dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sources In order to produce a reliable assessment of the crop type mapping system, 12 sites spread all over the world were used. The list includes 4 sites in African countries where the stakes of food security are highest: Morocco, Madagascar, Burkina Faso and South Africa. To give an answer to the global agricultural variability, also sites in Asia (China, Pakistan), Europe (France, Belgium, Ukraine, Russia), North America (USA) and South America (Argentina) were included. A large variety of climate types and agricultural practices are therefore represented. For all of these sites, satellite imagery (Section 2. 1. 1) and in situ data (Section 2. 1. 2) were available.
In this case, the blue, green, red, red-edge and NIR bands were used. Although these images were provided with 5-m resolution, they were resampled to 20 m using the SPOT4 sampling grid as a reference. The quantity and the quality of these data were not homogeneous across sites, depending mainly on the observed cloud cover. We briefly describe each dataset below. Geographical coordinates of the center of each site, as well as their sizes are given. The dataset provides good coverage with a good image every week, except for the beginning of the cycle (December/January). • Ukraine (30. 008751E, 50. 060014N, 3593 km 2 ), moderate: April, May and June are covered, which corresponds to the end of the winter crops and the beginning of the summer crops. From December to March, there is no image due to clouds.
In Situ Data Added to the satellite imagery, crop type in situ data were made available by site managers (most of the sites belong to the JECAM network). Except for Belgium and the USA (where official Land Cover databases were available for 2013), the cropland data were obtained by field observations during the main growing season of the year 2013. Non-cropland classes were also requested in order to have a complete dataset allowing a proper validation of the cropland products. These last data were obtained either from field observations or from institutional data. The amount of data and the number of crop classes in the nomenclature varies from site to site. The choice of the final nomenclature for each site was made to ensure that the main crops of every site were included. The main crop types are defined as those covering a minimum area of 5% of the annual cropland and for which the cumulative area reaches more than 75% of the annual cropland in the region.
This exploratory phase was aimed at evaluating a high number of combinations of the processing steps listed above and at defining the appropriate ranges for the values of the parameters of the different algorithms. For this exploratory phase, only 2 test sites were used. France and Morocco were selected, as they offered the best combination of available images and in situ data. In terms of algorithms, only the feature extraction and classification steps were studied. The classifiers used were: support vector machines (SVM  [39] ), decision trees (DT  [40] ), gradient boosted trees ( GBT  [41] ) and random forests (RF  [42] ). The implementation of the selected algorithms we used is the one available in the Orfeo Toolbox  [43]  free software library. Using metrics, such as the κ coefficient, the overall accuracy  [44]  and the F-score  [45]  (see Section 2.
Final List of Strategies The result of the exploratory phase allowed narrowing down the list of supervised classifiers to 2, validating the use of the linear gap-filling of the time series and choosing the input image features to be used. The full benchmark on the 12 sites could therefore focus on the comparison of the 2 selected classifiers and the impact of the smoothing and the temporal resampling described above. The fusion of the output of the 2 selected classifiers was also evaluated. The objective of this evaluation was to assess the complementarity of the results of the 2 classifiers. The fusion approach used was the Dempster-Shafer rule  [48]  using the confusion matrices (see Section 2. 2. 3) for the estimation of the masses of belief  [49]. The final list of 5 strategies to be benchmarked on the 12 test sites was:  The data preparation consists of the following steps: 1.
France The main crops covering 75% of the crop surface according to the FAO statistics are wheat, maize, barley, rapeseed and sunflower, which coincide with the main crops of the in situ data. The individual results (see Figure  4 ) of the main crops are satisfactory, except for barley. This class is also under-represented in the in situ data, and the confusion of this class with wheat is high. This is a classic confusion, as both crops have nearly identical phenological cycles and spectral responses. Tables  3  and 4  show the confusion matrices for RF and SVM, respectively. The classification produced by the RF classifier based on field surveys made by our teams was also compared to the RPG (Registre Parcellaire Graphique) database, which corresponds to the farmers' declaration of their cultivated areas.
This comparison allowed evaluating the results in areas far from the in situ data location. For this comparison, the nomenclature of the RPG (with more classes than the one of the benchmarking) was used. Figures  5  and 6  show the results of the crop type map with the RPG polygons overlayed. The image of Figure  5  corresponds to an area located about 40 km north of the area where the in situ data were collected. The image of Figure  6  corresponds to an area with a higher altitude close to the Pyrenees mountain range. A confusion matrix was also calculated using RPG polygons as a reference dataset. All of the comparable classes have F-score values higher than 0. 7 excluding barley, which has the lowest value (0. 48). The OA was 0. 8.
Ukraine The main crops according to the FAO statistics (wheat, sunflower, maize, barley and potatoes) are all present in the in situ dataset. However, potatoes and barley represent less than 2% of the total cropland surface. As soybean is a main crop within the five main crops of the in situ dataset, it was included in the final legend (maize, soybean, wheat, sunflower). Figure  7  presents the results for the two classifiers. >From February to March, the site is covered by snow, and during the summer season, after June, the cloud coverage is high, which results in only a good coverage of the end of the winter crops and the start of the summer crops. Some explanations of the previous results can be found by analyzing the detailed confusion matrices (Tables  5  and 6  for RF and SVM, respectively).
Samples of winter wheat are indeed correctly classified, but confusion with other classes (spring wheat, spring barley, other cereals) is present. Maize is also well classified, but other summer crops (soybeans, sunflower) are also classified as this class. All of the main crops according to the FAO statistics (sorghum, cow peas, millet and maize) are represented in the in situ data; however, the millet class represented less than 1. 5% of the reference cropland area, and it was not included in the final legend. On the other hand, cotton was included, since it amounted to 38% of the in situ dataset. Figure  8  summarizes the results for the two classifiers. Tables  7  and 8  show the confusion matrices for RF and SVM, respectively. Only Landsat 8 images were used in this site, which was not among the SPOT4 (Take5) sites, and the available dates barely cover the growing season going from June to October.
Acknowledgments This study has been conducted in the frame of the Sentinel-2 for Agriculture Project funded by the  Data User Element of the European Space Agency (ESA). The Take5 initiative (SPOT4 and RapidEye) was sponsored by  CNES  (the  French Space Agency)  and  ESA. The authors would like to thank the JECAM site managers who made the in situ data available:  Diego de Abelleyra (Argentina), Aline Léonard, Damien Jacques (Belgium) ,  Agnes Begué  ( Burkina Faso , Madagascar),  Wu Bingfang ,  Miao Zhang ,  Zheng Yang  (China),  Claire Marais-Sicre ,  Jean-François Dejoux ,  Valérie Demarez  (France),  Michel Lepage  (Morocco),  Ijaz Ahmad Bhutta (Pakistan) ,  Igor Savin  (Russia),  Terry Newby  ( South Africa ),  Nataliia Kussul ,  Andrii Shelestov  (Ukraine) and  Andrew French (USA).
• Belgium (4. 953034E, 50. 635297N, 3829 km 2 ), moderate: Due to high cloudiness, the image dataset is not very rich, but has at least one acceptable image per month during the summer crop season (from March to September), covering also the end of the winter crop season. • Burkina Faso (3. 669765W, 11. 257590N, 6734 km 2 ), bad: Very few images in the growing period. • China (116. 503414E, 36. 817027N, 3847 km 2 ), moderate: The dataset has a high presence of aerosols in February and March. April and May are well covered, as well as the beginning of June. The end of winter crops will be well covered, and it was possible to work (not in the best conditions) with the summer crops. • France (0. 570392E, 43. 628161N, 16,903 km 2 ), good: This dataset covers the end of the winter crops and the complete summer crop cycles with more than one image per month, lacking the starting of the winter crops.
• Madagascar (46. 987573E, 19. 641356S, 14,402 km 2 ), moderate/bad: Images were taken almost weekly for the middle end of the growing season (from the end of February to the end of April). However, many them have clouds for the eastern half of the image, where the validation data are located. • Morocco (7. 858731W, 31. 476627N, 14,068 km 2 ), very good: There was very good coverage from January to June (one image approximately every 5 days). • Pakistan (72. 557226E, 31. 477121N, 1199 km 2 ), bad: The dataset has at least one non-cloudy Landsat in June, one in July, one in September and one in August (June and July with some aerosols).
Quantification was done using ImageJ (National Institutes of Health, Bethesda, MD, USA) software by comparing Alexa Fluor 555-staining density of nuclei overexpressing JMJ14ΔFYR-YFP-HA to that of the local neighboring wildtype nuclei. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http:// creativecommons. org/licenses/by-nc-sa/4. 0/ Shuaibin Zhang et al. Cell Discovery | www. nature.
Acknowledgements We thank the  Arabidopsis Biological Resource Center  for providing T-DNA insertion lines,  Dr David Baulcombe  for JAP plants. This work was supported by the  National Basic Research Program of China  (grant nos. 2011CB915401  to  X Cao  and  2013CB967300  to  Xia Cui ), the  National Natural Science Foundation of China  (grant nos. 31271363  to  X Cui  and  31330020  to  X Cao ),  Genetically Modified Breeding Major Projects  (grant no. 2014ZX08010-002  to  X Cao ), the  State Key Laboratory of Plant Genomics , and the post-doctoral fellowship to (BZ and  Xiekui Cui ). Work in the Jacobsen laboratory was supported by  National Institutes of Health  grant  GM60398. SEJ is an investigator of the  Howard Hughes Medical Institute.
TaqMan Probes and Primer Design Multiple sequence alignments of the T. cruzi SL-IR, cytochrome oxidase subunit II (COII), 18S ribosomal DNA (18S rDNA) and 24Sα-rDNA genes were performed using the ClustalW algorithm in MEGA 5. 2 software  [43]. Reference sequences were retrieved from the GenBank database. The PrimerQuest and OligoAnalyzer tools (provided online at the website http://www. idtdna. com) were used for the final design of specific primers and probes (Table  2 ). To minimize nonspecific detection, the oligonucleotides were compared with all relevant sequences using the BLAST database search program (provided online from the National Center for Biotechnology Information [NCBI]). cycling condition for the 24Sα-III/IV reaction was an initial cycle of 10 min at 95°C followed by 40 cycles at 95°C for 30 sec and 57°C for 1 min in a Rotor-Gene 6000 (Corbett, UK) device.
Acknowledgments We are grateful to  Paula Marcet Atlanta, USA) ,  Arturo Muñoz-Calderón  ( Instituto de Medicina Tropical ,  Universidad Central de Venezuela, Caracas ,  Amaia Izeta ,  Carlos Ibarra-Cerdeña  and  Eduardo Rebollar Tellez  ( Centro Regional de Investigación en Salud Pública, Chiapas, México ),  Hugo Ruiz Piña (Centro de Investigaciones Regionales "Dr. Hideyo Noguchi", Universidad Autónoma de Yucatán, Mexico) ,  Gustavo Enriquez ,  Julián Alvarado-Otegui  and  Marta Victoria Cardinal  ( Universidad de Buenos Aires, Buenos Aires, Argentina ),  Rodrigo Gurgel-Gonçalves  ( Universidade de Brasilia, Brasilia DF, Brazil ) and  Mercedes Gómez Samblas  ( Instituto de Biotecnología, grupo de Bioquímica y Parasitología Molecular Universidad de Granada, Granada, Spain ) for providing aliquots from their collections of DNA samples from different geographical regions.
Acknowledgements The ALICE collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector:  State Committee of Science, World Federation of Scientists (WFS)  and  Swiss Fonds Kidagan ,  Armenia ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European Community's  Seventh Framework Programme ;  Helsinki Institute of Physics  and the  Academy of Finland ;  French CNRS-IN2P3 , the  'Region Pays de Loire', 'Region Alsace', 'Region Auvergne'  and  CEA, France ;  German BMBF  and the  Helmholtz Association ;  General Secretariat for Research and Technology, Ministry of Development, Greece ;  Hungarian OTKA  and  National Office for Research and Technology (NKTH) ;  Department of Atomic Energy and Department of Science and Technology of the Government of India ;  Istituto Nazionale di Fisica Nucleare (INFN)  and  Centro Fermi -Museo Storico della Fisica e Centro Studi e Ricerche "Enrico Fermi", Italy ;  MEXT   Grant-in-Aid for Specially Promoted Research, Japan ;  Joint Institute for Nuclear Research, Dubna ;  National Research Foundation of Korea (NRF) ;  CONACYT, DGAPA, México, ALFA-EC  and the  EPLANET Program  ( European Particle Physics Latin American Network) Stichting voor Fundamenteel Onderzoek der Materie (FOM)  and the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Netherlands ;  Research Council of Norway (NFR) ;  Polish Ministry of Science and Higher Education; National Authority for Scientific Research -NASR  ( Autoritatea Nat ¸ionalȃ pentru Cercetare S ¸tiint ¸ificȃ -ANCS) ;  Ministry of Education and Science of Russian Federation, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and The Russian Foundation for Basic Research; Ministry of Education of Slovakia ;  Department of Science and Technology, South Africa ;  CIEMAT ,  EELA, Ministerio de Economía y Competitividad (MINECO) of Spain, Xunta de Galicia  ( Consellería de Educación ),  CEADEN, Cubaenergía, Cuba , and  IAEA (International Atomic Energy Agency );  Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW) ;  Ukraine Ministry of Education and Science; United Kingdom Science and Technology Facilities Council (STFC) ; The  United States Department of Energy , the  United States National Science Foundation , the  State of Texas , and the  State of Ohio.
, 2010)  was performed using custom Arabidopsis 28K microarrays (GenBank Gene Expression Omnibus [GEO] Platform ID no. GPL15543). Differentially expressed transcripts were identified using a false discovery rate (FDR) , 0. 05 and a module of logged fold change (FC) on base 2 (|log 2 FC|) $ 1 (GEO accession no. GSE66980). With this filtering criteria, the transcript levels of 16 genes increased, and those of 4 genes decreased in a statistically significant way in rosette leaves of both transgenic lines compared with the wild type (Table  I ). Gene Ontology functional categories of these genes were enriched in response to stress and defense against other organisms (P , 0. 001). Notably, onethird of the genes significantly induced in both 35S:AnPGII lines compared with the wild type encoded proteins putatively involved in cell wall structure and/or ROS homeostasis, including EXTENSIN4 (EXT4; At1g76930), a pectin esterase (At4g02330), copper/zinc superoxide dis-mutase1 (CSD1; At1g08830), and a CIII Prx (At5g64120).
4B ). Lines 22 and 24, which showed higher expression of the transgene (Fig. 4A ), exhibited a marked reduction of rosette size (Fig. 4B ) and a significant reduction of rosette fresh weight (Fig. 4C ). These lines also accumulated very high levels of guaiacol Prx activity in their leaves (Fig. 4D ) compared with untransformed plants. Taken together, these data indicate that high levels of AtPRX71 expression have a negative impact on plant growth. It has been proposed that CIII Prxs can restrict cell expansion by the formation of cell wall cross links  (Passardi et al. , 2004 (Passardi et al. , , 2005)). We therefore hypothesized that the effects of AtPRX71 on Arabidopsis growth may depend on its ability to influence cell size. To verify this hypothesis, the area of the epidermal cells of rosette leaves was measured in wild-type, atprx71-1, atprx71-2, and 35S:AtPRX71 plants.
Gene Expression Analysis Leaves or seedlings were frozen in liquid nitrogen and homogenized with an MM301 Ball Mill (Retsch) for about 2 min at 30 Hz. Total RNA was extracted with Isol-RNA Lysis Reagent (59-Prime) according to the manufacturer's instructions. Full-genome transcriptome analysis was performed on custom Arabidopsis 28K v2. 0 Microarrays (GenBank GEO Platform ID no. GPL15543) synthesized using a CombiMatrix B3 Synthesizer (Combimatrix) according to manufacturer's instructions. Total RNA (1 mg) was amplified, and 6 mg of antisense RNA was labeled using the RNA AmpULSe Amplification and Labeling Kit with Cy5 for CombiMatrix Arrays (Kreatech Biotechnology) according to the manufacturer's instructions. Labeled samples were hybridized to the microarrays according to CombiMatrix protocols.
Scanning was performed on a GenePix 4000B Scanner (Molecular Devices). Data extraction was done using Microarray Imager software (Combimatrix), and quantile normalization of data was performed using Blist v0. 6 software (Combimatrix). Differentially expressed transcripts were identified using Limma R package using an FDR , 0. 05 and a module of logged FC on base 2 |log 2 FC| $ 1  (Smyth, 2004). Expression data are available from the National Center for Biotechnology Information (GEO accession no. GSE66980). Gene Ontology categories enrichment of differentially expressed genes was determined with the aid of the AgriGO Toolkit  (Du et al. , 2010). For reverse transcription PCR analysis, RNA was treated with RQ1 DNase (Promega), and first-strand complementary DNA was obtained using Improm II Reverse Transcriptase (Promega).
Materials and Methods Setting: Three teaching hospitals located in each of Northern, Eastern and Southern Taiwan. A list of all TB patients notified to health authorities from 2005-2010 who were managed by these three hospitals was obtained from the national TB registry at Taiwan CDC. Objectives: to investigate whether the influence of DM on clinical manifestations of pulmonary TB is related to glycemic control and whether the impact of chronic hyperglycemia on outcome of TB treatment is mediated through diabetes-related comorbidities. Patient material and definitions: Notification data and patients' medical records were reviewed to identify culture positive pulmonary TB patients. Patients who 1) were treated with insulin or diabetes-specific hypoglycemic agents, 2) had been assigned a diabetes-related International Classification of Diseases 9th Revision (ICD-9) code during admission, 3) had been assigned a diabetes-related ICD-9 code 2 or more times on an outpatient visit, or 4) had a history of diabetes, were considered as probably having diabetes and were assessed.
Patients who were found to have transient hyperglycemia at the initiation of anti-TB treatment were excluded. A non-diabetic culture positive pulmonary TB patient who had never been documented to have 1) glycated haemoglobin A1C (HbA1C) ≧6. 5%, or 2) fasting plasma glucose≧126 mg/dl, or 3) post-prandial plasma glucose≧200 mg/dl, or 4) random plasma glucose≧200 mg/dl and was notified to health authority immediately prior to each probable diabetic TB patient was identified as a comparison. Culture positive TB patients who started anti-TB treatment within 3 months of sputum collection were included in this study. Study procedures: Data were collected from medical charts using a structured questionnaire. Data collected included age, sex, pretreatment smear (negative vs positive, and positivity grade, scanty, 1+, 2+, 3+ 4+), type of TB case (new vs retreatment), smoking status (ever vs never), comorbidities, culture at 2 months, and outcome of treatment.
Data entry and analysis To ensure accuracy of data entry, the data set was double entered and validated using EpiData Entry 3. 1 (EpiData Association, Odense, Denmark). Discrepant records were checked and corrected against the original data on the questionnaires. STATA Version 12 (StataCorp LP, College Station, Texas, USA) was used for statistical analysis. Categorical data were analyzed by Pearson Chi-square test. Logistic regression models were constructed for outcome variables with 2 categories (symptoms, pretreatment smear, 2-month culture, and treatment outcome) and multinomial logistic regression for that with 3 categories or more (smear positivity grades). In treatment outcome analysis, outcome was further dichotomized into successful (treatment success) and unfavorable (died, failed, loss-to-follow-up).
38, 95% confidence interval (CI) 1. 52-3. 73), cough (adjOR 1. 35, 95% CI 1. 01-1. 84), hemoptysis (adjOR 1. 85, 95% CI 1. 26-2. 72), tiredness (adjOR 1. 70, 95% CI 1. 14-2. 52), and weight loss (adjOR 1. 75, 95% CI 1. 37-2. 24) as compared with non-diabetic patients. The presence of symptoms in diabetes was influenced by HbA1C. Fig. 2  shows that the proportions of patients with any symptom, cough, hemoptysis, tiredness and weight loss were all highest in diabetic patients with HbA1C>9% as compared with other groups. Diabetic patients with HbA1C>9% were significantly more likely to have any symptom, hemoptysis, tiredness and weight loss in multivariate logistic regression models adjusted for age, sex, type of case and smoking as compared with non-diabetic patients (data not shown). Of the 1,473 culture positive TB patients, 862 (58.