dataesr/scientific-paragraphs-categorization huggingface dataset:
This means that when the number of infected persons in the population increases, the absolute number of severe cases and deaths may also increase. Therefore, the studies based on the community-based testing dataset reported an increase in mortality in patients infected with VOCs. The absolute risk increase affecting individual patients is likely minimal. Our study has some limitations. We screened papers published only in English and reported in PubMed, Web of Science and Google Scholar. Ongoing research projects have not been used. For example, in our University Hospital Institute, a large cohort study aiming at comparing the demographic and clinical characteristics of patients infected with several new variants of SARS-CoV-2 virus during July to September 2020 is ongoing. In addition, a new variant, L18F, has been recently reported with 1186 spike L18F VOC genomes in the UK.
Ref. Country Where Patients Were Sampled Period of Time Number of Patients Type of Samples Sequencing Methods/Data Availability Variants Outcome 1. 1. Studies showing no impact 1. 1. 1. In vitro and/or animal model studies Hamsters infected with the [18] NA NA Animal model study with a mutant virus and a wild-type virus NA NA SARS-CoV2 D614 and G614 variants in the Spike glycoprotein two variants exhibited comparable viral loads in lung tissues as well as similar amounts of virus shedding in nasal washes No significant increase in infectivity was observed in these cell lines for any of [19] NA NA In vitro study NA NA 501Y. V2 variant the pseudotyped viruses with 501Y. V2-related mutations compared to the reference 614G variant 1. 1. 2. Studies based on SARS-CoV-2 genomes downloaded from electronic databases or based on community-based testing dataset None of the recurrent 46,723 complete SARS-CoV-2 mutations [20] Various 46,723 ND SARS-CoV-2 genomes 12,706 variable positions were associated with downloaded from GISAID increased viral transmission Sequencing of SARS-CoV-2 [21] Singapore 22 January to 22 April 2020 The first 10,000 COVID-19 cases were extracted from the Ministry of Health database.
2. 2. Studies analyzing SARS-CoV-2 genomes downloaded from electronic databases or based on community-based testing dataset SARS-CoV-2 strains for [9] 23 countries Approximately 20,000 case reports ND each country were extracted from NextStrain open-source project. Amino acid sequences of ORF3a protein were downloaded from NCBI 218 viral strains from 15 countries were further analyzed for amino acid mutations from NextStrain database Mutation in ORF3a protein was associated with increased infection of SARS-CoV-2 protein database.
The ORF1ab variation (p. 5828P [64] - 23 December 2019 to 21 March 2020 1962 SARS-CoV-2 genomes - COVID-19 infection, mortality, and recovery rates were collected from the USA Centers for ORF1ab (p. 5828P > L and p. 5865Y > C), NSP13 (P504L and Y541C) > L and p. 5865Y > C) and NSP13 variation (P504L and Y541C) were associated with high Disease Control and infection and fatality rates Prevention (CDC) and virusncov [65] - - 27,304 - 27,304 SARS-CoV-2 sequences were downloaded from GISAID database Clade G A23403G (S:D614G) variant Clade G viruses was associated with higher death rates.
1. 526 (3679 patients), B. 1. 1. 7 (1815 patients) and non-variant of concern (VOCs)/variant of interest (VOIs) variants rate was observed between B. 1. 526 and non-VOI/VOC variants. Patients infected with the B. 1. 1. 7 variant were more likely to be hospitalized than those with non-VOI/VOC infections Data analyzed 327 B. 1. 427/B. 1. 429 more COVID-19 B. 1. 427/B. 1. 429 frequently cause [69] USA 4 January to 20 March 2021 327 cases retired from Colorado Department of B. 1. 427/B. 1. 429 discernible and severe illness than nationally Public Health and circulating lineages Environment database do overall.
g  Cappelen et al. , 2007; Saito, 2013)  do not offer specific predictions. NoRef N = 44 N = 46 N = 44 Ref N = 52 N = 48 N = 46 11 Due to a mistake in the presentation of the lotteries in the online quiz, some answers are not properly recorded. As a consequence, these data are noisy, and not used in the core of the paper. More details are available upon request. 12 Data, as well as the scripts needed to replicate our results are publicly available on the OSF platform (https:/doi. org/10. 17605/OSF. IO/5MFPT). 13 For instance, the effect size for the comparison of the regular dictator game and the dictator game with a take option (Experiment 2) in  Bardsley (2008)  yields an effect size of d = 0. 65, according to our calculation based on the data retrieved on the publisher's website. 14 We invited 12 participants for each sessions, but due to the no-show, we do not have a perfect balance across conditions.
provided valuable technical assistance. Julien Benistant  thanks the  Fondation pour la Recherche Médicale  for financial support ( FDT20170436847 ). This research was funded by the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissement d'Avenir"  ( ANR-11-IDEX-007 ) operated by the  French National Research Agency (ANR). It received the approval of the  Gate-lab research Ethics Committee  (ref. :  2018-12 ).
This was permitted by the technological advances with the increasing availability of small, light and affordable high spatial resolution cameras and autonomous vehicles. Unmanned ground vehicles (UGV) provide access to detailed phenotypic traits  [14] -  [16]  while being generally expensive and associated with throughputs of the order of few hundreds of microplots per hour. Conversely, unmanned aerial vehicles (UAV) are very affordable with higher acquisition throughput than UGVs. When carrying very high-resolution cameras, they can access potentially several traits  [17] ,  [18]  including plant density  [19] ,  [20]. Image interpretation methods used to estimate plant density can be classified into three main categories. The first one is based on machine learning where the plant density measured over a small set of sampling area is related to other canopy level descriptors including vegetation indices derived from RGB and multispectral data  [21] -  [23].
However, this type of method may lead to significant errors due to the lack of representativeness of the training data set as well as the effect of possible confounding factors including changes in background properties or plant architecture under genetic control. The second category of methods is based on standard computer vision techniques, where the image is first binarized to identify the green objects that are then classified into plants according to the geometrical features defined by the operator (e. g. [24] ,  [25] ). The last category of methods is based on deep learning algorithms for automatic object detection  [26] -  [28]. The main advantage of deep learning methods is their ability to automatically extract low-level features from the images to identify the targeted objects. While deep learning methods appear very promising, their generalization capacity is determined by the volume and diversity of the training dataset  [29].
While large collections of images can now be easily acquired, labeling the images used to train the deep models represents a significant effort that is the main limiting factor to build very large training datasets. Few international initiatives have been proposed to share massive labelled datasets that will contribute to maximize the performances of deep learning models  [30] -  [34] , with however questions regarding the consistency of the acquisition conditions and particularly the ground sampling distance (GSD). The use of UAV images for plant detection at early stages introduces important requirements on image resolution, as deep learning algorithms are sensitive to object scales with the identification of small objects being very challenging  [35] ,  [36]. For a given camera, low altitude flights are therefore preferred to get the desired GSD.
Later, Fromm  [38]  and Magoulianitis  [39]  showed improvements in object detection performances when using the super-resolution methods. The more advanced superresolution techniques use deep convolutional networks trained over paired high-and lowresolution images  [40] -  [42]. Since the construction of a real-world paired high-and lowresolution dataset is a complicated task, the high-resolution images are often degraded using a bicubic kernel or less frequently using gaussian noise to constitute the low-resolution images  [43]. However more recent studies have shown the drawbacks of the bicubic down-sampling approaches as it smoothens sensor noise and other compression artifacts, thus failing to generalize while applied to real world images  [41]. More recent studies propose the use of unsupervised domain translation techniques to generate realistic paired datasets for training the super-resolution networks  [44].
We propose here to explore the impact of image GSD on the performances of maize plant detection at stages from three to five leaves using deep learning methods. More specifically, three specific objectives are targeted:  (1)  to assess the accuracy and robustness of deep learning algorithms for detecting maize plants with high-resolution images used both, in the training and validation datasets;  (2)  to study the ability of these algorithms to generalize in the resolution domain, i. e. when applied to images with higher and lower resolution compared to the training dataset; and (3) to evaluate the efficiency of data augmentation and preparation techniques in the resolution domain to improve the detection performances. Special emphasis was put here on assessing the contribution of two contrasting methods to up-sample lowresolution images: a simple bicubic up-sampling algorithm, and a more advanced superresolution model based on GAN (generative adversarial network) that introduces additional textural information.
Study sites This study was conducted over 8 sites corresponding to different field phenotyping platforms distributed across the west of France and sampled from 2016 to 2019 (Figure  1 ). The list of sites and their geographic coordinates are given in Table  1. Each platform included different maize microplots with size 20 to 40 square meters. Depending on the experimental design of the platform, the microplots were sown with two to seven rows of maize of different cultivars and row spacing varying from 30 to 110 cm. The sowing dates were always between mid-April and mid-May. Table  1  The dataset used for the training and validation of the object detection models are listed here. , is the training high-resolution dataset, is the validation high-resolution dataset and is the validation lowresolution dataset.
Data acquisition and processing UAV flights were carried out on the eight sites approximately one month after the sowing date, between mid-May and mid-June (Table  1 ). Maize plants were in between three to five leaves stage, ensuring that there is almost no overlap among individual plants from near nadir viewing. Three different RGB cameras were used for the data acquisition: Sony Alpha (ILCE-6000) with a focal length of 30 mm, DJI X7 (FC6540) with focal lengths of 24 mm and 30 mm and the default camera with DJI Mavic 2 pro (L1D-20c) with a focal length of 10. 26 mm mounted on AltiGator Mikrokopter (Belgium) and DJI Mavic 2 pro (China). To geo-reference the images, ground control points (GCPs) were evenly distributed around the sites and their geographic coordinates were registered using a Real Time Kinematic GPS.
The flights were conducted at an altitude above the ground ranging between 15 and 22 meters, providing a ground sampling distance (GSD) between 0. 27 and 0. 35 cm (Table  1 ). For Tartas and Selommes sites, an additional flight was done at a higher altitude on the same day providing a GSD between 0. 63 and 0. 66 cm. The flights were planned with a lateral and front overlap of 60/ 80% between individual images. Each dataset was processed using Photoscan Professional (Agisoft LLC, Russia) to align the overlapping images by automatic tie point matching, optimize the aligned camera positions and finally geo-reference the results using the GCPs. The steps followed are similar to the data processing detailed by Madec  [15]. Once ortho-rectified, the multiple instances of the microplot present in the overlapping images were extracted using Phenoscript, a software developed within the CAPTE research unit.
Experimental Plan To evaluate the effect of the resolution on the reliability of maize plant detection, we compared Faster RCNN performances over training and validation datasets made of images of high (GSD0. 30 cm) and low (GSD0. 60 cm) resolution. Three training datasets built from  ℎ (Table  1 ) were considered: (1) the original  ℎ dataset with around 0. 32 cm GSD; (2) A dataset,   ℎ→ where the images from  ℎ were down-sampled to 0. 64 cm GSD using a gaussian filter and motion blur that mimics the actual low-resolution imagery acquired at higher altitude as described later (section 2. 6. 1); (3) A dataset, where the original  ℎ high-resolution dataset was merged with its low-resolution transform,   ℎ→. This  ℎ +   ℎ→ is expected to provide robustness of the model towards changes in GSD.
Note that we did not investigated the training with the native low-resolution images because the labeling of low-resolution images is often difficult because plants are not easy to identify visually and to draw accurately the corresponding bounding box. Further, only two flights were available at the high altitudes (Table  1 ) that were reserved for the validation. A specific model was trained over each of the three training datasets considered (Table  2 ) and then evaluated over independent high-and lowresolution validation datasets. We considered three validation datasets for the high-resolution images: (1) the native highresolution validation dataset,  ℎ acquired at low altitude with GSD around 0. 30 cm (Table  1 ). (2) a synthetic high-resolution dataset of GSD around 0. 30 cm obtained by up-sampling the native low-resolution dataset, acquired at high altitude, using a bicubic interpolation algorithm as described in section 2.
6. 2. It will be called   →ℎ. (3) A synthetic high-resolution dataset,   →ℎ , obtained by applying a super-resolution algorithm (see Section 2. 6. 3) to the native lowresolution dataset   and resulting in images with a GSD around 0. 30 cm. Finally two lowresolution datasets will be also considered:  (1)  The native low-resolution validation dataset,   (Table  1 ), with a GSD around 0. 60 cm. (2) A synthetic low-resolution dataset,   ℎ→ , obtained by applying a Gaussian filter to down-sample (see Section 2. 6. 1) the original high-resolution dataset,  ℎ , and get a GSD around 0. 60 cm.
Methods for image up-and down-sampling 2. 6. 1 Gaussian filter down-sampling To create the synthetic low-resolution datasets   ℎ→ and   ℎ→ , a Gaussian filter with a sigma=0. 63 and a window size=9 followed by a motion blur with a kernel size=3 and angle=45 were applied to down-sample the native high-resolution datasets  ℎ and  ℎ by a factor of 2. This solution was preferred to the commonly used bicubic down-sampling method because it provides low-resolution images more similar to the native low-resolution UAV images (Figure  3 ). This was confirmed by comparing the image variance over the Selommes and Tartas sites where both native high and low-resolution images were available: the variance of the   ℎ→ was closer to that of   whereas the bicubic down-sampled dataset had a larger variance corresponding to sharper images.
Super-resolution images derived from Cycle-ESRGAN The super-resolution (SR) is an advanced technique that artificially enhances the textural information while up-sampling images. We used a SR model inspired from  [53]. It is a twostage network composed of a CycleGAN network that generates synthetic paired data and a ESRGAN network capable of image upsampling. The CycleGAN  [54]  performs unsupervised domain mapping between the native low-resolution and bicubic downsampled low-resolution domains. Thus, for any given input image, CycleGAN is trained to add realistic image noise typical of low-resolution images. The ESRGAN-type super-resolution network  [42]  up-samples by a factor of two the low-resolution images. The paired high-resolution and "realistic" low-resolution dataset generated by the CycleGAN was used in the simultaneous training of the ESRGAN-stage of the network.
The CycleGAN stage of the network was initially trained for a few epochs following which the two stages (CycleGAN + ESRGAN) were trained together simultaneously. It should be noted that during inference, only the ESRGAN stage of the network would be activated. The training parameters and losses reported by Han  [53]  were used for the model training. The model weights were initialized over the Div2k dataset  [55]  and finetuned on the UAV dataset detailed below. The Cycle-ESRGAN network was implemented using Keras  [56]  deep learning library in Python. The codes will be made available on Github at the following link: https://github. com/kaaviyave/Cycle-ESRGAN A dedicated training dataset for the super-resolution network was prepared using UAV imagery belonging to the following two domains: • Native high-resolution domain: 2234 microplot extractions from four sites with an average GSD of less than 0.
33 cm. Some of the sites belonging to the  ℎ dataset was used as a part of the training. • Native low-resolution domain: 1713 microplot extractions from three sites with an average GSD of 0. 46 cm per site. None of the validation sites (  and  ℎ in Table  1 ) were used in the training of the super resolution model. The synthetic downsampled dataset used to train the CycleGAN was prepared by bicubic downsampling the native high-resolution domain by a factor of 2. The images were split into patches of size 256 x 256 pixels for the high-resolution domain and into 128 x 128 pixels for the low-resolution domain.
AP: The AP is a frequently used metric for the evaluation of object detection models and can be considered as the area under the precision-recall curve. Where TP is the number of True Positive, FP is the number of False Positive and the FN is the number of False Negative. For the calculation of AP, a predicted bounding box is considered True Positive (TP) if its intersection area over union area (IoU) with the corresponding labelled bounding box is larger than a given threshold. Depending on the objective of the study, different variations exist in the AP metric calculation and the choice of IOU threshold used to qualify a predicted bounding box as TP. After considering several IoU threshold values, we decided to use an IoU threshold of 0. 25 to compute AP. This will be later justified. The Python COCO API was used for the calculation of the AP metric  [57].
Accuracy evaluates the model's performance by calculating the ratio of correctly identified plants to all the predictions made by the model. A predicted bounding box is considered true positive if it has a confidence score of more than 0. 5 and an IoU threshold of 0. 25. Accuracy is then calculated as: The relative root mean square error (rRMSE) between the number of labeled and detected plants across all images belonging to the same dataset: where is the number of plants labeled on image and is the number of images predicted by the CNN (confidence score > 0. 5 and an IoU > 0. 25) and. is the average number of labeled plants per image.
The accuracy increases up to 0. 89 when introducing few images from the validation sites in the training dataset. Kitano  [27]  proposed a two-step method: they first, segment the images using a CNN-based method and then count the segmented objects. They report an average rRMSE of 0. 24 over a test dataset where many factors including image resolution vary (ranging from GSD0. 3cm to 0. 56 cm). They report that their method is sensitive to the size and density of the objects. In the following, we will further investigate the dependency of the performances to image resolution.
The Faster-RCNN model is sensitive to image resolution and apparent plant size The performances of the model were evaluated when it is trained and validated over images with different resolution. When Faster-RCNN is trained on the high-resolution domain ( ℎ ) and applied to a dataset with low-resolution (  ), both AP and Ac decrease almost by 30% (Table  3 ) compared to the results where the model is trained and applied over high-resolution images. The rate of true positive drops because of the drastic increase of false negatives indicating a high rate of misdetection (Figure  6 , [ ℎ ,   ]). This degradation of the detection performances impacts highly the rRMSE that increases up to 0. 48. This indicates that the model is sensitive to the resolution of the images. We further investigated if this was linked to the apparent size of the plants and therefore up-sampled the validation low-resolution images with a bicubic interpolation method (  →ℎ ) to get plants with the same size as in the native highresolution images ( ℎ ).
52 (Table  3 ). This result confirms the importance to keep consistent the resolution and plant size between the training and the application datasets since Faster-RCNN tends to identify objects that have a similar size to the objects used during the training. We thus evaluated whether data-augmentation may improve the performances on the low-resolution images (  ): the Faster-RCNN model trained on the simulated low-resolution images (  ℎ→ ) shows improved detection performances as compared to the training over the native high-resolution images (Table  3 ) with a decrease of the rRMSE down to 0. 29 (Table  3 ). When this model trained with synthetic low-resolution images (  ℎ→ ) is applied to a dataset downscaled to a similar resolution (  ℎ→ ), the performances improve dramatically with Ac increasing from 0.
Further, the jpg recording format of the images may also significantly impact image quality. Recording the images in raw format would thus improve the detection capability at the expense of increased data volume and sometimes image acquisition frequency. 4 ) performances similar to those observed when the model is trained only over high ( ℎ ) or synthetic low (  ℎ→ ) and validated on the same resolution ( ℎ or   ℎ→ ) (Table  3 ). This proves that data augmentation could be a very efficient way to deal with images having different resolution. Further, this model trained on augmented data ( ℎ +   ℎ→ ) (Table  4 ) surprisingly beats the performances of the model trained only on the high-resolution images ( ℎ ) as displayed in Table  3. This is probably a side effect of the increase of the size of the training dataset (Table  2 ).
Up-sampling with the super-resolution method improves the performances of plant detection on the native low-resolution images If the training is difficult with the native low-resolution images because plants are visually difficult to identify and label, the training should be done over low-resolution images derived from the high-resolution images using a more realistic up-sampling method than the standard bicubic interpolation one. Alternatively, the training could be done using the high-resolution images and the low-resolution dataset may be up-sampled to a synthetic high-resolution domain using bicubic interpolation or super-resolution techniques. Results show that the super-resolution technique improved plant detection very significantly as compared to the native low-resolution (  ) and bi-cubic up-sampled (  →ℎ ) images (Table  5 ).