dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Cécile Duvillier  holds a PhD grant from  Grenoble Alpes University. The authors are grateful to  Margherita Maggioni , the anonymous referee and  Yves Bühler , whose insightful comments helped produce a better paper. IGE/INRAE is a member of  LabEx OSUG. Financial support. This research has been supported by  Grenoble Alpes University  and the  French National Research Agency  through the " Statistical modelling for the assessment and mitigation of mountain risks in a changing environment" (SMARtEN ) programme  (grant no. ANR-20-Tremplin-ERC8-0001 ). Review statement. This paper was edited by  Yves Bühler  and reviewed by  Margherita Maggioni  and one anonymous referee.
Code availability. The proposed PRA detection method has been developed using the free QGIS software and environment. It combines existing algorithms from the free software QGIS, SAGA, GDAL and GRASS and from ArcGIS (for watershed delineations). Scripts can be requested from Cécile Duvillier. Data availability. The source data and results of this study are available at https://doi. org/10. 5281/zenodo. 6517730  (Duvillier et al. , 2022). These data can be used to reproduce all the results of https://doi. org/10. 5194/nhess-23-1383-2023 Nat. Hazards Earth Syst. Sci. , 23, 2023.
The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks twenty-one years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000), and provides a detailed overview of global genotype and antimicrobial resistance (AMR) distribution and temporal trends, generated using open analysis platforms (GenoTyphi and Pathogenwatch). Compared with previous global snapshots, the data highlight that genotype 4. 3. 1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR.
Infant pose estimation is crucial in different clinical applications, including preterm automatic general movements assessment. Recent infant pose estimation methods are limited by a lack of real clinical data and are mainly focused on 2D detection. We introduce a stereoscopic system for infants' 3D pose estimation, based on fine-tuning state-of-the-art 2D human pose estimation networks on a large, real, and manually annotated dataset of infants' images. Our dataset contains over 88k images, collected from 175 videos from 53 premature infants born <33 weeks of gestational age (GA), acquired within the Neonatology department of the Centre Hospitalier Universitaire de Saint Etienne, France, between 32 and 41 weeks of GA. This framework significantly reduced the pose estimation error compared to existing 2D infant pose estimation networks.
However, this method remains subjective and time-consuming. This motivates the need for automated GMA, especially with the continuous progress in computer vision and artificial intelligence tools. To achieve this goal, a primary task is to track preterm infant movements and estimate their poses in the three dimensions of space using a markerless tool adapted to this specific population. Thanks to the advances in deep learning, many reliable neural models are available for automatic 2D and 3D human pose estimation  [6, 8, 38]. However, as these models are trained on general-purpose datasets containing mainly adult persons  [2, 20] , they perform poorly for infants who are anatomically different. To solve this problem, researchers retrained 2D neural networks either on synthetic datasets  [16]  or on datasets collected from the internet  [7].
In both cases, the training data was not representative enough of the target population of preterm infants hospitalized in the Neonatal Unit. Moreover, 2D movement analysis remains limited since it does not exploit the overall infants' movement information in space, and information loss can go up to 53% due to dimensionality reduction  [3]. On the other hand, 3D analysis can be advantageous since it provides a complete analysis of infant movements, which allows an objective classification of these movements as normal and abnormal according to their complexity, variability, and fluidity. The purpose of this work was first to introduce a real and representative dataset of preterm infants and second to propose a markerless, accurate, and safe 3D pose estimation framework to automatically assess preterm infants' spontaneous movements.
The main contributions of this work are  (1)  to propose the first framework for 3D infant pose estimation from stereoscopic images dedicated to preterm infants hospitalized in the Neonatal Unit;  (2)  to create a fully annotated dataset of real preterm infants' images with a clinical protocol that follows the guidelines for the GMA  [9] ;  (3)  to provide models for the main state of the art convolutional neural network (CNN) architectures specifically retrained with the new dataset;  1   (4)  to perform an evaluation of these models for 2D and 3D infant pose estimation, providing a new baseline in this field.
These motion images were used to calculate Motiongrams, Motion quantities, and motion centroids. A step further was made with the use of optical flow algorithms. Stahl et al  [37]  performed motion extraction using optical flow then feature extraction using wavelets and frequency analysis to classify infants' fidgety movements. Many others opted for large displacement optical flow (LDOF)  [27, 30, 31]  to track the movements and obtain the movements' velocities. These methods have many limitations, especially in the case of occlusions and illumination changes. In the last decade, convolutional neural networks have revolutionized the task of pose estimation for adults and achieved very promising results on the different state-of-the-art datasets (e. g. COCO  [20]  or MPII  [2] ). This inspired many works on infants' pose estimation  [11, 22, 24, 32, 33] , and all of them are based on the Openpose architecture  [6]  which was trained on datasets containing only images of adults who are structurally and anatomically different compared to infants  [34].
To address this problem, Chambers et al. [7]  have retrained the Openpose network with their own labeled dataset of infant pose consisting of 9039 infant images collected partially from videos on Youtube and from clinical data, which reduced the mean error by 60%. In addition to the fact that Openpose lacks sufficient scaling of network depth and its computational insufficiency  [14] , the ground truth body joints annotation process using Vatic  [7]  was not very accurate and was missing many occluded and not visible joints as can be seen from their publicly available dataset. The absence of open-source infants pose images mainly for privacy concerns is a problem that many researchers have tried to address. Hesse et al. [15]  introduced a synthetic dataset of infants, called MINI-RGBD for Moving INfants In RGB-D, created from only 12 RGB-D videos of moving infants using a textural mapping to a 3D model mesh called SMIL for Skinned Multi-Infant Linear model.
Both its simplicity and exclusively synthetic nature cause the pose estimation models trained on MINI-RGBD not to generalize well on real-world infant images  [16]. This is why Huang et al. [16]  created a hybrid synthetic and real infant pose dataset (SyRIP) from real images collected from YouTube and Google images. They used a similar approach to SMIL, by fitting the 3D model mesh to a real infant image and getting synthetic images with changing backgrounds, texture maps, lightning, and camera position, resulting in 700 real and 1000 infant synthetic images. This small dataset was used to train an architecture (FIDIP) that contains a domain classifier that promotes a feature extractor to retain the ability to extract keypoints information but also to ignore the differences between the real and the synthetic input images  [16].
The group tested the effect of retraining three existing models as backbone networks on their SyRIP dataset in different ways, but what we found questionable was mainly the small number of images used for testing (100 & 500) and the use of mean average precision (mAP) evaluation metric with OKS thresholds  [20]  which was previously implemented specifically for the COCO dataset (explained in Section 3. 2). In this study, we provide a PCK-based evaluation of this model using our dataset containing real images captured in a clinical environment.
Dataset The AGMA dataset was built by recording real images of 53 premature infants enrolled in the AGMA 2  study, born from October 2020 to June 2021 (see Fig. 3 ). All infants born before 33 weeks of gestational age (GA) and hospitalized in the Neonatology department of the Centre Hospitalier Universitaire de Saint-Étienne, France, were included. Exclusion criteria were the ongoing presence of ventilatory support, contraindication of a radiant heat warmer, and absence of written parental consent. The features of the included children are summarized in Table  1. The video recording protocol was controlled according to Prechtl's method of GMA  [9]. Infants wearing a diaper were placed in a radiant heat warmer in a supine position. The room light illuminance was controlled between 60 and 120 lux.
Videos were recorded for one hour using the ZED 2 (Stereolabs, San Francisco, CA) stereoscopic camera positioned perpendicularly to the warmer, with frames of 1280x720 resolution at 30 FPS. Each included infant was recorded from one to three times, with a period ≥ 7 days between two consecutive recordings. The dataset contains 44,250 stereoscopic images (88,500 in total) from a selection of 175 videos from 53 infants acquired between 32 and 41 weeks of GA. All images were manually annotated and reviewed. The keypoints annotation process was done according to the COCO keypoint detection task, using 17 points forming the infant skeleton with a bounding box around them  [20]. The annotation process was performed manually by a group of annotators specifically trained for this task. They were given a composite video containing the two rectified left and right views, and the task consisted of successively pointing each skeleton joint on both views for each video frame.
The consistency of the annotation across time was subsequently checked for each video. Finally, the 3D annotation was obtained by triangulation using (1). AGMA dataset was divided into three independent sets for training, validation, and testing. Each set contains distinct subjects with various poses, skin colors, and ages. The training set is composed of 156 stereoscopic videos of 5 seconds duration (46800 frame pairs), the validation set is composed of 13 videos of 5 seconds duration (4500 frame pairs), and the test set comprises of 10 videos of one minute (36000 frame pairs). It should be noted that the AGMA dataset cannot be directly compared to state-of-theart publicly available datasets such as SyRIP and MINI-RGBD as our objective was to capture images following Prechtl's method within a clinical setting.
The MINI-RGBD dataset comprises images derived from 12 entirely synthetic videos featuring simplistic infant poses  Abbreviation: SD, standard deviation  [16] , whereas SyRIP is a partially synthetic dataset that does not accurately represent the context and poses encountered in the GMA assessment method (see Fig. 4 ). Thus, the AGMA dataset stands as the first dataset captured within a genuine clinical environment, adhering to a controlled protocol that aligns with Prechtl's method of GMA  [9]. It specifically concerns infants population aged between 32 and 41 weeks of gestational age which is very hard to record due to medical constraints and practical considerations which makes the AGMA unique and the first of its kind. Table  2  summarizes the characteristics of the three datasets.
Evaluation metrics Even if our dataset ground truth annotations were performed according to the COCO keypoint detection task  [20] , its respective evaluation metric (AP, AP50, AP75) was not used since COCO AP is calculated using the Object Keypoint Similarity metric. As shown in equation (4), OKS uses per-keypoint constants that are calculated only from images in COCO validation set  [10] , which cannot be representative of another kind of datasets. O K S = i exp - d 2 i 2s 2 k 2 i δ(υ i > 0) i δ(υ i > 0) (4) where d i is the Euclidean distance between the detected keypoint and the corresponding ground truth, υ i is the visibility flag of the ground truth, s is the object scale, and k is a per-keypoint constant that controls falloff. The Percentage of Correct Key-points (PCK) is more suitable for infants' 2D pose estimation evaluation.
Experiments The goal of this section is twofold: First, to compare the different selected deep learning architectures and their retrained versions on our dataset. Second, to quantify the precision and accuracy of the keypoint detection in the perspective of clinical use. The OpenPose network which was originally trained on adult human pose estimation, is retrained by Chambers et al. [7]  on an infants' dataset of 9039 images collected mainly from Youtube. This retrained network is widely used to automatically assess infants' movements. So we compare and test this model without further training on our images and check whether it can generalize well on data from a clinical environment only. The DarkPose + FiDIP network  [16]  was a suggested solution to solve the lack of clinical data. Based on a hybrid dataset of synthetic and real images collected from the internet (SyRip), a domain classifier was trained to promote a feature extractor to retain the ability to extract keypoints' information but also to ignore the differences between the real and the synthetic input images.
This network is also tested and compared without extra training as an existing solution in addition to OpenPose retrained version. The experiments in  [38]  have shown that HRNet networks with higher input resolution (384 × 288) improve the AP compared to networks with lower input resolution (256 × 192). For that reason, two HRNet trained networks (W32 & W48) are chosen with an input resolution of 384 × 288, in addition to two HigherHRNet trained networks (W32 & W48) with an input resolution of 640 × 640, and finally two DarkPose on top of HRNet trained networks (W32 & W48) with input resolution of 384 × 288. These networks are tested with and without training on our infants' clinical dataset. For training, we used only 15 epochs since we are only adjusting the network weights. An Adam optimizer was used, and the learning rate was set to 10 -4 throughout the whole process.
2D pose estimation Figure  6  shows the results obtained after training and testing the different architectures. The error is calculated in pixels as the Euclidean distance between ground truth and predicted keypoints and averaged for each image (36k images with 880x720 pixels resolution). The impact of retraining the networks on our infants' dataset is obvious, as it led to a noticeable decrease in the mean error across all architectures, as well as a reduction in the standard deviation. Specifically, for the HigherHRNet32 and HigherHRNet48 networks, the mean error diminished from 30. 7 to 3. 7 pixels and from 28. 5 to 5. 3 pixels respectively. The two networks OpenPose and FiDIP, have been evaluated without any additional training. With a confidence score threshold of 0. 1, it was observed that OpenPose exhibited the lowest mean error of 6 pixels (see Fig.
6 ). This result is remarkably low; however, it should be noted that the mean error calculation exclusively considered successfully predicted joints across the entire dataset and the corresponding mean detection rate for this network was approximately 5 keypoints per image, in contrast to the other networks that achieved a detection rate of 17 keypoints per image (100% of joints detected). This discrepancy becomes evident when assessing the network's performance using PCK evaluation at various thresholds, as illustrated in Table  3. In fact, OpenPose performed poorly in comparison to networks that were not trained on any infants' images. As referred in Table  3 , the FiDIP network exhibited a comparable PCK (Percentage of Correct Keypoints) when compared to the non-retrained DarkPose48 network (95.
35% versus  95. 32% for PCK@0. 2). Despite being retrained using the SyRIP dataset, which comprises 700 real and 1000 synthetic images, the FiDIP network did not demonstrate any advantage over DarkPose48. This lack of improvement can be attributed to the characteristics of the images within this dataset (refer to Fig. 4  and Table  2 ), which were sourced from the internet and encompass infant poses that do not adequately represent the typical video acquisition protocol for assessing infants. Consequently, retraining on synthetic images does not inherently enhance the efficiency of networks when processing real images captured in a clinical setting. This underscores the need for a real and significant dataset in this particular research domain, which cannot be overlooked or substituted. The HigherHRNet networks demonstrated a satisfactory level of performance in terms of PCK, but not as good as HRNet networks.
3D pose estimation The 3D pose estimation results were in accordance with the results obtained in 2D analysis (see Table  4 ). Openpose had the least 3DPCK at all thresholds with the same detection rate All the retrained networks gained 5% minimum of 3DPCK@2. 5cm, and their mean error was reduced. The retrained version of DarkPose32 achieved the best 3DPCK at all thresholds, with a minimum mean error of 1. 72 cm, which is a very promising result regarding the complex poses in our testing dataset. The results for DarkPose48 network after training were very similar to the 32 version with only a 3mm difference in mean error. Moreover, when evaluating the results of this latter network for each group of joints separately, the mean error can be even lower, particularly for keypoints that are easy to define, such as the nose for which the retrained DarkPose32 network achieved 99.
In summary, the main conclusions that can be drawn from these results are: (1) it is particularly important to use a dedicated annotated dataset to train pose detection models for infants in a real medical environment; (2) compared to existing retrained models Openpose and FiDIP, models built on the HRNet network provide better precision, particularly Dark-Pose32 which is the best performing one in our case; (3) the latter network provides a 3D localization error of 1. 7cm with a 3DPCK at 2. 5cm of 83% and a 3DPCK at 5cm of 97%.
Conclusion The 3D automatic GMA method is a challenging field of study. An accurate estimation of infant poses is needed to automatically analyze the complexity, variability, and fluidity of infants' movements. This study is the first stereoscopic 3D infant's pose estimation framework 123 dedicated to preterm children hospitalized in the Neonatal Unit. Compared to other existing automatic infant movement analysis tools, AGMA stereoscopic framework has successfully shown the possibility of estimating accurate 3D infant poses without the use of any markers or infrared cameras. Three state-of-the-art 2D human pose estimation networks (HRNet, HigherHRNet, and DarkPose) were retrained on a dataset of 88,500 preterm infant images collected in a real medical environment and manually annotated. The networks were tested with 18000 stereoscopic images and compared to the latest works on 2D infant pose estimation retrained on real or synthetic images.
The minimum mean 3D error on joint position achieved was 1. 7 cm with a 3DPCK at 5cm of 97%. This study demonstrated that retraining on synthetic images did not consequently make networks efficient on real images captured in a clinical environment and that an adapted real dataset was more advantageous. In addition, it showed that an appropriate heatmap decoding process could improve infants' pose accuracy instead of using the standard coordinate decoding method. Another advantage of the presented framework is that any other advanced deep neural network can be used in the future for 2D pose estimation and then 3D pose reconstruction. Beyond GMA, AGMA 3D framework could also pave the way for the development of new tools based on the analysis of preterm infant movements, such as the automation of clinical seizure detection  [28]  and sleep quantification  [5].
75 3DPCK@5cm 53. 20 3DPCK@10cm 57. 12 Mean error (cm) 2. 44 STD (cm) 5. 60 Retrained - 3DPCK@1cm - 3DPCK@2. 5cm - 3DPCK@5cm - 3DPCK@10cm - Mean error (cm) - STD (cm) - 1 Retrained on our infants' images dataset 123.
This model can also incorporate LRs derived from variant frequency in cases and controls. Recently, casecontrol information derived from genotype data for 20 variants was incorporated into a comprehensive multifactorial likelihood analysis of BRCA1 and BRCA2 variants by ENIGMA  [11] , using a method incorporating gene-and age-specific penetrance of PV carriers only. Such casecontrol LR calculations take into consideration gene-and age-specific penetrance values, and hence they might be expected to outperform the statistical measures currently recommended by ACMG/AMP for the analysis of casecontrol data (i. e. , OR or RR estimates). In this paper, we present a novel case-control LR method, based on the same principle as used in Parsons et al. 's  [11] , that incorporates age information in both 5 Human Mutation carriers and noncarriers in the dataset.
The method can be used to obtain evidence in favor or against pathogenicity for rare variants in any gene for which there exist known age-specific penetrance estimates based on data obtained from case-control studies. We illustrate the use of this method to calculate LRs for 24 BRCA1 and 68 BRCA2 variants from breast cancer case-control genotype data generated by the Breast Cancer Association Consortium (BCAC) as part of the large-scale OncoArray project  [12]. We further demonstrate the utility of this case-control LR approach to aid in the interpretation of the clinical significance of variants using evidence aligned to ACMG/AMP code strengths or other classification methods.
Case-Control Datasets 2. 1. 1. Simulated Case-Control Dataset. Genotype data simulations were performed using the R (v3. 6. 1) (https://www. rproject. org/) statistical computing language. To create casecontrol datasets, genotypes for cases and controls were simulated using a Poisson distribution with lambda (λ) equal to the mean number of events (variant carriers) in the given interval, expressed as λ Cases = N × RR × MAF, λ Controls = N × MAF, 1 where N denotes the sample size, RR denotes the relative breast cancer risk of the causal variant and MAF denotes the minor allele frequency of the variant in the general population. Ages were simulated using a normal distribution, with the mean and standard deviation following the genespecific age distribution in the CARRIER population-based study  [13].
Genotype data simulations were carried out for variants conferring a RR of 1 (indicating no increased risk), 2, 3, 4, 5, 6, 7, 8, 9, or 10, minor allele frequency in controls of 0. 0001, 0. 00005, or 0. 00003, and sample size of N = 20,000 (20,000 breast cancer cases and 20,000 controls), 30,000 (30,000 breast cancer cases and 30,000 controls), or 50,000 (50,000 breast cancer cases and 50,000 controls). For each of these 90 scenarios, we simulated 10,000 replicates. Additionally, in order to account for the possibility that age information is not available, we repeated the analysis using same age for all individuals. 2. 1. 2. BCAC OncoArray Dataset. Genotype data were generated as part of the BCAC component of the OncoArray project  [12]  (studies included in the analysis are listed in Supplementary Table  S1 ) and were available for 75,657 breast cancer cases and 52,987 controls of European ancestry.