dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, because deploying different types of resistance is likely a promising approach to benefit from their respective advantages, we also investigate the best strategies to combine a major resistance gene with an APR gene. To study these questions, we use a general simulation framework implemented in the R package landsepi  (Rimbaud L et al. , 2018b). The model is flexible enough to vary parameters related to the deployed resistance genes, and to encompass various pathogen epidemiological traits. Thus, although this work is motivated by rust diseases of cereal crops (for which there is considerable empirical data), our broad conclusions may, to some extent, apply to numerous pathosystems.
For the simulation experiments, we parameterised the model using available data from the empirical literature to represent wheat rust infection caused by a range of fungal pathogens in the genus Puccinia (Table  1 , details on model calibration in  Rimbaud L et al. , 2018c) , supporting information). The model is available in the R package landsepi version 1. 1. 1  (Rimbaud L et al. , 2018b).
Discussion To the best of our knowledge, adult plant resistance (APR) has never been explored in mathematical models dealing with plant resistance deployment  (Rimbaud L et al. , 2021) , despite its presence in numerous resistant cultivars of cereals and other crops  (Burdon JJ, 1987 p56; McIntosh RA et al. , 1995; Boyd LA, 2005; Chen XM, 2005 ; Develey-Rivière M-P & E Galiana, 2007;  Chen W et al. , 2014). Therefore, and because APR may affect different pathogenicity traits, in a delayed and potentially incomplete manner, we used the mathematical model implemented in the R package landsepi  (Rimbaud L et al. , 2018c)  to explore three parameters associated with this type of resistance: target pathogenicity trait, efficiency and time to expression. The main objective was to evaluate the impact of these parameters on resistance durability (evolutionary pathogen control) and disease limitation (epidemiological control).
Fundings This work benefited from  GRDC  grant  CSP00192 ,  ANR  project " ArchiV " ( 2019-2023 , grant  n°ANR-18-CE32-0004-01 ),  AFB Ecophyto II-Leviers Territoriaux Project  " Médée " ( 2020-2022 ), and the  CSIRO/INRAE linkage program.
This integration enables the extraction of multi-scale features and facilitates the capture of both local and global contextual information. 3. The decoder part of the framework effectively integrates deconvolution and attention units to recover spatial information and boost the localization of weeds and crops in drone images. 4. The performance of the proposed framework is evaluated on a publicly available benchmark dataset. From the experiments' results, it is demonstrated that the proposed framework precisely identifies weeds and crops in complex drone images and consequently beats other state-of-the-art methods in terms of performance standards. The remaining sections of this paper are structured as follows: Section 2 discusses the related works, Section 3 presents the proposed framework, Section 4 provides a description and discussion of the experimental results, and finally, Section 5 concludes the paper and presents future perspectives.
Dataset In this study, the performance of the proposed framework is assessed by using a publicly available dataset called the rice-weed dataset. The dataset was curated by Huang et al. [23]  and was collected at the Zengcheng Teaching and Research Bases, South China Agricultural University. The data acquisition was carried out using a DJI GS PRO unmanned aerial vehicle (UAV) flying at an altitude of 6 m. The UAV's camera features a 1-inch CMOS sensor capable of capturing 12-megapixel images. The dataset consists of 91 images, each with a pixel resolution of 4000 × 3000 pixels and a spatial resolution of approximately 0. 3 cm. These images exhibit diverse scenes with varying densities of weeds and rice plants. Within each image, there are three distinct categories: rice, weeds, and other elements.
The rice pixels are labeled as green, the weed pixels as red, and the background pixels as gray. Given the high resolution of images and due to a limited GPU memory, each image is sorted into twelve tiles, where the size of each tile is 1000 × 1000 pixels. This approach is also applied to the ground truth segmentation masks, ensuring consistency in the methodology. In this way, a total of 1092 tiles are generated, where 892 tiles are randomly selected for training and the remaining 200 are reserved for the testing phase. The number of images in the original dataset is limited; however, to address this problem, data augmentation techniques are employed by rotating, scaling, and flipping the original images. Specifically, these variations are incorporated by rotating images at 90-and 180-degree angles, adjusting their scales by enlarging or reducing the sizes of the images, as well as flipping the images horizontally and vertically.
Through these diverse transformations, the size of the dataset is expanded, ensuring a more extensive and representative collection of training data for the network. During the testing phase, a similar approach is followed where the test image is divided into 12 tiles. Each tile is individually inputted into the model for prediction. After obtaining the masks for all 12 tiles, they are stitched together to form the final segmentation mask. This final mask is then adjusted to align with the size of the original input image. A few sample frames and their corresponding ground truth masks are illustrated in Figure  3.
Evaluation Metrics To evaluate the effectiveness of the proposed framework and compare it to state-ofthe-art methods, the Jaccard similarity index, also known as Intersection over Union (IoU), is employed. The Jaccard similarity index serves as a widely adopted evaluation metric for assessing the performance of segmentation models. It is defined by the formula Ω g ∩Ω p Ω g ∪Ω p , where Ω g is the ground truth mask (each pixel is annotated with a class label) and Ω p is the predicted mask. By calculating the Jaccard similarity index for each class individually, and subsequently computing the mean Intersection over Union (mIoU), a comprehensive summary of the methods' performance is provided. Moreover, for a comprehensive assessment of the performance of various models across different categories within the dataset, Precision-Recall Curves are employed.
These pre-trained models served as a valuable starting point due to their ability to learn general visual representations from a diverse range of images. These models are then fine tuned on a weed-crop dataset. This strategy enables the networks to re-adjust the learned weights and parameters to better suit the requirements of the weed-crop segmentation task. To ensure a fair comparison among the different methods, same training and testing ratios are maintained across all the evaluated methods to avoid the potential bias introduced by varying the data splits. The performance of these methods in terms of Jaccard similarity and mean IoU is reported in Table  2 , while a comprehensive evaluation of these methods in terms of Precision-Recall curves is reported in Figure  4. From Table  2  and Figure  4 , it is obvious that the proposed method achieved superior results compared to other reference methods.
On the other hand, DeepLab achieves a comparable performance as indicated by its higher scores in all three categories and mIoU among the evaluated methods. The superior performance can be attributed to the large receptive field of the network which is achieved via the effective incorporation of dilated convolutions. These dilated convolution operations enable the network to capture contextual information. Additionally, DeepLab incorporates the Atrous spatial pyramid pooling (ASPP) module that further enhances the model's ability to extract features at multiple scales, aiding in a better segmentation performance. From Table  2 , it is observed that all the competing methods achieve a good performance in the "Others" category, a lower performance in the "Rice" category, and the worst performance in the "Weeds" category.
This signifies that the proposed method achieves high precision while maintaining a high recall or vice versa, demonstrating a robust ability to correctly identify positive instances in the rice-crop category (high precision), while also capturing a substantial portion of the true positive instances in the dataset (high recall). Furthermore, the curve for DeepLab covers a smaller area than the proposed method, while Huang et al. exhibits an even smaller coverage than both the proposed method and DeepLab for the rice-crop category. Figure  4b ,c illustrates the performance of difference methods in terms of the Precision-Recall curve for the weed category and other categories, respectively. From Figure  4b, c , it is obvious that the proposed method achieves a good balance between precision and recall by covering a large area under the curve, highlighting its effectiveness in accurately segmenting weed instances.
Conclusions This study introduces a novel weed-crop characterization framework for drone images, employing an encoder-decoder pipeline. The encoder integrates a novel DenseInception network with the Artous spatial pyramid pooling module, enabling the capture of global contextual and multi-scale information. The decoder incorporates deconvolution layers and attention units to recover spatial information and emphasize relevant regions. Extensive experiments on a challenging dataset demonstrate the effectiveness of the proposed framework. Quantitatively, the framework achieves notable success in identifying weeds and crops from drone images, showcasing its superiority. Specifically, the model achieves an impressive performance of a 0. 81 mIoU score across the whole dataset (test images). These quantitative results highlight the framework's robust performance, making it a promising solution for weed-crop segmentation in precision agriculture applications.
After filtering for participants and variants, the final dataset includes 11,640 participants with 3,892 variants for the ADSP cohort and 461,579 participants with 4,967 variants for the UKB. For prediction accuracy, we present the results of the models trained on the ADSP cohort and tested on independent test data from the ADSP and all data from the UKB. We split the data into training and test and further divided the training set into training and validation sets with stratification based on the phenotype labels. We consider self-reported ancestry labels in the training dataset to train the disentangling autoencoder and other ancestry-specific linear and non-linear models. The independent test data consists of 2,101 participants for the ADSP and 461,579 participants for the UKB. To evaluate the performance of the proposed method to predict AD status in minority populations or admixed individuals, we estimated ancestry percentages from genome-wide data using SNPWeight v2.
1  25. Then we used these ancestry estimates to divide the test data into different ancestries to present the results. This is to ensure that the partitions accurately reflect the actual genetic-ancestry background and enable a more rigorous evaluation of the methods. Details for dataset preparation are explained in the Methods section. Although unconventional, we also consider the models trained using other non-European ancestries like African American population (AFR) to understand the effect of training models with training participants other than European ancestry. Furthermore, we also considered adversarial learning to capture domain invariant representations where we treated ancestry as domains (Adv). Finally, we also train these different models on participants from UKB cohorts and evaluated them on the portion of held-out test dataset from UKB cohort.
Since UKB primarily comprises European participants, we set the threshold low to non-EUR participants for our analysis. In both datasets, individuals of EUR ancestry correspond to the largest proportion (48% for the ADSP and 98% for the UKB). For the ADSP, we consider two sub-groups of test datasets: i) African participants (n = 169) (ADSP AFR) (Panel B), and ii) admixed individuals (n=916) (ADSP admixed) (Panel C). This is to evaluate different models' performance on non-European minority populations. For the UKB, we again consider two sub-groups of test datasets: i) South Asian participants (n=8,083) (UKB SAS) (Panel D), and ii) European participants (n= 423,357) (UKB EUR) (Panel E). This is to evaluate different models' performance on dataset shift (or distribution shift), i. e. , the application of models trained on one cohort to participants of a separate cohort.
Across all cases, DisPred achieves the best result. The Lasso is the second-best model in all cases, demonstrating its superiority in sparse domains above standard Neural Network models. PRS EUR is the worst performing model across all cases, including no predictive power when applied for ADSP AFR. Furthermore, in the case of dataset shift, DisPred performs better in all the cases, including when models trained on EUR participants (ADSP) are applied to the EUR participants (UKB). We also note that although we did not leverage data from SAS, we significantly improved prediction accuracy for UKB SAS. The proposed Disentgl-AE utilizes all the available training participants to separate the effect of ancestry from the phenotype representation. As such, the obtained phenotype representation demonstrated the best predictive abilities.
We believe this is an appealing feature of DisPred as there has been a long-standing debate on the use of self-reported race/ethnicity/genetic ancestry in biomedical research. The scientific, social, and cultural considerations make it challenging to provide an optimal label for race or ethnicity, which could result in ambiguity, contributing to misdiagnosis. Moreover, at the minimum, such ancestry information may not be available. As such, a method like DisPred that does not require ancestry information is beneficial to get around this critical issue. Similarly, dataset shift, i. e. , different training and test data distributions (or cohorts), although a common practical scenario, makes generalization challenging for machine learning models. We showed that compared to existing methods, DisPred demonstrated better predictive abilities in the presence of dataset shift.
Dataset preparation In this study, we consider two datasets: the ADSP and the UKB. We use the p<1e-5 threshold to obtain the candidate regions of 5,014 GWAS variants, obtained from Jansen et al. , 2019  23  and Andrews et al. , 2020  24. We remove SNPs with more than a 10% missing rate to ensure marker quality. We then remove participants with absent AD phenotype or ancestral information. For the ADSP, we have a dichotomous case and control label for AD phenotype. For the UKB, we use the AD-proxy score defined in Jansen et al. , 2019  23  , which combines the self-reported parental AD status and the individual AD status. Since AD is an age-related disease, we removed control participants below age 65 (age-at-last-visit). For the ADSP, since case and controls are well defined, we perform this step for the whole dataset, but for the UKB, we first split data into training and test and remove AD proxy score less than 2.
The most striking is formed by two juveniles embracing each other (Fig. 1c ). Such an arrangement is rare, but other examples have been found in Neolithic and Protohistoric times, such as in Diyarbakir (Turkey, 6100 BC) or Valdaro (Italy, 3000 BC). Double burials are often considered as a lover's embrace, but arguments for this explanation are often elusive  31. To better understand the origin of the Shomu-Shulaveri population and the structuration of this community, we performed paleogenetic studies of some of the individuals found in the collective burial. The genetic data obtained are then compared to those of another individual from the structure already published and to contemporaneous southwestern Asian genomes.
Genetic structure of the Neolithic South-Caucasus. We merged our genome-wide data with the Human Origins dataset (HOdataset)  1  , as well as with 3529 previously unrelated published ancient genomes (Supplementary Data 2). To decipher the genetic relations between the new Mentesh Tepe individuals and other ancient populations from the Caucasus, Anatolia, the Near East, and the Middle East, we performed: (1) a PCA  32  on the modern dataset on which the ancient genomes have been projected and (2) an unsupervised ADMIXTURE  33  analysis with the HO and the ancient dataset (Fig. 2a , b, Supplementary Fig. 2 ). The PCA shows that the Mentesh individuals overlap with some other previously published Neolithic or Chalcolithic individuals from the South Caucasus  22, 23  , but the individual from Aknashen falls a bit closer to CHG than the main neolithic cluster (Fig.
Methods Archaeological sampling. We obtained ethical authorisation from the Institute of Archaeology and Ethnography of Azerbaijan to study samples from Mentesh Tepe. Extraction was attempted on the 30 samples found in Structure 342 of the site, except MTT001 (Individual 1), which has already been published elsewhere  23. Petrous bone fragments from 23 among 30 individuals of the SSC structure S342 have been selected (Supplementary Data 1) for ancient DNA analysis based on overall bone preservation. Ancient DNA extraction. All pre-amplification steps were carried out in the clean room dedicated to ancient DNA of the Paleogenomic and Molecular genetics platform, set in the Musée de l'Homme (Paris). As in ref. 45  , ancient DNA extraction was performed using a protocol adapted from ref. 46. Briefly, 50-200 mg of petrous bone was powdered by drilling and incubated in 1 ml of lysis buffer (0.
For the three selected samples (endogenous DNA content >1%), genomic capture was performed using the myBaits Expert Whole Genome Enrichment (WGE) kit (Arbor Biosciences), and the manufacturer's instructions were followed. Baits were formed from the genomic DNA of three individuals of different (African, European and Asian) ancestries. Sequencing. Aliquots of the amplified libraries were first pooled and sequenced on MiSeq instrument (2 × 75 bp) on the IGenSeq platform. Captured libraries were sequenced on a NextSeq 500 (2 × 75 bp) instrument. Data processing. Considering that our kinship analysis protocol makes use of previously published ancient individuals as a surrogate population, a separate datapreprocessing workflow mirroring that of ref. 23  was devised to make our results as comparable as possible with these previous analyses.
0. 6 51 (Supplementary Fig. S1 ). Quality scores within the bam files were then rescaled accordingly with the latter software, using default parameters. We extracted reads overlapping known variants from the 1240K dataset from the rescaled bam file using samtools mpileup, while disabling per-Base Alignment Quality (-B flag) and skipping any alignment carrying a base and/or mapping Phred-quality score which was lower than 30 (-q and -Q flags). Random pseudo-haploid variant calling was then performed using SequenceTools PileupCaller-v1. 5. 0 (Download: https://github. com/stschiff/sequenceTools). To evaluate contamination, we used AuthentiCT  52  , and for the male samples, we calculated contamination using the X chromosome  53  with ANGSD  54. (Supplementary Data 1). Merging genomic data. We selected 3529 published ancient human genomes from Eurasia (Supplementary Data 2) from the Palaeolithic to Middle Age, whom DNA sequencing data were generated with whole genome shotgun or hybridisation capture technics from the merge dataset v42.
4 available at https://reich. hms. harvard. edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-presentday-and-ancient-dna-data and in two recent paper about the South Caucasus and Anatolia  22, 23. We retained the nonrelated individuals with more than 10,000 SNPs hit on the 1240k panel. We analysed ancient data with 1587 Eurasian individuals from the Human Origin dataset. We merged our genome-wide data to this dataset using mergeit from the EIGENSOFT package suite  55  , and we haploidised them by randomly selecting one allele per position. The final merge includes 597,573 SNPs for 5116 individuals, and we call it the HO-dataset. For the analysis requiring more SNPs, we use a merge without the modern individuals from the HO dataset, covering 1,233,013 SNPs, which we call the 1240k dataset.
PCA. We ran PCA with smartpca 55 using the HO-dataset, on 1390 European and  Middle Eastern  individuals, and we projected all the ancient samples. We used the default parameters with lsqproject: YES, and numoutlieriter: 0 settings. ADMIXTURE. We computed ADMIXTURE 33 analysis using the HO dataset, where all populations were downsampled to a maximum of 20 individuals. We performed the analysis on 1266 Eurasian modern individuals, including  East Asian , and 2526 ancient samples from the first dataset on a subset of 365,075 SNPs pruned for linkage disequilibrium (by using PLINK --indep-pairwise 200 25 0. 4 function). We ran 20 replicate ADMIXTURE analyses for K between 2 and  14 , from which we kept the most likely. D-statistics. We performed D-statistics on the 1240k dataset using the qpDstat program of the ADMIXTOOLS package 56 with Mbuti as outgroup for both statistics.
Observation with a magnifying glass or under a microscope made it possible to see the fine characters, such as the sexual and asexual organs, the rhizines, and the structure of the thallus. In this work, the identification was based on the observation of the morphological characteristics by comparing the data obtained with other works, while being based on the following criteria: the type of ramification, the color, the consistency, the attachment organs, and the aspect of the thallus. Consultation of the WoRMS (World Register Marine Species) and Algea Base databases also facilitated the identification of taxa. All the species collected are described morphologically, photographed to build a photo library, and establish identification keys. Among these harvested and identified microalgae, four algae were selected on the basis of the dominant species.
In this work, the identification was based on the observation of the morphological characteristics by comparing the data obtained with other works, while being based on the following criteria: the type of ramification, the color, the consistency, the attachment organs, and the aspect of the thallus. Consultation of the WoRMS (World Register Marine Species) and Algea Base databases also facilitated the identification of taxa. All the species collected are described morphologically, photographed to build a photo library, and establish identification keys. Among these harvested and identified microalgae, four algae were selected on the basis of the dominant species. All of these algae belong to chlorophycophytes ( Figure  2 ). The four chlorophyta are filamentous taxa Chara (Charophyceae), Cladophora (Cladophoraceae), Spirogyra, and Zygnema, which constitute the group Zygnemataceae.
Methods Earth system model. We use Version 6 of the Institut Pierre-Simon Laplace (IPSL) low-resolution ESM, IPSL-CM6A-LR, developed in the runup for the sixth phase of the CMIP6  47. This ESM comprises the LMDZ atmospheric model Version 6 A-LR, the NEMO oceanic model Version 3. and the ORCHIDEE land surface model Version 2. 0 with a 144 × 143-grid spatial resolution. ORCHIDEE is a process-based terrestrial biosphere model that calculates energy, water, and carbon fluxes between the land surface and the atmosphere. The current CMIP6 version does not include a fire module and does not account for crop irrigation. Disturbances include biomass harvesting. In the model, droughts reduce NPP but do not increase mortality. Nutrient limitation of photosynthesis is modelled via a downregulation mechanism through a logarithmic function at CO 2 concentrations above 380 ppm.