dataesr/scientific-paragraphs-categorization huggingface dataset:
In total, the present version of the MnDB includes data from 272 experiments. There are generally several dose levels within a single experiment, with a separate record created for each dose level. Some studies are described in more than one article: in this event, information from these articles was combined so as to avoid duplication in the MnDB. Upon completion of the MnDB, a draft ordinal severity scoring matrix covering the spectrum of health outcomes in the MnDB was created. A three-day workshop was held at Risk Sciences International in Ottawa, Canada at the end of January 2013 with participation of experts in epidemiology, toxicology, medicine, veterinary sciences, and risk science. The expert panel was charged to review and modify the ordinal scale of severity scores and apply it to the health outcomes in the computerized database.
Literature Search and Exclusion Criteria To develop a robust categorical regression database, it was important to first identify relevant scientific reports for inclusion in the database. To achieve this, the International Manganese Institute (IMnI) electronic library reference list as well as Ovid Medline/Embase and Toxline bibliographic databases were searched. Search terms are provided in Appendix B. No limits were applied to publication date; studies published as early as 1930 and as late as 2013 were included in the analysis. References of identified articles were also searched to identify further relevant publications. Case reports, epidemiological studies and in vivo experimental studies were considered as potentially eligible for inclusion. The international expert panel also guided the modification of exclusion criteria.
For example, it was suggested that studies with transgenic animals with altered metabolic profiles that might be of limited relevance to human health risk assessment be excluded from database until scientific data (e. g. , PBPK modeling data) is available to compare dose metrics against conventional animal models. It was also suggested that these studies be retained as a separate group in the MnDB for possible use in categorical regression sensitivity analysis. Similarly, arguments for inclusion of metabolic/pharmacokinetic and in vitro studies could be made, as these studies may be useful in elaborating toxicity pathways for Mn. For example, in experiments in which neurotransmitters were evaluated, in vitro studies might be useful in determining severity level of the potential adverse outcomes.
Characteristics of the Database The database was created in Microsoft Access and contains a wide collection of variables, ranging from qualitative inputs related to data abstraction/storage to quantitative inputs associated with exposure. The identifier variable is an ID automatically assigned to each record. The identifier variable also contains the first author's last name, publication year, and the full reference. Note that a common study ID is assigned to all experiments within the same study. Characteristics of study subjects, such as species, strain, sex, and life stage at first exposure (e. g. newborn, weanling, adult, aged) appear in the database. Furthermore, the characteristics of exposure, namely, the manganese compound, exposure route (oral or inhalation), exposure medium (food, drinking water or gavage for oral exposure; dust or fume for inhalation exposure), dose of Mn (mg/kg bw/day or concentration of Mn in air (µg/m 3 )), and duration of exposure in days also appear in the database.
Each outcome under investigation was described in a separate text field. An ordinal severity score was assigned to each outcome on the basis of a severity system described in the following section. Because neurotoxic effects are "critical" for Mn health risk assessment, each experiment in the database has an indicator of whether or not this experiment contains at least one neurotoxicity-related outcome. The highest severity score (lowest severity score for deficiency) associated with a neurotoxicity-related outcome at each dose level in each experiment was extracted into a separate field. While neurotoxic outcomes are of interest, CatReg modeling exercises could consider any and all health outcomes, not only neurotoxicity. Following complete data abstraction from the scientific publications included in the database, the data was made available to the expert panel for their independent review and assignment of severity scores for each health endpoint measured and included in the database.
The distribution of existing data on Mn doses from basal diet was examined visually for rats and mice, the two species with the greatest numbers of experiments in the database (Table  3 ). Due to presence of outliers, the median basal diet Mn concentration is preferable to the mean concentration. In experiments where data on Mn in the basal diet were unavailable, median doses of Mn from basal diet were assigned according to the values provided below in Table  2.
Results Each observation in the database corresponds to a single dose level from each study, with the severity score(s) corresponding to the adverse health outcome(s) seen at that dose. For each data point, information is provided on the species, sex, age, route of exposure, animal strain, exposure level, and duration of exposure. The database incorporates information from eight different species, males and females of all ages, inhalation and oral exposure routes, as well as experimental and observational studies, providing a comprehensive repository of information for exposure-response assessment. Table 3 demonstrates the vast majority of studies included in the database were performed on rodents, with males, and via the oral route of exposure. Studies on humans tend to focus on marginal to moderate effects due to both Mn deficiency and excess.
Distribution of Study Characteristics in the MnDB Quasi-separation occurs when exposure level C yields Y=0 and Y=1; this often occurs when exposure-response data from different studies are combined. As a consequence, human and animal data will likely need to be combined when conducting categorical regression analysis. The CatReg software permits model parameters to be stratified by animal species: a categorical regression model can be parametrized so that human data are used to estimate the intercept, while animal data are used to characterize the slope  (Haber et al. , 2001). The common response scale was applied to the MnDB. In the data abstraction stage, the group size from each experiment was also recorded. In determining the distribution of severity scores, the group level entries were converted to individual level entries, and their distributions are presented above in Table  4 , which highlights the extensive data available for excess exposures.
Discussion An important contribution of this work was the development of an 18-point severity scoring matrix designed to standardize health endpoints onto a common scale for the application of categorical regression. This matrix can be adopted as a general template for all metals. Since all metals exhibit different toxicological properties, this general template could be modified to accommodate the characteristics of the metal under study, providing a stepping stone to begin to look at essential elements known to exhibit both health benefits and health risks that may be balanced using categorical regression modelling techniques. The MnDB offers the largest, most current library of data abstracted from relevant Mn studies for exposure-response assessment. The database has proven effective as an organizational tool to synthesize information abstracted from scientific articles.
A review of the database reveals considerable diversity among the available studies with regards to species, route of exposure, sex, and age, indicating stratification is an essential aspect in the categorical regression analysis. The database is also useful for identifying gaps in the literature, such as the limited amount of data on Mn toxicity due to deficiency. Future directions for this work include more accurate exposure characterization; one such example is developing biomarkers which can be used to quantitate exposure. As additional information on Mn toxicity due to deficiency accrues in the future, a more complete description of the U-shaped dose response curve for Mn as an essential element demonstrating toxicity due to both excess and deficiency may be possible.
An example is the measurement of vibrations and forces in machines; in case a pre-established value is reached, the order of repairing is triggered before being reached the state of stop by failure. Furthermore, the new trends and the so-called Industry 4. 0 is based on SCADA (supervisory control and data acquisition) systems, which are an integral part of industrial process Sensors 2017, 17, 1547 2 of 23 control and equipment. Sensing elements for parameters such as pressure, flow, temperature or strain, among others, are one of the key elements of such systems. Strain is usually measured with strain gage transducers. These widespread components are composed of a thin metallic sheet or wire whose electric resistance changes as it is stretched or compressed; this change is then converted into the strain value.
Interrogation Unit The sensors were interrogated by the SENSeOR wideband radio-frequency transceiver WR D005. This unit operates in the [430. 5, 449. 5] MHz frequency band. It provides digital RS232/USB and analogue outputs. The maximum sampling rate/sensor is, according to the data sheet, 150 Hz (depending on the application). The dimensions of the interrogation unit are 18. 4 × 10. 9 × 3. 0 cm 3. Once the interrogation unit is turned on, the user is prompted to choose a configuration. Different types of measurements are possible: quarter-bridge, half-bridge, half-bridge with temperature compensation or temperature alone. After the configuration has been chosen, the user may configure the filtering and recording parameters and correct the frequency offset. After this configuration, the user can start acquiring data.
To guarantee the uniform heating of the cell, each heating cycle was followed by a 1 h waiting period. For task (ii), the measurements were performed at three different temperatures (25, 47. 5 and 60 • C), with rotation speeds between 0 and 2300 rpm with increments of ≈300 rpm in both rotation directions and without applied torque. For each experimental condition, 1000 readings of the resonant frequencies of the temperature and strain sensors were recorded. The average resonating frequencies and corresponding standard deviations were then computed from the experimental data. In order to improve the accuracy of the measurements, the maximum standard deviation allowed to accept an interrogation cycle data frame was 3 kHz and 22 frequency readings were averaged to extract a data point. For task (iii), the tests were performed at room temperature and at a rotation speed of ≈150 rpm.
Once ∆ f r for a given temperature is known, the calculation of T with Equation (  4 ) is straightforward. However, the behavior of the temperature sensor mounted in the load cell may not be accurately described using the temperature coefficients taken from the sensor's datasheet and, as such, the calibration of the temperature sensor was required. The instrumented load cell was placed inside the thermal chamber (as described in Section 2. 1), the sensors and the interrogation unit were connected to dipole antennas and the response of the temperature sensor was recorded at different temperatures. The difference between the relative resonant frequencies with temperature is plotted in Figure  7a. The data were fit to a second order polynomial, allowing for the determination of the first and second order temperature coefficients C 1 = 5.
Acknowledgments: This work was sponsored by  Clean Sky project RTMGear 641593  and obtained financial support from  FCT/MEC  through national funds and when applicable co-funded by  FEDER-PT2020  partnership agreement under the projects  UID/EMS/00481/2013-FCT  and  CENTRO-01-0145-FEDER-022083. J. C. Mendes acknowledges the  FCT for POPH (Programa Operacional Potencial Humano) post-doctoral grant   SFRH/BPD/90306/2012 , co-financed by European ( FSE-Fundo Social Europeu ) and national ( MCTES-Ministério da Ciência, Tecnologia E Ensino Superior ) funds.
A single national social security number (AMKA) was introduced in October 2009. Furthermore, computer-based information systems were present in practically all hospitals, primary healthcare units and pharmacies although as recently as 10 years  ago this was not the case (see also Sect. 7. 2. 2). Additionally, standards for information codification like the International Classification of Diseases (ICD), the Anatomical Therapeutic Chemical (ATC) classification and HL7 Clinical Document Architecture (CDA) were already mature and readily available. E-prescription was built upon these enabling components and would have been challenged without them. Moreover, software architectural styles that allow client-server separation of concerns and simplify modular implementation (such as REST) were established.
So, the development of the software capitalised on relevant technical knowledge and experience. The installed base included also a complicated arrangement of multiple social security funds and national actors including the Ministry for Health, the Ministry for Labour Social Security and Welfare, the National Organisation for Medicines, Doctors and Pharmacists Associations. All these actors were involved by setting rules and providing datasets required for digitising the prescribing process. The Chief Executive Officer of IDIKA stated in an interview in 2012 that the main challenges faced were not related to the technical development but rather to the coordination of all involved actors  (Sfyroeras 2012a). He also stated that the lack of interoperability among systems and the absence of a national registry for the beneficiaries of healthcare were assessed as showstoppers by some participants during the early stages of the initiative.
We observed this during the Immersed condition, but not the Mindful condition. Stimuli. Food image stimuli validation. To validate the food stimuli, an additional group of 100 Chileans ranked, via an online questionnaire, 300 items from the bank Food-pics, an image database for experimental research on eating and appetite  18  , according to their attractiveness on a 5-point Likert scale (from "very attractive" to "not attractive"). From the results of this questionnaire, we selected 60 images with the highest scores in the "very attractive" and "attractive" categories, and 60 images with the highest scores in "neither attractive nor unattractive" and "not attractive" categories to be displayed in the experiment.
Acknowledgements This work was supported by the  National Committee of Science and Technology of Chile (CONICYT) , through  Beca Nacional de Doctorado   N° 1070761  given to  Baquedano C. Also  this work has been supported by the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) an  European Research Council  grant  ERC-Consolidator 617739-BRAINandMINDFULNESS  to AL, and a gift from the  Adam J. Weissman Foundation  to AL. DC acknowledges support by  Iniciativa Científica Milenio  from the  Ministerio de Economía, Fomento y Turismo , Project  IS130005 :  Millennium Institute for Research in Depression and Personality-MIDAP , and  FONDECYT de postdoctorado  grant No. 3160403  to a R. V. AL thanks  John Dunne  for an earlier discussion on the AAT paradigm.
2008) , so that no general assessment has yet been performed. In order to produce a broad and general picture of emerging patterns for alien species invasion, we propose a macroecological approach that simultaneously analyses occurrence patterns within several taxonomic groups (animals, plants, protists and prokaryotes) belonging to multiple freshwater ecosystems, taking advantage of the biodiversity dataset from Italian water bodies recently collated by the Lifewatch Italy infrastructure  (Boggero et al. 2016). The Italian peninsula is a useful model for such a study, as it represents a limited, connected area, with high heterogeneity of habitats ranging from wet and cold alpine areas to dry and warm Mediterranean ones. Moreover, studies of alien species often focus on large, charismatic and/or pest species due to the difficulty in observing the occurrence of alien species in understudied taxa.
Our study minimised this bias, because it's based on a dataset derived from broadscale and long-term monitoring projects with highly detailed taxonomic resolution for several taxonomic groups usually neglected in studies on alien species, across orders of magnitude of body size. We explored the drivers of alien species occurrence in freshwater ecosystems by assessing the relative contribution of the three main actors in the PAB framework. We explored explicit hypotheses for invasion success related to the effects of (i) propagule pressure, (ii) abiotic features of the receiving sites, and (iii) biotic features influencing the invading organisms. As an indirect proxy for propagule pressure and human frequentation of a habitat, we used site accessibility  (Nelson 2008; Uchida and Nelson 2010) , defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel.
Dataset preparation In this study we used the occurrence data collected in  Boggero et al. (2016) , which extended the dataset of a previous macroecological analysis on the same geographical area (4300 occurrence data points -  Boggero et al. 2014). The dataset spans approximately 50 years of monitoring programs on different taxonomic groups from a wide variety of permanent freshwater habitats (including springs, streams, lakes, fountains designated through the EUNIS classification system) covering almost all taxa, from prokaryotes to fish and macrophytes. For this reason, the dataset is particularly well suited for a study exploring invasion biology through a macroecological approach. The information on species presence at each site comes from published papers, including reports from universities and research institutions, as well as notes in technical reports from local authorities.
Unfortunately, the dataset does not report the exact dates of species findings in a site so we cannot analyse invasions as a dynamic process through time. We considered the dataset (covering a 50 year period) as representative of recent invasion processes and we are confident that this approximation does not affect our results. For more detailed information about the dataset, the sites and their typology classification, the geographic coverage and the LifeWatch Italy network organisation see  Boggero et al. (2016). Previous studies focused on the fact that man-made aquatic environments or reservoirs could be more prone to alien invasions  (Havel et al. 2015)  or could disproportionately contribute to the secondary spread of invasive species  (Hulme 2006 ) by functioning as invasion hubs for freshwater invaders  (Muirhead and MacIsaac 2005).
Therefore, in this study we explicitly chose to focus on natural and semi-natural environments only, excluding man-made sites (EUNIS code J5: highly artificial man-made waters and associated structures). We also excluded macrophytes because of the difficulties in obtaining a reliable estimate of their size. Furthermore, because of the macroecological focus of this work we included only those sites where at least two different phyla were sampled. The original dataset from  Boggero et al. (2016)  was thus pruned by removing macrophytes and non-natural sites. The final dataset included 5299 occurrence data from 139 sites (85 lentic and 54 lotic) distributed across the Italian peninsula (see Appendix 1 and Figure  1 ). Of the 1630 species (22 phyla, 244 families) included, 51 are alien species, representing ~ 3% of the total species diversity.
The frequency of alien species presence in our database is consistent with percentages previously reported for Italian freshwaters  (Gherardi et al. 2008; Boggero et al. 2014) , suggesting that our data were not biased towards a misleadingly larger representation of native versus alien species. The dataset was aggregated at the family level to provide comparable levels of diversity for the statistical analyses to be applied. We are aware that species within a family may differ in autoecological requirements. However, we analysed very different families so that intra-family diversities are almost negligible with respect to inter-family differences. Thus, aggregating species at the family level allowed us to group species with homogeneous ecological features and body size. Native species richness and alien species richness were calculated for each family in each water body.
The family-level and site-level aggregated dataset, together with the associated covariates, is available in Appendix 2. Estimates of body size for each species (maximum length in mm) were obtained from literature; we mostly used monographs on Italian species (e. g. ,  Ruffo 1977 Ruffo -1985)). When data were not available in monographs, we searched for additional literature at the European scale (e. g. ,  John et al. 2002; Wiederholm 1983). Given the wide range in body size, from cyanobacteria (0. 001 mm) to fish (> 1 m), we assumed that small differences in body size between populations of the same species would not affect our inferences. The body sizes of species were averaged per family and subsequently log-transformed. In this work, we focus on the available data for Italian freshwater systems and we used the lentic/lotic dichotomy only as random effect in the models below.
The varied geographic coordinates for lakes (north) vs rivers (central, south) (see Figure  1 ) supports our choice to exclude man-made systems, which usually occur in central-southern Italy to provide drinking water in areas more likely subject to prolonged drought. Unfortunately, there are insufficient data available to analyse in detail the different probabilities of alien species occurrence in northern and southern Italy. This is due to national monitoring programs based on a family/genus level approach undermining the results that could be achieved through a species level approach. Site bioclimatic variables were obtained from the Worldclim database  (Hijmans et al. 2005). Values for five bioclimatic variables, namely annual mean temperature, temperature mean diurnal range, temperature seasonality, annual precipitation, and precipitation seasonality, were extracted from 2.
Statistical analyses We investigated the effect and the importance of the set of biological and environmental variables previously described as predictors of the probability of occurrence of alien species. We used generalized linear mixed models (GLMM) to determine which variables best predicted alien species occurrence. GLMMs have the flexibility of generalized linear models in handling non-normal data distribution  (Crawley 2013) , and the advantage of accounting for potential violations of the assumption of independence of observations  (Bunnefeld and Phillimore 2011; Beckerman 2014). GLMMs were performed using the package lme4 1. 1-7  (Bates et al. 2014 ) in R 3. 1. 1 (R Development Core Team 2014). We evaluated the invasibility (i. e. , the probability of occurrence of an alien species) as the proportion of alien species observed in each site by taxonomic group.