dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of VR Programs Bliss is a type 1 medical device with European accreditation (EN 50581: 2012; manufacturer: Effet Papillon Company, Laval, France). It is a 3-dimensional interactive VR application. The program runs on a smartphone and a GearVR head-mounted display. We proposed 4 imaginary VR environments with a median duration of 15 minutes to 40 minutes: Nohara (dream-like walk on the country side), Kaitei (seabed exploration), Uchuu (space walk), and Mori (dream-like walk in the forest; Figures  1 2 3 4 ). All these environments were designed to induce a state of relaxation and light sedation through slow passive contemplative exploration without inducing a hypnotic state. For total immersion, the patients wore headphones with binaural sound. Synchronous sounds in the virtual environment contributed to stereophony and immersion in a 3-dimensional environment by increasing the concentration and eliminating other sources of noise.
Data Recorded the Day of the Biopsy Blood pressure was measured before the biopsy and 15 minutes after the biopsy. Anxiety was assessed using 2 questionnaires: a local fear of pain questionnaire before the biopsy (presence of fear, intensity of fear by VAS, and causes of fear) and the adapted Spielberger State-Trait Anxiety Inventory (STAI) form Y before and 15 minutes after the biopsy  [29]. The STAI score ranges between 20 and 80 points: the higher the score, the more anxious the patient. We defined 5 categories (very low: <36; low: 36-46; average: 46-56; high: 56-65; very high: >65). Nausea, headache, and dizziness were recorded to assess tolerance in each arm. The sense of presence (feeling of being immersed) was assessed for the VR group using 3 questions (feeling of being immersed, dreaming, and escaping).
Statistical Analysis The primary end point was to measure the intensity of pain in each group using the VAS and to demonstrate a 1. 5-point reduction in pain in the VR group. We enrolled 120 patients to validate this hypothesis with an SD of 2. 5, power of 90%, and type I error rate of 5%. We included 6 more patients, considering that 5% could not be evaluated using 2-tailed tests. Intention-to-treat analysis included patients for whom pain assessment was available. Variables are presented in tables; the Student t test was used to compare quantitative data, and the chi-square test or Fisher exact test was used to assess qualitative data. Correlation analysis was performed using the Spearman test. All statistical analyses were conducted using SAS 9. 3 software (SAS Institute Inc) with a significance of 5%.
We do not know if the patients had already been exposed to VR during their leisure time (movies, video games; data not available). Patient choice of the scenario was not recorded in this study, so programs were not compared with each other. Patients also could not compare them from one procedure to another (only 1 biopsy per patient). Pain and anxiety are very subjective symptoms that vary from patient to patient and are influenced by the self-experience. Physiological markers (clinical, biological parameters, neuroimaging markers) to objectively assess pain and anxiety have been published: blood pressure and heart rate variations, pupil reflexes, stress hormonal changes, electro-encephalography, magnetic resonance imaging, and positron emission tomography to assess brain activity  [30]. These parameters could be evaluated in a randomized study comparing all distraction methods (VR, hypnosis, music, television, video games).
Acknowledgments The authors would like to thank the patients and their families for their participation and  Melanie Peron ,  Etienne Lepoutre , and  Magali Balavoine  for their intellectual input and advice. This investigation was supported by a grant from the  French Lymphome Espoir Association  and the  Fundings of Hoffman-Roche lab  and the  Elsan group. The study was supported by  Hoffmann-La Roche , the  Elsan group , and the  France Lymphome Espoir Association.
Regarding the literature, we choose not to record the menstrual cycles. Indeed, even though it is well recognized that menstrual cycles may influence HRV measurement, recent research has shown no relationships in the HRV-BFB protocol  [36]. A priori power analysis was performed using Power IN Two-level designs software designed to estimate standard errors of regression coefficients in two-level hierarchical linear models for power calculations  [40]. If α is chosen at 0. 05, we expect a medium effect size, and if a power of 0. 80 is desired, then a sample of 25 participants along six measurement points is required for the model, including most of the variables in the present study. The study was part of the ASDP project validated by the ethics committee of Alliance Universitaire Bretagne under the number 2303077 and was carried out following the Declaration of Helsinki.
Procedure Data Analysis Multilevel growth curve analyses (MGCA) examined the linear and/or quadratic and/cubic trajectories of athletes' recovery-stress states and cognitive appraisals  [47]. All analyses were conducted using the R package labelled lme4  [48]. Separate analyses were conducted for each of the psychological (i. e. , general, specific and total scores of stress and recovery; perceived control and stress) states. Multilevel models extend multiple regressions to nested data (hierarchically structured data). Specifically, repeated measurements (Level 1 units of analysis) were nested within individuals (Level 2 units of analysis). Multilevel models are a flexible approach that can be applied to evaluate inter-individual differences in intra-individual changes over time (i. e. , each participant has their own curve).
The working group developed the clinical questions to be investigated after consultation with external experts from diverse geographic locations as well as a patient representative. Critically important outcomes for clinical questions focused on intervention were formulated and voted by the working group members as deemed necessary. Subsequently, PACOs and PICOs were created which were reviewed by the IWGDF Editorial Board. Next, a systematic review of the literature was performed to address the clinical questions. The systematic review for this guideline is published as a separate document. 22 Studies that reported on CNO patients with a foot ulcer were excluded as this may affect diagnosis and treatment, unless the data of patients without an ulcer were reported separately or when this was unlikely to influence the outcomes.
ACKNOWLEDGEMENTS The authors would like to acknowledge the expert review of the clinical questions and guideline draft by  Fran Game  and the  IWGDF Editorial Board  and the following international experts:  Rasmus Bo Jansen ,  Denmark ;  Patrick Burns ,  United States of America ;  Robert Frykberg ,  United States of America ;  Venu Kavarthapu ,  United Kingdom ;  Armin Koller ,  Germany ;  George T. Liu ,  United States of America  (also on behalf of the  American College of Foot and Ankle Surgeons );  Nicholas Lowery, United States of America ;  Fermin Martinez , Mexico;  Andrew Meyr ,  United States of America ;  Luca Dalla Paola , Italy;  Dario Pitocco , Italy;  Lee Rogers ,  United States of America  (also on behalf of the American Podiatric Association),  Juan Manuel Rios Ruh , Spain;  Luigi Uccioli , Italy;  Vijay Viswanathan , India (also on behalf of D-Foot International).
Clinical question: In persons with diabetes mellitus and active Charcot neuro-osteoarthropathy with intact skin who have been treated and are in remission, is therapeutic footwear preferred to conventional footwear to prevent re-activation of the disease? WUKICH ET AL. CNO is associated with high complication rates and the risk benefit ratio needs to be considered when intervening surgically. A large database study compared outcomes of ankle fusion in a matched cohort of patients with diabetes and CNO (n = 3815) and patients with diabetes but without CNO (n = 3815). 104 Significantly higher rates of amputation, hardware removal, wound dehiscence, acute kidney injury, pneumonia, and surgical site infection, were observed in patients with diabetes and CNO compared patients with dia- betes but without CNO.
This study was not included in our sys- Recommendation tematic review as a main limitation of this database study was that 25. Footwear and/or orthoses that best accommodate and support the timing of surgery (active or remission stage) could not be the shape of the foot/feet and ankle to help prevent re- determined, but these data highlight the risks of surgery in patients activation of Charcot neuro-osteoarthropathy (CNO) are rec- with CNO. ommended in a person with diabetes mellitus, intact skin, Although CNO reconstruction is associated with high upfront treated for active CNO with an off-loading device and who is costs, reconstruction early in the disease process is, in our opinion, now in remission (Strong; Moderate). justified for patients who cannot be managed successfully with total 26.
Clinical data Functional assessment of the outcome of transfer was made on GAS  [8, 9] , to provide an objective evaluation of functional success according to the preoperative contract between team and patient. The evaluation of GAS was done at 6 months follow-up. Using a validated form, the patient expressed his or her expectations and their relative importance with respect to the disability, and then the patient and the medical team assessed the result postoperatively. If the result was as expected, it scored 0; if better than expected, + 1 or + 2 depending on how much better; and if there was no functional improvement, then - 1 or, if there was any aggravation, -2. Three or four objectives can be assessed, for a total score out of 50. The T score used was: where: Xi = GAS score Wi = weight of each GAS scale (2/1) ρ = 0.
Functional results We included 22 patients operated for post-stroke SEVF, two of them were lost of follow up. Demographic and clinical data are shown in Table  1. 20 patients had a gait analysis preoperatively and at 6 months postoperatively. GAS assessment was performed at the last follow-up at 6 months. Table  1  summarizes all the surgical procedures performed. The GAS objectives, their results and the walking aid Necessity before and after surgery are shown in Table  2. For the T score calculation, GAS objective 1 had a weighting of 2 and objective 2 had no weighting. Considering binary GAS 90% (18/20) patients had a GAS improvement and 50% (10/20) had an improvement in walking technique as evidenced by the cessation of the use of a walking aid. Regarding the adverse events, one patient had lateral scar disunion after Split Anterior Tibial Tendon Transfer (SPLATT) treated by directed.
Following this motivation, they have been of interest to downstream tasks, such as supervised learning, compression and data augmentation. In supervised learning, it can be used as an input feature when building classifiers or other predictors to improve predictive performance  [18] , reduce sample complexity  [19]  and offer interpretability  [20]. For compression  [21] , disentangled representations are compact and low-dimensional, thus minimizing the cost associated with storing underlying factors of variation in data. Further, they can be used to generate novel examples not found in the original dataset  [21]. Such feature learning also supports a variety of other applications, such as super-resolution  [22] , multimodal application  [23] [24] [25] [26] [27] , medical imaging  [28, 29] , video prediction  [30] [31] [32] [33] [34] , natural language processing  [35] [36] [37] , transfer learning and zero-shot learning  [38].
Hence we can calculate the gradient of the last term of Equation  (7). However, the first term is a bit trickier as it requires an estimation by sampling from q φ (z|x i ). Kingma et al. [39, 72]  propose to estimate the marginal likelihood lower bound of the full dataset using mini-batches of M data-points and then average the gradient over these mini-batches. However, stochastic gradient descent via back-propagation cannot handle stochastic variables within the network. To solve this problem and generalize back-propagation through random sampling, they propose another way to generate samples from q φ (z|x i ); this solution is called the "reparameterization trick"  [39, 73]. The key behind this trick is to define z as a deterministic function z = g(φ, x i,ξ ), then sample from the posterior q φ (z|x i ) using z = µ(x i ) + σ(x i ) * ξ.
β-VAE was performed using a number of benchmarks with known ground truth factors, such as CelebA  [76]  (202,599 color images of celebrity faces), Chairs  [77]  (86,366 color images of chairs), Faces  [78]  (239,840 gray-scale images of 3D faces) and 2D Shape  [79]  (737,280 synthetic images of 2D shapes such as heart, oval and square). The size of the images in the four datasets is 64 × 64 pixels.
InfoMax-Variational Auto-Encoder As we can see from Equation  (10) , learning representation depending only on q φ (z|x) may result in meaningless representations and, therefore, a poor reconstruction quality. To avoid collapsed representations, Rezaabad et al. [44]  report a simple yet practical method to build a meaningful representation. They propose to extend the evidence lower bound (ELBO) with a regularizer term that maximizes the mutual information between the data and the latent representation. In so doing, the model is pushed to maximize the information about the data (input) stored in the inferred representation (latent representation)  [44]. This solution is referred to as InfoMax-VAE. They end up with the following ELBO: max φ,θ E q(x) L β-VAE + αI q φ (x, z) (11) where β and α ≥ 0 are regularization coefficients for the KL divergence and mutual information.
They propose a simple yet practical way to do so: first of all, and thanks to the reparameterization trick  [72] , they draw samples from q(x), (x i , z i ) ∼ q φ (x, z) = q φ (z|x)q(x). Afterward, to get samples from the marginal q φ (z), they choose a random data point x j , followed by sampling from z ∼ q φ (z|x j ). The InfoMax-VAE was performed using the CelebA dataset and has been shown to be capable of learning meaningful and disentangled representation and outperforms β-VAE.
The VAE parameters are finetuned using the objective in Equation (  16 ) with the total correlation term changed to its approximation in Equation  (17). Factor-VAE was performed using several benchmarks, such as CelebA, Chairs and Faces. Moreover, two synthetic datasets were used: 2D Shapes and 3D Shapes containing 480,000 64 × 64 × 3 RGB images of 3D shapes  [79].
According to Chen et al. [41] , the total correlation term in the ELBO is the one that affects disentanglement. To verify this claim, the TC-term is evaluated using a Monte-Carlo approximation  [86]. A unique integer index is assigned to each training sample, and they use the following estimator given a mini-batch of samples {n 1 , n 2 ,. , n M }: E q(z) [log q(z)] 1 M M ∑ i=1 log 1 N M M ∑ j=1 q(z(n i )|n j ) (19) where q(z|n i ) is close to 0 for a randomly sampled component but large if z comes from component n i. To achieve better disentanglement, the authors up-weight each term of the ELBO individually. Re-writing the β-TCVAE objective: L β-TCVAE = L β-VAE -αI q (z; n) -βKL(q(z)|| ∏ j q(z j )) (20) β-TCVAE was performed using the same datasets as Factor-VAE (CelebA, Chairs, Faces and 3D Shapes).
Since q φ (z|x) ∼ N µ φ (x), ∑ σ(x) , Equation (  22 ) becomes: Cov q φ (z) [z] = E p(x) ∑ σ φ (x) + Cov p(x) µ φ (x) (23) Kumar et al. [43]  explored two options for disentangling regularizers to get this term close to the identity matrix: (i) regularizing the deviation of Cov p(x) µ φ (x) from the identity matrix, which they refer to as DIP-VAE-I. (ii) regularizing Cov q φ (x) [z], which they denote as DIP-VAE-I I. Maximizing the objective of either DIP-VAE-I Equation (  24 ) or DIP-VAE-I I Equation (  25 ) leads to better disentanglement. L DIP-VAE-I = L VAE -λ 1 ∑ i =j Cov p(x) µ φ(x) 2 ij -λ 2 ∑ i Cov p(x) µ φ(x) ii -1 2 (24) L DIP-VAE-II = L VAE -λ 1 ∑ i =j Cov q φ [z] ij 2 -λ 2 ∑ i Cov q φ [z] ii - 1 2 (25) DIP-VAE was performed using three datasets: CelebA, 3D Chairs and 2D Shapes.
2. Generate a batch of vectors, where the value of the selected factor f k is held fixed in the batch while the other generative factors f k-1 are randomly selected. For a batch of L samples: p 1 = (x 1,1 ,. , x 1,L ) 3. Map each generated vector to latent code using the inference model: q(z|x) ∼ N(µ(x), σ(x)) 4. Normalize each variable within the latent representation using its empirical standard deviation calculated on the dataset. For a batch of L samples: (z 1 /s. z L /s).
Most of the existing metrics meet one or two out of the three criteria. Only the mutual information gap score is robust to these criteria and able to give a general measure of the disentanglement quality. However, some limitations remain unresolved and could be addressed in further work in order to improve the relevance of disentangled representation learning approaches in future work. (i) Despite the empirical success, existing disentangled representation learning approaches tend to ignore the latent variables and produce unrealistic, blurry samples with a significant reconstruction error when applied to complex datasets. There are several papers that discuss the issue of latent variable collapse  [91] [92] [93] [94] , but more analysis on this issue is needed. (ii) Most of the approaches are based on VAE or GAN, more research on other potential models, e.
g. , diffusion model  [95] , would allow new ways for disentangled representation learning. (iii) Although disentangled representation learning has achieved several successes in generalization to unseen scenarios, state-of-theart approaches for learning such representations have so far only been evaluated on small synthetic datasets. It will be interesting to explore the ability to generalize on complex real-world datasets. (iv) Finally, recent works discussed how to handle visual attacks and anomaly detection using dimensionality reduction, such as the SVD algorithm for neural networks and GAN  [96] [97] [98]. Inspired by these works, it will be interesting to explore the application of disentangled representation learning while preventing visual attacks. Moving forward, this survey is not only useful to provide insights for researchers that are currently working in the related area but can also be used as a basis for the implementation of new approaches.
Methods of data analysis All preparatory manipulations with the series of fluorescence spectra, their analysis, and the subsequent fitting of the titration traces were performed using our SpectraLab software  [16]. The latest version of the software package is freely available on the author's website  [17]. The series of the excitation spectra were first sorted in the order of the increasing ketamine concentrations, smoothed with a 15-point window 3 rd -order polynomial smoothing, and resampled to the 300 -430 nm region with a 2 nm step. The amplitude of the spectra was corrected to take into account differences in the sensitivity of the plate reader between different wells of the 96-well plate. For this correction, we used a set of 96 correction factors obtained with a plate with all wells filled with 160 µl of a standard solution of a fluorescent dye (we used Cumarin-460 laser dye with excitation at 395 nm and emission at 468 nm).
Acknowledgments This work was financially supported by an  ERC Consolidator Grant Photo-MedMet  to G. G. ( GA 681679 ), an  ANR grant  ( COSETTE Project ) and has received support under the program '' Investissements d'Avenir'' launched  by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL  (G. G. ). This research was funded in part by the  Austrian Science Fund (FWF)  [ I5721-N  to T. LM]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Y. W. thanks the  China Scholarship Council  for financial support.
Unlike the expected hippocampal atrophy normally seen in AD patients, an increased hippocampal volume was observed bilaterally in two of the patients, including the one who showed improved cognitive performance after one year. By contrast, bilateral hippocampal volume increase was never observed among the 25 matched subjects from the AD Neuroimaging Initiative database  179. In a follow-up double-blinded phase II clinical trial, DBS was applied bilaterally to the fornix of 42 patients with mild AD  180, 181. Half of the patients received DBS immediately after implantation, while the other half only received DBS after 1 year of sham stimulation (i. e. with the stimulator turned off). Fornix DBS significantly increased cerebral glucose metabolism after 6 months in the group receiving stimulation, but it did not reveal any statistically significant effect on cognitive functions.
Acknowledgements This work was supported by the  Bordeaux Neurocampus  junior chair programme cofunded by the  Région Nouvelle-Aquitaine  and the  University of Bordeaux Initiative of Excellence (IdEx)  (F. B. W. , Neurocampus junior chair), and the  European Union 's  Horizon Europe research and innovation programme  under  European Research Council (ERC)  Starting Grant agreement No. 101040391  (F. B. W. , MEMOPROSTHETICS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the  European Research Council. Neither the  European Union  nor the granting authority can be held responsible for them.
Tier 3 (NB): At least nominally significant in the discovery analysis and at least Bonferroni-significant in the replication analysis. For primary and sensitivity analyses, we conducted two-sample MR taking summary statistics for genetic associations with plasma TGs from one dataset (Global Lipids Genetics Consortium, GLGC)  (Willer et al. , 2013)  and summary statistics for genetic associations with outcomes from a second, independent dataset (UKB). The CARDIoGRAMplusC4D Metabochip study by GLGC involved 63,746 cases and 130,681 controls  (Deloukas et al. , 2013). UKB is a longitudinal, population-based cohort study with genetic and phenotypic data on over 500,000 participants aged 40-69 years at recruitment from across the United Kingdom during 2006-2010  (Sudlow et al. , 2015). The sociodemographic and health-related characteristics of UKB participants have been described elsewhere  (Fry et al.
Genetic instruments For primary and secondary analyses, we identified 3,086 single nucleotide polymorphisms (SNP) associated with plasma TG levels in GLGC at a genome-wide significance threshold of p < 5 × 10 -8. A total of 141 independent SNPs were then selected at a linkage disequilibrium (LD) threshold of r 2 < 0. 05 using the 1000 Genomes LD European panel as the reference population  (Auton et al. , 2015). To select independent SNPs, we used the. PLINK software's LD clumping command  (Purcell et al. , 2007)  with the following options: -clump-p1 0. 00000005 --clump-p2 0. 0005 --clump-r2 0. 05 --clump-kb 250. For replication analysis, we identified 1,388 SNPs associated with plasma TG levels in UKB after restricting for genome-wide significance (p < 5 × 10 -8 ) and LD-clumping. The SNPs were selected at an LD threshold of r 2 < 0.