dataesr/scientific-paragraphs-categorization huggingface dataset:
Contacts during 24-month home study period Participants will have identical planned contact visits during the two study arms. Participants/parents will be contacted by email/telephone within 1 week after initiation of the respective study arm. Thereafter, participants will be followed up at 3-monthly intervals. These study visits can take place at the hospital clinic, home or other suitable location. The purpose of these visits is to record any adverse events, device deficiencies, changes in insulin doses, other medical conditions and/or medications. An HbA1c sample will be taken and data from study devices downloaded. At each follow-up visit, participants of both study arms will be fitted with a masked CGM sensor. The sensor will be worn at home for up to 14 days. If the sensor fails or sensor function is interrupted prematurely, another sensor may be applied.
Interviews An integrated qualitative substudy will explore parents' and youth's views about using CL systems; the impact of using CL systems on diabetes management practices and everyday family life; parents' and youth's information and support needs when using CL systems; and their views about how the technology could be improved for future users. In-depth interviews will be undertaken at 12 months with a subset of up to 20 youth and 20 parents in the CL arm. Purposive sampling will be used to ensure diversity in terms of youth's age and gender, parents' occupation/ education and family forms. Where possible, parents and youth from the same family will be interviewed. Participants will be interviewed separately unless a joint interview is requested. Recruitment to the interview study will continue until data saturation is achieved.
PAtIEnt And PublIC InvolvEMEnt The research question and study endpoints are based on feedback from participants of previous studies and in line with prioritisation by stakeholders. Study design and assessment of the burden of the intervention were reviewed by focus groups. Results will be disseminated to participants and general public through social media and will be made available on the sponsor's website. stAtIstICAl AnAlysIs All analyses will be conducted on an intention-to-treat basis. Data from all randomised participants with/ without protocol violation including dropouts and withdrawals will be included in the analysis. The statistical analysis plan can be found in the online supplementary information.
safety analysis The following events will be compared between treatment groups: ► Number of subjects with severe hypoglycaemic events. ► Number of episodes of severe hypoglycaemic events per subject and incidence rate/100-person years ► Number of subjects with Diabetic Ketoacidosis (DKA) events. ► Number of episodes of DKA events per subject and incidence rate/100-person years. ► Number of any other adverse events reported per subject. ► Number of any other severe adverse events reported per subject. Safety data will be tabulated for all subjects in the two intervention periods, including dropouts and withdrawals, irrespective of whether sensor glucose data are available and irrespective of whether CL was operational. For the binary safety outcomes, a repeated measures logistic regression model will be used to compare treatment arms.
The World Health Organization conditionally recommended VOT as an alternative to DOT in 2017, but the evidence was graded as weak because only few randomized controlled trials had been published  [14, 15]. In the VOT method, the patients either connect via a computer or a smartphone to health care workers who will observe them swallowing the medication in real time  [16, 17]  or alternatively, the patients record videos of themselves taking the pills and then send the videos to the health care workers who can view these videos at their convenience  [18]. We developed a comprehensive software for the management of patients with TB by using video monitoring through mobile phones, thereby combining the advantages and the convenience of the VOT method mentioned above. The patients visit their health posts weekly to collect their medications and send videos of themselves taking their pills daily in real time to the health care workers who can review these videos at their convenience.
Study Design and Participants We assessed the effectiveness of the comprehensive TB management system that was implemented in September 2017 as the routine management procedure in the Center for Chronic Disease Control (CCDC) of the Nanshan district of Shenzhen, China. Patients beginning treatment for TB between September 2017 and August 2018 were enrolled into the VOT group, and their data were compared with the retrospective data collected from patients who began TB treatment between January 2016 and August 2017 and were managed with routine DOT. All health care workers involved in the management of patients with TB in the Nanshan District participated in the study, and they had managed patients on DOT before VOT was implemented. The inclusion criteria were as follows: (1) patients with pulmonary drug-sensitive TB who were treated and managed in the Nanshan CCDC; (2) patients who received a regimen containing only oral medication with fixed-dose combination (FDC) pills at an outpatient facility; and (3) patients who voluntarily provided informed consent to allow their data to be used in research studies.
Comprehensive TB Management System The software for the comprehensive TB management system was independently developed by the Nanshan CCDC. It consists of an app loaded onto the mobile phones of both patients and the medical personnel and a web-based monitoring platform. This app can be used in smartphones with Android or iPhone operating systems, but it is currently available only in Mandarin (Figure  1 ). This app has multiple functions, including an easy interface to report the side effects, reminders for scheduled medical appointments during treatment, and quantity management of the medicines for the health posts. The patients in the VOT group received training for the following modules in the app: setting up an account, logging into the account, setting the time for their medication reminder, recording and uploading a video, reporting side effects, and communicating with a health care worker.
Data Collection Responding to questionnaires before and after treatment is the standard procedure for all patients with TB. These questionnaires were handwritten by the patients in the DOT group and electronically completed by the patients in the VOT group. The questionnaire consisted of questions on the sociodemographic variables. After the patients completed the treatment, they were asked to assess their acceptance and satisfaction of their treatment management as well as their incurred costs. The data on compliance and clinical results were collected from handwritten records of the patients on DOT and from the comprehensive TB management system records for the patients on VOT. All patient information was made irreversibly anonymous. After the implementation of the project, we asked 66 health care workers to complete an electronic questionnaire on their attitudes toward patient management with DOT and VOT.
Acknowledgments We thank the physicians, the supervisors who helped to collect the data, and all the 66 health care workers who participated in this study. We are grateful for the cooperation and invaluable feedback provided by each of the participating health departments. This study was funded by the  National Science and Technology Major Project of China  ( 2018ZX10715004-002 ),  Medical Scientific Research Foundation of the Guangdong Province of China  ( C2018044 ),  Natural Science Foundation of the Guangdong Province of China  ( 2018A030313123 ), the  Sanming Project of Medicine in Shenzhen  ( SZSM201603029 ), and the  Scientific and Technology Research of the Bureau of Science and Technology Innovation of Nanshan  (2018054, 2018061, 2018076).
Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows.
We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline. the targets is made possible using precise geolocation information from Global Navigation Satellite Systems (GNSS), and orientation data from an Inertial Measurement Unit (IMU). When combined, the positional accuracy of the derived ALS data sets provide sub-meter measurement accuracy of surfaces, providing best-available data describing the threedimensional structure of landscapes. As a result, ALS has been widely used for the generation of bare Earth terrain models (Furze et al. ,.
Airborne Laser Scanning (ALS) Airborne laser scanning (ALS), also known as LiDAR (Light Detection and Ranging) technology has revolutionised data acquisition and resource quantification in natural sciences and engineering. ALS is an active remote sensing technology, that uses laser pulses to measure the time, and intensity, of backscatter from three-dimensional targets on the Earth's surface  (Wulder et al. , 2008). Retrieval of the position of pulses to pass through small openings in forest canopies facilitate the direct measurement of a number of key forest attributes such as tree height or canopy cover. Laser energy returned to the sensor can be recorded as either a series of discrete xyz locations (the most common data storage format), or a fully digitised return waveform  (Mallet and Bretar, 2009).
To date, the majority of ALS providers globally provide data in discrete return format largely due to established processing streams. Development of methods for processing full waveform data are ongoing and not yet considered to be conventional practice. In forestry, two well-established methods have been developed for deriving forest attributes: individual tree segmentation (ITS) and the area-based approach (ABA). ITS allow both individual tree tops to be located, and tree crowns delineated  (Hyyppä and Inkinen, 1999; Jakubowski et al. , 2013) , followed by derivation of individual tree attributes within each delineated crown. This method depends on the accuracy of tree identification and can be prone to errors that result from over-or under-estimation of tree crown dimensions  (White et al.
Historical software development for ALS processing The widespread interest and adoption of ALS-based technologies in the forestry arena over the past decade have generated a need for visualisation and processing software and scripts. Forest inventory data are often stored within geodatabases linked to aerial photographic interpretation (API) information represented by polygonal topology. As a result, many forest industry professionals and managers are accustomed to a vector-based analysis framework for data manipulation and analysis. The advent of ALS-based datasets in the early 2000s facilitated the need for software solutions to display and analyse three dimensional point cloud data. Initially, software solutions were developed in-house, by university research groups or data providers, with more professional solutions following as the user market grew.
FUSION also allows integrated large-scale batch processing of ALS datasets, allowing for simple computation of wall-to-wall metrics, which at the time was cumbersome and could not be easily completed in commercial GIS or image processing environments without extensive pre-processing. As the user-base of ALS forestry applications grew, so did the requirements for software solutions. A number of commercial companies started to develop ALS-specific software. Today, many GIS and image processing tools have graphical user interfaces and the ability to process ALS data with add-ons available for ESRI software, Whitebox, and image processing stand-alone software such as ENVI. A number of open source tools exist that allow users to capitalise on the complexity of ALS datasets. CloudCompare is an example of a commonly used open source software suite that allows ALS data to be analysed, manipulated, and merged.
Among commercial software packages, LAStools is also commonly used for various large-scale ALS processing tasks. LAStools has been specifically designed for processing ALS data, from plots to large landscapes, and is free to use on small datasets, while commercial solutions unlock advanced capabilities for in-depth processing and broad area implementation. A number of modules available within LAStools are open source, including the compressed LAZ format that reduces storage size of original uncompressed files  (Isenburg, 2013). Table  1  shows a selected set of active free and open source software currently capable of processing LiDAR point clouds and that include useful tools for forestry and ecology applications.
The move towards free and open-source software The current software landscape for ALS data processing, in particular for forestry applications, contains a mix of solutions. In some cases, tools are open-source and cross-platform (e. g. PDAL). In other cases, software are free to use, but the source code itself is not publicly available and use is restricted to the Microsoft Windows platform (e. g A set of open source software tools for processing laser scanning data (i. e. , LiDAR), including data captured from airborne and terrestrial platforms. Bunting et al. (2011 Bunting et al. ( , 2013) )  FUSION, LAStools). Some solutions propose free and open-source components on multiple platforms, but also contain closed source functionalities (e. g. LAStools). There are also a number of closedsource, single-platform commercial solutions requiring a paid subscription or licence.
Common to all closed source solutions is the use of specific algorithms or workflows for ALS that are essentially black boxes to the users. For example, it is not possible to know what is used internally to segment ground points in software like TerraScan. While it can be guessed that it is based on the Progressive TIN Densification (PTD)  (Axelsson, 2000) , this is not documented nor verifiable. Similarly, the LAStools documentation states that "a variation of the PTD" is used without providing further details of the variant. If a closed-source classification routine misclassifies points, it is impossible to glean from the documentation why this happened, and consequently difficult to tune the corresponding settings or improve the method. This example of ground classification also applies to other routines and algorithms, including tree segmentation, point cloud registration, point cloud classification, etc.
Manuscript organisation and intentions The lidR package has been rapidly adopted and is now widely used, especially in academic research. It is widely cited internationally (see section 6) and there are now several training courses designed to teach the package that have been created independently of the lidR development team. Consequently, this paper was written to clarify the goals and the design of the package for a broad audience in environmental sciences, and to inform the community of our development choices. While the package is comprehensively documented, we have never explicitly set out our motivations underpinning its development because this kind of information does not belong in the user manual. lidR is part of a large suite of available tools that all bear their own strengths and weaknesses.
Learning new tools can be laborious, so we believe it worthwhile to clarify what lidR is designed for, to help potential users and developers decide if it will meet their analysis needs upstream of the learning curve. This paper also serves as a general review of the various steps available to the practitioner for processing ALS-based point clouds, with an emphasis on the forestry and ecology contexts. However, our intent was not to provide detailed descriptions of the algorithms hosted in lidR that can be used to perform these steps because these are already documented in detail in peer-reviewed papers, which are all referenced in the text. In this manuscript we focus on four components of the lidR approach. In the first part (section 2), we focus on the architecture of the package. In the second part (section 3), we focus on key processing algorithms for data acquired from discrete return ALS systems, given their prevalence in the ALS market today, to demonstrate the efforts we have made in the lidR package to provide a wide range of tools from the peer-reviewed literature.
We follow a likely conventional ALS workflow involving ground classification and terrain interpolation, height normalisation, construction of digital canopy models, and extraction of ALS metrics to allow ABA model development. In the third part (section 4) we highlight the versatility of the lidR package in its fundamental formulation, which allows for a highly flexible programming environment to implement less common, or more innovative processing approaches. We provide some specific examples on how this flexibility can be used and leveraged. We conclude (section 5) with general comments on the processing speed and future functionalities of the package. As lidR is constantly evolving, it is important to state that this manuscript reflects the state of the package in its 3. 0 version. While some information might become outdated, such as the supported formats (section 2.
Architecture and design lidR is a point-cloud oriented R package designed to be integrated into the R spatial analysis ecosystem by supporting inputs/outputs in formats defined by the packages sp  (Pebesma and Bivand, 2005; Bivand et al. , 2013) , sf  (Pebesma, 2018)  and raster  (Hijmans, 2019). It is designed with the FOSS philosophy in mind: the source code is open and freely modifiable, and the development is both driven by feature requests from users and open to third-party modifications/additions, thus allowing the package to constantly evolve. The lidR package was created to provide a variety of customisable processing strategies with the goal of enabling users to manipulate and analyse their data easily. In addition to a set of internally-defined functions, lidR offers the possibility to implement user-defined functions, so that processing can be specialised and tailored to meet individual management and research needs.
ALS data formats supported Discrete return ALS sensors record three coordinates per point as well as several types of supplementary data for each point. Reading, writing and storing these ALS data is a critical preliminary step before any subsequent analysis. ALS data is most commonly distributed in the LAS open format, which is specifically designed to store LiDAR data that is standardised, and officially and publicly documented and maintained by the American Society for Photogrammetry & Remote Sensing (ASPRS, 2018). LAS format enables point cloud data to be stored using an optimised amount of memory but without being compressed. The requirement to store and share ALS data provided impetus for improved data compression  (Pradhan et al. , 2005; Mongus and Žalik, 2011). Since there is currently no official standard to compress point cloud data files, several different schemes were developed over the last decade to compress LAS files, such as 'LizardTech LiDAR compressor' (LizardTech), 'LAScompression' (Gemma lab) or 'zlas' (ESRI), many of which are proprietary, resulting in difficulties sharing the data between users or platforms.
Common processing workflow in lidR ALS data processing relies on recurring steps that are systematically applied to a dataset. These steps include ground classification, digital terrain generation, digital surface model generation, height normalistion of the point cloud and, in the case of forestry applications, an ABA and/or ITS analysis. While, the lidR package was designed primarily to explore processing beyond these steps (section 4), it embeds the required tools to apply these routines. All tools dedicated to such common processing steps are derived from the peer-reviewed literature. As far as possible, lidR aims to provide a wide range of processing options implemented from original papers or published methods, with a view to promoting reproducible science. Strictly speaking, lidR contains source code to process point clouds, which are decoupled from any environmental context.
These three methods are well known spatial interpolation methods with decades of documentation (e. g. Mitas and Mitasova, 1999). While these methods do not bring much novelty, their availability again demonstrates the importance we put on providing several state-of-the-art options to users. Because lidR is a constantly evolving package other methods such as bivariate interpolation  (Akima, 1978)  or  Multilevel B-Spline Approximation  (Lee et al. , 1997 ) could be added in future releases, or as third-party extensions (the package supports additional plug-ins).
Data normalisation A common third step in ALS data processing is the subtraction of the terrain surface from the remaining ALS returns (Fig. 2 ). Point cloud normalisation removes the influence of terrain on above-ground measurements, thus simplifying and facilitating analyses over an area of interest. The most common approach to normalise non-ground returns is to subtract the derived raster DTM from all returns. This method has been widely used (e. g. Wang et al. , 2008; van Ewijk et al. , 2011; Li et al. , 2012; Jakubowski et al. , 2013; Ruiz et al. , 2014; Racine et al. , 2014)  and is simple and easy to implement. For each point in the dataset, the algorithm selects the value of the corresponding DTM pixel, then subtracts this value from the raw elevation value of each point. The approach, while simple, can lead to inaccuracies in normalised heights due to the discrete nature of the DTM and the fact that the DTM was created and interpolated using regularly spaced points, which do not match the actual location of the ground points in the dataset.
The package does not embed any statistical models from the peer-reviewed literature because they are too specific to individual studies. Instead, lidR enables users to compute any metric so that any existing model from the literature can be reproduced. To ensure this is the case and to facilitate their use, some lesser-used metrics from the literature, such as a rumple index  (Blanchette et al. , 2015) , leaf area density  (Bouvier et al. , 2015)  or vertical complexity index  (van Ewijk et al. , 2011)  have been embedded into the package.