dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of statistical model We tested how well our statistical model predicts missing data, known as external predictive validity, in two different tests. In the first test, we held out all data from 10% of countries with data (that is, created the appearance of countries with no data where we actually had data). The countries for which the data were withheld were randomly selected from the following three groups: data rich (5 or more data sources, with at least one data source after the year 2000), data poor (1 data source) and average data availability (2-4 data sources). In the second test, we assessed other patterns of missing data by holding out 10% of our data sources, again from a mix of data-rich, data-poor and average-data countries, as defined above. For a given country, we either held out a random half of the data of a country or all of the 2000-2018 data of the country to determine, respectively, how well we filled in the gaps for countries with intermittent data and how well we estimated in countries without recent data.
Strengths and limitations The strengths of our study include its scope in making consistent and comparable estimates of trends in blood cholesterol and its cardiovascular disease mortality burden, over almost four decades for all of the countries in the world, including global estimates of non-HDL and HDL cholesterol. We used a large amount of population-based data, which came from countries in which 92% of the global adult population lives. We used only data from studies that had measured blood lipids to avoid bias in self-reported data. Data were analysed according to a consistent protocol, and the characteristics and quality of data from each country were rigorously verified through repeated checks by NCD-RisC members. We pooled data using a statistical model that took into account the epidemiological features of cholesterol, including nonlinear time trends and age associations.
Acknowledgements This study was funded by a  Wellcome Trust  ( Biomedical Resource & Multi-User Equipment  grant  01506/Z/13/Z ) and the  British Heart Foundation  ( Centre of Research Excellence grant   RE/18/4/34215 ). C. T. was supported by a  Wellcome Trust   Research Training Fellowship  ( 203616/Z/16/Z ). The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.
These histograms are calculated within superpixels of different sizes in order to provide a multi-scale characterization of the neighborhood, while preserving the geometry of the image objects. This method is evaluated on two data sets presenting different spatial, temporal, and spectral resolutions, and each case is compared with a D-CNN in terms of class accuracy, but also of the quality of the geometry in the produced map. Experiments on the Sentinel-2 time series show that HACCS provides equivalent thematic accuracy compared to the D-CNN, while exhibiting a higher degree of geometric accuracy. On very high spatial resolution imagery (SPOT-7), the D-CNN provides significantly stronger thematic accuracy, but this comes at the cost of a lower level of geometric accuracy.
For instance, in  [12] , the authors combine a regular CNN with an edge detecting CNN, the Holistically-Nested Edge Detection network  [13] , in order to improve the classification performance in these sensitive areas. However, the authors of these studies do not address the case where no dense reference data are available for training. Generally speaking, CNNs are challenging to use for land cover map production, especially over large territories, as the reference data used for training are only available in a sparse form, in other words, not every pixel of the training patches is labeled. This is very often the case in land cover mapping, as the reference data come from a combination of existing geodatabases  [5] , which each contain specific classes from the desired nomenclature. This means that the fine details of the geometry, the class edges, and the spatial relations between various classes are not directly present in the training data.
Multi-Scale Superpixels as Spatial Supports The choice of which spatial support to use to calculate the histogram of the class labels is not a simple one. Two commonly used spatial supports used for calculating contextual features are sliding windows and objects  [19]. Previous studies on the geometric precision of land cover mapping have shown that the use of a sliding window neighborhood with a so-called unstructured feature can increase the risk of blurring out high spatial frequency image features  [43]. Unstructured features do not take into account the spatial arrangement of the pixels in the neighborhood. Other examples of unstructured features are the sample mean and variance. In fact, this phenomenon is observed on experiments on the Sentinel-2 and SPOT-7 datasets, shown in Section 4.
Patch-Based Network Patch-based methods involve taking a standard CNN architecture such as the ones applied on the ImageNet database  [11] , and considering that the output label, which was originally meant to describe the entire patch, is assigned only to the central pixel of the patch. This way, all of the pixels in the image can be labeled by applying the network to a window around each pixel, like a sliding window. Furthermore, this is adapted for sparsely labeled training data, where each training sample is a labeled pixel. This allows for existing network architectures originally used for image classification to be re-used to achieve a dense classification, and therefore allows neural networks to be applied to land cover mapping  [48, 49]. Recently, a patch-based network was successfully applied on a five-class land cover mapping problem, including context-dependent classes such as roads and urban cover, which are commonly confused by pixel-based classifiers  [50].
The high-level decision layers are situated after several pooling layers which reduce the spatial resolution, meaning that a geometrically precise decision can be difficult to obtain. For this reason, fully convolutional networks like U-Net introduce skip connections, which pass full resolution information to the deep layers of the network. Generally speaking, if a neural network has never been trained on a labeled pixel that lies near the boundary between two classes, it might produce a result with an edge displaced towards one of the two classes. In practice, this often translates in a degradation of the high spatial resolution elements in the output classification. Sharp corners are rounded, and fine elements are either thickened, or entirely erased. The lack of dense training data also explains the speckle-like label noise observed near object boundaries in  [50].
Description of the SPOT-7 Data Set The second data set was based on optical imagery from SPOT-7, which measures surface reflectance in the visual RGB bands, as well as the Near Infrared band (NIR), at a spatial resolution of 6 m. Combined with a Panchromatic band at 1. 50 m, a pan-sharpened RGB-NIR image could be obtained. The training data contains five classes: urban cover, roads, water, vegetation, and crops, which came from the BDTopo and Registre Parcellaire Graphique (RPG). This data set was identical to the one proposed in the experimental section of  [50]. The challenging classes in this problem were the buildings (urban cover), the roads, and the water, due to common confusions between water and shade, which both appeared dark, and between the tones of gray that made up certain roofs and streets.
Moreover, if HACCS is applied to such images it must be done with care, as if the number of contextual features largely exceeds the number of pixel features, it may result in a degradation of the geometry. For HSR imagery (Sentinel-2) with more pixel features, HACCS seems to be the preferred solution, seeing as it provides a similar performance with a lower computational cost. The source code for the HACCS process is based on the use of the open source software Orfeo ToolBox, and is freely available  [68].
Acknowledgments: We would like to thank  Andrei Stoian  from  Thales ThereSiS Lab ,  Vincent Poulain  from  Thales Services , and  Victor Poughon  from the  Centre National d'Etudes Spatiales  for providing us with the results of the FG-Unet on the Sentinel-2 data set. Furthermore, we are thankful for the participation of the team at the  Institut Géographique National , in particular  Tristan Postadjian ,  Clément Mallet  and  Arnaud Le Bris  for providing us with the training data, validation data and results of the patch-based CNN on the SPOT-7 data set.
Extracellular vesicles (EVs) are small, heterogeneous and difficult to measure. Flow cytometry (FC) is a key technology for the measurement of individual particles, but its application to the analysis of EVs and other submicron particles has presented many challenges and has produced a number of controversial results, in part due to limitations of instrument detection, lack of robust methods and ambiguities in how data should be interpreted. These complications are exacerbated by the field's lack of a robust reporting framework, and many EV-FC manuscripts include incomplete descriptions of methods and results, contain artefacts stemming from an insufficient instrument sensitivity and inappropriate experimental design and lack appropriate calibration and standardization. To address these issues, a working group (WG) of EV-FC researchers from ISEV, ISAC and ISTH, worked together as an EV-FC WG and developed a consensus framework for the minimum information that should be provided regarding EV-FC.
This framework incorporates the existing Minimum Information for Studies of EVs (MISEV) guidelines and Minimum Information about a FC experiment (MIFlowCyt) standard in an EV-FC-specific reporting framework (MIFlowCyt-EV) that supports reporting of critical information related to sample staining, EV detection and measurement and experimental design in manuscripts that report EV-FC data. MIFlowCyt-EV provides a structure for sharing EV-FC results, but it does not prescribe specific protocols, as there will continue to be rapid evolution of instruments and methods for the foreseeable future. MIFlowCyt-EV accommodates this evolution, while providing information needed to evaluate and compare different approaches. Because MIFlowCyt-EV will ensure consistency in the manner of reporting of EV-FC studies, over time we expect that adoption of MIFlowCyt-EV as a standard for reporting EV-FC studies will improve the ability to quantitatively compare results from different laboratories and to support the development of new instruments and assays for improved measurement of EVs.
Introduction Extracellular vesicles (EVs) have tremendous potential value as biomarkers and therapeutic targets  [1, 2]. However, EVs are small, heterogeneous and therefore difficult to measure. Sensitive and reproducible methods for single EV analysis are essential to understand the biogenesis, diversity and fate of EVs and to apply this understanding to improve human health. Flow cytometry (FC) is a powerful method that provides accurate and precise enumeration of single cells and measurement of their molecular components. For these reasons there is an interest in applying FC to the measurement of individual EVs. However, most commercial flow cytometers, and the assays that employ them, (i) were designed for the analysis of cells that are orders of magnitude larger than EVs, (ii) are not readily adapted to the measurement of EVs and (iii) generate data which can only be interpreted if all experimental details are reported.
Limitations in instrument sensitivity, assay specificity and a general failure to adequately report experimental details together have produced a scientific literature that is rife with artefacts. General guidelines regarding methods and data reporting have been developed for both flow cytometry (Minimum Information about a Flow Cytometry experiment, MIFlowCyt)  [3]  and EV research (Minimum Information for the Study of EVs, MISEV)  [4, 5]. In addition, the EV-TRACK initiative provides an open access platform for documenting essential information for EV publications  [6]. However, these resources do not comprehensively address reporting component allowing full interpretation of FC data on EVs, which is critical for the validation and improvement of flow cytometry-based EV analysis. Here, a group of researchers active in the International Society of Extracellular Vesicles (ISEV), International Society for Advancement of Cytometry (ISAC) and/or International Society for Thrombosis and Haemostasis (ISTH) Vascular Biology Scientific Standardization Committee, have been working to develop a framework for reporting EV FC methods and results, named MIFlowCyt-EV.
The WG feel that currently there is not a gold-standard EV flow cytometry workflow, and the field will continue to struggle to identify robust methods until EV researchers adopt a standardized framework for reporting data in a consistent, benchmarked manner. As well as a reporting framework, MIFlowCyt-EV can also serve as a guide for the development of educational resources for researchers desiring to measure EVs. We expect that this framework will support the development and validation of new technologies for EV analysis.
MIFlowCyt-EV structure and utilization MIFlowCyt-EV utilizes the MISEV guidelines from ISEV for reporting preanalytical variables  [4, 5] , and the MIFlowCyt guidelines from ISAC for reporting FC experimental design and general variables related to FC  [3]. The proposed framework for MIFlowCyt-EV integrates and extends these two established societal guidelines to promote more standardized reporting of single EV analyses using FC. The MIFlowCyt-EV framework addresses seven areas: 1) preanalytical variables and experimental design, 2) sample preparation, 3) assay controls, 4) instrument calibration and data acquisition, 5) EV characterization, 6) FC data reporting, 7) FC data sharing. Figure  1  shows an overview of the framework and its components, whereas Table  1  provides a table for completion of the framework.
Report EV-FC manuscripts should provide a brief description of the experimental aim, keywords and variables for the performed FC experiment(s) using MIFlowCyt checklist components: 1. 1, 1. 2 and 1. 3, respectively  [3]. Completion of the entire MIFlowCyt checklist at this point is also advised here, in order to address subsequent MIFlowCyt checklist components that are integral to the MIFlowCyt-EV framework components, such as Components 2. 1, 4. 1 and 6. 1, below.
5. 2 Particle refractive index was derived from the ratio of side and forward scatter signal (i. e. Flow-SR). NIST traceable polystyrene beads with known size and refractive index (Exometry, Netherlands) were used to create a mathematical model of the optical configuration of the flow cytometer using FCM PASS software. Using this model, a Flow-SR versus diameter lookup table was calculated, which allows determination of the particle diameter from the measured Flow-SR. The determined diameter was subsequently used to derive the refractive index from a lookup table of side scatter versus diameter. Lookup tables were calculated for diameters ranging from 10 to 1000 nm, with step sizes of 1 nm, and refractive indices from 1. 35 to 1. 80 with step sizes of 0. 001.
Rationale The MIFlowCyt checklist was developed in order to provide a standardized method of reporting experimental and flow cytometer parameter pertinent to the experiment interpretation and reproducibility. These include the reporting of instrument settings such as laser power, detector settings and filter sets. It should be noted that many instrument settings are automatically saved to the ". fcs" -files that are generated by flow cytometers. FC software analysis packages also have built-in functions to record MIFlowCyt checklist parameters to the experiment workspace, which in turn can also be uploaded to public repositories such as FlowRepository. When uploaded to public repositories such as FlowRepository which read each ". fcs"-file, instrument settings are also imported and are therefore easily accessible  [3, 37].
4) and detergent controls (Component 3. 8), which were 77% and 74%, respectively. The poll showed varied expectations for reporting of instrument data acquisition and calibration components (Category 4). The reporting of trigger channel(s) and threshold(s), and flow rate/volumetric quantification was expected to be reported in all or most EV-FC manuscripts by ≧88%, while fluorescence calibration and scatter calibration were voted for in all or most EV-FC manuscripts by 55% and 37%, respectively. The reporting of EV characterization (Category 5) in terms of diameter, refractive index and epitope number using FC was the least expected information to be required in all or most EV-FC manuscripts, voted for by ≦20%. The poll showed that the expectation for reporting of FC data (Category 6) components in all or most EV-FC manuscripts was varied, with EV concentration reporting unanimously expected, the completion of the MIFlowCyt checklist (85%), and the calibration of detection channel range along with reporting EV brightness expected in all or most manuscripts by 41% of the participants.
The WG authors in this manuscript have outlined a set of components that should be broadly considered for all single-FC experiments, regardless or instrument or assay and feel the utilization of these components will aid in the development of more standardized small particle literature. MIFlowCyt-EV has been developed specifically for the reporting of single EV-FC experiments and are generally applicable to other small particles, for example viruses. A future framework aiming at alternative bulk-EV analysis methods using FC, such as capture-bead assays, may also be useful. Due to the EV-field being in its infancy, the MIFlowCyt-EV is not yet exhaustive and will likely adapted and updated in time as the field progresses. The utilization of this reporting framework will be key in the development of: (i) standard operating procedures, (ii) reference materials (iii) and validation studies.
Acknowledgments • JAW is an  International Society for Advancement of Cytometry (ISAC)   Marylou Ingram Scholar   2019-2023. • RN, FC, EvdP, LdR acknowledge funding from the  Netherlands Organisation for Scientific Research-Domain Applied and Engineering Sciences (NWO-TTW) , research programmes  VENI 13681  (FC) and VENI 15924 (EvdP) and  STW  perspectief  CANCER-ID 14195  (LdR). • IG acknowledges  NIH-U01-OD-019750 ;  NIH-R01 CA218500 ;  NIH U01-HL126497 ;  NIH UG3 TR002881. • AG is an  International Society for Advancement of Cytometry (ISAC)   Marylou Ingram Scholar   2019-2023. • AH and ODW are supported by  Fund for Scientific Research (FWO), Foundation against Cancer (STK) ,  Stand up to Cancer (KOTK)  and  Special Research Fund Ghent University (BOF). • SFWML was supported by the  Dutch Technology Foundation STW (Perspectief Program Cancer ID , project  14191 ), which is part of the  Netherlands Organization for Scientific Research (NWO)  and which is partly funded by the  Ministry of Economic Affairs.
Public access to data has been found to improve the reporting accuracy in manuscripts and provides important transparency for referees and readers of published work. For these reasons many funding agencies require public release of data. All human subject's data with Personally Identifying Information (PII) must be deidentified prior to collection or data deposition. FlowRepository was developed in tandem with the MIFlowCyt guidelines as a public data repository to support open access of data from published and unpublished work and aids the completion of the MIFlowCyt components (Component 1. 2, Component 6. 1) by archiving data from the FC ". fcs"-files [37]. Data repositories tailored to EV-FC data are yet to be devel- oped, but standard format (FC) data may be deposited at a number of data repositories (FlowRepository, Immport, Mendeley, Zenodo, figshare, etc.
First, we provide a "sparse" option, where LDpred2 truly fits some effects to zero, therefore providing a sparse vector of effects. Second, we also provide an "auto" option, where LDpred2 automatically estimates the sparsity p and the SNP heritability h 2 , and therefore does not require validation data to tune hyper-parameters. We show that LDpred2 provides higher predictive performance than LDpred1 (LDpred v1. 0. 0), especially when there are causal variants in long-range LD regions, when the proportion of causal variants is small, and when GWAS sample size is large. We also show that the new sparse option performs equally well as the non-sparse version, enabling LDpred2 to provide sparse effects without losing any predictive accuracy. Moreover, LDpred2-auto, which does not require any validation set, performs almost as well as the main LDpred2 model that tunes hyperparameters from a grid in a validation set, provided some quality control is performed on the summary statistics.
Overview of methods Here we present LDpred2, a new version of LDpred  (Vilhjálmsson et al. 2015). LDpred2 has 3 options: 1) LDpred2-inf, which provides an analytical solution under the infinitesimal model of LDpred1; 2) LDpred2grid (or simply LDpred2) that is the main LDpred model, where a grid of values for hyper-parameters p (the proportion of causal variants), h 2 (the SNP heritability), and possibly the sparsity option (as a third hyper-parameter) are tuned using a validation set; 3) LDpred2-auto, which automatically estimates p and h 2 from data and therefore is free of hyper-parameters to tune. Note that the sparse option in LDpred2-grid slightly modifies the Gibbs sampler in LDpred2 to be able to fit effects that are exactly 0. For users who do not particularly care about sparsity, they can still tune this parameter in LDpred2-grid to explore a larger parameter space.
Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for conservation purposes and supporting the delineation of marine protected areas, but model predictions are rarely associated with extrapolation uncertainty maps. In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.
Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i. e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e. g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species. Considering the unexpectedly high levels of extrapolation uncertainty measured in SDM predictions, we strongly recommend that studies report information related to the level of extrapolation.