dataesr/scientific-paragraphs-categorization huggingface dataset:
AMSU description This study focuses on the stratosphere temperature monitoring by using the temperature data from AMSU-A. This spectrometer is a cross-scanning microwave instrument that is composed of 15 channels between 50 and 58 GHz in the oxygen band, allowing the observation of the temperature structure of the atmosphere from the surface to around 50 km. More specifically, six channels (from 9 to 14) sound the stratosphere with vertical weighting functions with half-width of around 10 km  (Karbou et al. , 2005). The weighting functions for these channels are centred approximately at 18, 20, 25, 30, 35 and 40 km, respectively. The AMSU instrument has been collecting observations on board several successive NOAA polar-orbiting satellites  (NOAA-15 through to -19, starting in 1998 (NOAA-15 through to -19, starting in , 2000 (NOAA-15 through to -19, starting in , 2002 (NOAA-15 through to -19, starting in , 2005 (NOAA-15 through to -19, starting in and 2009 respectively) respectively) , National Aeronautics and Space Administration Earth Observing System (NASA EOS-Aqua) satellite (since May 2002), and Metop  (Metop-A in 2006 and Metop-B launched in 2012).
Such differences are small; however, they induce uncertainties on trend calculations. Fortunately, the AMSU series exhibit several overlapping periods. When temperatures obtained for the same period from different AMSU instruments are compared, systematic differences can be noted (Figure  2 (a) for 10 • N to 10 • S latitude band). At some levels (channels 12 and 13, Figure  2(c ) and (d), respectively) and for some coupled AMSU, drifts can also be observed with the largest amplitudes for data series corresponding to the upper altitude levels. NOAA-15 and NOAA-16 AMSU-A, which provide the longest period of operation and a long overlapping period, show obvious drifts for channels 12 and 13 (AMSU channel 14 on NOAA-15 failed in October 2000). Such drifts are assumed to be associated with atmospheric tides since orbit drifts induce changes of the times of measurement (Figure  3 ).
ncdc. noaa. gov/cdr/operationalcdrs. html. This dataset takes into account and corrects biases found in the pre-launch AMSU observations, including: relatively stable intersatellite biases between most satellite pairs, instrumental drifts, sun-heating-induced instrument temperature variability in radiances, and scene temperature dependency in biases due to inaccurate calibration nonlinearity. NOAA also made available the full operational AMSU data along with data with limb-correction following  Goldberg et al. (2001). When using only near-nadir data to construct the time series, the range of temperature differences between NOAA-15 and NOAA-16 from both IMICA and operational AMSU are basically unchanged (Figure  4 ), whereas using all fields-of-view to calculate monthly means yields temperature differences smaller for IMICA than for limb-corrected AMSU, within however the same order of magnitude and same sign of slope (not shown).
Quantification of tidal effects AMSU data from NOAA-15 and NOAA-16 provide a good opportunity to investigate the source of the differences and the possibility to correct them as both provide the longest period of operation and a long overlapping period since January 2001. Channel 13 was chosen, as the temperature differences are  obtained for the longest period while both instruments on NOAA 15 and 16 are in operation simultaneously over a long period (decade). Drifts evolve in the opposite direction, and in mid-2008 both satellites presented measurements at nearly the same time of day. Data obtained during this specific period can be used to calculate temperature differences that can be associated with instrumental differences, as temperature anomalies induced by atmospheric tides are similar.
This systematic difference is in good agreement with that of about -0. 08 K inferred by the bias adjustment scheme used in the ERA-Interim data assimilation system  (Simmons et al. , 2014) , which does not show strong sensitivity to orbital drift. In the summer stratosphere, the main tidal mode is a 24 h oscillation  (Keckhut et al. , 1996). The diurnal tides exhibit a seasonal cycle with amplitudes that increase with latitude, and the amplitude of the semi-diurnal mode increases too  (Keckhut et al. , 1996) , leading to a tidal behaviour that is more complex at high latitudes. This simple tidal model explains the appearance of an annual oscillation in the temperature difference between AMSU instruments. Here, only diurnal tide is considered and only summer data for each respective hemisphere (June, July, August and December, January, February respectively for Northern and Southern Hemispheres) are used to derive a tidal correction.
, 1998). AMSU summer data are selected to compare raw and tidal corrected temperature data with those derived by the Table Mountain Facility (TMF) lidar. The corresponding altitude between lidar and AMSU channel 13 has been determined following the same method as that developed by  Funatsu et al. (2008). Raw AMSU temperature series show differences on tendencies (around 15%) with lidar time evolution for both instruments on board NOAA 15 and 16 (Figure  7 ). The NOAA-15 temperature dataset shows reduced tendencies (-0. 5 ± 0. 5 K decade -1 ) and NOAA-16 shows larger tendencies (-0. 9 ± 0. 5 K decade -1 ). The tendency difference for correct/non-correct NOAA series is nearly significant (95%) while uncertainties are all provided for the 2σ interval. When NOAA temperature series are corrected for atmospheric tides, the tendencies converge to a similar median value (-0.
Discussion and conclusions Drifts of the orbits for the instruments in space are a key issue for stratospheric temperature analysis due to atmospheric tides. It is not easy to disentangle the contributions of atmospheric tides from the instrument drift to explain observed temperature drifts between two simultaneous instruments in space on different orbits. In this study, in assuming that atmospheric tides are the main reason for observed temperature drift between measurements by AMSU on NOAA 15 and 16, diurnal tides are derived for summer months. These compare well with simulated tides, showing that most of the observed temperature drifts may be due to tides. The impact on temperature trend estimates is large and the observed trend induced by tidal effects is of the same order. Such a correction was possible for summer data when the diurnal mode dominates.
Characteristics of atmospheric tides in such a variable atmosphere are still not well known. However, a sensitivity study shows that long-term changes of tide amplitudes due to expected atmospheric changes remain smaller than 10%  (Morel et al. , 2004). Only non-migrating tides are considered in this study; however, migrating tide should also contribute, mainly if data are investigated over longitudes. In most cases, the temperature monitoring is ensured by a succession of instruments, due to their operational status for weather forecasting and temperature monitoring (identified as sentinel instruments), using overlap periods. AMSUs on NOAA 15 and 16 exhibit an unexpectedly long lifetime. However while the instruments are similar, if their times of measurement are different some bias due to tides is superimposed on instrumental bias, as was demonstrated by using AMSU IMICA data (which eliminated most instrument and scene biases).
Acknowledgements This work was performed in the frame of the  FP7   ARISE  project. This tide issue was discussed during the  Stratospheric Temperature Trend Assessment group  of the  SPARC/WCRP  programme and the  ISSI (International Space Science Institute ) working group 'Characterizing the diurnal variation of ozone'. AMSU data were provided by the  INSU-CNES ICARE  thematic centre (http://www. icare. univ-lille1. fr)with the assistance of the IPSL expert centre ESPRI. NDACC data used in this work are created by  NASA-JPL  and are archived in the NOAA database at http://www. ndsc. ncep. noaa. gov/. The AMSU IMICA and limb-corrected operational AMSU data used in this study were acquired from NOAA's National Climatic Data Center (http://www. ncdc. noaa. gov). AMSU IMICA was originally developed by  Cheng Zhi Zou  and colleagues at  NOAA  though support from  NOAA 's  CDR Program.
Acknowledgments. We greatly acknowledge the MesoNH assistance, especially  C. Lac  and  J. Escobar. Computer resources were provided by  CINES (Centre Informatique National de l'Enseignement Supérieur  http://www. cines. fr/,project no. lac6309 ) and  CCUR  ( Centre de Calcul de l'Université de la Réunion ) for the access to super-computer. We also thank  ORA  (http://www. atmo-reunion. net/) for free access to their data. Finally, we would like to thank  OSU-R, OMNCG federation, Reunion University  and  LEFE/INSU program  for funding this work.
Body temperature drops at parturition  [40] [41] [42] [43]. We used the rise and drop in body temperature of pregnant females to calculate the dates of implantation and parturition and to document the gestation period of free ranging brown bears in Sweden. We compared the body temperature of pregnant females before, during, and after the gestation period and also with the body temperature of nonpregnant females. Hibernating pregnant female brown bears are more active during pregnancy than afterwards. Their activity levels increase at the end of November, remain elevated, and then drop sharply to a lower level in late January/early February, similar to the progress of body temperature that is reported for pregnant females during hibernation  [44]. We compared if activity data (recorded in GPS collars) and body temperature data (recorded in implanted temperature loggers) would yield the same dates of implantation or parturition.
We implanted abdominal temperature data loggers (DST Centi, Star Oddi, Iceland), programed to record body temperature every 30 minutes (see Arnemo et al. [50]  for further details on the implantation procedures). These temperature data were stored in the logger's internal memory with a realtime clock reference for each measurement. After recapturing the bears, we recovered the temperature loggers and uploaded the body temperature data with SeaStar software and the Communication Box (Star Oddi, Iceland), which served as a wireless interface between the logger and a PC. Only females with verified reproductive status in a given year were included in the data set. Pregnant females were defined as solitary-hibernating females that had been observed with cubs of the year (hereafter referred to as cubs) after den emergence in spring, or which had been captured shortly after den emergence and showed signs of lactation and that cubs had used the nipples (to exclude cases of pseudopregnancy).
In central Sweden, 22% of the brown bears change winter dens, most often early in the denning period, when human hunting activities are still high  [53]. Activity levels during hibernation are also lowest during midwinter  [44, 55]. For these reasons, occasional high activity peaks often occur early in hibernation, when implantation also occurs. To minimize the effect of high activity peaks, we used the moving averages (5 th order) of the mean daily activity levels when defining the dates of implantation. The first day when this moving average exceeded the individual's mean hibernation activity level for at least the next 2 weeks was defined as the day of implantation. We used the dates of implantation and parturition calculated with body temperature data to compare the recorded activity and body temperature data during the gestation period with that obtained from two other periods: 14 days before gestation (preimplantation period) and 14 days after parturition (lactation period).
We used relatively short periods of 14 days, because we wanted to compare data collected only during the hibernation period. Implantation may occur some weeks after the start of hibernation, and the time in den during lactation may be short for females that give birth to cubs very late. The body temperature of hibernating nonpregnant American and Asian black bears show multiday cycles, whereas pregnant females remain normothermic during gestation  [41, 42]. We compared the mean body temper-ature and also the daily variation in body temperature during the preimplantation, gestation, and lactation periods for pregnant and nonpregnant bears. For nonpregnant bears, we used the mean date of implantation and parturition determined from pregnant bears with body temperature recordings to define the periods of preimplantation, gestation, and lactation.
Data analysis We tested for relationships between mean daily activity and mean daily body temperature during hibernation with a general linear mixed model with normal distribution and with individual identity as a random factor. A second-order polynomial term for mean daily body temperature was included into this analysis to account for nonlinear effects. We used paired-samples t-tests to compare the dates of implantation and parturition and the gestation period between the estimates based on activity and body temperature data. Activity and body temperature data during the preimplantation, gestation, and lactation periods were compared with paired Wilcoxon signed rank tests. To evaluate the effect of the day of gestation on body temperature, we used a general linear mixed model with a normal distribution and with individual identity as a random factor.
A second-order polynomial term for day of gestation was included into this analysis to account for nonlinear effects. Mann Whitney U tests (MWU) were used to compare body temperatures during preimplantation, gestation, and lactation periods between pregnant and nonpregnant females. We evaluated the factors affecting the date of parturition with a general linear mixed model with a normal distribution and assessed the effects of the following factors: age, primiparity (as binomial variable, with no = 0; yes = 1), litter size, date of hibernation start, and the environmental condition index. Because some mothers contributed several litters to our datasets during their lifetime, we included individual identity as a random effect to account for nonindependence. Year was not included as a random effect, because the environmental condition index was included as a fixed variable to describe the different environmental conditions among years.
667, df = 5, P = 0. 045; Table  1 ). Mean activity during preimplantation, gestation, and lactation periods was 0. 3960. 3 (median: 0. 33), 1. 061. 08 (median: 0. 72), and 0. 2960. 27 (median: 0. 27), respectively. Activity during the gestation period was significantly higher than during both the preimplantation and the lactation periods (paired-sample Wilcoxon test; preimplantation vs. gestation: Z = 2. 02, P = 0. 043; gestation vs. lactation: Z = 22. 20, P = 0. 028; Fig. 2B ). Activity during the lactation period was significantly lower than during the preimplantation (paired-sample Wilcoxon test: Z = 2. 02, P = 0. 043; Fig. 2B ). We used a data set of 46 hibernation events from 30 females with only activity data to investigate which factors influence the date of parturition and the start of hibernation.
Activity Parturition dates estimated using activity and body temperature data differed by only one or two days and were the same for 50% of the females. Thus, we consider that either activity data or body temperature can be used to determine dates of parturition. However, because of the high variation in mean daily activity during the early hibernation period, it was more difficult to estimate the dates of implantation. We used the moving 5 th -order average, because in some cases, activity did not reach the mean hibernation level for more than a few days before implantation, in other cases activity rose before the implantation calculated from the body temperature. Raised activity during this period could be caused by hormonal changes prior implantation, or because activity is in general higher during the beginning of the hibernation period than during midwinter  [44, 55].
Bridges et al. [65]  observed later parturition in pregnant female American black bears , 5 years old. However, in our study, only 2 females were , 5 years. A larger dataset of young pregnant females might be necessary to document an effect of age on the date of parturition. Studies on captive brown bears have shown that larger females give birth earlier during winter than smaller females  [39]. However, in our study, favorable environmental conditions correlated with late parturition. Although we had no information about the females' body mass prior to denning, we expected that food availability was the most important factor affecting the environmental condition index  [58]  and that the females were heavier when the environmental conditions had been favorable. With this reasoning, our results differed from those found in captive bears  [39].
Data management and statistical analysis Data gathered during the study was recorded on a Microsoft Access TM database. This database was developed specifically for the study by the research team in order to record the large amount of data. Data input was the responsibility of only a few people and was checked to ensure that all records were correctly introduced. A cluster analysis (hierarchical model ---Ward method) that considered CO 2 concentrations, temperature and humidity indoors, measured in Phase I, was used in order to select the 20 DCCs for Phase II. DCCs that were further apart in the dendrogram were chosen. An exploratory analysis of the variables of interest was carried out using classic descriptive statistics to calculate frequencies, means, medians and associated dispersion measures and confidence intervals for population demographics, DCC characteristics, exposure values and health parameters.
Therefore, adjustment of clutch size to nest size is likely to have evolved before hole nesting. First, using data in classical experiments on great tits by L€ ohrl  (1973, 1980) , we investigated the effects of nest box size on clutch size, when great tits had access to differently sized nest boxes, and when the size of nest boxes was manipulated after laying had started. The latter design prevented differential access of specific females to boxes of a particular size. Second, we examined the plasticity of clutch size to nest size in hole nesters where nest box sizes were varied experimentally within a population. Third, we analyzed the relationship between clutch size and nest size using studies of four secondary hole-nesting species with both single or multiple nest box sizes. We predicted that if hole-nesting birds use a single type of plasticity as a clutch size response to cavity size, then we should find a homogeneous slope linking clutch size to cavity size.
Data sets This study was based on three different data sets: (1) L€ ohrl's  (1973, 1980)  data on great tits; (2) a data base on clutch size and nest box size in four secondary hole-nesting birds; and (3) a exhaustive database on the relationship between clutch size and nest size in 21 species of birds for which data on clutch size and nest size were available (Table  S1 ). We defined nest size in hole-nesters as the two-dimensional area of the nest box or nest hole in case the study was based on natural cavities (i. e. , nest base area). Similarly, we defined nest size for open nesting species as the horizontal maximum area of the nest including nest cup and nest rim (i. e. , nest base area). Nest size refers to this definition in the remainder of the article. We used this measure of nest size because it was available for almost all studies.
07, r 2 = 0. 99, P < 0. 0001, slope (SE) = 1. 197 (0. 083)), the open nesting barn swallows Hirundo rustica (A. P. Møller, unpubl. data: F 1,1265 = 866. 17, r 2 = 0. 40, P < 0. 0001, slope (SE) = 1. 202 (0. 041)) and rufous bush warblers Cercotrichas galactotes (M. Mart ın-Vivaldi, unpubl. data: F 1,48 = 24. 28, r 2 = 0. 34, P < 0. 0001, slope (SE) = 0. 937 (0. 190)) using log 10 -transformed data. We extracted data from L€ ohrl's  (1973, 1980)  pioneering studies of great tit clutch size, choice of nest box size and experimental manipulation of nest box size after start of laying. We used these data to estimate the relationship between nest size and clutch size, and in the nest box exchange experiment the relationship between clutch size and the change in nest box size. We made an exhaustive attempt to obtain information on clutch size, nest size and ecological variables from all studies of four common species of secondary hole nesters in Europe and North Africa, as described in detail elsewhere  (Møller et al.
Statistical analyses This study presents the results of 11 statistical analyses of the relationship between clutch size and nest size within and among species of birds (we have found no comparable data on any other taxa). This study differs from that by  Møller et al. (2014)  by analyzing an exhaustive compilation of data on both hole and open nesting species and a wider range of factors hypothesized to affect the relationship between clutch size and nest size. We re-analyzed L€ ohrl's  (1973, 1980)  data to allow distinction between the effects of original and subsequent nest box size on clutch size following exchange of nest boxes during the laying period. We did so by analyzing clutch size (the response variable) in relation to original and experimental nest box size. We estimated slopes of the relationship between clutch size and nest base area.
0264 (0. 0104)). This relationship and that reported when great tits were allowed to choose box size were not significantly different, as shown by their overlapping 95% CI (Table  1 ). These data also allowed a test of whether the relationship between clutch size and change in box base area between the original and the new box was statistically significant. That was indeed the case. The slope was half that of the previous estimate (Table  1C ), although the 95% CI of the parameter estimate overlapped with the previous two estimates (Table  1A , B). Mean nest base area in the second dataset for great tits weighted by sample size across all 3447 samples was 118 cm 2 , range 36-400 cm 2 , SD = 123 cm 2. This implies that an increase by one SD in nest base area was associated with an average increase in clutch size of the great tit by 0.
Based on these 116 datasets, a Welch analysis of variance for unequal variances with ranked slope as the response variable, species as a fixed factor and sample size as a weight revealed a significant effect of means of species (Fig. 2 ; F 7,3660. 6 = 6. 23, P < 0. 0001), and a Levene's test showed a significant 'difference in variance among species (F 7,95 = 5. 06, P < 0. 0001). This shows a general positive relationship that differed significantly  among species, and the variances within species also differed significantly. Because ambient temperatures during the breeding season are predictably warmer at lower latitudes, we expected a stronger relationship between clutch size and nest base area in southern populations than in northern populations. However, adding latitude (F 1,93 = 1. 43, P = 0.
Heat Shock Protein 70 expression We focused our study on a narrow area of 2DE gels where spots were previously identified as HSP 70 proteoforms. 4 We confirmed this identification by mass spectrometry analysis (data not shown). Four spots were discriminated and for each one and each replicate, normalized volumes were calculated. Means of normalized volumes were compared between groups (C, A and F) for Yport (Figure  3A ) and Le Havre (Figure  3B ). For each site, results showed an over-expression of HSP 70 proteoforms in A and F groups compared to control groups, except for one spot at Le Havre (spot 4). In accordance with literature, the HSP 70 levels markedly increased after heat stress in mussels living in temperate intertidal area. 3, 5, 6 or pristine site, an over-expression of roughly 2.