dataesr/scientific-paragraphs-categorization huggingface dataset:
Sera and IgG Purification Pemphigus sera were collected throughout a randomized clinical trial (RITUX 3: ClinicalTrials. gov number, NCT00784589, accessed on 6 August 2021); patients were treated with oral prednisone alone or a combination of rituximab and short-term prednisone regimen  [24]. Anti-DSG1 and anti-DSG3 IgG antibody titers were measured by ELISA (Euroimmun, Lübeck, Germany). The mouse monoclonal antibody (mAb) AK23 was purified from AK23 hybridoma culture supernatant, kindly provided by Dr Masayuki Amagai (Keio University, Minato City, Tokyo, Japan). AK23 is an anti-DSG3 mAb generated in a PV mouse model that is pathogenic both in vitro and in vivo  [26, 27]. AK23 mAb or serum IgG from anti-DSG3 + patients or from healthy donors (HD) were purified by automated protein-G affinity purification on an ÄKTA Start chromatograph using HiTrap ® Protein G High Performance (GE Healthcare Chicago, IL, USA ) according to the manufacturer's instructions.
Antibodies The following antibodies were used in immunofluorescence assays: anti-DSG3 Alexa Fluor 647 (clone: 5H10; Santa Cruz, Dallas, TX, USA), anti-flotillin-2 (Human Protein Atlas number: HPA001396; Sigma, Saint-Louis, MO, USA ) and then revealed by the secondary antibody Alexa Fluor 488 (Abcam, Cambridge, UK).
Samples were prepared for analysis as a mixture with dihydroxybenzoic acid (DHB) used as matrix. This matrix was freshly dissolved in 20 mg/mL of an 80% methanol solution. Permethylated N-glycans were solubilized in an acetonitrile/0. 1% trifluoroacetic acid 70/30 v/v. Samples and matrix were spotted in ratio 1/1 v/v. These spectra were recorded in reflectron positive mode with an accumulation of minima 10,000 shots with FlexControl 3. 4 software. Mass spectra were acquired using a mass range m/z 900-4500 and analyzed with FlexAnalysis 3. 4 software (Bruker Daltonics, Bremen, Germany). Based on the m/z ratio and report from the literature on IgG N-glycosylation, each ion was associated with an N-glycan structure, which were drawn according to the international nomenclature recently updated  [32].
All data were collected from an electronic medical record system (DIAMM Ò , Micro6, Villers-Lès-Nancy, France). Every individual patient file was reviewed, in particular to identify the initial intended mode of delivery: planned CD or planned vaginal delivery. Maternal characteristics included maternal age, parity, tobacco use during pregnancy, body mass index, previous abdominal surgery, coagulopathy or thrombophilia, diabetes mellitus, hypertensive disease, and previous CD. Maternal morbidity measures included intraoperative surgical complications (accidental extension of the uterine incision into the lower uterine segment, and incidental cystotomy), bowel or vascular laceration, uterine dehiscence (incomplete uterine scar separation with intact serosa), uterine rupture (complete scar separation), postpartum hemorrhage >1000 mL of blood, need for blood transfusion, puerperal febrile morbidity (>38 °C on two or more occasions in any 48-hour period excluding the first 24 h after delivery), wound infection with a need for antibiotics and/or drainage, evacuation of hematoma, venous thrombosis, pulmonary embolism, and need for hospital readmission within 2 months postpartum.
Categorical variables are presented as numbers and percentages and then compared between groups with Pearson's v2 test or Fisher's exact test, as appropriate. Multivariable logistic regression models were then built to predict maternal and neonatal composite outcomes. The predictors were selected with a backward stepwise procedure (Pvalue 0. 20 for entry and P-value 0. 05 for removal). Adjusted odds ratios (aORs) and their 95% confidence intervals (95% CIs) were computed for each predictor. A P-value < 0. 05 was defined as statistically significant. The statistical software package SAS 9. 3 (SAS Institute Inc. , Cary, NC, USA) was used for all data analyses. The National Data Protection Authority (n°2001404v0, Commission Nationale de l'Informatique et des Libertés) approved the study and the database.
Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of audio segment duration, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7 to 15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
Unfortunately, these motor symptoms appear once 50 to 60% of dopaminergic neurons in the substantia nigra  [22]  and 60 to 80% of their striatal endings  [14]  have degenerated. That is why detecting PD in the early stages remains a big challenge, in order to test treatments before the occurrence of Voice impairment is one of the first symptoms to appear. Many articles have used voice analysis to detect PD. They observed vocal disruptions, called hypokinetic dysarthria, expressed by a reduction in prosody, irregularities in phonation and difficulties in articulation. The classification performances (accuracy rate) using voice analysis ranged from 65 to 99% for moderate to advanced stages of the disease. Fewer studies focused on early detection of PD through voice. Moreover, they usually worked on rather small databases (around 40 subjects) and analyzed men or mixed-gender groups  [54, 45, 41, 51].
Likelihood scores of test subject MFCC against the two GMM models (PD and control) are then calculated  [29, 38]. GMM can also model the MFCC distribution of each subject. Means of Gaussian functions (forming a "supervector") are then fed into a classifier such as SVM  [5]. When not enough speech data is available to train the GMM models, which mainly occurs when GMM are used to model each subject (rather than a group), GMM can be adapted from Universal Background Models (UBM) previously trained with a bigger dataset  [49, 5]. More than that, a more recent speaker recognition technique, called ivectors, has been adapted for PD detection  [17, 38]. This approach consists in removing the UBM mean supervector and projecting each supervector onto a low dimensional space, called total variability space.
We also tested different types of speech tasks (text-dependent or text-independent) and Parkinson's Detection from X-vectors different datasets for the DNN training, in order to assess their impact on PD detection. In order to achieve the best performance, we also considered men and women separately. This is usually done in speaker recognition and has been proved to enhance vocal pathology detections involving MFCC features  [15]. Moreover, this allowed to analyze the effect of gender on PD detection from speech. We also made different methodological choices. We studied the effect of important x-vectors methodological aspects, such as the audio segment duration and data augmentation. Finally we assessed the advantage of considering an ensemble method for the classification. For each condition, we compared different classifiers: cosine distance (with and without LDA) and PLDA, which are commonly used with x-vectors, and as a baseline, the MFCC-GMM technique we used in  [29].
PD patients were pharmacologically treated and their voice were recorded during ON-state (less than 12 hours after their last medication intake). Data from participants with technical recording issues, language disorders not related to PD (such as stuttering) or when deviation from the standardized procedure occurred, were excluded from the analysis. The ICEBERG cohort (clinicaltrials. gov, NCT02305147) was conducted according to Good Clinical Practice guidelines. All participants received informed consent prior to any investigation. The study was sponsored by Inserm, and received approval from an ethical committee (IRBParis VI, RCB: 2014-A00725-42) according to local regulations.
Preprocessing and MFCC extraction The first preprocessing regarding our high-quality microphone recordings was spectral subtraction  [7]. The aim of this denoising technique was to compensate for the mismatched environments, by removing additive and stationary noises. We applied it with the Praat software  [6] , using the 5s silence recorded at the end of each participant's session for the calibration. Regarding the telephone recordings, spectral subtraction was not performed because acoustic environments were not different between PD subjects and HC. We then extracted the log-energy and 19 MFCC, using Kaldi software  [46] , on 20ms overlapping windows, with a 10ms step. For the high-quality recordings, the 23 triangular mel bins covered a frequency range of. As for the telephone recordings, the frequency range of the mel bins was [300-3700Hz].
More details about the MFCC extraction methodology can be found in  [27]. The first derivatives (Deltas) and second derivatives (Delta-Deltas) were then computed and added to the feature vectors. Once the MFCC and their deltas extracted, we carried out Vocal Activity Detection (VAD), based on the log-energy, in order to remove silent frames. Finally, to complete denoising, cepstral mean subtraction  [48]  was performed on 300ms sliding windows, reducing linear convolutional channel effects on both databases.
Distribution modeling with Gaussian Mixture Models We split the databases into three groups per gender: one group of PD subjects and one group of controls for training, and the remaining PD and control participants for testing. In the laboratory setting database, we took 36 PD and 36 HC for the male training groups and 38 PD and 12 HC for the male test group. As for women, we considered 30 PD and 30 HC for training and 11 PD and 13 HC for the test. For the telephone database, we selected 30 PD and 30 HC for the male training groups and 33 PD and 6 HC for the male test group. For females we used 20 PD and 20 HC for training and 18 PD and 5 HC for the test. During the training phase, we built multidimensional GMM to model the MFCC distributions of each training group (see Figure  1 ). Means, SD and weights of the Gaussians (characterizing the GMM) were estimated via an Expectation-Maximization algorithm.
DNN training Since DNN training usually requires a lot of data, we used DNN trained on large speaker recognition databases and available online (http://kaldi-asr. org/models. html). For the analysis of our telephone recordings, we considered the pretrained DNN SRE16 model, described in  [60]. This DNN was trained on 5139 subjects from LDC catalog databases, including the Swichboard (Phase1,2,3 and Cellular 1,2), Mixer 6 and NIST SREs corpora. These databases contain telephone conversations and data recorded with a microphone, with English as the dominant language. Some data were directly sampled at 8 kHz, and the 16 kHz sampled recordings were then downsampled to 8 kHz. For the analysis of our high-quality microphone recordings, we used the voxceleb model, trained on the voxceleb database  [40].
Data came from video interviews of 7330 celebrities posted on Youtube. Voices were sampled at 16kHz. Finally, data augmentation, as described in section 2. 2. 2. 3, was applied to all these DNN training datasets. Those DNN were trained in the context of speaker identification. Inputs are log energy and MFCC extracted every 10ms from [2-4s] audio segments. For SRE16 model, 23 MFCC were extracted with a MEL bin range of. For voxceleb model, 30 MFCC were extracted with a bin range of. As for the MFCC-GMM analysis, a voice activity detection and cepstral mean subtraction were performed. Deltas and Delta-Deltas were not computed because the temporal context was already taken into account in the DNN. DNN architecture is detailed in Table  3. The neural networks were composed of 3 parts: -A set of frame-level layers taking MFCC as inputs.
These layers constituted a Time Delay Neural Network (TDNN) taking into account a time context coming from neighboring frames. -A statistics pooling layer aggregating the outputs (taking mean and SD) of the TDNN network across the audio segment. The output of this step was a large-scale (3000 dimensional) representation of the segment. -Last part was a simple feed forward network composed of two segment-level layers taking as input the result of the pooling layer, reducing its dimensionality to 512, and ending with a softmax layer. The softmax layer gave the probabilities that the input segment came from each speaker of the training database. For the results presented in section 3. 5, we trained a DNN with our own data (telephone recordings). The only difference in the DNN architecture was the size of the softmax layer output, which was two.
X-vector extraction In order to extract x-vectors from each subject of our databases we had to extract MFCC in the same way as it was done for the pretrained DNN. We extracted the log energy and 23 MFCC each 10ms for our telephone recordings (like SRE16 model) and 30 MFCC with log energy for our high-quality recordings (like voxceleb model). For the high-quality microphone recordings, we first had to downsampled them to 16kHz (from 96kHz), in order to match the sampling frequency used for the DNN training. Moreover, for this database as for the MFCC-GMM analysis, we carried out spectral subtraction to compensate for mismatched background noises. Voice activity detection and cepstral mean subtraction were also performed on both databases, like SRE16 and voxceleb models and as for our MFCC-GMM analysis.
Data augmentation Recently, enhanced speaker recognition with i-vectors and xvectors has been noted by augmenting data  [60]  for DNN and PLDA training. Data augmentation consisted in duplicating the data, adding additive noises and echo to the copies. Thus, this led to increased quantity and diversity of samples available for the training. In our analyses, data augmentation was performed during DNN training and we assessed its effect on LDA and PLDA training. We used 4 different types of data augmentation: -Echo: a reverberation was simulated by taking the convolution of our data with Room Impulse Response (RIR) of different shapes and sizes, available online (http://www. openslr. org/28). -Additive noise: different types of noises, extracted from MUSAN database (http://www. openslr. org/ 17), were added additively, every second.
-Additive music: musical extracts (from MUSAN database) were added as background noise. -Babble: three to seven speakers (from MUSAN database) were randomly selected, summed together, then added to our data. MUSAN and RIR NOISES databases were sampled at 16kHz, we downsampled them to 8kHz for the telephone recordings analysis. Half of the four augmented copies were finally randomly picked and added to our training database, multiplying by three the size of the latter.
RESULTS AND DISCUSSION In the following section we present the results of x-vector analysis compared to MFCC-GMM for both sexes and for both recording types (high-quality and telephone). We analyzed the effect of audio segment duration, data augmentation, gender, type of classifier (for each speech task), dataset used for DNN training and the choice of an ensemble method. More details about MFCC-GMM analysis (men only) can be found in  [29] , in particular regarding the comparison of high-quality microphone vs. telephone recordings, as well as speech task effects. Performances were measured with the Equal Error Rate (EER), i. e. the error rate corresponding to the threshold for which false positive ratio is equal to false negative ratio (i. e. sensitivity equal to specificity).
Impact of data augmentation In this section we assessed the impact of augmenting LDA and PLDA training data. Results obtained with and without data augmentation for LDA and PLDA training are detailed in Table  5  for the free speech and sentence repetition tasks and in Table  6  for DDK task. We observed 2-3% enhancement with data augmentation for the free speech task, but no consistent improvement for sentence repetition tasks or DDK tasks. This can be explained by the fact that data augmentation added phonetic variability which may have damaged the specificity of the phonetic content of the text-dependent tasks (like sentence repetitions, reading or DDK tasks). Data augmentation seems to be more suited to text-independent tasks (like free speech).
This was certainly due to the fact that these discriminant analyses reduced intra-class variance, and thus tackled the MFCC variability issue in women. LDA and PLDA reduced the classification performance gap between genders but did not suppress it entirely. The remaining differences may be explained by other factors. First, less pronounced brain atrophy  [61]  and less network disruptions  [23]  have been observed in the first stages of PD in women. In addition, the onset of symptoms is delayed on average by two years in women compared to men  [23]. A possible protective role of estrogen on PD has often been suggested to explain gender differences in early PD manifestations. Besides we can notice in our age-matched database a lower UPDRS III motor score in PD women compared PD men (see Table  1  and 2 ).
Comparison with DNN trained with our own data In order to make the DNN more suitable for the particular type of DDK tasks, we carried out an additional experiment, training this time the DNN with DDK tasks of our own data. The subjects used for DNN training were the same as those used for the constitution of the average x-vector P D and x-vector HC and the LDA and PLDA training. Remaining subjects were used for the test. The results obtained are presented in Table  6. We noticed a clear performance degradation when data augmentation was applied on LDA and PLDA training. This is consistent with the fact that data augmentation, while adding noises, impairs the specificity of the DDK phonetic content. Results obtained with cosine distance + LDA and PLDA, without data augmentation, were similar to those obtained with the previous pretrained DNN.
CONCLUSION According to the literature, the latest speaker recognition system, called x-vectors, provides more robust speaker representations and enhanced recognition, when large amount of training data is used. Our goal was to assess if this technique could be adapted to early PD detection (from recordings done with a high-quality microphone and by telephone) and improve the detection performances. We compared x-vector classification method to a more classic system based on MFCC and GMM. At first, we recorded 221 French speakers (including PD subjects recently diagnosed and healthy controls) with a high-quality microphone and with their telephone. Our voice analyses were based on MFCC features. The baseline consisted in modeling PD and HC distribution with GMM. For x-vector technique, MFCC were used as inputs of a feed-forward DNN from which embeddings (called x-vectors) were extracted then classified.
Since DNN training usually requires a lot of data, we used a DNN trained on large speaker recognition databases. All the analyses were done in a separate way for men and women, in order to avoid additional variability due to gender, and to study a possible gender effect on early PD detection. We varied several experimental and methodological aspects in order to analyze their effect on the classification performances. Influence of segment duration: We observed that using short audio segments that were matched between training and test provided better results. Comparison of back-end analyses: We compared different back-end analyses used with x-vectors. We noticed that the addition of LDA clearly improved the cosine distance classification and performed as well as a PLDA classifier. This improvement due to discriminant analyses was even more pronounced in women, whose voices are known to contain more variability.
Gender effect: We noticed lower performances in PD detection in women compared to men, with MFCC-GMM. This is consistent with a higher MFCC variability in women. X-vectors combined with LDA or PLDA handled this variability and led to 7 to 15% classification improvement. Differences between speech neural circuits in men and women and a disease less pronounced in women at the first stages may explain the remaining classification performance differences. Influence of dataset used for DNN training: In order to make the DNN more specific to DDK tasks, we carried out an additional analysis by training it this time with our database (from DDK tasks). The performances obtained were not improved compared to the pretrained DNN, showing the importance of data quantity on DNN training. Influence of ensemble method: Finally, we observed a 2-3% classification improvement when the ensemble method was used, for both MFCC-GMM and x-vectors classifiers.
ACKNOWLEDGMENTS The authors would like to thank  Samovar laboratory (especially Mohamed Amine Hmani and Aymen Mtibaa), CIC Neurosciences (especially Alizé Chalancon ,  Christelle Laganot  and  Sandrine Bataille ), sleep disorder unit and CENIR teams. The authors are also grateful to  Obaï Bin Ka'b Ali  and  Fatemeh Razavipour  for the manuscript revision. Finally, the authors would like to express their sincere acknowledgments to all the subjects who have participated in this study.
FUNDING L. Jeancolas was supported by a grant of  Institut Mines-Télécom ,  Fondation Télécom and Institut Carnot Télécom & Société Numérique  through " Futur & Ruptures" program. The ICEBERG study was partly funded by the program " Investissements d'Avenir "  ANR-10-IAIHU-06  ( Paris Institute of Neurosciences -IHU ),  ANR-11-INBS-0006 ,  Fondation EDF ,  Fondation Planiol ,  Société Francaise de Médecine Esthétique  ( Mr. Legrand ) and Energipole ( Mr. Mallard ).
Many analyses, such as read mapping  (Yorukoglu et al. , 2016; Shajii et al. , 2021) , genome assembly  (Pevzner et al. , 2004) , and taxonomic profiling  (Lu and Salzberg, 2020; Nazeen et al. , 2020) , have benefited from milestone advances that effectively compress, or sketch, the data  (Loh et al. , 2012) , for e. g. , fast full-text search with the Burrows-Wheeler transform (BWT)  (Burrows and Wheeler, 1994) , space efficient graph representations with succinct de Bruijn graphs  (Chikhi et al. , 2019) , and light-weight databases with MinHash sketches  (Ondov et al. , 2016). Large-scale data re-analysis initiatives  (Edgar et al. , 2020; Lachmann et al. , 2018)  further incentivize the development of efficient algorithms, as they aim to re-analyze petabases of existing public data. However, there has traditionally been a trade-off between algorithmic efficiency and loss of information, at least during the initial sequence-processing steps.
Ultra-fast, memory-efficient, and highly contiguous assembly of real HiFi reads using rust mdbg We evaluated our software, rust-mdbg, on real PacBio HiFi reads from D. melanogaster, at 100X coverage, and HiFi reads for human (HG002) at ~ 50× coverage, both taken from the HiCanu publication (https://obj. umiacs. umd. edu/marbl_publications/hicanu/index. html) (Nurk et al. , 2020). Since our method does not resolve both haplotypes in diploid organisms, we compared against the primary contigs of HiCanu and hifiasm. In our tests with D. melanogaster, the reference genome consists of all nuclear chromosomes from the RefSeq accession (GenBank: GCA_000001215. 4). Assembly evaluations were performed using QUAST  (Gurevich et al. , 2013)  v5. 0. 2 and run with parameters recommended in HiCanu's article  (Nurk et al.