dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual tree segmentation An alternative method to the area based approach consists of calculating summaries of the point cloud at the scale of individual trees  (Chen et al. , 2006; Koch et al. , 2006 ). An accurate segmentation of individual trees to extract a database of tree-level position and attributes such as height, diameter, volume and biomass is a much desired outcome of research on the use of ALS for forestry applications  (Hyyppä et al. , 2001; Popescu, 2007; Zhang et al. , 2009; Kwak et al. , 2010; Yao et al. , 2012; Gleason and Im, 2012). Individual tree detection algorithms can be generally divided into two types i. e. those based on a digital canopy models and those utilising the point-cloud directly. The body of literature on individual tree segmentation is considerable, and has been the focus of a number of comprehensive reviews and comparisons  (Ke and Quackenbush, 2011; Wang et al.
lidR as a toolbox In the previous section we have shown how the lidR package has the capability to apply conventional ALS workflows using up-to-date implementations of a number of ALS methods presented in the peerreviewed literature. By providing transparent and functional versions of published routines, they immediately become easy to use, thus extending their value beyond their publication-based formulation, which is commonly the only publicly available format. The true power of lidR however lies in its design as a "toolmaker" to provide users with the capability to create their own applications. In fact, lidR provides a programming environment intended to be used by R users who wish to test and explore new processing workflows. While it is not possible to show a complete list of functionalities, some examples are provided below to demonstrate the flexibility brought by the package.
Development of user-defined metrics As expressed in previous sections, the derivation of metrics from ALS point clouds is inherent to processing ALS data for any forestry or ecology applications. Conventionally, most software solutions allow users to compute predefined and hard coded statistical summary metrics over an even-sized regular grid system. Unlike common software, lidR allows the computation of any user-defined metric for a wide variety of regularisations, and/or within user-defined objects. When metrics are computed at the level of individual points, they can serve for classification purposes i. e. to assign them to classes, such as building, vegetation, water, power lines, etc. When computed at the pixel level, metrics feed directly into conventional ABA analyses. When computed at the individual-tree level, the metrics can be used to estimate crown characteristics, and then to classify species.
Apply user-defined routines to large coverage ALS data are divided into many smaller files (known as tiles) that together make a contiguous dataset covering an area of interest. The above-mentioned tools work on point-clouds loaded to memory; however, real applications require upscaling of routines to facilitate processing of large datasets that do not fit into memory. A powerful engine has been developed to achieve this in lidR. Once users have designed a feature that works for a small point cloud, lidR offers the capability of applying the function over the entire coverage area using the "LAScatalog processing engine" (See section 2 and Fig. 7 ) that offers several features including: • The capability to iteratively process 'chunks' of any size. The chunks do not need to respect the original tiling pattern of the data acquisition.
• An error-handling manager (Fig. 7 ) that displays if a chunk pro- duced a warning or an error. In brief, the LAScatalog processing engine provides all the tools to apply and extend any user-defined routine to an entire acquisition, taking care of all the internal complexity of on-the-fly buffering, parallelism, error handling, and progress estimation, etc. Users have full access to the engine with the catalog_apply() function that is also heavily used internally in almost every function. Combining the versatility of functions, which goes beyond the short summary provided in section 4. 2, to the processing engine, a lot of processes that cannot be explored in traditional software can easily be designed by research teams using R. A further illustration of this point can be seen in Fig. 1d  where an algorithm to retrieve the position of the sensor was first designed and then applied to a broader area using the engine.
An example of the ALS-focused design of lidR is the use of an internal spatial index that is optimized for points evenly-spread on x -y axes with proportionally little variation on the z axis; this means that it will often process TLS point clouds sub-optimally. However, it does not mean that lidR cannot be used to process other sources of point clouds. Some examples of successes in these areas include the TreeLS package (de Conto, 2019), which is designed for TLS tree segmentation and is built on top of the lidR architecture. The viewshed3d package  (Lecigne, 2019; Lecigne et al. , 2020)  also uses lidR and TreeLS to process TLS data and address ecological questions. By paying great attention to memory usage using the tools offered by the package, we have also succeeded in integrating lidR into a DAP point cloud workflow.
Computational considerations Expansive ALS acquisitions can result in large amounts of data that need complex processing. As a result, ALS processing software needs to be as efficient as possible when reading, writing, and processing 3D point clouds. As discussed previously, the primary goal of lidR is to create a straightforward and versatile toolbox within the R ecosystem. This choice comes at a cost of memory usage and runtime, when compared to what could be achieved with specialised software. However, a significant part of the lidR package code that drives the most demanding computations is written in C++, and is natively parallelised at the C++ level whenever possible. Memory allocations are reduced by recycling the R allocated memory whenever possible and the reliance on third-party packages is focused on efficient tools, such as the data.
10GHz with 64GB of RAM running Windows 10. The C++ code of lidR as well as other dependency packages were compiled with g++ with level 2 optimisation (-O2), which is the default in R packages for Microsoft Windows and most GNU/Linux distributions. We did not take advantage of the multicore or multi-machine capabilities of the package for the results presented in this paper. To ensure the reproducibility of the benchmarks, we have provided supplementary materials with an extensive set of tests that can be run on other computers to perform time comparisons. These tests include a dataset of 25 1 km 2 tiles at a mean density of 3 points/m 2 , which was used to perform the following benchmarks and some extra multi-core parallelisation examples. For comparisons with existing R packages, we examined how fast the lidR package performed a simple rasterisation task compared with the rasterize() function available in the raster package  (Hijmans, 2019)  using a set of 3 million randomly distributed points.
It is the specialisation that has made lidR markedly faster than other more generic equivalent tools available in R. However, the versatility of the package comes at a cost and a drawback is often a longer computation times and a greater memory usage. In lidR, versatile functions are offered to prototype new tools that can subsequently be implemented in pure C++, if required. For example, in the current version of the package a user can compute a simple DCM using the versatile grid_metrics() function, but lidR has a specialized grid_canopy() function that is faster by an order of magnitude (Fig. 10. a). In lidR, these 'specialized functions' are functions that could be replaced with other versatile functions of the package, but that were considered to be of sufficiently high interest to be specialized for much faster processing, less memory usage and, whenever possible, native parallelisation at the C++ level.
In contrast to lidR, LAStools is less versatile but is designed to process very large amounts of data extremely efficiently (both in terms of speed and memory usage). Given the different focus of LAStools, we expected it to perform much faster. Fig. 11  shows the results of these comparisons and it can be seen that lidR is either close to or much faster than FUSION, but always slower than LAStools; an outcome that was expected. The goal of this set of benchmarking tests is not to identify the fastest among a set competing software tools. LAStools will always be faster than lidR by design and one may find tasks that FUSION can perform faster than lidR, and vice versa. Moreover, most tasks can simply not be compared in a direct way, such as ground segmentation or point cloud decimation, as no two software systems use the exact same methods.
The lidR book, which can be found at https://jeanromain. github. io/lidRbook/ is a guide that contains tutorials for both new and advanced users. Users can also find help from the community at https://gis. stackexchange. com/ using the lidr tag that currently hosts more than 100 questions. The package being in constant evolution, new features are already in development, such as support for full waveform data, processing speed improvements, additional methods from the peer-reviewed literature, new methods for mapping water bodies, forest roads segmentation, power line and transmission tower segmentation, and versatile tools for mesh processing. We also aim to provide better support for processing TLS data by implementing a 3D spatial index that is suitable for this type of point cloud.
Conclusion The use of ALS data for forestry and ecological applications, including the derivation of highly detailed and accurate terrain models, canopy height model development, and forest inventory estimation at both the plot-and individual tree-scale, is well established, with many researchers and managers regarding ALS as a mature and implementable technology that can be applied in an operational context. Considering the rapid integration of the technology globally, ALS provides a key success story of the evolution of a new technology from research and development to production. A simple scan of the available software platforms to process ALS data, however, demonstrates that while the technology is mature, the use of validated, repeatable, transparent and readily available tools is not.
In this context, the lidR package provides a significant set of algorithms implemented from the peer-reviewed research literature. We showed that lidR always provides at least two options for any given task to enable users to rely on potential alternatives when one method does not suit a given scenario. Also, lidR aims to provide a space where new algorithms can be tested. By making the methods from the literature available within the package, we intend to enable the community to figure out their strengths and weaknesses across various contexts, which may (or not) eventually lead to wider adoption. In other words, lidR is designed as a laboratory software. This design choice makes lidR different to other existing software but its open-source and cross-platform nature, its ease of use and its versatility already make it broadly accepted by the research community, as evidenced by the numerous citations of the package in the peerreviewed literature, as well as some newly developed tools based on the lidR architecture not only focused on ALS point clouds but also TLS, UAV and DAP point clouds.
Acknowledgements This software development was partly funded by  AWARE  (the Assessment of Wood from Remote Sensing  NSERC   CRDPJ462973-14 , grantee  Prof. Nicholas Coops ,  UBC ), in collaboration with the  Canadian Wood Fibre Centre (CWFC) , and  FPInnovations. Current support for the development of the package is provided by  Quebec's Ministry of Forests, Wildlife and Parks, Quebec ,  Canada.
Tumor-infiltrating lymphocytes (TILs) in gastric cancer are closely related to clinical prognosis; however, little is known regarding the immune microenvironment in this disease. Thus, RNA-sequencing data from gastric cancer patients were downloaded from the Gene Expression Omnibus (GEO). The proportion of immune cells was determined based on a deconvolution algorithm (CIBERSORT), and gene expression profiles were analyzed in the context of clinical outcomes to construct an immune risk score. Data were analyzed using least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression, to identify prognostic markers of gastric cancer survival. The model included four immune cell types: neutrophils, plasma cells, activated CD4 + memory T cells, and T follicular helper cells.
Analysis of a large number of cancer samples will increase the statistical power of any such evaluation, lending credibility to the outcomes. Recently, a new gene expression matrix-based deconvolution algorithm known as CIBERSORT was developed, which can be used to assess the diversity characteristics of tumorinfiltrating lymphocyte (TIL) populations. CIBERSORT has proven highly effective in controlling for statistical noise and distinguishing among closely related cell types, making it a useful application for studying cell heterogeneity in multiple tissue types, including solid tumors  (14). Here, we applied CIBERSORT to transcriptomic data collected from multiple tissue types, to quantify the relative proportions of 22 types of immune cells. RNA-sequencing data from gastric cancer samples were obtained from the Gene Expression Omnibus (GEO) to investigate the role of immune cells in the OS of gastric cancer patients.
Gastric Cancer Datasets and Processing Gastric cancer gene expression data were obtained from the GEO database  1. Small datasets (<50 samples) were excluded from the analysis, as were the data of patients with an OS time <1 month, or with insufficient data regarding age, gender, or TNM stage. Based on these criteria, we identified a single study (GSE84437) of patients with gastric cancer. Normalized matrix files for the dataset as well as the platform files were downloaded.
Estimation of Immune Cell Type Fraction To determine the proportions of the 22 infiltrating immune cells in the normalized gene expression datasets, the CIBERSORT algorithm and LM22 gene signature were used. CIBERSORT, which is a deconvolution algorithm for analyzing gene expression data, uses a series of gene expression barcodes (comprising a "signature matrix" of 547 genes) for characterizing the proportion of each immune cell type. Briefly, the gene expression datasets were uploaded to the CIBERSORT web portal 2  , and the deconvolution algorithm was run using the LM22 gene signature matrix (1,000 permutations). CIBERSORT derives a p-value for the deconvolution of each sample using Monte Carlo sampling, as a measure of confidence in the results; only samples with a p-value < 0. 05 were considered for further analysis.
Maps for Protest Maps for protest can be broadly divided into two main groupsdmaps that seek to challenge the official framing of a governance issue and maps that seek to expose unlawful actions. They both fall along a continuum of dissent that can be more or less open and embattled, and there is sometimes a fine line between the two. Exposing decisions and actions as morally and legally reprehensible may lead to firmly putting hitherto overlooked issues on the political agenda. First, it should be noted that the advances in digital technologies have provided administrationsdand citizensdwith a wide variety of communication tools to exchange georeferenced data with ease. Maps based on direct user input are defined as contributive or collaborative. One such platform is FixMyStreet which allows citizens to report problems to local authorities.
While complaint platforms based on georeferencing empower citizens with direct access to local authorities and help to democratize hyperlocal governance, overreliance on them may also exacerbate spatial inequalities. It has been shown that collaborative maps act as direct channels for citizens' grievances to reach local government bodies. But they can also be powerful vehicles to reach a global media audience. One such collaborative map, which might be termed "advocacy maps," is the Global Atlas of Environmental Justice by the EJAtlas group. Created in 2012 by a collective of academics at the Universitat Autonoma de Barcelona, it maps social conflict around environmental issues. The EJAtlas is based on a collaborative database which collects information directly from the activists involved in environmental conflicts on the ground.
It documents and aggregates conflicts across a wide range of topics, like environmental degradation and depletion, soil and water contamination, land grabbing, and the concomitant forced evictions and violent relocations. The data sets are made available online to the public under an open-source license. In 2017, the EJAtlas listed 2100 environmental conflicts across the globe, and in each case, compiled data about the involved advocacy groups, activists, and scholars, the conflict background, the impacts, the claims, and outcomes for the stakeholders. The whole project supports community-led science as it also caters to the needs of scientists, journalists, and teachers for research data directly provided by activists. For instance, the EJAtlas data have been used to investigate the forms and networks of environmental activism or survey global opposition to fracking.
That is why the EJAtlas was designed with citizens and elected representatives in mind. Its underlying premise is that people can discover, discuss, and implement alternative policy choices. At the far end of the protest spectrum, there are maps that defy state sovereignty and openly challenge the state monopoly on representing and transforming space. Two emblematic figures particularly stand out in the history of maps to protest. One of them is Otto Neurath, an Austrian political economist in Vienna who devised a system of statistical graphics in the 1920s to support his radical views on economic planning. He created Isotypes (International System Of TYpographic Picture Education) to visualize and compare statistical data in time and space. As a fervent Marxist, he did so with the express intent of advancing class struggle and freeing the people from bourgeois influence.
The latest advances in data visualization and mapping services have expanded the realm of possibilities for producers of maps to protest. They now have the ability to circumvent state and media censorship and reach out to the citizenry without any intermediaries. Take for instance "The Blood-Stained Housing Map" (link), a collaborative map created in October 2010 by an anonymous Internet user to document the violent evictions induced by development projects in China. The ambitious goal set for the map is to reveal the full extent of the land grabs and evictions and elicit/provoke a national and international response that rises above the emotional stir caused by individual cases. The project which started out as a blog runs on Google's mapping tool Google Maps and uses a wiki-based platform which allows online communities to collect, publish, verify, and discuss their data.
The project founder was driven by the desire to provide the public with an archival database of violent evictions and jumpstart a civic response from Chinese real estate buyers who are called upon to boycott development projects built on unethical practices. The "Blood-Stained Housing Map" can therefore be included in the corpus of maps to protest that expose and condemn state violence and the violation of human rights, be they collaborative or based on a forensic exploration of massive sets of documents and data. To name but a few contemporary examples of data-driven maps to protest that have garnered a lot of media attention, we can mention the mapping of the Iraq and Afghanistan war logs from WikiLeaks data or the mapping of the CIA's secret detention facilities, made by critical legal scholars after Freedom of Information requests.
10 ). Contemporary versions of drifts and hijacked maps have supported an even more militant and radical rhetoric. In 2010, to oppose the overreaching surveillance of urban spaces, the anonymous artist collective The Institute for Applied Autonomy (IAA) developed a computer program that charted itineraries through global cities along the urban routes least subjected to CCTV surveillance. Named i-See, the piece of software generates maps containing user-generated data including positioning of surveillance cameras in several international city centers. Before the i-See project, the IAA had collaborated with the artist Steve Rowell on a map called "SITE-R" which located street surveillance cameras in Manhattan. The collective described its approach as "tactical," concerned with disruptions within existing systems of power and control.
Data collection Endothelial function and the EG were investigated using three techniques: concentration of syndecan-1, measurement of the tissue oxygen saturation (StO 2 ) during the vascular occlusion test (VOT), and contemporary measurement of flow-mediated dilation (FMD). Data and blood samples were collected before surgery (T0), at 1-3 h post-surgery in the recovery room (T1) and 24 h post-surgery in the surgical ward (T2). The concentration of syndecan-1, a marker of EG shedding, was measured by classic sandwich enzymelinked immunosorbent assay (ELISA), based on the manufacturer's instructions (Syndecan-1 ELISA kit; Tebubio, Boechout, Belgium). Blood samples were collected in dry tubes, centrifuged to obtain the serum and stored at -80 °C for a maximum of 6 months before final analysis.
The following StO 2 -derived variables were analysed: StO 2baseline, StO 2 -ischemic slope and StO 2 -reperfusion slope (Image 2 in « Additional File 2 » shows the evolution of Fig. 1  Goal-directed haemodynamic protocol. Goal-directed protocol for the management of peri-operative fluids and haemodynamics: systolic volume variation (SVV) and mean arterial pressure (MAP), as indicated on FloTrac™ (Edwards Lifesciences Corp. , Irvine, CA, USA) StO 2 during the test in one participant). The FMD was assessed by automated edge detection software (FMD Stu-dio™ (CardioVascular Suite™); Quipu srl, Pisa, Italy)  [19] , based on the experts' guidelines  [20]. The following FMD-derived variables were analysed: brachial artery baseline diameter, FMD-maximum (i. e. the maximal diameter during reperfusion) and the area under the curve of estimated shear rate of hyperaemic flow until FMD-max (Image 3 in « Additional File 3 » shows the evolution of the brachial artery diameter and shear rate during a test in one participant).
Results We assessed 68 patients for eligibility and excluded 28 patients (seven patients had ≥1 exclusion criteria, 13 patients refused to participate, and eight patients were excluded for technical problems related to the connection between the ultrasonography transducer and the FMD detection software and thus the impossibility to perform measurements on the brachial artery). Trial recruitment was stopped when 40 patients were included, 20 for each group, as planned. All patients were included in the final analysis (Fig. 2 ). The patients' baseline characteristics for both groups are summarised in Table  1. Comorbidities and medications that influence baseline endothelial function and FMD measurements are reported in Table  1. The type of surgical intervention and whether it was conducted by laparotomy, laparoscopy or a combined procedure are reported in Table  2.