dataesr/scientific-paragraphs-categorization huggingface dataset:
With a view to assessing exposure to vehicle emissions, Wei et al. used State NO x emission data obtained from the air database for 1990 (the earliest year available) to study the incidence of female breast cancer observed during the period 1986-2002  [31] , whereas Hystad et al. used proximity to the road network to derive the number of years participants had resided within 50, 100 and 300 meters of a highway or major road during the 20-year exposure period  [23]. To investigate the long-term effects of urban air pollution in a case-control study of lung cancer in Stockholm, Nyberg et al. estimated annual levels of SO 2 and NO x /NO 2 for each year between 1950 and 1990 using retrospective emission data and linear extrapolation and interpolation  [8]. Using a spatial smoothing model and a Geographic Information System, Hart et al.
estimated annual average levels of PM 10 , SO 2 , and NO 2 from 1985 through 2000  [2]. Several cohort studies used Land Use Regression (LUR) models to assess small-scale spatial levels of outdoor pollution and assigned exposure for all participants. Using this approach in a Canadian province, Hystad et al. calculated an average concentration of NO 2 for the 1975-1994 exposure period  [23]. In the European ESCAPE study, the authors used LUR models to estimate concentrations of NO 2 , PM 10 and PM 2. 5 in 20 European areas, with 20 sites per area  [32, 33]. Finally, more sophisticated air dispersion models (such as the CHIMERE chemistry-transport model)-thanks to increasingly available monitoring data and geostatistical modeling-were recently used to retrospectively model outdoor air pollution in France and reconstruct annual average concentrations of NO 2 , PM 10 and PM 2.
Step1: Modeling the Annual Averages of NO 2 Concentrations between 2002 and 2012 at the Census Block Level Annual mean concentrations of NO 2 were estimated at a fine spatial scale (IRIS) throughout the 2002´2012 period by Airparif (the Paris metropolitan area air quality monitoring network: http://www. airparif. fr) [44]. Firstly, NO 2 background concentrations were determined by combining monitored NO 2 concentrations from monitoring stations and those modeled at a regional scale from the ESMERALDA inter-regional platform for air mapping and forecasting (www. esmeralda-web. fr). The ISATIS software was used to conduct geostatistical analysis for data assimilation. Secondly, NO 2 road traffic concentrations estimated from the STREET software model  [45]  were added to NO 2 background concentrations.
The software evaluates the annual levels from roads according to traffic characteristics and the close environment, as well as weather conditions. Several types of input data were used, including point sources and road transport emissions and meteorological data (temperature, wind speed and direction, relative humidity, barometric pressure). More than 200 point sources were selected from the regional emission inventory. Emissions for road traffic were estimated using the regional traffic network and the COPERT III European database for the 2002-2006 period, and COPERT IV for the 2007-2012 period. Concerning meteorological data, the Mesoscale Meteorological model (MM5: www. mmm. ucar. edu/mm5) developed by the Division of the NCAR Earth System Laboratory (NESL) was used. The areas of direct influence of the axes and the decreasing concentrations away from the latter are taken into account.
Step 2: Assessing the Spatial Area Representative of the Air Quality Monitoring Stations Using daily NO 2 concentrations measured by fixed monitoring stations (including background stations and traffic stations) located within the city of Paris and available over the 2002-2012 period, we identified the area for which the air quality monitoring stations provided good estimates of daily concentration variability. The hierarchical agglomerative clustering (HAC) is the most commonly used approach as it provides intuitive similarity relationships between any one sample and the entire dataset. The HAC was chosen and applied for Paris to associate each census block with a background permanent monitoring station. This cluster analysis allowed grouping census blocks and stations into clusters (groups) on the basis of similarities within a class and dissimilarities between different clusters.
Using a mobility matrix, constructed from the national census database, describing inhabitant movements intra-area and outside the study area (i. e. , another municipality, department or region) from one year to a previous year, the model estimate's cumulative population exposure is an average measure between E N j and the weighted [E ip j´1q ] where i represents the census blocks of residence in the (j-1) year. In our example, i can be the census block U, V, W and C. The weighted [E ip j´1) ] (noted W[E ip j´1) ] in following text) is defined according to the following equation: E cp j´1q is the average exposure level of the location C in year (j-1) extracted from the meta-analysis of French studies conducted at the municipal and regional scales (e. g. ,  Bentayeb et al. , 2014). From Equation (1), we deduct that a low level of mobility for census block N is quantified by a probability P N N , close to 1 (approximately 100% of inhabitants live in census block N in year j and in the year (j-1)) and as a consequence, others P uN , P vN , P wN and P cN will be near to 0 (and vice versa).
Estimation of the Cumulative Exposure Accounting for Residential Mobility To take into account residential mobility (the PNN, PuN, PvN and PwN, notations in Section 3. 2. 1. ); we used a database extracted from the 2006 National Census by INSEE (Institut National de la Statistique et des Etudes Economiques). The spatial distribution of the proportion of people living in 2006 since for five or ten years in the same census block in Paris is presented in Figure  7 : in half of the census blocks, 60% and 40% of people living in 2006 in a given census block resided in the same place five and ten years before, respectively. Unfortunately, because INSEE currently provides data of residential moves within Paris at the arrondissement and not census block level, we applied our theoretical approach (described in Section 3.
2. 1) at this larger spatial scale for a five-year latency period (the L in our notations, Section 3. 2. 1. ). Using a mobility matrix (see Supplementary Materials Figure  S1 ), constructed from the national census database, we determined for each arrondissement the number of inhabitants changing their arrondissement of residence over the last five years (the arrondissement where people lived five years earlier) and the origins of the inhabitants from another arrondissement. In our example, we only consider population movements inside the study area, ignoring at this stage movements "outside" (movements to or from other cities) (see the Matrix in Supplementary Materials Figure  S1 ).
Estimation of the Cumulative Exposure Accounting for Residential Mobility To take into account residential mobility (the P NN , P uN , P vN and P wN , notations in Section 3. 2. ); we used a database extracted from the 2006 National Census by INSEE (Institut National de la Statistique et des Etudes Economiques). The spatial distribution of the proportion of people living in 2006 since for five or ten years in the same census block in Paris is presented in Figure  7 : in half of the census blocks, 60% and 40% of people living in 2006 in a given census block resided in the same place five and ten years before, respectively. Unfortunately, because INSEE currently provides data of residential moves within Paris at the arrondissement and not census block level, we applied our theoretical approach (described in Section 3.
2) at this larger spatial scale for a five-year latency period (the L in our notations, Section 3. 2. ). Using a mobility matrix (see Supplementary Materials Figure  S1 ), constructed from the national census database, we determined for each arrondissement the number of inhabitants changing their arrondissement of residence over the last five years (the arrondissement where people lived five years earlier) and the origins of the inhabitants from another arrondissement. In our example, we only consider population movements inside the study area, ignoring at this stage movements "outside" (movements to or from other cities) (see the Matrix in Supplementary Materials Figure  S1 ). Table  3  gives the cumulative NO 2 exposures over five years by arrondissement with and without taking into account residential movements inside the study area.
Moreover, the illustration of the estimation of cumulative exposure accounting for residential mobility at arrondissement level has revealed significant differences between estimations with and without residential mobility over a five-year period. However, the mobility matrix must be completed at the census block level in order to combine this approach with the retrospective constitution of NO 2 concentrations at the census block level. To overcome the fact that residential mobility data is not currently provided at the census block level, different apportioning solutions are available for the disaggregation of population data from arrondissement to census block level via the combination of several data sources, including topographic and land use databases. A major limitation of our method is that it remains relatively cumbersome, labor-intensive, and computer-intensive, requiring extensive data inputs that are generally difficult to obtain.
To achieve this, we will apply the retrospective method to other years-those for which the dispersion model has already provided validated estimates at the census block level. For instance, using data from 2005 to 2012, we will estimate retrospective concentrations for the period 2002 to 2004. Then, we will quantify the margins of error inherent to our approach, by estimating the error between the concentrations retrospectively obtained, and the modelled NO 2 concentrations.
Conclusions In order to explore the health effects of chronic exposures, we seek to combine several sources of data routinely collected and modeled so as to address the accuracy of cumulative estimates of NO 2 concentrations over periods of years and, based on these, on population exposure, taking into account residential mobility over time, its duration being characterized according to the latency delay for development of the disease. Our framework explains how to surmount two principal difficulties in cumulative long-term exposure assessment: retrospective reconstitution of past ambient air concentrations and consideration of residential mobility in assigning exposure levels to neighborhoods; and, as appropriate according to study design, to individuals. We have provided guidelines on how census database and geostatistical methods may inform an approach for correcting the above-described sources of cumulative exposure misclassification.
In the "Background" section, we provide basic definitions; in particular, we extend ideas from topological precursor sets as defined in  [5]  in order to incorporate stoichiometry, and we discuss the relationship between topological and stoichiometric solutions. We then describe the sasita algorithm for enumerating all stoichiometrically feasible minimal precursor sets. In the "Results" section, we discuss in detail the comparison with respect to Eker's et al. proposal. Here we observe that sasita is the first publicly available software to enumerate minimal stoichiometric precursors sets both with the steady state and the machinery duplicating model. Experiments show that sasita can be applied to large genome-scale metabolic networks and we discuss the obtained results.
Results and discussion In this section, we present the experiments we realised and discuss the results we obtained. We start by comparing the method we developed with the one of Eker et al. [8]. We then show the performance of sasita versus the approach where minimal SPSs are obtained from combinations of minimal topological factories in the many-to-one network. Finally, we apply sasita to some genome-scale metabolic networks, obtained from Monk et al. [16]. The objective of this last part is both to illustrate how our method can be used and to validate it by reproducing the findings of the authors. All the experiments were performed using an Intel QuadCore i7-4770 computer with 16 GB of RAM memory. The algorithm sasita is coded in Java (OpenJDK IcedTea) and uses cplex (IBM ILOG AMPL/CPLEX 12.
Comparison between sasita and combinatorial approach We ran both approaches, i. e. sasita and a combinatorial approach (called combi) where minimal SPSs are obtained from combinations of minimal topological factories in the many-to-one network, on several instances. Our objective was to analyse the differences in the running times between both approaches, so we set cplex into single thread mode for sasita. Table  1  shows clearly that the MILP approach is more efficient than the combinatorial one. The networks of S. muelleri, C. ruddii and B. aphidicola were obtained from metexplore, filtering out ubiquitous metabolites and pairs of co-factors. We obtained the E. coli core model from http://www. systemsbiology. ucsd. edu/InSilicoOrganisms/Ecoli/EcoliS-BML. As sources we considered all compounds that are not produced by a reaction or those that are produced by reversible reactions only.
For the E. coli strains, we used the same networks from Monk et al. [16]  and considered as sources the compounds from Table  3. We set a time limit to the combinatorial approach of 2 h. sasita is by far more efficient on genome-scale networks where the combinatorial approach did not finish within the time limit. To be able to show an example where both approaches finish and sasita outperforms combi, we removed all compounds from the E. coli core network if they are consumed and produced by more than ten reactions. This is the case for M_atp_c, M_ nad_c, M_nadh_c, M_h2o_c, M_h_e, M_h_c. The resulting network is denoted by a superscript a. Notice that the number of reactions remains the same because we remove only the above-mentioned compounds from the reactions. The difference in the time spent to solve the problem is remarkable.
The three solutions detailed above and the solutions presented in Table  7  therefore represent metabolic capabilities that are specific to the pathogenic strains analysed here when compared to the commensal strain E. coli K-12, in agreement with the niches occupied by such strains. These results suggest that sasita can depict pathotype and niche-specific metabolic capabilities which allow broad in silico studies of strains or species interactions. For instance, an extension of the analysis presented in this paper to a larger dataset of E. coli strains including both pathogenic and commensal biotypes could help   predict in silico sets of commensal strains that would prevent the colonisation of pathogens due to a consumption by the native microbiota of the nutrients required by the pathogen (see the experimental study of mutant phenotypes in Maltby et al.
Funding RA acknowledges  CNPq/Brasil  for the financial support and VA for the support of Fondecyt 1140631,  CIRIC-INRIA Chile and FONDAP Center for Genome Regulation. CCK and MW were recipients of a grant from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant Agreement No [247073]10 SISYPHE.
Electromagnetic simulations Dispersed dielectric microspheres with high permittivity represent a Mie resonance-based metamaterial, where the effective response relies on the resonance of individual elements while the coupling between isolated microparticles has only minor influence on the resonance properties. This allows us to simplify the theoretical investigations: we consider a periodic array of the microspheres and neglect the influence of the disorder. Note that this metamaterial behavior fundamentally differs from that of photonic crystals where the optical response is controlled mainly by the coupling between the constituents  [14]. We employed a finite-difference time-domain (FDTD) simulation software package MEEP  [15]  to calculate the transmission and reflection spectra of TiO 2 microspheres with diameter d = 35 µm arranged in a square lattice a × a and embedded in the centre of a slab with dielectric permittivity ε H and thickness a.
The Infinium MethylationEPIC array (EPIC array) contains probes covering more than 850 000 CpGs in the human genome, including more than 90% of the probes on the 450K array. Of particular interest, the additional probes on the EPIC array have been designed to cover ENCODE enhancer and open chromatin regions, FANTOM5 enhancers, DNase hypersensitivity sites, non-CpG methylation sites identified in stem cells, and miRNA promoters, each of which has the potential to yield functionally relevant data in a variety of phenotypes. In this report, using technical replicates of DNA from human brain, blood, and saliva, we combine oxBS with Illumina's EPIC array (oxBS-EPIC) to evaluate whether the EPIC array is suitable for studying 5hmC in these tissues. With information on more than 400 000 relevant loci, we show this technology to be suitable for large-scale 5mC and 5hmC analyses.
Data processing Illumina's Infinium assays utilize methylated and unmethylated probes that bind to specific loci to determine the (hydroxy)methylation values at a given CpG. Beta values represent the measured (hydroxy)methylation values, based on the intensities of these two probes. Beta values range from 0 to 1, and can be thought of as a methylation percentage. For example, a beta value of 0. 2 can be thought of as 20% (hydroxy)methylation. Processing and statistical analysis of the EPIC array data was performed using the R statistical language version 3. 3. 0 and various packages of the Bioconductor project  [17]. Specifically, minfi version 1. 18. 2  [18]  was used to load, apply SWAN normalization  [19] , annotate probes, and filter probes with high detection P-values (>0. 01) across multiple samples (30%), probes mapping to known SNPs, and non-CpG methylation probes.
Representative gene analysis Gene models as defined in the Bioconductor package TxDb. Hsapiens. UCSC. hg19. knownGene version 3. 2. 2 were used to define TSS5000 (5 kb upstream), gene body, and TES (5 kb downstream) regions. Genes for which one region overlapped with any region of a different gene were excluded. The remaining 13 695 TSS5000, gene bodies, and TES5000 were each split in 100 bins of equal relative size. Site measurements (b BS , b ox , Db BS-oxBS ) were averaged within each bin and then averaged across genes.
However, with the high cost associated with whole-genome studies, alternative approaches became necessary. In this manuscript, we combine oxBS with the EPIC array to study both 5mC and 5hmC. Our results demonstrate not only the replicability of this methodology in all three tissue types, but also its ability to effectively study 5hmC in >400 000 loci in human brain. The increasing number of publications showing 5hmC in different tissue types brings to light several important caveats to consider when looking at previously published bisulfite-based results. Data generated using a bisulfite alone, whether combined with whole-or reduced-genome sequencing arrays, prevent researchers from dissociating 5mC from 5hmC. Certainly, this issue will be more prominent in studies using DNA from mammalian brain, regardless of platform, but it should not be overlooked in studies using tissues with lower levels of 5hmC or in studies using previous array technologies.
Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are two-fold: first, we show significant similarities in people's mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual's urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people's tendency to revisit few favourite venues using the shortest-path available.
Furthermore, people's habitual set of itineraries is characterized by its confinement, i. e. , people roam close to their main physical address  [4]. Datasets are of enormous importance to the analysis of human mobility. They provide the convenience of a non real-time analysis, that is, one can analyze mobility after its parameters (e. g. , timestamp, geographic coordinates) have been collected and logged. In the context of large-scale mobility and networks, where real-time analysis is arduous due to the enormous amount of individuals and parameters, logged datasets are widely used as primary source of information. Most work in the literature study human mobility predominantly from GPS datasets. Although this allows for a fine-grained mobility investigation, datasets collected in large urban scenarios are rarely publicly available.
In particular, the experiments to collect human mobility data generally involve people carrying GPS-capable devices which regularly collect their precise positioning. Due to the complexity of those experiments, they tend to be limited in number of participants (e. g. , up to 35), time duration (i. e. , a few weeks), and space as in university campuses  [5] , or shopping malls  [6]. Lausanne campaign  [7]  and GeoLife  [8]  represent some of the few relatively large experiments with around 200 participants, that attempt to collect fine-grained human mobility. The dataset collected from the former is not publicly available, while the one from the latter is. Furthermore, human mobility datasets covering large areas tend to rely only on automobile transportation, which is not in the scope of this paper  [9, 10].
More recently, datasets collected from cellular networks are being considered by the networking research community. Such datasets, named Call Detail Records (CDR), constitute another source of human mobility. CDR is a metadata record that describes phone communication using a series of data fields, e. g. , the identification of callee and caller, call type (voice call or SMS), starting time, ending time, duration of the call, and GPS location of the caller's cell tower  [11]. CDR datasets are usually released by Telco operators to a limited number of partners under a non-disclosure agreement and with limited access. As both mobility and network traffic are susceptible of giving away private users' information, entities responsible for such data are careful on providing it to third-parties anonymizing sensitive information.
Besides, it is important to understand the limitations of such CDR datasets. For instance, when modeling mobility using CDRs, one has to know its two biggest limitations: sparseness in time and coarseness in space. Time sparseness occurs because records are generated only when a subscriber sends or receive a call or a SMS, which makes he invisible at all other periods of time. Space coarseness is due to the size of a cell tower sector, which leads to a location uncertainty of about 1 square mile  [12]. It is important to consider that those two characteristics are not uniformly distributed in time due to the fact that subscribers tend to place their calls in bursts, then staying nonactive for long periods  [13] , around 70% of the total time  [12]. Finally, sparseness and coarseness play a negative role on the understanding of some specific human mobility aspects, such as the usage of shortest-path.