dataesr/scientific-paragraphs-categorization huggingface dataset:
37 g DW /OD 600 ) and calculate specific DHA uptake rates (mmol g DW Ϫ1 h Ϫ1 ), formate production yields (mmol g DW Ϫ1 h Ϫ1 ), and biomass yields (g mol Ϫ1 ). Experiments were performed in triplicate to calculate averages and standard deviations. In silico analysis of DHA metabolism. In silico analyses of DHA metabolism were carried out with the E. coli genome-scale metabolic model iJO1366 (37) (Biomodels identifier MODEL1108160000) constrained with experimental uptake fluxes of the wild-type strain (DHA ϭ 5. 2 mmol g DW Ϫ1 h Ϫ1 , formate ϭ 3. 2 mmol g DW Ϫ1 h Ϫ1 , glycolate ϭ 1. 0 mmol g DW Ϫ1 h Ϫ1 , and acetate ϭ 0. 1 mmol g DW Ϫ1 h Ϫ1 ), using Sybil package (v2. 1. 2) (63) of the R environment (v3. 2. 4)  (64, 65). The model and all the scripts used to run calculations are available at https://github.
ACKNOWLEDGMENTS We thank  MetaToul-MetaboHUB (Metabolomics & Fluxomics Facitilies, Toulouse, France , www. metatoul. fr, ANR-11-INBS-0010 , http://www. metabohub. fr/) and its staff members for technical support and access to NMR facilities. We also thank students from  INSA Toulouse ,  Ophélie Besse ,  Johan Benestad ,  Anaïs Bonnin ,  Maéva Brunet ,  Tanguy Soulié ,  Marion Urvoy , and  Maxant Vivier , for their help in producing transcriptomic data and initiating the data analysis. This work was funded by the  ANR  ( ANR-16-CE20-0018-01 ). C. P. performed the physiological analysis, P. M. performed the modeling experiments, A. D. S. built the strains, E. C. and L. P. performed the NMR analysis, B. E. performed the transcriptomic analysis, and S. H. designed the study and wrote the paper with the help of all the coauthors.
The teachers from the Biology Department of the University of Lille have been developing for the last two years, a digitalization program of naturalist collections. This project aims to preserve this scientific heritage and to use it for animal and plant biology teaching. Specimens are digitalized by a photo 3D capture system, that produces a 360° and/or hemispheric images of the objects starting from high-resolution pictures. Based on the use of this particular imaging technology, teachers realized multimedia eBooks and a series of files "at the glance" for practical works in animal biology for Bachelor students in Life Sciences. These supports, enriched by graphic complements, texts, legends and interactive animations, are available on the pedagogic platform Moodle. These digital tools are viewable on computers and can be handled on smartphones and tablets for a nomad utilisation.
Interactive multimedia files and eBooks The available photo 3D images were used to produce a series of files describing teeth's features and evolution in Mammals (http://photo3d. univ-lille1. fr/dents_et_regimes/index. html). Teachers entirely conceived these documents. Multimedia engineers treated single images with Photoshop (Adobe) to enrich them with legends and colours displaying specific elements of the different samples. Animations were realised with the Adobe Muse software. We recently produced several multimedia eBooks made available for students for practical session preparation and review before learning feedback. These digital supports contain text, schemes, images, photo galleries, a glossary and other interactive widgets to explain and complete the work made during the lab session.
The integration of hypertext links and QR codes into the eBook gives the students the possibility to directly point to the photo 3D database described above to observe and virtually handle the different samples. These digital resources, made with free software iBooks Author distributed by Apple, can be exported in iBook or pdf format for Apple or PC/Android users, respectively. These supports are so far available in French but English versions are being programmed for our Bachelor students of the bilingual (English-French) option.
Results The University of Lille recently acquired a 3D photo capture system aimed to digitalize its scientific heritage. The Biology Department is producing a database of plant and animal collections to be used for didactic purposes (http://pod. univ-lille. fr/video/5946-unenouvelle-vie-pour-des-collections-naturalistes-dantan/). Based on this context, a group of teachers developed a distance-learning project in animal biology for Bachelor in Life Sciences students aimed to illustrate, by digital tools, Mammals' teeth features according to their diet. The main learning objective of these innovative supports is to illustrate with texts, schemes and interactive 3D photos the set of teeth of different Mammals described during academic lectures and practical sessions. These supports are compatible with any digital device (computers, tablets or smartphones) and Internet browser.
Acknowledgements The project " Mammal teeth and diets " is funded by  UNISCIEL (Université des Sciences  en ligne, http://www. unisciel. fr). Digital textbooks were granted by calls from the  University of Lille  for the production of educational media resources. Authors thank  Thierry Danquigny  and  Teodorina Tibar , educational designers from the  EdTech & Digital Pedagogy Department of the University of Lille , for their accompaniment throughout the production of these digital supports.
Therefore, such interfaces require adequate models to track the state and evolution of missions  [8] , as well as knowing the state and preferences of operators. Currently, operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their work, possibly by adapting the information and available commands displayed to operators. This paper addresses the study of an approach to improve teleoperated multi-robot missions. To address this study, a videogame inspired by a search and rescue scenario was designed. In this videogame, a team of and aerial and ground robots have to explore a maze and locate victims. The movements and plays of several users were recorded and used as input data for the modeling and prediction of the operators' behaviors.
Analysis A total of 136,858 events in 259 levels of 72 games developed by 21 players were collected during one month. This data was split into training and test sets (80% and 20%, respectively) in order to develop and validate the algorithms. Tactical, technical, and performance data was stored in logs by the game server. The analysis is split into three steps: definition of profiles (Section 4. 1), classification of operators (Section 4. 2), and prediction of preferences (Section 4. 3).
Equation (  9 ) defines the weight w v for each variable V v by multiplying the average values of the rest of the variables V i in all of the clusters C j. In this manner, the metrics are normalized and their differences compensated. G(C k ) = n v ∑ v=1 w v (C k ) * V v (C k ) (8) w v (C k ) =    -∏ i =v 1 n c * ∑ n c j=1 V i (C j ), if V v = DB ∏ i =v 1 n c * ∑ n c j=1 V i (C j ), otherwise. (9) All of these clustering techniques and evaluation criteria were implemented in MATLAB R2017a, in order to simplify the preparation of data, integration of clustering and prediction algorithms, and analysis of results. Table  2  summarizes the results of operator classification and cluster evaluation. The best score is obtained by the PAM algorithm for all methods (mean, linear, and quadratic), followed by SOM, K-means, and hierarchical.
Prediction of Preferences The prediction of operator preferences is performed by considering different data (previous data of the specific operator, cluster of similar operators, and the whole population of operators) and methods (considering simple and weighted means of previous data). As the variables of this study range from 0 to 1, the predictions are performed by building Bernoulli distributions from the three types of profiles computed by means of three methods (mean, linear, and quadratic). The predictions are performed with the training set, which contains 80% of samples, and validated with test set, which contains the other 20%, and each of them is repeated a hundred times to generate significant results. Table  3  shows the results of the predictions with different configurations.
Conclusions The modeling of the behavior of multi-robot operators during a simulated search and rescue mission has been carried out. This study helps to prove that it is possible to have an adequate prediction of their preferences during the mission, which can be very useful for the development of an adaptive interface. To obtain the models and predictions, a systematic methodology consisting of three steps was used: first, the definition of profiles, then the classification of operators, and finally, the prediction of their preferences. The data was obtained during a set of extensive experiments performed by means of a multi-robot search and rescue videogame and then processed using the aforementioned methodology. The results demonstrate that predictions based on the previously collected data increase the probability of success by 19% when compared to a random.
Related work Imaging applications for clinical methods and pathology study need high performance and efficiency. Several image processing environments and processing architectures exist for the medical imaging application, but to the best of our information, a programmable and highperformance scalable processing system is required for medical imaging applications. Ibanez et al. [5]  designed an open-source medical imaging toolkit called the Insight Toolkit (ITK). The developed toolkit supports a number of platforms and gives programmers with an extended C++ framework of software tools for image understanding. Schroeder et al. [6]  developed an open-source Visualization ToolKit (VTK) for three-dimensional processing and visualization. VTK toolkit assists C++ libraries and algorithms for different parallel processing technologies and provides an interface to integrate with different databases.
Wolf et al. [7]  introduced Medical Imaging Interaction Toolkit (MITK) that assists to program medical image-based clinical software. The MITK gives an application programming framework that links with the ITK and the VTK libraries. The proposed HPMIS provides a medical imaging programming model, which supports data registration, memory management, and processing in hardware, and is easy to program. The HPMIS programming model is scalable for different architectures having reduced instruction set computing (RISC) multi-processors, application specific hardware cores, and vector processors. Bakalash et al. [8]  suggested the MediCube system for 3D medical imaging. The system gives the reconstruction and visualization of three-dimension complex medical images. The processing system of the MediCube uses a RISC processor that performs the parallel processing for real-time voxel representation, whereas the HPMIS handles 3D medical imaging using a specialized local memory system and uses reconfigurable processing cores to process the incoming data.
Memory system The HPMIS memory system uses three types of memories: the program memory, the specialized medical memory, and the main memory. The HPMIS program memory utilizes the memory access descriptors [APMC][PPMC][PMC] that contain the knowledge of imaging data registration, data transfers, storage device, and processing cores. The descriptors provide the medical imaging programmer to choose a processing core for image processing and explain the complicated image structure. A High-Performance System Architecture for Medical Imaging http://dx. doi. org/10. 5772/intechopen. 83581.
Programming The HPMIS Medical Application Programming Model (MAPM) helps medical application programmers to develop applications without acknowledging hardware arrangements and details. The MAPM uses image processing functions for image segmentation, reconstruction, features extraction, and computation; and it also provides memory management and registration task and separate task for visualization. The MAPM presents various function calls, which include medical imaging flow, control, and processing. The MAPM data transfer tasks help disordered, random, strider 1D, 2D, 3D, and automated blocking for image/video transfer processes and move data between the medical imaging devices and the HPMIS memory system. The HPMIS MAPM intelligently pipeline, overlap, and parallelize image processing tasks based on hardware processing and memory resources.
Descriptive and multivariable analyses were performed using SAS version 9. 3. Results: The average patient age was 57 (SD: 14) years of age and the majority were female (53%). The mean overall PACIC score was 2. 6 (SD: 1. 1) at baseline and 3. 6 (SD: 0. 9) at 6 months. The experience of care improved for 62% of the participants based on the minimal clinically important difference (MCID). No significant determinants of overall PACIC change score were identified in the multivariable regression models.
Participants And The Interdisciplinary Program Based on the framework of CCM, a primary care interdisciplinary program was developed by the Centre of Expertise in Chronic Pain (Quebec, Canada). Program development integrated needs assessment and evidence-based guidelines. The composition of the interdisciplinary team was determined by the assessment of the needs for individuals suffering from LBP and included a nurse, a physician, a physiotherapist, and a psychologist. We identified relevant assessment tools through literature review and validated them by clinician experts and health system decisionmakers. The model included referral criteria; a treatment algorithm; standardized clinical process and assessment tools for the interdisciplinary team; provision of self-management support for patients; and defined administrative and clinical indicators supported by an electronic data collection and management system for the clinicians and for evaluation.
Primary care physicians referred individuals with subacute (4 to 12 weeks) and chronic (>12 weeks) LBP to the program which was implemented at four Health and Social Service Centres (CSSS). Participants received an interdisciplinary evaluation at the start of the program and individualized evidence-based treatments including pharmacological, physiotherapy and psychological therapies, and structured self-management support. We collected data on socio-demographic status, impact of pain, physical and mental health, function, and quality of life using selfreport and standardized questionnaires. The inclusion criteria for this study were i) individuals aged 18 years or older; ii) suffering from sub-acute and chronic LBP ≤ 1 year; and iii) that answered at least 10 questions of the Patient Assessment of Chronic Illness Care (PACIC) at baseline and 6 months.
5. Responder analysis is an approach that permits to assess clinical relevance of the effect of interest, particularly while using PROM or PREM instruments. 75 We conducted multivariable regression to evaluate the relationship between patient and staff and process covariates and the change score of PACIC as the outcome. We first estimated univariate models, and all significant covariates (95% confidence interval does not include the null value) in the unadjusted models were included in the multivariate models. We conducted the hierarchical multiple regression by adding more predictors to each successive model. Patient sociodemographic covariates were entered in the model first, followed by patient health status, and staff and process variables. The analyses were performed using SAS ver. 9.
Given the fact that there is always some implementation variations,  76  the absence of significant association of the overall PACIC change score with sites may indicate a negligible impact of implementation variation on the PACIC across program sites. 8] [79] [80] [81] [82] [83]  The higher PACIC scores obtained in our study suggest better patient experience. This may be due to the unique characteristics of the IDT program implemented and/or the study population (LBP). Additional data obtained from the implementation of similar IDT programs within LBP population will be needed to support these hypotheses. Similar to some previous findings,  77, 80, 84, 85  no significant association with potential determinants of PACIC was found. However, other authors reported some significant associations.
While the currently low signal-to-noise of the w δ g κ CMB (θ) and w γκ CMB (θ) correlation functions limits their cosmological constraining power, we show that including them in the joint analysis can make the cosmological constraints more robust to multiplicative shear biases. This work builds on several recent DES collaboration papers that analyze two-point functions of DES observables. These include the analysis of cosmic shear  [24] , the analysis of galaxy clustering  [25] , the analysis of galaxy-galaxy lensing  [26] , and the joint analysis of all three two-point functions in Abbott et al. [5]. The layout of the paper is as follows. In §II we describe the datasets used in this work; in §III we describe the modeling steps required to compute a likelihood for the observed two-point functions given a cosmological model; in §IV we describe our procedure for characterizing systematic biases in w δ g κ CMB (θ) and w γκ CMB (θ) that are specific to the κ CMB maps; in §V we describe the motivation for our choice of angular scale cuts.
A suitably normalized quadratic combination of filtered CMB temperature maps then provides an estimate of κ CMB. The SPT maps used for this purpose are from the SPT-SZ survey  [38]. The combined map produced from the SPT and Planck datasets is sensitive to a greater range of angular modes on the sky than either experiment alone: Planck cannot measure small scale modes because of its 7' beam (at 143 GHz), while SPT cannot measure large scale modes because of time domain filtering that is used to remove atmospheric contamination. The κ CMB map from Omori et al. [36]  is restricted to the area of sky that is observed by both SPT and Planck. The overlap of this region with the DES Y1 survey region is approximately 1300 sq. deg.
On the other hand, this approach carries some associated modeling uncertainty, which we will attempt to constrain below. The cluster sample used to generate the simulated tSZ map combines the redMaPPer  [29]  cluster catalog from DES Y1 data with samples of tSZ-detected clusters from SPT and Planck. We use redMaPPer clusters with richness λ > 20, SPT clusters with detection significance ξ > 4. 5  [69]  and the entire Planck tSZ-detected cluster sample  [70]. Each of these samples probes a different range of mass and redshift. The redMaPPer sample captures low mass clusters, but only over the redshift range of DES. The SPT cluster sample captures only very massive clusters, but out to high redshift. The Planck cluster sample, on the other hand, captures very massive clusters at low redshift which are missed by both SPT and DES.
VII. DISCUSSION We have presented the methodology for jointly analyzing the combination of five two-point functions that can be formed from the combination of the δ g , γ and κ CMB observables (not including the κ CMB autocorrelation). This methodology will be applied to a forthcoming analysis using data from DES, SPT and Planck. Essential to this analysis is the characterization of the bias in maps of κ CMB induced by the thermal Sunyaev-Zel'dovich effect. Our estimate of this bias suggests that it could be quite large at small scales. Given the uncertainties associated with this estimate, we do not attempt to model tSZ bias in our analysis. Instead, we remove angular scales that are estimated to be strongly affected by the bias, at the cost of increasing our statistical errorbars. This degradation is significant: the total expected signal-to-noise of the w γ t κ CMB (θ) and w δ g κ CMB (θ) crosscorrelations is roughly 20; after the scale cuts, this is reduced to 8.
ACKNOWLEDGEMENTS This paper has gone through internal review by the DES collaboration. The contour plots in this paper were made using ChainConsumer 7 [90]. EB is partially supported by the  US Department of Energy  grant  DE-SC0007901. The Melbourne group acknowledges support from the  Australian Research Council 's  Future Fellowships scheme  ( FT150100074 ). PF is funded by  MINECO , projects  ESP2013-48274-C3-1-P ,  ESP2014-58384-C3-1-P , and  ESP2015-66861-C3-1-R. ER is supported by  DOE  grant  DE-SC0015975  and by the  Sloan Foundation , grant  FG-2016-6443. Support for DG was provided by  NASA  through  Einstein Postdoctoral Fellowship  grant number  PF5-160138  awarded by the Chandra X-ray Center, which is operated by the  Smithsonian Astrophysical Observatory  for  NASA  under contract  NAS8-03060.
Based in part on observations at  Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-88861 ,  FPA2015-68048 ,  SEV-2012-0234 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Metabolites, lipids, and other small molecules are key constituents of tissues supporting cellular programs in health and disease. Here, we present METASPACE, a community-populated knowledge base of spatial metabolomes from imaging mass spectrometry data. METASPACE is enabled by a high-performance engine for metabolite annotation in a confidence-controlled way that makes results comparable between experiments and laboratories. By sharing their results publicly, engine users continuously populate a knowledge base of annotated spatial metabolomes in tissues currently including over 3000 datasets from human cancer cohorts, whole-body sections of animal models, and various organs. The spatial metabolomes can be visualized, explored and shared using a web app as well as accessed programmatically for large-scale analysis.
Until recently, the progress and applications of imaging MS were hampered by the lack of algorithms for metabolite identification, namely for associating ion peaks and images with their corresponding molecules. We have recently addressed this issue and published a method for confidence-controlled metabolite annotation 13. 2  Here, we present METASPACE, a public knowledge base of spatial metabolomes in health and disease populated by a large community of users (Figure  1a ). The cornerstone of METASPACE is a computational engine for metabolite annotation which searches for metabolites, lipids, and other small molecules in an imaging MS dataset. The engine estimates the False Discovery Rate (FDR) of metabolite annotations that provides quality control and -as demonstrated in other -omics  16  -makes annotated spatial metabolomes comparable between datasets, experiments, and laboratories  13.