dataesr/scientific-paragraphs-categorization huggingface dataset:
In other words, the acyl chains of the lipid structures were randomly scattered using a naïve chain-avoidance algorithm. Multiple commercial chemical rendering programs were investigated to resolve this aesthetic issue, but without success. In the end, an in-house program was developed called 'MetBuilder' which generates lipid structures with all acyl/alkyl chains uniformly oriented in a parallel direction. The difference between the two types of lipid structure rendering can be seen in Figure  1  for the Cardiolipin(16:0/16:1/16:1/22:6). Obviously, the generation of lipid structures in this manner creates images that are more visually pleasing and more easily viewed or pasted into figures and slides. However, it is important to note that these pleasant-looking lipid structures are highly strained and the resulting MOL, SDF, and PDB files must be properly minimized if these files are to be used in any structural studies.
Furthermore, it was not able to handle all structure inputs nor was it fully mapped or easily mapped to other chemical classification schemes used by ChEBI, LipidMaps, or PubChem (MeSH). With the finalization of the ClassyFire chemical classification system  (23)  and its chemical ontology (ChemOnt) in 2016, all 114,100 compounds within HMDB 4. 0 have now been reclassified and updated to comply with the latest ClassyFire/ChemOnt release. Furthermore, the taxonomic mappings to other chemical classification schemes in other databases is now largely complete. More importantly, the ClassyFire classification scheme (which is fully open access) is now used by most other chemical/metabolomic databases  (23). This means that the metabolomics community (and the chemical/biochemical community) now has a consistent, fully exchangeable standard for chemical classification.
In addition to substantially increasing the number of metabolites, spectra, and pathways in HMDB 4. 0 as well as improving the quality of the structures and the chemical taxonomy in the database, our curation team has also added to the HMDB in many more ways. For instance, there are now many more compound names and synonyms (a 370% increase), substantially more compounds with biofluid concentration or tissue location data (a 50% increase), more data on normal and diseased metabolite concentrations (a 72% increase), and more data on biofluid and excreta metabolomes, including new data on urine  (33) , saliva  (34) , sweat, and feces. In particular, the number of metabolites (expected and detected) in human serum, urine, cerebrospinal fluid, saliva, sweat, and feces now stands at 25 424, 4225, 440, 1234, 90 and 1170, respectively.
Data quality and data currency continue to be two of the top priorities for the HMDB curation team. Because new metabolites from different tissues and biofluids are constantly being identified, re-characterized, re-measured or rescinded, the HMDB curation team continuously combs the literature to update existing MetaboCards (the data files associated with each metabolite in the HMDB) or to add new MetaboCards. As a result, thousands of metabolite descriptions, disease associations, biofluid/tissue concentrations, and metabolism data fields have been added, removed, rewritten and/or expanded over the past five years.
New data fields and data content As noted earlier, HMDB 4. 0 now includes an entirely new class of compounds called 'Predicted' metabolites. This has been done in response to the growing metabolite identification crisis in metabolomics. Typically, fewer than 2% of confidently identified MS peaks in most metabolomic studies are being identified, even via simple mass matching  (16, 17). That number has largely remained unchanged over the past five years. It is clear that today's chemical databases (including the HMDB) are not providing the necessary metabolite coverage to allow researchers to identify these unknown or unidentifiable compounds. While a good deal of thought has gone into speculating what this metabolic 'dark matter' (  16 ) might be, a growing consensus is that these unknowns are actually metabolites-ofmetabolites  (17).
As a result, there are now 9548 'Predicted' metabolites in HMDB 4. 0, which represents about 8% of the current collection of metabolites in the HMDB. Every 'Predicted' metabolite is as fully (or almost as fully) annotated as a 'Detected' or 'Expected' metabolite. In particular, all 'Predicted' metabolites have a name (and synonyms), all have a textual description indicating the parent compound, the biotransformation or biotransforming enzyme, and a description of the chemical structure generated via ChemOnt  (23). Additional nomenclature, structural, physico-chemical data, spectral data and biological (enzyme and reaction) data is also provided. All of the predicted compounds in HMDB 4. 0 are labeled as 'Predicted' and users may easily exclude or include these compounds through HMDB's data filters as they wish.
HMDB's 'Predicted' compounds are intended to serve as guides to help with the identification or characterization of unknown compounds. The substantial expansion of annotated metabolites in HMDB 4. 0 has necessitated an important change to the HMDB's original 5-digit accession numbers. Since its inception, the HMDB has used unique accession numbers consisting of a 4-letter prefix (HMDB) and a 5-number suffix. With the release of HMDB 4. 0, the 5-digit accession numbers have had to be adjusted to a 7-digit suffix to maintain consistency. This has been accomplished by appending two zeroes after the 4-letter prefix and before each 5-number suffix (e. g. HMDB00001 becomes HMDB0000001). In order to minimize disruption in dependent applications, the original 5-digit accession numbers have become secondary accession numbers to their 7-digit counterparts.
0. In total, HMDB 4. 0 contains 279 972 predicted MS/MS spectra for 98,601 compounds and 38,277 predicted GC-MS spectra for 26 880 compounds. All of the predicted MS spectra are labeled as 'Predicted' and they are intended to serve as guides to help with the identification or characterization of known or unknown compounds. Another completely new set of data added to HMDB was a functional ontology. While ClassyFire (23) offers a very useful approach for structure-based classification and description, it does not solve all of the ontological problems with metabolomics or metabolites. In particular, the metabolomics community still needs a functional ontology similar to the Gene Ontology (GO) used in genomics and proteomics  (36). In GO, genes and proteins are classified according to (i) cellular component, (ii) molecular function, and (iii) biological process.
A more complete description of this chemical functional ontology (CFO) will be forthcoming in a future publication. All 114 100 compounds in the HMDB 4. 0 have now been classified or partly classified using this new CFO. Many of the assignments were using a combination of manual annotation and a data mining software tools called PolySearch2  (37). Many other new and significant additions to HMDB 4. 0 have also been made. One of the more important ones includes the addition of pharmacometabolomics data. Pharmacometabolomics involves the analysis of how metabolite levels are changed in tissues, cells, or biofluids as a consequence of drug dosing  (38). The pharmacometabolomic data was first mined from the literature and then manually curated and formatted so that users may identify which drugs increase or decrease metabolite levels in which tissues, cells, or biofluids.
All drug names are hyperlinked to DrugBank  (39)  and all of the pharmacometabolomic data is browsable and searchable through a specially designed interface. In total, 764 drugs are linked to 494 metabolites through 2497 associations, which were compiled through 1317 publications. In addition to this pharmacometabolomic data, HMDB 4. 0 also features a much richer collection of metabo-genomic or metabolite-SNP (single nucleotide polymorphism) data. With more and more metabolite-SNP studies being published each year, it is now clear that genetic mutation and polymorphisms do have a significant effect on baseline metabolite concentrations  (40). While not as severe as the effects seen with inborn errors of metabolism, SNP-related metabolite ef-  fects can increase the risk for certain disorders.
In total, HMDB 4. 0 has 3056 metabolites linked to 2192 SNPs describing 6777 metabolite-SNP interactions. Metabolite levels are also known to differ with age, BMI, diurnal cycle, and gender. Given these effects, the HMDB now maintains a separate catalogue of 2901 metabolites that are known to co-vary with age, BMI, diurnal cycle, and gender. These data indicate which metabolites increase or decrease with these covariates and, where possible, by how much. Finally, a significant amount of new data is appearing in HMDB 4. 0 concerning the 'Exposome'  (41). The exposome is defined as the totality of chemical exposures that an individual may come into contact through daily living and along life course. These include food compounds, cosmetics, dyes, pesticides, pollutants, drugs, etc. All compounds that are considered to be part of the exposome are labeled as such and may be searched or categorized using HMDB's Advanced Search engine.
New and enhanced interface features Web technologies continue to advance and these advances can be exploited to make many web applications faster, more interesting, or more compelling. As always, the HMDB team continues to strive to improve the database's user interface, to enhance its search utilities, and to respond to user suggestions. For HMDB 4. 0, we have introduced a number of interface improvements including (i) improved pathway viewing tools, (ii) improved spectral viewing/browsing tools, (iii) improved spectral searching, (iv) enhancing the advanced search, and (v) better resources for data exchange and interoperability. HMDB 4. 0 uses the PathWhiz system for pathway viewing. PathWhiz is a JavaScript enabled tool that allows users to interactively draw, view, zoom and click on colourful pathway images that are richly annotated with cellular organelle data, organ/tissue data, enzyme cofactor information, and protein quaternary structure information.
All images are extensively hyperlinked (to UniProt, DrugBank, and HMDB) and all pathways are carefully described or summarized with an explanatory paragraph. To facilitate pathway rendering, two types of pathway views are now available including a (default) data rich pathway rendering, and a simplified KEGG-like pathway rendering which is more amenable for slides, figures, or manuscripts. All of the pathways rendered in HMDB can be saved and downloaded in static image formats (PNG and SVG) as well as in a variety of common data exchange formats such as SBML (systems biology mark-up language), BioPax, and PathWhiz's own markup language (PWML). The large quantity of spectral data in HMDB precipitated a need to improve the HMDB's spectral viewing tools. Previously static PDF images or relatively crude 'stick' renderings were the norm for most of HMDB's spectral data.
With HMDB 4. 0 all MS and NMR spectra are now viewable with JSpectraViewer, a locally developed JavaScript spectral viewing tool that is able to read and render nmML and mzML formatted spectra. For NMR spectra, JSpec-traViewer allows users to interactively hover over spectral peaks and to see 1 H spectral assignments and the corresponding protons on a rendered image of the molecule. For MS spectra, JSpectraViewer allows users to interactively hover over spectral peaks and to see the structures of the fragment ions that are predicted (via CFM-ID) to correspond to the observed m/z value. The improvements to the HMDB's spectral viewing tools have also led to a number of improvements to HMDB's spectral searching tools. In particular, HMDB's MS spectral search and MS spectral results page has been redesigned to better reflect the needs and expectations of MS spectroscopists.
In particular, the MS Search page has improved the way users can select adducts by reformatting and simplifying 'Adduct Type' field. In addition, an 'm/z calculator' has been introduced in MS Search results page to facilitate rapid comparisons and calculations and the page has been further modified to display more information including the chemical formula, the monoisotopic mass and the mass differences from the query mass (i. e. Delta(ppm)). We have also included KEGG and HMDB identifiers in the extractable CSV file for simplifying data analysis and data extraction. Furthermore, all MS spectra in HMDB are now mapped to a SPLASH (SPectraL hASH) key  (21). This spectral hash identifier allows MS spectra within HMDB to be easily searched or identified via the web through a standard Google (or other search engine) query.
With the con-tinued additions of new data fields and growing requests for new kinds of queries from its users, the HMDB team has also been actively improving and correcting HMDB's Advanced Search. The interface is more intuitive and powerful. The Advanced Search allows you to build structured queries containing predicates such as 'matches/does not match', 'starts/ends with', 'greater/less than', and 'is/is not present'. It has been corrected to include negative value in search. The result interface now allows one to browse the results through pagination. Because much of the data in the HMDB is downloadable and much of its data is being used to create or supplement other metabolomics resources, the need for improved data exchange standards and improved interoperability within the HMDB has become much greater.
In response to these needs, all of HMDB's chemical structures are now accessible in canonical SMILES, SDF, MOL, PDB, InChI, and InChIKey formats. Furthermore, all experimentally observed and theoretically predicted MS/MS, GC-MSand NMR spectra are stored and downloadable in mzML  (22)  and nmrML (www. nmrml. org) formats and all MS/MS and GC-MS spectra are assigned SPLASH keys  (21)  for rapid spectral querying and matching. Likewise, all sequence (DNA and protein) data is stored in FASTA format and all remaining textual data is stored in XML and JSON format. All of these HMDB 'component' files can be freely downloaded (for academics) from the HMDB 4. 0 website.
CONCLUSION Over the past 10 years the HMDB has grown from a small, somewhat specialized chemical database to the largest, most comprehensive, organism-specific metabolomic 'knowledgebase' in the world. Throughout this evolution we have strived to maintain the highest quality and currency of data, to continuously improve the quality of its user interface, and to attentively listen to all members of the metabolomics community. With this latest release of the HMDB we have undertaken the most significant update in its history. Many existing data sets have grown in size by factors of 3-30fold. In addition to this significant enhancement to the HMDB's existing data collection, we have also added substantial quantities of new and very important data for the entire metabolomics community. In particular, HMDB 4.
0 now includes predicted metabolites, predicted NMR, GC-MS, and MS/MS spectral data as well as predicted retention time data. These data sets are intended to address the metabolite identification crisis, which is severely limiting compound coverage for much of today's metabolomic studies. Other kinds of 'metabo-omic' data such as pharmacometabolomic data and geno-metabolomic data have also been added to make the HMDB much more compatible with the increasing number of multi-omic or systems biology studies that are being undertaken. Other improvements to the HMDB's interface, spectral and pathway viewing tools, searching utilities and data exchange standards should also make the HMDB much easier to work with and more enjoyable to explore. All of these additions and enhancements are intended to facilitate research in human.
A sub-study was designed to investigate innate immune parameters. Considering the important role of NK cell maturation for their effector properties, we investigated the NK cell compartment with a combination of markers known to characterize sequential steps of the NK cell differentiation pathway. Thirty primary HIV-infected patients peripheral blood mononuclear cell (PBMC) samples were investigated at inclusion and 3 months after the onset of combination antiretroviral therapy (cART). Because cell populations expressing unexpected combinations of markers might be expanded in pathological conditions, new bioinformatics methods were applied to analyze multiparametric cytometry data in an unsupervised approach  (22). This allowed the identification of a relationship between profiles of NK cells skewed to immaturity or maturity and virological and immune parameters reached naturally a few weeks after infection and after early cART.
Cells were incubated for 20 min at RT with reagents premixed in PBS, washed, and then fixed with 4% PFA. Data were acquired on a LSRII-SORP (BD Biosciences) equipped with four lasers (405 nm/100 mW, 488 nm/100 mW, 560 nm/50 mW, and 630 nm/40 mW). Photo-multiplicators were set using unstained and fully stained samples and linked to the cytometer standardization using the acquisition setting tool. Also, 7. 74 × 10 6 ± 3 × 10 5 events were recorded. Compensations were performed with beads individually stained with corresponding reagents.
automatic clustering The flowClust implementation version 3. 4. 11on R version 3. 1. 2 under Linux Cent OS 6 was applied on the following parameters: CD56-PC7, CD16-APCH7, NKG2A-PacBlue, NKG2C-PE, CD57-FITC, to compute clusters as described  (22). The number of clusters that fit the data optimally was estimated by computing flowClust on a predefined range and comparing them with the statistical criteria BIC and ICL (not shown). This number was estimated to be above 20; therefore, 27 clusters were computed in the events within the NK gate of the 60 samples (30 patients, T0, and M3) and one healthy donor. Two samples failed to be computed. MFI values and event counts of the 1,593 clusters generated after computation were exported in an Excel table.
Computation, through the consideration of all parameters at the same time allowed exploration of the full dataset and finding discrete subsets in an unsupervised approach  (22). The description of the NK cells of patients into 12 subsets, whereas only six would have been searched according to previous reports (CD56 bright , four populations of CD56 dim and CD56 neg ), provides an added value to the comprehension of the ongoing interplays between NK cell and HIV. One example is the finding of four different CD56 neg subsets and their clustering near four different NK cell populations, including CD56 bright. This result suggests that the defect affecting the NK cell can touch the lineage at different levels of maturation. Among 12 NK populations, four co-expressed NKG2C in various combinations spanning the whole NK maturation process.
, 2016) , i. e. when the diffusion approximation holds. However, for low values of the presynaptic frequencies, we can be far from this approximation, the additional coefficients are used to capture the firing response in those domains. Those coefficients are listed on Table  2  for the two cell types (RS & FS). The fitting procedure was identical to  Zerlaut et al. (2016) , it consisted first in a linear regression in the phenomenological threshold space of Equation 4, followed by a non-linear optimization of Equation 3 on the firing rate response. Both fitting were performed with the leastsq method in the optimize package of SciPy.
Stimuli were local Gaussian blobs with a s. d. of 0. 5°in space. Stimuli were presented at 0. 5°on the left of the vertical meridian and 3°below the horizontal, except where noted. Three different durations were used: 20, 50, 100 ms. Data analysis. Stacks of images were stored on hard-drives for offline analysis with MATLAB R2014a (MathWorks), using the Optimization, Statistics, and Signal Processing Toolboxes. The evoked response to each stimulus was computed in four successive basic steps. First, the recorded value at each pixel was divided by the average value before stimulus onset ("frames 0 division") to remove slow stimulusindependent fluctuations in illumination and background fluorescence levels. Second, this value was subsequently subtracted by the value obtained for the blank condition ("blank subtraction") to eliminate most of the noise due to heartbeat and respiration  (Shoham et al.
e. they enter into Equations 22 and 23). Parameter scans were performed on a grid of parameters with the following boundaries:  50, 200] ms. We scanned 5 points per dimension, i. e. we simulated and analyzed 15625 configurations. For a given session (see examples in Fig. 9B ), the optimal parameters of the model were taken as those minimizing the square residual of the difference between spatio-temporal pattern. To obtain the residual of the difference between the model and VSDi data, one needs to have a common sampling of the pattern. We thus subsampled the model data over time to reach the sampling rate of the VSDi data (i. e. f smpl =110Hz). Data were aligned with respect to the local maximum over space and time (tcenter, xcenter). The temporal window was taken as a 400ms window, with 100ms before tcenter and 300ms after tcenter.
Acknowledgments Research supported by the  CNRS , the  ICODE excellence network , the  European Community  ( Human Brain  Project  H2020-720270  to A. D. ,  FET  Grant  BrainScaleS FP7-269921  to A. D. and F. C) and the  ANR BalaV1  and  Trajectory  ( ANR-13-BSV4-0014-02  to F. C). Y. Z. was supported by fellowships from the  Initiative d'Excellence Paris-Saclay  and the  Fondation pour la Recherche Médicale  ( FDT 20150532751 ).
The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area.
By contrast, standard cameras are now available at very low cost but are equipped with infrared blocking filters to limit image capture in the VIS range. Here we examined how the spectral characteristics of plant leaves could be included in a low cost, portable and automated imaging system to determine isolated plant leaf area in a wide range of conditions. We describe such an efficient solution using the widely spread Raspberry Pi  [38]  computer with a modified version of a standard camera module  (Pi NoIR)  where the IR filter was removed to extend light capture beyond the VIS range. A blue filter (provided by the manufacturer) was also added. Scripts were developed for the resulting images to determine the projected leaf area of plants. We demonstrate that plant segmentation with this new device is efficient for various species and background environments, while standard methods often fail to correctly estimate plant leaf area.
Reliability in field conditions is illustrated by data obtained on lettuce plants grown below different configurations of photovoltaic panels (PVPs). The concept of growing plants in the partial shade of PVPs emerged in 1982  [39]  to cope with the detrimental impacts of climate change on plants and increase global land productivity  [40]. At first glance, shading crops with photovoltaic panels is thought to severely hamper plant growth. However, an increase in plant efficiency to intercept radiation (radiation interception efficiency, RIE) has been reported for lettuce grown below PVPs due to the acclimation of leaf expansion to shade and resulting in growth maintenance  [41]. To get insights into these acclimation processes, we developed a field-phenotyping cart where several Raspberry Pi devices were chained to monitor leaf area for hundreds of lettuce plants grown below different configurations of PVPs.