dataesr/scientific-paragraphs-categorization huggingface dataset:
838; p \ 0. 001), had B 4 years of education (63%; X 2 = 17. 097; p \ 0. 001), and was in NHYA class II-III (84%; X 2 = 5. 470; p = 0. 027). In the regression model, age (p = 0. 003), education (p = 0. 041) and NYHA class showed (p = 0. 051) independent association with coexisting CI and frailty/pre-frailty. Conclusions: Acknowledgements: Supported by ERDF through POCI-01-0145-FEDER-007746, funded by COMPETE2020, National Funds through FCT, within CINTESIS(UID/IC/4255/2013), and Project ''NORTE-01-0145-FEDER-000026-Symbiotic Technology for Societal Efficiency Gains:Deus ex Machina'', financed by NORTE2020 Results: The records of 10656 patients, of hospitalization episodes, were reviewed. There were 54. 1% females and median age of 66 years. The age group C 65 years was 53. 2%, and with age C 75 was 33%.
London: Stationery Office intervention, study design and phases of rehabilitation process during the transition from inpatient rehabilitation to their home 65. 25 ± 5 and 64. 95 ± 4. 79, respectively. The results of t-test including analyses of psychometric properties • Sub-group analyses environment. This risk increases with cognitive impairment (CI). showed that there was a significant difference between mean score of of people with cognitive impairment (PWCI). Aim of this study: The investigation of the change of motor per- BDI-II before and after intervention (P \ 0. 047). Methods: Established databases (PubMED, Cochrane CENTRAL, formance in geriatric patients with CI and HPF in the transition CINAHL, PEDro) were systematically searched. Inclusion criteria: between inpatient rehabilitation and home environment.
Methods: This was a retrospective analysis of 30-day, 6-month and 1-year mortality in patients discharged to SNF following hospital- ization between January 1, 2009 and June 30, 2014. Braden scores, demographic, comorbidity, and mortality data obtained from clinical and administrative databases were analyzed to compare mortality rates in patients with Braden scores in the at-risk category during hospital stay to those not at risk. P-304 Comparison of two fall risk screenings and fall prevention interventions in older in-hospital patients: an health economic evaluation Lilli Neumann 1 1 Albertinen-Haus, Geriatrics Centre, Scientific Department at the University of Hamburg, Hamburg, Germany Introduction: In-hospital falls are reported with 1. 3-8. 9 falls/1000 hospital days. They are associated with prolonged hospital stay, worsened immobility, disability, institutionalisation and cause added use of monetary resources (e.
1 Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Methods: For this observational study, participants were consecu- referral. We will present a comprehensive overview of results and the University of Heidelberg, Heidelberg, Germany tively recruited from September 2015 to September 2016 in a geriatric foundation it offers for triage methods. rehabilitation center in Oldenburg, Germany. At admission, malnu-Introduction: Effect of supported (by procedural learning potentially affecting procedural memory) and non-supported exergame training trition was assessed by Mini Nutritional Assessment-Short Form using simultaneous motor and cognitive tasks have not been studied before. Methods: Eight electronic databases were searched to identify relevant nutritional intervention studies in patients aged C 60 years, admitted to a geriatric rehabilitation setting.
The aim of the study is to detect whether there is any relation between malnutrition and OH. Methods: 862 geriatric patients, who had undergone comprehensive geriatric assessment (CGA), were included in the retrospective study. OH was identified as 20 and/or 10 mmHg dropped for systolic and/or diastolic blood pressures with the active standing test when patients Results: Mean LTPA was 5. 7 ± 5. 6 h/week and the most common Conclusions: CI was the best anthropometric index for estimating activities were walking and gardening. Mean concentration of cardiovascular risk in elderly analyzed. 25OHD was 66. 7 ± 28. 1 nmol/L and 8. 5/21. 2% were below 30 and Funding: This work was supported by the 4 IE project (0045-4 IE-4-50 nmol/L, respectively. Obese participants (n = 84) had lower P) funded by the Interreg V-A Espan ˜a-Portugal (POCTEP) 25OHD (-11.
For this reason, gerontology information system GeroS/ CEZIH was implemented with web-service Nutritional Risk Screen- ing 2002 (NRS-2002) that is able to provide quick screening of nutritive status focused on gerontologic-public health indicators such as age group, sex, functional ability, main and accompanying diag- noses of geriatric patients, basic anthropometric measurements and similar. Methods: Gerontology-oriented public health analysis of specific determinants (1) in different age groups of elderly through NRS-2002/ GeroS/CEZIH web-service was performed. A total of 187 patients from the database were randomly included in the analysis during the period from March to May 2016 and were stratified three groups; younger-elderly (65-74 years), middle-age''-elderly (75-84 years) and old-elderly ([ 85 years).
1 , Ficheur Gregoire 2 , Beuscart Jean Baptiste 2 , Boulanger Eric 2 , Demarle Sylvie 3 , Puisieux Francois2   To assess the association between institutionalization and the degree of kinship with their family caregiver among ADOD patients. Methods: A cross-sectional study analysing the first visit in a Memory Center in France for eachADOD patient, over 75, between  2011 and 2014 (French National Alzheimer Database). A multivariate logistic regression was conducted to assess the association of interest adjusted by age, gender, MMSE, educational level, and type of dementia. Results: 52 874 old patients were included. The primary caregiver is mostly a child (54. 8%) or the spouse (36. 7%). Compared with the category of reference ''spouse'', all other categories are significantly at risk of institutionalization.
There were seven cohorts of aging, three retrospective hospital series and one database The prevalence and severity of delirium and dementia related of cohorts. Diabetes was recorded via self-reporting/hospital records. behavioural disturbances on medical wards Cognition was assessed using a variety of scales. There was hetero-geneity in neuropathological quantification. Diabetics demonstrated mainly cerebral infarction and lacunes but not Alzheimer pathology. 1 M. Z. Vettasseri 1 , C. Hewkins 1 , A. Mamarelis 1 , B. Baath 1 , R. H. Harwood 1 , A. Ali 1 A synergistic relationship emerged where those with diabetes and infarcts, or with diabetes and Alzheimer pathology, had lower cog- 1 Department of Internal Medicine and Gerontology, Jagiellonian 1 Nottingham University Hospitals NHS Trust, Nottingham, UK nitive scores than those with diabetes, infarcts or Alzheimer University, Medical College, Krakow, Poland, 2 Department of Introduction: Behavioural disturbances associated with delirium and pathology alone.
Methods: Pubmed, Embase, SciELO, and LILACS databases were reviewed systematically (period: January 2007-March 2017) using search terms 'E. coli' and 'invasive disease'. Total burden of E. coli BSI was estimated by multiplying age-specific pooled incidence and case-fatality rates by 2016 US and EU population estimates. Since 2015 several recurrent syncope, sometimes traumatic and preceded by chest pain, dyspnea and palpitations. On March 2017: the patient was referred to our Syncope Unit: the physical examination showed the presence of right carotid bruits and the Orthostatic Hypotension test was positive. Second diagnosis: orthostatic hypotension. An Echocardiogram showed mild aortic valve stenosis. The Carotid Doppler Ultrasound revealed mild carotid atherosclerosis without significant stenosis.
In our previous retrospective study of 571 frail elderly who underwent a comprehensive geriatric assessment (CGA) over the years 2005-2013 we did not find any association between OH and mortality. The aim of the present study was to assess possible asso-ciations between OH and mortality in a broader sample that included not only patients who underwent CGA, but also those who underwent a geriatric consultation (GC) in the same unit. Methods: Individuals who were evaluated in the Outpatient Com-prehensive Geriatric Assessment Unit between January 2005 and December 2015 and who had data on orthostatic hypotension were included in the study. The database included socio-demographic characteristics, body-mass index, functional and cognitive state, geriatric syndromes, co-morbidity and mortality data.
Video Head Impulse Test (vHIT) The vHIT was recorded using a lightweight portable vHIT device (Hardware: ICS Impulse, GN Otometrics, Taastrup, Denmark, Software: Otosuite R Vestibular software). The method for this evaluation as well data analysis has been described in detail in a previous study  (12). The vHIT was performed 15 mn after DVA testing. Outward horizontal and vertical head impulses were performed by experienced examiner standing behind the patient. For the evaluation of the horizontal SCCs the head was tilted forward to align the plane of the horizontal SCCs with the horizontal plane. A minimum of 5 valid horizontal head impulses were realized in each direction with a target speed > 200 • /s. For the evaluation of the vertical SCCs vestibulo-ocular function, the head was turned 40 • to the side to align the horizontal gaze direction with the plane of the simulated SCC pair [right anterior-left posterior (RALP) or left anterior-right posterior (LARP)] (13).
It is clear that a greater FFV reduction is recorded when the crosslinking agent is a short rigid compound (i. e. , n-ethylamine and n-butylamine) or contains hydrogen bond donor/acceptor functional groups (i. e. , ethylene glycol monosalicylate). A more bulky component such m-xylylene diamine showed a lower FFV reduction, owing to its large aromatic group which hinders compaction of the chain packing. Please note that there is no data or discussion on 6FDA-ODA:DABA crosslinked with EG anhydrous, because of that we were unsuccessful in producing the self-standing film. It is believed to be due to over-crosslinking by the short-length and rigid EG, instigating a very high rigidity polymer chain and causing membrane brittleness and cracking. DSC measurements (Table  1 ) show the synthesized 6FDA-ODA transitioned to a rubbery polymer at 309 • C (glass transition temperature, T g ), closed to the reported data at 294-303 • C  [24, 25] , whereas the synthesized 6FDA-ODA:DABA (8:2) revealed two T g at 263 • C and 327 • C.
It is clear that a greater FFV reduction is recorded when the crosslinking agent is a short rigid compound (i. e. , n-ethylamine and n-butylamine) or contains hydrogen bond donor/acceptor functional groups (i. e. , ethylene glycol monosalicylate). A more bulky component such m-xylylene diamine showed a lower FFV reduction, owing to its large aromatic group which hinders compaction of the chain packing. Please note that there is no data or discussion on 6FDA-ODA:DABA crosslinked with EG anhydrous, because of that we were unsuccessful in producing the self-standing film. It is believed to be due to over-crosslinking by the short-length and rigid EG, instigating a very high rigidity polymer chain and causing membrane brittleness and cracking.
Overview The prediction of mutagenic activity of new chemical entities (NCEs) based on their structure and potential reactivity towards DNA has been used for some decades, and in silico tools are now accepted for regulatory decision-making in the area of genotoxicity of drug candidates and impurities in pharmaceuticals  (Amberg et al. , 2014). Beside this, a lot of effort has been put into the prediction of organ toxicities, such as DILI, using different computational models  (Kotsampasakou et al. , 2017; Mulliner et al. , 2016a) , which achieved accuracies in the range of 70 and 80%. New perspectives for in silico, read-across, and modeling approaches are resulting from the emerging availability of "big data" in toxicology  (Hartung, 2016; Clark and Steger-Hartmann, 2018). One opportunity to push investigative toxicologists to embrace the 3Rs principles lies in developing new in silico approaches, and also in effectively integrating existing in silico tools with in vitro technologies, as well as with preclinical and clinical databases  (Rovida et al.
PBPK modelling is becoming indispensable for QIVIVE  (Basketter et al. , 2012; Leist et al. , 2014). Specifically, reverse dosimetry PBPK is being used to estimate human exposures that lead to concentration-time profiles that are equivalent to sufficiently There have been significant advances in machine or deep learning technology in recent years. Although deep learning approaches have been shown to yield accurate predictions  (Mayr et al. , 2016) , they require large, costly datasets. When it is practical to generate a relatively small dataset, researchers often seek to test a diverse set of compounds in their assay. Because of the complexity of compound space as well as the assay results within that space, diversity selection of compounds does not always yield an optimally predictive model.
In addition, improved bioinformatics tools that integrate large omics datasets into co-regulated gene networks, allow quantitative analysis of the association between such gene networks and adverse outcomes  (Stiehl et al. , 2017; Sutherland et al. , 2018). There is a rapid development of sequencing strategies, where chromatin-immuno-precipitation (ChIP) sequencing will contribute to a further refinement of the transcription factors that drive these transcriptional networks in different target tissues. These complementary sequencing approaches should ultimately define the quantitative relationships between both safe and adverse ranges of pathway activation that will determine the safety margins. The IMI TransQST project will contribute to these quantitative systems toxicology evaluations 12.
Omics profiling Omics technologies, which have gained prominence and relevance over the last few years, can be divided into four parts that focus on different steps in the generation of bioactive molecules: genomics, transcriptomics, proteomics, and metabolomics. Systematic studies on transcriptome analysis within large consortia and industry settings have helped establish extensive datasets of drug-induced transcriptome profiles in different target organs as well as in vitro in cells. This is exemplified by the TG-GATEs 10 and DrugMatrix 11 datasets that are available in the public domain  (Igarashi et al. , 2015; Ganter et al. , 2005). The initial hope for toxicogenomics as the solution for the ultimate prediction of target organ toxicity has not been completely fulfilled as the technology and the diversity of transcriptional profiles has turned out to be more complex than anticipated  (Pognan, 2007).
But, also in the field of regulatory toxicology, ontologies of toxicological observations are not fully harmonized  (Hardy et al. , 2012a,b). How this can be improved was demonstrated in the ontology discussion within the IMI eTOX 5 project. Histopathology ontology was developed with the intent to standardize histopathology findings, and it has now been made openly available  (Ravagli et al. , 2017). This is a key enabler, making searches on one of the most important data types in toxicology studies possible and providing a database for new in silico and in vitro models 13. Further aspects that were found to be critical and challenging for an effective development of new tools (e. g. , models, methods) include the definition of performance standards that lead to successful proof of concept studies.
Acknowledgements Hajar Maleki  acknowledges nancial support by the  Austrian FWF  for the  Lise Meitner fellowship  (project number:  M2086-N34 ). Transmission electron microscopy, TEM, was carried out using facilities at the  University Service Centre for Transmission Electron Microscopy, Vienna University of Technology, Austria. Lawrence Whitmore  acknowledges nancial support from  Interreg Österreich-Bayern   2014-2010  Project  AB29   Synthese ,  Charakterisierung und technologische Fertigungsansätze für den Leichtbau "n2m " (nano-to-macro). We gratefully acknowledge the help of Karin Whitmore in the preparation of ultramicrotome specimens.
Forecasting model framework The forecasting modeling framework was based on five major steps summarized hereafter. First, the entomological and environmental data were collected and stored into a spatial-temporal database for data analysis. Second, a calibration step aimed at selecting the best combination of predictors and statistical models that best fit the observed spatio-temporal patterns of eggs abundance. Third, the ability of the forecasting model to project unforeseen future events in new locations was tested on independent validation dataset. Four, the forecasting model projections were extended to the entire Balkan countries (Albania, Montenegro, Macedonia, Serbia, Kosovo, Greece, Croatia, Slovenia, Bulgaria and Rumania) at high spatial (one km) and high temporal (week) resolution between 2009 and 2013.
Dataset Entomological survey. Oviposition activity of Ae. albopictus was monitored in 26 sites across Albania by ovitraps, i. e. black cylindrical vessels (9 cm high, 11 cm in diameter with an overflow hole at 7 cm from the base) filled with ~300 ml tap-water with no attractants, and internally lined with heavy-weight seed germination paper  [27]. No specific permissions were required for these sites/activities. Landowners gave permission to conduct the studies on their properties. The field studies did not involve endangered or protected species. In the latter 10 sites, monitoring was carried out in August-September 2012 every two weeks by 5 ovitraps/site and the number of eggs/week was calculated by dividing the total number of eggs collected over the two weeks by two. Eggs laied on germination paper were counted and identified to species level based on their color, size, shape and surface sculpting  [28].
Environmental data. Climate and landscape data expected to influence Ae. albopictus ecological dynamic were used to derive relevant predictors in the model  [21, 22, 23]. The mean land surface temperature (LST) were extracted from Modis data, by averaging night and daily LST data, then rescaling data to the weekly resolution using spline interpolation to match the sampling unit of the study. Altitude data were based on the Shuttle Radar Topography Mission database (SRTM;  [29] ). For each site, the dominant class of Corine land-cover 1km resolution raster  [30]  was extracted among: artificial surface, agriculture, forest, wetlands or water body.
The variables included: i) three biological predictors referring to the respective eggs abundance recorded at each three weeks before the sampling; ii) four climate predictors including the mean temperature recorded at the sampling week and at each of the past three weeks of sampling, in order to take into account the role of climate variables during the larval development in affecting A. albopictus adult abundance and survival  [23] ; iii) two geophysical predictors related to Corine land-cover and altitude; iv) one seasonal predictor denoting the week of the year the sample was recorded. Considering eggs abundance as count data, Poisson (hereafter referred as 'poisson') or negative binomial (hereafter referred as 'nb') errors distribution families were assumed in the modeling setup. In addition, simple GLMs (hereafter referred as 'glm') as well as more complex types of model enable to account for the presence of large number of zeros in the database (> 50%) were also tested; namely hurdle (hereafter referred as 'hurdle') and zero-inflated (hereafter referred as 'zeroinf') models  [31].
This allows projections to be made in any location where the predictor covariate data is available. Similarly, the Max modeling component defined maximal condition values at each step of the forecasting process, preventing the Core model to project some excessive or unreliable eggs abundance values above those maximal condition values. Based on the calibration dataset, the mean and maximum weekly eggs abundance over the three years of sampling was calculated at each site. The Init and Max models were setup individually, by regressing the mean (for the Init model) and the maximum (for the Init model) weekly eggs abundance against the mean altitude and the week of the year (as a second polynomial degree). GLMs models with Poisson and negative binomial distribution families were tested. Models selection.
All predictors were transformed to normality and scaled to zero mean and unique variance. All models were calibrated using the calibration dataset. Dynamical feature. The dynamical feature of the forecasting model was designed to refine the accuracy of projections while integrating meaningful ecological information related to the biological cycle of Ae. albopictus. Technically, this was implemented in 4 steps by: i) initializing the Core model at a given week of the year (Wt) using the Init Model by estimating starting egg counts; ii) Output from the Init Model were used to derive the egg count predictor variables to be included in the Core Model; iii) Core model projections were then calculated while paying attention that they do not exceed Max model projections, otherwise they were given the corresponding Max model projection value; iv) Core model outputs at time Wt were used as egg count predictors at time Wt-1 to derive Core model projections at time Wt+1; v) The model was run iteratively until the ending week of the year, by repeating step iii) and iv).
Forecasting model calibration and validation The forecasting model was calibrated using the calibration dataset while validated using the validation dataset. Both dataset were assumed to be spatially independent, so that the validation step provided a suitable assessment of the forecasting model ability to extrapolate projections to higher spatial extents. Since the validation data were only available in weeks 32, 34, 36 and 38 for year 2012, the goodness-of-fit assessment was made for this period only. Model goodness-of-fit and uncertainty was evaluated throughout bootstrap approach under 100 iterations. For each iteration, 70% of the calibration dataset was randomly sampled (with replacement) to calibrate a single forecasting model. The projections derived from the 100 forecasting models were then assembled to calculate the 10 th , 50 th and 90 th percentiles projections values.
Forecasting model extrapolation to the Balkans Once validated, the forecasting model was extrapolated to Albania and its surroundings countries (Montenegro, Macedonia, Serbia, Kosovo, Greece, Croatia, Slovenia, Bulgaria, Rumania) at 0. 01 degree spatial resolution (WGS84 coordinate reference system), and at a weekly interval from 2009 to 2013. Model projections are provided as supplementary information data S1 Dataset. The forecasting model assumed stationarity, so that the relationships fitted between the abundance of eggs and its predictors was supposed to remain stable beyond the spatio-temporal extent of model calibration. All statistical analyses were performed under the R software environment (version 3. 1. 1) using packages pscl  [33] , visreg  [34] , dplyr  [35] , and ggplot2  [36].
Model selection The first model selection step consisted in identifying the most suitable statistical models for the Core, Init and Max modeling components, independently. For the Core modeling component, zero-inflated negative binomial model displayed the best goodness-of-fit results, with particularly low AIC, RMSE and dispersion parameters values (zeroinf-nb; Table  1 ). The best statistical model was selected based on the following criteria: normality, homogeneity and absence of strong spatial and temporal autocorrelation patterns in the residuals (Fig 2 ), minimizing dispersion, RMSE and AIC parameter values. All models were built upon the calibration dataset. In regards with the Init and Max modeling component, best results were obtained using GLM statistical model with negative binomial distribution family (glm-nb; Table  1 ).