dataesr/scientific-paragraphs-categorization huggingface dataset:
Esketamine treatment Of the 49 patients with a completed treatment initiation form and available data, treatment was initiated in 27 fully hospitalised patients (55. 1%) and in 22 partially hospitalised (one-day admission for treatment administration purposes) ambulatory patients (44. 9%). Patients were monitored for a mean (SD) duration of 123. 3 (25. 4) minutes post-administration, mainly by nurses. The starting dose of esketamine was 28 mg in patients aged >65 years (n ¼ 13), and 56 mg in patients aged 18-65 years (n ¼ 36). The dose of esketamine was increased from 56 mg to 84 mg in 24 (48. 0%) patients at a median of 16. 0 (range, 8. 0-108. 0) days after initiation. The median treatment exposure time to esketamine was 30. 0 (range, 10. 0-148. 0) days in 47 patients with available cumulative data collected during the ATUc period from 23 September 2019 to 25 March 2020.
Clinical benefit For 46 out of the 71 patients included in the ATUc, the treating psychiatrists spontaneously reported at least two MADRS total scores, at esketamine initiation and during the induction period. Of these 46 patients, 25 patients had a MADRS total score reported at the b None of these 25 patients had suicidal behaviour in the last 3 months. c Among the 71 patients included in the ATUc, 50 were treated by esketamine and had at least one follow-up form. end of the induction period (Day 28), with the mean MADRS total score decreasing by 36. 8% from a baseline (Day 0) score of 30. 9 (n ¼ 46) to 19. 5 (n ¼ 25) at Day 28. Changes from baseline in MADRS total score during the induction period are shown in Figure  3. As reporting of the MADRS total scores was not mandatory, the number of patients for whom MADRS total scores were reported by prescribing psychiatrists gradually decreased towards the end of the 4week data collection period.
Discussion The first real-world data for use of the esketamine nasal spray in patients with TRD, who had no suitable alternative treatment options, were collected in a French early access programme. All 66 patients who were granted access to and treated within the programme were experiencing TRD, defined as failure to respond or no remission after two or more pharmacological treatments of two different classes of ADs during the current depressive episode, and who were unable to receive ECT due to a contraindication, no access, resistance or patient refusal. The treatment access criteria were stringent, recruiting patients only if they were diagnosed as having TRD, per the previously mentioned definition, and were lacking a therapeutic alternative. The criteria for 'no access' were similar to the exclusion criteria of the Phase 3 clinical trials  (Fedgchin et al.
2019; Popova et al. 2019; Ochs-Ross et al. 2020). At the time of treatment request, these patients were experiencing a high disease burden and had received a mean of 3. 6 adequately conducted AD treatment lines prior to esketamine initiation; 81. 8% of patients were being prescribed at least one antipsychotic as part of a potentiation regime; and 31. 8% and 42. 4% of patients were being prescribed non-pharmacological treatments, rTMS and ECT, respectively, of which 39. 0% were having self-reported resistance to ECT (Stage V of the Thase and Rush staging model) (Ruh e et al. 2012). There is a lack of accessibility to the rTMS and ECT treatments in France for a number of reasons, including no recommendations issued by the French National Authority for Health for rTMS, no reimbursement by the public healthcare services, a lack of French cost-utility data for rTMS, and insufficient care for patients due to various issues concerning the implementation and management of psychiatric neuromodulation units in France  (Etcheverrigaray et al.
8% (84 mg); 52. 5% (56 or 84 mg); and 17. 5% (28, 56 or 84 mg) in the esketamine plus oral AD arm of the TRANSFORM-1, -2 and -3 short-term trials, respectively  (Fedgchin et al. 2019; Popova et al. 2019; Ochs-Ross et al. 2020 ). However, there is a need to gather further real-world evidence in the post-ATUc period to confirm these trends and to complement the clinical trial findings. The safety data collected in this ATUc are noteworthy, as the cohort included patients with the most difficult-to-treat depression seeking treatment in real-world practices. In the open-label, long-term safety trial, SUSTAIN-2, most treatment-emergent AEs were mild or moderate in intensity  (Wajs et al. 2020). The treatment-emergent AEs in this ATUc were also mild to moderate, observed after dosing and generally resolved on the same day.
Conclusion The population of patients with TRD assessed in this ATUc was generally similar to that recruited in the previous European and French observational cohort studies. The clinical characteristics observed in patients at the time of treatment demonstrate a high level of resistance to conventional treatments. The results from this ATUc provide the first real-world data for esketamine treatment in patients with  TRD who had no suitable alternative therapeutic options and suggest that the safety and effectiveness profiles of esketamine in clinical practice are consistent with those seen in Phase 3 clinical trials. No new safety signals were identified. By granting this first early access programme, the French health authorities recognised the high unmet medical need in this population and further long-term follow-up would be of great interest.
Figure2. Flow chart of patients granted access into the ATUc. ATUc, Temporary Authorisation for Use cohort. a Rejected owing to treatment access criteria not met: 2 patients presented with bipolar disorder, 1 presented with psychotic disorder and 1 had a history of aneurysm. b Seven requests were still under investigation at the ATUc end date. c Five patients were included in the ATUc but were not treated, as the esketamine nasal spray pharmacy order was not received. d Esketamine nasal spray was ordered by the pharmacist, but no data were reported by the psychiatrist about treatment administration in these patients; however, treatment administration was confirmed through medical contact. e One patient had several follow-up forms but had no treatment initiation form. f For 1 patient, Janssen-Cilag was informed of treatment discontinuation through a pharmacovigilance event report; the date of discontinuation is unknown, and no endof-treatment form was completed by the physician.
Early recognition of untrimmed handwritten gestures is the task of recognizing as soon as possible gestures drawn in a continuous stream, one after another. This is particularly challenging for multi-touch gestures because it is impossible to know when the gesture has started and finished. For mono-stroke gestures, in an application context where the finger is never removed from the device between gestures, the recognition is even more complex. In this work we present an extension of the Online Long-Term Convolutional 3D (OLT-C3D) network to address the task of early recognition of untrimmed gestures which have been addressed by very few works. To evaluate our approach, we created two synthetic datasets using freely available benchmarks, MTGSetB and ILGDB, simulating the streaming data in two different application scenarios.
We extend it to address the untrimmed context by regularizing the network with the CTC (Connectionist Temporal Classification) loss  [8]  which improves the system stability and robustness. To handle this task we defined a new evaluation protocol. We created two artificial datasets to simulate gestures stream, and we propose a new evaluation metric. The contributions of this paper are summarized as follows: stability over time we propose to regularize the network with the CTC loss. • We propose two speed-independent gesture representation strategies for multi-touch and mono-stroke gestures. • We built two new challenging datasets of gesture sequences generated to evaluate our system on applicative scenarios and we propose a new metric to specifically evaluate early recognition of untrimmed gestures.
B. Synthetic Datasets Generation To evaluate our approach on the task we generated two datasets from ILGDB  [19]  and MTGSetB  [20]. ILGDB is a mono-stroke gesture dataset containing 21 gesture classes performed by 38 users. These 21 classes are divided into 7 groups of 3 classes, where these 3 classes shared a common begin, making nearly impossible early detection before the bifurcation of these 3 gestures. We generate sequences with between 4 and 8 randomly selected gestures per sequence. The sequence is generated in order that the last point of a gesture is the same point as the first point of the following gesture. Following the original train/test split, 119 sequences are used for training and 210 are used for testing. This dataset is particularly challenging because sequence does not have any breaks so it's very hard to determine the starting and the ending of gestures.
Moreover it has few training examples. To tackle the few amounts of data, we generated an augmented dataset using size scaling (5 different scales) and using the same gesture into multiple sequences (each gesture put into 5 sequences). At the end, each training gesture is used 25 times in the sequences, leading to 2621 training sequences. An example of a generated sequence is given in figure  1. MTGSetB is a multi-touch gesture dataset containing 31 different classes made by 33 users. We built sequences of 4-8 random gestures in order to being unable to differentiate an inter-stroke blank and an inter-gesture blank, each gesture is re-centered according to the previous gesture into the sequence, making impossible a spatial segmentation between gestures. According to the original user-separated train/test split, it leads to 607 gestures sequences for training, and 672 for testing.
1) Trimmed Early Recognition Results: To compare our approach with previous works, we evaluated our approach in a trimmed context. In this context, only the first detection is used because an application would know that only one gesture is being drawn. The comparison with the MTGSetB dataset is shown in table I. Detections happen a little later than  [7]  but it is well balanced with a significant improvement of the TAR (True Acceptance Rate) and the FAR (False Acceptance Rate) values. Also, almost no gesture is rejected (RR: Reject Rate). This shows particularly the good impact of the CTC regularization, even in trimmed context. The figure  5a  shows the behaviour of the system according to the normalized gesture completion. 50 % of the gestures were detected before 27 % of their completion.
Among them, 95. 4 % were correctly classified. As shown in figure  5b , the earliness can vary drastically between gesture categories, from near 100 % for A 02 to less than 15 % for B 04. This depends on the length of the common begins between gestures. Regarding accuracy without earliness, taking the prediction at the last frame for each gesture, we obtain a score of 97. 33 % versus 94. 45 % for  [7]. 2) Untrimmed Early Recognition Results: We evaluated our approach in the untrimmed context on the two datasets previously described with the BOD Metric. The results are shown in table II for MTGSetB and ILGDB. The method has been evaluated with different metric parameters combination. For MTGSetB we obtained a FMeasure from 83. 6 % to 76. 1 % depending on the ∆ value, without the possibility to correct the error, and from 88.
2 % to 86. 8 % if the model can correct itself. These results show that the network is able to predict well until the end of the gesture for this dataset. For ILGDB, the results vary much more depending on ∆, this means that the network is not able to identify correctly when the gesture finishes, this is consistent with the difficulty of this dataset because the gestures are totally chained, and the transitions between gestures are not easily identifiable. 3) Impact of CTC Regularization: To show the importance of the CTC regularization we evaluated the system with and without this regularization, the results are shown in table III. On both datasets, the CTC regularization has a significative impact on the results, especially on ILGDB. Because of the CTC loss which encourages the predictions to be consistent between frames, the precision and the earliness is particularly impacted: +1.
4) Qualitative Results: Figure  7  (top) shows an example of a sequence of MTGSetB with the predictions and detections of the network. For the first gesture, we see that the network rejects predictions (i. e. , confidence below 0. 5) until the beginning of the second stroke of the "X" gesture to be sure not to confuse with the "W" gesture which is also contained in the dataset. For the next two gestures, it also waits until the common parts with other gestures are passed. We observe a similar behaviour on ILGDB (figure  7 , bottom), but due to the difficulty of this dataset, it has difficulties to keep a consistent confidence score, which can produce false positive and missed detection.
V. CONCLUSION We presented an approach to address the challenging task of early recognition of untrimmed gestures. First, new representation strategies are designed to consider sequences of multistroke or mono-stroke gestures. We proposed to regularize the spatio-temporal CNN using the CTC loss to bring predictions stability. Moreover, we propose a new evaluation protocol with a new metric, and two artificial datasets. Our method obtained superior results when compared with other approaches in trimmed context. Good results are obtained in untrimmed context, which will be a strong baseline for future works. Applying the CTC loss can be an open door to weaklysupervised training, using only the sequence-level annotation, this will be explored in future works. We will also address the task of early action recognition of a full 3D human body.
Study participants This study used data from the Bt20+ cohort, a population-based longitudinal study that followed-up children from birth to adulthood  [33]. The initial sample included 3,273 mothers and their singleton children born during a 7-week period in 1990 in Soweto (a historically informal settlement in Johannesburg), South Africa. Mothers were recruited from public antenatal clinics in the area in late 1989, when researchers began interviewing women who were predicted to deliver their babies during the study's enrolment period. The aim of the Bt20+ cohort was to describe the effects of rapid urbanization on the physical and psychosocial development of children during a period of dramatic political and social change in South Africa, when violence and social disorder peaked. The study is still ongoing, and the last data collection was performed in 2018 when participants were 28 years of age.
In over 22 waves of data collection, the investigators assessed social and economic circumstances, family relationships, children's growth and health, schooling and employment, and mental health (S1 Fig). To overcome potential language problems, data collection was performed in the participants' language of choice (isiZulu, Sesotho, or English); consensual agreement on the phrasing of questions in the different languages was reached for each item when instrument validation was performed  [34]. For this study, we analyzed data from a sample of 2,020 participants with at least 1 measure of suicidal ideation at ages 14, 17, 22, or 28 years (of the initial 3,273 participants data on suicidal ideation were not available for 1,253 participants). This analytical sample differed from the original cohort on a number of variables, including maternal age and schooling, household crowding, and assets (S1 Table ).
This approach to the multivariable modeling was implemented following the comment of a reviewer. Initially, only variables that showed evidence of an association (P < 0. 05) in the univariable analysis were used in the multivariable model. The 2 methods led to consistent results. To account for missing data in childhood factors, we estimated our model using multiple imputations: 50 imputed datasets were generated using the Amelia II package in R, which relies on a bootstrap expectation-maximization algorithm to impute missing multivariate data  [47]. Models were then estimated across all imputed datasets and results pooled. The amount of missing data is shown in S2 Table. Analyses were performed in R version 3. 6, and the statistical significance level used was P < 0. 05, 2 sided. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).
Strengths and limitations A key strength of this paper is the use of longitudinal data from the longest birth cohort in Africa to prospectively examine the association of the childhood environment with suicidal ideation in youth. Assessments relied on validated measures and care was taken to adapt them to the local setting. Additionally, the majority of childhood factors were reported by mothers while suicidal ideation was self-reported by offspring, thus reducing bias due to shared method variance. The longitudinal nature of this study also enabled us to assess risk factors multiple times throughout participants' childhood. In addition to these strengths, certain limitations of the study should also be acknowledged. Attrition was substantial, although comparable with other longitudinal cohorts  [59].
Acknowledgments MNA was funded by a  Vanier Canada Graduate Scholarship  from  Canada's Social Sciences and Humanities Research Council. SB is a  CIFAR Azrieli Global Scholar  in the Brain,  Mind and Consciousness Program. The support of the  DSI-NRF Centre of Excellence (CoE) in Human Development at the University of the Witwatersrand, Johannesburg  in the Republic of South Africa toward this research is hereby acknowledged. We thank the Birth to Twenty study team and all participants who enable this research to continue. The Bt20+ sample has been funded by the  Wellcome Trust , the  South African Medical Research Council , the  Human Sciences Research Council , the  University of the Witwatersrand , and the  Bill & Melinda Gates Foundation  among others.
• Preprocessing All data processing was performed using Letswave 6 (https://www. letswave. org/, accessed on 29 July 2021) and MATLAB 2021 (MathWorks). The EEG signal was cropped in 68-s segments (2 s before and 6 s after each sequence), bandpass filtered (0. 1 to 100 Hz) using a fourth-order Butterworth filter, and downsampled to 256 Hz. For three participants who blinked on average more than 1. 5 SD above the mean (the average number of blinks per second across participants = 0. 09, SD = 0. 08), we applied independent component analysis via the runica algorithm  [68]  and removed the component that accounted for most of the variance. Noisy channels were linearly interpolated using the three spatially nearest electrodes (not more than 5% of the electrodes, i. e. , three electrodes, were interpolated).
5 Hz). Two measures were used to describe the responses: SNR to visualize the data  [70]  and baseline-corrected amplitudes to quantify the data  [67]. We computed SNR spectra by dividing the amplitude value of the target frequency bin by the average amplitude of the 20 neighboring frequency bins (i. e. , 10 bins on each side of the target frequency bin, excluding the immediately neighboring bins and the two bins with the most extreme values). Baseline-corrected amplitudes were computed by subtracting the amplitude of the frequency bin of interest by the average amplitude level of the 20 surrounding bins. To define the harmonics that were significantly above the noise level, the FFT data -averaged across all participants and across all electrodes in the regions of interest (ROIs)were transformed into Z-scores by dividing the difference between the amplitude at each frequency bin and the mean amplitude of the corresponding 20 surrounding bins, by the SD of the amplitudes in these 20 surrounding bins  [70] [71] [72].
Orthogonal Data Analysis Both accuracy and reaction times were calculated to evaluate the detection of the fixation cross-color changes. A key press was considered correct if it occurred in the 100 to 2000 ms time window following an actual color change. Hence, for these correct responses, the corresponding reaction time was calculated. Accuracies and reaction times were averaged across the four sequences, resulting in an overall accuracy and reaction time per participant per treatment condition. One participant was excluded from this analysis due to technical difficulties resulting in the loss of the numeric triggers for the fixation cross color change and key press recording.
Statistical Analysis Linear mixed models (LMM; "afex" package version 1. 1. 0) [73]  were performed in R version 4. 1. 3 [74]  with treatment condition (OT, PL), stimulus type (face, house) and ROI (LOT, ROT, MO) as fixed effects. As half of the participants started with the PL condition and the other half received OT during their first session, the session order was included as a nuisance covariate to adjust for its potential effect on neural responses. In order to account for the clustered nature of the repeated measurements, the random intercept and slope were included for each of the fixed factors per participant. A total of 26 of 744 data points were detected as outliers using the median absolute deviation and removed. Post-hoc Z-tests and holm-corrected p-values were used to compare means using the emmeans package version 1.
7. 3  [75]. LMMs were also performed on the accuracy and reaction times of the orthogonal task. Treatment condition (OT, PL) was included as the fixed factor and session order was included as a nuisance covariate. When no significant treatment effect was revealed by the LMMs, we computed Bayes Factors (BFs) to validate the null findings ("BayesFactor" package version 0. 9. 12-4. 3 [76] ). To compute the BFs, we compared the fitting of the model as described in the LMMs versus the fitting of the same model without the factor "treatment condition". Consistent with the classification scheme  [77, 78] , a BF-value of 1 indicates that the data are equally likely under either model, whereas values between 3 and 10 are taken as moderate evidence for the model in the numerator, values between 10 and 30 as strong evidence, and values between 30 and 100 as very strong evidence.
Nevertheless, our study shows the possible value of the Bathysmed® protocol. The protocol is a whole and must include psychoeducation to be effective. Diving alones might have been less effective but this still needs to be investigated. Amongst the limitations, we did not collect data on acceptability but all subjects in the diving group passed their first level diving diploma. The feasibility is more difficult. It happened far from home, diving is expensive. The cost makes it difficult to repeat the experience so often under the same conditions. Finally the limited number of subject and the diversity of their usual treatment did not allow us to take them into account. Further work is needed to confirm the initial results obtained with this pilot study. The protocol is currently used in another pilot study in France including French military veterans (NCT03995992).