dataesr/scientific-paragraphs-categorization huggingface dataset:
Trait measures were normalized by quantile transformation prior to the analyses. To assess the prevalence of anemia, we analyzed hospital discharge diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) together with self-declared illnesses. This information is available under data fields 41202 and 41204 (respectively main and secondary diagnoses) and data field 20002 of self-reported non-cancer medical conditions. The individuals we considered to be anemic were those with ICD-10 codes D50, D51, D52, D53, D55, D58, D59, D60, D61, and D64, and self-reported noncancer illness codes 1328, 1330, 1331, 1332, and 1446, as in Crawford et al. 29  A participant was coded as anemic if he or she had a diagnosis or self-reported the condition.
In both animal facilities, mice were provided water and food ad libitum and housed in 12 h long cycles of light and darkness. SMRT Sequencing of Bola2 À/À Mouse Genome Genomic DNA was isolated from the buffy coat layer of the blood of a male null mouse with the lacZ and neo cassettes excised. 32 We prepared one DNA fragment library (40-160 kbp inserts) with no additional shearing. After SMRTbell preparation, the library was size-selected using the BluePippin system (Sage Science) at a minimum fragment length cutoff of 40 kbp. Final library average size was 90 kbp. Single-molecule, real-time (SMRT) sequence data were generated using the PacBio Sequel instrument with Sequel Binding and Internal Ctrl Kit 3. 0, Sequel Sequencing Kit 3. 0, MagBead cleanup, diffusion loading, and acquisition times of 10 h movies.
Acknowledgments We thank families participating in the Simons Variation in Individuals Project (SVIP) and the 16p11. 2Consortium cohorts. G. G. is recipient of a  Pro-Women Scholarship  from the  Faculty of Biology and Medicine, University of Lausanne. This work was supported by grants from the  Swiss National Science Foundation  ( 31003A_160203  and  31003A_182632  to A. R. ), the  National Institutes of Health  ( R01HG002385  to E. E. E. ), Horizon2020 Twinning Project ePerMed ( 692145  to A. R. ),  Simons Foundation Autism Research Initiative  (SFARI # 303241  to E. E. E. , # 198677  to  R. A. B. , and # 274424  to A. R. ),  INSERM ,  CNRS, University of Strasbourg  ( ANR-10-LABX-0030-INRT  and  ANR-10-INBS-07-03 PHENOMIN  to Y. H. ),  and framework program Investissements d'Avenir  ( ANR-10-IDEX-0002-02  to Y.
Follow-up The course of the surgical procedure and of the postsurgical period was analysed. All patients were assessed daily for two days after the repair surgery and one week after in the outpatient setting. After the mean follow-up time of 9 years (8-12), 185 patients (70. 1%) treated using the Valenti method and 186 patients (71. 3%) treated using the Lichtenstein method were clinically assessed. In most cases, the reason for the loss to follow-up was a lack of contact, a non-treatment-related death, relocation, or refusal to give an answer (Fig. 1 ). A comparison of groups of patients lost to follow-up and of fully clinically assessed patients is presented in Table  1. All clinical data were registered in the National Hernia Registry. The rate of recurrence was assessed as the primary outcome in the study groups.
Standardised photographs were used for goniometer measurements of the following ROMs: forwards elevation, abduction, ER with the elbow by the side (ER1), ER with the arm abducted at 90° (ER2), IR with the elbow by the side (IR1), and IR with the arm abducted at 90° (IR2). IR1 was assessed according to the Constant score  [10]  (Table  1 ). IR1 was deemed satisfactory if the patient could actively place the hand on or above L3 and unsatisfactory otherwise. The L3 cut-off was chosen because it reflects the ability to independently perform toileting and dressing tasks. The pre-operative radiological work-up included radiographs taken under fluoroscopic guidance (antero-posterior views in neutral, internal, and external rotation and Neer's view) and computed tomography (CT) to assess the rotator cuff and to perform an automated analysis of glenoid version and inclination using 3D image analysis software (Glenosys; Imascap, Brest, France).
The radiographs were read and the parameters measured by two observers, who resolved any disagreements by consensus. Cases of osteoarthritis were classified as either concentric gleno-humeral osteoarthritis without massive rotator cuff tears or non-concentric gleno-humeral osteoarthritis with massive rotator cuff tear (Figure  1 ). The following were recorded on the pre-operative images: Gh0 angle defined by the horizontal and a line tangent to the glenoid, as described by Falaise  [2] , whose mean value was 94°±10. 2° (Figure  2 ); glenoid version and inclination as computed by the Glenosys software, whose mean values were 15°±12° (range, 6°-38°) and 7. 5°±11° (range, 17°-41°), respectively; and fatty muscle degeneration, recorded according to the Goutallier classification  [11]. The same clinical parameters were recorded for all patients during a visit 6 months after surgery.
The stem was cemented in 7 patients. The glenoid baseplate was positioned flush with the inferior rim of the glenoid cavity, taking care to tend towards an inferior orientation. Baseplate size was 25 in 22 patients and 29 in 14 patients. Glenosphere size was 36 in 32 patients and 42 in 4 patients. An eccentric glenosphere (2 mm) was used in 2 patients. After implantation of the final humeral prosthesis, trans-osseous sub-scapularis reimplantation was performed in 23 patients. This procedure was not feasible in the remaining 13 patients, due to excessive soft tissue tension (n=11) or to pre-operative absence of the subscapularis (n=2). The arm was immobilised on an abduction pad for 3 weeks after surgery. Gentle rehabilitation therapy was started immediately. Statistical tests were performed using XL STAT software (Addinsoft).
Middernacht et al. [23]  demonstrated that the reverse shoulder prosthesis functions like a hinged door and the anatomical prosthesis like a revolving door. IR requires combined extension and abduction  [16]. Lädermann et al. [15]  reported that extension was the main factor in the development of notching after RSA, due to the rapid development of impingement on the posterior scapular pillar. In any case, inferior glenosphere overhang places the humerus at a greater distance from the scapular pillar and correlated with better IR1 in our study and most previous reports. In contrast, neither glenosphere lateralisation using Bio-RSA nor post-operative glenosphere inclination were associated with IR1 outcomes. However, pre-operative glenoid inclination as measured by Glenosys software seems associated with better IR outcomes, perhaps by promoting optimal glenosphere positioning.
script to quantify leg activity levels. Regions of interests (ROI) for analysis were automatically drawn based on the center of mass (CM) of the thorax of the fly. Video images were converted into binary values using a threshold, and a time averaged image was calculated. Because the thorax of the fly was glued to a coverslip it was the only part of the fly that remained stationary throughout the video. To isolate pixels corresponding to the fly thorax, we identified those that did not change in intensity for more than 90% of the video. Pixels that did were converted to a background-related pixel. Next, the thorax of the fly and its CM, were extracted using the connected components method. From this binarized image, we calculated the area moment of inertia and aligned and centered the flies to the vertical axis.
The following primary antibodies were used: mouse monoclonal anti-Ubx (FP3. 38  [79]  1:500 from the Developmental Studies Hybridoma Bank) and chicken anti-GFP (Abacam Probes, 1:3000). The secondary antibodies were anti-mouse Alexa Fluor 555 (Invitrogen Molecular Probes, 1:1000) and anti-chicken Alexa Fluor 488 (Invitrogen Molecular Probes, 1:1000). RNA in situ hybridization in adult ventral nerve cords for the precursor RNA transcripts of miR-iab-4 was performed by designing 48 unique 20nt-probes labeled with Quasar 570 in the Stellaris platform from Biosearch Technologies, and using an adapted version of the protocol by  Raj A. , et al. , 2010 [80]. Images were acquired with a Leica SP8 confocal microscope, processed and analyzed using FIJI ImageJ  [81]. The VT006878 nerve-ending varicosities were quantified by measuring the puncta they covered in VT006878-labeled by myr::GFP in leg or VNC.
Composition of saline solution was as used previously  [83] : 108 mM NaCl, 5 mM KCl, 2 mM CaCl 2 , 8. 2 mM MgCl 2 , 4 mM NaHCO 3 , 1 mM NaH 2 PO 4 , 5 mM trehalose, 10 mM sucrose, 5 mM HEPES pH 7. 5. All imaging experiments were performed on a MOM-type two-photon microscope (designed by W. Denk, MPI, Martinsried; purchased from Sutter Instruments/Science Products) equipped with a mode-locked Ti:Sapphire, Chameleon Vision-S laser set at 927nm. Emitted fluorescence was detected with F48x573, AHF/Chroma, and a water immersion objective 20x/1,0 DIC M27 Zeiss was used for image acquisition. For image collection we used custom-made software running under IGOR pro 6 for Windows (Wavemetrics)  [84] , at 64 3 64 pixel resolution with 7. 8 frames/s image sequences for activity scans or 512 3 512 pixel images for high-resolution morphology scans.
All data analysis was performed using IGOR Pro 8 (Wavemetrics) and Fiji (NIH). In brief, image sequences were averaged and the ROIs corresponding to VT006878 neuron activity were defined semi-automatically by custom software  [85]. Then, Ca 2+ traces for each ROI were extracted and baseline correction applied, followed by z-normalization based on the time interval 1-6 s at the beginning of recordings using custom-written routines under IGOR Pro. And the expression of amplitude responses is represented by SD.
ACKNOWLEDGMENTS We thank members of the  Alonso lab  for helpful discussions and comments. We wish to thank  Filip Janiak ,  Takeshi Yoshimatsu , and  Tom Baden  ( Sussex Neuroscience ) for their assistance with multiphoton microscopy experiments and  Clare Hancock  for preliminary observations in this project. We thank  Welcome Bender ,  Ernesto Sanchez-Herrero , the  Bloomington Stock Centre , and the  Vienna Drosophila Resource Center  for fly stocks and  Alexandre Laborde  for assistance with software development. thank Collet and  Michael F. Land , FRS for valuable comments on the manuscript. This research was funded by a  Wellcome Trust  ( United Kingdom )  Investigator Award made  to C. R. A. ( 098410/Z/12/Z ).
2T YPOLOGY OF MULTI-LABEL DIMENSIONALITY REDUCTION METHODS Throughout the paper we consider a dataset with N instances described by a set of n x features and labeled by a set of n y labels. We denote by X (resp. Y ) the N × n x (resp. N × n y ) matrix describing the features (resp. the labels). As usually done in the literature, X (resp. Y ) also refers to the feature (resp. label) space when there is no ambiguity. The objective of the multi-label classification is to predict the right label vector y ∈ R ny for any feature vector x ∈ R nx. During a training phase, given the feature matrix X,a classifier is adjusted to fit its prediction to the label matrix Y. The vast majority of multi-label classification approaches based on dimensionality reduction follows a two-step process : (1) reduction of X or Y or both, (2) prediction of the labels from the reduced spaces with a classifier.
Coupling Dimensionality Reduction with the Classifier Objective As previously pointed out, a large majority of the reduction approaches are applied as a data pre-processing independent of the classification stage. But this procedure can turn out to be lacking in flexibility in some cases: its performances may be high for some problems and degrade some others. Indeed, it has been observed on many benchmarks that the impact of a reduction method on the classification performances varies with the classifier and the datasets  [48]. To overcome this limitation, some works have started investigating the coupling between dimensionality reduction and classification. At first glance, this approach consists in setting the coupling as a multi-objective optimization problem which tries to optimize both the reduction and the classifier objectives (resp.
With the large set of algorithms to be considered, this would require a considerable time-consuming effort, and consequently the exploitation of the outcomes of the existing published research works appears as a more realistic alternative. The experimental protocols (datasets, classifiers, performance measures, etc. ) varying from one publication to another, we here propose a new meta-analysis methodology. Often defined as "the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings"  [148] , the meta-analysis has known an increasing development from its pioneering works in the 30's  [149]   [150]. One of its favored field is medicine where aggregation of the available pieces of information is required to make as rational as possible decisions.
More precisely, the algorithms have been re-implemented (Python language) and tested in the same computational environment (standard computer with 16Gb of RAM). We used ten multi-label datasets often selected in previous multi-label learning studies. They are divided into train/test sets in the Python library scikit-multilearn 2. The feature (resp. label) dimensionality varies from 72 to 1836 (resp. from 6 to 983). The reduction methods are combined with the ML-kNN classifier and the parameter settings are extracted from the publications. For each method, ten dimensions k x have been tested (from 10 to 100 percent of the feature dimensionality n x ) and the best results with the F1-score in the measure set M and with the Hamming Loss (H) are presented in Figure  4. With the average rank criterion, SSMDMM outperforms MVDM and MDDM for both measures and MVDM is better (resp.
worse) than MDDM for M (resp. H). However, when getting into details, we observe that the results may depend on the datasets: e. g. SSMDMM obtains poor results on the Birds' dataset. This confirms the interest of the meta-analysis which aggregates results gathered on different experiments involving a variety of datasets. Even if a method outperforms the others on average on a limited number of datasets, it is also worth considering those that do not dominate it in a meta-analysis. The community C 2 is very small in G D (H): the authors of the algorithms of C 2 are mostly interested in data with a large number of labels and give more importance to ranking measures than to global classification errors such as Hamming Loss. Consequently, there are no methods of C 2 simultaneously dominant for the two measures.
This result confirms the interest of this double analysis. The dominated methods for the two measures are HSL, DMLDA, MLSI, and CCA. They all belong to C 1 or C 1-2 due to the lack of Hamming Loss measurements in C 2. They illustrate the bibliographic effect: they are early methods that have been dominated by more recent proposals. More precise interpretations should be made very carefully. HSL performs poorly in the available experiments but it has been considered only once in the corpus C. Moreover, CCA might have been implemented in the majority of papers with a version that is not the optimal one. In the experiments, the feature, label, and feature/label covariance matrices are often badly conditioned due to the multi-label dataset characteristics and it is known that the integration of a slight regularization with a Penrose generalized inversion leads to much better performances  [92].
A dual examination of domination relationships completes the analysis by pointing out the methods dominated for the two measures. However, from a methodological point of view the generalization of the conclusions should be considered cautiously. As numerous pairwise comparisons are absent of the published experiments, the meta-analysis has been computed on a non-complete graph. Moreover, the heterogeneity of both the datasets used in the different studies and the number of times each algorithm was evaluated add biases to the comparisons. However, despite these limitations, we believe that this first metaanalysis can help identify recurrent properties in the most efficient approaches and also flaws in the experimental protocols (e. g. the lack of some pairwise comparisons). More broadly speaking, the growth of publications in machine learning will certainly foster meta-analysis procedures in the near future.
Area Under Curve (AUC) Calculations and Statistical Analyses Slope calculations by linear fitting of initial accumulation rate and statistical analyses were carried out with GraphPad Prism 5 software (GraphPad Software, San Diego, CA, USA). The slope values are expressed as means ± SD of three independent experiments. For all single comparisons between each dose of sample and control, one-tailed paired Student's t-test was performed after checking for distribution normality. For all statistical analyses, p < 0. 05 was considered significant.
mMCD referred to surgical cases with blurred grey-white matter boundaries and excess of heterotopic neurons in cortical layer 1 (mMCD1) or in the deep white matter (mMCD2). Blood samples from patients and their parents (when available) were collected. Genomic DNA was extracted according to the standard procedures from blood samples and frozen brain sections or bulk tissue. This study was approved by the ethical committee CPP Ile de France II (N° ID-RCB/EUDRACT-2015-A00671-48) and is registered on ClinicalTrials. gov (N° NCT02890641). Written informed consent was obtained from all participants (or their parents on behalf).
Detection and validation of somatic variants Hybrid capture sequencing targeting coding exons and exon-flanking junctions (10 bp) were performed (Roche NimbleGen SeqCapEZ Human custom panel). We used three updated versions of a panel comprising mTOR pathway and FCD candidate genes, including SLC35A2. Genes common to all versions are: AKT3 (NM_005465), DEPDC5 (NM_001242896), MIOS (NM_019005), MTOR (NM_004958), NPRL2 (NM_006545), NPRL3 (NM_001077350), PIK3CA (NM_006218), PTEN (NM_000314), SEC13 (NM_183352), SEH1L (NM_001013437), TSC1 (NM_000368), TSC2 1 3 (NM_000548), WDR24 (NM_032259), and WDR59 (NM_030581). BRAF (NM_004333) was included in the two last versions of the panel, and SLC35A2 (NM_005660) and RHEB (NM_005614) were included in the last panel only. A full list of the genes included in each version of the panel is available in Supplemental Data (Online Resource 1).
Droplet digital PCR (ddPCR) To validate recurrent somatic variants and assess the neural cell subtypes carrying the variants, we performed droplet digital PCR (ddPCR) (QX200 system, Bio-Rad Laboratories), a highly sensitive and specific technique based on sample partitioning following a Poisson distribution and allowing DNA absolute quantification. All reactions were prepared using the ddPCR Supermix for probes according to the manufacturer's protocol. Specific ddPCR Mutation Detection Assays (FAM + HEX) were purchased from Bio-Rad to detect the MTOR variants p. Ser2215Phe and p. Thr1977Lys and the PIK3CA variant p. His1047Arg. Data were analyzed using the QX200 droplet reader and Quantasoft Analysis Pro software version 1. 0. 596. Validation of recurrent variants was performed using 50 ng of bulk brain DNA and blood DNA, leading to a statistically reliable quantification of the VAF in each sample.
Balloon cells (BCs) counting To calculate the density of BCs in selected samples (five panel-negative and four panel-positive FCD2b cases), we performed H&E staining on three 20 μm frozen brain section adjacent to the sections used for DNA extraction, scanned the slides with a Nanozoomer scanner (Hamamatsu) at a 40X resolution, captured the images with the software NDP. view2, and counted the number of BCs present in each entire section. For every patient analyzed, we subsequently calculated the BCs density mean by dividing the total BCs count by the total area of the section.
We discovered a strong VAF enrichment (up to 45%) in both pools of DNs and BCs compared to morphologically normal neurons (down to 1. 4%) and glial cells (down to 1. 4%) or bulk tissue (Fig. 6 ). This finding indicates that > 90% of DNs and BCs indeed carry the pathogenic variants. We further confirmed these results in two additional FCD2a cases with MTOR:p. Ser2215Phe variant (Supplementary Table  2 , Online Resource 1). In the PIK3CA:p. His1047Arg HME case, we found an enrichment of the somatic variant in glial cells ( ~ 60% of cells), probably reflecting an early mutational event in common neuroglial progenitors. Demographic clinical data for the full cohort or stratified along with the nature of the mutated gene (numbers of patients in each group is indicated). For patients who underwent multiple surgeries, clinical data and histopathological diagnoses from the last surgical procedure are indicated.
As an example, FCD2 patients for whom surgery and more than two antiepileptic drugs failed to control seizures are enrolled in an ongoing phase-3 clinical trial using the mTOR-inhibitor everolimus (NCT03198949). In SLC35A2-related patients, a galactose supplementary diet may improve seizures as previously reported in a case of UDP-galactose transporter deficiency  [13]. Recently, groundbreaking results were obtained with the administration of PIK3CA inhibitors to patients with CLOVES syndrome, a genetic disorder resulting from somatic gainof-function variants in PIK3CA  [47]. HME patients, sometimes associated with CLOVES syndrome, might represent good candidates for PIK3CA inhibitors-based treatment. These novel therapeutic approaches should be considered in patients not eligible for surgery or after an incomplete resection (when seizure freedom is not achieved) or before surgery in patients in which mosaic variants can be detected in peripheral samples (blood, buccal swab or cerebrospinal fluid), as in TSC cases.
Acknowledgements We thank the children and families that took part in this study, and neuropediatricians who referred the children to the  Rothschild Foundation hospital. We also gratefully acknowledge  Eric Noé ,  Delphine Taussig ,  Mustapha Talatizi  and  Ida Viken  for their assistance on this project. We also thank the ICM core facilities: iGenSeq, Histomics, DNA and cell bank. This work was funded by the  European Research Council  ( No 682345  to SB), the program " Investissements d'avenir "  ANR-10-IAIHU-06 , the  Fondation pour la Recherche Médicale  ( ECO20160736027  to TR and  FDT20160736468  to EM).
Considering the tertiary referral nature of the enrolling centers, multimodal imaging was available for all subjects. Imaging included: infrared reflectance (IR), blue-light fundus autofluorescence (BAF), and structural spectral domain optical coherence tomography (SD-OCT) that were all performed using an HRA þ OCT system (Spectralis; Heidelberg Engineering, Heidelberg, Germany). Fluorescein angiography (FA) and indocyanine green angiography (ICGA) were also performed and the data analyzed when available (e. g. , in case of suspect NV). FA and ICGA were performed using an HRA þ OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany) or a retinal camera (TRC-501X; Topcon, Inc. , Tokyo, Japan). For pseudophakic patients, AL measurements were derived from the biometric calculations, acquired with a biometer (IOLMaster; Carl Zeiss Meditec AG, Jena, Germany).