dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, in the present study, the delineated units were close to the duration of an oar stroke (or shorter), reflecting the importance of each cycle in experiencing the race. The first component of a phenomenological experiential unit refers to a current action [i. e. , Action (A)], defined as the fraction of activity that the individual can show, tell, or comment on at a given moment. This component is the closest to the syncretic experience of the actor in the situation. It is assumed to emerge as a physical action, a communicative exchange, or an interpretative act. The researcher identified this component within the verbalization data sets by determining what the participant was doing and what he was thinking. The second component refers to the current involvement [i. e. , Involvement in the situation (I)], defined as the individual's concerns at a given moment.
This component was identified within the verbalization data sets by identifying the participant's significant concerns in relation to the specific situation. The third component refers to current expectations [i. e. , Expectations (E)], defined as what is expected by the individual in the situation at a given moment. It was identified within the verbalization data sets by identifying the participant's expectations about the current situation arising from his concerns and from the previous events in the setting (e. g. , what result he/she was anticipating). The fourth component refers to knowledge elements [i. e. , prior mobilized Knowledge (K)], defined as the individual's past knowledge that is relevant to the current situation. This component was identified within the verbalization data sets by identifying the prior elements of knowledge used by the participant.
The fifth component refers to the perception [i. e. , Perception (P)], defined as elements of the situation significant to the individual at a given moment. It was identified within the verbalization data sets by identifying what the participant considered to be a meaningful element of the situation. The sixth component refers to the construction, validation, or invalidation of knowledge, defined as the component of activity that modifies elements of knowledge at a given moment [i. e. , Refashioned Knowledge (RK)]. This component was identified within the verbalization data sets by identifying what knowledge was being constructed, validated, or invalidated by the participant at the considered instant. For further details on the method or the framework, see  Theureau (2003). Table  1  provides an example of each of these components [i.
Computing Mechanical Indicators at Various Levels of Description Mechanical indicators were calculated for each rower's stroke to account for individual-, interpersonal-, and boat-levels of description. These indicators were analyzed for each full oar stroke. Each stroke was decomposed into four phases to better assess changes within the mechanical signatures, specifically, the first and second halves of the drive phase (i. e. , during the propulsive phase; when the oar was in water) and the first and second halves of the recovery phase (i. e. , during the replacement phase; when the oar was out of water). Raw data (oar angles, forces applied to the oarlocks, acceleration and velocity) were filtered with a low pass Butterworth filter, with a 5 Hz cutoff frequency. Continuous angular velocities were then computed as the first derivative of the angular position, using the central difference formula.
Identifying the Mechanical Signatures of the Typical Modes of Experience by a Subjectivity-Based Sampling Method To combine phenomenological and behavioral data (i. e. , typical modes of experiencing the race and the mechanical signatures at various levels of description), we performed a subjectivity-based sampling procedure. The procedure involved first scrutinizing the phenomenological data (i. e. , the rowers' course-of-experience) to delineate the samples of behavioral data to be compared (i. e. , various ways of experiencing the strokes give rise to various delineated sections within the race that will be further processed/compared). Such a subjectivity-based sampling method has been well developed in enactivist neuroscience (e. g. ,  Rodriguez et al. , 1999; Lutz et al. , 2002; Lutz and Thompson, 2003; Froese et al.
, 2014a,b). To our knowledge, this has not been used in the field of human movement or sports science. The principle is to guide the observational study (e. g. , brain dynamics observation, behavioral dynamics observation) using phenomenological data collected during the same task. This procedure includes the human experience as a valuable facet of the activity under study and investigates the observational (i. e. , behavioral) measures that contribute to their emergence. To utilize this method, the time code of each typical mode of experience was recorded (i. e. , starting/ending point of the given mode) to identify all intervals falling under the same typical mode of experience, subsequently, we aggregated them in a corresponding sample. Various samples of mechanical data were built from this procedure (i.
e. , respecting the time codes of the typical modes of experience), each of them thus reflecting different ways of experiencing the joint action. Each instant of the joint action (i. e. , each cycle) was further characterized in terms of the similarity of the individual experiences of the rowers, using the three individual modes of effectiveness experiences captured during the thematic analysis of each participant's activity. From the collective level of description of the lived experiences, we delineated four collective phenomenological categories (i. e. , four samples) in our overall data set. Each of these categories comprised mechanical indicators measured for each cycle under consideration, resulting in multiple quantitative time series. The first collective phenomenological category was labeled Simultaneously and Similarly Experienced as Meaningless (SSE-M).
The second category was labeled Simultaneously and Similarly Experienced as Detrimental (SSE-D). The third category was labeled Simultaneously and Similarly Experienced as Effective (SSE-E). The fourth category was labeled Simultaneously Diverging Experiences (SDE). Statistical analysis was carried out on the mechanical signatures of each of the four categories using the SPSS 17. 0 statistical software package (SPSS, Inc. , Chicago, IL, USA). Descriptive statistics were reported using the mean and the standard deviation (mean ± SD). Differences between the four categories regarding each mechanical indicator were analyzed using a multivariate analysis of variance (MANOVA). When the main effect was significant, ANOVA, or two-way ANOVA, with Tukey's HSD post hoc test was applied to the categories (SSE-M, SSE-D, SSE-E, and SDE) and the rowers (Rower 1 and Rower 2) as independent variables and the mechanical indicators listed above as dependent variables.
DISCUSSION The objective of the present study was to understand how individual experts in rowing experienced the effectiveness of their joint action when they rowed together at the first stage of their team coordination learning process. To achieve this objective, we collected the data related to their real-time lived experience (i. e. , at a pre-reflective level of the activity) and to the related mechanical properties during a 12-min race. We were thus able to explore the mechanical signatures of various shared lived experiences. The discussion of the results is organized around our research questions. The results first suggested that (a) the extent to which rowers simultaneously experienced salient, meaningful sensations of effectiveness (i. e. , effective or detrimental) in their joint action correlated with the extent to which supporting a mechanical signature captured expert-like pattern of team coordination.
It allowed us to process quantitative and behavioral data only, which de facto included the phenomenology that prevailed in such data. However, as is usual in behavioral research, the processed team coordination data (i. e. , the explained variable) include mostly the experimental condition, specifically external/contextual constraints that have been observed (i. e. , the explanatory variables). The procedure performed in our study guided the mechanical analysis of the phenomenological data, and thus ideally illustrated how a full enactivist approach could be used with behavioral data. The subjectivity-based sampling appeared to be a good method for interdisciplinary research. Moreover, by including a phenomenological methodology that uses retrospective interview techniques, the research design permits activity to be studied based on the reconstruction of the natural and specific conditions of the activity to reveal how participatory sense-making develops in a real-world setting.
We analyzed data for 170 patients in South Korea who had laboratory-confirmed infection with Middle East respiratory syndrome coronavirus. A longer incubation period was associated with a reduction in the risk for death (adjusted odds ratio/1-day increase in incubation period 0. 83, 95% credibility interval 0. 68-1. 03). T he incubation period of an infectious disease is the time from the moment of exposure to an infectious agent until signs and symptoms of the disease appear  (1). This major biological parameter is part of the case definition and is used to determine duration of quarantine and inform policy decisions when mathematical modeling is used  (2). Incubation periods vary from person to person, and their distribution tends to be right-skewed and unimodal  (3). Variability in incubation periods for infection with Middle East respiratory syndrome coronavirus (MERS-CoV) has been described  (4) (5) (6) (7) (8).
The Study We retrieved publicly available data from the Korea Center for Disease Control and Prevention, the Korean Ministry of Health and Welfare, the World Health Organization, and local news reports in South Korea to compile a list of all confirmed cases that had been reported by July 26, 2015  (6). Exposure data were available for 109 (64%) of 170 patients. For most cases, information on exposure was recorded as intervals >2 days during which infection was believed to have occurred, rather than exact dates of presumed infection. For the subset of patients without available exposure data, we assumed that their incubation time was 0-21 days because 21 days was the longest incubation period reported  (9, 10). Data for patients is provided in online Technical Appendix 1 (http://wwwnc. cdc.
However, this analysis could not account for potential confounders. Therefore, we specified a multivariable logistic regression model in which death was the binary response variable and predictors included age, sex, and the incubation time for each patient  (9). We performed this analysis by using an exact likelihood approach and incubation times resampled from the 10,000 posterior samples in each iteration (online Technical Appendix). All analyses were conducted by using R version 3. 0. 2 (R Foundation for Statistical Computing, Vienna, Austria). Raw data and R syntax enabling reproduction of results are available from the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. v3456). Of 170 patients in this study, 36 (21%) died. Mean patient age was 54. 6 years, and 98 (58%) were male.
This study was supported by the Harvard Center for Communicable Disease Dynamics (National Institute of General Medical Sciences grant no. U54 GM088558); a commissioned grant from the Health and Medical Research Fund of the Health, Welfare and Food Bureau of the Hong Kong SAR Government; and the Research Grants Council of the Hong Kong Special Administrative Region, China (project No. T11-705/14N). B. J. C. has received support from MedImmune Inc. (Gaithersburg, MD, USA) and Sanofi Pasteur (Lyon, France). He is also a consultant for Crucell NV (Leiden, the Netherlands). Dr. Virlogeux is a MD/PhD student at the University of Medicine Lyon-Est and at the Ecole Normale Superieure in Lyon, France. His research interests include infectious disease epidemiology, and the mathematical modelling of epidemic dynamics.
THE NEUROMUSICOLOGY OF DISORDERS OF CONSCIOUSNESS Different methodologies have been used to investigate the effect of music on the brain functioning of DOC patients. The present issue reports four articles in which the interest of autonomic recordings, EEG and functional MRI connectivity are evaluated. First, Kotchoubey et al. reviewed empirical data on different functions (perception, cognition, emotions, and motor functions) that musical stimulation can address in DOC patients. This is supported by an original study in which Riganello et al. investigated the link between music listening and heart rate variability, and in which they found differences between DOC and healthy subjects, characterized by lower values in VS/UWS patients. Lord and Opacka-Juffry, in an opinion paper, claim that EEG studies of coherence can be used to assess the effects of auditory stimuli in DOCs, for example during the course of music therapy.
, 2003; Takayasu et al. , 2006; Bruzzone et al. , 2007). 26RFa-containing neurons are exclusively located in the LHA and the ventromedial hypothalamic nucleus  (Chartrel et al. , 2003). Central administration of 26RFa stimulates food intake and causes obesity in rodents  (Chartrel et al. , 2003; Moriya et al. , 2006; Takayasu et al. , 2006). In addition, 26RFa mRNA is up-regulated in the hypothalamus of obese ob/ob and db/db mice  (Takayasu et al. , 2006)  indicating that the expression of the 26RFa gene is modulated in condition of energy imbalance. Circadian profile of plasma 26RFa was investigated in AN patients. The data showed that 26RFa levels are significantly increased all over the day in patients with restrictive AN as compared to healthy subjects  (Galusca et al. , 2012). Detailed mapping of GPR103 mRNA in the brain revealed that the 26RFa receptor is expressed in the VTA, the amygdala, and the NAc  (Bruzzone et al.
, 2013). All these data show that there are modifications in gut microbiota of AN patients, not necessarily severely malnourished. These changes include an increase of methanogen archea directly involved in energy efficiency. Indeed the increase of M. smithii can lead to the optimisation of food transformation of nutrients into calories. This increase of M. smithii could have preceded undernutrition and could play a central role in the outbreak of anorexia nervosa. In order to explore this pathway the gut microbiota of patients with AN should be assessed before the beginning of weight loss, during nutritional therapy and after recovery of weight loss. In summary (Figure  4 ), at least two hypotheses can be proposed concerning the role of gut microbiota in anorexia nervosa: (1) some pre-existing changes in gut microbiota induce and/or participate to food restriction in these patients and chronic constipation could induce and/or facilitate this change in gut microbiota.
Participants Twenty-six volunteers participated in this study, recruited from the community through advertising. Five participants were excluded from data analyses due to either non-completion of scan (one), excessive head movement during structural scans (three), or identification of anomalous anatomical features (one). Twenty-one healthy volunteers (13 females and eight males; M age = 24. 7 years, SD = 4. 9, range = 17. 7-33. 5) were included in the data analyses. All were right-handed, as determined by the Edinburgh Handedness Inventory  (Oldfield, 1971) , and reported no current or past history of neurological or psychiatric disorders or psychotropic medication use. All participants, and a parent or guardian for those aged less than 18 years, provided written informed consent and were screened for physical or medical conditions affecting eligibility for magnetic resonance imaging (MRI) scanning.
Apparatus Visual stimuli were presented using E-Prime software (version 2. 0, Psychology Software Tools, Pittsburgh, PA, USA) on a laptop PC (Intel 2 Ghz, 256 mb Nvidia Video Card) that was interfaced with the magnetic resonance scanner during fMRI data acquisition. Stimuli were projected onto a screen located near the feet of the participant, who viewed the screen via a mirror attached to a 32 channel head coil. Behavioral responses were recorded using a scanner-compatible two-button box (Fiber-Optic response pad, Current Designs, Philadelphia, PA, USA). fMRI data were acquired at Swinburne University (Hawthorn, Australia) using a Siemens Tim Trio 3T MRI scanner (Erlangan, Germany).
Functional Magnetic Resonance Imaging Data Acquisition Functional magnetic resonance imaging data for the Go/No-go tasks were acquired using 180 (gradient) echo-planar imaging (EPI) sequences which provided T2 * (time constant for loss of signal in sequence). Weighted BOLD activity measurements were obtained for each functional run with the following parameters: repetition time (TR) = 2 s; echo time (TE) = 36 ms; flip angle (FA) = 90 • ; 192 mm field of view; and 38 contiguous slices of 4 mm slice thickness. An oblique 30 • orientation to the anterior-posterior commissure line was employed in data acquisition. The first two volumes of each run were discarded prior to data analysis to account for transients in the magnetic field of scanner. Eight functional runs were collected for each participant.
Go/No-go Data Behavioral data was assessed using E-Prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA, USA), IBM R SPSS R Statistics Version 18 (Chicago, IL, USA), and Microsoft Excel (2007). To assess whether attention diversion improved response inhibition, and whether the outcome of the inhibition influenced performance, a two-way repeated measures analysis of variance (with pair-wise comparisons for significant effects) was conducted. The F-statistic was used to determine whether there was a significant difference between means. Confidence intervals were calculated using SPSS and provided additional information regarding the statistical power of each comparison. The dependent variable, mean response inhibition accuracy, was varied by two factors (a) level of attention diversion (High/Low), and (b) future reward (Delayed reward/No delayed reward).
Functional Magnetic Resonance Imaging Data Functional magnetic resonance imaging data were processed using Analysis of Functional NeuroImages (AFNI) software  (Cox, 1996). Following image reconstruction and concatenation of runs, functional data were time-shifted using Fourier interpolation to adjust for difference in slice acquisition times, aligned to corresponding anatomical data, and warped to standard Talairach space. Motion was corrected using threedimensional volume registration with the third volume from the first run as a base. Volumes were blurred using a 4. 1 mm fullwidth half max filter, each voxel was then scaled to a mean of 100 and values over 200 were clipped. To examine the influence of reward and attention diversion on inhibition performance, an event-related analysis was performed that estimated BOLD activity during correct No-go trials.
Hemodynamic response functions were calculated using deconvolution of each successful No-go trial response. Activity related to errors, Go-Money trials, feedback screens and motion, were modeled as additional regressors to avoid contamination of baseline and event-related data. TR pairs were censored when the Euclidian Norm of the motion derivative exceeded 1. 0. The baseline estimate was the mean activation recorded during the ongoing trial period (Go trials). Thus, the activation observed during successful Nogo trial and Go-Money responses represented activation that differed from that required for the ongoing trial period (or Go) responses. The absence of collinearity between regressors within AFNI X-matrices was confirmed during deconvolution using xmat_tool. py. Event-related map voxels for each regressor of interest were extracted, resampled to anatomical data resolution (1 mm 3 ), and masked using a group-averaged EPI mask dataset.
Such an approach can be considered a knowledge coproduction process in which participants are the key informants  (Lowry et al. 2004 ). The assessment team collaborated in an iterative and transparent way that included several virtual meetings and a 2-day face-to-face workshop in 2015. The online meetings served mainly to clarify the aims of the joint work and to share and discuss findings from the existing literature, whereas at the workshop we developed the structure and content of the template. An independent facilitator helped workshop participants share and reflect on their personal experiences in CS projects. Participants selected key questions and developed a unified template for the case study review. We used the template to collect secondary data from 14 environmental CS projects covering a broad geographical range across Europe (Supporting Information).
Assessment of Case Studies The assessment and interpretive synthesis of the 14 case studies (details in Supporting Information) demonstrates the variety of ways in which learning takes place in CS. All the case studies were biodiversity related and focused on surveying, measuring, monitoring, or observing the natural world. Some projects concentrated on certain groups of species. Other projects collected data on the flora and fauna of a given area or explored the cultural ecosystem services of a landscape. Results for each case study relevant to level of citizen engagement, program objectives, learning outcomes for scientists, and learning outcomes for citizen participants are available as Appendix S2.
Case Studies Nearly all 14 programs examined had the general aim of contributing knowledge to support decision making and clear scientific ecological interests such as obtaining distribution data and detecting threats to species. Other scientific interests included the development and testing of new methods and social-scientific objectives such as understanding citizens' perceptions and values of nature and citizens' relation to nature. The objectives and Conservation Biology Volume 30, No. 5, 2016 learning outcomes for the 14 case studies are in Supporting Information. Half the reviewed cases focused explicitly on environmental education, awareness raising, and increasing interest in conservation issues. Empowerment of citizens or engaging them in conservation activities were also mentioned as objectives.
In most cases it was assumed that the citizens involved had gained some ecological knowledge and understanding, and in some cases it was proven that citizens learned how to use scientific methods. The ecological literacy of citizens was perceived to have increased because of learning about certain species or ecological conditions through data collection and because of developing an overall picture of species and ecosystems by seeing larger data sets or maps. Learning how science is approached was more difficult to detect, but some case studies indicated citizens also gained knowledge about this, for example, by exploring new technologies (i. e. , GIS-based observations and generation of data sets in the UUSIMAA and Sibbesborg cases), by talking with scientists, or by adopting a scientific method and using it to challenge the primacy of scientific knowledge production (Wolf case).
In 4 cases, the knowledge created was reported to have raised awareness of conservation issues. In 2 of these (Ladybird Survey in the United Kingdom  [Ladybird]  and Vadonleső) increased awareness led to spin-off projects. In the other 2, citizen action emerged (Observation and Amphibians). Empowerment of citizens was mentioned as a goal in half the cases, including through problem-solving skills gained through the use of the scientific method. In the Reindeer case, for instance, increased understanding of other social groups was one of the major outcomes of the project. As a consequence, the reindeer herders were empowered because they learned to use scientific data to discuss their issues with other land-user groups and to find common management strategies. Learning arrangements can enhance not only citizens' learning but their commitment and motivation.