dataesr/scientific-paragraphs-categorization huggingface dataset:
This publication documents the latest information on the subject, global database on the adoption and distribution of biotech crops since the first year of commercialization in 1996, country situations and future prospects of the technology in the country and the world. ISAAA Briefs is the most cited reference in the field of modern agribiotechnology due to its credibility and accuracy. In 2016, global hectarage of biotech crops increased from 179. 7 million hectares to 185. 1 million hectares, a 3% increase equivalent to 5. 4 million hectares. Predictions made by James, C. (2015) that the slight decline in biotech crop area in 2015 due to the low global commodity price would immediately reverse once crop prices revert to higher levels was realized. It is noteworthy that fluctuations in biotech crop hectarage of this order (both increases and decreases) are influenced by several factors including global commodity price, demand for biofuels, need for livestock and poultry feeds, environmental stresses, disease/pest pressure, country policies, and consumer perception.
BRazil In   5 ). From 2003 to 2016, Brazil has approved 57 events for import for food, feed processing and cultivation including 33 maize events, 12 cotton events, 10 soybean events, one bean event and one eucalyptus. In 2016 alone, Brazil approved a maize event MON89034 x TC1507 x NK603 x DAS40278 for food, feed and cultivation (ISAAA GM Approval Database, 2016).
As an EU member state, Slovakia can only grow maize with the MON810 event which has been approved by the EU for all of its 28 member countries. Slovakia grows maize for grain and silage. Data on total plantings of maize in 2016 was not available when this Brief went to press, however, FAOSTAT (2016) reports a 216,816 total maize hectares harvested in 2014. There is thus, a huge potential for new insect resistant biotech maize with herbicide tolerance such as the various IR/HT stacked maize events. In addition, introduction of new biotech crops and traits that will limit the import of raw materials for feeds such as soybean would be beneficial to the livestock and poultry industries.
Number of GM events approved The number of GM events approved peaked in 2015 with only a few event approvals in 2016 (Figure  14 ). This could be an expected after effect of a large number of event approvals the previous year, while technology developers monitored approvals and adoption in other events approved, while events with more than one trait made up at least 43% (HT + PC, IR + DR, and HT + PQ) of the approved events. This trend will likely continue into the future since farmers demand more traits in an event, especially in maize. Maize still has the most number of events in the database. This is probably due to the number of single maize events which can be combined with other events to form the desired event.
GM approvals for 2016 The total food, feed and cultivation approvals for 2016 were 115, 87, and 49, respectively, for an over-all total of 251. These approvals are divided among 87 events from seven crops, and were granted by 16 countries (Table  37 ). The number of approvals for 2016 is lower than the previous two years (318 in 2015 and 302 in 2014). Argentina had the highest number of approvals which were mostly maize events. Since 2007, the number of stacked events approved has dominated the single events approved. In 2016, the stacked events approved made up 82. 6% of the total approved events. This is indicative of the increasing demand by farmers for events with more traits to further increase their profit. From 1992 to 2016, 40 countries have given 1,777 food approvals, 1,238 feed approvals and 813 cultivation approvals, scattered among 477 events from 29 crops (ISAAA GM Approval Database).
New developments and Products in the Pipeline According to Parisi et al (  2016 ), the number of GM events at the commercial cultivation, pre-commercial or regulatory stages has more than doubled between 2008 and 2014. The roster of new approvals recorded in the ISAAA GM Approval Database (Trends in GM Approval section) shows the predominance of insect resistant and herbicide tolerant traits, as well as gene stacking in the four major crops. Industry biotech crops in the pipeline published by CropLife International (2016) listed more stacking of the IR/HT traits and some few specialty products for soybean and canola. Examples of biotech crops in the pipeline in some public sector research institutes (Table  43 ) are also geared towards yield and biomass improvement, disease resistance and improved nutrition and product quality.
aCKNoWledGMeNtS ISAAA would like to acknowledge and extend its appreciation to the legion of colleagues, too numerous to name, who provided the data on adoption of commercialized biotech crops from the public and private sectors in industrial and developing countries. Without their collaboration, this publication would not be possible. Special accolades goes to  Dr. Clive James , founder and emeritus chair of ISAAA who has laid the foundation, authored the 20 annual reports (1996 to 2015) and institutionalized the ISAAA Brief, making it the most credible source of information on biotech crops in the last two decades. Very special thanks to  Dr. Paul S. Teng , Chair of  ISAAA's Board of Trustees  for his valuable support to this publication, and  Dr. Randy A. Hautea , ISAAA Global Coordinator and Director of the ISAAA SEAsiaCenter and his staff for always providing excellent and expeditious assistance in the preparation of the manuscript.
Special thanks to: Mr. Bhagirath Choudhary  for preparing four country chapters: India, Pakistan, Bangladesh and Myanmar;  Dr. Margaret Karembu  for contributing the chapter on Africa;  Dr. Wynand J. van der Walt  for preparing the South Africa chapter; ISAAA staff:  Clement Dionglay  for formatting all texts, tables, and figures, editing, and for overseeing the printing;  Ian Mari E. Reaño  for compiling data on GM crops regulatory approvals; and for the assistance of  Kristine Grace N. Tome ,  Eric John F. Azucena ,  Zabrina J. Bugnosen ,  Mario DC Generoso ,  Panfilo de Guzman , and  Domino del Prado. ISAAA takes full responsibility for the views expressed in this publication and for any errors of omission or misinterpretation.
In addition, any updates or validations of identified models that continued to meet the selection criteria were retained. Only full articles (ie, not conference abstracts) and articles published in English were considered eligible. Of those studies identified in the initial systematic review,  5  TS reviewed their summaries and referred to individual abstracts and full texts where required. Those with the potential to be included were reviewed by a second author (DCM/IT). In the extension to the systematic review, the search results were collated in the reference management software Endnote X7. After the removal of duplicates, the resultant publications' titles and abstracts were screened by TS, and those deemed to have the potential to contain eligible models were retained. These were then independently reviewed by two authors (TS and DCM/IT) to identify those papers containing eligible models, using the full text where available and required.
Validation cohorts We used two large, multicentre, population-based cohorts to validate the eligible colorectal cancer risk prediction models. The European Prospective Investigation into Cancer and Nutrition (EPIC) is a multicentre prospective cohort study comprising 521 324 participants aged 17 to 98 years at baseline (though predominantly 35-70 years) who were recruited between 1992 and 2000 across 23 centres in 10 European countries. 11 Participants were enrolled from a variety of sources and included blood donors, screening participants, health conscious individuals and the general population (more detailed information regarding the characteristics and eligibility criteria of the individual centres has been published elsewhere). 11 Baseline data on each participant's diet and lifestyle were generally collected through self-completed questionnaires with anthropometric measurements being recorded subsequently at a recruitment centre; however, there was variability between centres.
GI cancer were identified by a number of methods including cancer registries, health insurance records, pathology registries and active follow-up. 12 he UK Biobank is a multicentre prospective cohort of over 500 000 participants aged 40-69 years at baseline who were identified through National Health Services registers and recruited between 2006 and 2010, across 22 assessment centres in Great Britain. 13 14 Baseline data recorded at the assessment centres included information about diet and lifestyle along with anthropometric measures, obtained through a combination of a self-completed questionnaire, computer-assisted interview and physical measurement. 14 From an initial total of 502 639 participants, 475 629 were available for external validation after the exclusion of participants with cancers prevalent at recruitment (except non-melanoma skin cancers).
In the presence of other missing predictor data, where it was less restrictive on the resultant size of the validation population, we conducted complete case analysis. As several of the models evaluated were time-to-event models, imputation was not undertaken as it tends to produce biased estimates. 16 The number of participants contributing to the evaluation of each model thus differed depending on the extent of missing data in its constituent predictors. Participants aged between 40 and 70 years at recruitment were included in the validation of all models, unless the model was developed in an older population, in which case we matched the minimum age of the original population. The eligible and ineligible populations for each model were contrasted, by the cumulative incidence of the cancer(s) the model predicted, the age of the participants and the presence of five modifiable risk factors: body mass index (BMI),  17  smoking,  18  alcohol intake,  17 18  physical activity  17  and processed meat consumption.
variable included in the original model; ○, two variables amalgamated to create a new variable that was included in the model. The names of these variables were: α, oestrogen status; µ, oestrogen. Family history of cancer varied by both cancer site and degrees of relatedness between models. ■, available in a construction directly suitable for at least one model; □, variable required to be derived from other variable(s) in the dataset for all models that used it; na, not applicable as the variable is amalgamated in the model in which it is used. See online supplementary table 1 for further details. colorectal cancer screening or lower gastrointestinal endoscopy with or without identification of polyps; Veg, vegetables; Vit, multivitamin. Colorectal cancer risk model discrimination in the published literature, EPIC and the UK Biobank.
Statistics showed there were 500 million users with more than 100 million daily users on Weibo by the third quarter of 2015  [6]. These activities generate an enormous amount of users data (also referred "Big Data"  [7] ) based on human mobility. Despite some limitations on representing check-in behavior, e. g. , bias of gender, a low sampling frequency, and bias of location category, check-in data has the ability to uncover check-in behavior within a city. Compared to the aforementioned traditional methods, LBSN data are highly available and low cost. Moreover, this data contains rich information about geolocation  [8] , which can be used to study check-in behavior. Thus, geo-location data offers new dimensions toward studying check-in behaviors and helps to create new techniques and approaches to analyze LBSN data.
Moreover, it seems that LBSN data can be a supplement to than a substitute of traditional data sources for policy making  [9]. Therefore, LBSN data can be considered as a supplement while taking policy decision related to urban planning and public services by identifying the sentiment about a topic or community detection and user analysis for identification of the actors involved  [10] [11] [12] [13] [14] [15] [16]. In this research, we reconnoiter the reasonable prospect of using LBSN data as a novel perspective to observe individual level check-in behavior and intensity of check-ins during the period within a city. We will explore check-in behavior in 10 districts (Baoshan, Changning, Hongkou, Huangpu, Jingan, Minhang, Pudong New Area, Putuo, Xuhui, and Yangpu) of Shanghai, China, which are interconnected to the boundaries of the city center.
We discuss an empirical exploration using Weibo (launched by Sina Corporation on 14 August 2009) dataset, which is a dominant social media site in China. Since each Weibo account carries information about the gender of the user, we can differentiate between LBSN usage behavior by males and females. Furthermore, we consider LBSN data can be helpful to observe check-in frequencies during weekday and weekend. The rest of the paper is organized as follows. Section 2 overviews related works. Section 3 describes the study area and data set used in the current study. Section 4 presents the methodology. Section 5 presents the results and discussion for the experimental results performed on dataset. Finally, Section 6 concludes the paper and proposes some further research issues.
Related Work Studying people's behavior toward services has long been constrained to analyze traditional datasets due to enhanced capabilities of capturing, analyzing, and processing geo-location data, and the field of spatial analysis has blossomed  [17]. The origin of social networks lies in the early 1990s with simple communication mechanism to meet people over the internet, where people could exchange ideas. The term "social network site" (SNS) refers to web-based services. It gives people three significant capabilities: (1) to construct a public or semi-public profile, (2) to identify a list of other users with whom a connection shared, and (3) to view and track individual connections and those made by others within the system  [18]. When SNSs first emerged, they were only accessible through personal computers  [19].
Also, previous research  [38] [39] [40] [41]  on LBSNs has also studied user's check-in data to predict user's location and mobility patterns. While  [42] [43] [44]  studied the uses and patterns of LBSN and examined the factors that predict the use of LBSNs regarding check-in. For instance, mobile phone datasets have been used to understand the crowd and individual mobility patterns  [45] [46] [47]. However, mobile phone data sets are not the only choice to study human mobility pattern analysis. Many other data sources of big data are collected and used, especially including geo-tagged data. This variety of new data sources is so diverse that it ranges log files from smart devices and websites, social media data and geo-tagged audio, video, and graphics data  [48, 49]. Ye, et al. [50]  Proposed a novel definition of life pattern by presenting LP normal form to formalize the definition of individual life patterns and LP-Mine, an abstraction-and-mining framework to effectively retrieve life patterns from GPS data.
Many researchers  [51] [52] [53] [54] [55]  have concentrated on human mobility patterns, venue tagging, and check-in behavior toward using location-based social networks. Automatic venue tagging is one of the new concepts to observe spatial differences in many applications  [56, 57]. However, Gao and Liu  [58]  argued that when human mobility is integrated into an application that ranked locations based on a user's check-in history, temporal features were shown to be irrelevant. Ye, et al. [59]  explored socio-spatial properties among different LBSN platforms, in another study Ye, et al. [60]  analyzed check-in patterns of Foursquare users. A place to healthy relationships has been explored in  [61]  to expand opportunities for public health. Scellato, et al. [41]  Presented a broad study of the spatial properties of the social networks arising among users in online location-based services and analyzed large dataset aimed to observe the inconsistency of urban spaces.
Noulas, et al. [62]  Explored user participation and provided insight of the city by analyzing social media data from foursquare in Seoul city and specially observed venues. Yu, et al. [63]  Applied DBSCAN algorithm to observe Weibo locations in Shanghai and compared with k-means. Location based datasets have now been used in many studies for urbanization and its environmental effects  [64] , development and prediction [65-67], travel and activity patterns  [68, 69]  and emergency response [70-72] and urban sustainability  [73]. Hong  [74]  Highlighted the use of an LBSN data to observe the willingness of buyers to pay for various factors. Visit frequencies can represent opinions and the geographical preferences of the individuals for places and given different motivations. Liu, et al. [75]  Identified the factors that might cause the outbreak of Ebola and investigated the reaction by China, using big data analysis and explored differences in check-in behavior by gender.
Study Area and Data Source In China, finding open and dependable data that describe geo location-based gender segregation is very hard. The LBSN dataset we are using in the current study comes from Chinese microblog Weibo during January-March 2016. Shanghai, China (lying between 30 • 40 -31 • 53 N and 120 • 52 -122 • 12 E [80] ) is located on the eastern edge of the Yangtze River Delta  [81]. According to Gu, et al. [82]  in 2015, Shanghai had a total area of 8359 km 2 , with a gross domestic product of 366 billion USD. The disposable income per capita of Shanghai is 7333 USD, where the income per capita of urban residents is 7788 USD and the income per capita of rural residents is 3412 USD  [83]. As of 2015, the agricultural land area in Shanghai was 317,926 ha. The construction land area was 301,709.
Study Area and Data Source In China, finding open and dependable data that describe geo location-based gender segregation is very hard. The LBSN dataset we are using in the current study comes from Chinese microblog Weibo during January-March 2016. Shanghai, China (lying between 30°40′-31°53′ N and 120°52′-122°12′ E  [80] ) is located on the eastern edge of the Yangtze River Delta  [81]. According to Gu, et al. [82]  in 2015, Shanghai had a total area of 8359 km 2 , with a gross domestic product of 366 billion USD. The disposable income per capita of Shanghai is 7333 USD, where the income per capita of urban residents is 7788 USD and the income per capita of rural residents is 3412 USD  [83]. As of 2015, the agricultural land area in Shanghai was 317,926 ha. The construction land area was 301,709.
27 ha, and the unused land area was 193,564. 46 ha. Shanghai is considered to be the most populated and dense community in the world (by urban area inhabitants), and a significant international center for trade, trade, tourism and fashion with a population of around 24. 15 million people. In 2016, Shanghai is divided into 16 county-level divisions: 15 districts (Baoshan, Changning, Fengxian, Hongkou, Huangpu, Jiading, Jingan, Jinshan, Minhang, Pudong New Area, Putuo, Qingpu, Songjiang, Xuhui, and Yangpu) and 1 county (Chongming)  [84]. Seven of the districts (Changning, Hongkou, Huangpu, Jingan, Putuo, Xuhui, and Yangpu) are located in Puxi (literally Huangpu West). These seven districts are referred as downtown Shanghai or the city center  [85, 86] , as shown in Figure  1. In addition to the information available in Weibo dataset like user id, date, and time, we also have additional metadata like gender, geo-location (longitude and latitude), venue name, and category, but no personal information like the name is available.
Therefore, check-in data records the In addition to the information available in Weibo dataset like user id, date, and time, we also have additional metadata like gender, geo-location (longitude and latitude), venue name, and category, but no personal information like the name is available. Therefore, check-in data records the daily life patterns and user's behaviors towards the services, and it reflects the average person's day-to-day operations. Table  1  describes the necessary information about Shanghai dataset.
Methodology In this paper, we analyzed geo-location data that includes the user(s) ID, time, geo-coordinates (longitude and latitude), and the venue name and category. Figure  2  presents the process flow of data collection and check-in behavior analysis. Figure  3  presents a general framework for check-in frequency analytics. The frequency analytics methodology is divided into two stages: LBSN data collection and data analysis. The primary task of data collection phase is to download a large number of Weibo data in JavaScript Object Notation (JSON) format by using a python-based Weibo API as shown in Figure  2. However, in the data analysis stage, the critical task is to extract and analyze the feature of check-in data by considering location, time and gender. The analysis phase uses statistical and network analysis and data visualization to produce density maps and trends.
Let "C" be a set of historical check-in data i. e. , C = {c 1 ,……, c n } where c i = <x,y> is a geo-location of the check-in 1 < i< n, of individual "i" and on time "t", where "C" is referred as the data set used. Figure  3  presents a general framework for check-in frequency analytics. The frequency analytics methodology is divided into two stages: LBSN data collection and data analysis. The primary task of data collection phase is to download a large number of Weibo data in JavaScript Object Notation (JSON) format by using a python-based Weibo API as shown in Figure  2. However, in the data analysis stage, the critical task is to extract and analyze the feature of check-in data by considering location, time and gender. The analysis phase uses statistical and network analysis and data visualization to produce density maps and trends.
Let "C" be a set of historical check-in data i. e. , C = {c 1 ,. , c n } where c i = <x,y> is a geo-location of the check-in 1 < i < n, of individual "i" and on time "t", where "C" is referred as the data set used. f KD (c|C, h) = 1 n n ∑ i=1 K h c, c i (1) K h (c, c i ) = 1 2πh exp (- 1 2 (c -c i ) t -1 ∑ h (c -c i )) (2) where "c" refers to the location of check-in in training dataset "C" with bandwidth "h". It is assumed that the value of "h" is dependent on the resulting density estimate "f KD " which generates smooth density surface around "C" on data point "c i. " (1) 𝐾 ℎ (𝑐, 𝑐 𝑖 ) = 1 2𝜋ℎ exp(- 1 2 (𝑐 -𝑐 𝑖 ) 𝑡 ∑ (𝑐 -𝑐 𝑖 ) -1 ℎ ) (2) where "c" refers to the location of check-in in training dataset "C" with bandwidth "h". It is assumed that the value of "h" is dependent on the resulting density estimate "fKD" which generates smooth density surface around "C" on data point "c i.
Results and Discussion For our experiments, we utilized the Weibo check-in data set and used KDE to analyze the density of check-in data. The overall density of check-ins during January-March 2016 can be observed in Figure  4 , and it can be observed that the center of the city has a high density of check-ins, which is a normal behavior for a big city due to easy accessibility of transport (i. e. , subway) and living facilities (i. e. , food, entertainment). Moreover, the high density of check-ins can be observed near the district borders of Baoshan, Changning, Minhang, Putuo, and Pudong New Area as compared to the center of these district. Compared with the grid maps, kernel density estimation provides smooth distributions by eliminating the local noise to a certain degree by providing a non-parametric probability distribution with optimal bandwidth used to minimize the error.