dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with deposition flux measurements With surrogate models of wet Se deposition, we can estimate the modeled wet Se deposition throughout the parametric uncertainty space. Comparisons of these modeled distributions of wet deposition with observed values could help to constrain the input parameters to which deposition is sensitive. However, we do not attempt at this stage to calibrate the parameters to existing measurements because of several challenges in comparing the compiled measurement dataset with our simulations. Se is a difficult element to measure at environmental concentrations, which might lead to inaccurate reported deposition fluxes. The most popular analytical method is inductively coupled plasma mass spectrometry (ICP-MS), which was used for 58 of the 73 sites in the database.
Code and data availability. The SOCOL-AER code is available upon request from the authors, after users have signed the ECHAM5 license agreement http://www. mpimet. mpg. de/en/science/models/license/. The relevant simulation data, along with the experimental design of the training runs, is available at: https://doi. org/10. 3929/ethz-b-000357105 (Feinberg et al. , 2019a). The compiled Se precipitation database is available in the Supplement. The NAtChem precipitation database is available online at: http://donnees. ec. gc. ca/data/air/monitor/ monitoring-of-atmospheric-precipitation-chemistry/metals-in-precipitation/. Selenium in precipitation measured by EMEP was extracted from annual reports on heavy metals and POP measurements available at: https://projects. nilu. no//ccc/reports. html. UQLAB is freely available for Academic and degree-granting institutions by registering under https://www.
(2013) Illinois, USA (4 sites) Pan and Wang (2015) China (10 sites) Lynam et al. (2015) Alberta, Canada Xing et al. (2017) Jiaozhou Bay, China Nie et al. (2017) Mt. Lushan, China Blazina et al. (2017) Plynlimmon, UK Savage et al. (2017) Bangladesh and Sri Lanka Uchiyama et al. (2019) Tokyo, Japan Suess et al. (2019) Pic du Midi, France Pearson et al. (2019) Alaska, USA (3 sites) Savage et al. (2019) Northern Ireland, UK NAtChem database Ontario, Canada (9 sites) EMEP network 9 sites in Germany and Czech Republic.
Ramankutty et al. (2008) importance of the input parameters to total Se deposition globally and in agricultural areas. For the cropland and pasture means, Sobol' indices are averaged over grid boxes that are covered by more than 25% cropland or pasture area in theRamankutty et al. (2008)database. emiss mar emiss vol emiss terr emiss ant acc coef k 8 rad aer Global k 6 Pasture Cropland 0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 Mean total Sobol index Figure 11.
In order to show how technological innovation and scientific innovation are linked in the course of research in human science, I present an account of a series of innovations made in our laboratory (Distal Glove -Tactos system -Intertact server -Dialtact module). We will see how research on the technical constitution of cognitive and perceptual activities can be associated with a process of innovation. The technical devices present at each stage carry an interpretative framework that prepares the following stages. Devices which were initially developed for the purposes of performing experiments contributed both to scientific inventions and to developments with a practical and social finality.
Dialtact -A module for tactile interaction On the technical level, making a portable version of our devices required above all that we sufficiently miniaturize the system of tactile stimulation (a 4 × 4 matrix of piezoelectric stimulators) to achieve an energetically autonomous module that could be mounted on the back of a Smartphone. One of the prime advantages of this device is that it can be used with just one hand: the thumb on the screen controls the movements of the receptor fields, whereas the index finger is placed on the matrix of tactile stimulators on the back of the Smartphone. It also allows for the exploration and perception of the graphic interface on the screen. But above all, the software Dialtact allows for rich inter-individual tactile interactions, each subject directly controlling the activation of the tactile stimulators of their partner (Figure  5 ).
At the scientific level, the observations made with the Intertact server have shown that a very simple design of interaction allows for the creation of a dynamics of perceptual crossing which conveys the feeling of the "presence" of another subject. Now, the Dialtact device makes it possible to envisage the study of the development of a form of tactile language which could be differentiated between different communities of users. We are currently making some initial observations on groups of alpha-test highschool children. At the social level, the aim of this device (and the financing which helps us to develop it) is to propose applications which may interest the general population, and not merely communities of blind users. Indeed, it is a question of exploring a new market, that of tactile interfaces for access to information in a discreet manner (without having to bend one's head to look at a screen, and without having to wear head-phones) for guidance and intimate communication.
Previous research has shown that a MALDI-MS technique can be used to screen for sickle cell disease (SCD), and that a system combining automated sample preparation, MALDI-MS analysis and classification software is a relevant approach for first-line, high-throughput SCD screening. In order to achieve a high-throughput "plug and play" approach while detecting "non-standard" profiles that might prompt the misclassification of a sample, we have incorporated various sets of alerts into the decision support software. These included "biological alert" indicators of a newborn's clinical status (e. g. , detecting samples with no or low HbA), and "technical alerts" indicators for the most common non-standard profiles, i. e. , those which might otherwise lead to sample misclassification. We evaluated these alerts by applying them to two datasets (produced by different laboratories).
Despite the random generation of abnormal spectra by one-off technical faults or due to the nature and quality of the samples, the use of alerts fully secured the process of automatic sample classification. Firstly, cases of β-thalassemia were detected. Secondly, after a visual check on the tagged profiles and reanalysis of the corresponding biological samples, all the samples were correctly reclassified without prompting further alerts. All of the samples for which the results were not tagged were well classified (i. e. , sensitivity and specificity = 1). The alerts were mainly designed for detecting false-negative classifications; all the FAS samples misclassified by the software as FA (a false negative) were marked with an alert. The implementation of alerts in the NeoScreening ® Laboratory Information Management System's decision support software opens up perspectives for the safe, reliable, automated classification of samples, with a visual check solely on abnormal results or samples.
Introduction Newborn disease screening programs are front-line public health measures, and as such must be based on robust analytical methods and data-processing software. Cost effectiveness is a further requirement and has prompted the implementation of high-throughput screening units that reduce unit costs. Lastly, the greatest possible use of automation enables the medical team to focus exclusively on abnormal samples. Our MALDI-MS facility and the associated data processing and interpretation software packages were designed to address these challenges. Quantitative analysis with MALDI-TOF MS was initially viewed as implausible and inherently irreproducible because the mass spectrum's signal intensity varies as a function of the sample's composition, the sample's morphology, the laser conditions, and sample depletion during continuous laser exposure  [1, 2].
Hachani et al. 's pilot study was the first to demonstrate the MALDI-MS technique's potential in sickle cell disease (SCD) screening  [10]. A more comprehensive study by  Naubourg et al. then demonstrated the relevance of the NeoSickle ® approach (i. e. , automated, standardized sample preparation, followed by MALDI-MS analysis) and the NeoScreening ® sample processing and classification software  [11]. Naubourg et al. 's results showed that most of the samples had been correctly classified; however, there was still room for improvement, with a view to obtaining a completely automated, safe tool that enables technicians and biologists to validate results much more rapidly. Some MS spectra are difficult to interpret directly by the classification software, and must be inspected visually by the operator.
After a visual check, the operator can validate (or not) the results generated automatically by the software. There are many reasons why a spectrum may be difficult to interpret: Poor-quality sample collection, processing and/or transport, late sampling, very or extremely preterm infants (as defined by the WHO  [12] ), low HbA levels, or the presence of atypical Hb variants. In various domains, software programs have been developed to analyze, process and interpret MALDI MS data  [13, 14]. However, software solutions for clinical applications of MALDI MS are rare. The companies Bruker Daltonik GmbH (Bremen, Germany) and Biomérieux (Lyon, France) have respectively developed Biotyper ® and Vitek-MS ® software packages for facilitating and accelerating the identification of microorganisms on the basis of MALDI MS data  [15, 16].
These programs compare the sample's spectrum with a database of reference spectra for a range of microorganisms. In the field of infectious disease, PILOT. 4lab software (Info Partner SAS, Vandoeuvre-Les-Nancy, France) makes it possible to compile data (including MALDI MS data) from various analytical sources. In order to make a technology like MS usable in the analytical laboratory, it is essential to combine the instruments with data interpretation software and a Laboratory Information Management system (LIMS). The NeoScreening ® LIMS from Biomaneo (Dijon, France) meets this objective by including a module (Neoclinical ® ) that interfaces with clinical and/or epidemiological databases, and a module (e-NeoSickle ® ) for the processing and analysis of clinical MALDI-MS data. It is essential that defective analytical procedures do not lead to misclassifications.
Data Collection The data came from a previously described study of 6701 biological samples  [11]. We analyzed residual blood spots from standard Guthrie cards (used in our laboratory's routine screening activity) for samples with full datasets (i. e. , with clinical data, and avalidated Hb phenotypes using reference methods-the French neonatal screening program for SCDs was set up in 1995 under the aegis of the Association Française pour le Dépistage et la Prévention des Handicaps de l'Enfant  [17] ; the neonatal two-tier SCD screening strategy developed in France combines two of the following technics: Isoelectrofocusing (IEF), capillary electrophoresis (CE), or high performance liquid chromatography (HPLC)  [18, 19]. In order to select our cohort of Hb phenotyped newborns, we have applied in our laboratory in first line the automated CE system Capillarys ® 2 (Sebia, Evry, France) and in second line the automated HPLC system VARIANT nbs (Biorad, Hercules, CA, USA).
Analytical Data Flow The NeoScreening ® algorithm automatically discriminates between normal samples and samples containing an HbS variant. All the analytical results were centralized via a secure data collector. The newborns' profiles were automatically classified as FA, FAS or FS. Furthermore, the NeoScreening ® software included several alert features. The first set of alerts (referred to hereafter as "alert set 1") detected an abnormally low HbA:HbF ratio. The normal range of β:γ and βA:βS ratios was established by reference to the HbA:HbF value given by capillary electrophoresis (CE). The HbA:HbF thresholds were adjusted so that each sample from an infant requiring close monitoring or specific medical care (e. g. , very or extremely premature infants) was recorded by the automated classification software.
Alert index Parameter taken into account in the alert "a" Low Beta chain peak intensity "b" Ratio intensity 15,837/15,867 m/z peak "c" Intensity of Beta chain peak "d" Low HbS chain peak intensity "e" Low HbS chain peak resolution "f" resolving power of the HbS and HbA peaks "g" Ratio HbS/HbA peaks There were no alerts for newborns with S-thalassaemia (Sβ + or Sβ • ), since these samples were always classified as FS by the NeoSickle ® software  [11].
Discussion Neonatal screening for SCD must be trustworthy under all circumstances. To avoid phenotyping errors, insufficiently reliable results must be identified and tagged with an alert. This requirement is justified by the nature of neonatal screening, which is at the forefront of preventive public health measures. The NeoScreening ® software was developed to make the interpretation of SCD screening results as easy and fast as possible. To this end, the results are color-coded so that operators can instantly visualize the samples' classifications: The samples classified as FA appear in green, those classified as FAS samples appear in orange, and those classified as FS appear in red. For samples that have triggered an alert, a question mark is used to attract the operator's attention. Several criteria have been programmed for the detection of a non-standard spectrum that may (depending on their phenotype) cause the corresponding sample to be misclassified.
Hence, several types of non-standard spectra have been defined. The worst error for automatic classification is the assignment of an FA phenotype to an FAS newborn. The automatic discrimination of "standard" and "non-standard" profiles (on the basis of CE results) was monitored for alerts; abnormal curves are displayed in red  [17]. As mentioned above, there are two types of alert in the NeoScreening ® software: "Technical" alerts are related to low-quality data, whereas "biological" alerts are related to the characteristics of the sample itself (late sampling, extreme prematurity (as defined by the WHO), low HbA, atypical variants, etc. ). The variables taken into account differ as a function of the type of alert. Low β/γ and β/βS ratios were used as markers of a neonate's clinical status, rather than as quality controls.
Samples for which the spectra are classified as FA in the absence of an alert do not undergo further analysis; hence, none of the FS or FAS samples must be included in this category. In the present study, all the samples classified as FA in the absence of an alert by the MALDI MS system were also classified as FA by the reference methods. In the future, we shall study the FAS and FA spectra that triggered alerts from set 1 in more detail because the latter concern the characteristics of the biological sample per se (extreme prematurity, transfused infants, infants with beta thalassemia, low HbA levels, etc. ). After analysis with the NeoSickle ® solution, these samples' biological characteristics will be systematically characterized and confirmed using an additional analytical method. All the FAS samples misclassified by the software as FA were tagged with an alert; an initial visual verification and then sample retesting enabled correct reclassification in all of these cases.
In the present study, 708 spectra were tagged in Dijon and 298 were tagged in Lille. Considering that all the FAS and FS samples must be reanalyzed, only correctly classified but tagged FA samples (63 in Dijon and 50 in Lille) prompted an unnecessary visual reanalysis. This corresponds to only 0. 9% and 0. 7% of the samples, respectively. Designing a set of alerts is a compromise between not validating misclassified spectra on one hand and avoiding an excessively high reanalysis rate on the other. In routine use, the NeoSickle ® solution tags suspect spectra and alerts the operator. After a visual check, the operator will validate or invalidate the result initially produced by the software. In the latter case, the operator may decide to reanalyze the same target (to confirm his/her decision) or to reinitiate the entire analytical procedure (i.
e. , sample processing, deposition and analysis). Given that the present study included a high proportion of FAS, FS, premature or extremely premature newborns, it is difficult to calculate the percentage of samples that will require reanalysis in routine use. Nevertheless, one can extrapolate the data and predict that less than 1% of the FA spectra will be mislabeled. Of these mislabeled spectra, around 3 in 10 will be reclassified solely on the basis of the visual check, 5 in 10 will be reanalyzed on the same MALDI target, and only 2 in 10 (i. e. , less than 0. 2% of all the samples) will require reinitiation of the entire NeoSickle ® analytical procedure or analysis with another approach. The potential correlation between the different alerts and the variants other than HbS will be studied after we have acquired a large number of spectra from the corresponding samples.
It is considered that certain variants of the hemoglobin chains are expressed to different extents, which might enable a quantitative approach to developing alerts for certain variants. With the inclusion of additional alerts in the NeoScreening ® LIMS's decision support software, all misclassifications reported in our previous article  [11]  were detected and tagged by an alert. We showed that the mathematical data processing model and the classification algorithm (which take account of variables such as the signal-to-noise ratio, the intensity and the resolution) make it possible to produce reliable results even when "degraded" spectra (according to visual criteria) are present. We now intend to test the automatic MALDI-TOF classification strategy for first-line SCD screening in a prospective, real-life study.
Several languages exist to describe resources while supporting HATEOAS, e. g. , HAL 2  and SIREN  3. In our work, we use Hydra  [20] , a lightweight vocabulary that defines fundamental concepts to describe RESTful services in a machine understandable form. Hydra is expressed via JSON-LD (JavaScript Object Notation for Linked Data) specification, which decouples the resource serialization format from the communication format between the server and the clients. JSON-LD is an easy to learn and simple format that maps resources properties, e. g. , their provided functions, to concepts defined in existing data models (e. g. , ontologies), allowing resources to be effectively exploited by automated tools. An example of a resource description expressed using Hydra in JSON-LD is available online  4  , showing resource links annotated using semantic relation types, i.
Moreover, the solution is used to crawl the related resources without identifying more than one resource for a required task, and depends from a complex logic language, Notation3, which is a superset of RDF. Notation3 is difficult to use, even for expert users, compared to Hydra (the language adopted in our solution) that is expressed through JSON, a comprehensible and easy to learn format for humans. Focused on hypermedia, a service description model is proposed in  [5]  allowing the generation of a graph that captures state transitions in an activity layer, as well as resources, transitions, and response semantics in a semantic layer. However, the method uses a resource language that does not support dynamic resources. Moreover, the solution requires that the user knows the Schema. org data model, which has been extended with a set of concepts to semantically annotate Dynamic resource discovery is an active research area in other domains, as sensor networks  [19]   [12] , and fog computing  [14]   [11].
D. Automatic K-Resources Discovery Process Our discovery solution identifies static and/or dynamic resources, and adapts several graph-based algorithms for RG traversal. The algorithm to be used is given as input to the resource discovery process based on a library of algorithms (i. e. , BFS or DFS in this work), which will be extended in future works. The discovery process uses an integer variable, K, defined by the solution administrator, to represent the maximum number of discovered resources realizing similar functions. Algorithm 1 presents the pseudo code of the defined discovery process having the following entry data: -algoType (string): denotes the algorithm type to be used -F' (array of string): F'⊂ F, contains the requested function f and the functions that precedes f in FG -Id (array of string): refers to the resource(s) id(s) from which the specified algorithm type starts its process -K (integer): refers to the maximum number of discovered resources providing similar functions The output is the discovered array, containing the pairs [f, id] that correspond to the discovered resources matching each function f ∈ F'.
A. Environment Setups The function and resource graphs are based on simulations that are dynamically generated, using several criterion, i. e. , number/order of functions, number/type of resources, and number of resources (K) providing the same function. This is done to study our approach in different functions/resources graphs topologies. In the tests 9 , conducted on a Linux Debian (64 bits) virtual machine, with 1 dedicated Intel R Core TM i7-46000 CPU @ 2. 10GHz 2. 70GHz processor and 1 GB RAM, we show the algorithm response time (in milliseconds) based on an average of 5 sequential executions.
CFHTLS T0007 The photo-z catalogue is obtained from the CFHTLS data release T0007 1. CFHTLS T0007 photo-zs were computed in the 154 deg 2 sky coverage of the CFHTLS from multicolour images in the u * g r i z filters of MegaCam at CFHT. The photo-zs were obtained with the LePhare software  (Arnouts et al. 1999; Ilbert et al. 2006). Details on the method are given in  Coupon et al. (2009). Briefly, the photozs were computed using 62 templates obtained after having optimized four templates from  Coleman et al. (1980)  and two starburst templates from  Kinney et al. (1996) , and linearly interpolated between them to better sample the colourredshift space using the VVDS spectroscopic sample (e. g. Le  Fèvre et al. 2005). A particularly crucial step of the process is the calibration of the zero-points using spectroscopic samples which help in removing biases.
The resulting statistical errors on photo-zs depend on the redshifts and magnitudes of the galaxies. Following the photo-z catalogue based on the CFHTLS T0007 data release, we define the dispersion as σ ∆z phot /(1+zs) = 1. 48 × median |∆z| (1 + z s ) , ( 1 ) which is the NMAD (Normalized Median Absolute Deviation) estimator defined in  Ilbert et al. (2006) , with ∆z phot = z phot -z s , where z phot and z s are the photometric and spectroscopic redshifts respectively. The catastrophic failure rate η is set as the proportion of objects with |∆z| ≥ 0. 15 × (1 + z s ). In our analysis, we only consider galaxies that are outside the masks provided with the CFHTLS T0007 release. These masks are located around bright stars or artefacts, and mark regions of lower photometric quality. Thus photo-zs in these regions would be of poorer quality than those outside the masked regions.
Cosmic filament reconstruction method Our reconstruction of the cosmic web from photometric redshifts is based on the DIScrete PERsistent Structure Extractor (DISPERSE Sousbie 2011), a software that extracts the cosmic web filaments as ridges of the density field from discrete point distributions either in 3D or 2D. The extraction is naturally scale-free and robust to noise. The software is based on discrete Morse theory and theory of persistence. We refer to  Sousbie (2011)  for a detailed description of the theoretical grounds of the algorithm as well as its implementation. Application of the software to astrophysical data can also be found in  Sousbie et al. (2011). Here we will briefly present the main features of the algorithm with specific details of our 3D and 2D use in the relevant sections.