dataesr/scientific-paragraphs-categorization huggingface dataset:
Complete remission was defined as proteinuria <0. 3g per 24 h and partial remission as proteinuria >0. 3 but <3. 5g per 24 h or a decrease in proteinuria by at least 50% from the initial value and <3. 5g per 24 h, according to international Clinical Practice Guidelines (KDIGO)  (46). Healthy donors of the same age and living in the same environment were also recruited. Patients and healthy donors with infection or breastfeeding at the time of sampling were excluded. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the appropriate institutional review committee (NCT02199145). Written informed consent was obtained from participants prior to inclusion in the study.
Data Collection Clinical and environmental data were collected during the medical consultation, using the patient's medical record and via a questionnaire which focused on lifestyle, usual area of residence and medical history before the first symptoms of MN. Exposure level to air pollutants (PM 2. 5 , PM 10 , NO X , CO, SO 2 , and C 6 H 6 ) was publicly available (Atmosud (a French public institution)). We used cumulative exposition during the year before the diagnosis or the relapse measured by Atmosud using sensors present at different points in the region studied (Supplemental Figure  1 ).
Supernatant IL-17A, IL-6, IL-1b, IFN-g, IL-12p70, TNF-a, IL-10, IL-5, IL-4, IL-13, and GM-CSF were measured after in vitro stimulation. The concentration of inflammatory cytokines IL-1b and IL-6 implicated in innate immune response was significantly higher in MN patients than in the healthy control  There was no difference in TNF-a, IL-5, IL-13 and GM-CSF concentrations between MN patients and healthy subjects (data not shown). These data suggest an activation of the Th17 and Th2 pathways and a deficit of the Th1 and Treg pathways in MN patients as observed in many autoimmune diseases  (29, 30, 39).
A large-scale study on a larger territory should be initiated to confirm the association between air pollution and the Th17 profile of MN patients. Second, we did not identify the environmental factor(s) associated with non-Th17 related MN in patients from rural areas. It would be interesting to study the role of certain more specific exposures of this population such as pesticides, wood combustion, the use of diesel engines etc. Third, this is an association study that cannot confirm the causal link between the urban environment, the modification of the cytokine profile of patients and the development of MN. In vitro studies on PBMC and podocytes, and in vivo studies on animal models are necessary to assess the impact of exposure to particulate matter on the cytokine profile. These three points are the objectives of a French hospital clinical research program called Immunopathological Analysis in a French National Cohort of Membranous Nephropathy (NCT04326218) which started including MN patients in July 2020.
(B) Calibration curve for determining PDMS pillar spring constants. The spring constants of PDMS pillars were determined at different heights with respect to the bottom of the PDMS well by measuring the force required for a defined bending (pillar deflection). A representative measurement of the force-deflection relationship at a height of 1. 75 mm is shown in the inset. The spring constant is then computed from the slope of the force-deflection relationship. Green data points indicate the spring constants of individual measurements at different heights. The dotted line shows the prediction of the Euler-Bernoulli beam theory, according to which the effective spring constant is proportional to the negative third power of the height every day. Differentiation medium consisted of DMEM supplemented with 2% horse serum, 1% penicillin-streptomycin and 0.
ACKNOWLEDGEMENTS This work was supported by  Deutsche Forschungsgemeinschaft (DFG)  grants  FA-336/12. 1 , FR-2993/23. 1, HA3309/3-1 ,  HA3309/6-1, HA3309/7-1 ,  TRR 225  project  326998133  (subprojects  A01 ,  B08 , and  C02 ), and the  Muscle Research Center Erlangen (MURCE). C. AD. was supported by a fellowship from  Ecole Polytechnique. Open  access funding was provided by the Projekt DEAL.
Together with the DLR and a consortium of originally 12 industrial companies from the former GDR states, the feasibility of such a mission was investigated in 1994. In 1995/1996 DARA selected it as a small satellite mission to be realized under the leadership of the GFZ. Specifications of the small satellite approach were: fast realization, low costs, ambitious mission objectives. Due to the unique combination of novel instruments for the simultaneous detection of gravity and magnetic field, as well as for the sounding of the atmosphere and ionosphere, the mission was named CHAMP (CHAllenging Minisatellite Payload). Launched in the year 2000, CHAMP started the Decade of Geopotentials, proclaimed by the IUGG in 1999, on schedule and provided Earth System Research with a unique and continuous geophysical data set over the entire decade.
A laser retro-reflector manufactured by GFZ on the bottom-side of the satellite which supports the SLR (Satellite Laser Ranging) measurement principle completed the instrumentation. A highly autonomous control and data processing system guaranteed safe operation over long periods of time (up to 12 h) without contact to ground stations. The data was stored in a mass memory with a capacity of 1. 2 Gigabit and sent to the receiving stations in Weilheim, Neustrelitz and Ny Ålesund during overflights. The pre-integration of the mechanical CHAMP structure was carried out at the Dornier company in Friedrichshafen (today Airbus Defense & Space GmbH), and that of the cold gas system at the space company RST in Rostock. The electrical integration and system tests were carried out in Jena at the company Jena Optronik, and the environmental tests were finally performed at the Industrieanlagen-Betriebsgesellschaft (IABG) in Ottobrunn.
These corrections transformed the initially slightly elliptical CHAMP orbit into an almost perfect circular orbit (e = 0. 0 ) which is the so-called frozen orbit, i. e. the orbit with smallest possible eccentricity. To maximize the mission duration    3 ). The most important characteristics of CHAMP are summarized in Table  4. CHAMP had reached a flight altitude of 260 km on the 10th anniversary of its launch. On 19 September 2010, it plunged into the Earth's atmosphere and burned up. A successful satellite mission requires not only a perfectly functioning satellite in space but also a complex, well-functioning infrastructure on Earth. This so-called CHAMP ground segment was designed from the very beginning in such a way that, in addition to the mission control data, all sensor data could be made available to the scientific users as quickly as possible in different processing stages over a long mission period.
In addition to the determination of averaged (static) gravity field models from the joint processing of monthly data series over the entire mission period, the secondary mission objective, as with CHAMP, was the use of GPS radio occultation measurements to obtain density and temperature profiles in the upper atmosphere. Month by month a new global image of the Earth's gravity field, which changes both spatially and temporally, should be created from GRACE data. During the planned fiveyear mission lifetime, the sequence of these highly detailed maps of the gravitational field was to be used to detect minute gravitational fluctuations and relate them to the physical properties of planet Earth, following the considerations and simulations made in the NRC report  (National Research Council 1997).
Launch, Mission History and Data Products The two GRACE satellites were launched on 17 March 2002 from the Plesetsk Cosmodrome in northern Russia with a ROCKOT launch vehicle, a converted Russian intercontinental rocket SS-19 with a manoeuvrable BREEZE-KM upper stage. The German-Russian space company EUROCKOT Launch Services, a joint venture of Astrium and Khrunichev, was responsible for the provision of the rocket and the preparatory work at the launch site. The satellites were injected with pinpoint accuracy into a very close polar ( i = 89 • ) and almost circular ( e = 0. 0003 ) orbit at an altitude of 500 km. From this point on the satellites were taken over by DLR's GSOC in Oberpfaffenhofen for further mission operations. After alignment of the satellites along their local vertical and horizontal axes, and separation to a mutual distance of 220 km, both satellites drifted freely under the influence of the gravitational field during the entire mission period.
3 During the 15-year mission period, which was three times longer than originally planned (Fig. 8 ), all instruments on board the GRACE satellites delivered almost continuously measurement and control data for satellite operation and monitoring of the instruments as well as for scientific evaluation. In September 2017, due to the agerelated failure of a large number of battery cells on GRACE-2 and outgoing fuel, tandem operation had to be discontinued. This marked the end of the extremely successful active GRACE long-term mission. On 24 December 2017 GRACE-2 burnt up in the Earth's atmosphere. The twin GRACE-1 followed the same fate on 10 March 2018. All measurement data obtained onboard GRACE were processed and archived in the GRACE Science Data System (SDS), jointly operated by JPL, CSR and GFZ, to form so-called level-0 (original raw observations) to level-2 (monthly gravity field models in terms of spherical harmonic coefficients) GRACE products.
The archiving of the products and supporting documents was performed in the JPL Physical Oceanography Distributed Active Archive Center (PODAAC) and the Information System and Data Center (ISDC) operated at the GFZ. Both archives were continuously and automatically harmonized. Worldwide users can retrieve all GRACE data and gravity field products calculated by the SDS from there. Figure  9  shows the number of registered users at the ISDC as of 2 January 2018 and statistics of publications resulting from GRACE until the end of 2017. The most important characteristics of GRACE are summarized in Table  5.
This confirms remarkable improvements achieved with the GFZ RL06 reprocessing and also depicts that even after more than 15 years of the first instrument data release a substantial gain in the quality of monthly GRACE gravity field products is still possible thanks to reprocessing efforts of Level-1 instrument data, continuously improved background  models, especially for tidal and non-tidal mass variations, and enhanced processing strategies. In addition, time series of weekly gravity fields were formed by the GFZ for RL05 by solving subsets of the monthly normal equation systems corresponding to the division of the GPS weeks. They are characterized by a higher temporal resolution, but at the expense of a lower spatial resolution. In addition to the SDS monthly up to weekly products mentioned above, time-variable GRACE gravity field solutions with different evaluation methods have been calculated and made available by e.
Static Gravity Field Models In addition to the time-variable gravity field models, the SDS team and various groups in Europe, the USA and China computed static gravity field models. For these static gravity models, i. e. models averaged over longer periods of time, a distinction was made between GRACE-only, satellite-only (from a combination of GRACE, GOCE, CHAMP and/or LAGEOS data) and combined models, in which additional terrestrial gravity data were included. From the great number of calculated models only some typical examples from the entire analysis period are listed: • First and more recent "GRACE-only" gravitational field models  (Reigber et al. 2005; Tapley et al. 2005; Jäggi et al. 2010; Chen et al. 2018; Mayer-Gürr et al. 2018 ). • "Satellite-only" gravity field models complete to high degree and orders and computed from  GOCE, GRACE and LAGEOS data (Pail et al.
2010; Bruinsma et al. 2010; Farahani et al. 2013; Zhou et al. 2019) , and • "High-resolution combination" gravity models, complete to very high degree and orders and computed from GOCE, GRACE, LAGEOS and surface gravity data  (Förste et al. 2014; Zingerle et al. 2020). All gravity field models are available for download in the ICGEM (International Center for Global Earth Models, http:// icgem. gfz-potsd am. de) database of the GFZ. The ICGEM offers, besides further static and time-variable models, also web-based visualizations of the models, as well as interactive service offers for the computation of various gravity field quantities for further use  (Ince et al. 2019).
GRACE Follow-on Mission With a mission duration of more than 15 years GRACE has delivered data three times longer than originally planned. This underlines the excellent work of Airbus Defense & Space (D&S) GmbH, which built the two satellites in Germany on behalf of NASA/JPL. In addition, the operations team, consisting of DLR/GSOC, JPL, CSR, Airbus D&S and GFZ, which together monitored the daily performance of the satellites and their instruments, did a great deal to extend the mission duration as long as possible. Already since 2011, the batteries on both satellites showed clear signs of ageing, and the core instruments had to be switched off regularly for limited periods of time. By October 2017, the mission lifetime was finally over, as the satellite tandem had used up all its fuel supplies.
Acknowledgements We would like to thank the two anonymous reviewers and the associate editor  Prof. Dr. Nico Sneeuw  for their valuable suggestions and  Dr. Thomas Gruber  ( TU Munich ) for preparing  Figure 1 and Table 8. NASA (1972) Earth and ocean physics applications program ,  Volume II Rationale and Program Plans NASA  ( 1984 ) Geopotential research mission (GRM),  NASA  conference publication 2390, (Conference at the  University of Maryland , October  29-31 NASA (1987 ) Geophysical and geodetic requirements for global gravity field measurements 1987-2000, Gravity Workshop,  NASA-GSFC, Colorado Springs NASA  (1991) Report of the NASA Coolfont workshop 1989: emphasis areas for solid earth science in the 1990s,  NASA Technical Memorandum 4256 ,  Program Plan National Academies of Sciences  (2018) Engineering and medicine.
Here, we also provide evidence that  The dataset based on SNAPE-pooled could therefore be useful for studies that rely on rare SNPs, such as those investigating recent demographic events  (Keinan and Clark 2012). SNAPE-pooled has several limitations though. First, it is only capable of handling Pool-Seq data. Second, because of the hard filtering that we are imposing with our posterior probability cut-off, some true SNPs are being called as missing data (see Materials and Methods). This problem is apparent when comparing the number of polymorphisms identified by SNAPE-pooled and PoolSNP (fig. 3 ). Third, any demographic inference done with SNAPE must be limited to cases where a SNP is discovered in at least three populations or more, because the caller appears to produce too many false positives when only two populations are considered (see fig.
Therefore, the more populations that are used for SNP calling by PoolSNP, the less likely PoolSNP is to identify private polymorphisms. Because PoolSNP filters out rare and private polymorphisms, it is less sensitive to sequencing or library preparation errors. Notably, the 20 flagged populations do not have elevated p N /p S with MAC > 50. Additionally,  Kapun et al. (2020)  demonstrated that these problematic samples did not affect population genetic inference based on common SNPs. The problematic samples derived from the DrosRTEC studies likely do not have a major impact on their results either as both  Bergland et al. (2014)  and  Machado et al. (2021)  imposed stringent MAF filters. PoolSNP has the added advantage that it can incorporate in-silico pooled data sets wherein haplotype or genotype information are collapsed into allele frequencies (see Materials and Methods).
However, any real-time monitoring activity will likely suffer from the rapidly changing landscape of sequencing technologies. One of the biggest challenges in the present "omics" era is the rapidly growing number of complex large-scale data sets which require technically elaborate bioinformatics know-how to become accessible and utilizable. This hurdle often prohibits the exploitation of already available genomics data sets by scientists without a strong bioinformatics or computational background. To remedy this situation for the Drosophila evolution community, our bioinformatics pipeline is provided as a Docker image (to standardize across software versions, as well as make the pipeline independent of specific operating systems) and a new genome browser makes our SNP data set available through an easy-to-use web interface (see supplementary figs.
S2 and S3, Supplementary Material online; available at https://dest. bio, last accessed September 6, 2021). The DEST data repository and platform will enable the population genomics community to address a variety of longstanding, fundamental questions in ecological and evolutionary genetics. The current data set might for instance be valuable for providing a more accurate picture of the demographic history of D. melanogaster populations, in particular in Europe and North America, and with respect to multiple bouts of out-of-Africa migration and recent patterns of admixture. Such analyses can be strongly affected by chromosomal inversions that are known to impact LD and haplotype variation  (Kapun and Flatt 2019; Durmaz et al. 2020). We have therefore provided frequency estimates for the seven most common cosmopolitan inversions (In(2L)t, In(2R)NS, In(3L)P, In(3R)C, In(3R)K, In(3R)Mo, and In(3R)Payne; Lemeunier and Aulard 1992), which allows accounting for the effects of inversions in population genetic inference (e.
2008; Kolaczkowski et al. 2011; Fabian et al. 2012; Bergland et al. 2014 Bergland et al. , 2016;; Reinhardt et al. 2014; Kapun et al. 2016 Kapun et al. , 2020;; Waldvogel et al. 2020; Bogaerts-M arquez et al. 2021; Machado et al. 2021). Another major opportunity provided by the DEST data set lies in studying the temporal dynamics of evolutionary DEST Population Genomics. doi:10. 1093/molbev/msab259 MBE change. Sampling at dozens of localities across the growing season and over multiple years will help to advance our understanding of the short-term population and evolutionary dynamics of flies living in diverse environments, thereby providing novel insights into the nature of temporally varying selection  (Bergland et al. 2014; Wittmann et al. 2017; Machado et al. 2021 ) and evolutionary responses to climate change (e.
g. ,  Umina 2005; Rodr ıguez-Trelles et al. 2013; Waldvogel et al. 2020). Moreover, by integrating these worldwide estimates of allele frequencies, those from lab-and field-based "evolve and resequence" experiments (E&R;  Turner et al. 2011; reviewed in Kofler and Schlo ¨tterer 2014; Schlo ¨tterer et al. 2014; Flatt 2020)  and those from mesocosm experiments (e. g. ,  Rudman et al. 2019; Erickson et al. 2020) , we might be able to gain deeper insights into the genetic basis and evolutionary history of variation in fitness components (e. g. ,  Flatt 2020). In addition to analyses of selection, the DEST data set can also be used for preliminary demographic inference. Although Pool-Seq data sets lack important haplotype information, they have been successfully used in the past to generate demographic and biogeographic insights into both model and non-model species (e.
g. ,  Gautier et al. 2021; Machado et al. 2021; Nunez et al. 2021 ; see fig. 7 ). Our analyses suggest that Pool-Seq data can be used for demographic model inference. A major caveat in this endeavor is that, to the best of our knowledge, Pool-Seq has not been exhaustively benchmarked for demographic inference. As such, and until proper validation has been completed, we present our results as tools for hypothesis generation and exploration. Our results from moments are in full agreement with basic biological expectations. For example, our estimates of h are concordant with previously reported values ($0. 005; Lack et al. 2016). Moreover, our estimate of mean divergence time between the eastern and western European clusters of D. melanogaster is 1,013 years. This estimate is subject to caveats, given the nature of Pool-Seq data and that future validation may need to be done using different types of data.
First, we observed a remarkable difference between the method used to discretize AFs from Pool-Seq, prior to SFS estimation. Discretizing the data based on direct counts results in noisier demographic estimates. Discretizing based on binomial probabilities, on the other hand, produced consistent results across comparisons. This behavior is due to the inherent noise of directly converting Pool-Seq AFs (which are heavily affected by coverage) to counts. Based on these observations, we recommend the use of the binomial method of AF discretization for Pool-Seq analysis  (Thia and Riginos 2019). Second, we also observed a difference in the estimator's behavior based on whether the PoolSNP or SNAPE-pooled data were used to build the SFS. In general, PoolSNP generated h estimates which converge toward 0.
Although our analyses of the DEST sequencing data already led to novel insights into the evolutionary history of Drosophila, we believe that the real value of the DEST data set lies in the future: its long-term utility will grow as natural and experimental populations are continually being sampled, resequenced and added to the repository by the community of Drosophila evolutionary geneticists. The pipeline that we have established will make future updates to the data repository straightforward. Furthermore, because it is not easily feasible for any single research group to sample flies densely through time and across a broad geographic range, the growing value of the DEST data set will depend upon the synergistic collaboration among research groups across the globe, as exemplified by the DrosRTEC and DrosEU consortia.
Data Sources The genomic data set presented here has been assembled from a combination of Pool-Seq libraries and in silico pooled haplotypes. We combined 246 Pool-Seq libraries of population samples from Europe, North America, and the Caribbean that were sampled through space and time by two collaborating consortia in North America (DrosRTEC: https://web. sas. upenn. edu/paul-schmidt-lab/dros-rtec/, last accessed September 6, 2021) and Europe (DrosEU: http://droseu. net, last accessed September 6, 2021) between 2003 and 2016. Of these 246 Pool-Seq samples, 121 samples represent previously unpublished samples generated by DrosEU, 48 DrosEU samples previously reported in  Kapun et al. (2020) , and 77 samples previously reported in  Machado et al. (2021). In addition, we integrated genomic data from >900 inbred or haploid genomes from 25 populations in Africa, Europe, Australia, and North America available from the Drosophila Genome Nexus data set  (DGN v1.
Sample Collection Most population samples contributed by the DrosEU and the DrosRTEC consortia were collected in a coordinated fashion to generate a consistent data set with minimized sampling bias. In brief, fly collections were performed exclusively in natural or seminatural habitats, such as orchards, vineyards, and compost piles. For most European collections, flies were collected using mashed banana, or apples with live yeast as bait in traps placed at sampling sites for multiple days to attract flies, or by sweep netting (see  Kapun et al. 2020  for more details). For North American collections, flies were collected by sweep-net, aspiration, or baiting over natural substrate or using baited traps (see  Behrman et al. 2018; Machado et al. 2021 for details). Samples were either fieldcaught flies (n ¼ 227), from F1 offspring of wild-caught females (n ¼ 7), from a mixture of F1 and wild-caught flies (n ¼ 7), or from flies kept as isofemale lines in the laboratory for five generations or less (n ¼ 4); see supplementary table  S1 , Supplementary Material online for more information.