dataesr/scientific-paragraphs-categorization huggingface dataset:
When available, additional annotation data found in the dataset's primary publication were manually added to the annotation information provided with the microarray data. The datasets cover a broad swath of human immunology studies, and encompass many cell populations, tissues, diseases, and study types as illustrated by a graphical representation of relative occurrences of terms in the list of diseases loaded into our tool (Figure  1 ). A wide range of cell types and tissues are represented. The collection includes in vitro as well as in vivo transcriptome profiling studies. Diseases in this collection include both autoimmune and infectious diseases, as well as cancer and pathologies of the heart, brain, and blood. This collection is comprehensive but not exhaustive; users can request specific studies be added to this tool via the envelope icon at the top of the main screen.
Data upload and processing Data can be imported to GXB from GEO or from labderived data (Figure  2 ). In either case, both expression values and associated annotation data are uploaded by the user. The libraries or samples identified in the first row of the data file must match the sample names in the first column of the annotation spreadsheet so that annotations can be automatically associated with samples. One of the specific strengths of GXB is the ability to take any number of annotations (categorical or numerical) for a given sample set without requiring an update of system database design; GXB is flexible to any and all data that can be associated with the samples. Importing GEO datasets reads both the expression data and annotations from either soft or series matrix formatted files.
Dataset navigation interface The first step in transcriptional data access required development of a means to easily navigate and filter the full compendium of datasets. The "Go to GXB" button on the landing page at  [14]  takes users to a searchable and sortable list of datasets. Datasets of interest can be quickly identified either by filtering on criteria from pre-defined lists on the left, or by entering a query term in the search box at the top of the page (Figure  3 ). By default, all available datasets are listed and sorted by title. Filters are applied to produce a list of datasets that meet specific criteria: for example, choosing "tuberculosis" in the Diseases filter box returns a short list of eight datasets that include samples from TB patients. Selecting "monocytes" under the Sample Source box returns a different set of 13 datasets, profiling monocytes across several disease types.
The table can also be sorted by other column headers such as platform, species, disease, sample source, and sample count. On the right of this list are icons that will take a user directly to the original dataset entry on NCBI's GEO (G symbol)  [26] , or to the linked PubMed article (M symbol). The paper clip icon will take the user to a list of associated files that are available for download directly from GXB. The database can also be queried for studies in which a gene of interest is differentially expressed between two groups. Entering an official gene symbol in the query box on the top left corner of the page will return a list of studies for which the queried gene meets a user-specified fold change cut-off. For instance, a query for IFITM3 with a twofold difference between groups returns 43 datasets.
These include several studies of vaccine responses, as well as a TB dataset. It should be noted that many  datasets include more than two study groups, therefore multiple comparisons are performed for any given gene. For simplicity, datasets are listed only once even though further examination may reveal that differences meeting the pre-specified cutoff can be found for more than one group comparison. Thus, a navigation interface with advanced query and filter capabilities has been implemented to provide users with the ability to quickly identify relevant datasets for further in-depth exploration and data browsing. While the interface is intended to be easy for users to quickly identify datasets of interest, we recognize that any new software tool can be complex. A video tutorial is available as a companion to the website  [29] , which covers the majority of functions described in this manuscript.
Data browsing and visualization interface Clicking on one of the studies listed in the dataset navigation interface opens a viewer designed to provide interactive browsing and graphic representations of large-scale data in an interpretable format. This interface is designed to navigate ranked gene lists and display expression results graphically in a context-rich environment. Selecting a gene from the rank ordered list on the left will display its expression values graphically in the screen's central panel. Ranked ordered lists (left panel): The gene list shown on the left panel is ranked by default based on a combination of fold change and expression level difference between two groups of samples. The ranked gene list includes all the probes on an array, or in the case of RNAseq datasets all Ensemble IDs.
This allows the user to change the method used to rank the genes, or to include only genes that are selected for specific biological interest. Gene lists come from the KEGG database  [30] , or are constituted of immune-relevant genes (e. g. cytokine ligands and receptors, T cell signaling), or of genes associated with known disease signatures (GVHD and SLE, among others). (b) To change sample groups (Group Set button). In some datasets, a user can switch between groups based on cell type to groups based on disease type, for example. (c) To sort individual samples within a group based on associated categorical or continuous variables (e. g. gender or age). (d) To toggle between the histogram view and a box plot view. Samples are split into the same groups whether displayed as a histogram or box plot.
Data interpretation Measurements have no intrinsic utility in the absence of contextual information. It is this contextual information that makes the results of a study or experiment interpretable. It is therefore important to capture, integrate and display information that will give users the ability to interpret data and gain new insights from it. We have organized this information in tabs directly above the graphical display (Figure  5 ). The tabs can be hidden to make more room for the data plot display, or revealed with the blue "show info panel" button on the top right corner of the display. Information about the gene selected from the list on the left side of the display is available under the "Gene" tab. Description of the gene function is parsed from the RefSeq database  [31]. Links to the gene's page on external resources at NCBI Gene  [32] , Wikipedia  [33] , and Wolfram Alpha  [34]  are also provided.
A list of titles for the most recent 25 PubMed articles mentioning the gene is available with a single click, and the titles link out to Pub-Med for quick access to relevant literature. Information about the study is available under the "Study" tab. Data interpretation requires an understanding of how the study or experiment was done and why it was done. This section provides background information about the study design, a reference to the primary publication associated with the dataset, and a link to the Pub-Med abstract. Information available about individual samples is provided under the "Sample" tab. Rolling the mouse cursor over a histogram bar while displaying the "Sample" tab lists any clinical, demographic, or laboratory information available for the selected sample. When a large amount of sample information is available it can be broken down over multiple tabs, in order to display all ancillary data associated with a sample.
This level of detail is rarely available for studies published in GEO, but we made use of the software application to display such information in a recent publication  [13]  and associated website  [35]. Most of this sample information can be overlaid on the gene expression data histogram as colored rectangles at the bottom of each bar for categorical variables (Figure  4 ), or as data points plotted on a separate axis (see  [35] ). In our example from the tuberculosis study, this includes demographic data like age, gender, and ethnicity, as well as clinical and laboratory data such as radiographic extent of disease and drug resistance patterns of the infecting bacterial strains. As mentioned above, clinical and demographic data can be used to sort samples within a group. This feature makes it easy for users to quickly visualize relationships between clinical variables and changes in expression data.
The customized expression data can be downloaded along with user-defined clinical overlays as publication/presentation-quality graphics with one click. Legends for both the histogram and the clinical data overlays are viewable within the tool and are downloadable as well. Finally, the "Downloads" tab allows advanced users to retrieve the original dataset for analysis outside this tool. It also provides all available sample annotation data for use alongside the expression data in third party analysis software. Thus this tool not only allows the navigation and querying of vast amounts of data with minimal user learning time, it is also capable of integrating heterogeneous ancillary information that is paramount for the interpretation of transcriptome data and generation of novel knowledge.
Knowledge sharing The envelope icon on the top right corner of the display will setup a new e-mail message in the user's default e-mail application. The e-mail is pre-populated with a web link that can load the user's current view of the dataset, including sample groupings, information overlays, and other plot options. Systems-scale datasets provide investigators with a vast source of preliminary data that can lead to new hypotheses and new discoveries. In addition to offering ease of use and rich contextual information that facilitates data interpretation, our tool provides users with the ability to gather, organize and share findings via the social media site Google Plus (G+). The G+ link available within the tool allows users to easily and quickly archive and organize gene expression profiles of interest.
Conclusions The web application presented here employs state of the art web programming to uniquely address Big Data's canonical "3 Vs", which are: Volume, Variety and Velocity  [36]. Increasingly larger Volumes of data are generated through widespread use of systems or large-scale profiling approaches. The storage and management of such large volumes of data has become a challenge, but the GXB tool has been designed specifically to handle the large collections of datasets generated through systemsscale profiling approaches. The second V stands for Variety. Datasets have become increasingly heterogeneous, especially in the context of clinical studies where a wide array of information about study subjects is available and needs to be captured. Here we have shown that GXB can incorporate and present to the user large amounts of heterogeneous ancillary information necessary for data interpretation.
There is also an obvious need to integrate data generated across studies. This need will continue to grow as more clinical research studies begin to employ large scale profiling technologies in concert (e. g. genome, transcriptome, microbiome), thus heralding the era of so called "multi-omics" approaches. The third V stands for Velocity. Acquisition, storage, and integration of vast amounts of data is an important goal but in order to prove useful, especially as a source of novel insight and knowledge, this data must be readily and seamlessly accessible to the user. A primary strength of the GXB tool is its ability to enable rapid querying, access, and visualization of large and heterogeneous datasets. Tackling big data in the biomedical sciences will undeniably require the continued development and use of sophisticated data mining solutions, which enable bioinformaticians to map relationships in systems data and thereby reduce Figure  6  Use of social media for information archiving and sharing.
Special emphasis has been put into user interface design; the GXB interface is as clean and simple as possible so that the vast amount of data appears clear and seamless to each scientific user. As demonstrated in one of our earlier publications, such interactive data visualization tools can be employed for the generation of interactive supplements to static figures in publications  [13]. This enables more democratic access to the data underlying each static figure, and creates the opportunity for others to derive additional insight from this vast dataset. Greater transparency is an added benefit to providing data via a webbased tool. This is especially important when publishing studies based on large-scale datasets, since those data are seldom easily visualized by reviewers or by the community.
For all these reasons, the use of interactive figures and data browsing software as companion to publications must be promoted and should become widespread in scientific publishing. We hope that the data browsing software tool that we have developed will further this goal. The GXB tool is continually being improved to better enable data sharing and analysis. In the immediate future, we will begin to support upload and analysis of RNAseq, both at the gene-level and exon-level, and of high-throughput quantitative polymerase chain reaction (qPCR) datasets. It should also be noted that the tool can be used to display other data types, such as protein or cellular measurements, regardless of whether they are high dimensional. For instance, a modified version of the tool can be used to plot results from flow cytometry studies while displaying FACS images (see  [13, 46] ).
In this work, a constitutive law describing the hydro-mechanical behaviour of wood is presented. The main novelty of developed 3D F. E. model, among literature hardening models, is the coupling between plasticity and moisture effect. The hydro-mechanical wood behaviour is analyzed within the framework of non-linear mechanical theory of Mechanical Continuous Media (MMC). The local integration of constitutive laws is carried out with an iterative implicit outline using plastic corrections and the calculus procedure is implemented on Abaqus software and validated in the context of dowelled timber joints. The results of simulation were compared with published experimental results, showing good agreements and demonstrating clearly the effect of the moisture on the plastic behaviour.
Article published by EDP Sciences -Guitard equations described in  [3]  are included in the proposed model to take into account the rate of moisture on the orthotropic elastic properties of wood. We generalized these equations in plastic field correcting the form of the yield in our model developed in references  [9] [10] [11]. -The analytical formulation of the proposed model based on an incremental approach is different from those used in references  [1] [2] [3] [4] [5] [6] [7] [8] , it is considered as an approach "advanced" compared to those available in the literature. In addition, the model proposed in this study is rich in state variables; it contains 28 parameters including 9 for elasticity, 9 for plasticity and 2 for moisture. The proposed finite element model (FEM) was implemented on Abaqus finite element code  [12]  to explore the hydro-mechanical behaviour of wood in dowelled timber joints.
Conclusion and perspectives In this work, a constitutive 3D F. E. model for hydro-mechanical behaviour of wood is presented and successfully implemented in the commercial Abaqus software. A theoretical model based on the thermodynamics of irreversible processes is presented to describe the hydro-mechanical coupling between the elastic-plastic anisotropic properties and moisture for timber. Theoretical and numerical modelling were discussed in detail. The numerical integration of the 3D constitutive equations of the model was performed by implicit methods. To validate the model, two different steel-to-timber joints tests were used: an embedding test and a multiplefastener connection. First simulation studies the embedding of a metal rod into a timber specimen, which is widely used in timber structures to simulate the behaviour of connections.
Numerical Solutions The solution of the present nonlinear problem is obtained by the finite-difference method using the implicit formulation. A Matlab  [35]  computing code was developed to solve the proposed model equations. To solve the equations of heat balance of the canopy and the substrate surface, we need to linearize the terms of power 4. According to Deardorff  [36] , we can write for Tp (still valid for Tg), at iterations n: [ ] [ ] [ ][ ] n p n p n p n p n p T T T T T - + = + + 1 3 4 4 1 4 ( 20 ) i CLOI i SI in i CI L z i SI i SI T T h z t z T i , , ; , , ) ( ) , ( ϕ λ - - = ∂ ∂ - = (11) The other nonlinear terms are linearized by using their Taylor expansion in the vicinity of their value in the previous time step. The set of the finite-difference equations can be expressed as follows: B X A =.
Weather Data The weather data in 2007 of Antananarivo, the capital city of Madagascar, which were provided by the public managed weather station of Antananarivo, were used for simulations. The months of July and March are chosen to represent, respectively, the typical winter and summer climate conditions. For the winter season, the day with the minimum temperature (25 July) and the day with the maximum solar radiation (20 July) are considered. For the summer season, the day with maximum temperature is considered  (16 March). The ambient temperature (Text), relative humidity (hr), wind speed (u), beam and diffuse global radiations (Rdirh and Rdifh, respectively), and the sky vault temperature (Tsky), related to the city of Antananarivo are the weather data inputs of the model. We mainly focus on the results that highlight and compare the thermal performance of the green roofs.
, 2005). Although there exist some different developments between iLOVECLIM and the LOVECLIM versions, both consist of the following coupled Earth system components: the atmosphere (ECBilt), the Coupled Large-scale Ice-Ocean Model (CLIO), and vegetation (Vegetation Continuous Description -VECODE) (Fig. 2 ). ECBilt, the atmospheric model  (Opsteegh et al. , 1998) , consists of a dynamical core with three vertical levels at 800, 500, and 200 hPa. It runs on a spectral grid with a triangular truncation (T21), which translates to a horizontal grid with a resolution of approximately 5. 6 • lat × 5. 6 • long. The CLIO module  (Goosse and Fichefet, 1999 ) is a 3-D ocean GCM with a free surface. It has 3 • × 3 • horizontal resolution and 20 vertical layers. VECODE, the vegetation module  (Brovkin et al.
We reclassified the original seven categories from the Global Lithological Map Database (GLiM) from  Hartmann and Moosdorf (2012)  into "Bedrock (Bed)", (e. g. , granitic and metamorphic rock), and "Sedimentary (Sed)" (e. g. , sandstone, limestone) (Table  3 , Fig. 5 ). In the case of Bed, the subsurface is assumed to be quite consolidated/compressed, resulting in a low water content  (Almén et al. , 1986; Gleeson et al. , 2014). Bed was thus assigned a low porosity of 0. 1, which based on sources that showed depth profiles of bedrock sites  (Schild et al. , 2001; Nováková et al. , 2012) , that stays constant with depth. On the other hand, similar to the case studies from  Kitover et al. (2013) , a depth porosity function from  Athy (1930)  was applied for the Sed class, where the surface porosity ( ) is 0.
Experimental setup The model experiments are performed over the whole globe, with the VAMPER model forced by ECBilt land surface temperatures. These values are the lower boundary layer of the atmosphere and are calculated using a surface heat budget  (Goosse et al. , 2010). Referring to Fig. 3a , this means that ECBilt passes temperature values to the VAMPER(S) model (right side of Fig. 3a ) but no data are returned to ECBilt (left side of Fig. 3a ), leaving the climate unaffected from permafrost or changes in permafrost. The model experiments also include the spatially varying parameter values of geothermal heat flux and porosity provided by the new maps (described in Sect. 2. 2. 3 and 2. 2. 4). Two different model runs were performed: one without the snow enhancement or any imposed surface offset (VAMPER) and one with the snow enhancement (VAMPERS).
As opposed to our generalized snowpack parameterization scheme, described in Sect. 2. 1. 1, high-resolution snow models are fitted to observational data by analyzing, for example, the physics of accumulation, areal distribution, and snowsoil interactions. Therefore, it is arguable from this lack of detail and the results shown in Fig. 7 , whether the better option is to include a snowpack in VAMPERS or not. However, we contend that the VAMPERS model is doing a reasonable job since it is producing the surface offset that would naturally occur from the snowpack  (Goodrich, 1982; Smith and Riseborough, 2002). The simulated global distribution of this surface offset is shown in Fig. 8. It is determined by calculating the difference between the mean annual ground temperature (MAGT) using VAMPERS and the MAGT using VAMPER (no snow option and no imposed surface offset).
, 2003). It is also identified in the "Circumarctic Map of Permafrost and Ground-Ice Conditions"  (Brown et al. , 2014)  and "The Last Permafrost Maximum (LPM) map of the Northern Hemisphere"  (Vandenberghe et al. , 2014). But it should be noted that not all the relict permafrost in western Siberia is of late Pleistocene origin and may be from earlier cold stages  (Zemtsov and Shamakhov, 1993; French, 2007). Another reason for discrepancies between modeled and observed data is that high-resolution features in the landscape and topography cannot be captured by iLOVECLIM due to the limited spatial resolution. Such factors as vegetation and the organic layer, which can vary due to local topography and micro-climatic conditions, have been shown to affect the ac- tive layer and ground thermal regime  (Shur and Jorgenson, 2007; Fukui et al.
Code availability The iLOVECLIM (version 1. 0) source code is based on the LOVECLIM model version 1. 2 whose code is accessible at http://www. elic. ucl. ac. be/modx/elic/index. php?id=289. The developments on the iLOVECLIM and VAMPER(S) source code are hosted at https://forge. ipsl. jussieu. fr/ludus but are not publicly available due to copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr). Edited by: J. Fyke.
Background: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. Results: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions.
Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. Conclusions: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github. com/alifahsyamsiyah/learningDL.