients with a poor exercise tolerance due to ventilatory limitation and candidate for NIV application during pulmonary rehabilitation, we propose to assess post-exercise quadriceps endurance with and without a ventilatory support. Combining the responses to cycling exercise endurance and to post-exercise quadriceps endurance could allow to adjust the training load in order to maximize quadriceps adaptations and would participate in the increased benefit of pulmonary rehabilitation with NIV.
METHODS We define resource partitioning as differences between species in how they acquire resources  (Schoener 1970 , 1974 , Roughgarden 1976 ). We conducted a review of the biodiversity-ecosystem functioning literature through March 2018 (Appendix S1). From this review, we collected data sets from an experimental species richness gradient greater than four species in a field or large controlled environment facility (Ecotron) context with typical grassland species (including both grasses and forbs). To measure vertical resource partitioning, we required that biomass be collected in layers by either soil depth or canopy height (see Appendix S1: Table  S2  for experiment details and supplementary methods for data requirements). In addition to biomass data sets, we also collected data sets where resource tracers were injected at different soil depths.
We were able to collect 21 total data sets: 12 aboveground biomass by canopy height, 6 belowground biomass by soil depth, 1 nitrogen uptake by soil depth, 1 nitrogen and potassium uptake by soil depth, and 1 water uptake by soil depth. Throughout, we refer to the last three studies as a group as "resource tracer uptake" studies. Of these 21 data sets, 19 reported either total community resource uptake or total community biomass. For the one data set where water uptake was measured, we could not calculate summed water uptake per plot (total community resource uptake, the measure of ecosystem functioning for resource tracer uptake studies) because water is not immobilized, stored, or accumulated in plants  (Guderle et al. 2018). For another data set, we could not determine total aboveground biomass  (Guimarães-Steinicke et al.
Individual data set analysis Prior to conducting our meta-analysis, we fitted linear mixed-effects models using the package lme4  (Bates et al. 2015)  with the package lmerTest to calculate Satterthwaite approximations of denominator degrees of freedom  (Kuznetsova et al. 2017)  for each individual data set. For each site, we fit the model with the dependent and independent variables and random effects and transformations that were relevant at the individual site. That is, if an experiment used a blocked experimental design, block was a random effect in the model. If a data set was collected over multiple years or seasons, then year or season was a random effect in the model. We log-transformed variables when the residuals of an individual model were significantly nonnormally distributed according to a Shapiro-Wilk test (see Appendix S1: Table  S3  for full individual model details).
Levins B is highest when all sources are used equally and lowest when one source is used exclusively by a species  (Levins 1968, Parrish and Bazzaz 1976). Levins B, which we selected because it performs well at low sample size, is equivalent to the inverse Simpson index  (Smith and Wilson 1996). Second, we asked, does the location of the resource partition change with increasing diversity (Fig. 1b )? For community biomass data sets, we measured this location change in terms of the weighted mean biomass allocation of the community, which we calculated as the community center of gravity: community center of gravity ¼ P j Biomass j Â Mean location i Total community biomass (3) where j is the soil depth or aboveground height layer from which the sample was collected, and the mean location is the mean height or depth of the layer.
Meta-analysis After conducting the individual linear mixed-effects models, we extracted Pearson's correlation coefficient r for each question for each data set (using the package MuMIN; Barto n 2018), standard error, and sample size. We then used the package metafor (Viechtbauer 2010) to calculate Fisher's z-transformed correlation coefficients (r z ). The Fisher's z-transformed correlation coefficient normalizes the distribution of Pearson's r. We used r z as the model input in mixed-effects meta-models with a nested random effect of site within experiment to control for the uneven distribution of our data sets among our sites and our sites within broader experimental units (e. g. , at the BIODEPTH experiment, there are eight experimental sites but similar experimental designs, while, in the Jena Experiment, there are several experiments with different designs all located at the same site).
For each dependent variable (total community biomass, total community resource uptake, community inverse CV, Levins B, community center of gravity, or proportional similarity), we ran up to five analysis sets of our total meta-data set: (1) all functions analysis set (above-and belowground biomass and resource uptake) used only for ecosystem functioning; (2) all biomass analysis set (above-and belowground); (3) abovegroundbiomass only analysis set; (4) belowground biomass only analysis set; and (5) resource uptake only analysis set. By subsetting the data and running joint and separate analysis sets, we were able to determine whether patterns were stronger (i. e. , had a larger r z ) for different subsets of the meta-data set. We performed all data analysis with R statistical computing software version 3.