dataesr/scientific-paragraphs-categorization huggingface dataset:
There are a number of points to be drawn from this. First, there were differences in sampling effort between the two sites, as the Akonolinga sampling regime consisted of 12 time points in the year, while the French Guiana regime consisted of 2 time points. This would be consistent with the idea that the bacterium is transiently present in different regions, and under-prediction would be expected in this case. Secondly, a potential complication results from differences in the ability of the SRTM dataset to delineate watersheds due to dense rainforest canopies in French Guiana  [52]. The shape of a watershed is sensitive to the quality of the elevation data used, errors in the digital elevation model, or man-made drainage structures, can have effects not captured by this model. Finally, we cannot rule out that the differences are a result of differences in M.
Acknowledgments We are grateful to the staff of the  Centre Pasteur and IRD  for their invaluable help in different phases of the study, notably during data collection. We also thank  Annelise Tran  of  CIRAD and Benjamin Roche of IRD  for invaluable discussions and insights on previous versions of the manuscript, the  ISIS Spot  programme for support in acquiring SPOT images, and  HerveChevillotte  ( IRD Cameroon ), for environmental data from the  IFORA  project ( ANR-Biodiv grant IFORA ).
Funding: This work was supported by a grant from the  French National Research agency  ( ANR 11-CEPL-00704 EXTRA-MU ) with additional funding from the  Young International Research Team of AIRD/IRD  ( JEAI ATOMyc ) and an '' Investissement d'Avenir '' grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) through its integrative research programme BIOHOPSYS on Biodiversity and infectious diseases. KC is funded by a PhD studentship from  ANR EXTRA-MU and LabEx CEBA  (grant  ANR-10-LABX-25-01 ), AG from a PhD studentship from the  EHESP , and AM from a PhD studentship from  Bournemouth University. GEGP received a post-doctoral fellowship from  Fondation pour la Recherche sur la Biodiversite(FRB)  and its  Centre de Synthese et d'Analyse sur la Biodiversite(CESAB, research programme BIODIS ).
e. signalspecific; 37 °C and pH 5. 5), but not after either of the physical parameters separately. Several other protein kinases (including regulatory subunits) and phosphatases also showed changes in phosphorylation during differentiation. This work constitutes the first genome-scale interrogation of phosphorylation dynamics in a parasitic protozoa, revealing the outline of a signaling pathway during Leishmania differentiation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (identifier PXD000671). Data can be viewed using ProteinPilot™ software. Molecular & Cellular Proteomics 13: 10. 1074/ mcp. M114. 037705, 1787-1799, 2014. Protozoan parasites of the genus Leishmania are the causative agents of leishmaniasis in humans, which can manifest as hepatosplenomegaly, ulcerative skin lesions, or destructive mucosal inflammation and damage to the mucosal tissues  (1, 2).
MS/MS spectra were acquired in a data-dependent manner over the course of three injections and analyses selecting the top two most intense eluting ions in the 400 -1600 m/z range with a 2ϩ to 5ϩ charge state. Following selection for MS/MS analysis, precursor ions were excluded from selection for MS/MS analysis for 180 s. Following the first 2 h of LC-MS/MS analysis, gas phase fractionation was used to increase the total number of identified peptides. All MS/MS data were analyzed using Protein Pilot 2. 0 (Applied Biosystems)  (22). Raw data files were searched against the Leishma-nia infantum proteome in TriTrypDB version 3. 3. Peptides with confidence Ͼ 95% were considered for further analysis. Acceptable mass tolerances were 0. 6 Da for MS and 0. 25 Da for MS/MS. Protein Pilot has these as well as a list of considered post-translational modifications preconfigured in the search settings; therefore, highconfidence results meet these criteria.
34. 2680), a nucleobase transporter (LinJ. 11. 0520), and an RNA-binding protein (LinJ. 35. 2240). The last two were among the few showing dephosphorylation under these conditions. Another serine/threonine-protein kinase (LinJ. 25. 2450) contained two sites (S 212 and T 381 ) that were phosphorylated by either temperature or pH alone, with an even greater (6-to 13-fold) increase upon exposure to the complete signal (Fig. 6D ). Possible Signaling Pathways Involved in Promastigote-toamastigote Differentiation-The analyses described above identified 12 protein kinases, two protein kinase regulatory subunits, and four protein phosphatases (supplemental Table  S1 ). For nine of these proteins, we have complete (or nearly complete) datasets for at least one phosphorylation site, allowing detailed analysis of the kinetics in response to the temperature and pH signals and during differentiation.
DISCUSSION The host-free system we developed to simulate Leishmania differentiation inside its host has, to date, provided significant insight into the mechanisms of this process. Comparative analysis of genome-wide time-course transcriptomic and proteomic datasets clearly indicated that promastigote-to-amastigote differentiation is a regulated and highly coordinated process. Here, we have provided an additional layer to these analyses by employing iTRAQ affinity tagging to quantitatively analyze changes in phosphopeptide abundance and describing the dynamics of protein phosphorylation during L. donovani differentiation. In a recent study, using shotgun phosphoproteomics, we demonstrated that in axenic L. donovani there is more stage-specific (i. e. in promastigotes or amastigotes exclusively) than constitutive (i.
Many offthe-shelf modules guarantee the compatibility with any kind of LVDT and the interface with industrial control systems through Profibus, ModBus RTU or analog interface communication standards. There are several reading techniques implemented on these modules: for instance the simple synchronous analogic demodulation or digital signal processing techniques, such as the Goertzel algorithm  [8]. Their reading uncertainty unfortunately is not below a few hundreds of micrometers. In addition these modules are not radiation tolerant, therefore in radioactive applications they should be placed in safe areas, even up to several hundred meters away from the LVDT, with a consequent reduction of the signal-to-noise ratio (SNR) at the module input and increasing of the reading uncertainty. The reading accuracy can be increased by adopting a ratiometric reading technique combined with the use of a three-parameter sine-fit algorithm for the extraction of the first harmonics of the secondary voltages  [9].
Flash based devices show higher tolerance to SEUs compared to SRAM based ones, but SRAM memories are faster to access, hence they are necessary in order to respect timing specifications. For this reason all data stored in the external FLASH memories is loaded at powerup within dedicated SRAM cells inside the FPGA: the SRAM blocks are constantly read, to realize the fitting algorithm and the primary signal, and rewritten with the FLASH memories contents, to mitigate the possibility of SEUs. In addition Actel SRAM blocks are designed so that two bits of the same SRAM word are never stored in nearby cells  [18] , whether in the horizontal or vertical direction, therefore the probability of multiple-bit upset in SRAM blocks is highly reduced. The functional block diagram of the module is shown in Fig.
Acknowledgments The authors would like to thank  M. Di Castro ,  M. Butcher , and  A. Danisi  for the fruitful discussions;  G. Foucard ,  E. Fadakis ,  P. Oser  and  G. Ruggiero  for the excellent work provided for the design and the preparation of the radiation tests;  J. Mekki  for the support and the advice on the subject.
In order to determine the feasibility of using reanalysis (ERA-Interim) for climate studies over the southern central Andes in South America, we have compared the most recent version of different data sets available to the community. The data sets include gridded in situ observations: Climate Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), and University of Delaware; merged satellite and in situ data: Global Precipitation Climatology Project; and satellite estimates: Tropical Rainfall Measuring Mission. We pay particular attention to the region between 30°and 37°S and also the period 1970-1976 to evaluate the data sets. This is the period of maximum availability of meteorological stations in the region. Whereas all data sets provide an adequate representation of mean winter, GPCC provides the best for winter precipitation due to the large number of stations included in gridding.
The CRU data set has fewer observations throughout the period. Although it cannot reproduce the localized maxima in the Andes, it provides a better representation of the regional precipitation and is best suited to evaluate trends. The temperature in the region is best estimated by CRU. We evaluate the ERA-Interim reanalysis to determine potential shortcomings. The trends in the region were analyzed during the period 1979-2010, and while CRU indicates a significant decrease in winter precipitation, ERA-Interim shows virtually no significant trends. Interannual variability is well represented by ERA-Interim, and the El Niño-Southern Oscillation, which has been proven to be the principal source of year to year precipitation variability in the region, is highly correlated there. RUSTICUCCI ET AL.
2. Global Precipitation Climatology Center (GPCC), WMO/DWD. This data set includes monthly, land surface precipitation from rain gauges built on GTS-based and historic data  [Becker et al. , 2013; Schneider et al. , 2011]. The GPCC version 6. 0 is available at spatial resolutions of 0. 5, 1, and 2. 5°for the period 1901-2010. The 0. 5°version was used in this study. 3. University of Delaware's data set (UDEL). Version 3. 01 of this data set is from monthly global gridded highresolution station (land) data. It is available at a 0. 5 × 0. 5°resolution for the period 1900-2010. Both air temperature and precipitation were used in this study. 4. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis, NASA. Product 3B42 V7 provides an estimate of daily combined microwave-IR-gauge precipitation on a 0.
25 × 0. 25°grid around the globe between 50°N and 50°S from 1998 to the present  [Huffman et al. , 2007]. 5. Global Precipitation Climatology Project data set (GPCP). A monthly precipitation data set  [Adler et al. , 2003]  that combines ground-based observations and satellite precipitation data into 2. 5 × 2. 5°global grids. It covers the period from 1979 to 2010. The period 1979-2010 was selected since it is the maximum period that ERA-Interim and the observational databases have in common, except for TRMM since it only extends back to 1998. Sometimes TRMM or GPCP are left out from the comparison with other data sets. As part of the methodology, we have defined the period June to September (JJAS) as winter (rather than only 3 months), since September values are similar to June values as shown by the annual cycle of the altitude of the 0°isotherm between two stations on each side of the Andes (not shown).
Masiokas et al. [2006]  also used the same 4 month period in their study of snowpack in the region. Daily radiosonde data were downloaded for this analysis from the webpage of the University of Wyoming (http://weather. uwyo. edu/upperair/sounding. html) for two stations in Chile and Argentina. The radiosonde station Quintero (32. 78°S, 71. 53°W, 3 m) in Chile was relocated approximately 100 km south along the coast of Chile in August 1999 and renamed Santo Domingo (33. 65°S, 71. 61°W, 75 m). Unfortunately, the stations did not coexist, so there is no overlapping period available to evaluate possible differences due to location. A Student's t test was applied to the altitude of the 0°i sotherm, seasonal means, to determine homogeneity in the series. There is no statistical difference between the variances; however, the means do not belong to the same population, at an annual basis and for every 4 month period except from March-April-May-June.
, 2007; Uppala et al. , 2008; Dee and Uppala, 2009]. Data from this reanalysis were originally created starting from 1989, but these were recently extended back to 1979 and these are continuously updated forward in time. Monthly 2 m temperature, temperature from 1000 to 200 hPa, and surface precipitation are analyzed for the period 1979-2010. Temperature is derived from assimilated observations (satellite data and surface reports from land stations, ships, buoys, aircrafts, dropsondes, and radiosondes) while precipitation estimates are produced by the forecast model. Finally, we have calculated the areal mean of precipitation in the SCA box and studied the temporal series from the gridded data sets and the reanalysis. When averaging data from ERA-Interim and TRMM over SCA box, grid points over the ocean were excluded using their land-sea mask available for each data set.
Precipitation 3. 1. 1. Southern South America The spatial distribution for the mean precipitation in winter (JJAS) for all databases in the period 1979-2010 (except for TRMM: 1998-2010) and the differences, calculated as ERA-Interim minus each of the observational data sets, are shown in Figure  2. When calculating the differences, all databases were interpolated into the ERA-Interim grid. All data sets show the winter maximum over the Patagonian Andes and the southern central Andes. A secondary winter maximum is located over southeastern Brazil, and the data sets all agree on its position, also consistent with previous studies using different periods and databases [e. g. ,  Garreaud, 2009; Boulanger et al. , 2005; Grimm et al. , 2000]. Note that while GPCP captures the position of the maxima, it is not useful for regional studies due to its coarse resolution.
However, the added value of this data set is that it combines in situ data over land with satellite precipitation estimates so it provides information from over the ocean. The TRMM data set is crucial in supplying much needed spatial coverage, which is in turn very valuable to assess the performance of the ERA-Interim reanalysis; however, the drawback is its limited length of time for climatological studies. A broad Journal of Geophysical Research: Atmospheres 10. 1002/2013JD021167 comparison of ERA-Interim with all the other databases indicates that the reanalysis satisfactorily represents the spatial distribution of winter precipitation. Note, however, that ERA-Interim shows precipitation where none of the others data sets do in central and northwest Argentina (approximately between À20°S and À30°S and around 67°W) and this is shown as an overestimation of precipitation in the differences figures.
In the summer season (figure not shown), the overestimation over this region is more evident. The biggest differences in magnitude between ERA-Interim and the rest of the databases are found in the south of Chile where ERA-Interim overestimates winter precipitation. In the southwest of Argentina, some data sets present higher values of precipitation than the reanalysis. Figure  3  shows the temporal correlations between ERA-Interim and every data set for the period 1979-2010. A Student's t test was performed, and significant correlations at 95% level of confidence are marked with crosses. All data sets were regridded into ERA-Interim resolution, and the correlation coefficient between every grid point was calculated. A generalized pattern of significant positive correlations is found in every data set, except with the TRMM database.
The magnitudes are also well represented by GPCC. Note that CRU does not capture the east-west gradient in precipitation at the border between Argentina and Chile. An important factor that should be considered when analyzing gridded data sets is the number of stations used in gridding. The number of stations per grid point was available online for GPCC (in the website http://kunden. dwd. de/GPCC/Visualizer) and for CRU. We have compared them in the southern central Andes region. For example, in the year 1974, the number of stations used by GPCC was 102 and only 41 for CRU, whereas  Viale and Nuñez [2011]  used 78 stations. This difference in the number of stations is reflected in the detailed spatial distribution shown by GPCC. Consequently, we conclude that the GPCC data set reproduces more accurately the spatial distribution determined from the best station observations available in the region for  1970-1976.
ERA-Interim captures the interannual variability, but it tends to show larger values than the observational data sets over the entire period. In the last decade, the time  The correlations between mean winter precipitations in every data set are presented in Table  3. Note that all the correlations were calculated over the period 1979-2010 except the correlations with the TRMM database that were calculated for the period 1998-2010. Almost all correlations are higher than 0. 9, implying broad agreement in the representation of interannual variability. In particular, ERA-Interim is well  Linear trends were calculated for every grid point of the GPCC, CRU, UDEL, and ERA-Interim data sets and the five meteorological stations in Chile and Argentina in the SCA region for the period 1979-2010 (Figure  7 ).
For example, in 1979, 98 stations in this region were used by GPCC to interpolate precipitation while at the end of the period only 18 stations were used. CRU also decreased the number of stations over this area from 32 in 1979 to 12 in 2010. Information on the number of stations used was not found for the UDEL data set. The influence of the El Niño-Southern Oscillation in different hydroclimatic variables in the region has been widely studied by various authors  [Montecinos and Aceituno, 2003; Rutlland and Fuenzalida, 1991; Compagnucci and Vargas, 1998]. A further analysis to assess the validity of the interannual variability of precipitation in ERA-Interim is to determine whether the reanalysis captures the ENSO signal. Correlations between winter precipitation in the SCA box for the different databases and the Niño 3.
4    4 ). A Student's t test was applied with a 95% level of significance. All correlations were significant at this level except for the TRMM database. The results from these databases indicate an excess of precipitation in the region during the El Niño phase of the Southern Oscillation, in agreement with previous studies. Therefore, it shows that ERA-Interim captures the most important interannual variability mode in this region. Although a relationship between hydroclimatic variables on the southern central Andes region and the Pacific Decadal Oscillation was suggested by previous studies  [Masiokas et al. , 2010 [Masiokas et al. , , 2006]] , in this study connections with the Pacific Decadal Oscillation index are not evident from the correlation coefficients (results not shown). Journal of Geophysical Research: Atmospheres 10.
Both GPCC and UDEL show qualitative spatial better agreement with their results than CRU, which presents less spatial variability. One is tempted to select GPCC or even UDEL as gridded ground truth against which to compare the results from ERA-Interim. As mentioned in the previous section, we know that GPCC included 102 stations in 1970, more even that the total number of stations used in the observational study  (78). But most of those stations were discontinued, so that the density of stations incorporated into the gridded databases decreased dramatically, down to only 18 stations by 2010. Unfortunately, we were unable to find the detailed Journal of Geophysical Research: Atmospheres 10. 1002/2013JD021167 numbers of stations as a function of time for UDEL, but given the more detailed spatial distribution, it must have also included many more stations than CRU in the 1970s.
As an example, if GPCC or UDEL databases were used for a study of decadal variability of precipitation, the changes in the spatial variability due to the decrease in station density would affect the significance of the derived trends. Therefore, selecting as ground truth a gridded database that has fewer stations incorporated (such a CRU), but a more consistent overall number throughout the decades, while counterintuitive, may be considered a better approach and it is the one that we recommend. After selecting CRU ground truth, we proceeded with the evaluation of ERA-Interim, focusing on the spatial pattern of the winter average and interannual variability in the SCA box. The analysis of the frequency distributions of the average winter precipitation from the gridded data sets and ERA-Interim (Figure  5 ) suggests several differences between them that should be taken into account.
In contrast, note that ERA-Interim shows virtually no significant trends in the SCA box from 1979 to 2010. This is a very important result and again supports our comment that the determination of trends from ERA-Interim for climate studies in the region should be accompanied with these caveats. Furthermore, studies of trends in the future from climate model simulations and the conclusions derived from those studies should be put into this context. The analysis of the surface temperature from the gridded data sets CRU and UDEL and ERA-Interim shows different results from the discussion presented above. Temperature is a continuous variable and more likely that reanalysis would capture the observed spatial and temporal variability. There is no significant bias in ERA-Interim, as shown in Figure  10 , where the reanalysis values are bounded by the gridded data sets and also display similar interannual variability.
Conclusions The The gridded data sets of surface land precipitation (GPCC, UDEL, and CRU) were compared in the southern central Andes region with the results of the most detailed observational study to date, to determine which one could be considered ground truth. The study of  Viale and Nuñez [2011]  analyzed the in situ observations from a large number of stations in that region from the period 1970-1976, many of which have since been discontinued. The evaluation of the spatial indicates that both GPCC and CRU are able to reproduce the results of  Viale and Nuñez [2011]. However, both these data sets (GPCC and CRU) have experienced a dramatic decrease in the number of stations used, more significant in GPCC. Our results indicate that the decrease in the number of stations used by the different data sets may introduce an unwanted bias in the estimates of trends over several decades.
Therefore, our evaluation not only included the spatial distribution but also this bias into account, to conclude that the CRU data set should be taken as ground truth against which ERA-Interim should be assessed. The interannual variability in the SCA box was captured well by ERA-Interim; this variability is dominated by El Niño-Southern Oscillation, as has been reported by different authors. Significant positive correlations were found in all data sets (except TRMM, possibly affected by its shorter period of data) between the El Niño 3. 4 index and JJAS precipitation in the SCA region, and ERA-Interim captures this mode of variability. However, the trends determined by ERA-Interim resulted not significant at the 95% confidence level, and studies focusing on trends should clearly include this caveat.