dataesr/scientific-paragraphs-categorization huggingface dataset:
life) database, which consists of the five interconnected resources: 'Human metabolism', 'Gut microbiome', 'Disease', 'Nutrition' and 'ReconMaps'. These resources are interlinked based on shared nomenclature and Users can search all resources, using the Quick Search bar (1), or specific resources through the 'Browse' button  (2)  or the resource panels available in the main page (3). At any point in time, it is possible to provide feedback or report issues with the VMH through the feedback button (4). If a user performs a quick search (e. g. 'h2o') different result grids will be available. Each type of entity will be displayed in its corresponding grid  (5). Each detail page (6) contains additional information and connections with other resources (both internal and external -7). For instance, by selecting 'Associated human reactions' a user can then navigate to a reaction detail page  (8)  and from there to other associated entities, such as human genes  (9).
The VMH also allows the visualization of metabolic pathways through the 'ReconMaps' resource  (10). Users can search for a metabolite using the side bar of the map interface  (11)  and get results as locations in the map panel  (12). It is also possible to search for specific reactions making it easier to investigate specific pathways of interest  (13)  and upload simulation or experimental data  (14)  through the interface or the COBRA Toolbox  (10). With the nutrition resource the VMH offers the ability to design in silico diets that can be used to perform simulations. In this interface, users can search foods from the 'Available foods' panel  (15)  and add them to the 'Selected foods' panel by specifying the portion size in grams  (16). During this process, the top of the 'Selected foods' panel will automatically update information about the diet  (17).
DATABASE DESCRIPTION The VMH database contains 17 730 unique reactions, 5180 unique metabolites, 3695 human genes, and 632 685 microbial genes as well as 255 diseases, 818 microbes and 8790 food items. Unique features of the VMH database include (i) metabolic reconstructions of human and gut microbes that can be used as a starting point for simulations; (ii) seven comprehensive maps of human metabolism that permit a visualization of omics data and simulation results; (iii) a nutrition designer that allows researchers to design personal dietary plans for computational simulations; (iv) a userfriendly web interface for browsing, querying and downloading the VMH database content; (v) a well-documented representational state transfer application-programming interface (API) for easy access to the database content; and vi) user feedback integration through the feedback button accessible in all pages of the website and the ReconMaps interface, which allows users to leave comments on specific reactions and metabolites (Figure  2 ).
'Human metabolism' resource The VMH database hosts the most recent version of the human metabolic network reconstruction, Recon3D (4), which describes the underlying network of 13 543 metabolic reactions distributed across 104 subsystems, 4140 unique metabolites and 3288 genes expressed in at least one human cell. The content of Recon3D has been assembled through an extensive literature review over the past decade by the systems biology community (4,14-16). Individual pages are dedicated to each reaction, metabolite and gene. These pages contain information on literaturebased evidence as well the relations of the page with other entities in the VMH database (Figures  1  and 2 ). Novel features of Recon3D include molecular structures and atom mappings  (4, 17) , which are visualized on the metabolite and reaction pages, respectively, in addition to thermodynamic information  (18).
The maps support low-latency content queries and custom dataset visualizations, which are either represented as a text file or automatically uploaded from the COBRA Toolbox  (10, 21). Tutorials have been developed demonstrating the visualization of data and simulation results onto the ReconMaps (https://opencobra. github. io/cobratoolbox/ stable/tutorials/tutorialRemoteVisualisation. html). Users can submit feedback through the map interfaces by rightclicking on specific elements. From each map entity, users can access the corresponding entry in the VMH database and obtain further information from external resources, such as the HMDB  (22) , KEGG  (23)  and CHEBI  (24). The VMH connects ReconMaps with the Parkinson's disease map, PDMap  (25) , which visualizes cellular processes known to be involved in Parkinson's disease through the 'Biochemical and disease maps' section on the Metabolite page, where possible.
We have identified 168 metabolites that are shared between these maps, providing a connection between the general human metabolism and Parkinson's disease related cellular pathways. Similarly, ReconMaps have been connected to the Atlas of Cancer signaling network resource (ACSN)  (26) , which visualizes pathways known to be deregulated in cancer cells, through shared 252 proteins implicated in 22 functional modules of ACSN and in 51 subsystems of ReconMaps. Further disease maps are currently assembled by the community  (27) , and we will continue to increase the connectivity of the VMH and the ReconMaps to these valuable resources. The disease map connections with ReconMaps enable for data analysis and visualization beyond metabolism.
The VMH database also hosts the Leigh Map  (30) , which represents a computational gene-to-phenotype diagnosis support tool for mitochondrial disorders. The Leigh Map consists of 87 genes and 234 phenotypes expressed in Human Phenotypic Ontology (HPO) terms  (31) , and they provide sufficient phenotypic and genetic variation to test the network's diagnostic capability. The Leigh Map is a first step in integrating diagnosis tools within the VMH. Further development of this resource will provide a detailed multi-layered overview of the connection between clinical features, genetic mutations and metabolic pathways facilitating better understanding of the underlying mechanisms of complex diseases.
'Nutrition' resource The 'Nutrition' resource consists of two parts: (i) a food database mapped onto the metabolites present in the VMH and (ii) a diet database listing the nutritional composition of 11 pre-defined diets. The food database was built by integrating the molecular composition information for 8790 food items distributed in 25 food groups obtained from the USDA National Nutrient Database for Standard Reference  (32). Of the 150 nutritional constituents, 100 could be mapped onto the metabolites present in the VMH database. Most of the remaining unmapped constituents represent general metabolite classes (e. g. fibers). The resource can be queried based on food items as well as their nutritional constituents. The diet database contains 11 diets that were formulated based on real-life examples and literature.
Resources connectedness We have focused the design of the VMH on the ability to navigate all its resources seamlessly. From any detail page in the VMH, it is possible to access related entities through links in association grids (Figure  2. B). In addition, each entity of the database contains a list of links to external resources with different purposes and focus (e. g. chemistry, nutrition and clinical). We continuously verify the integrity of our links and where possible, use the resolving system Identifiers. org (34). Overall, the VMH connects to 57 different external databases (Table  1 ). This focus on connectedness will continuously increase the amount and the depth of knowledge that can be accessed through the VMH, thereby Nucleic Acids Research, 2019, Vol. 47, Database issue D623 increasing the database's utility to the scientific community beyond the systems biology community.
The VMH beyond computational modeling A growing number of studies link microbial composition with diet and disease  (35, 36). The generation of novel hypotheses about the functional implications of observed correlations, e. g. between microbial abundances in disease states, is hindered by the lack of online databases to facilitate such work. In particular, the 'diet designer' tool in conjunction with computational modeling permits the generation of in silico hypotheses that could then be experimentally tested. Moreover, the use of synthetic microbial communities is of great value for hypothesis testing, and the VMH database could facilitate the design of defined microbial communities with specified metabolic capabilities.
Accessing the API The VMH API can be reached at https://www. vmh. life/ api. This page displays some of the available resources that can be used to retrieve data. Each of these is reachable through a Uniform Resource Identifier (URI), which provides data in different formats, such as HTML, JSON or flat file format (CSV). For each of these identifiers, additional query parameters can be applied, which allow to further refine the search (e. g. search a metabolite with a given HMDB identifier). All API endpoints and query parameters are detailed at https://www. vmh. life/ api/docs, where users can test the API usage and get code templates, in different programming languages, to integrate access to the VMH in their applications or scripts.
DATABASE IMPLEMENTATION The VMH database was implemented with MySQL 5. 6 (https://dev. mysql. com/). The front-end is reachable via web browser at https://vmh. life and was developed in Sencha ExtJS 5. 1 (https://www. sencha. com/). The API was developed using Python 2. 7 via the DJANGO framework and the Django Rest Framework package. The diagram editor CellDesigner (version 4. 4) (  19 ) was used to manually draw the metabolic maps of the 'Recon-Maps' resource. Continuous quality control was achieved using a dedicated MATLAB (Mathworks, Inc. ) code for map correction and manipulation. This code and the corresponding tutorial are freely available in the COBRA Toolbox (10) (https://opencobra. github. io/cobratoolbox/).
CONCLUSION The VMH database captures information on human and gut microbial metabolism and links this information to hundreds of diseases and nutritional data. Therefore, the VMH database addresses an increasing need to facilitate rapid analyses and interpretations of complex data arising from large-scale biomedical studies. Unique and distinguishing features of the VMH database are the following three key factors. First, the VMH database is a comprehensive, interdisciplinary database that permits complex queries. Sec-ond, the VMH database provides a graphical representation of the 'Human metabolism' resource through the 'Re-conMaps' resource, thus allowing for the analysis of complex, multi-faceted omics data in the context of the biochemical knowledge captured in the VMH database. Third, the VMH database represents a starting point for computational modeling of human and microbial metabolism in healthy and diseased states by providing information and simulation constraints and being fully compatible with the COBRA Toolbox  (10).
Figure1. Overview of the Virtual Metabolic Human (VMH) database. The VMH database is divided into two interfaces, and its database contains five distinct but connected resources. Users can interact with the database using the two available interfaces: (i) a user-friendly web interface and (ii) an application-programming interface that allows programmatic access to the information contained in the database. At the core of the database is the representation of reconstructions as sets of reactions. The database connects the five resources through shared nomenclature: (i) the 'Human metabolism' and 'Gut microbiome' resources share metabolites and reactions, (ii) the nutrients in the 'Nutrition' resource are mapped to metabolites that can be shared by the human and gut microbes and (iii) the diseases in the 'Disease' resource include affected genes and metabolite biomarkers present in the 'Human metabolism' resource.
The VMH also allows the visualization of metabolic pathways through the 'ReconMaps' resource (10). Users can search for a metabolite using the side bar of the map interface (11)  and get results as locations in the map panel (12). It is also possible to search for specific reactions making it easier to investigate specific pathways of interest (13)  and upload simulation or experimental data (14)  through the interface or the COBRA Toolbox (10). With the nutrition resource the VMH offers the ability to design in silico diets that can be used to perform simulations. In this interface, users can search foods from the 'Available foods' panel (15)  and add them to the 'Selected foods' panel by specifying the portion size in grams (16). During this process, the top of the 'Selected foods' panel will automatically update information about the diet (17).
FUNDING Luxembourg National Research Fund (FNR)  through the  ATTRACT program  [ FNR/A12/01 ];  FNR CORE program  [ C16/BM/11332722 ];  FNR OPEN grant  [ FNR/O16/11402054 ];  FNR National Centre of Excellence in Research (NCER) on Parkinson 's disease; European Union's  Horizon 2020 research and innovation program  under grant agreement [668738];  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [757922]. This study received generous support from the  Art2Cure ASBL  for the generation of ReconMaps. Funding for open access charge:  Luxembourg National Research Fund (FNR)  through the  ATTRACT program  [ FNR/A12/01 ].
DATA AVAILABILITY The VMH database and its content are freely available at https://www. vmh. life. Metabolic reconstructions and additional materials are available in the 'Download' section, and search results are directly downloadable from the grid interfaces. Users can provide feedback through the different platforms on the website. Detected issues will be addressed and integrated into the database in subsequent releases. The API can be accessed by third-party applications and is also accessible via web browser at https://www. vmh. life/ api. Detailed documentation for the API is available at https://www. vmh. life/ api/docs.
' Chemspider 'ChemSpider is a free chemical structure http://www. chemspider. com/ 1357/5180 = 26. 2% database owned by the Royal Society of Chemistry. It provides fast access to over 60 million structures, properties, and associated information. ' Drugbank 'The DrugBank database is a unique https://www. drugbank. ca/ 271/5180 = 5. 2% bioinformatics and cheminformatics resource that combines detailed drug data with comprehensive drug target information. ' EPA 'United States Envrionmental Protection https://comptox. epa. gov/dashboard/ 793/5180 = 15. 3% Agency -Chemistry Dashboard. ' Foodb 'FooDB is a comprehensive resource on food http://foodb. ca/ 1354/5180 = 26. 1% constituents, chemistry and biology. It provides information on both macronutrients and micronutrients. ' HMDB 'The Human Metabolome Database (HMDB) http://www.
hmdb. ca/ 5008/5180 = 96. 7% is a freely available database containing detailed information about 114 100 small molecule metabolites found in the human body. ' KEGG 'The Kyoto Encyclopedia of Genes and http://www. genome. jp/kegg/ 4773/5180 = 92. 1% Genomes integrates genomic, chemical and systemic functional information. KEGG is an integrated database resource consisting of eighteen databases. ' MetanetX 'MetaNetX. org is an online platform for https://www. metanetx. org/ 4989/5180 = 96. 3% accessing, analyzing and manipulating genome-scale metabolic networks and biochemical pathways. ' METLIN 'METLIN is a comprehensive MS/MS https://metlin. scripps. edu/ 1372/5180 = 26. 5% metabolite database. METLIN includes 961 829 molecules ranging from lipids, steroids, plant & bacteria metabolites, small peptides, carbohydrates, exogenous drugs/metabolites, central carbon metabolites and toxicants.
' ModelSEED 'ModelSEED is a resource for the http://modelseed. org/ 1606/5180 = 31. 0% reconstruction, exploration, comparison, and analysis of metabolic models. ' PDMAP 'The PD map is a manually curated knowledge https://pdmap. uni. lu/MapViewer/ 282/5180 = 5. 4% repository established to describe molecular mechanisms of PD. It compiles literature-based information on PD into an easy to explore and freely accessible interactive molecular interaction map. ' PubChem 'Pubchem is an open chemistry database that https://pubchem. ncbi. nlm. nih. gov/ 4979/5180 = 96. 1% collects information on chemical structures, identifiers, chemical and physical properties, biological activities, patents, health, safety, toxicity data, and others. ' KNApSAcK 'KNApSAcK is a comprehensive http://kanaya. naist. jp/KNApSAcK/ 446/5180 = 8.
6% species-metabolite relationship database. ' Wikipedia 'Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation. ' https://www. wikipedia. org/ 757/5180 = 14. 6% Reactions BRENDA 'BRENDA is the main collection of enzyme functional data available to the scientific community. ' http://www. brenda-enzymes. org/ 14864/17730 = 83. 8%.
Each sequence was repeated until completing 8 pulses. A custom software was developed in LabView (National Instruments) to control the whole system. The sequence of electroporation and measurement signals is detailed in Fig. 1b. Compensation for the effects of the system frequency response produced by the microelectrodes, cables, amplifiers, etc. , was achieved by means of a three-references calibration method  [39]. The references were obtained by measuring, with the same setup, saline solutions with three different conductivities in the range of 0. 1 S/m ± 70 %.
However, the dynamics of this change are in the order of minutes and not seconds. The absence of cell swelling was confirmed after visualizing the cells under bright field microscopy during the first 5 minutes after the pulses application. Finally, an increase in temperature due to Joule heating could also produce a reduction in impedance. However, the accumulated temperature change was calculated and resulted to be negligible to explain the observed change (≈0. 3 °C for 8 pulses of 100 µs at 1400 V/cm). -Fig. 3. Impedance spectrum evolution during electroporation. Data shown corresponds to a representative example for C2C12 cells exposed to an electric field intensity of 1000 V/cm. a) Bode plots showing the evolution in the impedance spectrum (magnitude and phase) pulse after pulse for a complete 8 pulses exposure experiment.
In Fig. 5a  a schematic representation of the model with the different components is shown. The multifrequency impedance data for each cell line corresponding to measurements during exposure of cells to three different electric field intensities (600, 1000 and 1400 V/cm, respectively) were fitted to the model in MATLAB. In the case of the C2C12 and Hela cells the only restriction imposed to the fitting algorithm was a lower boundary to the parameter Rint corresponding to its initial value. Under the considered experimental conditions this parameter is expected not to decrease during the electroporation procedure. The rest of parameters were left to freely vary during the fitting process. In Fig. 5b  the evolution of both Rpores and Rext , normalized with respect to their initial values, is shown for representative examples of the three electric field intensities assayed in C2C12 and Hela cells.
In order to quantify the extent of permeabilization, at least three different fields of each sample were recorded under the fluorescence microscope and subsequently automatically counted with the free image processing software ImageJ (MacBiophotonics). The mean number of cells obtained for the maximum electric field was considered as 100 % permeabilization and the rest of results normalized with respect to this value. Following, the total % of impedance variation extracted from the value at the end of the treatment (t=8 s) (see Fig. 6 ) was compared with the obtained permeabilization rates. In Fig. 7  (left panels) both values are plotted together (notice that the scales in both axes are not the same). The results in Fig. X  show a clear correlation between impedance changes and permeabilization rates for the three cell lines analyzed.
3 -recorded in the super-linear responsivity regime-one finds that the interferometric autocorrelation trace (blue line) exhibits a 7:1 ratio, whereas the intensity autocorrelation trace (red line) yields a 3:1 ratio. These values nearly correspond to those expected for quadratic detection, although the power dependence of the APL Photonics 3, 051705 (2018) responsivity is cubic at the given gate voltage. These ratios can be reproduced assuming given power dependencies and the intensity ratio of 1:5 between the different interfering arms. At zero gate bias, the measured intensity autocorrelation trace (data not shown) exhibits a 12:1 ratio due to the strong nonlinearity of the responsivity [see the inset of Fig. 1(b) ]. The data of Fig. 3 (a) and additional ones at other gate bias voltages also allow us to estimate the gate voltage dependence of the intrinsic response time (τ int ) of the transistor.
Vision plays a crucial role in children's mental development. Therefore, early diagnosis of any vision disparities and implementation of a correct therapy is very important. However, carrying out such a procedure in case of young children and especially children with brain dysfunctions poses some limitations due to cooperation problems. The vision diagnostics and treatment (VisDaT) system presented in this paper is meant to help therapists in proper diagnosis and treatment involving such children. It utilizes a computer connected to two monitors and equipped with a specialized software. The main system components are as follows: an eye tracker recording child's eye movements and a digital camera monitoring online child's reactions. The system is equipped with a specialized software, which creates the opportunity to stimulate children's vision with a dedicated stimulus and post hoc analyses of recorded sessions, which enable making decision as to the future treatment.
2. 1. Registered Data Processing. Ensuring the possibility of registering eye position is only the first step towards obtaining information about children's vision quality. There are some additional steps, which have to be undertaken to change registered data into valuable information. The aim of the first step is to adjust an eye tracking environment to a particular child, which may be obtained during the calibration process. The commonly applied calibration procedure consists of two stages. During the first one, the examined person's eye movements are registered when looking at stimuli in known locations of a scene-usually there are points evenly distributed over a screen. In the case of an experiment engaging adults, there are 9 points presented. Subsequently, for correlating an eye tracker output with appropriate points, a function mapping registered eye positions to points of viewed scene is defined.
It is further used to provide coordinates of user's gazes  (1). Because of idiosyncratic features of the oculomotor system, this process is conducted independently for each examined person  [13]. x s = f x e , y e , y s = f x e , y e , 1 where x e and y e represent the data obtained from an eye tracker and x s and y s are the estimated gaze coordinates on a screen. The function f may be defined in various ways  [14] ; however, the solution commonly used is the second degree polynomial regression. Not every term of the polynomial must be utilized in the mapping function. It may be adjusted to a particular environment during its configuration, based on the accuracy of provided gaze points. The simplest way for its assessment is the usage of the root-mean-squared error (RMSE), measuring the difference between the stimulus location and the mapped gaze point: x s = A x x 2 e + B x RMSE = 1 n 〠 i G i x, y -Ĝi x, y 2 , 3 where G i is an observed value and Ĝi is a value calculated by the mapping function.