dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Since the early eighties, the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) has been developing MORET, a Monte-Carlo calculation code for criticality calculations. It was delivered to French criticality safety practitioners within the CRISTAL package  [1]  in a multigroup version using macroscopic cross sections calculated by the APOLLO2 cell code. A large validation database composed of more than 2500 experiments was established for this package, based on the selection of critical experiments covering most operations of the nuclear fuel cycle. For its 5 th release, the MORET code  [2]  also embeds a continuous energy route, allowing the use of various nuclear data libraries. The continuous energy route has also its own validation database comprising 1136 experiments derived from the 2500 previous ones.
Calculation code The continuous energy version of the MORET 5. C. 1 code  [2]  is used for the calculations. It employs cross sections processed via the GAIA tool for different evaluations, such as JEFF-3. 1. 1 and JEFF-3. 2. The unresolved energy range treatment of cross sections is ensured through a probability table methodology. Other capabilities were implemented in the MORET 5 code, such as sensitivity calculations of k eff to nuclear data  [4]. a e-mail: nicolas. leclaire@irsn. fr.
Intermediate energy range Nine series of benchmarks were retained (HCI-004, HMI-001, -006, HMM-002, -003, HCM-001, ICI-001, MCI-005 and BFS experiments). In order to contribute to 235 U nuclear data validation, highly enriched uranium powders were investigated. These critical experiments consisted of cans in variously sized arrays containing enriched uranium dioxide (93%) with different water content and various interspersed moderation configurations. A 15. 24 cm-thickness polyethylene reflector surrounded all configurations. Although categorized as MIXED in ICSBEP Handbook, some experiments also concern the fast and the thermal energy range. Regarding the validation of U, Pu and Pu in the intermediate energy range, the experimental program performed in BFS reactor by IPPE (Russia) has been investigated  [5].
Data Collection NOTE: Request beam time as early as possible. For ESRF, guidelines on available access types and on how to submit an application can be found at: http://www. esrf. eu/UsersAndScience/UserGuide/Applying. After the acceptance of the proposal and an invitation for the experiment, all participants have to complete a safety training. After validation of the training, fill in the "A-form" (via the ESRF user portal) to declare the researchers visiting the beamline for the experiment, along with the required safety information on the samples. Contact the local contact person to discuss the experiment. 1. SEC-SAXS data collection NOTE: For online purification, use the high-performance liquid chromatography (HPLC) system installed at BM29. The system consists of an in-line degasser, a binary pump, a valve for buffer selection and gradients, an autosampler, a UV-VIS array photospectrometer, and a conductometer.
Discussion For many macromolecules, a final purification step using chromatography is required prior to SAXS data collection to obtain a good quality data set. However, not all samples remain stable; they may be prone to aggregation or re-equilibration to a mixture of oligomerization states. Therefore, a final online purification step on the beamline is required to minimize the time between purification and data collection in order to obtain the best-quality SAXS data. Depending on the biophysical properties of the protein of interest, SEC-SAXS or IEC-SAXS might be chosen to obtain optimal sample quality. Here, on a protein construct derived from the helicase/primase D5, both techniques are explained and discussed. Acquisition of SEC-SAXS data is becoming more and more standardized and is available on many BioSAXS beamlines.
Acknowledgements We acknowledge financial support for the project from the  French  grant  REPLIPOX ANR-13-BSV8-0014  and by research grants from the  Service de Santé des Armées  and the  Délégation Générale pour l'Armement. We are thankful to the  ESRF  for the SAXS beam time. We thank  Andrew McCarthy ,  Gordon Leonard ,  Wim Burmeister , and  Guy Schoehn  for financial and scientific support.
Data collection. Every run consisted of periods and each period consisted of (GroupSize * ) activations. We chose activations because we want to compare results with GrooFiWorld and in GrooFiWorld individuals interact approx. times per period. Data were collected from period onwards to exclude any bias caused by transient values. For the social network analysis, we collected data from , individual activations. We chose this value since it has been shown that a minimum of , observations is necessary to obtain meaningful network statistics and because higher values would increase network density beyond values observed in empirical data (Voelkl et al. ). Data collection consisted of every social interaction: dyadic fights, coalitions, and grooming behaviour. We recorded, for fighting interactions the identities of ( ) the attacker and its opponent, ( ) that of the winner/loser, ( ) the updated Dom values of the individuals; for coalitions, identities of ( ) target, ( ) recipient of support, and ( ) supporter; and for grooming interactions, the identities of ( ) groomer, ( ) the groomee, and ( ) the updated Anxiety values.
Statistical analyses of social networks. All analyses were performed in R statistical so ware version. ( ). We analysed the e ects of group size and intensity of aggression on each network metric. To do so, we built General Linear Mixed Models (GLMM). For all metrics, we used Gaussian distributions and identity link functions except for modularity. We transformed the values of density since otherwise the assumption of a normal distribution of the residuals was not met (Box-cox transformation, see: Peltier et al. As regards the analysis of values of modularity, due to the high frequency of zero values at low group sizes, we built two di erent GLMMs. First, we modelled the zero and nonzero data using a binomial distribution with a logit link function. Then we modelled the non-zero data (n = ) using a gamma distribution with a log link function.
Instead, reciprocation emerged from the more frequent interactions among some partners than with others. The REAPER model, however, failed to reproduce some socio-behavioural patterns typical of despotic and egalitarian macaque societies (Thierry ). For instance, at high intensity of aggression, aggression was bidirectional instead of unidirectional, and individuals did not groom more frequently partners of similar rank  ( , in Table ). Further, at low intensity of aggression individuals did not appear to interchange grooming for support and/or vice versa  ( -, Table ). Furthermore, analyses of social networks from the REAPER model revealed that the structure of these networks bore little resemblance to the structure found in social networks from empirical data and the GrooFiWorld model. For example, in the REAPER model, the modularity of the networks did not di er between simulations at high and low intensity of aggression, had low values, and decreased as group size increased (Table  , Figure ) ; and the centralization of the networks decreased as group size increased  (E in Table , Figure ).
This is contrast with findings in empirical data  (Kasper & Voelkl ; Pasquaretta et al. ; Sueur et al. b ) and the GrooFiWorld model (Puga-Gonzalez & Sueur, unpublished data). In both, empirical data and GrooFiWorld, networks from despotic societies have higher modularity than those of egalitarian societies and, independent of dominant style, as group size increases so does the modularity and centralization of the network. Overall, thus, our results suggest that the mechanism of preferentially interacting more with 'friends' than with 'no-friends' is enough to generate patterns of cooperation (reciprocation and interchange), but not to generate other socio behavioural patterns and social network structure observed in macaque societies. This suggests that the spatial structure of the group may have a bigger e ect than preferential interactions with 'friends' on the distribution of social interactions.
In GrooFiWorld, due to the spatial structure with dominants in the center and subordinates at the periphery, individuals of similar rank are closer and thus interact more frequently giving then rise to this correlation (Puga-Gonzalez et al. ). As regards the analyses of social networks, results show that the features of the social networks in the REAPER model were not similar to those found in empirical data. Group size had a negative e ect on the modularity and centralization of the networks (E in Table ; Table ); and intensity of aggression had no e ect on the modularity of social networks. This is in contrast with findings from empirical data and the GrooFiWorld model. In societies of despotic macaques, a higher modularity is thought to be the result of the higher frequency of interactions among kin than non-kin related individuals  (Sueur et al.
We assume the latter discussions to be more intensive than those with each individual consumer and producer, which is represented by the (arbitrary) factor in the algorithm below. In NetLogo code this is represented as follows: to apply-opinion-dynamics ; let agents interact and update their opinions repeat 5 [ ask consumers [ interact-with one-of ngos interact-with one-of other-consumers interact-with one-of retailers ] ask producers [ interact-with one-of ngos with [category = "producers organization"] interact-with one-of other-producers interact-with one-of retailers ] repeat 100 ; (intensive mutual interactions between NGOs and retailers) [ ask ngos [ interact-with one-of other-ngos interact-with one-of retailers] ] ] end. When an agent encounters another agent according to the above algorithm, they exchange and update opinions.
Publicly funded research has provided a wealth of genetic and environmental data. The All of Us Research Program announced in 2015 aims to further support data-driven science by building a national research cohort of 1 million or more US participants with genetic and environmental data (https://www. nih. gov/ research-training/allofus-research-program). As these types of resources grow, the challenges of big-data analysis are a significant consideration for genomic and exposome research. Analyzing data sets that include both types of data only exacerbate these issues further. GWASs have had remarkable success in combining analyses across diverse studies to improve power. However, for exposome studies, challenges remain in unifying measurements and defining analytical procedures. When such analyses can be achieved, several challenges emerge.
1 to 0. 26  (41, 42). The majority of studies calculate heritability from total expression levels, but allele-specific expression is increasingly complementing these estimates. Buil et al. (41)  used allele-specific expression measured in a twin study of approximately 400 female twin pairs with RNA-sequencing data from different tissues (fat, lymphoblastoid cell lines, skin, and blood) to measure the relative contribution of genetics and environment. In support of previous studies, they found little evidence for shared environmental effects. However, they found significant evidence of unique or individual-specific environmental effects, explaining approximately 10%-20% of expression variation in each of the tissues, on par with cis or cis-trans effects, and they further estimated that 38%-49% of variance observed in allele-specific expression was not explained by additive effects and was due to gene-gene or G×E effects.
For example, Wei et al. (45)  combined gene-based testing with pathway enrichment analyses to look for G×E associations with lung-cancer susceptibility. The second potential strategy is to use pathway information to reduce the search space for interactions, as in candidate-based approaches. To facilitate selection of candidate variants and study of G×Es, the CardioGxE database has extensively curated G×E variants in the literature  (46). Further, as discussed in previous sections, a common strategy in G×E screening is to assume SNPs involved in interaction effects also display a marginal effect. Similarly, one can argue that SNPs involved in interactions might be enriched in pathways related to the outcome or the exposure in question. Building on this idea, Rava et al. (47)  proposed a strategy to select genes for G×E for associations with asthma (a trait for which earlier G×E studies did not find strong evidence of associations).
This study also suggested a negative correlation between somatic mutations in the axonal guidance pathway and smoking-nonsmokers were more likely to have somatic mutations in this pathway than smokers. For the first time, both GWAS and somatic mutation data pointed to the same biological pathway that might interact with smoking in modifying cancer risk. To integrate this type of biological knowledge into an analysis, bioinformatics tools such as Biofilter can be used. Biofilter is a knowledge integration tool developed to allow for annotation, filtering, and model building of genomic data  (51, 52). The underlying database of biological knowledge within Biofilter is called the Library of Knowledge Integration (LOKI) and consists of multiple public database sources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.
Tools like Biofilter make performing candidate-pathway, or gene-set, approaches more efficient because they integrate knowledge from multiple public data sources and enable researchers to use all of the sources simultaneously. Because of their novelty and the limited number of applications that have been developed so far, it is challenging to assess the relevance of these approaches. Obviously, their performances rely on the validity of the underlying statistical and biological assumptions, such as valid and correct biological/pathway knowledge. For example, in the later candidate-based approach, substantial gain in power might be achieved if G×E are indeed enriched in outcome/exposure pathways, but might have decreased power otherwise. Also, as in the case of marginal-effects testing of genes and pathways, the potential success of these strategies lies in their ability to cope with multiple causal genes whose effects might be heterogeneous across populations.
ACKNOWLEDGMENTS Center, Houston, Texas (Peng Wei );  Department of Genetics ,  Stanford University School of Medicine, Stanford, California (Stephen B. Montgomery) ; and  Department of Pathology ,  Stanford University School of Medicine, Stanford, California (Stephen B. Montgomery ). M. D. R. and S. B. M. contributed equally to this work. Research reported in this publication was supported by  National Institute of Environmental Health Sciences ;  National Library of Medicine; National Institute of Allergy and Infectious Diseases ;  National Cancer Institute ;  National Heart, Lung and Blood Institute ;  National Human Genome Research Institute ; and  National Institute of Mental Health of the National Institutes of Health  under award numbers  R21HG007687  to H. A. ;  R01CA140561 ,  R01CA201407 ,  P01CA196569  to D.
During the µCMM measurement process, contamination gradually builds up on the surface of the stylus tip and affects dimensional accuracy of the measurement. Regular inspection of the stylus for contamination is essential in determining the appropriate cleaning interval and preventing the dimensional error from becoming significant. However, in situ inspection of a µCMM stylus is challenging due to the size, spherical shape, material and surface properties of a typical stylus. To address the challenges, this study evaluates several non-contact measurement technologies for in situ stylus inspection and based on those findings proposes a cost-effective microscopy approach. The operational principle is then demonstrated by an automated prototype, coordinated directly by the CMM software MCOSMOS, with an effective threshold of detection as low as 400 nm and large field of view and depth-of-field.
Focus variation microscopy Focus variation microscopy (FVM) technologies computes topographic data from a stack of images of the sample surface captured at numerous heights at the sample surface. The image stack is then analysed to determine the surface height at each pixel location by searching for maximum pixel contrast through the stack. As the technology relies on the change in contrast, surface roughness is required in order for the instrument to function properly. Lateral resolution of FVM is determined by the image and optical resolution as well as the contrast searching algorithm, and cannot be easily determined. Therefore, the estimated lateral resolution specified by the instrument software and recorded along with the measurement data is given here. Figure  7  shows the topography of a µCMM stylus tip obtained with FVM at 50× magnification using coaxial light illumination.
Various versions of proprietary and free software are readily available, where the main difference lies in the contrast searching method and user interface. This study has chosen Zerene Stacker for reconstruction of the FDoF image, from which better results have been obtained for this particular application. The same microscope evaluated in the last section has been used to capture the stack of images. To demonstrate the capability of the proposed microscopy approach, two µCMM styli with ruby tips and diameters of 300 µm (Renishaw A-5000-7800) and 125 µm (Zeiss 620151-8401-000) respectively have been inspected using the following steps:  Choose an appropriate magnification to image the tip in the FoV. Adjust sample height to focus onto the nearest face of the sphere, as shown in Figure  10.
Prototype device for in situ stylus inspection As the performance of the microscopy approach has been demonstrated, this section further demonstrates the effective implementation of this technology under the more relaxed constraints of a conventional CMM system. While demonstration of the proposed approach was previously conducted using a stand-alone microscope and obtaining of the stack of images was performed through manual adjustment of the height of the stylus, the implementation of this approach described in this section will further demonstrate the integration of the prototype inspection device with the CMM, where stylus inspection is performed in situ on a CMM and without human intervention; high precision movement of the stylus is performed automatically by the CMM; the capture of the stack of images is automated through communication with the CMM software.
Setup The implemented inspection system, as shown in Figure  15 , consists of the following components: (i) a microscope with reasonably small size, (ii) a digital camera with an adapter to the microscope, (iii) wired or wireless communication with the PC (not shown in the figure) running CMM measurement programs, and (iv) software for image processing. An upright microscope with epi-illumination setup is positioned on the platform of Mitutoyo Crysta-Apex C-121210 CMM. Measurement task is performed by probing the specimen placed in the workspace, which is paused at regular intervals to perform stylus inspection. The captured image stacks are automatically processed by Zerene Stacker and the amount of contamination is quantified using an image processing procedure. The stylus mounted on the CMM probe has a ruby sphere of 300 µm diameter.
Contamination quantification For in situ stylus inspection, a quantified measure of the amount of contamination is often preferred by the user along with the image. In this study, Matlab is used to process the reconstructed FDoF images and estimate the area of the sphere surface being contaminated. The following image processing steps are followed: (i) an edge detection algorithm is used to find the edge of the features, (ii) a 2D median filter is used to reduce false contrast due to imaging noise, (iii) the features are dilated to compensate for reduced feature size due to the median filter, (iv) the closed edges are filled, and (v) the remaining false edges due to glare and the sphere azimuth are corrected. Figure  16  shows the FDoF images of a 300 µm stylus tip before and after being contaminated in three separate occasions after repeatedly probing on a nickel specimen.
In situ stylus inspection To demonstrate the efficacy of the proposed microscopy approach for in situ stylus inspection, a measurement program has been prepared and executed in the software MCOSMOS, where inspection of the stylus was conducted in-between measurements without human intervention. A stylus with 300 µm tip diameter was used to probe on a nickel specimen. Both the stylus and the specimen had been cleaned in an ultrasonic bath immediately before the measurement program was executed. Inspection of the stylus was set to commence after every 50 contact points until 300 contacts have been made, after which inspection was set to commence after every 300 contacts. Progressive build-up of contamination on the stylus tip throughout the measurement program is visualised in Figure  17. Using the quantification method described in section 4.
Acknowledge This study was funded by the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. The authors would like to thank Prof. Richard Leach for his inputs on instrument costs, and Cyril Lacorne and Gustavo Queiroz Fernandes for their work in contamination quantification. The authors would also like to appreciate the support of Sensofar Merology and the Precision Manufacturing Centre at the University of Nottingham for assisting this project.
With this latest release of ReMap (http://remap. cisreg. eu), we present a unique collection of regulatory regions in human, as a result of a largescale integrative analysis of ChIP-seq experiments for hundreds of transcriptional regulators (TRs) such as transcription factors, transcriptional co-activators and chromatin regulators. In 2015, we introduced the ReMap database to capture the genome regulatory space by integrating public ChIP-seq datasets, covering 237 TRs across 13 million (M) peaks. In this release, we have extended this catalog to constitute a unique collection of regulatory regions. Specifically, we have collected, analyzed and retained after quality control a total of 2829 ChIP-seq datasets available from public sources, covering a total of 485 TRs with a catalog of 80M peaks.
The rapid accumulation of ChIP-seq data in public databases provides a unique and valuable resource for hundreds of TR occupancy maps. There is a strong need to integrate these large-scale datasets to explore the transcriptional regulatory repertoire. Unfortunately, the heterogeneity of the pipelines used to process these data, as well as the variety of underlying formats used, challenge the analysis processes and the underlying detection of TF binding sites (TFBSs). Integrative studies would offer significant insights into the dynamic mechanisms by which a TF selects its binding regions in each cellular environment. ReMap has been the first large scale integrative initiative to study these data, offering significant insights into the complexity of the human regulatory landscape  (4). The ReMap 2015 resource created a large catalog of regulatory regions by compiling the genomic localization of 132 different TRs across 83 different human cell lines and tissue types based on 395 non-ENCODE datasets selected from Gene Expression Omnibus  (2)  and ArrayExpress  (3).