dataesr/scientific-paragraphs-categorization huggingface dataset:
madagascarica and An. coustani near Lake Kinkony and An. pauliani and Ma. uniformis near Lake Soamalipo  [36]. In our study, Ad. madagascarica and An. pauliani were the two species found to be positive for WNV. Unfed mosquito abdomens rather than whole specimens were screened for WNV infection. Detecting WNV in the abdomens of unfed mosquitoes suggests the potential for these two species to be involved in the maintenance and/or transmission of WNV in Madagascar. However, to incriminate these species as vectors of WNV transmission, vector competence studies should be undertaken to show infectious virus in the saliva  [18, 48]. CDC-light traps are suitable for studying mosquito seasonal dynamics as evidenced by data obtained in Morocco  [15] , in sub-Saharan Africa  [40] , and in Madagascar  [57, 58].
Acknowledgements. We thank  Dr. Thomas Walker  and  Benjamin Rice  for critical reading of the manuscript. This study was conducted and funded as part of  Animal Risk , a research program on emerging animal infectious diseases in the Indian Ocean Region, and the  Institut Pasteur de Madagascar. We thank all the Mitsinjo district farmers, veterinarians, and veterinary technicians for their participation in the fieldwork. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.
However, in the aftermath of the Great Recession, some of the most segmented and crisis-ridden counties in Southern Europe implemented substantial deregulation, including France, Greece, Italy, Portugal, Slovenia and Spain (OECD 2013;  Gama et al. 2015). Shown in Fig. 5 , EPL reforms-as well as reforms in other policy areas-can be mapped using the European Commission's LABREF database  (Turrini et al. 2015)  and the ILO inventory on labor market reforms  (Gama et al. 2015). In line with this we can observe major changes in the aggregate EPL index developed by the OECD and available until 2013. Here, it has to be noted that some of the most recent and probably most substantial reforms have not yet been incorporated.
Acknowledgements A draft of this paper was presented at the  IZA/ILO Conference  on ' Assessing Labor Market Reforms ' held at the ILO in Geneva on 10 March 2016. The authors are grateful to  Guy Mundlak  and other colleagues participating in the discussion for their comments on the earlier version. We are also grateful to two anonymous referees and to  Sarah Stahlmann  for her excellent research support and to  Richard Forsythe  for editorial suggestions.
Acknowledgments: This project was supported by the  USDA National Institute of Food and Agriculture ,  McIntire-Stennis  project number # ME041616  through the  Maine Agricultural & Forest Experiment Station. Maine Agricultural and  Forest Experiment Station  Publication Number 3553. Author Contributions:  Shokoofeh Ghasemi  and  Mehdi Tajvidi  conceived and designed the experiments;  Shokoofeh Ghasemi  performed the experiments;  Shokoofeh Ghasemi  and  Mehdi Tajvidi  analyzed the data;  Douglas Gardner ,  Douglas Bousfield  and  William Gramlich  contributed to the analysis and discussions;  Shokoofeh Ghasemi  wrote the paper.
05792105 µm -2 , B 2 = 0. 00167917 µm -2 , C 1 = 238. 0185 µm -2 , and C 2 = 57. 362 µm -2  17. In the full model of air refractivity, we calculate the refractive index of air with Eq. (  1 ) including the entire HITRAN-database manifold of molecular transitions in air  18. The GVD of atmospheric air can then be found as k 2 = ∂ 2 k(ω)/∂ω 2 , where k(ω) = ωn(ω)/c, c is the speed of light in vacuum, and n(ω) is calculated using Eq. (  1 ). Dispersion of atmospheric air within the 3. 5-4. 2-μm wavelength range is dominated by the 00°01-00°11 asymmetric-stretch rovibrational band of atmospheric CO 2 (here, the v 1 v 2 l v 3 r notation is adopted for a superposition of v 1 symmetric-stretch, v 2 bending, and v 3 asymmetric-stretch vibrations with an angular momentum l, and Fermi-resonance perturbation parameter r).
5-4. 2-μm range have no effect on the dynamics of ultrashort probe pulses in our experimental conditions, as they show up on a very long, ~10-ps time scale as extremely weak features, whose intensity is about eight orders of magnitude lower than the intensity of the laser probe (Fig. 1e ). As is also seen in Fig. 1e , the temporal envelope of the transmitted laser pulse on the ~10-100-ps time scale is dominated by rotational echo revivals (e. g. , those   4 ) with the full model of air refractivity [Eq. (1)] that includes the entire manifold of molecular transitions in air from the HITRAN database (blue line). The input laser probe is shown by the red line. centered at η ≈ 55 and 100 ps in Fig. 1e ) -a standard behavior of a laser probe in time-resolved studies of molecular rovibrational modes  20, 21.
Two of these curves represent measurements performed with an almost transform limited (blue line) and negatively chirped (green line) probe pulses. The red line shows the results of calculations performed with the full model of air dispersion including the entire manifold of HITRAN-database infrared transitions. Both the overall behavior of the group delay as a function of the wavelength and the absolute values of Δτ g achieved in experiments (blue and green lines in Fig. 1d ) agree remarkably well with theoretical predictions (red line in Fig. 1d ), verifying the predictive power of the model of atmospheric dispersion used in this work. The differential group delay Δτ g is seen to be a monotonically growing function of the wavelength within the entire wavelength range covered by the spectrum of the laser probe, clearly indicating anomalous GVD within this wavelength range.
Acknowledgements Research into nonlinear microscopy and attosecond spectrochronography is supported by the  Russian Science Foundation  ( 17-12-01533 ). This research was supported in part by the  Russian Foundation for Basic Research  ( 16-02-00843 ,  15-02-08792 ,  17-52-53092 ,  17-02-01131 ),  Welch Foundation  (Grant No. A-1801 ), and  ONR  (Award No. 00014-16-1-2578 ).
93% of homology). A total of 71 out of the 85 genes carried by plasmid pO157 were present in the MC2 assembly (average homology, 99. 74%), suggesting that MC2 carries at least one major plasmid homolog to pO157. Comparison of MC2 and Sakai genes using BLASTn (70% coverage and identity cutoff) demonstrated that 4,608 of the MC2 functional genes were present in E. coli O157:H7 Sakai, showing an average homology of 99. 91%. Only 803 genes were unique to MC2. The high homology between the two genomes suggests that the bovine E. coli strain MC2 possesses the characteristic of an EHEC strain potentially responsible for clinical cases. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number NJDB00000000. The version described in this paper is version NJDB01000000.
ACKNOWLEDGMENTS This work was performed in collaboration with the  GeT core facility, Toulouse, France , and was supported by  France Génomique National  infrastructure, funded as part of the " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ) (the funders had no role in study design, data collection or interpretation or in submission of the work for publication). P. A. was supported by  CASDAR (Compte d'Affectation Spéciale Développement Agricole et Rural ;  France Agrimer   EDP 0914000451 ) and  Interbev  (Interprofession bétail et viande). We are grateful to the  Genotoul Bioinformatics Platform Toulouse Midi-Pyrenées (Genotoul Bioinfo)  for providing computing and storage resources.
Above Earth's surface, the annual lower troposphere temperature was record high according to all datasets analyzed, while the lower stratospheric temperature was record low according to most of the in situ and satellite datasets. Several countries, including Mexico and India, reported record high annual temperatures while many others observed near-record highs. A week-long heat wave at the end of April over the northern and eastern Indian peninsula, with temperatures surpassing 44°C, contributed to a water crisis for 330 million people and to 300 fatalities. In the Arctic the 2016 land surface temperature was 2. 0°C above the 1981-2010 average, breaking the previous record of 2007, 2011, and 2015 by 0. 8°C, representing a 3. 5°C increase since the record began in 1900. The increasing temperatures have led to decreasing Arctic sea ice extent and thickness.
An overview of findings is presented in the Abstract, Fig. 1. 1, and Plate 1. 1. Chapter 2 features global-scale climate variables; Chapter 3 highlights the global oceans; and Chapter 4 includes tropical climate phenomena including tropical cyclones. The Arctic and Antarctic respond differently through time and are reported in separate chapters (5 and 6, respectively). Chapter 7 provides a regional perspective authored largely by local government climate specialists. A list of relevant datasets and their sources for all chapters is provided as an Appendix.
S3 AUGUST The following ECVs, included in this edition, are considered "fully monitored," in that they are observed and analyzed across much of the world, with a sufficiently long-term dataset that has peerreviewed documentation: • Atmospheric Surface: air temperature, precipitation, air pressure, water vapor, wind speed and direction. • Atmospheric Upper Air: earth radiation budget, temperature, water vapor, wind speed and direction. • Atmospheric Composition: carbon dioxide, methane, other long-lived gases, ozone. • Ocean Surface: temperature, salinity, sea level, sea ice, current, ocean color, phytoplankton. • Ocean Subsurface: temperature, salinity. • Terrestrial: snow cover, albedo. ECVs in this edition that are considered "partially monitored," meeting some but not all of the above requirements, include: • Atmospheric Upper Air: cloud properties.
These high levels of moisture were also apparent in the water vapor column, which peaked in line with the El Niño in early 2016 and reached record levels over the oceans. The global average fraction of photosynthetically available radiation (FAPAR) was also above average. Levels of precipitation globally were above average, although by how much depended on the dataset analyzed. Global atmospheric burdens of the three dominant greenhouse gases (CO2, CH4, and N2O) continued their long-term upward trends in 2016. Increases in the global annual mean mole fractions of CH4 and N2O from 2015 to 2016 were fairly consistent with decadal trends, but the 3. 5 ± 0. 1 ppm (0. 88% ± 0. 03%) rise in global annual mean CO2 from 2015 to 2016 was the largest annual increase ever observed in the 58-year atmospheric measurement record.
3°-1. 7°C century -1 ). From 1891 to 1980 a new temperature record was set on average every 8-11 years, depending on the assessed in situ dataset. Since 1981, a new temperature record has been set every three to four years on average. However, having three consecutive years  (2014, 2015, and 2016)  set a new global surface temperature record is unusual. The question is, how unusual is it? To estimate the probability of the most recent three years consecutively setting records, a large number (10 000) of plausible time series of globally averaged temperatures was generated by Monte Carlo simulation, whereby scenarios are created that exhibit similar statistical properties to the observed record. The probability is calculated as the percentage of the scenarios that show the last three years each setting a warm record.
In accordance with  Argüez et al. (2013) , only the years since 1975 are simulated, as there is virtual certainty that all earlier years in the assessed in situ datasets were cooler than the most recent warm years. Statistical modeling suggests that randomized simulations of natural variability since 1975 is appropriate, and these random scenarios are superimposed on the trend since 1975. This approach results in a probability of 0. 8%-1. 5%. While the warming trend over the last few decades makes such a feat substantially more likely than if the global surface temperature were stationary, it is still quite remarkable for the most recent three years to set consecutive records. Spatially, the year 2016 was characterized by muchwarmer-than-average conditions across much of the world's surface, with the most notable warm anomalies across parts of the higher northern latitudes  (Plate 2.
1a; ). The only land areas with temperatures below their 1981-2010 averages were parts of eastern Canada, southern South America, western Australia, and parts of Antarctica and eastern Asia. Several locations across the south-ern oceans and the northwestern Pacific Ocean had below-average temperatures, along with persistent cooler-than-average conditions across the northern Atlantic Ocean south of Greenland. Across the land areas, the globally averaged annual temperature was 0. 77°-0. 82°C above the 1981-2010 average. All six assessed datasets [including Berkeley Earth  (Rohde et al. 2013) ] rank the 2016 global land-only surface temperature as the highest on record, surpassing the previous record of 2015 by 0. 08°-0. 21°C. The globally averaged annual temperature over the ocean was the highest on record at 0.
36°-0. 41°C above the 1981-2010 average, surpassing the previous record set in 2015 by 0. 01°-0. 03°C (see Chapter 3b for a more detailed analysis). The ocean's temperature departure from average tends to be smaller than the land's temperature departure because it requires a larger amount of energy to raise the ocean's temperatures; subsequently the ocean heats up at a slower rate. Global average surface air temperatures are also estimated using reanalyses, which blend information from a numerical weather prediction model with observations. Reanalysis produces datasets with uniform spatial and temporal coverage of the whole globe. According to ERA-Interim  (Dee et al. 2011) , the 2016 globally averaged, adjusted (see  Simmons et al. 2017 and Simmons and Poli 2015  for details) 2-m temperature was the highest since records began in 1979.
May, A. M. Paterson, A. Rimmer, J. A. Rusak, S. G. Schladow, M. Schmid, S. V. Shimaraeva, E. Silow, M. A. Timofeev, P. Verburg, S. Watanabe, and G. A. Weyhenmeyer Observed lake surface water temperature (LSWT) thermal anomalies in 2016 are placed in the context of the recent warming observed in global surface air temperature (Section 2b1) by collating long-term in situ LSWT observations from some of the world's best-studied lakes and a satellite-derived global LSWT dataset. The period 1996-2015, 20 years for which satellite-derived LSWTs are available, is used as the base period for all LSWT anomaly calculations. Warm-season averages (July-September in the Northern Hemisphere and January-March in the Southern Hemisphere) are analyzed to avoid ice cover, in line with previous LSWT analyses  (Schneider and Hook 2010; Hook et al.
3°C. Higher-than-average LSWTs were also evident in the Southern Hemisphere, with Lakes Rotorua and Taupo  (New Zealand)  showing an average LSWT anomaly exceeding +1°C, and the smaller lakes in the Bay of Plenty region (New Zealand) experiencing an average anomaly of +1°C in 2016. Satellite-derived warm-season LSWTs generated within the Globolakes project (www. globolakes. ac. uk) for 681 lakes are used in this analysis to investigate global variations in LSWT. LSWTs were retrieved during the day using the retrieval methods of  MacCallum and Merchant (2012)  on image pixels filled with water according to both the inland water dataset of  Carrea et al. (2015)  and a reflectance-based water detection scheme  (Xu 2006). The satellite temperatures represent mid-morning observations throughout the record.
E. Perkins-Kirkpatrick, and M. G. Donat As in the previous year, 2016 set the highest global annual mean temperature on record, and again, it experienced various anomalously high temperature events, especially with respect to minimum temperatures  (Plates 2. 1c, d). During 2016, Australia and western North America experienced the highest number of warm days and nights (defined below, using the 90th percentiles of the maximum and minimum daily temperatures) in the observational records dating back to 1951. Other than this, only a few regions exhibited record extreme temperatures throughout the year, but the globally averaged temperature for 2016 was the highest on record (see Section 2b1). As in previous reports, the GHCNDEX dataset  (Donat et al. 2013 ) is used to monitor global temperature extremes.
GHCNDEX is a quasi-global gridded dataset of land-based observed temperature and precipitation extremes. A suite of temperature and precipitation extremes indices  (Zhang et al. 2011 ) is first calculated for daily station time series from the GHCN-Daily archive  (Menne et al. 2012) , before interpolating the indices on global grids. As in previous reports (e. g. ,  Donat et al. 2016) , some of the indices fields have limited spatial coverage for 2016, especially those derived from minimum temperatures across central and eastern Asia, compared to those calculated from maximum temperatures. Results are presented here for a number of the temperature indices in GHCNDEX: TX90p (frequency of warm days, defined as number of days above the seasonal 90th percentile of daily maximum temperatures; all percentiles in GHCNDEX were calculated over the 1961-90 base period), TX10p (frequency of cool days, defined as number of days with maximum temperatures below the seasonal 10th percentile), TN90p and TN10p (analogous definitions using the minimum temperature yielding the frequency of warm nights and cool nights, respectively).
The 2016 annual globally averaged temperature of the lower troposphere (LT: the bulk atmosphere below 10-km altitude or roughly the lower 70% by mass) was 0. 45° to 0. 75°C above the 1981-2010 mean, depending on the dataset. The year 2016 proved to be the warmest of the past 59 years in all datasets, exceeding the previous major warm phase ENSO (1998) by +0. 02° to +0. 38°C (Fig. 2. 6). In general, the radiosonde-based datasets displayed greater warming relative to 1998, likely due to their more limited geographic coverage. Direct measurements of the LT bulk temperature utilize radiosonde (balloon) datasets beginning in 1958 which are complemented by satellites since late 1978, described in  Christy (2016). In addition to radiosonde and satellite estimates, three reanalysis products [ERA-I, MERRA-2, and JRA-55; see  Christy (2016)  for details] are examined with ERA-I used to provide spatial depictions of the temperature anomalies (Plate 2.
16 ± 0. 02°C decade -1 and based on both radiosondes and satellites (starting in 1979) is +0. 16 ± 0. 04°C decade -1 , where the range represents the variation among the datasets and thus serves as an estimate of structural uncertainty in Fig. 2. 6. Major volcanic events in 1963 2. 6. Major volcanic events in , 1982 2. 6. Major volcanic events in , and 1991 contributed contributed  to cooler temperatures at those times that affected the early part of the LT record, especially in the satellite era, thus increasing the upward trend to some extent. The layer centered on the midtroposphere (MT) is used for tropical atmospheric temperature monitoring and is able to utilize more independently constructed datasets. The top of the effective MT layer reaches higher into the stratosphere than LT, but in the tropics where the tropopause is approximately 16 km in altitude, there is only a slight influence  from the stratosphere on MT.
Satellite monitoring of this layer is most sensitive to the mid-to upper troposphere, which in the tropics is the layer that typically responds most strongly to thermal forcing of any kind, for example, ENSO, enhanced greenhouse gases, etc. [See  Christy (2016)  for more details. ] Examining the various datasets of tropical MT trends  in Online Fig. S2. 9), it is noted that the 2016 spike in tropical temperature due to the warm phase ENSO has increased trend values relative to those presented last year. Radiosonde values cluster closely around +0. 11°C decade -1 while satellite results vary considerably from +0. 08° (UAH) to +0. 17°C (NOAA) decade -1. The notable difference between UAH and the other satellite datasets is most evident over the lower latitude oceans (Online Fig. S2. 9). This suggests the discrepancy is not due to corrections for diurnal (east-west) drift of the spacecraft, because these are negligible over the oceans, but to differing assumptions regarding basic calibration issues, primarily between 1990 and 2005.
Understanding this discrepancy is an active area of research. Tropical temperature trends are displayed in online Fig. S2. 10 to demonstrate their vertical structure, as well as the MT bulk layer values at the top and in Table  2. 2. Included are trends from the output of 102 CMIP-5 RCP4. 5 model simulations  (Flato et al. 2013) , demonstrating the theoretical expectation of basic lapse-rate theory in which trends are magnified with altitude into the upper troposphere  (National Research Council 2003; Christy 2014). The results (Online Fig. S2. 10; Table  2. 2) through 2016 continue to show that the observed MT trends tend to be below estimates anticipated from theory. The tropical surface trend (from NOAA/NCEI) is +0. 13°C decade -1. The median MT trend of all datasets examined here, including reanalyses, is +0.
The various observation systems differ slightly from each other during this period since 1994. The four radiosonde datasets  (RAOBCORE, Haimberger et al. 2012; RICH, Haimberger et al. 2012; RATPAC, Free et al. 2005; and   UNSW,  Sherwood and Nishant 2015)  show different global trends and variability (Fig. 2. 8): RAOBCORE and RICH are similar and highly variable during this period, but RATPAC and UNSW behave more like the satellite and reanalyses. However UNSW (for which 2016 data are still awaited) has a relatively flat trend, while RATPAC has a cooling trend. The three satellite records (RSS,  Mears and Wentz 2009; STAR, Zou and Wang 2010; and UAH, Christy et al. 2011)  have very minor differences in the global anomalies and show a relatively flat trend from 1994 to present. The four reanalyses (ERA-Interim,  Dee et al.
2011 ; JRA-55,  Kobayashi et al. 2015; MERRA-2, Bosilovich et al. 2017; and CFSR, Saha et al. 2010)  show varying global trends since 1994. ERA-I and MERRA-2 have positive trends, while the CFSR and JRA-55 are near neutral. Reanalyses assimilate observations beyond the radiosondes and MSU and AMSU radiances, and the positive trends of ERA-I and MERRA-2 may be due to the usage of GPSRO data beginning in 2006. Finally, trend variability with latitude is examined. Figs. S2. 12a,b  show the latitudinal annual trends (°C decade -1 ) for the four reanalyses and the three satellite TLS datasets. There is a large range of trends in the tropics among the four reanalyses from a negative trend in JRA-55 to equally positive trends in ERA-I and MERRA-2, to the CFSR neutral trend. The satellite trends in the tropics are all slightly negative.
The GTN-P recently launched a sophisticated data management system (http://gtnpdatabase. org), which includes 1350 temperature boreholes and 250 active layer measurement sites worldwide  (Biskaborn et al. 2015)  in order to assess the thermal state of the permafrost at the global scale. Permafrost temperature in the Arctic increases along a north-south bioclimatic gradient from <-14°C in High Canadian Arctic to just below 0°C near the limits of permafrost distribution, but differences in site-specific conditions, such as snow depth, vegetation, and soil properties, complicate this general geographic trend (Fig. 2. 10a). The permafrost temperature at 20-m depth in the European Alps is generally above -3°C and is above -1. 5°C on the Tibetan Plateau. Continental Antarctica is characterized by permafrost temperature below -15°C, with the exception of Antarctic Peninsula where permafrost temperature is generally higher than -3°C.