dataesr/scientific-paragraphs-categorization huggingface dataset:
Responses are expressed as mean response percentage with 95% confidence intervals represented by error bars. Numbers of effect size comparisons are given as # of data points. rice and wheat were conducted under irrigated conditions, as SSNM had initial focus on favorable growing environments. However, our analysis shows that SSNM improved both grain yield and N use efficiency of the three crops under rainfed as well as irrigated conditions  (Haefele and Konboon, 2009; Banayo et al. , 2018). Grain yield gain and N use efficiency benefit with SSNM over FFP tended to be greater for transplanted than direct-seeded rice (Fig. 3 ). While only 12 studies evaluated SSNM under direct-seeded rice, our results suggest the need to fine tune SSNM recommendations for these systems, which are increasingly replacing transplanted rice in response to water and labor scarcity  (Cabangon et al.
Discussion Our study acknowledged the presence of publication bias and employed analytical tools to reduce the bias  (Borenstein et al. , 2009). We applied strict selection criteria for inclusion or exclusion of literature, which left us with a seemingly small number of studies (  61 ), but this allowed us to conduct a more consistent and objective meta-analysis, with close to 400 paired comparisons for grain yield. We also acknowledge that the dataset used in our study was geographically biased, with very few studies coming from Africa, highlighting the paucity of data in the region. Nonetheless, we know of no other agronomic intervention that has increased crop yield, profitability, and N use efficiency across three cereal crops and geographies in such a robust manner. This suggests that SSNMthrough field-specific tailoring of fertilizer applications -is a highly effective management strategy that maximizes positive outcomes, contributing to food security attainment with economic and environmental benefits.
irri. org/i n/od/rcm/, http://webapps. irri. org/in/br/cmrs/; 2017 to early 2021), whereas in West African countries (2014-2020) RiceAdvice (htt ps://www. riceadvice. info/en/) generated 100,000 recommendations. The high number of recommendations in the Philippines reflects stronger support by the Philippine government, with Rice Crop Manager recently becoming part of a country-wide agriculture digital advisory service (https://rcm. da. gov. ph/). Uptake of the SSNM recommendations in the Philippines has reached about 30%, but there are still a myriad of constraints that farmers face. In China, a Nutrient Expert software platform (http://www. nutriente xpert. cn/) of web and smartphone apps, including social media (WeChat), is being used to develop, validate and disseminate SSNM solutions for 23 different crops, including grain crops, cash crops, vegetables, and fruits.
Germline variant calling and filtering Exome sequencing data analysis was performed using the pipeline we developed for this project. Briefly, BWA (v. 0. 5. 9) and the Genome Analysis Toolkit (GATK)  18  were used to align the sequenced reads to the reference genome (UCSC hg19) and to perform local realignment of reads around small insertions and deletions (indels). PCR duplicate reads were marked using Picard (https://github. com/broadinstitute/picard). The coverage of consensus coding sequence (CCDS) bases was calculated by GATK, resulting in a mean coverage of 141. 3X (ranging from 26. 9X to 250. 6X) and 97. 3% of CCDS bases were covered by at least 10 reads. Germline variants were called individually for each sample using HaplotypeCaller from GATK and annotated by wAnnovar  19. From the generated list of germline variants, variants were filtered out if they fulfilled any one of the following quality criteria: (i) genomic position of variant covered by <5 reads; (ii) <2 reads support the alternative variant; (iii); allelic fraction between < 0.
Somatic Copy Number Analysis and LOH detection With GATK CNA, read coverage counts across the target were collected. We created a separate copy number variation (CNV) PoN to reduce read coverage data noise to obtain denoising copy ratios against the PoN. Then we collected counts of reference versus alternate alleles allelic imbalance. We used GISTIC  26  to assess recurrent focal amplifications and deletions in tumors. We used FACETS  27  and Sequenza 28 to infer the integer copy number of each pair of alleles as well as LOH. Hemizygous deletions were defined as the loss of one allele in the tumor. Homozygous deletions were defined as the loss of both alleles in the tumor. Amplification was defined as a copy number status ≥ 6 per tumor for ERBB2, MYC, and ZNF703.
Candidate variant selection algorithm A list of candidate pathogenic variants was generated using our variant detection algorithm (Supplementary Fig. 5 ). ClinVar annotations were used to classify variants already reported, and when no ClinVar data was available, we used the ACMG criteria. Variants classified as likely benign or benign by ClinVar were filtered out. The master list of variants was comprised of truncating, missense, or splice-site alterations found in gene candidates. For non BSCG, missense and splice-site variants predicted to be deleterious by at least 4 of 8 bioinformatic prediction tools (SIFT, PolyPhen2, MutationTaster, FATHM, Provean, MetaSVM, MetaLR, and CADD) were considered for further analysis. For known BSCG genes, we considered all variants regardless of scores as these can be evaluated by expertise.
ACKNOWLEDGEMENTS This study, including the acquisition of archived samples, was funded by a  Quebec Breast Cancer Foundation (QBCF)  grant to W. D. F. and P. T. Tumor banking was supported in part by the  Banque de tissus et de données of the Réseau de recherche sur le cancer of the Fonds de recherche du Québec Santé (FRQS)  affiliated with the  Canadian Tumor Repository Network (CTRNet). Y. T. is supported by  Israel Science Foundation  grant. A. O. is supported by  CCSRI   Innovation Grant  and  Canada Research Chair Tier 2 in Genome Stability and Hematological Malignancies. P. P. is supported by the  Jimmy V foundation scholar award.
CODE AVAILABILITY The bioinformatics analyses were performed using open-source software, including Burrows-Wheeler alignment tool (v. 0. 5. 9), GATK (v. 4. 1. 6. 0), samtools (v. 1. 10), bcftools (v. 1. 10), htslib (v. 1. 10), Picard (v. 2. 24. 2), vcf2maf (v. 1. 6. 17), R (v. 4. 0. 3), CNV_facets (v. 0. 16. 0), Maftools (v. 0. 9. 30), FishHook (v. 1. 0), Bioconductor (v. 3. 12), MutationalPatterns (v. 3. 0. 1), deconstructSigs (v. 1. 0), SigMA (v. 1. 0), Sequenza (v. 3. 0. 0) and ScarHRD (v. 1. 0).
DATA AVAILABILITY The data generated and analyzed during this study are described in the following data record: https://doi. org/10. 6084/m9. figshare. 14802954  39. Tumor and germline datasets generated during the current study are not publicly available as the consent provided by study participants did not include a provision for widespread disclosure. However, direct requests can be made to the corresponding authors for access. The following files are openly available as part of the figshare data record: 'Master_list_variant. xlsx' (underlying Table  1 ), 'Genes assessed initially. xlsx' (underlying Supplementary Table  1 ), 'Candidate Genes list. xlsx' (underlying Supplementary Table  2 ). All other data are housed on institutional storage and are not openly available in order to protect patient privacy as informed consent to share participantlevel data was not obtained prior to or during data collection.
Objective: The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals. Methods: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users.
Data Extraction and Preprocessing The IDs from a total of 1,499,421 tweets, without retweets, from March 11, 2020, through January 31, 2021, were obtained using the data set maintained by Georgia State University's Panacea Lab  [22]. These tweets were collected by the Panacea Lab using the following 13 keywords: COVID19, CoronavirusPandemic, COVID-19, 2019nCoV, CoronaOutbreak, coronavirus, WuhanVirus, covid19, coronaviruspandemic, covid-19, 2019ncov, coronaoutbreak, and wuhanvirus. The Panacea Lab can only provide the tweet IDs  [23] , which need to be hydrated to recover the full tweet data. During the tokenizing stage, we used the gsub function in R (R Foundation for Statistical Computing) to extract the tweets whose language field in the tweets' metadata was specified as English. All text mining was performed using RStudio Version 1.
4. 1103 on a Mac computer (Apple Inc) running Big Sur, version 11. 2. 2. We converted the text of all the tweets to lowercase. We further filtered the tweets by the following keywords: vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. We prepared 2 batches of tweets, 1 for text mining and 1 for sentiment/emotion analysis. The data processing procedures for the 2 batches are almost the same except at the beginning: for sentiment analysis, we converted all the emojis to words, whereas for text mining, we removed all the emojis. Next, we created a script to remove the URLs, mentioned names, non-American Standard Code for Information Interchange (ASCII) characters, and anything other than English letters or spaces (eg, "1" and "?"). Using the R package dplyr, version 1.
Furthermore, we used the tweetornot package [24], version 0. 1. 0, to remove users that were identified as bots with a 95% probability or higher. The final cleaned data set consisted of 1,499,421 unique tweets from 583,499 different users. We further cleaned the tweets by removing words and characters that were of little or no analytical value (eg, "the", "very", "&"). We performed this task by creating our own list of stop words by appending the 13 keywords related to COVID-19 and the 7 keywords related to vaccine to the English stop words list from the R package tidytext, version 0. 2. 6; this step was performed because we already knew that every tweet would contain one or more of those keywords, and retaining them in the tweets would not further our understanding of the main content of the tweets.
We performed the LDA algorithm on the data using the R textmineR package, version 3. 0. 4. The LDA algorithm requires manual input of the number of expected topics. We ran the LDA algorithm on the data by varying the topic number from 2 through 40. For each topic number, we calculated the coherence score using the textmineR package. We chose 16 as the topic number for our final topic model, based on two considerations: first, the topic number 16 corresponded to the highest coherence score (see Multimedia Appendix 1); second, in comparison with topics that appeared in the other topic models, the topic model with 16 topics strikes a balance between one too narrow that the model would risk excluding important topics and one so broad that it would dilute the main focus. The top 8 terms from each of the 16 topics were generated.
We also used the geo_freqpoly function in the R package ggplot2, version 3. 3. 2, to generate the frequency polygons (see Figure  2 ) to visualize the weekly frequency of the 16 topics from March 11, 2020, to January 31, 2021. For each tweet, the LDA assigned a probability to each of the 16 topics. We assigned the topic with the highest probability to each tweet, and we grouped the tweets according to the most prevalent topics. To obtain representative tweets for each topic, we randomly sampled 100 tweets from each topic; two of the authors then independently examined the sampled tweets, followed by a group discussion to select the most representative ones. If one of the authors thought that no conspicuous topics had emerged from the first 100 sampled tweets, another 100 tweets would be sampled and further reviewed; the authors continued this process until they judged that there was a clear common topic and they reached a consensus (see our previous paper  [27]  for more details).
Sentiment and Emotion Analyses Sentiment analysis can be used to classify the polarity of a given document; it can assign a score to a document to indicate whether the expressed opinion is positive, negative, or neutral. Emotion analysis goes beyond simple polarity and can give scores to different emotions, such as anger, fear, anticipation, trust, surprise, sadness, joy, and disgust (the so-called Plutchik wheel of emotions)  [28]. The syuzhet package (Jockers, 2017)  [29]  is the most popular and efficient R package for sentiment/emotion analysis. To perform the emotion analysis, we used the National Research Council of Canada Emotion Lexicon developed by Turney and Mohammad in 2010  [30]. It is the most comprehensive dictionary for this task  [31]. In Figure  3 , we show the weekly average polarity (sentiment) scores from March 11, 2020, to January 31, 2021; we fitted the data points with the best linear fit and obtained a slope of 0.
Numbers of Tweets We downloaded 144,332,894 tweets posted from March 11, 2020, through January 31, 2021 (for a total of 327 days) using the Panacea Lab database. After cleaning, a total of 1,499,421 tweets from 583,499 different users were included in the analysis. As shown in Figure  5 , the number of daily tweets continually increased; the daily average for the month of January 2021 was 22,202 tweets. Before November 9, 2020, the number of daily tweets was around or below 5000, with only one exception, on August 11, 2020 (n=7486), when Russia approved the world's first COVID-19 vaccine  [32]. The first wave of an exponential increase in the number of daily tweets started on November 9, 2020 (n=12,720), when Pfizer stated that its vaccine is 90% effective  [33]. The second wave of a surge in the number of daily tweets started around January 3, 2021, when more COVID-19 variant cases were confirmed in North America by the CDC, and the highest number of tweets in a single day (n=31,197) occurred on January 29, 2021, when Johnson & Johnson's and Novavax's vaccines showed 85% and 89.
Process Modeling with Papyrus Papyrus is a modular tool based on Eclipse IDE that includes a modeling module by default, as well as an execution engine called "Moka". It has a graphical editing tool that allows the editing of all types of UML 2. 0 diagrams. It provides an integrating UML profile mechanism that allows extensions definition to basic standards for better modeling flexibility. The UML profiles are the mechanisms specific to the UML 2. 0 standard that allow the extension of the metaclasses related to the existing metamodels. By doing this, the UML profiles can be adapted to any context. Papyrus also has a UML model execution plugin: Moka, an execution engine compatible with the OMG foundational subset for executable UML models (fUML) standard  [41]. The objective of this standard is to serve as an intermediary between "surface" models (used to describe business processes) and "platform" languages (can be executed on machines).
Industrial Simulation Using JaamSim JaamSim is a free Java-based and open-source simulation package developed by an Australian Engineering company named Ausenco. The main feature that differentiates JaamSim from other off-the-shelf commercial DES (discrete event simulators) is that users can design and make their own high-level object palettes  [40]. Users can use the GUI of JaamSim to add entities and create the simulation model or write/edit a configuration file (. cfg) in which all entities/objects can be added and configured. Some users may prefer the graphical user interface (GUI) to drag and drop their entities and configure them on the graphical user interface (GUI). Others, especially programmers, will find it faster and easier to create or modify the configuration file (. cfg). This software is used in our research work, because of its transparency, reliability, and recent adaptation to the HLA standard.
JaamSim was not originally designed for communications to external systems and is not suitable for DS; it is considered to be a black-box simulator. This contribution uses an extended version of JaamSim  [42]  which opens it to distributed simulation via the HLA standard. In  [42] , the author proposes to edit the source code of the tool in order to make it communicate with different instances of JaamSim via the HLA standard. We will use this proposal to make JaamSim, as Papyrus, collaborate with external systems.
Papyrus Extension for Coupling to External Components Distributed components are designed to gravitate around the Papyrus tool: the simulation orchestrator component. Its role is to coordinate the different tools to obtain a single and homogeneous platform. Its modeling tool (extensible through UML profiles) contains the global business simulation scenario referring to related functions. Its Moka execution engine (also extensible) interprets the scenario and interacts with connected tools in Papyrus. Each of the technologies exploited by this contribution (risk management and HLA) is handled by a Papyrus software layer (UML profile + Moka extension), which is stacked to build the overall proposal illustrated in Figure  4 ). Each element of the UML diagram is linked to its associated visitor.
Papyrus Extension for Coupling to External Components Distributed components are designed to gravitate around the Papyrus tool: the simulation orchestrator component. Its role is to coordinate the different tools to obtain a single and homogeneous platform. Its modeling tool (extensible through UML profiles) contains the global business simulation scenario referring to related functions. Its Moka execution engine (also extensible) interprets the scenario and interacts with connected tools in Papyrus. Each of the technologies exploited by this contribution (risk management and HLA) is handled by a Papyrus software layer (UML profile + Moka extension), which is stacked to build the overall proposal illustrated in Figure  4 ).
Risk-Management Layer The risk-management layer consists of a UML profile added to the papyrus UML modeling tool, and an extension of the Moka runtime allowing a customized behavior. We emphasize that the function of a UML profile is to semantically customize a basic language. Here, we add the notion of a unique identifier ("ID" in Figure  5 ) to each task (UML Action in Figure  5 ) of the general model. The risk-management layer consists of a UML profile added to the papyrus UML modeling tool, and an extension of the Moka runtime allowing a customized behavior. We emphasize that the function of a UML profile is to semantically customize a basic language. Here, we add the notion of a unique identifier ("ID" in Figure  5 ) to each task (UML Action in Figure  5 ) of the general model. Industrial risk information is stored in a remote database; thus, the link between database and Papyrus is done through the definition of a new Moka extension.
The Eclipse EMF Generator provides the basis for the implementation of a Papyrus extension: a set of classes and methods for implementing an additional advice to the visitor of each task extended by the UML "dbActionFailure" profile. When the model is executed, the Moka engine identifies a running task and performs a time-stamped query in the risk database. Upon receipt of this query, the risk-management tool identifies the impact of the current risks as a function of the simulated time and returns to Papyrus the value of the temporal impact on the simulation.
FMI-Environmental Risks Layer The implementation of communications between Papyrus and the distributed FMU components is established through an additional Papyrus software layer. To ensure communication between the Papyrus tool and the simulation environment provided by the FMI standard, we are adding three components to the system: First, a UML profile (superimposed on the previous one) allowing a user to define a link between a task in the Papyrus diagram and an FMU component. The "Path" parameter in Figure  6  contains the path to the FMU file to be executed; When performing a task extended by the described profile, the Moka engine makes a time advance request (time value defined by the configuration file) to the FMU indicated by the "Path" field in Figure  6. Upon receipt of this request, the FMU advances its own time, and returns values to the extension, which are recorded in the FMU configuration file.
Risk-Management Tool The consequence of a risk is to cause the degradation of the duration of one or several tasks. It is necessary that this disruption acts on the durations during the simulation runtime. In order to have the most flexible tool possible, the user defines each of the risks present in the simulation in an external database. Therefore, there is a risk-modeling phase to be undertaken in parallel with the process modeling. The risks can be identified in different ways depending on the context. A risk can be used in two different ways and can have two different objectives: It can be an item referenced by a unique identifier that can have an impact on the duration of one or more tasks of a Papyrus process. This impact can be a constant, the result of an equation, or based on a semirandom factor; It can also be an element aggravating the impact of another risk in the simulation.
 The delivery of raw materials for the solar field, and construction steps (raw materials, GC1, GC2, GC3) are tasks simulated by Papyrus and disrupted by the industrial risk-management database;  The delivery of a mobile factory is simulated by Papyrus and disrupted by the environmental risk-management tool. This external component is a system composed of different states performing meteorological queries in order to disrupt the progress of a task. In our case, this is the first time that a Papyrus task uses the FMU. It is therefore in its initial state: queries on the humidity rate (precipitation) of a geographical area for a time described in the configuration file of the simulation. The next task of the orchestration model concerns the deployment of the power plant. Here, Papyrus once again relocates the complexity of the simulation through a JaamSim model impacted by industrial risks.
Conclusions and Perspectives The Papyrus tool is an efficient way to model and simulate an industrial process. We have shown in this article that the extension of Papyrus can be untied to other methods and technologies. The legacy effect that Papyrus extensions have between them allows us to cumulate contributions one after the other. Thus, we can add modeling and simulation components without impacting the basic model. In this project, we assembled three Papyrus extensions into a single tool by first connecting a database on industrial risks to Papyrus, so that this information can impact the simulation results. We also open the tool to an external real-time communication standard (FMI 2. 0). We demonstrated the use of a component capable of extracting data from the environment (through the FMU making queries to a weather web service).