dataesr/scientific-paragraphs-categorization huggingface dataset:
Embodied mouse in the Neurorobotics Platform The full musculoskeletal model of the virtual rodent controlled by the spinal cord model was simulated in the Neurorobotics Platform (NRP) developed in the Human Brain Project  (Falotico et al. , 2017). The main components of the NRP are a world simulator, a brain simulator and the mechanism that enables the data flow between the two in a closed-loop. The connection between the body and the brain is specified through a domain specific language  (Hinkel et al. , 2015 (Hinkel et al. , , 2017)) , via Python scripts called Transfer Functions. In these scripts the output of devices that read neuronal output data can be processed and passed as input for the virtual body actuators, and vice versa, the sensory information from the virtual body sensors, in this case muscle length data, can be passed to devices that map sensory data to neural input.
Simulation of the experiment on goal-directed movements As the first component of the proposed framework ("Movement-driven models"), we simulated the experiment on goal-directed forelimb pulling in the virtual environment and validated the simulation on experimental data. In the in-vivo experiment, two healthy mice were trained on the M-Platform to perform active pulling of the forelimb. As we expected, the contralateral motor cortex showed a highly coherent activation with the kinetic data. The coherence between the force applied by the animal and the signal recorded in the CFA was evident both in the low and in the high frequencies band (Fig. 8 ). For the data that were later used in simulations we focused on the high band (300 to 40k Hz); in particular we found an high activation of the motor cortex around the force peaks for both multi-unit activity and single units analysis.
In Fig. 8  we show the results for a simulation trial and a comparison with data recorded from a physical experiment. We employed kinematic data recorded alongside neural activity in the same in-vivo experiment: position of the slide and force applied to the slide through the trial. As expected, by comparing the activation levels with the normalized force applied by the mouse to the slide we can observe that the flexor muscles are active when there is also a force recorded, and conversely, there is low activation when the slide is still. It is also worth mentioning that, although the two muscles receive the same inputs from the descending stimuli, their activation levels are different due to the feedback circuitry of the spinal cord and the activity of muscle spindles, which are different for the two muscles.
DATA AVAILABILITY STATEMENT The datasets and data description on wide-field calcium imaging generated for this study can be found here: Allegra Mascaro, A. ,  Conti, E. , Sacconi, L. , & Pavone, F. (2019). The raw datasets on neurophysiological recordings for this study can be found here: Pasquini, Maria,  Spalletti, Cristina, Caleo, Matteo, and Micera, Silvestro. (2019). Recordings of Caudal Forelimb Area in healthy mice during a forelimb pulling task [Data set]. Zenodo. http://doi. org/10. 5281/zenodo. 3546068 Processed data and source code that can be used to reproduce the neurophysiological recordings experiment can be found in the following repository: https://gitlab. com/lore. ucci/closed-loop-mouse-stroke-simulation The BNM model and all the relevant information to reproduce the simulated BNM data can be found in the following repository:.
Statistical Analysis Data analyses were performed using the SPSS program (Statistical Program for Social Sciences, version 2. 1, IBM, São Paulo, SP, Brazil). The normality of continuous variables was verified using the Kolmogorov-Smirnov and Shapiro-Wilk tests, as well as graphical analyses. Nominal variables were expressed as absolute frequency and relative frequency (n (%)), while continuous variables were expressed as mean (standard deviation). In the comparison between the HIV (+) and HIV (-) groups, the Mann-Whitney U test was used for the continuous variables and the Chi-square test or Fisher's exact test for the nominal variables. As a sample calculation was not performed, the effect size for each variable was calculated. Thus, after applying the statistical test for the comparison between the groups (obtaining the level of significance for each analysis), the effect size for each variable was calculated.
Study Design and Data Collection This cross-sectional study was carried out in the capital city of Qinghai Province of China, i. e. , Xining, in November 2018. Healthcare professionals from four county-level health alliances (2 general health alliances and 2 Chinese traditional health alliances) in Minhe county and Ledu district were invited to participate in this survey. All four leading county-level hospitals in those two counties were included in this study. Township health center was selected by a random sampling method, 18 township health centers were finally included, and the cluster-sampling method was used to recruit participants in each hospital and township health center. Temporary or visiting staff was excluded from this study. The formula of the sample size calculation is N = Z 2 a ×P(1-P) d 2  [18].
The study relied on a cross-sectional analysis of the data from wave seven of the ELSA study consisting of 7385 participants (with an average age of 67), of whom 3056 had mild hearing loss, 755 had severe hearing loss and 834 used a hearing aid. Cognitive functions were measured with tests of memory (10-word list immediate and delayed recall test) and executive function (verbal semantic fluency task). Hearing loss was associated with poorer cognitive scores. However, this association was evidenced only in the individuals who did not use hearing aids. For the subsample of respondents who used hearing aids, there was no evidence of association between hearing loss and cognition. Interestingly, the analysis involved an index of social isolation. After introducing this index in the statistical model, the results suggested that social isolation acted as a mediating factor, in particular in those participants who did not use hearing aids.
Cognition was measured with subtests of the Halstead-Reitan neuropsychological test battery and the Wechsler-Bellevue Form II test. Pre-implantation assessment was not available, so changes in cognition pre-versus post-implantation could not be measured. Post-implant test results showed that 11 participants scored within normal limits and two showed impaired cognitive scores. Another study analysed the data from 46 post-lingually deafened adults aged 19 to 75 years (mean age 48) who underwent cochlear implantation with a single-channel device  [63]. The patients performed an extended cognitive battery (Wechsler Adult Intelligence Scale assessing general cognitive ability, Graham-Kendall Memory for Designs measuring immediate recall, Trail Making Test assessing executive functions and Bender Visual Motor Gestalt and Interference Test for visual motor functions) before implantation, one year post-implantation, and, for some patients, 2 years after implantation.
The results showed that there was no damage in cognitive function post-implantation and that some individuals showed improvement in several of the cognitive tests. A third study examined the psychological status pre-and post-implantation of 30 adults with profound post-lingual hearing loss aged 14 to 80 years (mean age 49) who received a multi-channel cochlear implant  [64]. Cognitive assessment included the Wechsler Adult Intelligence Scale-Revised. The results showed patients' improvement in communication abilities early after implantation. However, the small number of subjects and lack of long-term follow-up did not allow conclusions to be drawn about changes in cognition after implantation. Based on these three studies, the conclusion of the review  [61]  was that, while many publications have shown that cochlear implants improve speech perception, social functioning, and overall quality of life, there is insufficient data to allow conclusions regarding the effects on cognition.
, 2008; Solaymani-Mohammadi and Singer, 2011; Huang et al. , 2013; Nowarski et al. , 2015; Zhao et al. , 2016). RT-qPCR was conducted with QuantiTect SYBR Green PCR Kit (QIAGEN, Toronto, Canada) on a Rotor Gene 3000 Cycler (QIAGEN, Corbett, Australia) and normalized to the b-actin gene. Primers and reaction conditions for detected genes are given in Supplementary Table  S1  (University Core DNA Services at the University of Calgary, Calgary, AB, Canada). Data were analyzed using a 2 ÀDDCT method and expressed as a fold change compared with WT non-infected mice or plain Caco-2 cells.
Statistical analysis Data were expressed as mean ± S. E. M. Data representation and statistical analysis were performed using GraphPad Prism 6 software for Macintosh (San Diego, USA). The normality of the data was assessed before statistical analysis. Statistical significance was determined by one-way ANOVA with the Kruskal-Wallis post-test for non-parametric data. Comparisons between groups with normal distributions were performed using one-way ANOVA followed by Tukey's test for multiple comparison analyses. Mann Whitney's U test was used to compare two sets of nonparametric data. P < 0. 05 was considered statistically significant.
Acknowledgements The authors thank  Dr. Peter Geldhof  ( Ghent University, Ghent, Belgium ) for providing the G. muris strain, and acknowledge the support of staff at the animal care facilities of the  University of Calgary (LESARC) ,  Canada. This work was supported by funding provided by the  Natural Sciences and Engineering Research Council of Canada , the  Canadian Institutes of Health Research, and Crohn's and Colitis Canada. TA and JAC were sponsored by post-doctoral fellowships provided by the  Eyes High program  at the  University of Calgary , and  Alberta Innovates for Health Research ,  Canada. JPM was funded by postdoctoral fellowships from the  Alberta Innovates Health Solutions (AIHS) ,  Canada  and from  European Union 's  Seventh Framework Programme   N°FP7-609398  (AgreenSkills+ contract).
Sediment lag deposits (mud drapes that settled typically on top of channel-bed sand; n = 24) were collected along the main rivers draining the Mano and Niida catchments after the typhoon season on 29 and 30 October 2019 (Figure  2b ). At each sampling site, 5 to 10 surface scrapes of deposited sediment, collected along a 5-10 m river reach with a plastic spatula, were composited into one sample. All soil and sediment samples were dried at 40 °C for 48 h. Then, they were sieved to 63 µm and placed in plastic boxes (15 mL) prior to analyses. All the data including spectrocolorimetric properties, geochemical properties, and radiocesium concentrations are available on the Pangaea data repository (https://doi. org/10. 1594/PANGAEA. 923582).
Spectrocolorimetric analyses All soil and sediment samples were analyzed using a portable diffuse reflectance spectrophotometer (Konica Minolta CM-700d) by providing data required to calculate characteristic colorimetric parameters. The spectrophotometer was calibrated before each measurement session by a white and a black gauge. The measurements were performed in triplicate on the plastic boxes in which the samples were stored (15 mL) with a minimum sample quantity of 0. 2 g. All measurements were performed with a 3 mm aperture target mask, the standard illuminant D65, and a 10°angle observer, excluding the specular component. The instrument measured the percentage of spectral reflectance for each of the 39 wavelength classes between 360 and 740 nm at a resolution of 10 nm. The XYZ tristimulus values were calculated on the basis of the color matching functions defined in 1931 by the International Commission on Illumination  [CIE, 1931].
Acknowledgments This research was funded by the  AMORAD  project supported by the  French National Research Agency (Agence Nationale de la Recherche  [ANR],  Programme des Investissements d'Avenir ; grant no. ANR-11-RSNR-0002 ). The support of  Centre National de la Recherche Scientifique (CNRS, France)  and  Japan Society for the Promotion of Science (JSPS)  is also gratefully acknowledged (grant no. PRC CNRS JSPS 2019-2020 , no. 10 ;  CNRS International Research Project -IRP -MITATE ).
Depression The Center for Epidemiologic Study -Depression Scale (CES-D  [45, 46] ), a 20-item questionnaire, is used to assess depressive symptomatology at baseline and after six months. With a score ranging from 0 to 60, persons with dementia have to assess the frequency of their symptoms, while caregivers assess their own symptoms and the person with dementia's. Decision making Involvement of the person with dementia and his/her caregivers and relatives in the decision to enter a nursing home are from the caregiver. We will also whether the person with dementia agreed with this decision with a dichotomous question. Admission Data on conditions of admission are collected: was it an emergency or planned admission? Place of stay before admission is also collected. We ask whether the participant with dementia was admitted from his own home, from a hospital or from any temporary housing.
Sample size In order to calculate the target sample size, we used data from Castro-Monteiro et al. [19]. They found a mean difference in QOL-AD scores between admission and after 6 months of 1. 7 point in people with dementia. Therefore, we considered this difference in QOL-AD scores as clinically significant. To observe such a difference in our study, we need a sample size of 130 subjects, with a 90% statistical power and a 5% maximum probability of type 1 error alpha. This sample size was calculated using the Rstudio software (power. t. test), and was rounded to 150 to account for attrition, some studies showing an attrition rate of about 30% after 3 months  [47].
Participants' characteristics at baseline and follow-up At baseline and follow-up, categorical data will be described as follows: frequency, number and percentage. We will compare baseline data of people who answered after 6 months with data of people lost to follow-up using a chi-square test. At baseline and follow-up, continuous data will be described as follows: mean, standard deviation, minimum, maximum and interquartile intervals. We will compare baseline data of people who answered after 6 months with data of people lost to follow-up using a Student T test.
Discussion Comprehensive research on the quality of life of people with dementia throughout the transition between home care and nursing home remains scarce. We can reasonably think that our study will contribute to identify which factors, before admission, predict quality of life and adjustment to nursing home. contribute to a better understanding of the impact of institutionalization on quality of life and the determinants of a "successful" nursing home admission. Given the time and memory sensitive nature of the data, main analyses on living conditions at home will be performed using caregivers' data. Comparison between the caregivers' and person with dementia's data in further analyses will offer interesting insight on how a person with dementia reconstructs the idea of life at home, and if this reconstruction reflects the objective conditions of her daily life and the perception of the main caregiver.
, 2014) , as well as methodological differences in the assessment of the exposures between cohorts and in the definition of the outcomes across cohorts and studies  (Lambert et al. , 2017). The CORINE Land Cover (CLC) is a European wide standardised land cover map spanning a large time scale managed by the European Environmental Agency. It has advantages in that it combines geo-spatial environmental information from national databases and satellite images into 44 land cover classes describing various types of artificial surfaces, agricultural land, forests, wetlands and water bodies  (Kosztra et al. , 2017). The aim of this study was to use CLC to estimate the percentage of green, blue, agricultural and grey spaces surrounding the residence of 8063 children, aged 3-14 years, from nine European cohorts to evaluate associations between these four broad land cover types and six indicators of allergy and respiratory health during childhood, including asthma, wheezing, allergic rhinitis and eczema.
Health outcomes and covariates In all studies, data on health outcomes, as well as on potential confounders, were collected through parental questionnaires. Children were classified as having: • "lifetime wheeze" (wheeze) if children were reported to have ever had wheezing or whistling in the chest at any time in the past; • "current wheeze" if the child had any wheezing in the last 12 months; • "lifetime asthma" (asthma) if the child had asthma at any time in the past; • "current asthma" if the child was currently taking medication for asthma and/or suffered an asthma attack in the last 12 months; • "lifetime allergic rhinitis" if the child ever had any nasal allergy or hay fever at any time of the past; • "eczema" if the child ever had an itchy rash on one or more parts of the skin which was coming and going or had been diagnosed with eczema.
Residential land cover The participant's residential addresses were geocoded and re-projected into CLC's Lambert equal area projection  (Kosztra et al. , 2017). The proportion of each CLC class surrounding each residential location was calculated by using buffer zones with four radii: 100 m, 300 m, 500 m, and 1000 m. The raster version of CLC with a pixel size of 100 m was used. Depending on the year of data collection for the specific cohort study, the nearest CLC layer was selected from the available  1990 , 2000 , 2006 , 2012 or 2018 years: 1990 for Pisa-2, 2006  for Eden, Fumane, PHIME-SLO, Turin and Viadana, and 2012 for Mubicos and REPRO_PL. 2012 was selected for ITR as it was the year closest to the visit dates of the children selected for this study. From the resulting proportions of original CLC classes, eight land cover features used in this study were calculated: percentages of green space, blue space, grey space, agricultural space, forest cover, coniferous forests, deciduous forests and mixed forests.
Land-cover main classes (i. e. green, grey, blue, and agricultural) were included in the model as continuous variables, and OR were estimated for a 10% increase of land coverage within a 500 m buffer. In each model, associations with health outcomes were estimated only in studies that had more than 10 cases in order to avoid data sparseness. In the second stage, fixed-effects meta-analyses were performed on the estimates calculated for individual studies using the inverse-variance method and overall OR were calculated. Fixed-effects meta-analyses were adopted under the assumption that land cover features have the same effects on health outcomes across all studies. Random effects meta-analyses using the DerSimonian and Laird methods were performed when a significant heterogeneity across the studies emerged  (Fisher, 2015).
Discussion Our study set out to investigate whether residential land cover was associated with the occurrence of respiratory and allergic symptoms in children. Meta-analyses were conducted based on data collected from nine paediatric cohorts from four European countries. We found that greater residential exposure to green space was associated with increased odds of wheezing and asthma (both current and lifetime) and lifetime allergic rhinitis, but not with eczema. Children living within a 500 m buffer including coniferous forests, in particular, were found to have odds of wheeze, asthma and allergic rhinitis that were 2-to 4-fold higher than those who did not. Grey (urban) and blue space were not associated with any of the studied outcomes, while agricultural space was moderately protective, although not at a statistically significant level, for all the respiratory outcomes.
, 2017) , who found no statistically significant associations for grey space determined by CLC within 300 m buffers and wheezing, asthma and allergic rhinitis. Another study by  (Ebisu et al. , 2011)  found a significant association between urban land use and wheeze severity in American infants, but the significance of this association disappeared when the models were adjusted for NO 2. Again, here is the question regarding the definition of exposure. In the case of  (Tischer et al. , 2017)  where CLC was also used at a buffer radius of 300 m, our results are also consistent with no association between grey space and any of the allergic and respiratory outcomes. In the case of  (Ebisu et al. , 2011) , their exposures are estimated from the U. S. National Land Cover Database, which may differ from CLC, and they also considered a much larger buffer surrounding the residence (1540 m).
The use of CLC, rather than NDVI to determine green space exposure, however, means we cannot directly compare our results with many other studies. CLC data does not capture small green spaces well which some have suggested renders its use inappropriate in urban areas  (Annerstedt et al. , 2012; Mitchell et al. , 2011). Consequently, our observations may be confounded due to exposure misclassification. Quantifying land cover is only an indirect way of assessing exposure. The chosen buffer size can also affect the results. Our choice of a 500 m buffer is supported by  (Browning and Lee, 2017) , and our results were consistent when changing the buffer radius to 300 m or 1000 m (Table  S5  in the Online Supplement). A further concern is that CLC is only available for specific years, which may mean that for some of the younger children the exposure was measured before they were born.
Conclusions Data collected from nine different European paediatric cohorts was meta-analysed to investigate potential associations between current residential land cover and common respiratory and allergic childhood diseases. Our results indicate that living close to large green space areas, in particular coniferous forests, may increase the risk of developing wheezing, asthma and allergic rhinitis in children. Our findings also support research showing that agricultural areas near children's homes may have a protective effect on respiratory health, while exposure to urban/grey space did not seem to impact the development of wheezing, asthma or rhinitis. Additional studies evaluating both the type and quality of green space and its use, as well as the interaction with air and soil pollution in relation to respiratory conditions, should be conducted in order to clarify the underlying mechanisms behind the adverse respiratory impacts associated with green space.
Framework Programme for Research, technological development and demonstration  under grant agreement  FP7-ENV-2013-603946. In addition, the following funding supported the individual cohort studies: PHIME-SLO work was supported by the  EU  through the  6th Framework Program  ( FOOD-CT-2006-016253 ,  PHIME  project) and the  CROME-LIFE  + project; the REPRO_PL cohort received financial support from the  National Science Centre, Poland , under grant No. UMO-2014/15/B/NZ7/00998 ; the Pisa2 study was supported in part by the  Italian National Research Council Targeted Project  ' Prevention and Control Disease Factors-SP 2 ' (Contract No. 91. 00171. PF41 ), by the  Italian Ministry of Labour and Social Security  (Contract No. 587-1997 ), and by the  CNR-ENEL (Italian Electric Power Authority) 'Interaction of Energy Systems with Human Health and Environment' Project  ( 1989 ).