dataesr/scientific-paragraphs-categorization huggingface dataset:
Just to mentioned few of them, e. g. , the site survey approach is not suitable for Big Data technology because it is time-consuming, labour-intensive, and only samples smallscale local data/information. On the other hand, the SAGIN approach has several limitations, e. g. , very high deployment and operational cost. In addition, it does not allow further Weimin Ding and Lei Shu are corresponding authors improvements in terms of the flexibility and scalability of deployed devices, which is clearly a bottleneck and highly desirable in the context of Agriculture. Thus, this work provides a platform that evaluates and proposes new data collection approaches to overcome the aforementioned limitations. Mobile Crowd Sensing (MCS) is a technique where a large number of individuals having mobile devices, e.
Currently, with the rapid development of the economy, even in underdeveloped countries, the proportion of owning smartphones is almost or at least one in every farmer's family  [30]. Moreover, farmers who equip with smart devices will obtain better agricultural data due to participating in the whole agricultural production process. Motivated by the above reasons, Motivated by the above reasons, we hope to utilize MCS to cover the existing agricultural data collection system shortage, i. e. , SAGIN. In our previous study  [31] , we just proposed the concept of AMCS and analyzed three benefits, i. e. , cheaper cost, superior scalability, and balance data granularity and excellent flexibility, of using MCS technology to obtain agricultural data through comparing with SAGIN. However, our previous work needs to improve further due to the lack of considering the challenges caused by integrating MCS with agriculture and the limitations of AMCS.
B. Existing data collection method 1) Space-Air-Ground Integrated Network (SAGIN) The rapidly developed sensing technologies, e. g. , remote sensing and wireless sensing, have significantly enriched agricultural information acquisition methods towards a good data collection system -SAGIN. It is used to gain the growth information of crops and improve the accuracy of farmland management, e. g. , irrigation, fertilization, and seeding. SAGIN mainly consists of three parts: • In the space, Remote Sensing Satellites (RSSs) are used to gather agricultural environment information by taking images, in which 3S technology, i. e. , Remote Sensing (RS), Geography Information Systems (GIS), and Global Positioning System (GPS), is adopted to acquire data. • In the air, Unmanned Aerial Vehicles (UAVs), including other vehicles, e.
However, the limitations of AMCSs in data integrity and quality still should be noticed. Thus, AMCS can become an integral approach of acquiring agricultural data for further improving the efficiency of data collection in different agricultural application scenarios rather than wholly replacing the SAGIN system. Besides, comparing with CSs, AMCSs have two advantages: 1) more flexible due to select participants based on location and 2) more convenient participation mode due to use their mobile phones of users to join in sensing tasks. For example, in  [39] , the crowdsourcer, who is the institution that requests the data, recruited farmers offline artificially for specific crowdsourcing tasks and provided smartphones for the participates. However, AMCS can choose appropriate participants based on real-time location and actual landform for performing multiple tasks.
E. Cooperative sensing with IAM Compared to traditional machinery in agriculture, Intelligent Agricultural Machinery (IAM) can increase the operational efficiency of production by installing a variety of wireless sensors, e. g. , flow sensors for precision sowing and precision fertilization. Based on these wireless sensors, a lot of meaningful data, e. g. , fertilization amount and seeding amount, can be collected, assisting farmers in judging the operational quality of IAM. Specifically, farmers can utilize the computing power of smartphones to visualize the received data collected by wireless sensors. Simultaneously, the collected data will be a historical record and upload to the server for storage. Thus, this application displays the cooperative sensing of IAM and AMCS.
Performance Evaluation For evaluating the proposed rate adaptive algorithm, the network simulator NS-3 has been used to perform the experiments. Table  3. defines the simulation parameters used for experimental work. Figure  4  shows the network topology implemented in this work for the single-user scenario. The used topology consists of three nodes, i. e. , server, router, and client nodes. The server is an Apach HTTP server running on Ubuntu 14. 04 LTS, while the client is implemented in python and runs on a Window 10 with Core i7 2. 6 GHz CPU and 8GB RAM. DASH server contains the Big Buck Bunny video sequence with framerate of 30fps and a resolution of 1280x720. Each video is divided into small video sequence of 2s and 4s. The client node downloads video segments from the server over the bottleneck link, whose bandwidth is shown in Figure  5.
One simple integration example (Another example of integration and reproduction of a model, 2021) involving two popular languages, python and MATLAB, demonstrates the problem of transition between languages. There is no real translation between languages. No general compiler exists between multiple languages and human efforts are required. Fortunately, there are standardization efforts among languages. The standardization problem is not new and was considered by modelers a long time ago, resulting in the Systems Biology Markup Language (SBML) (Systems Biology Markup Language, 2021) that is a very helpful format that can help transport models between systems. SBML has a track record of success and allows transporting models between hundreds of systems. However, despite its popularity, it is not an official standard and the community decided not to go in that direction (Recent SDO/ COMBINE legal entity issues, 2021).
Models Are Hard to Locate Many times model location is a difficult task since models are published in different sources. Despite many repositories available there are many ways models are published including journal papers, conferences, preprint services such as BioArxiv, web sites, and code repositories such as GitHub. In some good cases, there are model archive/linking web sites such as BioModels (BioModels, 2021), SimTK (SimTK, 2021), IMAGWiki (IMAGWiki, 2021), and in the future modeleXchange  (Malik-Sheriff et al. , 2020). However, currently there is no one aggregator that helps locate all models and many times community members cannot agree on location and attempt to create more repositories rather than centralize efforts. Moreover, simulation workflows are even harder to find. For example, BioModels primarily focuses on models.
Lack of Common Platforms for Executing Models and Simulations Even if models can be located, their simulation is a different issue. Due to the existence of many partially supported standardization efforts in this field, it is often difficult to know what tool needs to be used with which model. It can also be difficult to find that tool, download it, install it, learn it, and to use it, especially for large simulations. These issues keep modelers in silos. Even formats associated with standardization efforts have difficulties. It is not possible, for example, to load a MATLAB/ SimBiology SBML L2V4 model into COPASI and someone adhering to latest standards implementing L3V2 SBML support for import will find difficulties in importing models. Moreover, it is difficult to find a common platform that supports all SBML versions.
Those examples are relatively simple integrations and in more complex integrations the adaptation effort is more significant and many more obstacles exist. One obstacle is lack of standards for describing composite models and software tools for merging models. One specification is SBML-comp, but it is cumbersome and few tools support it. Another tool is SemGen  (Neal et al. , 2019a) , but it focuses on finding mappings between similar models. To the point here, SBML-comp is designed to compose models that were not intended to be composed. Instead, composition needs to be deeply ingrained into the entire community so that models are anticipating the needs of composition from the beginning. Note that adaptation towards standardization also requires matching terminology, and especially matching of units of measure, as well as proper documentation which we will address in the next topics.
Unit Standardization Unfortunately, units of measure are not yet standardized, an open problem despite many attempts to resolve it by multiple standardization bodies such as IEEE, CDCIC, and NIST. One indication of the severity of the problem is that a Github search for "unit conversion" shows over a thousand results. Another good example of the severity of the problem is ClincialTrials. Gov that aggregates quantitative data from around the world and this database shows over 24 K different units of measure  (Barhak and Schertz, 2019). One attempt at solving this standardization issue using machine learning is ClinicalUnitMapping. com, yet this project requires more effort. Unit mismatches become particularly problematic when trying to integrate models across different spatial or time scales.
Data Availability and Measurement Definitions When attempting to integrate models, the phenomenon being reproduced by the models or the data they are based on might not be the same. This is especially true when model definitions evolve or can change in many ways. Examples include International Classification of disease (ICD) codes (Wikipedia, 2021a) that went through multiple versions through the years, or even a disease definition that has evolved for sepsis  (Gary et al. , 2021). Even outcomes of clinical trials change if counted using different definitions as seen in (clinicaltrials. gov/NCT00379769 -GlaxoSmithKline, 2017). Those definitions can hinder connecting different models together. Possible solutions are machine learning techniques that can transfer interpretation or modeling techniques that merge human interpretation from multiple experts into the modeling process  (Barhak, 2020a).
More recently, non-infectious virus particle concentrations are being measured using PCR. In this technique, the number of segments of a particular piece of RNA are measured. While this unit is more tangible and consistent than the infectious viral titer units, viral kinetics models often consider only infectious virions. Although noninfectious viruses are starting to be incorporated into models, the relationship between infectious and non-infectious virions changes over the course of an infection  (Petrie et al. , 2013) , making it difficult to use these measurements to get at the underlying infectious virus dynamics. New measurement techniques and strategies for more direct measurement of infectious virions are being developed  (Cresta et al. , 2021). Data availability to rationalize calibration and validation of models is crucial but often not possible because of data sharing policy and privacy (especially for individual human data).
, 2020; Wang et al. , 2020). Additionally, viral loads were measured via nasal swab, though it is not clear that the viral load in the nose is correlated to the viral load in the respiratory tract, which is the infection location simulated in the models. Other viruses can also infect internal organs that are difficult to access for frequent measurements without invasive procedures. Immune responses are often measured using levels in the blood, which is typically not the site of infection and often not the location of viral load measurements either. Since models are attempting to replicate virus and immune dynamics at the site of infection, these data collection limitations make it difficult to collect the data needed to accurately parameterize such models for humans. A further methodological issue with some of the available in vitro and in vivo experimental data is that it often does not represent the infection time course in a single individual or single experimental well.
Missing Annotations in Models In biosimulation models, documentation about the intended semantics of the model is captured by annotations -additional information that describes the model, and the biological entities included in that model. Further, these annotations can leverage the rich resources of bio-ontologies-consistent nomenclatures and terminologies that describe the biological world in great detail. COMBINE has recognized these challenges for understandability and reuse of models and is working hard to disseminate best practices around semantic annotation. COMBINE consensus around annotation is described in  (Neal et al. , 2019b). This paper describes some key tenants for improved semantic annotation: First, these annotations should be written using a standard format, and one that is independent of modeling languages.
Thus, COMBINE recommends RDF as a simple triplebased representation to connect model elements to annotations and knowledge resources (e. g. , ontologies). Next, COMBINE recommends that annotations should be stored externally from the source code of the model. Obviously, the annotations should be linked to elements within the model source code, but in order to be language independent, they should be stored separately. Finally, COMBINE recommends that modelers and model building communities provide policies and rationale for choosing which knowledge resources to use for which types of annotations. Otherwise, the same biological entity may look different if different modelers annotate the entity against different bioontologies. Annotations can be useful for multiple tasks such as: annotation of semantic meaning where biology or real-world relevance is explained, annotation of provenance where the origins of the model and its creators are referenced, and annotation of verification indicating tests the model should undertake and pass.
Methods Our discrete-time and discrete-space simulation model was written in NetLogo  (Wilensky, 1999)  version 6. 2. 0. We interacted with the model and designed our experiments using the nlrx R package  (Salecker et al. , 2019). We provide below a summarised description of the model, before presenting the simulation experiments we ran using it. A detailed description of the simulation model using the ODD protocol  (Grimm et al. , 2010; Grimm et al. , 2020)  can found in Supplementary Material S1, and a copy is attached with the model and analysis code (Data availability).
Results for 𝒓 𝟎 = 𝐥𝐨𝐠(𝟓) For both velocity and neutral genetic diversity, the best statistical models were the ones including information about both  0 and density-dependence (Fig. 2 ). For velocities,  2 was highest when we used traits after evolution to predict velocities, while for neutral diversity it was highest when we used traits measured before expansion (Fig. 2 ). Based on the best model, expansion velocity increased with  0 (Fig. 3 ), and for a given  0 , expansions with positive density-dependent dispersal were consistently faster (Fig. 3 ). Increased mortality costs decreased velocity overall and reduced the effect of  0 and density-dependence on velocity, but did not change the overall direction of the effect. Predictions are based on the best model Fig. 2 ; predictions for the effect of  0 are made assuming no density-dependence ( -0 = 0), and predictions for the effect of  -0 are made setting  0 to its average value in the simulated dataset.
Data availability Netlogo model code and R scripts to reproduce all analyses presented in this manuscript are available on Github (https://github. com/mdahirel/pushed-pulled-2020-heritability-IBM) and archived in Zenodo (https://doi. org/10. 5281/zenodo. 5830993). Simulation outputs are also archived in Zenodo (https://doi. org/10. 5281/zenodo. 5830996).
The first oral sirolimus administration as a compassionate treatment in refractory LMs was reported in 2008 in a 9-month-old boy  23  with multiorgan failure due to Proteus syndrome. Since then, sirolimus has been applied in different vascular anomalies  13, 24  , with 2 RCTs of LMs currently under way (NCT00975819, Adams, United States [active] and NCT02509468, Maruani, France [completed])  25. However, despite an overall good safety profile (absent or mild side effects in 2 of 3 patients with vascular anomalies, including children), significant dermatologic (oral mucositis), hepatic (elevated liver enzymes, hepatitis) or hematologic side effects may occur. Grade III or IV drug-induced cytopenia is reported in up to 27% of treated patients, with some severe infectious complications also being reported  13, 24.
Data collection methods LMLMs are a rare skin disease for which neither evaluation guidelines nor purposely developed evaluation instruments exist. The PGA scale is a tool widely used in dermatology. It represents a global evaluation of disease severity. We use a 6-point PGA scale: 0, clear; 1, almost clear; 2, mild; 3, moderate; 4, severe; 5, very severe. The PGA scale is widely used in psoriasis but is also used in a wide array of skin diseases  38, 39  and as an easily obtainable global evaluation of disease severity is an interesting tool. The DLQI and cDLQI are widely used instruments to assess health-related quality of life in dermatology. The cDLQI is validated for children from age 4 years up to 16 years  36. Study staff with their own access right to the study database will enter/capture data from source documents corresponding to a participant into the protocol-specific electronic Case Report Form (eCRF).
Data management Data management will be performed by the INSERM CIC-P 1415. An eCRF will be developed by using the Ennov Clinical software. eCRF management will be performed in agreement with the INSERM CIC-P 1415 Standardized Operating Procedures (SOP). The Clinical Research Associate in charge of the study will be trained in the eCRF and will be in charge of the investigator training. Data will be entered in investigating centers via a secure web site, monitored by Clinical Research Associates and potential queries will be edited by data managers, in agreement with a prespecified data management plan. Data will be reviewed before the database is locked. The database will be locked in agreement with the INSERM CIC-P 1415 SOPs, and data will be extracted in a SAS or other format according to statistical requirements.
Statistical methods A detailed analysis plan will be a priori defined. SAS 9. 4 and R 3. 3. 1 (or latest versions) will be used for analysis. The level of statistical significance will be set at 5%. The primary outcome (PGA) will be treated as a continuous variable. For analysis, we will use the model described by Hooper et al  36. Analysis will rely on a mixed linear regression model with a random effect for participant. For each participant, we will include the 7 PGA measurements from W0 to W24 (assessed every 4 weeks). The model will be as follows, denoting   as the PGA for individual i= 1,. , n in sequence j=1,…, T at time t=0,…, T after randomization: 𝑌 𝑖𝑗𝑡 = 𝛽 + 𝜏 𝑡 + 𝐴 𝑗𝑡 + 𝜂 𝑖𝑗 + 𝜀 𝑖𝑗𝑡 where   ~(0,   2 ) is the participant random effect;   ~  (0,   2 ) is the random error; and   and   are all independent, and,.
Harms All serious and non-serious events will be reported on the adverse event reporting form (initial or follow-up declaration), as thoroughly as possible, within the regulatory time limits for reporting. All adverse events will be monitored until they are completely resolved. The investigator will immediately notify the sponsor of any serious adverse event. SUSARs will be reported to Eudravigilance (European pharmacovigilance database), the French Health Authorities (ANSM), and the investigators.
Similarly, the stepped-wedge design allows every patient to benefit from the experimental treatment regimen. Indeed, although safety data in the literature is reassuring, notably the very low sirolimus systemic passage, data on the exact setting of topical application in LMLMs are lacking. Safety procedures were then designed for a "worst-case scenario" of a total sirolimus systemic diffusion, which would amount to an oral 0. 5-to 1-mg per day oral sirolimus regimen. We also gathered a DSMB in that respect. In the end, if the TOPGUN trial demonstrates a relevant clinical benefit and a good safety record, it will be supported by good-quality evidence, and could pave the way for a rapid change in first-line clinical practices in LMLMs.
French law in force (Code de Santé Publique), the recommendations of French and international Good Clinical Practices, the Helsinki Declaration (Ethical Principles for Medical Research involving Human Subjects), and the European regulations related to clinical research. The study will be conducted in accordance with this protocol. With the exclusion of emergency situations requiring specific therapeutic actions, the investigators undertake to observe the protocol in all respects. This research is registered in the European EudraCT database in accordance with article L1121. 15 of the French Public Health Act.
Acknowledgements We are indebted to  Mr Wiebe de Jong  from the  Délégation à la Recherche Clinique et à l'Innovation, CHRU Tours, 37000, Tours, France ,  Mrs Michèle Carriot  and  Frédérique Musset ,  Clinical Investigation Center, INSERM 1415, CHRU Tours, 37000, Tours, France  and  Mrs Emiliène A. Edee , from the  Department of Dermatology and Reference Center for Rare Diseases and Vascular Malformations (MAGEC) , We also deeply thank  Dr Alice Phan  ( Department of Dermatology, Hospital University Centre of Lyon, France ),  Dr Claire Abasq  ( Department of Dermatology, Hospital University Centre of Nantes, France  and  Prof Mahtab Samimi  ( Department Of Dermatology, Hospital University Centre of Tours, France ) for participating in the adjudication committee for the primary outcome and  Dr Bénédicte Lebrun-Vignes , Pharmacologist,  Department of Pharmacology (AP-HP, Hospital Pitié-Salpêtrière, France ),  Dr Catherine Droitcourt , Dermatologist,  Department Of Dermatology, (University Hospital  of.
French law in force (Code de Santé Publique), the recommendations of French and international Good Clinical Practices, the Helsinki Declaration (Ethical Principles for Medical Research involving Human Subjects), and the European regulations related to clinical research. The study will be conducted in accordance with this protocol. With the exclusion of emergency situations requiring specific therapeutic actions, the investigators undertake to observe the protocol in all respects. This research is registered in the European EudraCT database in accordance with article L1121. 15 of the French Public Health Act. This protocol has been granted both ANSM (French Regulatory Authority) and CPP (Comité de Protection des Personnes, French Ethical Review Committee) approval (19. 03. 28. 46025).
We present a complete comparison of semi-inclusive ν μ -12 C cross section measurements by T2K and MINERνA collaborations with the predictions from the SuSAv2-MEC model implemented in the neutrinonucleus event generator GENIE and an unfactorized approach based on the relativistic distorted wave impulse approximation. Results, that include cross sections as function of the final muon and proton kinematics and correlations between both, show that the agreement with data obtained by the relativistic distorted wave impulse approximation approach, that accounts for final-state interactions, matches or improves GENIE-SuSAv2 predictions for very forward angles where scaling violations are relevant.