dataesr/scientific-paragraphs-categorization huggingface dataset:
Fried et al. [13]  described frailty in older adults as a phenotype that could be identified through five criteria: unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity. These criteria have been identified as predictive of health degradation in a number of situations related, for instance, to ageing, cardiovascular problems or renal insufficiency  [14] [15] [16]. In that way, it would be of great interest to provide general practitioners with data collected from Fried's scale in a continuous manner. Beyond the fact that this scale has been widely diffused and used to assess physical frailty, scoring its items is easy to perform by means of simple technological tools, hence well adapted to a quantified-self approach. In that way we developed a technological package (the ARPEGE Pack  [17] ) whose objective was to associate each Fried's criterion with a wireless device which calculated the criterion value and sent it to a local receiver (typically a tablet or a smartphone) with remote communication capability.
Material The ARPEGE Pack aims to provide the data for the five Fried's criteria by means of technological devices. The weight (then weight loss after successive measurements) is obtained by a connected bathroom scale which, in addition, includes a functionality evaluating balance quality (BQT: Balance Quality Tester  [21] ). In Fried's work, weakness is evaluated by means of grip strength measurements using a Jamar hand-held dynamometer. In order to make it easy and motivating to use in long-term monitoring, we developed the "Grip-ball"  [19] , a ball including all functionalities for measuring and wirelessly communicating the pressure exerted on it by the subject. The Grip-ball can be associated with a serious game which ensures the motivation side of the device  [22]. As clinicians generally refer to force instead of pressure, a regression model has been developed to convert pressure into force  [23].
In a second step subjects were asked to declare their current weight, test their maximal grip strength using the reference device, i. e. the Jamar dynamometer  [36] , as well as to walk 15 ft at their usual pace, the measure being the time to cross the distance. All data described above were first captured manually in a paper document and then electronically recorded on the Reims University server for further processing. Subjects were also asked to use the ARPEGE Pack to measure their weight (stepping on the bathroom scales), maximal grip strength (using the Grip-ball prototype), and walking speed (walking 15 ft at usual pace in the radar direction), then to answer the questions related to activity level and exhaustion (multiple choice on the tablet). All tests were achieved under the supervision of a clinical investigator.
Data processing As the objective is to compare the criterion values produced by clinical evaluation and by the ARPEGE Pack, a linear regression model was calculated between quantitative data under the condition that they were Gaussian. Normality was verified for weight, grip strength and walking speed using the Kolmogorov-Smirnov statistical test (threshold p = 0. 05). The scores produced by the questionnaires being obviously non-Gaussian, the comparison between clinical tests and ARPEGE questionnaires was achieved by means of the Spearman correlation coefficient. The good agreements between the two approaches was assessed using the Cohen's kappa test. All statistical tests were performed using the XLSTAT statistical package (XLSTAT Version 2015. 6. 01. 23953, Addinsoft, Paris, France) and the Statistical Package for Social Sciences (SPSS Inc.
Conclusion Remote monitoring of physical the frailty state and its evolution is part of the great challenge aiming at allowing older people to live at home while preserving their autonomy and well-being. There are two main conditions that have to be followed by a monitoring system in order to be efficient over time. The first condition is the relevance of the data produced by the sensors with respect to reference clinical tests. The ARPEGE Pack roughly fits this condition, especially for the Fried's indicators objectively measured by technological tools (weight, grip strength, walking speed). The second condition relates to acceptability and motivation. When people are requested to achieve a specific action day by day, this action must be either designed to provide pleasure in doing it, which is the case for the Grip-ball associated with a serious game, or a normal part of everyday life, like using the bathroom scales.
A method called iTEKA, which stands for iterative time elastic kernel averaging, was successfully used for averaging time series. In this paper, we adapt it to GPS trajectories. The key contribution is a denoising procedure that includes an over-sampling scheme, the detection and removal of outlier trajectories, a kernelized time elastic averaging method, and a down-sampling as post-processing. The experiment carried out on benchmark datasets showed that the proposed procedure is effective and outperforms straightforward methods based on medoid or Euclidean averaging approaches.
Experimentation The experimentation was carried out using the training dataset provided by the Averaging GPS segments competition setup  Fränti and Mariescu-Istodor (2019)  at University of Eastern Finland (https://cs. uef. fi/sipu/segments/). It consisted in estimating a cleaned GPS trajectory segment given a set of GPS trajectories corresponding to the same segment. Only training data corresponding to a set of road segments were delivered along with ground truth trajectory for each considered road segments. It is important to note that the assessment measure used to evaluate the competing approaches was not explicitly provided. However, on the training data, the challenge site makes it possible to obtain the average value of the assessment measure obtained by a given method by submitting the set of solution trajectories produced by this method.
The HC-SIM (H for hierarchical) measurement was derived from the C-SIM measure by varying the size of the cells and providing a weighted average as output. The details of this measure have not yet been published by the authors. However, as mentioned above, an evaluation program allowed producing the results presented below. The HC-SIM measure gives a percentage of similarity between two trajectories (hence, it varies in [0, 100%]). We evaluated eight approaches: (i) four medoid based models, namely Euclidean, DTW, K rdtw and soft-DTW with medoid; and (ii) four centroid based models, namely Euclidean, DBA, soft-DTW and iTEKA centroid methods. All approaches shares the T meta parameter, which defines the size of the resample trajectories. In addition, K rdtw , iTEKA as well as the soft-DTW medoid and centroid methods require the set-up of two meta parameters, ν and γ, respectively, the bandwidth of the local kernel parameter (Equation (  4 )) and T , the length of the trajectories, once they have been resampled after the second preprocessing step.
With the limited training data, it cannot be guaranteed that the comparative results presented here are effectively significant. However, it clearly emerged from our experimentation that centroid-based approaches outperform medoid-based approaches. Furthermore, the algorithmic complexity is clearly in favor to centroid-based approaches, since it is linear with the size of the sets of trajectories that are processed, whereas it is quadratic for medoid-based approaches. As a perspective, to improve the HC-SIM quality of the cleaned trajectory estimation, one should consider the optimization of the meta parameters (T and τ essentially, as ν or γ may be considered as a function of T ) on each segment of road (instead on the whole set of segments), according to its topology. In that line of improvement, one should try first to clusterize the GPS datasets according to the segment shapes, and then optimize for each cluster the meta parameters.
Background Phylogenetic trees are often combined with extrinsic traits to reconstruct evolutionary scenarios in various research fields, including phylogeography, molecular epidemiology, and ecology. The analysis starts with the computation of ancestral annotations from extrinsic traits (discrete variables such as geographic origin, resistance to a treatment, and life history traits), associated with the sampled sequences used to build the phylogenetic tree. It continues with the study of the evolutionary changes from the tree root to its leaves to characterize evolutionary scenarios such as the spread of a disease, the dynamic of a drug resistance, or shifts in ecological habitats. There are numerous methods and software applications in ancestral character state inferences, among the most used are SIMMAP  [1] , BEAST  [2] , SpreaD3  [3] , and various functions implemented in R packages  [4, 5].
Implementation PastView is written in Tcl/Tk. It is an open source, cross-platform, standalone editor available for Windows and Unix-like systems including OSX. The PastView process includes five steps (Fig. 1 ): (1) data input (tree and annotations), (2) tree and/or annotations edition, (3) computing and displaying of ancestral annotations, (4) study of ancestral annotations transitions, and (5) comparative analysis between different methods of ancestral annotation inference. The PastView user interface/architecture (Fig. 2 ) of PastView is based on multi-maps, where each map is subdivided into a main view for the tree display and secondary view(s). PastView's features are grouped into five toolboxes (Fig. 2c ), following its five main steps: The "Input/output" toolbox includes controls for I/ O management: loading a phylogenetic tree (Newick format) and ancestral annotations or primary annotations (i.
e. annotations for leaves only, see below for computing ancestral annotations) in CSV format. The user can also import data in the NEXUS format, which merges tree topology with ancestral annotations (e. g. BEAST output). PastView's graphical output format is PostScript and/or SVG for high quality graphics and CSV for annotations. The "Editing trees and annotations" toolbox includes controls for basic editing of trees and annotations: tree layouts, rooting, swapping, ladderizing, zooming in/out, etc. The third toolbox is related to the "Computation and display of ancestral annotations". PastView computes ancestral annotations using parsimony: DELTRAN and ACCTRAN  [6] [7] [8] , and maximum likelihood (ML) assuming an F81-like model of character evolution. The branch lengths (typically obtained from sequences) are rescaled to fit the rate of evolution of the studied character.
Results In the following, we present two published examples of virus phylogeographic studies re-examined with Past-View. The first one is related to the HIV-1A epidemiological history in Albania. The second is related to the global phylogeography of Dengue serotype 1 virus. Fig. 4  Output of a PastView analysis for the study of HIV-1A epidemiological history in Albania  (Salemi et al. , 2008 [15] ). The countries associated with the tree sequences are used to compute ancestral areas by three methods: maximum a posteriori (MAP), the joint most likely scenario, and parsimony (DELTRAN). (a) A country color-code is used to color nodes and branches if their associated ancestral annotations are the same for the three methods; if not, bubbles are displayed. (b) A filter (threshold based on MAP probability minus 40% of its value) is used to display pie charts of posteriors for ambiguous nodes.
Example 2 Study of the global phylogeography of dengue serotype 1 virus. The second example is related to the study of the worldwide phylogeography of Dengue virus of serotype 1 (DENV-1). The dataset, tree (269 strains), and origin of the sequences (13 locations) were obtained from  [16]. Tree and ancestral annotations are first computed with BEAST (same parameters / model as  [16] ), then imported in PastView. Another set of ancestral annotations is computed with PastView (F81-like method, MAP, computation time is ~40s with an 3. 1 GHz Intel I7 computer). The tree is then foreground color-coded (Fig. 5a ) if the ancestral annotations (MAPs) are the same between the two methods. If not, pie-charts are displayed (from a root to tips reading: left = BEAST, right = PastView). Both analyses reach the same main conclusions as described in  [16] : Southeast Asia countries are found to be the most likely origin of the virus dispersion and India played a crucial role in the establishment, evolution and dispersal of the Cosmopolitan (Africa, America, Carribean, East & Southeast Asia) DENV-1 genotype (Fig.
Conclusion The main contribution of PastView is to assemble a series of numerical and graphical tools into a multi-map interface dedicated to computing, searching and viewing ancestral scenarios. One challenge will be to develop and integrate new methods to compute ancestral annotations combining the advantages of the nuanced outputs of the ML/Bayesian settings, and the overly stringent outputs of the joint and MAP methods, such as the recent decision theory-based PastML approach  [12]. In the long-term, a next step will be to integrate statistical tools and information visualization methods to quickly identify robust evolutionary scenarios, such as a consensus of transition maps between different methods (tree computations, ancestral annotation inferences, see for instance  [17] ), or for more elaborated transition maps in the case of huge datasets.
Funding This work was supported by The  PALADIN  project (GC), publicly funded through the  French National Research Agency  under the " Investissements d'avenir" program  with the reference  ANR-10-LABX-04-01 Labex CEMEB , and coordinated by the  University of Montpellier , by the  EU-H2020   Virogenesis  project (grant number  634650 , OG), and by the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG).
Availability and requirements Project name: PastView Project home page: http://pastview. org/ Operating system(s): Platform independent Programming language: Tcl/Tk Other requirements: ActiveTcl 8. 6. 8 or higher License: GNU GPL Any restrictions to use by non-academics: none Availability of data and materials PastView source code and data (tree, primary and ancestral annotations) of the two examples used in the manuscript are available on http://pastview. org/ and in the PastView package. PastView and the source code is publicly available on www. pastview. org.
1 1 Fig. 4Output of a PastView analysis for the study of HIV-1A epidemiological history in Albania(Salemi et al. , 2008 [15]). The countries associated with the tree sequences are used to compute ancestral areas by three methods: maximum a posteriori (MAP), the joint most likely scenario, and parsimony (DELTRAN). (a) A country color-code is used to color nodes and branches if their associated ancestral annotations are the same for the three methods; if not, bubbles are displayed. (b) A filter (threshold based on MAP probability minus 40% of its value) is used to display pie charts of posteriors for ambiguous nodes. (c), (d) Tree-like representations of transitions (MAP and joint inferences, respectively); numbers indicate the numbers of identical transitions having the same ancestor in the transition maps type 1.
Counting labeled cells, after immunofluorescence or expression of a genetically fluorescent reporter protein, is frequently used to quantify viral infection. However, this can be very tedious without a high content screening apparatus. For this reason, we have developed QuantIF, an ImageJ macro that automatically determines the total number of cells and the number of labeled cells from two images of the same field, using DAPI-and specific-stainings, respectively. QuantIF can automatically analyze hundreds of images, taking approximately one second for each field. It is freely available as supplementary data online at MDPI. com and has been developed using ImageJ, a free image processing program that can run on any computer with a Java virtual machine, which is distributed for Windows, Mac, and Linux.
Introduction When evaluating viral infections in vitro, fluorescence microscopy is commonly used to monitor the expression of a viral protein following immunostaining. However, this method requires a high content screening apparatus to count large numbers of fluorescent cells. Manual evaluation is feasible when analyzing few images, but it can result in subjective evaluation by the researcher. Furthermore, it is very time-consuming when working with hundreds of images, containing thousands of cells per image. ImageJ is a free image-processing program that was developed 20 years ago by Wayne S. Rasband at the National Institute of Health, and has become a valuable tool for researchers  [1, 2]. It is a Java-based software that can run on any computer using a Java virtual machine. It is thus available for Windows, Mac, and Linux.
ImageJ can convert images into numerical values that can be exported and further processed with other software for statistical analysis. Furthermore, a major strength of ImageJ is the possibility to record macros that enable the automatization of image analysis. In this technical note, we present QuantIF, an ImageJ macro for determining the percentage of fluorescent cells following immunofluorescence staining. QuantIF can be used when the specific staining in the cytoplasm and/or nucleus of a cell is diffuse. The macro automatically determines the total number of cells and fluorescently labeled cells for a series of images corresponding to different conditions. For each condition, two pictures of the same field must be taken, the first one corresponding to the specific staining and the second one corresponding to the DAPI staining.
Macro Description QuantIF was developed using ImageJ version 1. 52e and Java version 8. It is freely available as supplementary data online at MDPI. com. In order to use the QuantiIF macro, it is necessary to save the QuantIF. ijm file in the "Plugins" folder of ImageJ. The macro will then appear in the "Plugins" menu. When QuantIF is launched, the folder containing the images for analysis must be selected. Then, parameter values should be entered in a dialog box (Figure  1a ), (i) the type/name of the specific staining, (ii) the staining threshold, and (iii) the size limits of nuclei. Once the parameters have been entered, the macro starts analyzing the images. They are first converted to 8-bit images, displaying 256 gray levels. Indeed, we recommend directly exporting images as 8-bit TIFF files, from the microscope software.
We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The 5 error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state.
Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated 10 with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint.
On the basis of prior knowledge in the literature for stochastic approaches and on what is feasible in terms of computational resources, we have selected a few sources of model uncertainties and we have excluded others, investigating: (a) Which are the main physical-biogeochemical model uncertainties in regional/coastal systems and how can we estimate them? (b) What are the impacts of the choice of 5 different set of physical-biogeochemical perturbations on the analysed ocean state, by means of multivariate ensemble-based DA? In order to answer the first question, in Section 2, we present a generic method to complement the NEMO stochastic modules for high-resolution configurations, following the work of  Brankart (2013) ,  Brankart et al. (2015)  and  Bessières et al. (2017). In Section 3, we explore possible sources of ocean-ecosystem model errors in the coupled system and present an ensemble-based innovation statistics framework to evaluate upper-ocean uncertainties.
The coupled ocean-biogeochemical model 15 In this study, we use a regional configuration based on NEMO, in its stable version 3. 6, covering the Bay of Biscay and the western part of the English Channel (Fig. 1 ). For a complete description of the numerical set-up, identical to the IBI-MFC within CMEMS, and for details of validation, the reader is referred to  Maraldi et al. (2013). The NEMO ocean engine OPA (Océan PArallélisé) is coupled on-line with the passive tracer package TOP2 and the biogeochemical model PISCES-v2 (Pelagic Interactions Scheme for Carbon and Ecosystem Studies volume 2;  Aumont et al. , 20 2015). The high-resolution configuration (hereafter BISCAY36) is inherited from earlier studies during MyOcean  (Quattrocchi et al. , 2014; Vervatis et al. , 2016). The meteorological fields are provided by ECMWF (European Center for Medium-Range Weather Forecasts).
Note that on-line 30 coupling means one-way ocean forcing to the ecosystem model, since no feedback is given back to the circulation model. The primitive equations are discretized on a 1/36° curvilinear Arakawa C-grid and the tracer transport model runs also in the same grid resolution (no coarsening). The TOP2 package controls the advection-diffusion equations of the passive tracers and the biogeochemical SMS (Sources Minus Sinks) terms. The numerical scheme for the biogeochemical processes is forward in time (Euler) and differs from the classical leap-frog scheme used in physics. The advection scheme is the same as 35 in physics, i. e. QUICKEST  (Leonard, 1979) , but using limiter of  Zalezak (1979). These options have been tested by  Gutknecht et al. (2016)  and now are used in the IBI-MFC operational system (http://marine.
/ • 𝜉 ( + 𝜎 • 1 -𝑒 -5 / • 𝑤 + 𝜇 • 1 -𝑒 -. / (1) where  is the model time-step,  is white Gaussian noise, ,  and  the mean, the standard deviation (uncertainty amplitude) and the correlation timescale, respectively. Brankart et al. , (2015)  performed independent Gaussian 30 autoregressive processes at every model grid point and introduced spatial dependence via Laplacian filtering to the  (:) 2D or 3D maps (grid index  = 1, … , ). In their work, an alternative is also proposed to solve an elliptic equation noting that both methods are flow dependent. In the present study, we aim at complementing the NEMO stochastic modules, by integrating a subroutine to calculate explicitly variable anisotropic 2D spatial scales. The latter is of high importance, especially in high-resolution regional 35 applications.