dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were extracted for all patients who had undergone a penile implantation between 1997 and 2013. Overall, 6982 PP were inserted over the last 17 years in France. We found that 2821 PP were implanted in France between 1997 and 2005 (i. e. mean number of 352. 6 PP per year), and 4161 PP between 2006 and 2013 (i. e. the mean number of 594. 4 PP per year) (Figure  1 ). Although the number of PP placements has increased considerably over this period, PP appears to be still underutilized in France compared with the USA, where they are used to treat ~ 10% of impotent men. 3  In addition, in the future, even more men are likely to develop ED associated with diabetes or other comorbidities, such as metabolic syndrome. Overall, we found that 1182 revisions (16. 9%) and 2264 explanations (32. 4%) of PP occurred over the 17 years period (Table  1 ).
IMAGE ACQUISITION The fMRI data were collected using a 1. 5 T MRI scanner (Magnetom Vision; Siemens Medical Systems, Erlangen, Germany). The functional images were acquired using a gradient-echo planar imaging sequence (TR = 3000 ms; TE = 60 ms; FOV = 240; flip angle = 90 • ; 64 × 64 matrix). Whole brain coverage was obtained with 25 axial slices (thickness = 4 mm; in-plane resolution = 3. 75 mm × 3. 75 mm). High-resolution structural T1-weighted images (TR/TE = 9. 7/4. 0 ms; flip angle = 12 • ; 160 slices; thickness = 1 mm; 256 × 256 matrix; FOV = 256 mm) were obtained during the same session. The presentation of a stimulus was synchronized with the acquisition of an image using a triggering circuitry. The subjects' head movements were restrained with foam padding.
Behavioral data The latency of the correct responses (reaction time) was analyzed using Statistica TM (7). Mean reaction times were determined for 19 of the 22 subjects; behavioral data from three subjects were not recorded due to technical problems. The reaction time analysis was performed with a two-way ANOVA with task ("Picture,""Easy Bar," and "Hard Bar") and valence (neutral and mutilated) as within factors. Statistically significant effects identified through ANOVA were further evaluated post hoc using the Newmann-Keuls method for pairwise comparisons.
fMRI whole-brain analysis The fMRI analysis was performed in BrainVoyager TM QX 2. 2 (Brain Innovation, Maastricht, The Netherlands) using a general linear model (GLM). The dataset was corrected for motion and slice timing, and it was spatially filtered (8 mm FWHM) and temporally filtered (high pass filter at 0. 01 Hz). Individual functional maps were normalized into the Talairach anatomical atlas  (Talairach and Tournoux, 1988). After pre-processing, first-level analysis was performed on each subject using the GLM with a boxcar waveform convolved with a canonical hemodynamic response function. Six regressors of interest were created that correspond to the experimental conditions: Picture NEU, Picture MUT, Easy NEU, Easy MUT, Hard NEU, and Hard MUT. After transformation into Talairach anatomical atlas, random effects group analysis was calculated using an ANOVA in a whole-brain voxelwise approach.
ACKNOWLEDGMENTS The authors would like to express our gratitude to  Prof. Carlos A. E. Montesco  and  Isabela V. P. Lobo  for important discussions. They also thank all the subjects who participated in the study and to  Khallil T. Chaim , and  Milena C. Gravinatti ,  Elcio A. Navas , and  Matheus Guerreiro  for technical support. CAPES, PRONEX-FAPERJ ,  MCT-CNPq, IBN-Net. MJ is funded by the  Agence Nationale de la Recherche Francaise  ( ANR-11-EMCO-01001  and  ANR-11-LABX-0042 ).
Subjects accurately detect changes in the generative process We first asked whether subjects could detect when the characteristics of the sequence changed suddenly. We assessed the accuracy of their detection in comparison to the actual position of jumps with a Receiver Operative Characteristic analysis. Subjects reported more jumps when transition probabilities were indeed changing (hit) than when they were stable (false alarm): the difference of hit minus false alarm rates was 0. 23 (standard error = ± 0. 03; t-test against 0: p<10 -5 ). To show that this difference is positive not because of chance, but instead because the detection is based on the actual evidence provided by the observed sequence, we used a more conservative test. Comparison with surrogate data indicates that the observed difference between the hit and false alarm rates is significantly higher than expected from a random detection process (p<0.
Subjective confidence reflected the Ideal Observer confidence on top of the general U-shape pattern. To quantify this additional effect, we performed a multiple regression of subjective confidence, without binning, including as predictors both the subject's U-shape transformed probability estimates and the optimal confidence. The data revealed that the Ideal Observer confidence indeed captures aspects of subjective confidence (t 17 = 3. 12, p = 0. 006) that are not accounted for solely by a quadratic effect of probability estimates. In our experiment, subjects reported their probability and confidence estimates sequentially. We therefore ran a control experiment to check that the nested relationship between probability estimates and confidence ratings (as shown in Fig  5A ) is a general property of human reasoning which cannot be attributed to sequential reporting.
5) 2. Trials were sorted by subjective probability estimates and, within each bin, into high and low Ideal Observer confidence according to a median split. Equally-filled bins were used for data visualization, not for data analysis. (B) The accuracies of probability estimates and confidence ratings are correlated across subjects. The accuracy of probability estimates was computed per subject as the correlation (across trials) of the subject's and the Ideal Observer's estimates. The same logic was used for confidence. One dot corresponds to one subject. (C) The link between probability estimates and confidence ratings goes beyond any mapping. Within each subject, we computed the correlation across trials between accuracies in probability estimates and confidence ratings. The accuracy of probability estimates was computed at the trial level as the distance between the subject's and the Ideal Observer's estimates.
Questions being asked only occasionally to the subjects, the subjective model revision cannot be estimated from their reports. Instead, we estimated the degree of model revision from the Ideal Observer. This ensures in addition that subjective confidence is regressed against a normative estimate in every subject. We observed the predicted negative correlation between subjective confidence and the amount of revision in the probability estimates relative to the previous observation (t  17   Second, the number of data samples accumulated since the last detected jump should affect the level of confidence: more samples should lead to more precise estimations. We counted the cumulative number of samples between the optimally detected jumps. As predicted, we observed a positive correlation between subjective confidence and the number of samples since the last jump (t 17 = 3.
Task and participants 18 participants (9 females, mean age 23, sem: 0. 74) were recruited by public advertisement. The task was delivered on a laptop using Matlab (Version R2013a) and PsychToolBox (Version 3. 0. 11). The experiment was divided into 4 blocks, each presenting a sequence of 380 stimuli (denoted A and B). On alternated blocks, A and B were either auditory or visual stimuli perceived without ambiguity, see Fig  1  for a description and the timing. A fixation dot separated the visual stimuli and remained present during the auditory blocks. The modality used in the first block was counterbalanced over subjects. The sequence was generated randomly based on predefined transition probabilities between stimuli, e. g. an 80% chance that A is followed by A and a 30% chance that B is followed by A.
Probing subjects more often would have provided more information on their internal estimates; however it would also have disrupted more their effort to integrate serial observations, which is critical to estimate transition probabilities. Asking every 15 stimuli is thus a compromise. The raw data are provided as Supporting Information (S1 Dataset, see S1 Text for a description). 20 participants (12 females, mean age 25, sem: 0. 76) were recruited for the control experiment. The key difference compared to the main task was that subjects were only asked the confidence question. The other task parameters were identical, excepted a minor modification: subjects used a four-step scale instead of a continuous scale to report their confidence level. Subjects first performed one session of the main experiment which served as training.
The sampling approach explicitly computes the likelihood of possible decompositions of the sequence into chunks, whereas the iterative approach computes the likelihood that a jump occurred at any given position, independently from the other potential positions. The sampling approach is computationally slower but it allows a straightforward estimation of jump-related statistics used here: 1) The likelihood that a jump occurred around a given position, e. g. within a window of ±5 stimuli; 2) The variance of the estimated length of the current chunk, which reflects the precision of the knowledge of the observer about the last jump location. The derivation of each algorithm is presented in detail below. Computations were performed numerically in Matlab using regular grids.
The same logic was used for confidence. One dot corresponds to one subject. (C) The link between probability estimates and confidence ratings goes beyond any mapping. Within each subject, we computed the correlation across trials between accuracies in probability estimates and confidence ratings. The accuracy of probability estimates was computed at the trial level as the distance between the subject's and the Ideal Observer's estimates. The same logic was used for confidence. The observed results are contrasted to two ways of shuffling the data (p-values are from onetailed t-test, see Methods).
PARTICIPANTS Participants were 328 preschoolers from 5 private preschools (Mai Ke Rui kindergarten, Havard Star kindergarten, Hai Xia Xin Cheng kindergarten, Liu Cheng Mei Yu kindergarten, and Duolaimi kindergarten in Chengdu, China). Seven of them were excluded from the final data analysis due to either their inability to follow decision rules (n = 3) or a potential confounding effect caused by the presence of their teachers during the experiment (n = 4). The remaining 321 participants were divided into 2 groups: 157 young children (2. 5-3. 5 years of age) and 164 old children (5. 5-6. 5 years of age). Due to the fact that gender difference in distributive behaviors have been observed in young children  (Stewart and McBride-Chang, 2000; Ongley et al. , 2014) , the present study included almost half males (n = 162) and half females (n = 159) (Table  1 ).
The unequal sample size between these two conditions makes it less sensitive to exploring the possible interaction effects between intergroup competition and age. In spite of this, the non-intergroup competition condition can still serve as an important benchmark condition against which participants' behaviors can be compared and therefore, help illustrate effects of intergroup competition on children's prosocial behaviors. Participants under the non-intergroup competition condition were only recruited from C, D, and E preschools. Seven participants were excluded from the final data analysis: In A, two participants were excluded due to the presence of their teachers. In B, one participant did not follow rules during the task, and one participant was excluded because of their teacher's presence.
Acknowledgements This research was supported by the  "Federation de Recherche   3C ",  Aix-Marseille University  and the  EU Euprim-Net- II  project (# RII3-026155 ). This work was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the programs  LICORNES  ( ANR-12-CULT-0002 ) and  ASCE  ( ANR-13-PDOC-0004 ). The staff of the  Rousset-sur-Arc CNRS  primate station is acknowledged for technical support.
Methods The international NPC Registry was initiated in May 2009 as a post-approval commitment to the European Medicines Agency (EMA) following approval of a new indication for miglustat for the treatment of progressive neurological deterioration in adults and children with NP-C  [4]. The Registry is a prospective, observational cohort study conducted in clinical practice settings. All patients with a diagnosis of NP-C are eligible for enrolment, regardless of the treatment they receive. A previous published report from this Registry described general methodological details and patient characteristics at enrolment  [14]. The NPC Registry collects data on demographics, diagnosis, disease characteristics and treatment. Before entering any clinical visit data, written informed consent is obtained from all patients (including children) and/or their legal guardians.
For the current longitudinal study, data were analyzed for patients that: 1) received miglustat therapy continuously between enrolment visit and last follow-up visit (where 'continuously' was defined as at least 90 % of the observation period with no single period without miglustat lasting more than 28 days); and 2) had a minimum of 1 year of observation time in the Registry. Functional disability status is recorded at enrolment and at each follow-up visit in the Registry using an expert-based disease-specific disability scale that assesses four key neurological domains in NP-C (ambulation, manipulation, language and swallowing)  [15]. The scoring system of this scale originally ranged from 0 (normal) to 4 or 5 (worst)  [16] , but has since been modified to provide an equal weighting in each domain, and ranges from 0 (normal) to 1 (worst)  [8, 15].
Statistical analyses are descriptive. All percentages were calculated relative to the number of patients with available data per parameter. Data are presented for all patients and stratified according to internationally agreed age at neurological onset categories (early-infantile onset [<2 years], late-infantile onset [2 -<6 years], juvenile onset [6 -<15 years], and adolescent/adult onset [≥15 years])  [1, 2]. One modification to these internationally agreed neurological onset categories was that in this analysis all patients with onset aged <2 years were included in the early-infantile subgroup due to the low number of cases. The participation rate of miglustattreated patients in Europe was calculated as the number of miglustat-treated patients enrolled in the Registry divided by the estimated number of NP-C patients treated with miglustat in European countries.
Patients As of the data extraction on 19 October 2013, a total of 283 patients from 28 centers in 13 European countries, Canada and Australia were enrolled in the NP-C Registry. The majority of patients were enrolled in Germany, the UK and France. Ninety-two patients received continuous miglustat therapy between enrolment and last follow-up visit, and were observed for at least 1 year (Fig. 1 ). The mean (SD) observation period was 2. 0 (0. 7) years. From this point onwards, all data refer to this continuously treated patient group (N = 92). Patient characteristics at enrollment stratified by age at neurological onset categories are summarized in Table  1. Amongst all patients, the mean (SD) age at neurological onset was 9. 8 (8. 6) years, ranging from <1 year to 44. 6 years, and the mean (SD) age at diagnosis was 13.
Safety observations Safety information recorded during the observation period is summarized in Table  3 ; no unexpected safety findings were identified. Known safety/tolerability factors associated with miglustat, including chronic diarrhea, thrombocytopenia and seizures, were frequently reported in the overall population. Chronic diarrhea (i. e. , diarrhea lasting >3 months) occurred in 11 % of patients during the observation period and in 2 % of patients before treatment. Thrombocytopenia was recorded in a total of 43 (54 %) patients during follow-up and in 18 (20 %) patients before miglustat therapy, and was mild or moderate in almost all (98 %) cases. Seizures were present in a total of 21 (23 %) patients before miglustat therapy, and 13 (33 %) patients with available data had new occurrences of seizures during the observation period.
5 years of miglustat treatment (i. e. , from treatment start), whereas the progression rate calculated for the Registry cohort was based on patients who had already received an average of 1. 9 years of miglustat therapy before the observation period. Hence, despite being further along in their treatment course, a large proportion of the Registry patients still showed no progression of neurological disease. Our analysis of annual disease progression revealed a trend for slower progression in the later neurological onset subgroups and more rapid progression in earlier neurological onset subgroups, which is in line with previous data  [1, 8, 15, 16]. Even though disease progression was fastest among the infantile-onset subgroups in this study, stable/improved neurological disease was observed in 33 % (early infantile) to 50 % (late infantile) of treated patients.
Seizures were present in 23 % of NPC Registry patients before miglustat therapy, and during the observation period, new seizures were reported in 33 % of treated patients. It is well known from the literature that seizures are a common feature of the natural history of NPC, and NPC Registry data on seizures that occurred before miglustat therapy ('pre-miglustat') support this. Effective seizure control using anti-epileptic agents is considered crucial in optimizing patient quality of life  [2, 19]. New occurrences of tremor and neuropathy were relatively infrequent during the observation period. Again, however, it should be borne in mind that 25 % of NPC Registry patients had tremor and 4 % had neuropathy before miglustat initiation. The clinical profile of miglustat-treated patients from the Registry presented in this paper is consistent with data from previous large cohort studies  [1, 11, 12, 20, 21].
The disease characteristics at enrolment of this miglustattreated group were similar to those reported for the whole NPC Registry cohort as of March 2012, where data from both miglustat-treated and untreated patients were pooled  [14]. In addition, the estimated overall participation rate for European countries at the time of data analysis was high (63 %). Given this high rate of patient/physician participation, as well as the wide global coverage of participating centers, the presented miglustat-treated cohort of NPC registry patients can be considered representative of the overall NP-C patient population treated with miglustat, both within the Registry and globally. This analysis comes with some limitations in terms of recruitment and data interpretation. The fact that the majority of patients with a confirmed diagnosis of NP-C are currently being treated with miglustat has the drawback that there are only a few treatment-naïve patients available for comparison.
Acknowledgements The authors thank the following principal investigators who enrolled patients in this international  NP-C Registry: Amado-Fondo A, Amraoui Y, Andria G, Arellano M, Audoin B, Azcona C, Barr C, Baruteau J, Baumgartner C, Bell L, Bembi B, Benneddif K, Bernard G, Bobocea N, Bodzioch M, Boettcher T, Bonnan M, Broue P, Bruni A, Caceres M, Camino R, Campbell E, Cances C, Cannell P, Cesar J, Chabrol B, Chakrapani A, Colao R, Collet A, Corsetti T, Cousins A, Covanis A, Cox T, Cuisset JM, Dardis A, Das A, Deegan P, Dengler T, Deodato F, Derralynn H, Di Donato I, Di Rocco M, Dinopoulos A, DomanskaPakiela, Eckehard S, Engelen M, Eyer D, Fecarotta S, Federico A, Filla A, Fiumara A, Fonseca MJ, Gabrielli O, Garcia T, Garrote J, Gissen P, Giugliani L, Greenberg C, Heron B, Hertzberg C, Higgins F, Hill A, Hiwot T, Hlavata A, Hörbe-Blindt A, Howley E, Hussain N, Illsinger S, Imrie J, Jacklin E, Jones S, Jovanovic A, Kaczmarek V, Kaphan E, Kibaek M, Kleinhans P, Klünemann KH, Koch SM, Koegl-Wallner W, Kolnikova M, Korenke GC, Korinthenberg R, Kumari S, Lachmann R, Lee Ann L, Likopoulou L, Lilienthal E, Link B, Lippold M, Lopez-Laso E, Luecke T, Lundgren J, Mackrell M, Madruga M, Maletta R, Malinova V, Manners J, Marinei R, Marquardt T, Martins E, Martins AM, Martins N, McAlister L, McCabe A, McKie M, McMahon S, Meehan M, Meldgaard Lund A, Mendozah C, Mengel E, Meyer A, Mielke S, Milligan A, Mir P, Moisa M, Mombelli C, Morris L, Müller vom Hagen J, Munoz B, Murphy E, Nagarajan L, Neto PB, Nevsimalova S, Nia S, Nicolai J, Niemann D, Niktari G, O'Callaghan MDM, Paucar-Arce M, Peers K, Peintinger L, Peralta M, Pérez J, Perez-Poyato M, Petrariu A, Pineda M, Puschmann A, Raiman J, Rask O, Rataj J, Raymond-Gaynor C, Reichelt G, Ribeiro E, Riches V, Roberts A, Roelants J, Rohrbach M, Rokicki D, Rolfs A, Russo C, Rutsch F, Saleem R, Santos M, Schmutz P, Schwahn B, Sedel F, Semotok J, Sharma R, Silska S, Silva A, Simmons L, Sivera R, Skorpen J, Sole G, Souza C, Stadlober-Degwerth M, Starling J, Temudo T, Tomas M, Tranchant C, Uziel G, Valayannopoulous V, Van den Hout H, Van der Tol L, Van Spronsen F, Vellodi A, Verdu A, Vilchez JJ, Vinaixa A, Visser G, Voelker J, Waldek S, Walter A, Walterfang M, Wein U, Widner H, Wilcke C, Wildish L, Wraith E, Wright N, Xaidara A, Yamamoto M, Zallocco F, Zielke S.
Such contexts are rare, as it is actually the nature of PAs that encourage and foster researcher presence, providing infrastructure, safety, and often history of known wildlife populations  (Sinclair et al. 2007 ). We measured the spatiotemporal distribution of snare and mammal encounters as a function of proximity to the researcher base station and overall search effort in the Issa Valley, Ugalla, western Tanzania. Data collection began late in the first year of the establishment of the Ugalla Primate Project -a continuous, ongoing study of woodland primates and medium-large mammals. Our study differs in three key ways from the aforementioned studies at Tai and Gombe. First, the Issa Valley lies in Open Area, belonging to Tanzania's central government, with no formal protective status. It is >30km from the nearest protected area (a forest reserve, also with no formal government surveillance).
Line transects: Data for both mammal and threat distribution and density come from line transects and reconnaissance (recce) walks. We established seven line transects in  Fall 2008, totaling 39. 8km (range: 4. 8-6. 1km ). From January 2009-March 2010 we walked each transect bi-weekly, at ~1km/hour, whilst from April 2010-December 2012, we walked these same transects once monthly. Researcher teams were always comprised of two experienced field assistants or researchers, who each looked for all direct or indirect (faecal, print, nest, feeding remains) evidence of mammal presence as well as for snares. We recorded perpendicular distance from the animal or object to the transect line using a measuring tape, as well as documenting vegetation type (woodland, open gallery forest, closed gallery forest, swamp), topography (valley, slope, plateau), and age (1-fresh, 2-recent, 3-old) of object.