dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, for the binocular protocol, we calculated both the absolute blue light PSPS as well as the difference in the PSPS between red and blue light. In this way, potential confounding central effects on the post-stimulus pupil response could be detected and controlled. Statistical Analysis. Statistical analyses were carried out using the SPSS 21 software. Descriptive statistics were performed to characterize the distribution of age, acuity, color vision, MD, RNFL, RAPD, logI 50 for rod-weighted and cone-weighted protocols, PSPS of melanopsin-weighted protocols and sleep questionnaire scores on three groups: controls, patients with unilateral AION and patients with bilateral AION. The data are presented as median values and the ranges. Due to the small number of subjects in each group, a non-normal distribution was assumed and non-parametric tests were applied for the comparisons.
01 1% 5% 14% 52% 28% mobile devices, students are able to send (0. 84) instant messages to teacher at the lecture podium to raise or answer questions. 4. 11 2% 3% 8% 56% 31% (0. 82) Classroom Exceptions Detection System: 3. 09 9% 21% 29% 34% 7% Through gesture detection shown on the (1. 09) screen at the lecture podium, the teacher is able to know students' classroom learning behaviors. 3D Virtual Reality/Animation-Based 3. 91 4% 10% 14% 36% 36% Learning Environment: The classroom is (1. 11) surrounded with 3D projection technologies to create simulative and immersive virtual reality for teaching and learning. Instant Scanning/Projection System: Group 3. 90 2% 5% 19% 51% 23% works or materials can be scanned/projected (0. 88) instantly and shared with the whole class. Class Activity Database System: Teacher 3.
81 2% 8% 19% 51% 20% and students are able to review certain class (0. 90) teaching/learning episodes and group activities to adjust teaching/learning. Interactive Whiteboard System: Students 3. 90 1% 7% 16% 53% 23% are able to write down answers to teacher's (0. 88) questions or group work results on the electronic tablet device to share with the whole class instantly on the electronic whiteboard. Class Content Retrieval System: Through 4. 17 1% 2% 10% 53% 34% voice-or text-based recognition, students are (0. 75) able to search for needed videos/text information from class databases. Automatic Learning Assessment System: 3. 92 1% 3% 19% 55% 22% With automatic assessment tools with quizzes (0. 80) and answers, students are able to have self- evaluation on what have learned in classes. Notes: Automatic Voice Recognition System: Relevant information including documents, pictures, and video clips can be instantly retrieved and shown on the screen when specific keywords and terms are spoken.
Scyther Overview Scyther is a free (GPL-2. 0) tool available on all operating systems (Linux, Mac and Windows). This tool can automatically prove security properties or provide an attack on a cryptographic protocol for bounded and unbounded numbers of sessions. One main advantage of Scyther is that it provides an easy way to model security properties, like secrecy and authentication. Scyther considers the Dolev-Yao intruder model. In this model, messages are abstracted by a term algebra, in order to formalize the cryptographic primitives used in the protocols. Moreover, the perfect encryption hypothesis is made, meaning that it is possible for a malicious node to decrypt an encrypted message only if it knows the associated secret key. Finally, in this model, the intruder is controlling the network, which means that all messages can be captured by the intruder.
Security Analysis We verified automatically all of our protocols using Scyther in a few seconds on a regular PC. Scyther concludes that all of our protocols are secure. More precisely, we proved the secrecy of all sensitive data exchanged (keys and nonces) and also the authenticity of the communication. Our Scyther codes are available here  [45]. The advantage of using an automatic verification tool that considers a powerful intruder is that we are sure that there is no attack, including replay or man-in-the-middle attacks. In all of our protocols, the authentication is ensured by using, in an appropriate manner, nonces, which also ensures the freshness of the messages to avoid, for instance, replay attacks. Moreover, the tool guarantees that our protocols preserve the confidentiality of the sensitive data and that the different participants communicate in an authenticated manner.
Diet recall interviews were done by telephone with the child and then confirmed with the parent who prepared the meals. Reported foods were entered into CANDAT (London, Canada) and converted to nutrients using the 2007b Canadian Nutrient File  [26]. Intake of sugar-sweetened beverages was computed as the mean daily mL of soft drinks and other sugar-sweetened drinks, excluding juices made from real fruits. Given a substantial positive skewness in its distribution, the variable was dichotomised to >50 mL/day (approximately 1 soft drink can per week) Vs. less. Participants' physical activity (PA) was measured using a uniaxial activity monitor (Actigraph LS 7164 activity monitor, Actigraph) for 7 days during the week following the baseline clinic visit. A minimum of 4 days with ≥10 h of wear time was required for data to be retained  [27].
Measurement of neighborhood environment characteristics Neighborhood environments were characterized using a geographic information system (GIS) for the study area. Canadian Census data from 2006 were used to obtain the following measures: % residents with a university degree, average value of owner occupied residences, % households living below Statistics Canada's low income cut-offs  [30] , % single parent families, % unemployment, % who have moved in the past year and % owner occupied residences. For each measure, population-weighted proportions or averages of Census dissemination areas overlapping 500 m network buffers centered on the child's residential address were computed. These variables were then reduced to 2 components using principal components analysis, namely neighborhood prestige (university degree and housing value) and neighborhood disadvantage (remaining Census variables described above), and then categorized into tertiles (see Additional file 1: Table  S1 )  [31].
This resulted in classification trees with 7 terminal nodes (Figure  1 ). Multivariable linear regression models were subsequently used to examine associations between the categorical variable that represents the recursive partitioning subgroups (terminal nodes) and BMI percentile while controlling for age, sex, puberty, and parental education. The lowest risk subgroup was the reference category; the remaining subgroups were identified using 6 indicator variables. Finally, associations between subgroup membership and BMI percentile at follow-up were examined while adjusting for BMI percentile at baseline. These analyses were conducted with SAS version 9. 3 (Cary, North-Carolina). Although a school-based sampling was used in QUALITY, clustering of participants in schools did not significantly influence estimates for associations (see Additional file 1: Tables  S2  and S3 ).
Acknowledgements Dre Marie Lambert  ( July 1952 -February 2012 ), pediatric geneticist and researcher, initiated the QUALITY cohort. Her leadership and devotion to QUALITY will always be remembered and appreciated. The QUALITY study is funded by the  Canadian Institutes of Health Research (CIHR) , the  Heart and Stroke Foundation of Canada (HFSC) , as well as the  Fonds de la recherche du Québec en santé (FRQS). The QUALITY Residential and School Built Environment complementary studies were funded by the  HSFC  and the  CIHR , respectively. A Van Hulst received support from a  CIHR/HSFC   Training Grant in Population Intervention for Chronic Disease Prevention , and a doctoral scholarship from the  FRQS. Y Kestens  holds a CIHR Applied Public Health Chair on Urban Interventions and Population Health.
MATERIALS AND METHODS Data for the present analyses were gathered through the M-SKIP project, which was previously described  (Raimondi et al, 2012). Briefly, we collected data from epidemiological studies on MC1R variants, sporadic cutaneous melanoma (CM), NMSC and phenotypic characteristics associated with skin cancer from 33 investigators who agreed to participate in the M-SKIP project. Participant investigators sent their data along with a signed statement declaring that their original study was approved by an Ethics Committee and/or that study subjects provided a written consent to participate in the original study. We created a pooled database, including data on 8301 CM cases, 3542 NMSC cases and 15 589 controls. For the present study, we identified in the M-SKIP database 8 independent case-control studies on NMSC  (Kennedy et al, 2001; Dwyer et al, 2004; Scherer et al, 2008; Brudnik et al, 2009; Liu et al, 2009; Nan et al, 2009; Ferrucci et al, 2012; Andresen et al, 2013)  that overall included data on 2587 BCC cases, 788 SCC cases, 152 cases with both BCC and SCC, and 9391 controls.
Coding and standardisation of the variables in the M-SKIP database has been described elsewhere  (Raimondi et al, 2012). For each study, we imputed missing data with multiple imputation models for variables with o20% of missing data, by using the iterative Markov chain Monte Carlo method, as previously described  (Schafer, 1997). We choose this method because several data sets presented non-monotone missing data patterns and because it is robust to minor departures from the assumptions of multivariate normality  (Schafer, 1997). For each imputation procedure, five data sets were generated, which was considered an adequate number for multiple imputation  (Rubin, 1996). The results from the five logistic regression models applied to the imputed data sets were then combined for the inference with proc mianalyze (SAS software, Cary, NC, USA).
We considered that statistically significant heterogeneity existed when the P-value was p0. 10. When significant heterogeneity was revealed, we performed sensitivity analysis and metaregression by year of publication of the study, geographic area where the study was carried out, MC1R genotyping methodology, deviation from HW equilibrium, type of controls and DNA source. To evaluate the robustness of the results, we also compared the pooled-OR obtained using the M-SKIP data set with the meta-OR calculated by pooling risk estimates reported in studies from notparticipating investigators also using DerSimonian-Laird randomeffects models. We computed the attributable risk (AR) in the population for the presence of at least one MC1R variant and for the MC1R variants found to be statistically significantly associated with NMSC by using the Miettinen's formula: (OR À 1/OR) Â proportion of cases exposed, with the corresponding 95%CI.
80 (1. 49-2. 17) for NMSC overall, 1. 70 (1. 36-2. 12) for BCC and 2. 10 (1. 60-2. 76) for SCC (Table  2 ). We observed a significant linear trend for one point increase in MC1R score for NMSC overall, BCC and SCC: per-point SOR (95%CI) were 1. 25 (1. 14-1. 36), Po0. 0001; 1. 22 (1. 11-1. 34), Po0. 0001 and 1. 28 (1. 17-1. 41), Po0. 0001, respectively (results not shown). We then restricted the analysis only to the studies that sequenced the MC1R gene. ORs for these studies were always higher than ORs obtained both on the whole group of studies and on the studies with MC1R not sequenced (Po0. 0001, results not shown). Association between single MC1R variants and NMSC. The nine most prevalent MC1R variants in the M-SKIP database were: V60L, D84E, V92M, R142H, R151C, I155T, R160W, R163Q and D294H.
As was previously pointed out  (Williams et al, 2011; Pasquali et al, 2015) , the inclusion of some MC1R variants in the reference category for the analyses would lead to underestimate the true risk of disease, because MC1R variants are very common among populations of European origins (66-67% of our study population had at least one variant). We sought to overcome this problem in our analysis by excluding from the reference category all the MC1R variants that were specifically assessed in each study. Since the nine most common variants were examined in the majority of studies, the reference category would mainly include WT and rare variants that were observed in B4% of the study subjects in the M-SKIP data set. We performed separate stratified analyses on subjects with and without red hair, skin type I/II and freckles, but unfortunately we could not compare subjects with and without any of these at risk phenotypic characteristics because they were collected jointly only in two studies  (Kennedy et al, 2001; Ferrucci et al, 2012).
ACKNOWLEDGEMENTS This work was supported by the  Italian Association for Cancer Research  (grant number:  MFAG 11831 ). The M-SKIP study group consists of the following members: Principal Investigator:  Sara Raimondi  (the  European Institute of Oncology, Milan, Italy ); Advisory Committee members:  Philippe Autier  (the  International Prevention Research Institute, Lyon, France ),  Maria Concetta Fargnoli  (the  University of L'Aquila, Italy) ,  JoseC Garcı ´a-Borron  (the  University of Murcia, Spain), Jiali Han (Brigham and Women's Hospital  and  Harvard Medical School, Boston, MA, USA) ,  Peter A Kanetsky (Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA) ,  Maria Teresa Landi  (the  National Cancer Institute, NIH, Bethesda, MD, USA ),  Julian Little  (the  University of Ottawa, Canada ),  Julia Newton-Bishop  (the  University of Leeds, UK) ,  Francesco Sera  (the  UCL Institute of Child Health, London, UK ); Consultants:  Saverio Caini  ( ISPO, Florence, Italy ),  Sara Gandini  and  Patrick Maisonneuve  (the  European Institute of Oncology, Milan, Italy ); Participant Investigators:  Albert Hofman ,  Manfred Kayser ,  Fan Liu ,  Tamar Nijsten  and  Andre G.
Acknowledgments The authors would like to thank warmly the panel of experts who agreed to participate in the Delphi process and made the consensus possible:  Penelope J. Allen ,  Sofia Androudi ,  Florent Aptel ,  Itay Benzion ,  Sanjoy Bhattacharya ,  Brian A. Bonanni ,  Christophe Chicquet ,  Antony Clark ,  Sophie Deng ,  P. Dickerson ,  Murat Dogru ,  Renée du Toit ,  K. David Epley ,  Céline Faure ,  Eric Gabison ,  Prashant Garg ,  Birgitte Haargaard ,  Julia A. Haller ,  Pedram Hamrah ,  Mingguang He ,  Amy L. Hennessy ,  Lawrence W. Hirst ,  Fung Rong Hu ,  Mark Juzych ,  Justin Kanoff ,  Alan Kreiger ,  Hsi-Kung Kuo ,  Davide Lazzeri ,  Ralph Levinson ,  Lance Liu ,  Tushar M. Ranchod ,  Heloisa Maestrini ,  Ezra Maguen ,  Gerald McGwin ,  Rachna Meel ,  Naik Milind ,  Kevin Miller ,  Danny Mitry ,  Inger Montfoort ,  Yvonne Ou ,  Rey Pangilinan ,  Noortje I.
The OVRO 40-m monitoring program is supported in part by NASA grants NNX08AW31G and NNX11A043G, and NSF grants AST-0808050 and AST-1109911. The National Radio Astronomy Observatory is a facility of NSF operated under cooperative agreement by Associated Universities, Inc. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team  (Lister et al. 2009 ).
Characterization Infrared spectra obtained by FTIR spectroscopy were recorded with a Bruker Vertex 70. The resolution of the infrared spectra was 2 cm -1. For all experiments, an effective precursor 1 film thickness of 2 µm was chosen and assessed by profilometry using an Altisurf 500 workstation (Altimet) equipped with a 350 µm AltiProbe optical sensor. X-Ray Diffraction patterns (XRD) were obtained on a PANalytical X'pert Pro diffractometer with fixed slits using Cu/Kα radiation (λ = 1. 5418 Å) and θ-2θ mounting. Before analysis, films on silicon wafers were directly deposed on a stainless steel sample holder. Data were collected between 1 and 10 ° 2θ degrees (XRD) with a scanning step of 0. 01° s -1. Morphologies of the samples were characterized by scanning electron microscopy (SEM) (FEI Quanta 400 microscope working at 30 kV).
Binding was first detected straightforwardly by optical and fluorescence microscopy (data not given). Additionally, we used FTIR spectroscopy to assess the yield of coupling. Figure  7  shows the IR spectra of the COOH-terminated hybrid to which the ligand L has been coupled. After activation, there is no band corresponding to residual carboxylic acid groups (C=O symmetric stretch of COOH, ≈ 1710 cm -1 ), which are replaced entirely by another blue-shifted band of similar intensity assigned to NH-C(O) amide groups (C=O amide stretch ≈ 1690 cm -1 ),  33  thus suggesting a quantitative coupling. Another evidence was given by the appearance of the characteristic v(N-H) band at 3440 cm -1. A benchmark experiment was performed by immersing a nonhydrolyzed film (1-P) in the same fluorescent ligand solution.
The saturation is reached earlier for Sc-44g than Sc-44m. An alternative production route is to use deuterons as projectiles through the 44 Ca(d,2n) reaction, which has an energy threshold equal to 6. 964 MeV. We did not find cross-section data with deuterons in the literature and we have therefore chosen to use the TALYS code version 1. 6  (11)  to estimate the production yields in order to compare with the proton route. TALYS is a computer program for simulation of nuclear reactions induced by light projectiles on nuclei heavier than carbon. It integrates many theoretical models that predict the behavior of various observables, among which the production cross-sections, in the energy range from 1 keV to 1000 MeV. TALYS can work with a limited number of input parameters: the type of projectile and its incident energy, the target element and its atomic mass.
Default settings are assigned to each nuclear model and several parameters can be modified to better describe the data. In this work, we used the default settings. Before using the code to obtain the (d,2n) cross-section values, we have compared the experimental cross-section for the reaction (p,n) measured by  Levkovskij (8)  with the values predicted by TALYS. The data are presented as symbols on Figure  2  whereas the TALYS values are presented as full lines on the same Figure. A good agreement is found between the data and the code values giving us confidence on the ability of this code to predict deuteron cross-section values. TALYS was then used to estimate the Sc-44g and Sc-44m production cross-sections associated to the (d,2n) reaction on Ca-44. On Figure  2 , the dashed line corresponds to the Sc-44g crosssection obtained with the TALYS code.
An original chemistry has been developed using DGA chromatographic column (5) from TrisKem International SAS (France). After the chemistry, the expensive target material is recovered for reprocessing. The target is generally irradiated with deuterons of 16. 4 MeV for 180 min with a beam intensity around 300 nA. Scandium is recovered as ScCl 3. Typical activity for Sc-44g is 90 MBq whereas it is 1. 8 MBq for Sc-44m at the end of the chemistry step (around 4 h after EOB). The activity ratio is then equal to 2%, which is coherent with TALYS calculations. If one except Sc-44g and Sc-44m, all radionuclides are below the detection limit using our HP-GE detector. Using an ICP-OES, the main stable contaminants have been identified (Al, Fe, Zn, and Sc) and the total content is lower than 1 ppm. These numbers show that deuterons can be advantageously used to produce Sc-44m with a high-specific activity.
When using a deuteron beam, the production of Ni-65 (which decays to Cu-65) and Cu-65 will have an impact on the final specific activity. In order to have some hints on their contributions, production yields have been calculated for these isotopes and for Cu-63. When available, data have been taken from Kinsey et al. (4). Otherwise, we have used the TALYS code with default parameters  (11)  to get cross-section values. In Table  4 , the calculated yield values are presented for a 1 h irradiation at 1 µA. The main stable copper contribution comes from the decay of Ni-65 either during irradiation or after. The latter contribution is strongly correlated to the time of the chemistry, the shorter the separation is, the lower the contribution is, whereas the first contribution increases with the irradiation time.
Acknowledgments The cyclotron Arronax is supported by  CNRS ,  INSERM ,  INCa , the  Nantes University, the Regional Council of Pays de la Loire , local authorities, the  French government , and the  European Union. This work has been, in part, supported by a grant from the  French National Agency for Research called "Investissements d' Avenir, " Equipex Arronax-Plus   ANR-11-EQPX-0004 , and  Labex  IRON  ANR-11-LABX-18-01.
Our main contributions are presented as follows: • Given a network represented as square grid cells with inverted files, an index tree is constructed, where grid cells correspond to the leaf nodes in this tree. A query expects to return sensory data for certain attributes in a set of neighboring grid cells. For simplicity, sensory data in a grid cell with an attribute is considered as an atomic unit for query answering and caching manipulation. A query is answered through composing sensory data that are: (i) cached in the sink node; and (ii) gathered from the network in real time. • The sink node is usually limited in storage and computational capabilities and can hardly cache sensory data of all sensor nodes in the whole network. Besides, a sub-region, rather than the whole network, is usually interested in applications within a certain time duration.
When this grid cell is interested in queries, sensory data of these sensor nodes, whose flags indicate a dramatic variation, are retrieved from the network in real time. • Extensive simulations are conducted for evaluating the effectiveness and efficiency of the proposed algorithms. The experimental results show that the technique proposed in this article outperforms another technique proposed by Zhou et al. [33]  where multiple-attribute query processing is also explored. Generally, our technique is more efficient than  [33]  in reducing the communication energy consumption and increasing the network capability, especially when the number of queries and the number of attributes interested in these queries are relatively large. The rest of this article is organized as follows. Section 2 introduces the energy model used in the following.
Query Processing with Two-Tier Cooperative Caching Mechanism Leveraging sensory data cached in the memory space of INs and the SN, we propose a two-tier cooperative caching mechanism for answering periodic queries. As presented in our previous work  [33] , the network region is divided into square grid cells. A two-dimensional matrix is used to represent grid cells, where each grid cell is accompanied by an identifier (denoted gc id ). gc id is computed based on the value of (i) row (denoted row) and column (denoted col) coordinates of the grid cell and (ii) the columns of the grid cell in the matrix (denoted cols). Specifically, gc id = row × cols + col. An example is shown in Figure  2 , where pi (i = 1, 2,. ) represents sensor nodes sensing various kinds of attributes. As for the grid cell containing sensor node p17, its grid cell ID is computed as 7 = 1 × 5 + 2, and this grid cell is denoted as gc 7.