dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast a significant over-representation of NS mutations was observed in the IDRs for all viruse (Figures  3  and S3 ). Altogether, those results indicate that IDRs are more prone to accumulate NS muta By contrast, for NS mutations, a large and significant difference between the experimental and simulated data could be observed (p < 0. 02, χ 2 for each of the four simulations, Table  2 ). Indeed, the ratio higher than 1 observed in the case of the experimental data indicates an over-representation of NS mutations within the IDRs compare to the ORs (Figure  3 ). In the case of PVY, this difference with simulated mutations was only verified for the PVY 2017 dataset  [12]  (Table  2 ). With respect to the analysis of natural diversity, no differences were observed between S mutations within IDRs and ORs, in agreement with simulated data.
The higher the score, the higher the likelihood of substitution. It was observed that the highest replacement probabilities are correlated to amino acids with similar physicochemical properties (e. g. , charges, hydrophilicity-hydrophobicity, amino acid size)  [21]. Upon amino acid substitutions, the more drastic physicochemical changes are (the lower the score value), the more destabilizing these changes are in terms of structure. For each virus (TEV, PVY, and TuMV), we assessed whether the natural selection discriminated differently within IDRs and ORs for amino acid substitutions with respect to their impact on biophysical changes. For each type of region (IDRs or ORs), a comparative statistical analysis (Dunn test) was thus performed between the natural diversity, experimental, and simulated datasets (Tables  3  and S4 ).
For PVY and TuMV, the BLOSUM62 scores associated to amino acid substitutions in IDRs of biological and simulated data belong to the same statistical group. In the case of TEV, the natural diversity data shows a slight difference for three of the four simulations. For TEV, this diversity was represented by a set of only 6 genomes, while those of PVY and TuMV were illustrated by 100 genomes each. This could explain the apparent discrepancy. In contrast, for both the natural diversity and experimental evolution of all three potyviruses, amino acid substitutions present in the ORs have a significantly higher BLOSUM62 score than the simulated data. The high BLOSUM62 score observed as associated to ordered regions supports the idea that amino acids substitutions occurring in those regions are globally poorly destabilizing at the physicochemical and structural level.
Discussion Potyviruses constitute, together with begomoviruses, the two largest viral genera described to date among plant viruses  [26, 27]. Potyviruses are very damaging to field crops and embrace a very wide host range  [28]. Most of them are generalists and, as such, provide a rich model for studying viral adaptation. In this study, we tested the hypothesis that among these viruses, the amount of non-synonymous mutations (NS) was greater in the disordered regions of their proteomes than in the ordered regions. We analyzed the distribution of mutations in the genomes of potyviruses belonging to three different species (i. e. , PVY, TEV and TuMV) which are representatives of their genus. The datasets used included viral genomes resulting from both shorter (experimental evolution) and longer evolutionary scales along with the use of natural diversity.
An analysis of the two datasets showed that IDRs and ORs are subject to different evolutionary mechanisms, with disordered regions evolving towards significantly more amino acid polymorphism than ordered regions. The selection pressure that applies to ordered regions thus tends towards a conservative evolution while that which applies to disordered regions rather supports a divergent evolution. It is quite easy to understand the evolutionary mechanism at work in the ordered regions. These protein regions have a strong structure-function relationship. At the molecular level, these regions are defined by geometries of constrained atomic interactions with few degrees of freedom and well-packed hydrophobic cores. These regions have significantly higher BLOSUM62 scores than would result from random substitutions.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/v14091959/s1, Table  S1 : Mutations in coding sequences detected during experimental evolution in PVY 2015 1. (A) and PVY 2017 2. (B) or in TEV 2015 3. (C). Table  S2 : List PVY, TuMV and TEV isolates used to the natural diversity dataset; Table  S3 : Adapted promotor score; Table  S4 : Differences in physicochemical properties associated with amino acid substitutions were assessed using scores derived from the BLOSSOM62 substitution matrix; Table  S5 : Dunn test (p value adjustment method : Bonferroni) of the order/disorder promoting score of each amino acid of the reference genome targeted by substitutions before substitution (A), of the amino acid.
A recent systematic literature review by  Liu et al. (2019)  suggested that the global prevalence of CHD continued to increase between the years 1970 and 2017 and identified regional differences in CHD prevalence. Khoshnood et al. (2013) , using data from the European Surveillance of Congenital Anomalies (EUROCAT) for 1990-2007, found that the direction of CHD prevalence trends changed over time, with the total prevalence having increased prior to 2004 and reduced thereafter. Similarly, a population-based study in Norway  (Leirgul et al. , 2014)  reported that the prevalence of severe CHD (heterotaxia, conotruncal defect, atrioventricular septal defect [AVSD], anomalous pulmonary venous return  [APVR] , left ventricular outflow tract obstruction  [LVOTO] , right ventricular outflow tract obstruction  [RVOTO] , or other complex heart defect) increased from 1994 to 2004 and decreased thereafter by 3.
| METHODS EUROCAT is a European network of population-based registries for the epidemiological surveillance of congenital anomalies (https://eu-rd-platform. jrc. ec. europa. eu/ eurocat_en). Forty-three registries in 23 countries use high quality multiple sources to collect data on congenital anomalies occurring in live births, fetal deaths from 20 weeks gestation and terminations of pregnancy for fetal anomaly at any gestation (TOPFA). All registries use the WHO International Statistical Classification of Diseases and Related Health Problems version 9 or 10 (ICD-9 or ICD-10) according to the birth year; from birth year 2005 the cases must be reported to the Central Registry of EUROCAT coded in ICD-10, with an additional digit extension (optional) for the British Paediatric Association (BPA) revision.
As the data from the different registries were for different time periods, a simple analysis of CHD prevalence over time might introduce confounding. Thus, a Poisson multilevel regression approach was used for all nonsyndromic CHDs and for the three CHD severity groups. The total number of CHD births per year were nested within registry and modeled by random-effects models (Poisson regression) with a random intercept, an offset equal to the log of the expected number of CHD births, and time (year) as a continuous predictor. The random component of the intercept was used to take into account heterogeneity that may exist across the registry. All analyses were performed using Stata software (version 14; StataCorp, College Station, TX) for both descriptive and multilevel analysis.
| DISCUSSION This study examined trends in prevalence of nonsyndromic CHD in Europe overall and by CHD severity group between the years 2008 and 2015 and investigated whether the rates have continued to decline as had been reported previously  (Rankin et al. , 2005). Based on data for 26,115 cases of nonsyndromic CHD from 25 European population-based registries, we found that both total and live birth prevalence remained stable overall and across the three CHD severity groups. Khoshnood et al. (2013)  using EUROCAT data for the period 1990-2007, found that the direction of trends in total prevalence of CHD cases not associated with a chromosomal anomaly changed over time with the total prevalence having increased prior to 2004 and reduced thereafter. However, the Khoshnood study found no trend in total prevalence of the most severe group (Severity group I), while the prevalence of Severity group II increased until 2000 and reduced thereafter.
, 2011). In our dataset, the maximum age at diagnosis varies according to the registry policies of registration (some registries include anomalies diagnosed up to 1 week, up to 1 year of after 1 year). This difference in methodology contributes to the difficulty in comparing prevalence between registries, especially in the less severe group of CHDs that can be diagnosed several months after birth and therefore may not reported in all registries. This is consistent with the study by Hoffman et al. who reviewed 62 studies and found large variations in the age of diagnosis (EUROCAT Central Registry University of Ulster, 2009). The main strength of this study is the use of highquality population-based data derived from an established network of European congenital anomaly registries where both multisource case ascertainment and accurate diagnoses are achieved.
| Offline analyses Maps were analyzed retrospectively using a noncommercial version of Carto ® 3 V8. The substrate map was analyzed using a new late potential map (LPM) software, which includes: a LAVA annotation algorithm; a localized bipolar voltage assessment; and an algorithm for automatic regional vector analysis, allowing to determine the directionality of the electrical wavefront and to display a quantitative assessment of LCV 12 -Supporting Information. Of note, localized bipolar voltage assessment allows automatic and dynamic adjustment of the window of interest focusing on the near-field signal and excluding any far-field component from the measurement. All electrograms (EGM) were reviewed by two operators (G. L. S. and N. C. D. ) and a third reviewer (J. S. ) gave his interpretation in case of discrepancies.
Introduction Learning with few examples, or few-shot learning, is a domain of research that has become increasingly popular in the past few years. Reconciling the remarkable performances of deep learning (DL), which are generally obtained thanks to access to huge databases, with the constraint of having a very small number of examples may seem paradoxical. Yet, the answer lies in the ability of DL to transfer knowledge acquired when solving a previous task toward a different, new one. The classical few-shot setting consists of two parts: • A base dataset, which contains many examples of many classes. Since this dataset is large enough, it can be used to efficiently train DL architectures. Authors often use the base dataset alongside a validation dataset. As is usual in classification, the base dataset is used during training, and the validation dataset is then used as a proxy to measure generalization performance on unseen data and, therefore, can be leveraged to optimize the hyperparameters.
However, contrary to common classification settings, in few-shot, the validation and base datasets usually contain distinct classes, so that the generalization performance is assessed on new classes  [1]. Learning good feature representations from the base dataset can be performed with multiple strategies, as will be further discussed in Section 2; • A novel dataset, which consists of classes that are distinct from those of the base and validation datasets. We are only given a few labeled examples for each class, resulting in a few-shot problem. The labeled samples are often called the support set and the remaining ones the query set. When benchmarking, it is common to use a large novel dataset from which artificial few-shot tasks are sampled uniformly randomly, what we call a run. In that case, the number of classes n (named ways), the number of shots per class k, and the number of query samples per class q are given by the benchmark.
This setting is referred to as n-way-k-shot learning. Reported performances are often averaged over a large number of runs. In order to exploit knowledge previously learned by models on the base dataset, a common approach is to remove their final classification layer. The resulting models, now seen as feature extractors, are generally termed backbones and can be used to transform the support and query datasets into feature vectors. This is a form of transfer learning. In this work, we do not consider the use of additional data such as other datasets  [2] , nor semantic information  [3]. Additional preprocessing steps may also be used on the samples and/or on the associated feature vectors, before the classification task. Another major approach uses meta-learning  [4] [5] [6] [7] [8] [9] , as mentioned in Section 2.
It is important to distinguish two types of problems: • In inductive few-shot classification, only the support dataset is available to the fewshot classifier, and prediction is performed on each sample of the query dataset independently of each other  [9] ; • In transductive few-shot classification, the few-shot classifier has access to both the support and the full query datasets when performing predictions  [10]. Both problems have connections with real-world situations. In general, inductive few-shot corresponds to cases where data acquisition is expensive. This is the case for FMRI data, for example, where it is difficult to generalize from one patient to another and collect hours of training data on a patient could be harmful  [11]. Alternatively, transductive few-shot corresponds to cases where data labeling is expensive.
Such a situation can occur when experts must properly label data, but the data themselves are obtained cheaply, for instance in numerous medical applications  [12, 13]. In recent years, many contributions have introduced methodologies to cope with few-shot problems. There are many building blocks involved, including distillation  [14] , contrastive learning  [15] , episodic training  [16] , mixup  [17] , manifold mixup  [1, 18] , and self-supervision  [1]. As a consequence, it can appear quite opaque what the effective components are and whether their performance can be reproduced across different datasets or settings. Moreover, we noticed that many of these contributions report baseline performances that can be outperformed with a simpler training pipeline. In this paper, we are interested in proposing a very simple method combining components commonly found in the literature and yet achieving competitive performance.
• We show the ability of the proposed methodology to reach or even beat state-of-theart  [9, 19]  performance on multiple standardized benchmarks of the field. We also propose a simple demonstration of our method using live video streaming to perform few-shot classification. The code is available at https://github. com/RafLaf/ webcam accessed on 14 June 2022. We first train multiple backbones using the base and validation datasets. We use two cross-entropy losses in parallel: one for the classification of base classes and the other for the self-supervised targets (rotations). We also use manifold mixup  [18]. All the backbones are trained using the exact same routine, except that their initialization is different (random) and the order in which data batches are presented is also potentially different.
AS: Then, for each image in the novel dataset and each backbone, we generate multiple crops, then compute their feature vectors, which we average. E: Each image becomes represented as the concatenation of the outputs of AS for each of the trained backbones. Preprocessing: We add a few classical preprocessing steps, including centering by removing the mean of the feature vectors of the base dataset in the inductive case, or the few-shot run feature vectors for the transductive case, and projecting on the hypersphere. Finally, we use a simple nearest class mean classifier (NCM) if in the inductive setting or a soft K-means algorithm in the transductive setting.
Data Augmentation First, data augmentation or augmented sampling are generally used on the base dataset to artificially produce additional samples, for example using rotations  [1] , crops  [20] , jitter, GANs  [21, 22] , or other techniques  [23]. Data augmentation on support and query sets, however, is less frequent. Approaches exploring this direction include  [15] , where the authors propose to select the foreground objects of images by identifying the right crops using a relatively complex mechanism, and  [24] , where the authors propose to mimic the neighboring base classes' distribution to create augmented latent space vectors. In addition, mixup  [17]  and manifold mixup  [18]  are also used to address the challenging lack of data. Both can be seen as regularization methods through linear interpolations of samples and labels.
Augmented Samples We propose to generate augmented feature vectors for each sample from the novel dataset. We did not perform this in the validation set as it is very computationally expensive. To this end, we used random resized crops from the corresponding images. We obtained multiple versions of each feature vector and averaged them. The literature has extensively studied the role of augmentations in deep learning  [41]. Here, we assumed most crops would contain the object of interest. Therefore, the average feature vector can be used. On the other hand, color jitter might be an invalid augmentation since some classes rely extensively on their colors to be detected (e. g. , birds or fruits). In practice, we used = 30 crops per image, as larger values do not benefit accuracy much. This step is optional.
Feature Vector Preprocessing Finally, we applied two transformations as in  [35]  on feature vectors z. Denote z the average feature vector of the base dataset if in the inductive setting or of the few-shot problem if in transductive setting. The ideal z would center the vectors of the few-shot runs around 0 and, therefore, would be the average vector of the combined support and query set. The number of samples being too small to compute a meaningful average vector in the inductive setting, we made use of the base dataset. In the transductive setting, queries are added to the support set for mean computation. The average vector is therefore less noisy and can be used to compute z. The first operation (C-centering of z) consists of computing: z C = z -z. ( 1 ) The second operation (H-projection of z C on the hypersphere) is then: z CH = z C z C 2.
(6) Contrary to the simple K-means algorithm, we used a weighted average where weight values are calculated via a decreasing function of the L 2 distance between data points and class barycenters-here, a softmax adjusted by a temperature value β. In our experiments, we used β = 5, which led to consistent results across datasets and backbones. In practice, we use a finite number of steps. By denoting c ∞ i the resulting vectors, the predictions are: ∀z ∈ Q : C tra (z, [c 1 ∞ ,. , c n ∞ ]) = arg min i z -c i ∞ 2. ( 7 ).
Ranking on Standard Benchmarks We first report results comparing our method with the state-of-the-art using classical settings and datasets. We used the following datasets: For each method, we specified the number of trainable parameters and the accuracy of 1-shot or 5-shot runs. Experiments always used q = 15 query samples per class, and results were averaged over 10,000 runs. Results are presented in Tables  1 2 3 4 5 for the inductive setting and Tables 6-10 for the transductive setting (the codes allowing for the reproduction of our experiments are available at https://github. com/ybendou/easy). Reported results for the existing methods are those specified by their respective papers. Some methods do not include their standard deviation over multiple runs. Let us first emphasize that our proposed methodology shows a new state-of-the-art performance for MiniImageNet (inductive), TieredImageNet (inductive 1-shot setting) and FC100 (transductive), while showcasing competitive or overlapping results on other benchmarks.
Ablation Study To better understand the relative contributions of components in the proposed method, we also compare, for each dataset, the performance of various combinations in Table  11  for the inductive setting and Table  12  for the transductive setting. Interestingly, the full proposed methodology (EASY) is not always the most efficient. We believe that for large datasets such as MiniImageNet and TieredImageNet, the considered ResNet12 backbones contain too few parameters. When reducing this number for ensemble solutions, the drop of performance due to the reduction in size is not compensated by the diversity of the multiple backbones. All things considered, only AS is consistently beneficial to the performance.
Discussion Regarding the inductive setting, the proposed method achieves state-of-the-art performance on MiniImagenet by a fair margin. On TieredImagenet, only S2M2R performs better in the five-shot setting. This can be explained by the fact that TieredImagenet is the largest of the considered datasets and it requires more parameters to be trained efficiently, reducing the effectiveness of the proposed ensemble approach. We also noticed subpar performance on CIFAR-FS, and we believe that is due to the small resolution of images in the dataset, which cripples the augmented sample step. On the FC-100 dataset, our results in the inductive setting overlap with  [46] ; however, our method has the advantage of having lower confidence intervals compared to other methods on the same benchmark. Regarding the transductive setting, our method achieves competitive results without any prior on the number of classes.
Figure1. Illustration of our proposed method. Y: We first train multiple backbones using the base and validation datasets. We use two cross-entropy losses in parallel: one for the classification of base classes and the other for the self-supervised targets (rotations). We also use manifold mixup [18]. All the backbones are trained using the exact same routine, except that their initialization is different (random) and the order in which data batches are presented is also potentially different. AS: Then, for each image in the novel dataset and each backbone, we generate multiple crops, then compute their feature vectors, which we average. E: Each image becomes represented as the concatenation of the outputs of AS for each of the trained backbones. Preprocessing: We add a few classical preprocessing steps, including centering by removing the mean of the feature vectors of the base dataset in the inductive case, or the few-shot run feature vectors for the transductive case, and projecting on the hypersphere.
Patients inclusion and data collection Every consecutive patient older than 18 years of age admitted to the medical intensive care unit (ICU) at Bordeaux University Hospital in October and November 2019 was prospectively screened to participate to Microbe study (NCT04131569) and included in this ancillary analysis. Data were prospectively recorded by physicians in charge of the patient by questioning the patients, patients' family and patients' general practitioners. Electronic worksheet was completed by two medical intensive care residents. Comorbidities were defined as follows: chronic obstructive pulmonary disease and asthma were defined according to lung function testing. Chronic heart failure was defined according to transthoracic echocardiography and chronic coronary disease based on stress test or percutaneous coronary intervention.