dataesr/scientific-paragraphs-categorization huggingface dataset:
For the routine clinical examination of patients, two other sensors were also placed in the back at L5 level and on the front of the head. These data, although of use for the description of the clinical syndromes we quantified, will not be considered further as this manuscript focuses on step detection. The signals obtained with both sensors were automatically synchronized by the acquisition software. All subjects were asked to: • stand quiet for 6 s • walk 10 m at preferred walking speed on a level surface • make a U turn • walk back • stand quiet 2 s For practical reasons, patients kept their own shoes. Average speeds ranged from 1 m•s -1 to 1. 8 m•s -1. The database is composed of 230 subjects who performed the protocol between 1 and 10 times, which totals 1020 recordings. The subjects' characteristics are presented in Table  1.
Healthy subjects had no known medical impairment. The orthopedic group is composed of 2 cohorts of distinct pathologies: lower limb osteoarthrosis and cruciate ligament injury. The neurological group is composed of 4 cohorts: hemispheric stroke, Parkinson's disease, toxic peripheral neuropathy and radiation induced leukoencephalopathy. The protocol includes 2 sensors (left and right foot), and each of them records a 9-dimensional signal (3D accelerations, 3D angular velocities, 3D magnetic fields), possibly with some re-calibrated data provided by the XSens TM software (such as the vertical acceleration in the direction of the gravity). Instead of considering all these dimensions, we decided to only use a subset of them, and select the most relevant in the context of step detection. This decision has been made based on observations of real data and physiological reasons provided by medical doctors (see Acknowledgments and  [2, 39, 40] ).
In that context, the evaluation of the method and the determination of the library of templates was therefore performed thanks to manual annotations provided by experts that were trained to deal with physiological signals. These experts have been trained on two gait validation tools (instrumented mat: GAITRite from CIR Systems and motion capture: CodaMotion) in a preliminary study, so as to learn how to annotate the signals. This learning phase was performed on five healthy subjects in laboratory conditions and the manual annotation error was calculated as 2 samples (std : 1. 0) with CodaMotion and 1. 7 samples (std : 1. 2) with the GAITRite. All steps were manually annotated by specialists using a software allowing to point with the mouse the starts (foot-flat) and the ends (heel-off) of the foot flat periods during which the sensor is not moving.
The annotations were performed thanks to the Z-axis acceleration (normal to the upper foot surface) which is the most sensitive direction to detect the movements of the foot with respect to the floor. For the tricky cases of pathological gaits, a first gross annotation was made and then refined by zooming in on each step. The uncertainty of this annotation is evaluated to less than 0. 2 s (20 samples) for each mouse click. In total, the database is composed of 40,465 steps (20,240 extracted on the right foot and 20,225 on the left foot). Even though they had a distinct shape, the U-turn steps were also taken into account. In the database, 2671 steps are considered as belonging to the U-turn. Examples of these 3 components (Z-axis acceleration, vertical acceleration and Y-axis angular velocity) recorded at the right foot are presented on Figure  2a ,b for respectively an healthy and hip-injured patient.
We therefore added a post-processing step, which, once a step is detected, compares the standard deviation of a neighborhood around the detected peak to a noise level. The size of the neighborhood, as well as the noise level, are optimized by grid search so as to give the best performances. In Ying et al. [21] , the parameters used are f min = 0 Hz, f max = 20 Hz, N inte = 0. 1 s. The peak search procedure is performed sequentially: they select one peak every other peak, starting with the second one. With these parameters, we obtain from our database a recall of 99. 53% and a precision of 51. 20%. In fact, the peak-search procedure is not adapted and tends to detect several peaks within a step except for one. This phenomenon has already been described by several authors  [29, 30]. In order to objectively compare our method to the Pan-Tompkins, we tested several values for f min , f max and N inte , as well as a more relevant peak-search procedure, which only selects the local maxima among the detected peaks, thus preventing multiple detections.
7, ∆End : -1. 0, ∆Duration : -0. 9). This tends to prove that the library is able to accurately detect the step boundaries and to adapt to various step durations. For 90% of the correctly detected steps, the errors for start and end times are lower than 0. 15 s (in absolute value) and 0. 21 s (in absolute value) for duration. These results are satisfactory when compared to the annotations uncertainties of experts and specialists, which are around 20 samples (0. 2 s-see Section 2. 2). Outliers are in fact due to two specificities of the database: tiny steps (under 50 samples) mainly located during U-turn (causing underestimation for start times and overestimation of end and duration times), and highly pathological steps for stroke subjects whose duration exceeds one second (causing upper outliers for start times and lower outliers on end and duration times).
For the pathological subject who suffers from osteoarthritis, the shapes of the initiation and U-turn steps differ from the standard shape of the steps. For these reasons, the algorithm still detects the first step but is less precise for the start/end times. The U-turn step is not detected by the algorithm as its shape is unusual and its amplitude is too low. In fact, it is likely that the patient (who suffered from severe gait troubles and obesity), dragged his foot during the U-turn, and did not perform a proper step. In particular, his foot did not leave the ground, thus making it very difficult to detect this phenomenon with accelerometers. Thankfully these cases remained rare in the database, and U-turn steps still had an 83% average recall. Nevertheless, future works will investigate these situations by conceiving dedicated templates.
Conclusions We described in this article a template-based method for step detection. This method, based on a greedy algorithm and a library of annotated step templates, achieves good and robust performances even with a small number of templates. When used on a large database composed of healthy and pathological subjects walking at different speeds, the method obtains a 98% recall and 98% precision. Moreover, the algorithm allows one to detect the start and end times of each step with an acceptable precision, even on pathological subjects. Thanks to its robustness and low computational cost, this method could be extended to process signals acquired in free-living conditions. Indeed, the actual protocol is composed of a no activity period and a U-turn, and there are no obstacles for testing the algorithm on unconstrained walking.
Acknowledgments: The authors would like to thank  Juan Mantilla ,  Alain Yelnik ,  Catherine De Waele ,  Danping Wang ,  Flavien Quijoux ,  Albane Moreau ,  Emna Jelili ,  Clément Provost ,  Thomas Grégory ,  Stéphane Buffat  and  Christophe Labourdette  for the useful discussions, their help in the data acquisition and clinical annotation.
METHODS The panel of experts organised by EPA/UNEPSA, who are the co-authors of the present review, met in person in June 2017. The aim of that meeting was to define the clinical questions of special relevance for the use of probiotics in paediatric health care, to propose the scope of the paper on the review and the outline what would be included, to discuss the research methods and to set the time limits. The document was further developed and discussed by email from June to October 2017, and the final recommendations for clinical practice, called practice points, were agreed on by all the panel members and finally approved during a teleconference in November 2017. We searched the PubMed and Cochrane Library databases up to September 2017 for any relevant guidelines, recommendations and position papers covering the paediatric clinical indications that were selected and retrieved the most recent high-quality evidence.
Such studies can be proposed by companies, and some have been agreed as part of PIPs (e. g. lenvatinib in an osteosarcoma phase II trial, NCT02432274), while others are executed without being part of a PIP (e. g. ruxolitinib phase III trial in GVHD, NCT02913261). The European Paediatric Medicine Regulation [(EC)-No1901/2006)] has dramatically improved the European regulatory environment for the development of paediatric medicines in the EU and has had an international impact  [27].
'Unfeasible' adolescent-specific trials A phase I trial of vemurafenib was opened internationally in January 2013 only for 12-to 17-year olds with stage IIIC and IV melanoma and B-RAF V600 mutations (NCT01519323), despite this disease being extremely rare in adolescents  [30] , while an adult study started recruitment from 16 years old upwards in March 2011 (NCT01307397). Confounding the situation was off label use of this drug in adolescents with no data formally collected. Only 6 patients were recruited to NCT01519323 despite it being open in 10 countries. Recently, adult studies have proven that the combination of vemurafenib with an MEK inhibitor provides a better therapeutic option than single agent vemurafenib  [31]. Similarly, the adolescent melanoma phase I/II ipilimumab trial was closed prematurely because of low accrual (NCT01696045), at a time when the melanoma standard-of-care had changed with the emergence of the anti-PD1 antibodies, and the PIP covering the paediatric melanoma development required recruitment of adolescents into ongoing adult trials.
Trials enrolling adolescents should always be conducted in an age-appropriate setting with clinical care provided by expert paediatric or AYA oncologists, to ensure best safety, care and compliance. This could be facilitated by having coprincipal investigators, with separate responsibilities for adults and adolescents. 4. Adolescents should be included in paediatric phase I, II and III trials where relevant (e. g. adolescents with paediatric cancers type or biological targets). 5. Young adult with paediatric cancer types should be offered to participate in paediatric phase II/III trials. 6. This approach should yield adequate data to support an adolescent indication at the time of the initial marketing authorisation application for a given anticancer drug, particularly where the disease crosses the age spectrum and has similar biological and clinical behaviour, or when diseases are histologically different but have similar targets present across the age spectrum.
Key success factors for the implementation of a joint adolescent-adult clinical trial platform Firstly, the early opening of adolescent slots in adult trials, i. e. before the adult recruitment is well advanced (e. g. denosumab in giant cell tumours, NCT00680992)  [15]  is crucial. The timing should be balanced between opening to adolescents once there is some adult safety and PK data, but not so late that few can realistically participate. This time point could be defined on a studyby-study basis (e. g. once safety data from the first 1-2 adult cohorts have been assessed; or at the point of adult expansion cohorts opening). This will avoid that adolescents being exposed to potentially sub-therapeutic doses, and maximise the chance for individual benefit, complying with the requirement that paediatric/adolescent patients participating in research have the prospect of direct benefit which justifies the risk of participation.
Industry corner Annals of Oncology the dose-finding data obtained from adults. Drug concentrations in adolescents can be determined during the trial and PK can be confirmed in late-phase adult trials. The number of adolescents to be recruited should be included within the upfront overall enrolment calculation, and the prespecified primary analyses designed to include the adolescent data. We propose not to pre-define a minimum number of adolescents to be recruited, in order to avoid delays in completion, reporting and publication of the trial and authorisation process. However, it is crucial that this upfront agreement is not merely a token gesture. Involvement of experts in both adult and paediatric oncology early drug development will help design the most appropriate studies to ensure that adolescent inclusion does not delay the study execution.
A consensus exists and progress is ongoing There are many benefits of including adolescents in adult phase I/II trials once safety and PK data are available from adult patients but without the need for prior paediatric data. Firstly, it accelerates access to new drugs for adolescents and potentially provides data to inform drug development in both children and adults, e. g. figitumumab in Ewing's sarcoma  [16] ; crizotinib in ALK/ MET aberrant tumours, NCT01524926. Where early signs of activity are seen in a paediatric disease present in adolescents, the opportunity exists for adolescent data to inform broader paediatric drug development. The development of drugs in adults might also be positively influenced by recruiting adolescents whose tumours harbour an oncogenic driver or drug target which might achieve proof-of-concept/proof-of-principle for drugs with a new MoA.
Funding This publication has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under the project  ENCCA  ( European Network for Cancer research in Children and Adolescents)  ( HEALTH-F2-2011-2614 ). LVM is funded by the  Oak Foundation  ( OCay-04-169 ). JS was supported by project no. LO1605 ,  1413  from the  National Program of Sustainability II  ( MEYS CR ) and by project  FNUSA-ICRC  no. CZ. 1. 05/1. 1. 00/02. 0123 ( OP VaVpI ) and  Czech Republic Ministry of Health  grant  16-33209A  and  16-34083A. JYB: supports for  NetSARC/RREPS/InterSARC (INCA) ,  LYRIC (INCA-DGOS 4664) ,  EuroSARC  ( FP7 278742 ).
Mobile mental health outreach teams recruited SZ/BD homeless patients in the street, emergency shelters, hospitals, and jails from August 2011 to April 2014. Evaluations were performed during face-to-face interviews by psychiatrists and research assistants in the offices of mobile mental health outreach teams that were located in the downtown area of each city. The quantitative data were analyzed using descriptive analyses, chi-square and Student's t-tests, generalized estimating equation regression models, and path analysis. Results: A total of 703 patients were included in this study. The proportion of women in the SZ/BD homeless population was 123/703 (17. 5%). In this population, women were more likely than men to declare that they were subjected to physical or sexual assault during the past 6 months, that they had been diagnosed with posttraumatic stress disorder (PTSD), and that they had experienced more severe depressive symptoms.
Data collection The following data were collected: • Sociodemographic information. • History of homelessness: Lifetime duration of homelessness, age at first homeless experience, location (street, emergency shelter, specific accommodation for homeless persons), and number of nights spent homeless over the past 6 months using a retrospective calendar. The location and the number of nights spent homeless over the past 6 months were recoded into the categories of the European Typology of Homelessness and Housing Exclusion, which describe all living situations, from sleeping in public spaces to living in extremely over-crowded spaces. 31  Clinical characteristics: The diagnosis of SZ and BD must have been based on the DSM-IV-TR criteria; psychiatric comorbidities, including current suicide risk and posttraumatic stress disorder (PTSD), were assessed using a structured diagnostic interview, the Mini-International Neuropsychiatric Interview (MINI);  26  perceived mental health was assessed using the Modified Colorado Symptom Index (MCSI).
Model fit was evaluated using the chi-square test of model fit, the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the weighted root mean square residual (WRMR). 38 Statistical significance was defined as p,0. 05 in two-sided tests. The statistical analyses were performed using the SPSS version 20. 0 software package and the R MCMC Generalised Linear Mixed Models package, and the path analysis was performed with Mplus version 6 software.
limitations The present study was based on retrospective data. No causal inference can be formally proposed, and our model should be interpreted from an associational point of view. Moreover, women with dependent/young children were excluded from the present study, which may limit the representativeness of the sample and may underestimate the proportion of women in the homeless population. Data were mostly selfdeclared and may have been underreported by participants because of having been humiliated as a victim. Future work should explore hetero-assessment, including interviewerassessed scales such as the Multnomah Community ability scale. Finally, although our study accounts for a large set of potentially relevant variables, other important data should be collected in future studies (eg, history of childhood).
Acknowledgments We would like to thank the following people for their help:  A Albisson ,  P Allenou ,  K Baumstark ,  E Beetlestone ,  J Billard ,  E Bourlier ,  JM Colucci ,  T Danel ,  J Debieve ,  A Djena ,  J Dupont ,  P Estecahandy ,  D Flahaut ,  C Fortanier ,  G Guetière ,  P Hérédia-Rodier ,  E Jouet ,  E Labeyrie ,  M Lefèbvre ,  JM Legagneux ,  A Loundou ,  F Olive ,  C Psarra ,  E Requier ,  D Sauze ,  E Simonnet ,  G Suderie ,  A Troisoeufs ,  PP Vandini ,  B Vincent , and  R Wulfman. Finally, we would like to thank the  Canadian At Home/ Chez Soi  team for help and advice in developing the  French Housing First program. This work was supported by institutional grants from the 2011  Programme Hospitalier de Recherche Clinique National  and the  French Ministry of Health  ( Direction Générale de la Santé ), as well as grants from  Janssen Pharmaceutical Companies  and the  Fondation de France  ( PHRC-12-024-0278 , Afssaps  B110684-50 ,  Comité de Protection des Personnes   2011-A00668-33 ).
D estructive leadership and employee silence have attracted increasing attention in the academy of organisational behaviour and human resource management. However, the research on the mediating mechanism and boundary variables of their relationship has received little attention. The main purpose of our research was to explore the underlying influence of negative leadership (specifically, destructive leadership) on employee silence by developing a moderated mediation model. Drawing from conservation of resources theory, role theory, and the job characteristic model, the new theoretical model concentrates on the role of stress with three dimensions as mediators and the job complexity as a moderator. Using 318 samples collected from multiple companies in southeast China, the model was tested through confirmatory factor analysis, correlation analysis, and the PROCESS program in AMOS and SPSS environments.
Sample and Procedure The data were collected in Shanghai Municipality and Hangzhou City in China from October to December 2016. With the assistance of the management committees of two industries, we randomly selected 12 manufacturing firms to conduct a questionnaire survey. Our team contacted the human resources management department of each company, and received their support. With the help of the HR managers, we selected permanent staff under a direct leader as the research target. In order to avoid common method variance, we paired the sample by randomly selecting 3-6 leaders with 3-8 of the direct subordinates in each firm. The leader filled out the questionnaire first and marked the employees' number, and then our research members found the corresponding employees to complete their questionnaires.
Conclusion As a hot issue in the field of negative organisational behaviour, destructive leadership has been given much attention by many scholars and entrepreneurs. The article was inspired by the present literature and the circumstances of China. Combining the COR theory, role theory and job characteristics model, our research adopted both qualitative and quantitative methods to discuss the influence results and role paths of destructive leadership in the Chinese cultural background and constructed a theoretical model of the impact of destructive leadership on employee silence. After collecting and analysing the data to validate the model, we obtained the following conclusions. (1) Disruptive leadership has a significant positive impact on employee silence; the more destructive the leadership, the more the employee silence in the organisation.
Signals were recorded with a low-noise multichannel DAM-80A amplifier (WPI, UK; low-pass filter 1 Hz; high-pass filter 3 kHz; gain x1000) and digitized online with a Digidata 1400A digitizer (Molecular Devices, CA, USA). Analysis of synaptic activities was done using Clampfit 10. 4 software (Molecular Devices, CA, USA). The spike detection threshold was defined as three times the standard deviation of the noise recorded in the bath solution. Spike frequency was calculated for control, isoguvacine and wash-out periods. Wash-out spike frequency that did not come back to control levels (±10% of control) was a criterion of exclusion of slices.
Whole-Cell Recordings Whole-cell recordings of CA3 pyramidal neurons were performed using a MultiClamp 700B amplifier (Molecular Devices, CA, USA). Data were low-pass filtered at 2. 4 kHz and acquired using a Digidata 1550A (Molecular Devices, CA, USA). Patch pipette solution contained (in mM): 130 K-gluconate, 10 Nagluconate, 7 NaCl, 4 Mg-ATP, 10 HEPES, 4 phosphocreatine, 0. 3 Na-GTP, pH 7. 3 with KOH. Spontaneous GABAergic postsynaptic currents (sIPSCs) were recorded for 15 min at the reversal potential for glutamatergic currents (+5 mV), and spontaneous glutamatergic postsynaptic currents (sEPSCs) were recorded for 10 min at the reversal potential for GABAergic currents (-70 mV). sIPSCs and sEPSCs were analyzed using Clampfit 10. 4 (Molecular Devices, CA, USA) and Mini Analysis 6. 0. 7 (Synaptosoft Inc, NJ, USA) softwares.
Ultrasonic Vocalizations Isolation-induced ultrasonic vocalizations (USVs) were recorded in pups of both sexes at P9 to test early communication behaviors as described previously  (Tyzio et al. 2014). In brief, after 30 min of habituation to the testing room, neonatal mice were individually isolated from their mother and placed in an isolation box (23 × 28 × 18 cm) located inside an anechoic box (54 × 57 × 41 cm; Couldbourn instruments, PA, USA) for a 3 min test. An ultrasound microphone (Avisoft UltraSoundGate condenser microphone capsule CM16/CMPA, Avisoft bioacoustics, Germany) sensitive to frequencies of 10-250 kHz was located in the roof of the isolation box. Recordings were done using Avisoft recorder software (version 4. 2) with a sampling rate of 250 kHz in 16 bit format. Data were transferred to SASLab Pro software (version 5.
Grooming Young-adult male mice 8-9 weeks old were used for the evaluation of grooming behavior. After 30 min of habituation to the testing room, mice were individually placed in a plexiglass cylinder (15 × 45 cm; Form X. L. , France) and video-taped by a camera placed on a bar above the apparatus for 20 min. The first 10 min of the test were used to habituate the mouse to the cylinder and the last 10 min for the actual test. Recordings were done with the Ethovison software (Noldus, the Netherlands) and analyzed manually for the time spent grooming, the number of grooming events and the latency to start grooming. Grooming behaviors were defined as described previously by  Pobbe et al. (2012).