dataesr/scientific-paragraphs-categorization huggingface dataset:
Primers were purchased from Invitrogen (Invitrogen, Life Technologies Corporation, Paisley, UK). The specific sequences are provided in Table  2. The expression stability of five candidate reference genes, (Cyclophylin A, β-actin, β2-microglobulin, Ribosomal Protein L32 (RPL32), and Hypoxanthine Phosphoribosyl transferase 1 (HPRT-1)) was analyzed across the different experimental samples using the BestKeeper software program  65. RPL-32 (SD = 0. 82; r = 0. 971) and β2-microglobulin (SD = 0. 94; r = 0. 962) were identified as the best reference genes for our dataset and used to normalize the values. ELISA and Western blot assays. Concentrations of TNF-α and IL-8 were measured in cell culture supernatants by enzyme linked immuno-absorbent assays (ELISA). Specific kits for porcine TNF-α and IL-8 (R&D Systems, Minneapolis, MN, USA) were used according to the manufactured instructions as already described  20, 66.
Benchmark dose analysis of PODs for the pro-inflammatory effects of FX. A benchmark dose (BMD) analysis of the point of departure (POD) for several cytokines was conducted to establish the intestinal pro-inflammatory potency of FX. The BMD is defined as the dose corresponding to a predetermined change in an adverse response (the benchmark response (BMR) compared with the response in the control group  61. As suggested by the U. S. EPA, one standard deviation above the control mean was used to set the BMR  61. The PROAST (Possible Risk Obtained from Animal Studies) software was used to calculate the BMDs and their lower confidence bounds (BMDLs). This procedure is extensively described elsewhere  61. The program has been developed by the Dutch National Institute for Public Health and the Environment (RIVM) in Bilthoven and is available from the RIVM website (http://www.
Acknowledgements. We thank the three reviewers and the editor for their thorough reading and constructive comments, which helped improve the manuscript. This study was performed in the framework of the " Climatically driven changes of Antarctic sea ice and their role in the climate system" (CATARSI)  project as part of the  Italian National Program for Research in Antarctica (PNRA). The financial support of the  Italian Ministry of Education, University and Research and the Ministry for Environment, Land and Sea (MIUR) , namely through the project  GEMINA , is gratefully acknowledged. Edited by: J. Hutchings Reviewed by:  R. Stevens  and two anonymous referees.
Data availability. The NEMO model is freely available under the CeCILL public license. The code can be downloaded from the NEMO website (www. nemo-ocean. eu). On registering, individuals can access the FORTRAN code using the open-source subversion software (http://subversion. apache. org/). The revision number of the code used for this study is 4510. The CMCC NEMOv3. 4 code includes some additional modifications, applied to the base code. In particular, we modified the North Pole folding condition, introducing an optimization of the north fold algorithm (Epicoco et  al. , 2014), which leads to an extra increase in model performances without altering any physical process. Please contact the authors for more information on the CMCC NEMOv3. 4 code. The numerical results used here are available under request.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n˚2012-305608 (EURenOmics; http://cordis. europa. eu/project/rcn/105363_en. html)and from  Bundesministerium fu ¨r Bildung und Forschung  (BMBF; https://www. bmbf. de) through the e-Rare initiative (PodoNet).
Furthermore, it is important to note that some bacterial genomes have a strong base compositional bias and, consequently, encode Sec-dependent proteins with non-canonical signal peptides  (Payne et al. , 2012). Several bioinformatics programs can be used to predict the presence of cleavable Sec or Tat signal peptides, such as SignalP (http://www. cbs. dtu. dk/services/SignalP) (Petersen et al. , 2011) , PSort (http://www. psort. org), which conveniently also provides a list of links toward other subcellular prediction programs, Pred-Tat, TatP or TatFind (see  Berks, 2015, for comparison). Lipoprotein signal peptides are a distinct class of Sec dependent signal peptides characterized by a C-terminal consensus sequence, the lipobox, which ends with an absolutely conserved cysteine residue that, after fatty acylation, becomes the first residue of the mature protein (www.
It proved difficult or impossible to identify sequence features in secretion substrates that indicate that they will use one or other of these pathways. Several machine-learning techniques have been developed recently for this purpose based on datasets of known T3SS and T4SS secretion substrates. They differ in terms of the machine learning methods, the curated data sets and the features used, and reach different levels of prediction performance. Several, but not all  (Meyer et al. , 2013) , of these tools were recently compared  (An et al. , 2016). All predictors are flawed, to various degrees, with false positives (secretion signal identified in non-secreted proteins) and false negatives (documented secretion substrates not predicted as such), so while they can be precious in orienting research, experimental validation is required.
ACKNOWLEDGMENTS We thank  Tony Pugsley  for critical reading of the manuscript. This work was supported by the  European Research Council   Starting Grant  NUChLEAR  N • 282046  to AS, the  Institut Pasteur  and the  Centre National de la Recherche Scientifique. The OF group is funded by the  French ANR  grant  FiberSpace  N •  ANR-14-CE09-0004.
For complex diseases such as cancer and type 2 diabetes, heritability ranges from approximately 10% to greater than 50%  (56). In a recent meta-analysis across 50 years of twin studies and nearly 18,000 traits, heritability across all traits was 49%  (46). Therefore, a significant proportion of phenotypic and disease variance can likely be attributed to nongenetic factors such as environmental exposures. However, aside from a handful of diseases such as lung cancer (of which >80% can be attributed to smoking), we have yet to describe much of the phenotypic variability for most complex diseases. Humans encounter numerous exposures over the lifespan. For example, recent catalogues contain up to 3,600 toxicants in the Toxic Exposome Database (71) and 13,000 in the Comparative Toxicogenomics Database  (9).
Infectious agents (e. g. , bacteria) are measured via immunological assays. The NHANES also includes other indicators of environmental exposures such as self-reported nutrient consumption (based on a food questionnaire), physical activity, and prescribed pharmaceutical drugs. Although these data are an example of the complex array of human exposures, they are neither comprehensive across all exposure domains nor longitudinal. For example, orthogonal external exposure data sources such as air pollution sensors and cell phones generate a wealth of complementary data that present opportunities for new analyses as well as shared challenges, including autocorrelation and missing data. Some challenges and opportunities regarding the use of external sensors to assess the expo-some are described in an accompanying review  (61).
We recommend that the scientific community devote efforts to extend NHANES-like epidemiological biomonitoring surveys to ascertain baseline and reference intervals of exposome-based measurements to capture the extent of variability of the exposome in diverse populations (Table  1 , recommendation 2). The human exposome data structure is a high-dimensional collection of highly heterogeneous exposure variables that may change upon repeated sampling during an individual's lifetime (Figure  1a ). Time-dependent and high-throughput genome-scaled phenotypic data types such as gene expression, protein expression, or metabolomics data are similar in structure. For example, to measure gene expression, an array of gene probes measures the amount of messenger RNA expressed in a tissue or cell. Similarly, an exposome array would measure the amount of a multitude of time-varying external exposures and biomarkers of exposures.
However, we have yet to develop an assay to operationalize an exposome-wide measurement (e. g. , 100-1000s of exposures measured with a single measurement platform). To this end, we recommend that the exposome community expand on the integration of 'omics technologies (e. g. , metabolomics) that allow for the comprehensive, agnostic, and high-throughput measurement of the personal exposome in humans (Table  1 , recommendation 2). Notable challenges remain in the metabolomics field, such as determining the exact chemical identity or structure of analytes that are detected in human tissue from a mass spectrum. Another nontrivial challenge involves the integration of data across the external and internal exposome domains. For example, how do (a) sensor-based and physical measures (e. g. , air pollution or noise) or (b) individual-level sociodemographic factors influence the distribution of internal exposome indicators? Data integration across these domains is often executed by merging across spatiotemporal coordinates, a resource-intensive but usually straightforward database task.
Similarities and Differences Between Exposome and Genome Science Much can be learned about design and analysis challenges in exposome association studies from parallels with GWASs  (23). From a design perspective, GWASs have highlighted the value of data sharing and replication in independent data  (25). From an analysis perspective, major concerns include mitigating chances for false-positive findings incurred when executing multiple statistical tests (known as multiple hypothesis testing) to achieve genome-wide significance (e. g. , p < 5 × 10 -8 ) and adjustment for the confounding effects of population stratification  (47). From a strategic perspective, a third major theme has involved leveraging external information now available in numerous genomic and pathway databases ("ontologies") such as gene set enrichment analysis  (57).
Associating Exposures with Disease "Environment-wide association studies" or equivalently "exposome-wide association studies," which are analogous methodologically to genome-wide association studies (GWASs), are a recently proposed analytic approach to systematically associate exposures with disease (Figure  1b ). In EWASs, multiple exposures are assessed simultaneously for their association with a phenotype or disease of interest. The false discovery rate (1) is controlled to adjust for multiple testing, and significant associations are validated in independent data  (38) (39) (40) 62). The main advantage of this approach is that it systematically investigates an array of exposures and adjusts for multiple testing, thus avoiding selective reporting while enabling discovery. Just as the literature for genetic associations in disease has become more reproducible owing to standardized and extensively validated analytical procedures  (22, 64) , we propose that an analogous process to associate the exposome with disease and health outcomes will result in more robust environmental associations.
Similar data standards are needed to enable the integration of exposome measurements across cohort data sets for large-scale analyses  (42). Genome science has also illustrated the value of open-source standardized analytic frameworks such as PLINK  (49). In addition, standardization in data representation and file formats [e. g. , variant call format (VCF) files for sequencing data] has enabled widespread reuse of genomic data. The development of similar analytical tools and resources for exposome science would standardize and accelerate research efforts. To this end, we recommend the establishment of data analytics standards and analytics code reuse (Table  1 , recommendation 5). Specifically, we recommend the creation of public software code repositories and the development of open-access software libraries to conduct standardized exposome-wide association studies.
Such an effort should include a network of core facilities that each exhibit expertise in measuring a component of the exposome, such as metabolomics. For example, such an effort would support exposome-related research that integrates data from platforms developed in the NIH Common Fund initiatives such as the Core Metabolomics. We recommend that funding bodies, scientific journals, investigators, and study participants consider methods and protocols for universal data sharing to promote reproducible research (Table  1 , recommendation 7). Specifically, many details will need to be understood and demonstrated to reproduce findings, including the data sets used for analysis, the analysis method/software tools, and the analysis parameters. Computational exposome research promises to be a complex exercise, and standardized measurement platforms and data analytic provenance will be instrumental for the development of computational methods and for reproducible research.
We recommend that studies akin to the NHANES establish background variability, geotemporal dependence, and prevalence of exposome factors of different populations around the world (e. g. , race/ethnicity groups, sex, age). Such data sets can also provide a substrate to develop data standards and promote the development of analytical methods. For example, through efforts such as the HapMap project  (16) , human geneticists have measured and cataloged the genetic diversity of human populations. This characterization has enabled large-scale investigations such as GWASs to understand the relationship between genes and disease. To understand the relationship between the exposome and health, we must understand how the exposome varies through space and time and in different segments of the human population.
However, individual-level exposome data sets must also be archived and shared for their continued use and integration for data-driven discovery (e. g. ,  44). Progress in archiving and sharing these types of data is dependent on the continued development, adoption, and dissemination of standardized data dictionaries and ontologies that enable exposure nomenclature (analogous to gene names) and that catalog how exposures are measured (e. g. , Figure  2 ). One example of such a catalog is the PhenX Toolkit  (19). Such a tool kit provides standard measures for association studies, including the name of the exposure (e. g. , cigarette smoke), related exposures that may be of interest, and protocols for an exposure's measurement in a study. For example, a recent EWAS of type 2 diabetes (17) successfully ascertained self-reported indicators of exposure by implementing the measurements cataloged in PhenX.
Considerations for a Universal Data Repository to Support Exposome-Related Investigation In conceptualizing an approach and determining the feasibility and usefulness of a data repository to share individual-level exposome data, all parties, be it funding bodies, researchers, and study participants, need to consider issues common to sharing individuallevel genomic information, such as ownership and sustainability (e. g. , who will own and maintain repositories), granularity (e. g. , which study data are stored and how to account for heterogeneity of multiple exposome measurement platforms), and compliance (e. g. , how will repositories guarantee confidentiality and handle data use agreements). The NCBI hosts repositories [e. g. , databases of genotypes and phenotypes (dbGaP)  (29) ] which have taken some of these considerations into account, such as data use compliance.
Findings Information to support the interpretation of primary findings will require the continued development of ontologies and databases that are relevant to the exposome. Two established databases that will be instrumental for developing standards for nomenclature and toxicological knowledge are the Toxic Exposome Database (T3DB; http://www. t3db. ca) (71)  and the Comparative Toxicogenomics Database (CTD; http://ctdbase. org) (9). T3DB currently provides information for more than 3,600 compounds and 2,000 targets, expression data sets for more than 15,000 genes, and extensive information on chemical concentrations in biofluids and referential chemical spectra data. CTD aims to help elucidate the molecular mechanisms by which environmental exposures contribute to disease etiologies by providing manually curated information about chemical-gene-disease interactions.
One significant challenge for exposome-based research includes continuing to evolve, harmonize, and raise awareness of the different standards for describing exposome data through community-driven processes that promote consistent use and ongoing maintenance and development. Software repositories such as GitHub (http:// github. com), development tools such as WebProtege  (20) , and portals such as BioPortal  (37)  and oboFoundry  (55)  are existing tools that can enable the communal and opensource development of ontologies.
Big Data Analytics and The Exposome: Implications for Cross-Cutting Training Discovery research with the human exposome is a big data analytics integration challenge that cuts across statistics, computer science, biomedicine, and public health. Thus, datadriven exposome research requires the training of anew breed of researchers who can bridge multiple fields of investigation and work in consortium/team science capacities. We recommend promoting exposome research among scientists in these different fields through educational outreach programs administered by both research funders and institutions (Table  1 , recommendation 8). Primary tasks for outreach include identifying example and publicly available data sets (e. g. , US NHANES) to develop new methods and to support research in a classroom setting.
In a and c, diet and gut flora are linked with genomic markers to type 2 diabetes (T2D) and blood pressure (BP). Analytic methods to discover exposures (EWAS), genotypes (e. g. , GWAS), and phenotypes (e. g. , PheWAS) are depicted in purple, green, and blue, respectively. High-level schematic of ExO integration within a broader biological context (adapted from Reference 32). CTD, Comparative Toxicogenomics Database; ExO, exposure ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MeSH, Medical Subject Headings; OMIM, Online Mendelian Inheritance in Man.
Acknowledgments This research was supported, in part, by the  Intramural Research Program  of the  National Institutes of Health (NIH) ,  National Institute of Environmental Health Sciences (NIEHS). C. J. P. was supported byR00  ES023504 ,  R21 ES025052 ,  NIH/BD2K U54 HG007963 , and aPhRMA  Foundation  fellowship. D. C. T. was supported by  R01 ES019876 ,  P30 ES 07048 , and  P30 CA014089. A. K. M. was supported by  NIH /BD2K U54 HG007963. C. J. M. was supported by  R01 ES014065  and  R01 ES019604. M. H. was supported by  F31 HG008588.
Methods Corpus: We trained our language models on a subset of the movie book project corpus  (Zhu et al. , 2015) ; the subset contained approximately 50M characters (10M words). The corpus was lowercased by its creators. We split the corpus into training, validation and test sets (80%, 10% and 10% of the data, respectively); this test set was used only to calculate perplexity (see below). The vocabulary we used to test our network in the lexical decision task only included words that oc-(a) 16 32 64 128 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 60% 80% 100% Nonword generation: We generated nonwords using a slightly modified version of Wuggy  (Keuleers and Brysbaert, 2010) ; we refer the reader to the original paper and our published code for further details.
g. , an SRN with 32 hidden units in one layer outperformed an SRN with 16 hidden units in two layers). 3 https://github. com/jcjohnson/ torch-rnn How much of the advantage of LSTMs is due to the fact that they have more parameters per unit? Figure  1b  plots the accuracy of the same networks, this time against the log-transformed number of parameters. While there remains a slight advantage for LSTMs over SRNs, especially as the number of parameters increases, it is evident that the number of parameters is an excellent predictor of the performance of the network. Of course, since the dependencies that the network needs to model to perform the lexical decision task are relatively short, this task may not bring out the competitive advantage of LSTMs, which are argued to excel in tasks that require long dependencies.
, 2004). Some challenges remain, however, before a quantitative comparison before humans and neural network language models can be performed. Existing large-scale human behavioral datasets are based on a speeded yes/no version of the task, in which participants are instructed to make a lexical decision on a single string of characters as quickly as possible  (Balota et al. , 2007) , whereas our evaluation is based on the forced choice task and does not incorporate time pressure. A behavioral dataset with the paradigm we have used should be easy to collect using crowdsourcing. Alternatively, direct comparison to existing human datasets could be made possible by developing reliable ways to map language model probabilities onto timed yes/no lexical decisions; our initial experiments suggest that some nontrivial challenges would need to be overcome before this direction can be pursued.
Acknowledgements We thank  Emmanuel Keuleers  for his assistance with the Wuggy nonword generator. This research was supported by the  European Research Council  (grant  ERC-2011-AdG 295810 BOOTPHON ) and the  Agence Nationale pour la Recherche  (grants  ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-0087 IEC ).
In this article, we describe the design and implementation of an einternship program that BioScience Project offers high school students over the summer. Project topics are in the areas of behavioral neuroscience and brain disorders. All research, teaching, and communication is done online using open access databases and webtools, a learning management system, and Google apps. Students conduct all aspects of a research project from formulating a question to collecting and analyzing the data, to presenting their results in the form of a scientific poster. Results from a pilot study indicate that students are capable of comprehending and successfully completing such a project, and benefit both intellectually and professionally from participating in the e-internship program.
eurekalert. org/), BBC Science (http://www. bbc. co. uk/science), Neuroscience News (http://neurosciencenews. com/) and YouTube (https://www. youtube. com/). Students are provided with as much mentoring as they need to complete the internship. Mentoring is requested and scheduled by email. All instruction and mentoring is provided by the project director, Dr. Anna Delprato. As the internship program grows, additional scientists will be recruited to assist with teaching. Students are not tested and there are no grades assigned. Teaching and communication is done through an online learning platform (Moodle; https://moodle. com/), one on one video conferencing (Skype or Google Hangouts), email, document sharing (Google Docs), and a Google group, which enables students to receive notices and communicate with one another.