dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements DC, TMK and JC acknowledge funding by the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig  funded by the  German Research Foundation  ( FZT 118 ). DC and TMK were additionally funded by the  Helmoltz Association  and by the  Alexander von Humboldt Foundation. Data collected by TMK and KB was achieved with support from the  National Geographic Society  and the  Pacific Island Climate Science Center. All authors thank data providers for collecting the data.
In order to quantify the hydrodynamics of the reservoir under different discharge conditions, two field campaigns were performed in 2015 (usual discharge condition) and 2016 (during a flood event). During these campaigns, aDcp measurements were carried out in order to evaluate the flow velocities along seven cross sections. These field campaigns are presented in details in section 2. The numerical codes TELEMAC-2D and SISYPHE (www. opentelemac. org) were used to simulate in 2D the hydrodynamics and the suspended sediment transport on this site. A ten kilometers long model was built and calibrated using the measured data from the 2015 and 2016 field campaigns, but also with measurements of sediment parameters that have been done separately (density, critical erosion shear stress). The numerical results are presented in section 3.
Contaminated sediments modeling Starting from this hydraulic and suspended sediment transport model, the dynamics of the contaminated sediments was simulated. In 2010, a core sampling campaign has been performed, and measurements of concentration have been done on 32 cores for several chemical agents. In this study, we focused on one example of chemical agent in order to built the modeling methodology. A 3D spatial distribution of the pollutant concentration in the reservoir was calculated with a kriging algorithm. The dimensionless results are presented in Figure  8. The SISYPHE code was used and modified for allowing the simulation of two cohesive sediment classes: one class for contaminated sediments (class 1), one class for the noncontaminated ones (class 2). With this approach, both sediment classes have the same physi-cal properties and are defined by a concentration threshold of pollutant concentration.
The rule-based approach Technology for making, running and analyzing large dynamic models, though in its infancy, is progressing significantly  (Bachman and Sorger, 2011; Cohen, 2015; Gyori et al. , 2017; Loew and Schaff, 2001). One powerful component are rule-based languages, such as Kappa  (Danos et al. , 2007a)  and BioNetGen  (Faeder et al. , 2009; Harris et al. , 2016)  for molecular biology and Mød  (Andersen et al. , 2016)  for organic chemistry. Common to rule-based languages are entities with a structure represented as a graph and rules that are graph-rewrite directives (Fig. 1a  and b ). The point of a rule is to distinguish between the transformation of a structure fragment and the reaction instance resulting from it in the context of specific entities. This distinction is a key organizing principle in chemistry (Fig.
Agents, patterns, embeddings and activity At the heart of languages like Kappa and BioNetGen lies the agent abstraction, Figure  1c , which conceptualizes a protein as an agent with an interface of sites that represent distinct interaction capabilities, such as binding and post-translational modification (Fig. 1c ). Through their sites, agents can connect into site graphs (Fig. 1 ). A site graph that exhibits the full interface and state of all its agents represents a molecular species. A rule r : L r ! R r involves two site graphs, L r and R r , which usually mention some but not all sites of their agents. L r and R r are therefore patterns, not molecular species (Fig. 1 ). The state of a system is itself a graph consisting of a (large) ensemble of disconnected site graphs, each representing one instance of a molecular species.
Panel (c): In rule-based languages for molecular biology agents stand for proteins and sites for their interaction capabilities, without, however, representing the underlying physical features and processes enabling them. A hypothesis or an assertion in the literature (i) typically mentions proteins whose agent abstraction (ii) involves several sites, collectively referred to as the agent's interface. The interface (ii) appropriate for a particular model can be assembled manually or automatically by scanning bioinformatic databases. The assertion (i) is converted, manually or with computerassisted reading, into a rule rendered graphically (iii) or textually (iv). Since a rule mentions only the sites and states necessary for a transformation, it is subject to revision as knowledge evolves. Sufficiency is often not within purview of experimental techniques, as not all biochemical aspects of an interaction can be observed distinguishable with respect to the mechanism expressed in r.
Kappa software agents The main Kappa ecology consists of several software agents that communicate through an ad hoc JSON-based protocol and expose highlevel functionalities through both a Python API and an HTTP REST service. At present these are: The Kappa Simulator (KaSim), the Kappa Static Analyzer (KaSA) and the Kappa Story Extractor (KaSTOR). A Python client enables scripting to tailor work flows and is available as the kappy package in pip. Modeling in a rule-based language is much like writing large and complex programs, which necessitates an integrated development environment. To integrate the new Kappa web services, we developed a browser-based User Interface (UI), which also comes packaged as a self-contained application for the Windows OS, Mac OS and Ubuntu. This effort raised several challenges pertaining to visualization and analysis that we address below.
KaSim is interactive, allowing a simulation to be paused and resumed at any time. A versatile intervention language can be used for defining perturbations, such as altering rate constants and adding/removing agents interactively or at time points set in the input file. The intervention language supports queries about the state of the system and requests for data dumps, such as snapshots of the mixture and rule influence matrices (Section 3. 3). Simulation is the main tool for studying the dynamical behavior of a rule-based model. However, some properties of a model can be determined by direct inspection and manipulation of the rules without execution. Such properties are static in the sense of being timeindependent, yet they relate to behavior because they demarcate its envelope. For example, a static property is the reachability of a specific molecular species, i.
The analysis made use of the initialization statements ('init') for ligand E and receptor R. (Static analysis only cares whether the initial copy number is non-zero. ) If we remove the first statement, KaSA responds in real-time by flagging all rules as unreachable-because of the absence of ligand. This can be useful to ensure that the initial condition of complex models covers the intended agent types. The command line version of KaSA provides many additional analyses that are not yet exposed in the software agent.
]), S(x[. ], y{u}) -> K(z[1]), S(x[1], y{u}) 'u' K(z[1]), S(x[1]) -> K(z[. ]), S(x[. ]) 'p' K(z[1]), S(x[1], y{u}) -> K(z[1]), S(x[1], y{p}) '*' S(x[. ], y{p}) -> S(x[. ], y{p}) Since causality is a relation between events, the identity rule '*' serves to transform our query into an event. Suppose a particular mixture contains three substrate agents S and two kinase agents K and that a simulation happened to produce the trace s ¼ bbubpuppuÃ, where each event is labeled by the rule that produced it and contains information about which agent identifiers matched the rule. KaSTOR, the causal analysis software agent, attaches to every site of every agent instance in the mixture a thread and records whether an event only tested the state of a site or whether it also modified it. In typical Kappa rules, a modification also implies a test.
Visualization: the DIN KaSim can be directed to compute influence matrices DI r;s t; s ð Þ, Equation (2), aggregated over user-specified time intervals t; t þ s ½. Typically, s and increments in t are chosen so as to create overlapping intervals for smoothness. A time series of such matrices can be uploaded to the DIN visualization server (DIN-Viz) (https://github. com/CreativeCodingLab/DynamicInfluenceNetworks), where the DIN is presented as a node-link diagram, leveraging a force-directed layout to position related nodes and clusters near to each other. The nodes represent the rules and the links convey the influence of rules on each other, color-coded with red (green) signifying a negative (positive) influence. This visualization enables the topological analysis of rule-fluxes in a KaSim simulation at the chosen time step scale.
A time slider controls the current time step, enabling the user to move through time or jump to a particular time step. Standard playback controls animate the simulation so that the user can observe changes in the DIN over time. Although force-directed layouts mitigate visual clutter in nodelink diagrams, dense networks can still be difficult to make sense of-an issue that is exacerbated when representing large datasets. DIN-Viz enables the user to manually create a layout of nodes or entire clusters through relocating and 'pinning' them to specified locations. This reorganization reduces clutter, but also helps users to distribute rules and clusters in a way that is cohesive with their thought process during exploratory analysis. When pinned, the spatial positioning and grouping of selected rules and clusters is preserved over the course of the entire animation, over-riding the normal layout behavior.
The challenge is to develop mathematical techniques and a sound software infrastructure for analyzing, visualizing, manipulating, simplifying-in short, reasoning with-models that are like empirical objects. We see two major challenges that rule-based approaches need to address. The first consists in developing formal and computational techniques for constructing explanations. The glimpse on causality in Sections 3. 4 and 4. 4 is just a beginning. Second, languages like Kappa and BioNetGen are not knowledge representations. Tying modeling to knowledge representation requires a structured staging area designed to formally organize the abstraction process leading from biochemically rich and grounded descriptions to logical, ungrounded, but executable expressions  (Basso-Blandin et al. , 2016; Harmer et al.
sRNAPipe evaluates 10-nucleotide 5′-overlaps of reads on opposite strands to test ping-pong amplification for putative PIWI-interacting RNAs, providing counts of overlaps and corresponding z-scores. Conclusions: sRNAPipe is easy to use and does not require command-line or coding knowledge. This pipeline gives quick visual and quantitative results, which are usable for publications. sRNAPipe is freely available as a Galaxy tool and via GitHub.
Reads of the chosen length (default: 18-29 nt) are selected and aligned against the reference genome using BWA allowing up to the selected maximal number of mismatches. The result of this first step is a SAM (Sequence Alignment/Map) file output by BWA sorted by genomic position and filtered out for unmapped reads using SAMtools  [8]. The corresponding bedgraphs are created using BEDtools genomecov  [9]. The R/Bioconductor package Sushi  [10]  is used to plot the coverage along the chromosomes or transposable elements. For the following steps, only the genome-mapping reads with the selected maximal number of mismatches are conserved and for accurate mapping of small RNA reads, ribosomal RNA, miRNA, tRNAs, snRNAs filtering was performed before mapping the resulting bonafide reads on transcripts and TEs.
Results and discussion The results can be retrieved in the main html output page. On the first page, the number of all genome-mappers and the number of genome-unique mappers (reads that map only once to the genome) are reported and used later for normalization. The length distribution of mapped reads is also provided, allowing to check whether it fits the expected length distribution (with the typical peaks according to the species). We evaluated our pipeline extensively and used it to analyse hundreds of small RNA-seq libraries with multiple configurations including different species, different genome releases and different RNA extraction protocols  [11] [12] [13] [14]. To present examples, the pipeline was run with the SRA dataset SRR4428936  [12]. Figure  2a  depicts a bimodal size distribution, with a peak at 22 nt that represents the miRNA population and an enrichment of small RNAs with sizes between 23 and 29 nt which represents the piRNA population.
Availability and requirements Project name: sRNAPipe Project home page: sRNAPipe is freely available via GitHub: https://github. com/brassetjensen/sRNAPipe, and in the Galaxy Toolshed: https://toolshed. g2. bx. psu. edu/ repository?repository_id=13a327665795142c For each TE, the table contains the sum of all overlaps, the sum of 10-nt-overlaps, the mean of all overlaps, the standard deviation, the z-score and corresponding p-value for 10-nt-overlaps Operating system(s): Unix Programming language: Perl Other requirements: Galaxy server License: GNU GPL Any restrictions to use by non-academics: none.
Comparison of the Accuracy in the Different Models. In statistics, the sum of the squares of residuals (deviations predicted from actual empirical values of data) is a measure of the discrepancy between the data and an estimation model. A small sum of the squares of residuals indicates a tight fit of the model to the data. However, in the present study, the comparisons of the accuracy in the different models cannot be based on the differences in the sums of the squares of residuals because the residuals in the power-law model corresponded to the logarithm of the residuals and because the individual regression of the first hyperbolic model (S Crit1 ) did not correspond to regressions between t lim and running speeds (S) but regressions between t lim and distances (D lim ). Moreover, it would be assumed that there was a homoscedasticity in the residuals of the running speeds, which could not be tested with only 4 datasets in an individual regression.
The 2-parameter models are easier to compute either by a nomogram  [48]  or by the current database software (Microsoft Excel, LibreOffice Calc, etc. ). The calculation of S Crit1 is much easier than the parameters of the other models. Particularly, it is very easy to calculate S Crit1 from two running performances: S Crit1 = (D lim2 -D lim1 ) (t lim2 -t lim1 ) (42) In addition, the S Crit1 model is the only model that can directly predict the performance corresponding to a distance from its parameters (ADC 1 and S Crit1 ): D lim = ADC 1 + S Crit1 * t lim t lim = (D lim -ADC 1 ) S Crit1 (43) In the present study, the other models can only predict performances corresponding to a value of t lim. In these models, the protocol presented in Section 3. 3 is necessary for the prediction of a performance corresponding to a distance.
Numerical Model Groundwater flow models, which provide adequate modelling for most hydrogeological problems from real situations, are a commonly used tool in hydrogeological research. Therefore, numerical modelling has become an important and valid method in groundwater research  [20, 32]. The method that is applied to the resolution of this study's model is based on code that solves the equation for the underground flow using finite 3D differences. This method also discretizes the environment of block modelling  [33] , which is freely available and widely disseminated and which has multiple prior examples  [22, 34] ; this gives the method a high degree of acceptance in the scientific research on groundwater, for which there is a lack of data. Researchers have used the Visual MODFLOW program, which simulates behavior under constant and transient regimes, as a processor in this method.
Model Setup and Geometry The model layers' geometry and the initial hydrogeological parameters were obtained from other studies that the Geological Survey of Spain (IGME) and other researchers have carried out  [3, [9] [10] [11] [12]. A schematic representation of the conceptual model is shown in Figure  3. Values for the piezometric levels of the observation points were obtained from the CHS and the IGME, and the rainfall data were obtained from the State Meteorological Agency. For the preparation of the initial geometry, topography (0) was obtained from existing maps (National Geographic Institute) and then digitized using computer-aided design programs. The model was framed using the Universal Transverse Mercator (UTM) coordinates (647,000, 4,155,000) and (720,000, 4,210,000). The study area was modelled using a finite-difference regular grid with grid cells spaced at 500 m × 500 m.
The piezometry was performed with well data from the CHS and IGME databases. IGME's piezometric measurements, which began in the mid-1970s, show that current stability was preceded by a gradual recovery of groundwater, linked to the initiation of the TSWT as an external source of water at the beginning of 1979. The available historical data help to reveal the aquifer's status before the IGME measurements began. At the initiation of water withdrawals in the 1920s, piezometric heads in the Pliocene and Messinian aquifer layers in the coastal area were respectively 25 m and 45 m above sea level (masl) (Figure  5 ). Such values are much higher than the ones provided by the IGME database and corroborate the description of artesian wells  [37]  by local newspapers at the beginning of the 20th century.
The data on the piezometric levels, which were obtained through the interpolation of non-linear functions between 1925 and 1975, as well as the IGME and CHS data for 1975 through to 2015, were evaluated and introduced into the model (in stress periods of 5 years) to determine the behavior of the system in both steady and transient states. The upper aquifers today have higher hydraulic heads than the deeper aquifers, which is the opposite of the situation a century ago (Figure  5 ). An inversion occurred in the vertical hydraulic gradient between the layers, and the movement of the flow between the aquifer layers through the wells is now the opposite of what it was, corresponding to these two phases that occurred during this time. In the last 20 years of data , the groundwater levels were stable, with measurements of 10-15 masl for the Quaternary and 15-25 m below sea level (mbsl) for the Pliocene; however, there were irregular but decreasing levels of 55-80 mbsl for the Messinian.
Calibration of the Model Seventeen observation wells were used for the calibration, along with piezometric data obtained from the IGME and CHS databases (Figure  1 ). A simulation of the last 90 years, starting in 1925, was conducted. The steady-state simulation reflects the original state of the aquifers in 1925, prior to the anthropization of the zone due to extractive pumping. Model calibration is usually an iterative process in which values of hydrogeological parameters are adjusted so that the differences between the variables that the model calculates and the field measurements are as small as possible  [40]. The method used in this study to calibrate the model was trial and error, and the most sensitive parameters were the recharge and the hydraulic conductivity.
Objectives: To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP). Methods: A survey of the literature was performed by the two editors of the NLP section of the International Medical Informatics Association (IMIA) Yearbook. Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed based on title and abstract. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017.
About the Selection Process Papers were retrieved relying on PubMed (http://www. ncbi. nlm. nih. gov/pubmed/) and the Association of Computational Linguistics (ACL) Anthology Searchbench (http:// aclasb. dfki. de/). This year, two PubMed queries using free-text keywords and minimal metadata were used sequentially (Figure  1 ). We do not rely on MeSH terms in the queries because at the time of retrieving articles for review, many have not been indexed yet. The ACL Anthology query restricted our selection to the most selective journals (Transactions of the Association for Computational Linguistics (TACL), Computational Linguistics), conferences (ACL, Empirical Methods in NLP (EMNLP), North American Chapter of the ACL (NAACL), Computational Linguistics (COLING)) and workshops (ACL BioNLP). It used the free text keywords "medical", "clinical", and "health".
The collection of papers using these queries brought back 414 titles and abstracts from the PubMed NLP query, 224 additional titles and abstracts from the PubMed Text Mining query and 71 from the ACL Anthology, resulting in a total selection of 709 papers. Then, we used the results of the 2017's selection to train a logistic regression classifier to automatically rank the selected papers from most relevant to less relevant  [5]. The section editors then used the BibReview tool (https://pypi. python. org/pypi/BibReview) to classify the papers based on titles and abstracts into four categories: (1) Off Topic (OT) which identified papers that focused on topics outside of the scope of clinical NLP, such as biological natural language processing, knowledge representation, psycholinguistics, or even image processing; (2) No (N) for papers that did not provide a contribution to either NLP methodology or clinical outcome.
The study offers an extrinsic evaluation. This is a valuable step in the direction of application-oriented evaluations of NLP systems. However, as pointed out by the authors in their candid limitation section, the system uses simplified queries and the results of the analysis should be confirmed on a more realistic task. Iqbal et al. [14]  present the development and evaluation of a multi-phase rule-based pipeline for the recognition and classification of adverse drug effects (ADEs) in psychiatric narratives. The system is open source and includes an improved version of the popular ConText algorithm. The dataset used is multi-centric as it comprises clinical notes from four UK-based psychiatric hospitals. The system shows good performance at detecting ADEs related to antipsychotics and antidepressant drugs.
They study the causes for performance reduction with an ANOVA analysis and provide a very interesting discussion of what transfer learning is. Sohn et al. [17] use birth cohorts from two US-based hospitals to characterize linguistic variations in reports of asthma in children. The authors use the results of their analysis to explore NLP system portability for asthma ascertainment in the context of retrospective studies. Castro et al. [4]  address the extraction and classification of mammography information from radiology reports written in English. These free-text reports contain information expressed using the standardized lexicon and classification system described in BI-RADS (Breast Imaging Reporting and Data System). As described below in more details this study tackled the problem from an application point of view and attempted to cover a large number of hospitals.