dataesr/scientific-paragraphs-categorization huggingface dataset:
This amounts to 150k, 40k, and 65k images in each set. The images are resized to ResNet-50's input dimension of 224 × 224, and the training dataset is augmented by applying horizontal flip, saturation, and rotation. 2) CATARACTS: The CATARACTS dataset, proposed in  [29] , contains 50 videos of cataract surgery. With the recent CATARACTS2020 challenge, the dataset has been released with step annotations. Similar to  [6] , we define a phase ontology for available step labels. Cataract surgery consists of 5 phases and 19 steps that are summarized in Table  I. The dataset is extended with phase labels that is automatically generated using the available step annotations and the ontology presented in Table  I. For each frame in a video, the phase label is obtained by a simple lookup of the step label in Table  I.
V. RESULTS AND DISCUSSIONS A. Bypass40 1) Effect of weak supervision: To quantitatively evaluate our method, the results of step recognition on the test set are presented in Table  III. The table contains the results of our model with a varying number of videos in the training set labeled with steps  (3, 6, 12, and 18)  along with the rest of the training set containing phase annotations. The introduction of dependency loss 'DEP' for weak supervision significantly improves the performance over the model (FSA) trained only on the step labeled subset of the dataset. We notice a 10-13% improvement of the model trained with 'DEP' loss containing only 3 videos annotated with steps. Similarly, we see a 10-13% and 5-7% increase in performance in all the metrics of the 'DEP' model in experiments corresponding to 6 and 12 step annotated videos, respectively.
Interestingly, our 'DEP' model, trained on a dataset with 50% of step and 50% of phase annotated videos, achieves performance close to the upper baseline 'FSA' model trained on the whole fully labeled dataset. Moreover, the results of Yu et al. [42]  semi-supervised method are also presented in Table  III  for different step annotated videos  (3, 6, 12, and 18)  used to train both teacher and student model. The student model's performance increases by 3-8% over 'FSA' in all the metrics for 6 videos with step annotations. Furthermore, an increase of 6% and 2% is noticed in recall and F1-score above 'FSA' with 12 step annotated videos. However, the method falls short of our proposed 'DEP' method. We notice a 10-15%, 2-6%, and 1-6% increase in performance in all the metrics of the 'DEP' model over Yu et al.
The semi-supervised method of Yu et al. achieves performance improvement of 16%, 8%, and 1. 5% over 'FSA' in F1-score for experiments corresponding to 3, 6, and 12 videos, respectively. However, as seen earlier, it falls short of 'DEP' by 5%, 0. 5%, and 0. 5% in the F1score for experiments corresponding to 3, 6, and 12 videos. Interestingly, Yu et al. achieves high recall on both datasets (Table  III  & V). On CATARACTS, it even outperforms the 'DEP' model in recall in all the experiments but falls short significantly in precision. This could be credited to the student model which learns from imperfect pseudo labels generated by the teacher model. Since our proposed 'DEP' model learns from true phase labels on additional videos its performance increases in both precision and recall. This validates the applicability of our approach to different surgical workflows.
C. Weak supervision on step predictions To visualize the effectiveness of our method, we visualize the step predictions of our method on the CATARACTS dataset which contains fewer phases and steps thereby enabling us to render a simple and clearer graphical diagram. We compare the step predictions of our 'DEP' model against 'FSA' for 2 best and 2 worst videos in CATARACTS in Fig. 3  for different labeled ratios  (3, 6 , and 12 videos with step annotations). Along with the step predictions we present the errors in the phase predictions for both models. The phase prediction error plot is computed as the errors in phase predictions derived from step predictions, using the step-phase mapping matrix, against ground truth phase predictions. Fig. 3  clearly depicts the effectiveness of our method for different labeled ratios.
As observed in Sections V-A. 2 & V-B. 2, it is beneficial to train the 'DEP' model utilizing all the additional phase annotated videos in the dataset for weak supervision. We also observe that in the lower data setting (6 videos with step annotations) model performance improves even when the phase annotated videos are increased from 12 to 18 (19 for cataracts). However, our study doesn't provide insights as to how many phase annotated videos are truly required to achieve the best performance by our proposed 'DEP' model. This is another limitation of our study, irrespective of the complexity of the procedure, that is hindered by the size of the available labeled datasets (24 in Bypass40 & 25 in CATARACTS). Understanding the extent of the 'DEP' model would require extending these datasets which is an important direction that needs to be pursued in future studies.
VI. CONCLUSION In this paper, we introduce a weakly-supervised learning method for surgical step recognition utilizing less demanding phase annotations. To model the weak supervision between steps and phases, we introduce a step-phase dependency loss and train a ResNet-50 + SS-TCN model end-to-end. The proposed method is extensively evaluated on a Bypass40 dataset consisting of 40 LRYGB procedures and on the CATARACTS dataset containing 50 cataracts surgeries. The proposed 'DEP' model significantly improves the step recognition metrics over the baseline 'FSA' model for all the amounts of step annotations available. We hope that this work will inspire and foster future research in weak supervision for surgical workflow analysis utilizing multi-level descriptions of the workflow.
This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  813782  -project  ATLAS. This work was also supported by  French  state funds managed within the  Investissements d'Avenir program  by  BPI France  (project  CONDOR ) and by the  ANR  ( ANR-16-CE33-0009 ,  ANR-10-IAHU-02 ).
Patients included in the survey This survey includes data from patients with respiratory allergy of all ages who initiate AIT with pollen, house dust mite, animal dander, and/or moulds as either SCIT, SLIT-drops, or SLIT-tablets according to real-life clinical standards of practice. Patients could be included in this survey if previous AIT courses had been performed. Patients undergoing more than one AIT could be included, by completing 1 PQ for each new Patients under AIT with Hymenoptera venoms or food could not be included in the survey.
Blinding design Doctors' anonymity is crucial in this survey, where professional criteria and other sensitive information are collected. Therefore, data are collected anonymously and without any traceability. For this purpose, 2 separate databases have been created. The first database contains names and contact details of participating doctors, together with an individual numerical identification code for the survey. The second database includes only the survey information extracted from the 2 questionnaires. The Survey's Data Managers are the only persons with access to the first database, enabling contact with any study doctor if clarification regarding data is needed.
Database The SurveyMonkeyÒ online instrument is used for this survey. This program allows the participating doctors to store all data collected on a centralized electronic database. The SurveyMonkeyÒ facility operates some of the most advanced technology for Internet security commercially available today. Secure Sockets Layer (SSL) technology protects user information using both server authentication and data encryption, ensuring that user data is safe, secure, and available only to authorized persons. SurveyMonkeyÒ is PCI-DSS compliant.
Statistical methods Statistical analyses will be carried out by an independent third-party using SPSS. Firstly, a general analysis of the DQ and PQ questionnaires will be performed to identify data entry errors, inconsistencies between variables. Once the database is cleaned, it will be locked. Prior to any analysis, generation of new variables or classifications of interest, based on the data collected in the questionnaires will be established including analysis of multiple combinations of variables/groups with multiple answers. A descriptive analysis will provide the basic global information of the survey with a fusion of data from both DQ and PQ. Tables of frequency to analyze associations between two variables, contrasting the percentage differences with the statistical tests required according to established objectives (Chisquare, Fisher, McNemar, Odds Ratios) will be prepared.
For the comparison of the distributions of quantitative variables, adequate techniques of analysis of the variance will be applied. Multivariate analysis with relevant objective variables will be carried out, with logistic regression and decision trees. Unsupervised cluster analysis, to obtain a typology or profile of patients according to the variables that will be determined for their interest and/or their significance in the previous statistical analysis, will also be considered. Database managers won't change throughout the study. Data are stored on the online SurveyMonkeyÒ platform and, once surveys are completed in each country, downloaded, and stored on an encrypted file on a computer connected to a secured server of the Allergy Unit of the University Hospital of Montpellier (France) and on another one of the Hospital Infantil Universitario Niño Jesús of Madrid (Spain), by each one of the two data managers.
Data quality assessment The survey's data managers carry out a monthly systematic review of the database, searching for inconsistencies, such as duplicated data entry, missed information or any other kind of potential error. They generate a query and contact the corresponding survey's participating doctor responsible for that questionnaire for clarification. When a mistake is identified, and after double checking, the mistake can be removed, and the correct data can be introduced into the database. Tracked changes of all errors detected are recorded as well.
Acknowledgements We are grateful to all the colleagues who are working in CHOICE-GLOBAL as national coordinators and all those participating doctors who are already collected data. We truly appreciate the support given as unrestricted educational grants by  ALK  and  Stallergenes Greer. CHOICE-Australia :  Prof Connie Katelaris ,  Dr Narinder Kaur ,  Dr Margaret Li; CHOICE-Canada :  Prof Susan Waserman  and  Prof Harold Kim ;  CHOICE-China :  Prof Rongfei Zhu ,  CHOICE-France :  Prof Pascal Demoly ,  Prof Jocelyne Just  and  Dr Sarah Saf ;  CHOICE-Germany :  Prof Oliver Pfaar  and  Dr Stephanie Dramburg ; CHOICE-Italy:  Dr Silvia Caimmi ,  Prof. Gian Luigi Marseglia ; CHOICE-Portugal:  Prof Ana Morete ;  CHOICE-Spain :  Prof Carmen Vidal  and  Prof Carmen Moreno; CHOICE-Sweden :  Prof Christer Janson ;  CHOICE-Japan :  Prof Motohiro Ebisawa ;  CHOICE-Costa Rica :  Prof Olga Patricia Monge ;  CHOICE-El Salvador :  Prof Marta Elvira Rubio; CHOICE-Guatemala :  Prof Edgar Antonio Moran ;  CHOICE-Honduras :  Prof Claudia Almendarez; CHOICE-Mexico :  Prof Sandra Gonzalez ;  CHOICE-Panama :  Prof Dayanara Herrera ; CHOICE-Dominican Republic:  Prof Patricia Latour ;  CHOICE-Argentina :  Prof Maximiliano Gomez  and  Dr Pablo Moreno; CHOICE-Chile :  Prof Maria Antonieta Guzman ;  CHOICE-Colombia :  Prof Jorge Mario Sanchez ;  CHOICE-Ecuador :  Prof Ivan Cherrez; CHOICE-Peru :  Prof Jose Ignacio Larco ;  CHOICE-United Arab Emirates :  Prof Marilym Karam; CHOICE-Jordan :  Prof Anan Al Fakih ; CHOICE-Kuwait:  Prof Mona Al-Ahmad; CHOICE-Lebanon :  Prof Carla Irani Nasr ;  CHOICE-Qatar :  Prof Maryam Al-Nisf ;  CHOICE-Kingdom of Saudi Arabia :  Prof Adeeb Bulkhi ;  CHOICE-Russia :  Prof Oksana Kurbacheva  and  Prof Andrey Kamaeb ; CHOICE-USA:  Dr Michael Lanz ; CHOICE-China Data-base Managers:  Dr Lin Yang  and  Dr Hao Chen ; CHOICE-Latin America Data-base Managers:  Dr Silvia Uriarte Obando  (Peru) and  Dr Cesar Fireth Pozo Baltran  (Mexico).
EXPERIMENTAL MODEL AND SUBJECT DETAILS All experiments were performed in accordance with the European ethical committee (European directive 86-609) and approved by the French Committee of Ethics Charles Darwin N o 5 (project 896. 01). Data were collected from 12 adult male mice: 6 L7PKCI mice and 6 wild-type littermate controls which underwent surgery at 3-7 months of age. All animals were bred in a C57BL/6 mouse strain background. In L7PKCI mice, the pseudo-substrate PKC inhibitor (PKCI) was expressed under the control of the pcp-2 (L7) gene promoter, ensuring a selective suppression of PKC-dependent mechanisms in Purkinje cells. Hippocampal functions (synaptic transmission and plasticity), known to be essential for navigation tasks, are normal in L7-PKCI mice. 11 Their behavior during vestibulo-dependent motor tasks is also normal, both in the light and in the dark.
Electrophysiology Extracellular signals from all electrodes were filtered through a unity-gain pre-amplifier (HS-16; Neuralynx, Bozeman, US). Spiking activity was sampled at 20kHz after filtering between 600 Hz and 9kHz. Local field potential (LFP) was filtered between 1 and 475 Hz and sampled at 1kHz. Data were digitized by a Power 1401 acquisition system controlled by Spike2 (CED, Cambridge, UK). MFB stimulation was delivered through a DS3 current stimulator (Digitimer, UK). Each MFB stimulation consisted of a 125 Hz train of 28 negative square wave pulses of 1-ms duration. 52 days after implantation, tetrodes were slowly lowered in the brain ($60 mm per day) until reaching the hippocampus. Hippocampal recording was identified by increased multi-unit activity and sharp-wave ripples during resting periods.
Spikes were detected and sorted offline using Kilosort2. 50 Single units were considered as putative pyramidal neurons when they had a mean firing rate < 5Hz, a burst index > 2 and a prominent peak in their spike auto-correlogram between 3 and 18ms. Only well-isolated single units (isolation distance > 10) were considered for analysis. For all single cell analyses, spikes and behavioral data were re-sampled at 100Hz and we only considered time points where the animals run speed was > 2. 5 cm. s -1.
To estimate the significance of spatial tuning strength, we measured place fields after circularly shifting spikes by a random delay (> 5s) for 500 times. Single units were considered as significantly tuned to positions when their spatial tuning strength was higher than the 95-percentile of the distribution of tuning strengths measured from the shuffle controls. Predicted firing rates were computed using a 10-fold cross-validation procedure: 10 test sets were defined by splitting the data into non-overlapping subsets containing 10% of the data. The response of a neuron was predicted for each test set from the place field estimated with the remaining 90% of the data. To estimate the significance of the predictive power of a place field, we performed a likelihood ratio test comparing the place field prediction to the prediction by a constant mean firing rate.
To construct B, spatial positions were binned into 5-cm bins; directions, into 20 o bins and run speed, into 5 cm. s -1 bins. Columns in B corresponded to a succession of 0 and 1 describing in which of those bins the animal was at any time t. Time points corresponding to position bins that were visited on less than 3 trials were excluded from the analysis. The best maximum likelihood solution (MLE) was estimated in Matlab, using the glmnet toolbox  56  (with a final regularization parameter set to zero). The estimated coefficients (X in Equation  8 ) corresponding to each behavioral variable were smoothed (after exponentiation) with a Gaussian (5-cm s. d. for position; 20 o for movement direction and 5-cm. s -  for speed). The predictive power of the model was quantified using a 10-fold cross-validation procedure to measure the log-likelihood of held-out data under the model.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical tests were performed by fitting generalized mixed effect models with nested random effects to account for variability across mice or sessions within mice (using Matlab function fitglme). 57 The model was fitted using a Laplace approximation method and significance was estimated by means of likelihood ratio tests between nested models. The use of mixed models was justified by the fact that in all tests performed, including random effects always significantly improved the goodness of fit (LR test of random effects, p < 10 -10 ). Data points further away from the median by 10 times the median absolute deviation were considered as outliers (in the limit of 5% of the data) and removed from statistical analysis. To test for significant differences in behavioral performance across genotypes, we considered genotype as a fixed effect and animals and training days as nested random effect (i.
ACKNOWLEDGMENTS This work was supported by the  Fondation pour la Recherche Medicale DEQ20160334907-France , by the  National Agency for Research   ANR-17-CE37-0015-01  and  ANR-18-CE16-0010-02  (LRR), by the  Human Frontier Science program  CDA  00058 -2019  (JF) and by the  Institut Universitaire de France (CR). This work also benefited from the support of the  CNRS, INSERM and Sorbonne Universite. We thank all members of the CEZAME team, especially  Aurelie Watilliaux  for helpful support in ethical procedures. We thank  C. I. De Zeeuw  for providing the L7-PKCI mice. We gratefully acknowledge the  IBPS animal facility  staff for their support.
Electrophysiology Once the microelectrode reached the target brain structure (STN or thalamus), the speed of the microdrive was reduced and neuronal activity was streamed to a loudspeaker, allowing the electrophysiologist to verify the targeted depth of the preplanned trajectory. The research task was initiated when a neuron showed stable activity for a few tens of seconds and the anatomical localization was confirmed by the electrophysiologist. Electrophysiological data was recorded from the 5 mm tip of the microelectrode, grounded and referenced to the guide cannula and an adaptive line noise canceller was applied. Data were sampled either using a Guideline 4000 LP+ amplifier (FHC, Maine, US) at 30 kHz (N = 21 surgeries), or using a Guideline 5 amplifier (FHC, Maine, US) at 32 kHz and resampled offline to 30 kHz (N = 14 surgeries).
Experimental procedure Participants performed a tactile detection task programmed in Matlab using the Psychophysics toolbox  (29, 30, 31). When possible, participants were trained a few days before the surgery (N = 18 / 36 surgeries). Participants sat in a reclining chair in a quiet room (training session) or in the operating room (main session). Every trial started with a 300 ms long auditory "go" cue delivered through an external loudspeaker placed near the participants. Following the end of the go cue and a delay of 500 ms, a 100 ms vibrotactile stimulus could be delivered at any time during a two second stimulation window (i. e. , uniform distribution between 0. 8 and 2. 8 s after the onset of the go cue; Figure  1A ) on the lateral palm contralateral to the deep brain implant. Stimuli were applied using a MMC3 Haptuator vibrotactile device from TactileLabs Inc.
Oxidative stress and phytohormone signaling are tightly intertwined but many of the metabolic links remain to be identified  [1, 2]. The catalase-deficient Arabidopsis mutant, cat2, shows oxidative stress because the capacity to remove H2O2 produced through photorespiration is decreased  [6, 24]. In this mutant, both SA and JA pathways are impacted by the increase in intracellular H2O2 availability  [25] [26] [27] [28] [29]. Our aim in this work was, first, to exploit three cat2 transcriptomic datasets to generate a robust account of how the expression of major metabolite-modifying gene families responds to enhanced intracellular H2O2. We then sought to investigate in detail how gene expression associated with SA and JA synthesis and metabolism are impacted by oxidative stress. To explore the potential functional impact of the changes in gene expression, we used parallel metabolite profiling to determine how oxidative stress affects compounds such as SA and JA, as well as their derivatives.
Transcript measurements by quantitative RT-PCR Total RNA was isolated in triplicates from leaves using Spectrum™ Plant Total RNA Kit (SIGMA, Germany), and samples were treated with DNase during RNA purification. RNA concentrations were determined using Nanodrop and 1 µg of RNA was used to perform reverse transcription using the qScript cDNA Synthesis Kit (Quantabio). Selected genes were chosen for qPCR analysis according to  [32]. Two or three independent experiments were performed, each with three biological replicates and each with at least 12 plants per genotype. qPCR was performed in technical triplicates and data were normalized relative to two reference genes, ACTIN2 and UBIQUITIN7. For each transcript, differences between cat2 and Col-0 were assessed for statistical significance by t-test of the three biological replicates in each experiment.
Transcriptome analyses Transcriptome profiling of cat2 and Col-0 plants were performed in three independent experiments using the growth conditions described above, with three biological replicates in each case. Transcript abundance data were retrieved from one microarray  [21]  and two RNA sequencing datasets (in-house datasets). For the RNA sequencing experiments, library preparation and sequencing were performed at the VIB Nucleomics Core (Leuven, Belgium), using TruSeq Stranded mRNA Library Preparation Kit (Illumina). Samples were sequenced on IlluminaNextseq 500 (75-bp single-end reads). Reads were aligned to the Arabidopsis genome by STAR (v2. 5. 2b)  [33]  using the Araport11 annotation  [34]. The number of reads per gene was quantified with the featureCounts function as implemented in the Subread package v1.
Transcriptomic data mining To generate the list of genes for CYP and GST subfamilies, the keyword research in Thalemine (bar. utoronto. ca/thalemine/) was used. For UGTs, a search in Uniprot (www. uniprot. org) for reviewed "IPR002213" Interpro accession in A. thaliana was performed. The gene annotations were crossvalidated using Phytozome (phytozome-next. jgi. doe. gov; Araport11). To visualize the datasets, dendrograms, heatmaps and PCA plots were generated using the average of Z score normalized data of each dataset. For CYPs, GSTs, and UGTs, only genes for which data are available in the three datasets were considered. To generate the dendrograms, the "ward. D2" method of the R "stats" package was used with a Pearson's correlation coefficient-based distance matrix. Genes associated with salicylate and jasmonate synthesis and metabolism were selected based on current knowledge of the different pathways (Supplemental Figures  S1-S6 ).
Sample solutions were injected onto a reversed phase column (Kinetex C18, 1. 7U, 100A, 2. 1 × 100 mm, Phenomenex) on an ACQUITY Ultra Performance LC™ System (Waters, Milford, MA, USA) linked to a Bruker MicrOTOF II time of flight mass spectrometer (Bruker Daltonik GmbH, Germany) equipped with an electrospray ionization (ESI) source operating in negative mode. Mobile phases were water (eluent A) and 0. 1% acetic acid in acetonitrile (eluent B). The injection volume was 5µL and chromatographic conditions were as described in  [36]. Eluting compounds were detected from m/z 50 to 800. Data were acquired and processed using the MicrOTOF-Control, DataAnalysis (version 4. 0 SP 1) and QuantAnalysis (version 2. 0 SP 1) software packages. Most compounds were identified by comparison of retention times with standards and by searching for ions according to the mass spectra of the standards.