dataesr/scientific-paragraphs-categorization huggingface dataset:
Note the non-distinction between irrigated and rain-fed crops in our data from available statistics despite the fact that most of the European irrigated areas are located in Southern Europe  [48]. The shorter yield time series and the lower number of districts included in our study for Southern Europe (only 10% of the total number of yields data in our database) may also explain the reduced RF   predictive ability in this region (electronic supplementary material, figure  S1 ). More generally, despite the efforts in the various European countries to use homogeneous methods and provide high-quality data, a certain degree of subjectivity is associated with these regional statistics, and could thus increase the amount of unexplained yield variation. Global improvements of the predictive quality of our models are possibly reachable using other climatic variables, or other temporal aggregations.
Furthermore, Ben-Ari et al. [21]  did not find any added value in considering climate aggregation based on precise estimation of the crop phase (i. e. vegetative and reproductive) compared to monthly, bi-or tri-monthly aggregation. Similarly, Sharif et al. [52]  did not find any advantages to considering fortnightly rather than monthly aggregated climate variables to predict yields. We estimated the impatcs of climate on yield anomalies in four European regions (based on pairs of climate and yield data for each district and year available). Europe (mean R 2 = 34%, across all crops) could also stem from local specific climatic conditions. Hungary, with its Pannonian climate, could experience significantly different climatic conditions than its neighbouring countries (e. g. in 2018, figure  7 ).
Acknowledgements. We thank all participants involved in the ERA5 climate data re-analyses. The work was informally supported by the  ICOS infrastructure. We would also like to gratefully acknowledge all the national statistical offices and all the interlocutors who facilitated access to the yield data of the 17 European countries. Our sincere thanks for the national data provision to  Dr Renáta Sándor  and  Dr Nándor Fodor  from  Agricultural Institute, Centre for Agricultural Research, Hungary  (complete database is available at country level between 1921 and 2018 and at NUT3 level between 1960 and 2018). We would also like to thank  Tamara Ben-Ari  for her advice during the design of the study and the data collection phase.
It has been reported that children with food allergy will develop other allergic diseases earlier and with a frequency 2 to 4 times higher than those without allergies  [10]. Epidemiological and ecological studies have shown that some foods are protective factors against allergic diseases, while others are risk factors leading to an increase in the prevalence of allergic diseases. In addition, a cross-sectional study conducted in sex French cities between 2000 and 2001, suggesting that a healthy diet rich in antioxidants such as vegetables, fruits, cereals, fish fats high in monounsaturated fatty acids, olive oil, low fat dairy products also reduced meat and poultry consumption during childhood can help reduce the risk of allergic diseases  [11, 12]. In this study, we aim to use data from the French study as part of the International Study of Asthma and Allergies in Childhood (ISAAC) III France survey at first to describe allergic phenotypes.
Statistical analysis The data collected was coded, entered and captured on the Statistical Package for Social Sciences (SPSS) software version 22 (SPSS TM Inc. , Chicago, IL USA). First, descriptive analyzes were performed using mean and standard deviation to present continuous variables, while percentages were used for qualitative variables. Second, bivariate analysis was conducted to investigate the relationship between the symptoms of asthma, allergic rhinitis, eczema and food allergy during the last 12 months on one side, and the following factors of the other side: diet habits, taking into account external and internal environmental, socio-demographic and socio-economic factors of children and their parents, history of personal and family atopy, stay in nursery, exposure to smoking, vaccination, medical history.
(H-I) The graphs correspond to the quantification of the radioactivity incorporated into LMN-1 [H] and [T1] fragments divided by the total amount of LMN-1 fragments quantified using tryptophan fluorescence (Stain-Free; Bio-Rad). The ratio obtained for the WT fragments was arbitrary defined as 100. The quantification of three independent experiments is presented. Error bars represent the standard deviation. (J) In vitro kinase assay was performed with C. e PLK-1 and the GST-LMN-1 [H] WT or mutated fragments as substrates. The samples were subjected to SDS-PAGE and the gel was revealed using tryptophan fluorescence (Stain-Free; Bio-Rad). The online version of this article includes the following source data and figure supplement(s) for figure  1.
Fragments were obtained with a Higher-energy Collisional Dissociation (HCD) activation with a collisional energy of 27%, and a quadrupole isolation width of 1. 6 Da. MS/MS data were acquired in the linear ion trap in top-speed mode, with a dynamic exclusion of 50 s and a repeat duration of 60 s. The maximum ion accumulation times were set to 250 ms for MS acquisition and 60 ms for MS/MS acquisition in parallelization mode.
LC-MS/MS data processing For the identification step, all MS and MS/MS data were processed with the Proteome Discoverer software (Thermo Scientific, version 2. 2) and with the Mascot search engine (Matrix Science, version 5. 1). The mass tolerance was set to six ppm for precursor ions and 0. 02 Da for fragments. The following modifications were allowed: Acetyl (N-term), Methylation (M), Phospho (ST), Phospho (Y). The peptide identification was performed on Caenorhabditis elegans protein database from Swissprot. Peptide identifications were validated using a 1% FDR (False Discovery Rate) threshold calculated with the Percolator algorithm.
For the analysis of the paired nuclei phenotype in live specimens by differential interference contrast (DIC) microscopy, embryos were obtained by cutting open gravid hermaphrodites using two 21-gauge needles. Embryos were handled individually and mounted on a coverslip in 3 ml of M9 buffer. The coverslip was placed on a 3% agarose pad. DIC images were acquired by an Axiocam Hamamatsu ICc one camera (Hamamatsu Photonics, Bridgewater, NJ) mounted on a Zeiss AxioImager A1 microscope equipped with a Plan Neofluar 100Â/1. 3 NA objective (Carl Zeiss AG, Jena, Germany), and the acquisition system was controlled by Axiovision software (Carl Zeiss AG, Jena, Germany). Images were acquired at 10 s intervals. Live imaging was performed at 23˚C using a spinning disc confocal head (CSU-X1; Yokogawa Corporation of America) mounted on an Axio Observer.
Quantification and statistical analysis GFP::LMN-1 intensity over time was measured using the Image J software in control and RNAi conditions after background subtraction. Anaphase onset was defined as time 0. Data points on the graphs are the mean of the normalized GFP intensity measurements in control and RNAi conditions for the same define region of interest (ROI). To allow direct comparison between control and RNAi conditions, the average signal intensity of GFP::LMN-1 at the NE 120 s before anaphase was arbitrarily defined as 1. The results are presented as means ± SEM. In all graphs, data were compared by Mann-Whitney test. All calculations were performed using GraphPad Prism version 6. 00 for Mac OS X, GraphPad Software, La Jolla California USA, www. graphpad. com. (Equipe labellisee, France).
Acknowledgements We thank  P Moussounda  and  R Servouze  for help with media preparation. We thank  V Archambault ,  B Cisneros ,  B Lanctot  for strains and reagents. We thank  X Baudin  and  V Contremoulins  for microscopy data acquisition and analysis. We are grateful to  Angela Graf  for technical assistance. We thank  R Karess  and  V Doye  for fruitful discussions during the course of the study and  R Karess  for critical reading of the manuscript. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of  IBiSA and France-BioImaging  ( ANR-10-INBS-04 ) infrastructures and the  Institut Jacques Monod 'Structural and Functional proteomic platform '. Some strains were provided by the  Caenorhabditis Genetics Center (CGC) , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ).
GVA was supported by the  Labex 'Who am I?' Laboratory of Excellence  No. ANR-11-LABX-0071 , the  French Government through its Investments  for the  Future program  operated by the  French National Research Agency (ANR)  under Grant no. ANR-18-IDEX-0001  and by the  CONACYT  grant  CVU 364106. SNN is supported by a  PhD fellowship  from the  Ministry of Research. NJ is supported by a funding ' Dynamic Research ' from  ANR-18-IDEX-0001 ,  IdEx Universite de Paris. Work in the laboratory of LP  is supported by grants from  'Agence Nationale pour la Recherche' (ANR, France  - ANR-17-CE13-0011 ) and by the  'Ligue Nationale Contre le Cancer '.
The melting curves were systematically monitored (temperature gradient at 1. 1 • C/s from 65 to 97 • C, five acquisitions/1 • C) at the end of the last amplification cycle to confirm the specificity of the amplification reaction. Each PCR assay included replicate samples (duplicates of RT and PCR amplification, respectively) and negative controls (reversetranscriptase-and cDNA-template-free samples, respectively). For the analysis of the mRNA levels, relative quantification of target gene expression was performed using the Roche Applied Science E-Method  Pfaffl (2001). The relative gene expression of ef 1α was used for the normalization of the measured mRNA in each tissue, as its relative expression did not significantly change over the sampling process (data not shown). In all cases, PCR efficiency was measured from the slope of a standard curve using serial dilutions of cDNA.
Data Analysis All data were analyzed using SPSS for Windows, version 12 (SPSS Inc. , Chicago, IL, United States). In order to determine the effects of glucose microinjection on the survival rate and glucose content, one-way analysis of variance (ANOVA) was performed. When significant differences were found among the groups, Tukey's method was used to rank the groups. In addition, an independent t-test analysis was conducted to evaluate the differences between the two groups at weeks 1 and 20: saline (0. 85% NaCl) versus glucose (2 M glucose) stimuli. After the nutritional challenge (week 24), the statistical factors consisted of the analysis of the effects of the glucose stimulus history, dietary carbohydrate level and their interactions. Twoway factorial ANOVA was also carried out. When the interaction of the factors was statistically significant, oneway ANOVA following Tukey's range test was performed to rank the treatment combination groups.
27 for MABIO and 0. 35 for MASED; Table  5 ). Finally, the final score (D²SI) on MABIO and MASED sites (Fig. 3G  Slightly disturbed on MABIO and Moderately disturbed on MASED. These results highlight the persistence of the impact following spoil deposition, with a higher impact on the conical site (MASED). A non-parametric Kruskall-Wallis test and multiple comparison tests were carried out on the MABIO and MASED data to study score differences between the three periods. These tests reveal significant differences between D²SI before-during dumping and before-after dumping (KW test = 8. 964; df = 2, p-value = 0. 011). However, many fluctuations are observed, with poor correlations between the final index and the dumped volumes (0. 13 on MABIO and 0. 39 on MASED; Table  5 ).
Additional approaches In this study, we use a calculation test by adding different weighting coefficients for each sub-index according to the consistency of the data (x3 for D²SIB, x2 for D²SIS and D²SIT, x1 for D²SII, D²SID and D²SIR). There is no statistical difference between the unweighted (D²SI) and the weighted (D²SIweighted) indices for the MABIO and MASED sites (KW test= 25. 462; df= 16; p-value= 0. 062) and both indices are significantly correlated (0. 95 for MABIO and 0. 95 for MASED; Table  5  and Fig. 4 A, B ). Nevertheless, D²SI is less strongly correlated with dumped volumes (0. 13 on MABIO and 0. 39 on MASED; Table  5 ) than D²SIweighted. The weighted index shows higher correlations with dumped volumes, especially on the MASED site (0. 25 on MABIO and 0. 51 on MASED). By giving a greater weight to the scores of sub-indices based on benthic compartment, sedimentary and thickness modification included in the final index calculation, we can achieve a better assessment of the impact of dumping according to the dumped volumes (Fig.
The D²SI is calculated by considering impacted (under the direct influence of harbour sediment disposal) and unimpacted stations (without direct or indirect influence of the harbour sediment disposal) and is composed of six sub-indices that take into account the biological and physical impacts generated by dredge disposal at sea (integrating data on benthic macrofauna, demersal fish, sedimentary types, bathymetry, resistance to dredge sediment deposition and drifting capacity). A final score is calculated using each sub-index, allowing us to classify the disturbance. In the absence of data on ichthyofauna and biological traits of life of macrobenthic fauna, an additional approach (D²SI2) shows that results based on only three sub-indices, including at least benthic macrofauna, sedimentary types and thickness, appear sufficient surrogate to provide a similar assessment of dumping pressure.
Seven campaigns were conducted on the MASED site one campaign before dumping (November 2011), two campaigns during dumping operations (July and December 2012) and four campaigns after dumping operations (September 2013, September 2014, March 2015 and December 2016). 1. 4. 3. Biological traits of life A literature search allowed us to create a database of biological traits characterizing 'Resistance to landfill' and 'Drifting' for taxa whose abundances represent 95% of the total abundance (40 taxa on MABIO and 35 taxa on MASED). 1. 5. Statistical analysis To validate the index, Spearman's correlation coefficients were calculated, and the non-parametric Kruskal-Wallis test and multiple comparison tests were performed with the R 3. 4. 1 software, after testing normality of data with the Shapiro test.
Acknowledgments The study was funded by the  Agence Française de la Biodiversité  (AFB;  INDICLAP  project:  INdicateurs d'Impacts de CLAPages  sur les habitats benthiques). This study also forms part of the PhD research work of  N. Baux  (Baux, 2018), funded by the  ANRT  ( Le Havre  harbour and the  French State). The authors thank  Gwenola De Roton  ( AFB ) for her very useful comments during the D²SI development and the members of the  Scientific Committee of INDICLAP  ( P. Galichon ,  S. Brasselet ,  S. Samson ,  P. Tournier ,  N. Bacq  and  J. Grall ) for their participation at regular meetings to evaluate the progress of the project. Part of this work was carried out at the CSLN in association with the  University of Le Havre  and its 22 representative Fabrice Durant, which the authors thank.
Acknowledgements The authors thank  KT Nam  and  BC Cho  of  Yonsei University (South Korea ) for their generous donation of Transgenic NF-κB reporter and SPC-Cre-ER T2 mice. Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology  ( NRF-2015R1D1A 1A01057695 , and  NRF-2018R1A6A1A03023718 ). In addition, this study was supported by a faculty research grant from  Yonsei University College of Medicine  ( 6-2018-0164 ) and the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP) ( 2020R1A2C3005787 ).
Financial/nonfinancial disclosures This work was supported by grants from the  Korea Healthcare Technology R&D Project ,  Ministry for Health & Welfare Affairs, Republic of Korea  ( HI08C2149 ). This work was also supported by the  Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF)  funded by the ministry of  Science, ICT & Future Planning  ( NRF-2014M3A9D5A01073865 ), and the  Basic Science.
Survey of multitreatment uplift modeling approaches The MTUM literature is still limited. This study categorizes the different MTUM approaches according to the classification proposed by  Devriendt et al. (2018)  for binary uplift models. The authors distinguish two main methods to obtain uplift estimates: the data preprocessing approach and the data processing approach. The former learns an uplift model by means of conventional machine learning algorithms by redefining the original outcome variable or by modifying the input space before training. The data processing approach comprises methods wherein standard machine learning algorithms are trained separately, or their internal structures are adapted to the multitreatment case. Table  1  provides an overview of the modeling strategies that are surveyed in this study.
Data sets Customizing treatment allocation is a main concern among decision-makers in different domains. This study evaluates uplift models with respect to eight multidisciplinary data sets. Table  3  summarizes the most relevant information in relation to the data sets. Because some data sets are not specifically designed to estimate individual treatment effects, the treatment groups are formed according to the observed values of a specific decision variable (see  Rzepakowski and Jaroszewicz (2012) ). To assure that the uplift estimate is unbiased, we assess the balance of pretreatment characteristics among treatment groups before training. When imbalance is detected, we implement propensity score matching as proposed by  Guelman (2015). This technique is further discussed in the next subsection.
The two data sets slightly differ in the way that the predictor variable time is processed. For the Colon1 data set, this variable is split into two factors: 1) the number of days until the recurrence event and 2) the number of days until the death event. In the Colon2 data set, time refers only to the number of days until death, since there is no recurrence. -The AOD data set corresponds to alcohol and drug usage  (McCaffrey et al. 2013). In this subset of 600 observations, three treatment groups are identified: "community," "metcbt5" and "scy. " We assigned individuals within the former category to the control group. Given that the outcome variable is continuous, we apply binary encoding by assuming that a positive case is an individual whose substance use frequency declines by the 12th month after the treatment is applied.
An important observation is that only 5 out of the 23 original pretreatment variables are available in this subset. Therefore, information on demography, substance use, criminal activities, mental health function and environmental risk is mostly absent. -The Bank Marketing data set  (Moro et al. 2014 ) is publicly available in the UCI repository. This set contains information regarding a direct marketing campaign conducted by a commercial bank. To obtain a multitreatment set, the categorical variable "contact" is chosen as the decision variable to determine the different treatment groups. Depending on the type of contact communication, individuals are assigned to either the "cellular" group or "telephone" group. The "unknowns" are the control group. The outcome variable is positive if a customer decides to open a term deposit with the institution.
Implementation The prescribed experiments are implemented in R (R Core Team 2017) and Python  (Van Rossum and Drake Jr 1995). For the analysis of selection bias, the RItools package is used to check the imbalance in pretreatment characteristics among treatment groups. In the case of detecting any imbalance, the MatchIt package applies optimal matching based on the propensity scores. In R, the caret package includes the standard Logistic regression and the Random forest algorithms. Furthermore, in this programming language, the uplift package  (Guelman 2014)  incorporates the CKNN ( upliftKNN), the Uplift random forest (upliftRF) and the Uplift causal conditional inference forest (ccif). For the setup of the modified outcome techniques, the randomForest algorithm  (Liaw and Wiener 2002)  and the Multinomial log-linear model algorithm (multinom)  (Ripley and Venables 2011)  are chosen.
Identifying and correcting selection bias We perform a PSM preprocessing step in the case of detecting any imbalance in the pretreatment characteristics among the treatment groups. The purpose of this corrective action is to decrease the possibility of obtaining a biased uplift estimate. Considering that all data sets in this study consist of two treatments and a control group, balance is assessed in a pairwise fashion, as shown in Table  5. To verify whether there is at least one pretreatment variable for which the two groups are different, we compute a χ 2test that performs the omnibus test proposed by  Hansen and Bowers (2009). Table  5  illustrates the results of the balance assessments and indicates whether matching is performed. The resulted p-values of the initial chi-square tests do not provide evidence of imbalance among the pairs of treatment groups that are part of the Hillstrom, Gerber and AOD data sets.
Therefore, matching is not required. However, the test suggests that at least one of the pretreatment variables in the Bladder, Colon1, Colon2 and Turnover data sets is creating an imbalance between the treatment pairs. Given this result, we apply matching. The p-values of the postmatching chi-square test (i. e. , final p-value) indicate that the imbalance is considerably reduced. An important relevant remark is that the prior imbalances among the groups of the bank data set are not successfully corrected by the chosen matching strategy. Therefore, in this specific case, the uplift estimates can be biased, given the differences in the pretreatment characteristics of the individuals.
Assessing model performance: the Qini metric Table  6  reports the results of the benchmarking study for the Qini metric. We consider two scenarios in which the MTUM technique is used to target the full sample of test cases (Panel A) and the top 10 percent of individuals most likely to respond favorably to the treatments (Panel B). The CKNN algorithm is not implemented in the Hillstrom, Gerber and Bank data sets, given its operational inefficiency for large data sets. The Qini metrics of the best performing models are in bold, and the corresponding standard deviations are within brackets. Overall, in panel A, it is observed that none of the MTUM approaches evaluated in this study outperform the others. Most of the techniques perform well for some data sets, but poorly for others. Nonetheless, in five out of the eight data sets, our proposed approaches perform better than current methods.
Among the recent algorithms such as CTS, XLearner and RLearner, only the former slightly excels beyond the performance of our proposed approaches with respect to  the Colon2 data set. Moreover, the proposed approaches generally exhibit reduced variability among folds. Their predictions are more stable, and therefore more reliable. The Friedman test is applied to the results of Panel A to corroborate whether there are statistically significant differences among the performances of the different models. The estimated p-value for this test is 0. 24, which allows us to conclude that there is no proof of a statistically significant difference in performance among techniques. The Qini metric for the top 10 percent of targeted test cases indicates how well an MTUM technique prioritizes treatment allocation.