dataesr/scientific-paragraphs-categorization huggingface dataset:
In conclusion, in this study we reached a particularly high accuracy in discriminating MCI-converters from healthy controls thanks to the application of a non-linear classifier based on SVM preceded by individuation of anatomo-functional meta-VOIs, normalization to cerebellum and evaluation of inter-hemispheric asymmetries. It has to be underscored that this quite complex and time-consuming data pre-processing was automated and simplified by the in-house created Matlab-based script encouraging its implementation in clinical routine to assist in the diagnosis of AD in aMCI possibly along with MRI and CSF biomarkers. Even in the era of amyloid PET imaging that has been licensed by regulatory Agencies to exclude rather than to confirm AD, FDG-PET maintains a role not only as a marker of disease evolution but also as an important diagnostic biomarker in the early stages of the disease.
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked datasets on the web. In order to benefit from this mine of data, one needs to access descriptive information about each dataset (or metadata). However, the heterogeneous nature of data sources reflects directly on the data quality as these sources often contain inconsistent as well as misinterpreted and incomplete metadata information. Considering the significant variation in size, the languages used and the freshness of the data, one realizes that finding useful datasets without prior knowledge is increasingly complicated. We have developed Roomba, a tool that enables to validate, correct and generate dataset metadata. In this paper, we present the results of running this tool on parts of the LOD cloud accessible via the datahub.
Introduction The Linked Open Data (LOD) cloud 1 has grown significantly in the past years, offering various datasets covering a broad set of domains from life sciences to media and government data  [3]. To maintain high quality data, publishers should comply with a set of best practices detailed in  [2]. Metadata provisioning is one of those best practices requiring publishers to attach metadata needed to effectively understand and use datasets. Data portals expose metadata via various models. A model should contain the minimum amount of information that conveys to the inquirer the nature and content of its resources  [9]. It should contain information to enable data discovery, exploration and exploitation. We divided the metadata information into the following types: -General information: General information about the dataset (e.
g. title, description, ID). This general information is manually filled by the dataset owner. In addition to that, tags and group information is required for classification and enhancing dataset discoverability. -Access information: Information about accessing and using the dataset. This includes the dataset URL, some license information (i. e. license title and URL) and information about the datasets resources. Each resource has generally a set of attached metadata (e. g. resource name, URL, format, size). -Ownership information: Information about the ownership of the dataset (e. g. organization details, maintainer details, author). The existence of this information is important to identify the authority on which the generated report and the newly corrected profile will be sent to. -Provenance information: Temporal and historical information on the dataset and its resources (e.
g. creation and update dates, version information, version number). Most of this information can be automatically filled and tracked. Data portals are datasets' access points providing tools to facilitate data publishing, sharing, searching and visualization. CKAN 2  is the world's leading open-source data portal platform powering web sites like the Datahub which hosts the LOD cloud metadata. We have created Roomba  [1] , a tool that automatically validates, corrects and generates dataset metadata for CKAN portals. The datasets are validated against the CKAN standard metadata model 3. The model describes four main sections in addition to the core dataset's properties. These sections are: -Resources: The actual accessible raw data. They can come in various formats (JSON, XML, RDF, etc. ) and can be downloaded or accessed directly (REST API, SPARQL endpoint).
-Tags: Provide descriptive knowledge on the dataset content and structure. -Groups: Used to cluster or a curate datasets based on shared themes or semantics. -Organizations: Organizations describe datasets solely on their association to a specific administrative party. The results demonstrate that the general state of the examined datasets needs much more attention as most of the datasets suffers from bad quality metadata and lacking some informative metrics needed that would facilitate dataset search. The noisiest metadata values were access information such as licensing information and resource descriptions in addition to large numbers of resource reachability problems. We also show that the automatic corrections of the tool increase the overall quality of the datasets metadata and highlight the need for manual efforts to correct some important missing information.
Related Work The Data Catalog Vocabulary (DCAT)  [8]  and the Vocabulary of Interlinked Datasets (VoID)  [5]  are models for representing RDF datasets metadata. There exist several tools aiming at exposing dataset metadata using these vocabularies such as  [4]. Few approaches tackle the issue of examining datasets metadata. The Project Open Data Dashboard 4  validator analyzes machine readable files for automated metrics to check their alignment with the Open Data principles. Similarly on the LOD cloud, the Datahub LOD Validator 5  checks a dataset compliance for inclusion in the LOD cloud. However, it lacks the ability to give detailed insights about the completeness of the metadata and an overview on the state of the entire LOD cloud group. The State of the LOD Cloud Report  [7]  measures the adoption of Linked Data best practices back in 2011.
Experimental Setup The current state of the LOD cloud report  [10]  indicates that there are more than 1014 datasets available. These datasets have been harvested by the LDSpider crawler  [6]  seeded with 560 thousands URIs. However, since Roomba requires the datasets metadata to be hosted in a data portal where either the dataset publisher or the portal administrator can attach relevant metadata to it, we rely on the information provided by the Datahub CKAN API. We consider two possible groups: the first one tagged with "lodcloud" returns 259 datasets, while the second one tagged with "lod" returns only 75 datasets. We manually inspect these two lists and find out that the API result for the tag "lodcloud" is the correct one. The 259 datasets contain a total of 1068 resources. We run the instance and resource extractor from Roomba in order to cache the metadata files for these datasets locally and we launch the validation process which takes around two and a half hours on a 2.
Results and Evaluation CKAN dataset metadata includes three main sections in addition to the core dataset's properties. Those are the groups, tags and resources. Each section contains a set of metadata corresponding to one or more metadata type. For example, a dataset resource will have general information such as the resource name, access information such as the resource url and provenance information such as creation date. The framework generates a report aggregating all the problems in all these sections, fixing field values when possible. Errors can be the result of missing metadata fields, undefined field values or field value errors (e. g. unreachable URL or syntactically incorrect email addresses). Figures  1  and 2  show the percentage of errors found in metadata fields by section and by information type respectively.
We observe that the most erroneous information for the dataset core information is related to ownership since this information is missing or undefined for 41 % of the datasets. Datasets resources have the poorest metadata. 64 % of the general metadata, all the access information and 80 % of the provenance information contain missing or undefined values. Table  1  shows the top metadata fields errors for each metadata information type. We notice that 42. 85 % of the top metadata problems shown in Table  1  can be fixed automatically. Among them, 44. 44 % of these problems can be fixed by our tool while the others can be fixed by tools that should be plugged into the data portal. We further present and discuss the results grouped by metadata information type in the following sub-sections.
General Information 34 datasets (13. 13 %) do not have valid notes values. tags information for the datasets are complete except for the vocabulary id as this is missing from all the datasets' metadata. All the datasets groups information are missing display name, description, title, image display url, id, name. After manual examination, we observe a clear overlap between group and organization information. Many datasets like event-media use the organization field to show group related information (being in the LOD Cloud) instead of the publishers details.
Access Information 25 % of the datasets access information (being the dataset URL and any URL defined in its groups) have issues: generally missing or unreachable URLs. 3 datasets (1. 15 %) do not have a URL defined (tip, uniprotdatabases, uniprotcitations) while 45 datasets (17. 3 %) defined URLs are not accessible at the time of writing this paper. One dataset does not have resources information (bio2rdfchebi) while the other datasets have a total of 1068 defined resources. On the datasets resources level, we notice wrong or inconsistent values in the size and mimetype fields. However, 44 datasets have valid size field values and 54 have valid mimetype field values but they were not reachable, thus providing incorrect information. 15 fields (68 %) of all the other access metadata are missing or have undefined values.
Looking closely, we notice that most of these problems can be easily fixed automatically by tools that can be plugged to the data portal. For example, the top six missing fields are the cache last updated, cache url, urltype, webstore last updated, mimetype inner and hash which can be computed and filled automatically. However, the most important missing information which require manual entry are the dataset's name and description which are missing from 817 (76. 49 %) and 98 (9. 17 %) resources respectively. A total of 334 resources (31. 27 %) URLs were not reachable, thus affecting highly the availability of these datasets. CKAN resources can be of various predefined types (f ile, f ile. upload, api, visualization, codeanddocumentation). Roomba also breaks down these unreachable resources according to their types: 211 (63.
17 %) resources do not have valid resource type, 112 (33. 53 %) are files, 8 (2. 39 %) a re metadata and one (0. 029 %) are example and documentation types. To have more details about the resources URL types, we created a key : objectmeta-f ieldvalues group level report on the LOD cloud with resources> format:title. This will aggregate the resources format information for each dataset. We observe that only 161 (62. 16 %) of the datasets valid URLs have SPARQL endpoints defined using the api/sparql resource format. 92. 27 % provided RDF example links and 56. 3 % provided direct links to RDF down-loadable dumps. The noisiest part of the access metadata is about license information. A total of 43 datasets (16. 6 %) does not have a defined license title and license id fields, where 141 (54. 44 %) have missing license url field.
Enriched Profiles Roomba can automatically fix, when possible, the license information (title, url and id) as well as the resources mimetype and size. 20 resources (1. 87 %) have incorrect mimetype defined, while 52 resources (4. 82 %) have incorrect size values. These values have been automatically fixed based on the values defined in the HTTP response header. We have noticed that most of the issues surrounding license information are related to ambiguous entries. To resolve that, we manually created a mapping file 6  standardizing the set of possible license names and urls using the open source and knowledge license information  7. As a result, we managed to normalize 123 (47. 49 %) of the datasets' license information. To check the impact of the corrected fields, we seeded Roomba with the enriched profiles.
Since Roomba uses file based cache system, we simply replaced all the datasets json files in the \cache\datahub. io\datasets folder with those generated in \cache\datahub. io\enriched. After running Roomba again on the enriched profiles, we observe that the errors percentage for missing size fields decreased by 32. 02 % and for mimetype fields by 50. 93 %. We also notice that the error percentage for missing license urls decreased by 2. 32 %.
Conclusion and Future Work In this paper, we presented the results of running Roomba over the LOD cloud group hosted in the Datahub. We discovered that the general state of the examined datasets needs attention as most of them lack informative access information and their resources suffer low availability. These two metrics are of high importance for enterprises looking to integrate and use external linked data. We found out that the most erroneous information for the dataset core information are ownership related since this information is missing or undefined for 41 % of the datasets. Datasets resources have the poorest metadata: 64 % of the general metadata, all the access information and 80 % of the provenance information contained missing or undefined values. We also show that the automatic correction process can effectively enhance the quality of some information.
Identifying the intramolecular motions of proteins and nucleic acids is a major challenge in macromolecular X-ray crystallography. Because Bragg diffraction describes the average positional distribution of crystalline atoms with imperfect precision, the resulting electron density can be compatible with multiple models of motion. Diffuse X-ray scattering can reduce this degeneracy by reporting on correlated atomic displacements. Although recent technological advances are increasing the potential to accurately measure diffuse scattering, computational modeling and validation tools are still needed to quantify the agreement between experimental data and different parameterizations of crystalline disorder. A new tool, phenix. diffuse, addresses this need by employing Guinier's equation to calculate diffuse scattering from Protein Data Bank (PDB)formatted structural ensembles.
The potential for improved statistics when relatively few observations are available has positioned TLS as a general refinement technique: roughly 22% of the structures deposited in the Protein Data Bank (PDB;  Bernstein et al. , 1977; Berman et al. , 2000)  employ TLS refinement in some form. TLS refinement is a component of many major structural refinement programs such as REFMAC  (Murshudov et al. , 2011; Winn et al. , 2001) , BUSTER-TNT  (Bricogne, 1993; Bricogne et al. , 2011)  and phenix. refine (Afonine et al. , 2012). These programs can select TLS groups automatically, based on biochemical intuition or with the assistance of external web servers  (Painter & Merritt, 2006a,b). TLS refinement naturally suggests concerted structural motions, which can be assigned biological significance and subsequently tested with additional experiments.
It is also possible to explicitly estimate each matrix element from molecular-dynamics trajectories  (Meinhold & Smith, 2007). The size of the covariance matrix scales as the square of the number of atoms, making full matrix calculations expensive to compute for large systems. This poses a significant challenge to quantitative diffuse scattering analysis. For these reasons, a straightforward method that calculates diffuse scattering from discrete multi-model PDB files may be preferable. To meet this need, we developed phenix. diffuse, a new tool within the PHENIX software suite  (Adams et al. , 2010)  which uses Guinier's equation to calculate diffuse scattering from multi-model (ensemble) PDB files. Thus, phenix. diffuse can be applied to any motional model represented as an explicit ensemble of related structures.
GpdQ refinement Based on the original refinement strategy of  Jackson et al. (2007) , we performed three different TLS refinements on the zinc-bound structure of GpdQ (PDB entry 2dxn): 'entire molecule', with one TLS group for all residues, 'monomer', with one TLS group for each of the two individual chains, and 'sub-domain', with one TLS group for each of the -sandwich domain (residues 1-196), the 'dimerization' domain  (residues 197-255)  and the 'cap' domain (residues 257-271) of each chain. The pre-TLS refinement R work and R free were 19. 1 and 23. 1%, respectively. After defining the TLS groups, each structure was re-refined for five macrocycles in phenix. refine. The strategy included refinement of the individual coordinates and isotropic B factors, water picking and refinement of TLS parameters for defined TLS groups.
phenix. diffuse can model these large-scale effects through the analysis of a 'supercell' containing multiple unit-cell copies, as implemented in several recent MD simulations of small proteins  (Janowski et al. , 2013; Kuzmanic et al. , 2014). Guinier's equation can be applied to arbitrarily sized crystalline regions; thus, a system of multiple unit cells allows analysis of motions that occur between and across unit cells. In line with previous diffuse scattering simulations  (Wall, Van Benschoten et al. , 2014) , our program calculates structure factors for each ensemble member at the Bragg lattice positions, from which each term in Guinier's equation is determined.
GpdQ TLS diffuse scattering simulation We simulated the diffuse scattering of each of the GpdQ TLS ensembles to 3. 0 A ˚resolution. Unless otherwise stated, all TLS groups within a given refinement were assumed to move independently of one another. Since the diffraction data for GpdQ in PDB entry 2dxn extend to 2. 9 A ˚resolution, our simulation should be sufficient for future comparisons with experimental maps. As the resulting diffuse scattering data are identical in format to descriptions of Bragg X-ray reflections, phenix. reflection_statistics was used to perform all statistical analyses. All reported correlation values are global Pearson correlation coefficients calculated between the described two sets of diffuse intensities. As previously mentioned (and described in  Wall, Ealick et al.
Despite this synergy between TLS refinement and diffuse scattering, there are many potential complications when applying TLS X-ray refinement to model protein dynamics. As the T and L matrices describe independent translations and librations, these motions must be physically sensible. Our review of protein structures deposited in the Protein Data Bank indicates that roughly 85% of refinements employing TLS (about 25% of the total PDB) do not satisfy this physical requirement  (Urzhumtsev et al. , 2015). We hypothesize that this discrepancy arises owing to a lack of restraints applied to refined TLS parameters to ensure their physical plausibility. Even if this criterion is met, current TLS refinement methods still do not impose chemical restraints between TLS groups, which can lead to displacements that are chemically unreasonable.
Acknowledgements JSF is a  Searle Scholar , a  Pew Scholar  and a  Packard Fellow. Work in the laboratory of JSF  is supported by  NIH   OD009180 ,  GM110580  and  NSF   STC-1231306. PDA, PVA and TCT are supported by  NIH  grant  GM063210. NKS was supported by  NIH  grant  GM095887. AU thanks the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI). MEW is supported by the  US Department of Energy  through the  Laboratory-Directed Research and Development program  at  Los Alamos National Laboratory. This work was supported by the  Program Breakthrough Biomedical Research , which is partially funded by the  Sandler Foundation.
Using the sidestream dark field (SDF) or incident dark-field illumination (IDF) imaging technologies to assess sublingual microcirculation, several studies have reported a link between microvascular flow index (MFI) and the proportion of perfused vessels (PPV) and outcome  [1, 4, 9, 10]. In one such study using a large database of patients with severe sepsis who had undergone evaluation of microvascular perfusion, De Backer et al. recently reported that PPV was a stronger independent predictor of mortality  [4]. Their results suggest that these imaging techniques might provide pertinent parameters to guide the hemodynamic management of ICU patients and to prevent organ dysfunction. However, the microvascular measurements were semiquantitative and required time to analyze videos with use of software, thus preventing their real-time utilization to titrate necessary hemodynamic support  [11, 12].
Study design and measurements After ICU admission and study inclusion, the nurse in charge of the patient performed a set of measurements of macrocirculatory and microcirculatory parameters every 4 h within the first 12 h after admission and before and after every hemodynamic therapeutic intervention, such as fluid challenge, transfusion of red blood cells (RBC) or change in catecholamine rate during this period. Macro hemodynamic data included heart rate, mean arterial pressure, delta pulse pressure (ΔPP) and cardiac index. Sublingual microcirculatory measurements were performed with an incident dark-field illumination device (Cytocam-IDF (incident dark-field illumination), Braedius Medical, Huizen, The Netherlands). Lactate concentration, hemoglobin concentration, norepinephrine or epinephrine dose during the procedure were gathered.
The values in each quadrant are averaged to give an MFI for each sublingual site at each time point  [14]. A quantitative measurement of the capillary density, the total vessel density (TVD, mm/mm 2 ), was also taken automatically with dedicated software (Cytocam video microscope, Braedius®, Netherlands). Each set of measurements was performed at the bedside in real time by the nurse in charge of the patient. The nurse calculated the MFI score (nurses' real-time MFI) once a 5-s video sequence had been obtained. For assessment of density, the nurse qualitatively evaluated the type of density as poor, normal or rich (nurses' realtime TVD). Videos initially analyzed by the nurses were associated to an alphanumeric code to enable offline and delayed analysis (physician's delayed MFI and TVD) by a physician experienced in Cytocam-IDF analysis (ST) blinded to all clinical data and nurses' real-time MFI values.
Thus, we considered that if the limit of agreement given by d-2 s is around 0. 5, a sample size of 75 would allow precision in the estimate of the limit of agreement around ± 0. 05. The sensitivity and the specificity of the nurses' real-time MFI and TVD to respectively detect impaired microvascular flow and low microvascular density were calculated using the physician's delayed MFI and TVD as the reference standard. An impaired microcirculatory flow was defined as an MFI <2. 5. A low TVD was defined as a TVD <8 mm/mm 2. In order to test the potential heterogeneous performances between nurses, we calculated the delta of MFI measured by nurses and by the physician and used one-way analysis of variance (ANOVA) to compare the nurses. The data were analyzed using Prism, San Diego, California 4® for Mac OS X®.