dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data were analyzed using SPSS for Windows (version 18. 0; SPSS Inc. , Chicago, IL). Continuous variables were tested for normality using Kolmogorov-Smirnov test. Values are expressed as mean (SD) or median (inter quartile range, IQR) for continuous variables and as a percent for categorical variables. Comparisons between groups were performed using analysis of variance or the MannÁ Whitney test for continuous variables and Pearson chisquare test or Fisher's exact test for categorical variables as appropriate. The association between continuous variables was tested using a Pearson correlation test. Unadjusted and multi-adjusted odd-ratios with 95% confidence intervals were calculated as an estimate of the risk of vitamin D deficiency/insufficiency for several potential risk factors.
The socioeconomic rank of children was not properly identified. However, based on parents' occupation, most participants have average to high socioeconomic status. Nutritional assessment was achieved in only 77% of participants and vitamin D intake was estimated using a non-Tunisian food database. Because the vitamin D content of foods is not given in the food composition database for Tunisia, the presumed concentrations of vitamin D were obtained from European food composition tables, which make estimations less precise. To overcome this issue, we considered the consumption of vitamin D food sources in Tunisian diet. The development and validation of a vitamin D database for Tunisian food is necessary to allow future estimations of vitamin D intake. Our study did not look for health problems that may be related to vitamin D deficiency, an aspect that is beyond the scope of the study.
Data are means ± SEM of 15 values obtained after quantification of three independent experiments. (h,i) Western blot analysis of PCNA expression in HL-60 cell lines (h) in nuclear fractions using 20 μ g protein/lane. Results were quantified by densitometry and normalized to lamin B1 loading control. The same experiments were performed with the whole lyzate using 10 μ g protein/lane. Results were quantified by densitometry and normalized to β -actin loading control. For (a-f) multicomparison ANOVA test (indicated by*) was used to evaluate the difference between HL-60S and HL-60R and Student's t test (indicated by #) was used to evaluate the effect of the drug on either HL-60S or HL-60R, *p < 0. 05, **p < 0. 01, ***p < 0. 001. fluorescence following LMB treatment was higher in HL-60R compared with HL-60S with a mean ± SEM of 90.
Methods Blast cells from AML patients and myeloid cell lines. Bone marrow or peripheral blood samples with more than a 70% blast cell content were obtained from 69 patients with newly diagnosed AML: 19 were followed by Didier Bouscary from the Department of hematology of Cochin Hospital; their characteristics are listed in Supplementary Table  3 ; 26 were intermediate risk AML patients from the GOELAMS AML2006IR trial (NCT00860639) and 24 were CBF (core binding factor)-AML patients from the ALFA-GOELAMS AML 2006 trial (ref NCT00428558) whose characteristics are listed in Supplementary Tables  4  and 5. Handling, conditioning and storing of patients samples were performed by GOELAMStheque (N° BB-0033-00073), the tumor bank of the GOELAMS group, Cochin Hospital, Paris. Cells from AML patients were collected after obtaining written informed consent in accordance with the approval from the Cochin hospital and INSERM institutional ethics committees of the GOELAMS protocols whose numbers are mentioned above.
Immunofluorescence analysis in HL-60 and blast cells from leukemic patients was performed, as previously described 9 using primary antibodies against PCNA (rabbit, Ab5, Calbiochem) or NAMPT/Visfatin (monoclonal mouse, Enzo Life Sciences). Nuclei were stained by Hoechst (Sigma, 2 μ g/ml). P21 peptide analysis in HL-60 was performed after incubation of FITC-coupled p21-peptide-YIRS as previously described  9. Slides were mounted using fluoprep medium and analyzed by confocal microscopy with a Leica TCS SP5 AOBS imaging microscope, model X63, with LAS AF version 1. 8 software. Image stacks were acquired with 0. 3 nm z step (63 × 1. 4NA oil objective; Leica) using Yokogawa CSU X1 Spinning Disk (Yokogawa, Tokyo, Japan) coupled with a DMI6000B Leica microscope (Leica Microsystems Gmbh, Wetzlar, Germany).
Acquisitions were made with MetaMorph 7 software. Image analysis and quantification of the intensity of nuclear PCNA fluorescence was performed using ImageJ (http://imagej. nih. gov/ij/). To establish the intensity of nuclear PCNA fluorescence, an automatic threshold was performed and a Hoechst binary mask was used to quantify the labelled area within the nucleus. Subcellular fractionation and western blot analysis. PCNA expression was evaluated by western blot analysis in HL-60 cells lyzed in PBS supplemented with 1% Triton-X100 and anti-proteinases or in the nuclear fraction after cell potterization, as previously described  55. Nitrogen cavitation was used to obtain protease-free cytosolic fractions, as previously described  56. NAMPT expression was evaluated by western blot analysis in blast cells after sonication to obtain the cytosolic fraction  9.
The association (ka) and dissociation rate (kd) constants and the equilibrium dissociation constant (KD) were calculated using the T200 Biaevaluation software. For analysis of the p21 peptide effect, p21 (2 μ M), NAMPT (120 nM) or a mixture of both at these same concentrations were injected at 20 μ l/min for 100 s over immobilized PCNA and dissociation was analyzed for 400 s. The specific binding signal was obtained by subtracting the background signal, routinely obtained by injection of the sample over an activated-deactivated surface as previously described  23. Evaluation of metabolism in HL-60 cells. Total intracellular NAD was measured using the Biovision assay kit according to the manufacturer's instructions. Briefly, 1. 5 ml containing 3. 75 × 10 5 of cells were incubated at 37 °C in 5% CO 2 with the p21-peptide-YIRS (10 μ M, 25 μ M) or 3-bromopyruvate (3-BrP) (20 μ g/ml, Calbiochem) for 6 hours.
Acknowledgements The authors greatly acknowledge  Dr. Samy Arroum  (Novartis),  Pr Catherine Lacombe  ( Cochin Institute ) and  Pr Etienne Weiss  ( Ecole Supérieure de Biotechnologie de Strasbourg ) for valuable discussions and all the people from  Cochin Institute  facilities:  Muriel Andrieu  and  Karine Labroquère of the Cochin Cytometry facility ;  Pierre Bourdoncle  of the Cochin Microscopy and Imaging facilities;  François Guillonneau ,  Cédric Broussard ,  Virginie Salnot  and  Christian Federici  of the 3P5 proteomic facility,  Université Paris Descartes  and the  Etablissement Français du Sang (Saint-Antoine Hospital, Paris France)  for providing us human blood samples. We thank the  SPR platform of the Grenoble Instruct centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
This work was supported by institutional grants to  VWS from Investissements d' Avenir programme   ANR-11-IDEX-0005-02 ,  Sorbonne Paris Cite ,  Labex INFLAMEX , the  Chancellerie des Universités de Paris  ( Legs Poix ), and the  DHU AUTHORS (AP-HP  and  Paris Descartes University ), by the  ARC  ( N° PJA 20131200141 ),  Vaincre La Mucoviscidose  (fellowship to DO), and Arthritis Fondation Courtin patient associations, and by a research subvention from  Novartis. This study was also supported by grants to DB from the  Institut National du Cancer (INCa ; Projet recherche translationnelle  TRANSLA13-087 ) and the  Ligue Nationale Contre le Cancer  (LNCC; Equipe Labellisée  EL2014. N° R14077KK ).
org/ng), while merosin expression appeared near normal. Molecular findings. 5] [6]  One participant (case 3) was compound heterozygous for 2 new variants in GMPPB, and case 6 had 1 new mutation not present in the databases, such as Exome Variant Server and 1000 Genomes Project. The c. 1039_1043dup (p. Ile349Serfs*) mutation is inherently pathogenic due to a frameshift, and the mutations, c. 464G. A (p. Arg155His) and c. 902C. G (p. Ser301Cys), affect highly conserved sites and are predicted to be pathogenic by 3 databases (Single Nucleotide Polymorphism/GO, SIFT, and PANTHER). The prevalence of LGMD2T in the Danish cohort was estimated to be 1. 5%. Creatine kinase (CK) level was elevated in all patients (table). Neurophysiology. EMG showed a myopathic pattern mostly in proximal muscles, and repetitive stimulation disclosed a decremental response primarily in the proximal muscles at 3-Hz stimulation of the radial, peroneral, and spinal nerves with recordings from the anconeus, tibialis anterior, and trapezius muscles (11%-29%).
Novel Screening Modalities and Readouts As more complex cellular systems are used in HCS, their analysis becomes more challenging. The SLAS Europe HCS conference was heavily focused on imaging, and several examples of advanced imaging applied at high throughput were presented. One of the emerging themes was smart microscopy, where the microscope acquisition is controlled by feedback from image analysis. Smart microscopy has been developed to allow acquiring images where something "interesting" has or is happening and avoid taking images with no object of interest, thereby reducing data quantity. Typically, the microscope surveys the plate for an event of interest and then switches to an acquisition mode that is generally more data intensive, such as high-resolution 3D stacks, fast livecell imaging, super-resolution microscopy, total internal reflectance (TIRF), or fluorescence recovery after photobleaching (FRAP).
Users can perform a low-resolution scan of a plate, determine objects of interest by image analysis, and use the coordinates of the objects to acquire high-resolution images. The use of smart microscopy will presumably grow in HCS, as it allows acquiring high-quality data while maintaining the amount of data within reasonable bounds. It is also to be expected that it will allow the introduction of advanced fully automated imaging modalities such as super resolution, FRAP, and TIRF. This, in turn, will allow the development of novel, informative assays to interrogate and study biology in the future. At the SLAS Europe HCS conference, Jan Huisken presented his automated single-plane illumination microscope (SPIM) for use in screening zebrafish embryos and other 3D objects. His group has developed software solutions to project the data on a 2D map, thereby preserving the 3D information but discarding the voxels that do not contain information.
It is to be expected that a model where licensing fees are paid for the usage of the software will appear. An alternative is the use of open-source software such as CellProfiler, KNIME, CellCognition, ImageJ, and other platforms. Pharmaceutical companies will need to settle upon a strategy to keep their software costs under control as the size of data increases. AstraZeneca, Janssen Pharmaceuticals, and Novartis have come up with three different solutions to the problem. AstraZeneca opted for purchasing Definiens, the company producing its favorite image-processing software (www. astrazeneca. com/media-centre/press-rele ases/2014/ medimmune-definiens-aquisition-tissue-phenomics-2611 2014. html). Janssen Pharmaceuticals developed its own image analysis solution (phaedra), and Novartis uses opensource software such as CellProfiler and KNIME.
Importance of Data Sources Data sources are a key aspect in the safety evaluation process. It is important to consider the different postmarket surveillance data sources and their limitations. Table  1  provides examples of types of post-market data sources for both NAIMDs and medicines. Many data sources are common to both products, but some have been explored more in one than in the other. For both NAIMDs and medicines, passive post-market data sources are easily accessible and well established. On the other hand, active data sources are further advanced for medicines than for NAIMDs. The main differences between NAIMDs and medicines is that prescription or pharmacy dispensing and electronic medical records (EMRs) or claims databases for NAIMDs are underdeveloped. This is primarily because of the lack of a unique device identifier (UDI).
UDIs will enhance post-market surveillance activities by providing a standard and unambiguous way to document device use in EMRs or healthcare utilization databases. The integration of the UDI into such databases could potentially support public health-related activities such as reducing use errors and the reporting and assessing of adverse events and other problems related to the NAIMD. It would also enable tracking of product withdrawals, assessment of patient outcomes and risk-benefit profiles of NAIMDs across different populations, as well as provide a viable source of device identification information to the various stakeholders. Integrating UDI information into such databases will increase the use of 'real-world' data in the decisionmaking process. The US FDA has indicated that establishing a medical device safety evaluation system to gain real-world evidence is one of its strategic priorities for 2016-2017.
Harmonization and Centralization Adverse event coding should be harmonized to improve the signal detection process. It is recommended that a global and centralized database, such as the World Health Organization (WHO) Vigibase, be established for the assembly of all medical device reports. Moreover, a worldwide evaluation system should be developed for medical devices and should include representatives from the different stakeholders  [12]. This system also does not yet exist for medicines and could use real-world evidence to support regulatory decision making. To ensure successful implementation of this system, three steps need to be incorporated: (1) the UDIs need to be consistently assembled within electronic health information, (2) all stakeholders need to ensure a continual use of the EHRs, including UDIs, and lastly (3) to link patient data, all data sources need to have interoperable linking capabilities  [30].
Coding harmonization to improve the signal detection (ISO, FDA) • Partnerships to build a worldwide system for medical devices in order to integrate all type of post-market data sources • Global centralized database for collection of reports related to medical devices • Harmonization of regulatory actions across jurisdictions • New benefit-risk evaluation tools: PMS Plan and PSURs • More post authorization studies developed and performed in order to address safety concerns • Robust signal management process for medical devices (CIOMS VIII) • The clinical data required to obtain the market approval should be defined in guidelines • User training as the most important risk minimization measure • Provide excellence customer service to the medical device user.
Examples of post-market data sources for non-active implantable medical devices and medicines AERS Adverse Event Reporting System, AOANJRR Australian Orthopaedic Association National Joint Replacement Registry, CDER Center for Drug Evaluation and Research, EEA European Economic Area, EMA European Medicines Agency, EMR electronic medical records, ESID European Society of Immunodeficiencies, EUADR Exploring and Understanding Adverse Drug Reactions, EUREQUO European Registry of Quality Outcomes for Cataract and Refractive Surgery, FDA US Food and Drug Administration, MAUDE Manufacturer and User Facility Device Experience, MedSun Medical Product Safety Network, MHRA Medicines and Healthcare products Regulatory Agency, NAIMD non-active implantable medical devices, NorPD Norwegian Prescription Database, PAS post-authorization study, PASS post-authorization safety study, PRAC Pharmacovigilance Risk Assessment Committee, SCAAR Swedish Coronary Angiography and Angioplasty Registry, WHO World Health Organization.
• Coding harmonization to improve the signal detection (ISO, FDA) • Partnerships to build a worldwide system for medical devices in order to integrate all type of post-market data sources • Global centralized database for collection of reports related to medical devices • Harmonization of regulatory actions across jurisdictions • New benefit-risk evaluation tools: PMS Plan and PSURs • More post authorization studies developed and performed in order to address safety concerns • Robust signal management process for medical devices (CIOMS VIII) • The clinical data required to obtain the market approval should be defined in guidelines • User training as the most important risk minimization measure • Provide excellence customer service to the medical device user.
The DAQ card allows to generate the driving signals for the galvanometer scanners and to generate a trigger signal for the line scan camera (the start of each A-scan is triggered). Data from the line scan camera is acquired using the frame grabber. The line camera can be operated at 70kHz with the full line width of 4096 pixels. However, the line rate can be increased by reading fewer pixels. We chose 896 pixels to read out which resulted in an A-scan rate of 200 kHz. Operation and evaluation software of the AO-OCT was developed in LabView (National Instruments, Austin, USA).
Healthy volunteers In a first step the imaging performance of the instrument was tested in several healthy volunteers. Figure  2  shows representative images recorded with the new instrument in a healthy eye. The large field of view fundus image (cf. Fig. 2(A) ) does not show small details as can be seen in the enlarged region (cf. Fig. 2(B) ). However the AO-fundus image (Fig. 2(C )) acquired at the same location (temporal = 1°, superior = 2° from the fovea) with the focus set at the photoreceptor layer and showing the same field of view as Fig. 2(B) , reveals the photoreceptor mosaic. AO-OCT data is acquired at the central part of the AO-fundus image and provides depth resolved information (Fig. 2(D) ). Visualization 1 shows a fly-through of AO-OCT B-scans in a linear grey scale. The different retinal layers can be clearly visualized, however, at this retinal location, the separation between end tips of photoreceptors (ETPR) and retinal pigment epithelium (RPE) is not pronounced.
It should be noted, however, that depth integrated AO-OCT images appear very similar to AO-SLO images as has been shown in  [25]. The images of patients show the potential of the instrument. For interpretation of patient data the additional information provided by AO-OCT is valuable. For example, the blurred patches which are visible in the fundus image of Stargardt's disease (cf. Fig. 4 (C)) are difficult to interpret. However, as can be observed in the AO-OCT data (cf. Fig. 4 (F)) these patches correspond to flecks that penetrate the photoreceptor bands. On the other hand, the remaining photoreceptor mosaic appears with higher contrast in the AO-fundus image. In addition to that, AO-OCT reveals structures that might not be observable with any other imaging modality. Such structures are for example the spots within the outer nuclear layer in the patient with Stargardt's disease.
Acknowledgements Financial support from the  European  project  FAMOS  (project number  FP7 ICT 317744 ) and the  Macular Vision Research Foundation (MVRF, USA)  is acknowledged. We thank  Boris Hermann ,  Abhishek Kumar ,  Mengyang Liu  and  Daniel Fechtig  from the  Center for Medical Physics and Biomedical Engineering at the Medical University of Vienna  for their support and fruitful discussion.
Activity Recognition Activity recognition is a topic of interest for automation and health applications in smart homes. It can be used for different applications. One of them is to adapt automation of the home to the circumstances of the command that is perpetrated. For instance, turning on the light can be done differently if it is to wake up the person in the morning or if it is to give the person some more light for cooking when the outside luminosity has decreased too much. A relatively complete state-of-the-art of activity recognition can be found in  [11]. It also lists the datasets that have been acquired to work on this topic and to create models of activity. Activity recognition is generally done using supervised classification algorithms. However, two types of algorithms can be found to perform this task:.
• Offline classification algorithms, such as K-Nearest-Neighbors (K-NN)  [12] , Artificial Neural Networks (ANNs)  [13]  , Decision Trees (DTs)  [14]  or Support Vector Machines (SVMs)  [15]. These algorithms rely on the creation of "frames" of data of a chosen length that will try to find the closest example(s) in the database for a test frame. These algorithms are based only on statistical evaluation of the dispersion of the data in a given space. Using ontologies,  [16]  defines the context and the Activities of Daily Living for a further recognition with rules-based algorithms.
• Sequential algorithms, such as Hidden Markov Models (HMMs)  [17] , Conditional Random Fields (CRFs)  [18]  or also Markov Logic Networks (MLNs)  [19]. Those methods add to the previously cited algorithms a notion of dependence between the different events of the frame or of the activity. This allows to identify spatio-temporal relationships between the data that are totally absent in the classical methods. HMMs have been, for a long time, a reference method for activity recognition. For all these algorithms, the conception of a dataset that is annotated is mandatory to obtain the models of each activity and to be able to test the performances. In the results that are obtained, the models are relatively generic as they are not dependent of the person performing the activity. However, they could depend on the sensors that are present in the environment and their disposition.
). Some of the user profile attributes will be learned automatically using the activity recognition system. For example, an attribute representing whether the person has a habit of practicing sport frequently can be learned/updated from the readings. Our approach is based on the automatic detection of the relevance between the learned preference (the user x prefers watching news media while practicing inside sports) and the attributes representing the environment (rainy day) and the user profile (practices sports frequently). Such relevance is an important key element for generalizing the learned adaptive behavior to new situations and unknown users and for decreasing the complexity of convergence to an optimal adaptive policy (decreases the size of needed dataset to reach an acceptable adaptive automation).
Global Architecture Figure  1  describes the different components (processes and databases) of the proposed architecture and shows the data flow from acquisition to decision making. The architecture contains five databases and three processes. The first database (called "raw data"), taking information from the smarthome directly, contains all the sensors' readings and all the profiles of the different users (the current version). A user profile can be partially completed directly by the user and then, while processing the available data, can be completed/updated automatically. The "data processing" process is responsible for three missions: 1. activity recognition, 2. detecting implicit feedback, and exporting potential preferences through analyzing each received explicit feedback and each detected implicit feedback, and 3.