dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank  M. de Paepe  and  E. Rocha  for fruitful discussions. We also thank  J. Mozziconacci  for helpful suggestions,  J. -F. Flot  for performing the original IDBA-UD assembly and Kraken analysis, and  T. Pedron  for providing us the mouse samples. Funding: This research was supported by funding from the  European Research Council (ERC)  under the  7th Framework Program  ( FP7/2007-2013) / ERC  grant agreement  260822  (to R. K. ). Author contributions: M. M. and R. K. conceived the analysis. M. M. performed the experiments. M. M. , L. B. , and A. C. performed the analysis, and M. M. and  R. K. interpreted the results. M. M. and R. K. wrote the manuscript. Competing interests: The GRAAL program is owned by  Institut Pasteur. The entire program and its full source code are freely available online for noncommercial purposes, but commercial usage requires a specific license.
We estimate a linear probability model with difference-in-differences regressions, in which the probability of pogroms in a grid cell in a year is a function of economic and political shocks and the occupational structure of Jews relative to the majority, controlling for grid-cell and year fixed effects, and adjusting the standard errors for both spatial and over-time correlations. We construct proxies for economic shocks using historical temperature data and government grain-assistance data, showing how they relate to grain yields and prices. We define political turmoil as a time dummy indicating historical events from a comprehensive list of violent successions (with revolt or assassination), defeats in wars, invasions of Russia's territory, general political strikes, and revolutions. We show that the main driver of pogroms is the interaction of three factors: economic shocks, political turmoil, and the domination of Jews in the middleman sector related to agriculture.
However, if Jews were more likely to choose middleman occupations related to agriculture in localities where the local majority was more antisemitic, then the results of the OLS regressions would exhibit an upward bias. We present several analyses showing that the latter is unlikely to be the case. First, we assembled historical data that allowed us to create two alternative proxies for preexisting antisemitism. In particular, we collected and georeferenced data on the locations of violence against Jews that took place before 1800. All the episodes of violence against Jews that took place on the territory which became the Pale of Settlement at the end of the 18th century, when the Russian Empire annexed them from the Polish-Lithuanian Commonwealth, were different from pogroms that we study because Jews as an ethnic and religious group were "neither the initial nor the principal targets" in these episodes  (Klier, 2011, p.
59) ; instead, they were victims of their economic ties to the primary target, Polish-Catholic nobility. 4 Assuming that Jews were more likely to become victims of violence directed against another group in places with higher anti-Jewish sentiment, we can use the locations of these events as a proxy for preexisting antisemitism. We also collected data on the publication place of all antisemitic and non-antisemitic books published in Eastern Europe in the main European and Eastern European languages throughout the 18th century. We use the geographical proximity to places of publication of antisemitic books conditional on the number of literate non-Jews as another proxy for preexisting antisemitism. The two measures of preexisting antisemitism are positively correlated with each other. They also positively correlate with the index of the occupational segregation between Jews and non-Jews across all occupations, suggesting that in more antisemitic places Jews and Gentiles tend to engage in different occupations.
DATA AND THE MEASUREMENT OF ECONOMIC AND POLITICAL SHOCKS To investigate the conditions under which pogroms broke out, we compile data on pogroms and the ethnic composition of the local population by occupation and construct measures of the negative economic shocks that hit the majority, of the episodes of political turmoil that undermined longterm economic relationships, and of prior antisemitism. In this section, we describe the data and present several checks on the measures we have constructed. Summary statistics of all variables used in the analysis are presented in Table  1  in the main text and Table  A1  in the Online Appendix.
5 degrees resolution that covers the period from 1800 to 1927. Because we aim to study politico-economic determinants of ethnic violence, we stop in 1927, because this is the last year before the start of Soviet collectivization, which put an end to individual farming in the vast majority of our sample geography. We measure violence against the Jews with a dummy variable that takes the value of 1 if a pogrom took place in a given year and grid cell, and 0 otherwise. Systematic data on the number of casualties do not exist: historians give widely varying estimates of the number of casualties and of the extent of property damage for many of the pogroms. There were a total of 638 pogroms in 385 different grid cell × year observations. Panel B of Table  1  summarizes the data on pogroms. The probability that a pogrom occurred in a given grid cell and year is 0.
The 1897 census also provides information on literacy levels in Russian language and in the native language separately for each native language. We use data on the literacy rate among non-Jews to proxy for how exposed they could be to antisemitic books. In the Pale of Settlement, the literacy rate among non-Jews was 20%; among Jews, it was 37%. We also use data from the 1926 Soviet Census and the 1931 Polish Census to measure the change in the share of Jews and in the share of Jews among traders and creditors from 1897. These two censuses are less detailed than the 1897 census: the 1931 Polish Census does not provide information about occupations by ethnicity, while the 1926 Soviet Census provides information about moneylenders and all traders as a single occupational category.
Seasonal temperature. To measure economic shocks, we combine data on grain yields, prices, and historical accounts of crop failures with data from climatologists. In particular, we use a data set that provides information on reconstructed historical seasonal temperature. These data were built by  Luterbacher et al. (2004)  and  Xoplaki et al. (2005)  and previously were used by  Ashraf and Michalopoulos (2015)  and  Buggle and Durante (2017). These temperature data were derived from indirect proxies such as tree rings, ice cores, corals, and ocean and lake sediments, as well as archival documents. Luterbacher et al. (2004)  and  Xoplaki et al. (2005)  reconstructed historical temperature by calibrating the indirect proxies to Climatic Research Unit (CRU) gridded data based on weather station observations by  Mitchell and Jones (2005)  for the 20th century and extending time series backward for earlier years.
Additional variables. To verify that our results are not driven by the effect of shocks on general crime rather than ethnic violence, we use data on the number of thefts, homicides, and arsons per capita. Data on the number of thefts and homicides exist for 1900-1912 and for the number of arsons for 1900-1910. (Data for other years are not available. ) The number of arsons comes from  MIA (1912). Homicides and thefts come from the annual volumes of the statistics of district courts and the chambers of justice published by the Ministry of Justice in St. Petersburg between 1904 and 1915. We use data from 1905 Land Census assembled by  Dower and Markevich (2017)  to measure land inequality with a Gini index and tax rates from  Nikolay (1890). We also digitized historical railway maps to measure proximity to railways.
108-144). It is a challenging task to measure shocks to agricultural incomes in the 19th century. We use several datasets to build a proxy for local economic shocks. First, we ask which climate shocks, measured by the reconstructed historical seasonal temperature, mattered for grain production. Agricultural scientists (e. g. Hall, 2001)  argue that extremely hot temperatures in the early growing season, often referred to as heat stress, causes grain yield to collapse. 15 In the Pale of Settlement, both winter and spring grains were cultivated, with winter grains constituting the majority. Winter grains in that area are planted in September, sprout in May and June, and are harvested in July and August; spring grains are planted in April and May, sprout in June and July, and are harvested in August and September (Joint Agricultural Weather Facility, and U.
S. Department of  Agriculture, 1992, p. 139). Given this agricultural calendar, for both winter and spring grains, spring in the reconstructed historical temperature dataset (March, April, and May) represents the early growing season, whereas summer and autumn (i. e. June-November) represent the harvesting season. Using the available historical data on grain yields, we investigate whether and how temperature shocks in each of the seasons, including the growing and the harvesting seasons, affected agricultural output. We regress log yield in a province and year on the temperature shocks in each season in the province and year controlling for year and province fixed effects (to single out the variation that is relevant for the subsequent analysis) and correcting standard errors for spatial correlation within a 250-kilometer radius and one spatial lag.
In addition, extremely cold weather during the later stages of the growing season may also negatively affect yields of both winter and spring crops  (Acevedo et al. , 2002). Consistent with these mechanisms,  Anderson et al. (2017)  find that colder growing seasons increased the likelihood of Jewish persecutions in the European cities between 1100 and 1600, but not between 1600 and 1800. As shown above, the incidence of extremely hot spring temperatures has substantial power in predicting crop failures. This prediction is not perfect, however. This is partly because natural calamities other than shocks to seasonal temperature also affected agricultural yields and partly because reconstruction of temperature from indirect sources is prone to errors. In particular, there is no detectable climatic anomaly in the reconstructed climate dataset during the 1880-1882 period.
In columns 4 and 5, we control for year fixed effects and show that prices rose for two years following a local economic shock, but only when this local economic shock occurred in areas particularly suitable for grain cultivation. In columns 6 and 7, we omit year effects, as the marketwide economic shocks variable varies only over time, and we verify that the marketwide economic shocks were associated with significantly higher prices. 22  Overall, the analysis presented in the two subsections above motivates the use of local economic shocks as a measure of local crop failure and the use of a marketwide economic shock as a measure of high grain prices. For a subset of years, for which the grain price data are available, we also use the price of grain directly to measure the market conditions for grain trading.
Acknowledgments. We thank the editor,  Nicola Gennaioli , the three anonymous referees,  Glenn Ellison ,  Roberto Galbiati ,  Sergei Guriev ,  Rachel Kranton ,  Erzo Luttmer ,  Debraj Ray ,  Nathan Sussman ,  Fabrizio Zilibotti , the participants of seminars at the  Paris School of Economics, University of Lausanne, University of Zurich, University of Toronto, Tel Aviv University, Hebrew University of Jerusalem, University of Warwick, London School of Economics, University of Pompeu Fabra ,  Sciences Po ,  London Business School , and the participants of  ISNIE Conference in Harvard Law School, Warwick Political Economy Conference in Venice, SOIE Conference in Columbia University , and Institutions and Diversity Conference in the New Economics School in Moscow for helpful comments. We thank  Dias Akhmetbekov ,  Zalina Alborova , and  Etienne Madinier  for outstanding research assistance and  Fabrizio Colella ,  Rafael Lalive , and  Mathias Thoenig  for sharing their code calculating standard errors corrected for spatial and temporal correlations with instrumental variables and fixed effects.
The sources of Polish bibliography are: https://www. estreicher. uj. edu. pl/home/ and https://catalog. hathitrust. org/api/volumes/oclc/3963468. html (accessed on 15 January 2018). The source of the Russian bibliography is: http://nlr. ru/rlin/ruslbr_v2. php?database=RLINXVIII (accessed on 24 January 2018). The bibliography of religious texts published in the Russian Empire is unavailable. Downloaded from https://academic. oup. com/restud/article-abstract/87/1/289/5280103 by King's College London -Journals Dept user on 03 March 2020.
Acknowledgments This research was partially supported by the research program of  UPV/EHU. T. Echániz  would like to acknowledge the  UPV/EHU  for its support through a postdoctoral fellowship. I. González de Arrieta  thanks the  Radiative and Transport Properties Group at CEMHTI laboratory (CNRS) in Orléans  for the opportunity to use its facilities and the useful discussions.
Renal function was expressed as eGFR, obtained by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation,  16  and the change of eGFR per year was also calculated. Current smoking status was investigated in Hospital database. The Kolgomorov-Sminorv normality test was used to characterize the data distribution. Results are presented as mean ± standard deviation, median (interquartile range) or percentage, as applicable. Comparisons between baseline and the follow-up data were analyzed using paired t-test. For comparisons between independent groups, Student's t test, Chi-square or ANOVA test were used. Multivariate regression analysis was performed to verify variables independent associated with annual changes in eGFR. Statistical significance was accepted as p values <0.
Thus, protein intake could be the responsible for improvement on creatinine and eGFR in patients adherent to LPD (diabetics and non-diabetics). However, the absence of data about dietary protein intake (g/kg/day) is the main limitation of the present study because avoid more accurate evaluations regarding independent factors associated with CKD progression; or specific effects of protein intake and clinical outcomes, such as bicarbonate levels. Moreover, the present study has others limitations. Firstly, the adherence to LPD was evaluated only by dietary methods because the 24-h urinary urea nitrogen excretion could not be determined due to logistical reasons. Although not considered the gold standard, the systematic use of dietary methods could be a strategy to assess the feasibility of dietary interventions because provides detailed intake data.
25 Although dietary data were collected, just the categorizing in "adherent" and "nonadherent" patients is available. The availability of original data would provide important information about the real protein intake (g/kg/day) and its correlations to other variables, such as serum bicarbonate levels. Secondly, kidney function was estimated only through eGFR (based on serum creatinine) because cystatin C or others renal biomarkers are not available in clinical practice. Thirdly, clinical outcomes closely related to CKD progression (such as blood pressure control and proteinuria) could not be evaluated. Consequently, the effects of LPD in the outcomes above mentioned, as well as adjustments for all possible confounders could not be performed due to retrospective study design. In this specific matter, randomized study design had important ethical concerns because if LPD promotes potential benefits to the nondialysis CKD patients, should we randomize patients to a LPD or unrestricted diet?  14  Even with all these limitations, the present study reports that low protein diet is nutritionally safe and it could have a positive effect on the improvement of creatinine and renal function in diabetics and nondiabetics patients.
46 In the adjuvant setting, the Phase III APHINITY trial (NCT01358877) is currently evaluating the benefit of its addition to trastuzumab. A press release on March 1, 2017, confirmed a significant improvement in invasive DFS (iDFS) with the addition of pertuzumab to trastuzumab in patients with early-stage HER2-positive breast cancer. Full results of the study are expected to be reported at the 2017 American Society of Clinical Oncology (ASCO) Annual Meeting in June. T-DM1 is an anti-HER2 antibody-drug conjugate that combines emtansine (a cytotoxic agent) to trastuzumab to further increase the toxicity of the monoclonal antibody by targeting its activity directly to cancer cells. 47 T-DM1 administration improves progression-free survival and OS in the metastatic setting in case of trastuzumab-resistant HER2positive breast cancer.
48, 49 So far, no data are yet available in early-stage breast cancer, but the compound is actually being investigated in three Phase III trials. 13 The KATHERINE study is comparing 14 cycles of adjuvant T-DM1 with trastuzumab in patients with residual disease following neoadjuvant chemotherapy combined with trastuzumab (NCT01772472). The KAITLIN trial is comparing the combination of T-DM1 and pertuzumab with a taxane after surgery and anthracyclinebased chemotherapy (NCT01966471). Finally, the ATEMPT trial is comparing T-DM1 alone with paclitaxel plus trastuzumab followed by trastuzumab in stage I HER2-positive breast cancer (NCT01853748).
51; 95% CI 0. 33-0. 77; P=0. 0013) than to those with hormone receptor-negative tumors (HR 0. 93; 95% CI 0. 6-1. 43; P=0. 74; P for interaction =0. 054). Longer follow-up data from the study are awaited to better understand the potential role of neratinib in this setting. In terms of toxicity, diarrhea is the most important side effect observed with the administration of neratinib (in the ExteNET trial, 55 and 40% of patients developed grade 1-2 and grade 3-4 diarrhea, respectively). 64 To overcome this issue, intensive loperamide prophylaxis during the first month of treatment has been shown to effectively reduce the occurrence of diarrhea and improves the overall tolerability to the drug. 65 An open-label Phase II study is currently assessing the incidence and severity of diarrhea when routine use of intensive loperamide prophylaxis is administered during the first month of neratinib therapy (NCT02400476).
The resulting probability that neratinib was superior to standard therapy was 95%, and the probability of success in a Phase III trial including 300 patients was 73%. There was limited activity of neratinib in patients with HER2negative disease independently of their hormonal receptor status, and the adaptive randomization algorithm stopped assigning these patients to receive the anti-HER2 tyrosine kinase inhibitor. Grade 3-4 treatment-related adverse events were mostly limited to diarrhea (38%) and were mitigated by dose reductions and/or symptomatic management. Results on survival from the I-SPY 2 trial are not mature yet. 60 ore recently, neratinib was also evaluated in the neoadjuvant NSABP FB 7 Phase II trial (NCT01008150). 68 his study had a backbone of weekly paclitaxel followed by doxorubicin and cylophosphamide.
We follow the thinning dynamics using an ultra high-speed camera attached to a microscope to have both maximal temporal and spatial resolution. For liquids with different ratios σ/ρ, we arrive at good agreement with eq 1 (Figure  2A ) with a value of the prefactor A of 0. 9 ± 0. 01 (Figure  2B ). For water, however, the identical experiment is not on the same line, and shows a systematic deviation. ] [9] [10]  The data of Chen et al. , although noisier, appear to agree with our data at least close to the breakup point, but deviate for longer times and the helium and mercury experiments give very different prefactors. From numerics,  10, 13  the prefactor was determined to be A ∼ 1. 4, which disagrees with most of the experiments. We therefore use the precise and universal prefactor obtained in our experiments on a series of other liquids of known surface tension.
The Times-In-Mode (TIM) and thrust values used in Open-ALAQS are based on fixed-point profiles derived from the Aircraft Noise and Performance Database (ANP2. 1). The fuel flow as well as the EI used in the calculations for a very large number of aircraft engines are derived from the ICAO Exhaust Emission Databank combined with the FOCA databank for pistons (and potentially FOI for turboprops). Coupled with the Lagrangian particle dispersion model AUSTAL2000, the official reference model of the German Regulation on Air Quality Control (Technische Anleitung zur Reinhaltung der Luft, TA Luft), Open-ALAQS can also calculate the spatio-temporal distribution of the resulting concentrations. The resolution of the model outputs can be defined by the user depending as a function of desired accuracy and computational constrains.
It is recommended for use within ICAO CAEP policy assessments, and this recommendation was endorsed by the CAEP Steering Group in June 2009. Moreover, it was designed to cover a large part of European airports and the population exposed to significant noise levels within the ECAC region (approximately 90% within the EU). Following a similar methodology as for LAQ, separate simulations are performed for a set of points defined to simulate an aircraft flight path, for which the Noise-Power-Distance (NPD) relationship and the lateral adjustments are known. The NPD as well as the performance profiles (e. g. , speed and thrust values as a function of the aircraft's location) data are taken from the Aircraft Noise and Performance (ANP) database which is hosted and maintained by EUROCONTROL on behalf of ICAO.
Generation of Local Impact Metric Using Population Data The environmental impact is calculated by combining the LAQ and noise results with the number of people exposed to the calculated NO x /SEL levels. A basic LAQ metric is the emitted amount of respective component relevant for air quality, e. g. , nitrogen oxides of particulare matter, under specific atmospheric height (3000 and 5000 feet). For trace compounds however, final metric proposed is a population-weighted value that is mapped back (distance, altitude) to the source to provide a measure of how a specific aircraft movement impacts local environment (within a 30 km radius from the airport reference point). The population-weighted SEL is calculated using the centroid of each population grid cell as receptor point. The simulation is repeated for each point separately until the whole domain is covered.
g. , 1 kg of NO 2 ) emitted at equidistant points on a 3D-grid of geographic position and altitude. Local meteorological data (METAR) preferably from airport stations close to the runway-are used to calculate the dispersion of the emissions for each simulation and thus the LAQ impact (i. e. , the derived NO x concentration increment) at receptor points in the vicinity of the airport. Together with information on affected population (by this degradation of air quality) an integrated measure of LAQ impacts on population can be calculated by the sum of all intersection points with the official EU population data given at a 1 km × 1 km resolution (GEOSTAT 2011) and the NO x concentration gridded datasets. This new metric is then used as MET information to TOM to make available an airport-specific ECF for local air-quality, relating to particulate matter and nitrogen oxide emissions.