dataesr/scientific-paragraphs-categorization huggingface dataset:
32 vs. 0. 27, p = 0. 09). Moreover, 24-h urine supersaturation indexes were similar between green tea drinkers and non-drinkers noteworthy for the calcium oxalate relative supersaturation index (CaOx RSS) and the calcium oxalate (CaOx) product (Table  3 ). However, a significant higher Ca/Ox ratio is noticed in female green tea drinkers, suggesting a relatively lower risk for oxalate-dependent stones (though the other Ca Ox indexes appear similar). As shown Table  2 , other biological data were similar between drinkers and non-drinkers in the male and female population in terms of noteworthy renal function, calcium phosphate homeostasis, and bone remodeling biomarkers. Among the 98 renal stones available, 48 samples were collected from female and 50 from male patients. Within male or female groups, comparison of the major stone component identified by IRS analysis revealed no significant difference between drinkers and non-drinkers.
e older participants were age matched to the Parkinson's group (Section 2. 1). e choice of ecologically valid music as cue types was included to differentiate from the auditory cues typically used in RAS therapy (i. e. , metronomes and "rhythmically enhanced music") so as to investigate the general use of music for people with Parkinson's as this may be more accessible in general and helpful to practitioners. e three movement modalities were chosen to enable comparison between strike-based type data from fingertapping studies during which there is no forward motion in that type of nonspatial "tapping. " In order to find common ground between finger tapping and RAS gait studies, "stepping in the spot" represented the type of "in place" locomotion or footfall stomping previously suggested  [23, 73].
Equipment. A stomp box (used by musicians to provide bass drum sounds, generally in acoustic music) (Acoustim8, Series 100 Foot Drum, UK) was used to collect finger-and toe-tapping data in order to provide an ergonomically appropriate way of collecting tapping data and enable quick and easy transition between finger (table) and toe (floor) tapping, thereby reducing experimental demand for people with Parkinson's. BioPac heel and toe strike transducers (Model RX111) attached to BioNomadix ankle sensors (Model BN-TX STRK2-T) gathered press and release data for stepping "on the spot" (Figure  1 ). e experiment was ran on Superlab software (Version 5, Cedrus Corporation, San Pedro, CA) connected to an MP150 (Biopac Systems Inc. , CA) unit running an STP100C Solid State Relay Drive, a UIM100C (for the StompBox), and two BioNomadix STRK2-R units (for stepping).
On the basis that >60% of participants (N � 50) agreed that the song excerpts were "easy to tap along with," nine songs were chosen for this study. e stimuli (including metronome beep tracks which were matched to each of the musical excerpts) were created in Logic Pro (Apple Inc. , CA). In common with other timing studies, for all stimuli, an eight-beat "count in" section was provided (with an accented beep on the first and fifth beats) to reduce data loss caused by initial listening and movement adjustment by participants  [31]. Stimuli were divided into slow, medium, and fast (Table  1 ) and analysed separately as various studies of timing in general and in Parkinson's have suggested tempo may be a mediating factor in motor abilities  [6].
Motor Timing Parameters. e interonset interval (IOI) refers to an audible pacing event (metronome beeps or musical beat). e interresponse interval (IRI) refers to the time interval between the onsets of two successive strikes produced by a participant. e mean IRI is commonly used to reflect the participants' capacity to accurately produce a timed motor interval  [31]. e mean IOI of the stimuli classified by tempo and the mean IRI for each group are provided in Table  3. In this study, data from the central ten bars in each of the three twelve-bar sections (Sync A, Continuation Task, and Sync B) were used in analyses. e concept of entrainment is operationalized in terms of how the auditory stimuli has been internalized by the participant, evidenced by being able to maintain their motor timing across the three consecutive sections, characterized by calculating the IRI % Error, a dependent variable demonstrating the percentage of absolute difference between each IRI and the reference IOI of a given trial (IRI % Error � (mean IRImean IOI)/mean IOI * 100).
Data Preparation. During preprocessing, trials were removed from analyses if less than 18 and more than 44 strikes were recorded in all movement modalities (i. e. , 10% above or below the required number of strikes). ese anomalies were due to either participant error and/or equipment failure. Table  4  shows the missing databy group, tempi, cue type, and movement modality. Finally, a 40% criterion (deviation from interonset interval in the stimuli) was calculated to remove outliers from the IRI mean based on  [31]. Similarly, for the Absolute Asynchrony, a 25% criterion was calculated to remove outlying data points based on  [82]. is final process amounted to the loss of 7. 65% data points, 7. 18% for metronome stimuli, and 8. 19% for music stimuli.
Statistical Analyses. Multifactorial repeated measures ANOVA were conducted by group (Parkinson's, older, and younger controls), across the three sections of the experimental paradigm for entrainment (measured using IRI % Error) and pace stability (measured using IRI CoV), and for Absolute Asynchrony using the data from Sync A and Sync B sections only in terms of comparing the strikes against the reference points in the auditory stimuli. Factors included cue type (metronomes and music) and movement modality (finger tapping, toe tapping, and stepping on the spot). Where significant main effects and interactions were observed, post hoc pairwise comparisons (Tukey HSD) further explore these data where the findings are considered meaningful in application for practitioners, although it is indicated when findings do not withstand Bonferroni adjustment for multiple comparisons (alpha p < 0.
Acknowledgments We gratefully acknowledge the contribution of the Ph. D. candidate  Rebecca Hadley , the dance teacher  Meryl Kiddier , and the technical staff  Noel Taylor ,  Julie McKenzie ,  Jon Guillard ,  Roy Mills , and  Malcolm Green  in preparing the equipment and logistics for the experiment. We thank the members of our  Parkinson's Advisory Group  for their input and help and also  Parkinson's UK Research  for their help with recruitment. We thank the participants for their time and patience and generosity of spirit. anks are also due to  Professor Olivier Senn ,  Dr. Chris Lee , and  Dr. Benjamin G. Schultz  for their helpful comments on the draft manuscript.
5 Objective PA assessment with accelerometers is a good low-cost alternative to DLW and captures validated information on habitual levels of physical activity. 6 e aimed to compare the PA of those with various chronic diseases to healthy individuals using accelerometer data from the UK Biobank study. The UK Biobank has collected the world's largest objective PA dataset to date using wrist-worn accelerometers on a cohort of 103 720 participants with links to various health records in the UK. 7, 8 This database, therefore, provides a unique opportunity to reliably compare any potential difference in PA between participants with both common and rarer chronic disease against their healthy peers.
Study design and participants The UK Biobank data set includes data from 502 656 UK adults between 40 and 69 years of age at recruitment (2006-10) via a centrally coordinated invitation from population-based National Health Service patient registers. 8, 9 Data were obtained by application to the UK Biobank, reference number 15 856. UK Biobank obtained ethical approval from the North West Multicentre Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. All participants provided written informed consent.
Outcomes PA was measured using Axivity AX3 wrist-worn triaxial accelerometers, collected from May 2013 until December 2015. Each individual had PA measured from their dominant wrist over a 7-day period at 100 Hz with a dynamic range of 68 g with cut points set to 5-second epochs. Details on data processing can be found elsewhere. 7 om the processed data, minutes of moderate and vigorous PA were calculated by considering the percentage of time spent above 100-milligravities (mg) activity intensity for moderate PA and above 400-mg intensity for vigorous PA. 12  As both moderate and vigorous PA are derived from activity intensity and are more intuitive to comprehend, mean acceleration is not described in detail in this paper but can be found in Supplementary Table  1. Of the 103 720 participants who provided accelerometry data, 7001 participants were excluded due to poor accelerometer wear time-defined as not having at least 3 days (72 hours) of data and or lacking data in each 1hour period of the 24-hour cycle scattered over multiple days.
Acknowledgements J. B. and T. D. developed the hypothesis for the paper and led the analysis of the data. All authors participated in interpreting the data and provided input to the writing, which was led by J. B. and T. D. A. D. contributed to the conception of the study, developed and implemented the physical activity exposure measures, contributed to the statistical plan and data analysis, and critically reviewed the manuscript. C. F. contributed to the paper conception, analysis plan and writing. K. S. B. was the primary statistician on the project. K. R. provided clinical insights and expert input on the analysis of the data and contributed to the design and writing of the manuscript. R. R. contributed to the writing of manuscript, data analysis and critical appraisal of the paper. M.
Asylum seekers are not included in this study. The total sample will include 400 participants. Data collection will consist of standardised questionnaires complemented by semidirected interviews in a subsample (n=38) of migrants qualifying for regularisation. The baseline data will be collected just before or during the regularisation, and participants will subsequently be followed up yearly for 3 years. The quantitative part will explore variables about health (ie, health status, occupational health, health-seeking behaviours, access to care, healthcare utilisation), well-being (measured by satisfaction with different dimensions of life), living conditions (ie, employment, accommodation, social support) and economic situation (income, expenditures). Several confounders including sociodemographic characteristics and migration history will be collected.
Intervention Participants eligible for legal status regularisation (intervention group) will receive a residency permit within 3-6 months after submission of their application to the canton authorities. This process will take place during the first year of data collection. The information about permit acceptance will be collected through participants and the NGOs. This permit is renewable annually. outcomes Primary outcome measure The primary outcome is self-rated health. It will be measured by comparing intragroup differences at baseline and after 3 years. Yearly measurements will allow for observations of fluctuations in such differences. This item will be measured using the 12-Item Short-Form Survey question 1, a 5-point Likert scale,  64  whose large-scale use also allows for comparing the results with other groups of population.
Additional variables under investigations The quantitative part explores four domains: (1) personal and family characteristics, (2) health, (3) living conditions, and (4) employment and economic situation. Table  1  presents the variables under study, to the exception of the main outcomes. These variables will be used as predictors for the main outcomes. The questionnaire was designed to provide data that allow for comparisons with the general population living in Switzerland, by using questions from population surveys such as the Household Panel Study or the Swiss Health Survey. 67 68 For specific domains, we use validated measurement tools selected for their validity and reliability in non-clinical settings. We apply the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scales to screen depression and anxiety, respectively.
Participant timeline Quantitative data will be collected by questionnaire at baseline and repeated annually during the following 3 years (four waves in total) (figure  1 ). Questionnaires will be filled face to face with participants in the intervention and the control groups. We restricted the follow-up to 3 years assuming that most changes in the domains under study will occur during the first 2 years, followed by a more stable situation. This restriction also aims to reduce the attrition rate. Qualitative interviews are conducted at baseline (year 1) and then every year for 3 years. They are conducted at a different time from the quantitative data collection to avoid participant fatigue.
sample size Considering an effect size of 0. 3 for the main health outcome (self-rated health), a type 1 error of 0. 05 and a power of 0. 8, the total sample size is 352, rounded up to Open access 400 to account for the possible dropout of participants over the follow-up. The purposive, qualitative, nested study sampling includes 38 participants selected from the intervention group of undocumented migrants eligible for regularisation. The sample size is estimated to be sufficient to reach data saturation. To ensure the diversity of the sample, participants are recruited taking into account their family characteristics, notably the presence or absence of children, while ensuring the diversity of the sample regarding origin, age and gender. recruitment According to the Geneva authorities, 1000-2000 undocumented migrants could be eligible for the regularisation policy.
Moreover, partner NGOs inform the communities of the importance of follow-up. Contact for the following data collection will be made by phone with up to five recalls and use of email messaging in case of non-response. In order to facilitate participation and retention into the study, the field investigators can move to the preferred participant's location to pass the questionnaire. Participants may withdraw from the study for any reason at any time. In case of withdrawal or loss to follow-up, all data will be included for analysis until the last participant's questionnaire.
Quantitative data collection The questionnaire is administered face to face with participants of both groups at their preferred location in the community. It is available in the four most frequent languages (Spanish, Portuguese, French and English) spoken by undocumented migrants in Geneva. We acknowledge there may be a potential limitation related to the limited language skills of the subsample of participants not completely fluent in one of these four languages. Data are recorded on a mobile tablet using Sphinx mobile software (Le Sphinx, France) and are immediately transferred on a secure server. Interviewers are trained and supervised by a senior staff during their first interviews.
Après avoir présenté quelques principes qui fondent l'approche psychodynamique de l'agitation, l'article revient sur le sentiment de crise que manifestent les professionnels rencontrés et sur les raisons qui peuvent en être à l'origine. Un détour par une analyse réflexive de nos relations d'enquête permet de mieux mettre en lumière les conséquences de ce climat sur le travail quotidien en général et sur les relations avec les partenaires institutionnels en particulier. Mots-clés : Enfance ; Centres de Soin ; Psychanalyse ; Troubles du Comportement ; Ethnographie. conducted observations and collected data in care centers (more than 300 observations of meetings, consultations and therapeutic workshops; creation of a database including more than 800 situations). In the care settings studied, we conducted surveys using interviews, often participating in observations and data collection.
Each subject completed 360 trials (90 trials in 4 sessions). Data analysis. The individual position data time series were digitally low-pass filtered with a bidirectional Butterworth filter (order 4, cut-off frequency of 8 Hz) for further analysis. Hand speed was computed from the filtered positional data by three-point central difference calculation. For each trial, we then computed the time of arm movement onset, the peak velocity, the movement time (elapsed time from the hand movement onset until the hand crossed the target's path), and the temporal error with respect to the target. Movement onset was computed offline by using the A algorithm reported in  23  on the hand speed. This algorithm (a) locates the temporal location at which the velocity is greater than or equal to a tolerance value which is a percentage (10% in our case) of the peak velocity, (b) proceeds iteratively from this location backwards updating the tolerance value (10% of the current velocity sample) until the value of the velocity is less than the tolerance value.
Button press experiment. Participants. Eight participants (age range 23-32 years, 5 males) participated in this experiment. All of them had normal or corrected-to-normal vision, and none had evident motor abnormalities. All subjects were right handed and gave written informed consent. The study was approved by the local research ethics committee of the University of Barcelona (Institutional Review Board IRB 00003099). The experiment was conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) Apparatus. Stimuli were shown on a Philips CRT-22 inch (Brilliance 202P4) monitor at a frame rate of 120 Hz and a resolution of 1024 by 768 pixels. The viewing distance was about 60 cm and the head was free to move. A custom program written in C and based on OpenGL controlled the presentation of the stimuli and registered the time of the button-presses by sampling an ancillary device at 125 Hz.
Apparatus. Stimuli were generated using the Psychophysics Toolbox extensions for Matlab ® 24,25 and displayed on a video monitor (Iiyama HM204DT, 100 Hz, 22″). Participants were seated on an adjustable stool in a darkened, quiet room, facing the center of the computer screen at a viewing distance of 60 cm. To minimize measurement errors, the participant's head movements were restrained using a chin and forehead rest, so that the eyes in primary gaze position were directed toward the center of the screen. Viewing was binocular, but only the right eye position was recorded in both the vertical and horizontal axes. Eye movements were measured continuously with an infra-red video-based eye tracking system (Eyelink II ® , SR Research Ltd. , 2000 Hz) and data were trans- ferred, stored, and analyzed via programs written in Matlab ®.
Background: Historically, governmental hospital organisation consisted in a heterogeneous distribution of staff and a fragmented logistical organisation without cross-functionality or sharing of resources between departments. This organisation could not last in a context of an evolving healthcare environment, changing patient profiles and hospital expenditure constraints. Cost-effective workforce regulation for optimal patient quality of care was urgently needed. The purpose of the study was to describe the reorganization that led to resource management no longer based on what has been achieved but based on a daily measured workload. Methods: This prospective study used nursing intensity indicator, mirroring patient care needs, which was reported daily using VALPAReSO® software. Indirect care activities were recorded in departments of medicine, surgery and obstetrics.
At the same time, the VALPAReSO® software was made available to the care units to enable the daily recording of nursing intensity scores by caregivers. With this software, scoring only requires three minutes per day and per sector. Moreover, to facilitate the logistical reorganization, providers were asked to reflect on organisational factors that needed to be improved. In each department and during dedicated work meetings, healthcare assistants thought together with the manager on reorganization to absorb tasks that were previously performed by cleaners due to task slippage (e. g. patient meal distribution), and which task to delegate to logistics professionals. Work meetings also happened between healthcare managers and managers from the supply centre, the pharmacy, the laundry department, the transport and the catering services.
Analysis The primary outcome was the nursing intensity score, mirroring patient care needs, which was measured daily from July 2011 to December 2015. The average score per month and per department was used. The other main outcome was the time spent on indirect care by nurses and healthcare assistants, measured over 7 days in 2012 and in 2015. Indirect outcomes like patient and health providers satisfaction were also surveyed. Means +/-standard deviations or medians and ranges, counts, percentages and confidence intervals were used to describe continuous and categorical variables, respectively. Normal distributions were verified and T-test or Mann-Whitney tests were used to compare continuous data of independent samples where appropriate. Chi-square or Fisher tests were used for categorical variables.
Therefore, we chose and implemented nursing intensity recording commonly used for statistical purposes, and converted it into a dynamic management tool through the VALPAReSO® software. In parallel, a reinforcement unit was created and was constituted by healthcare assistants and highly qualified nurses. The objective was to continuously adapt human resources to the workload of patient-centred care as measured by the nursing intensity score. The culture of measuring the burden of care has become an integral part of caregiver daily lives. The availability, comparison and presentation of the results to the healthcare managers at department and unit level made cross-functional management possible. The results translated into significant elements for all professionals (intensity of basic, technical and relational care, patient profile, and care workload indicator) were shared and used as decision support tools.
Swabs were incubated at 37°C for 48h on different types of agar. Bacterial identification was conducted by mass spectrometry (MALDI-TOF). Automatic agar reader-incubator assessed antibiotic susceptibility (SirSCAN 2000 automatic  ). The number of CFUs was recorded. Bacteria were classified according to their usual reservoir (skin flora, oropharyngeal flora, digestive flora and environmental flora) and according to their pathogenicity (Table ). As coagulase negative Staphylococcus (CNS) was reported to be present in almost every mobile phone, we focused on pathogens colonization, excluding CNS. Categorical covariates were compared across HCWs and administrative staff using Fisher's exact test for count data or Chi-squared test when deemed appropriate. Association between http://mc. manuscriptcentral.
The Borg Scale is frequently used in post-stroke rehabilitation programs to quantify the PE and intensity of physical activities  [3] [4] [5]. Previous studies have demonstrated the relationship between the PE score as estimated by the Borg Scale and the effort as measured by a heart rate monitor (r=0. 46, p<0. 05)  [6]  or a breathing gas analyzer (r=0. 33, p<0. 001)  [7]. However, despite these tools being recommended by the guidelines, they are not used by many in everyday practice due to their cost, bulky size and unsuitable use for pathology  [8]. Another method used to assess the intensity of physical activity in post-stroke patients is the use of accelerometry. Wearable sensors are reliable and have been validated to monitor physical activity at all stroke phases  [9]. They present two main advantages: the assessment of physical activity intensity is not based on the patient's cognitive skills, and data can be collected over time periods ranging from several minutes to several days.