dataesr/scientific-paragraphs-categorization huggingface dataset:
The delivery method is important to consider. Mixed delivery methods showed greater improvements than web-based, telephone, or DVD-based interventions alone. This could be because of the reported lack of adherence to web-based interventions that do not involve personal contact with the clinicians  [27, 37]. The personalized contact in mixed delivery interventions could support participants to continue with the intervention using "live" telephone calls by clarifying queries arising from web-based materials and addressing other concerns that arise  [7]. Although multiple review papers have analyzed research in this field to date, there has been no umbrella review. One of the unique aspects of an umbrella review is that it considers the overlap of studies included in other review papers. It is thus able to review when data are being reported multiple times and synthesize the data, giving a clearer insight into the effectiveness of, in this paper, the eHealth interventions.
The CCA score was used to ascertain the overlap of the studies included in the reviews. A measure of overlap was selected owing to the presence of 1 study  [44]  in 12 of the reviews  [15, 16, 22, [24] [25] [26] 28, 30, 31, 33, 36, 37]. It is suggested that CCA can be misrepresentative of the true overlap by the inclusion of several "index publications. " The overall moderate score of 7% overlap, yet the high scores of comparisons between individual papers, suggests that the overall score may have been skewed by a high number of "index publications"  [20]. The high overlap between several review papers could mean that despite "new" systematic reviews being conducted, there is a false impression of new evidence. This could mask the lack of research on this topic. A limitation of reviewing the reviews is that the data were limited to what the previous authors reported on.
Acknowledgments This work is part of a PhD studentship "Adaptation of an e-health intervention 'iSupport' for carers of people living with rare dementias. "and is linked to the  Rare Dementia Support Impact  project (The impact of multicomponent support groups for those living with rare dementias,  ES/S010467/1 ) and is funded jointly by the  Economic and Social Research Council (ESRC)  and the  National Institute for Health Research. ESRC  is part of  United Kingdom Research and Innovation. The views expressed are those of the authors and not necessarily those of the  ESRC, United Kingdom Research and Innovation , the  National Institute for Health Research  or the  Department of Health and Social Care. Rare Dementia Support is generously supported by the  National Brain Appeal [47 ].
, 2012)  in Variant Calling Format (VCF) with the following conditions: minimum of 3 reads overlapping the SNP, minimum average base quality of 20, minimum variant allele frequency of 0. 8, and a p-value of 0. 05. Locations where SNPs were detected from CDC Bethune reads mapped on the reference genome (false-positive SNPs) were excluded from the analysis with VCFtools v0. 1. 17 (Danecek et al. , 2011). Validation of the SNP discovery pipeline was carried out by comparing the SNP alleles detected by the pipeline with those already known in genotyping projects (TERRE DE LIN private SNPs; KASP technology, LGC) on two accessions (332 SNPs on CC109 accession and 337 SNPs on CC123 accession). Visualization of SNP density plots was carried out using the CMplot v3. 6. 2 R-package (https://github. com/YinLiLin/ CMplot).
Private SNPs belonging strictly to fiber, oil, and wild groups were tracked using BCFtools  (Danecek et al. , 2021)  and visualized along the 15 chromosomes using the circlize v0. 4. 13 R-package (https://github. com/jokergoo/circlize). High-quality SNPs were retained (wild accessions excluded) using BCFtools for subsequent analyses based on a missing rate (MR) < 10% and on a minor allele frequency (MAF) ≥ 5%.
Principal Component Analysis, Population Structure, and Linkage Disequilibrium Analysis Prior to population structure analysis, we first imputed the missing data using Beagle v5. 1  (Browning, 2008; Browning and Browning, 2016)  with default parameter settings. As principal component analysis (PCA) and population structure analysis are affected by linkage disequilibrium (LD) between markers, we pruned the genotyping matrix using PLINK's -indep-pairwise function with a scanning window of 50 SNPs (step size of 5 SNPs) and a LD threshold of r 2 > 0. 4  (Purcell et al. , 2007). We performed two analysis of the genetic structure of the sampled flax panel: (i) a PCA using the "prcomp" Rfunction; (ii) a discriminant analysis of principal components (DAPC) using the R-package adegenet v2. 1. 3 (Jombart et al.
, 2010). For DAPC, the optimal number of selected principal components in the PCA step was based on the a-score criterion. In the K-means clustering step, the optimal number of clusters k was inferred after exploring k = 3-7, and considering the Bayesian information criterion (BIC) to assess the best supported model. To determine the most likely k-value in this flax panel, we also considered the correspondence between identified groups and breeder's knowledge of the varieties present in the diversity panel. The membership probabilities of each individual to the k different groups were obtained from DAPC analysis. Pairwise F ST between sub-populations identified by DAPC were computed with a R-script using the method proposed by  Weir and Cockerham (1984). Linkage disequilibrium distribution between pairwise SNPs across the whole genome was evaluated from a random subsample of 20,000 SNPs of the imputed matrix without the wild accessions using the estimator of Rogers and Huff  (Rogers and Huff, 2009)  with the Rutilstimflutre v0.
170. 0 R-package (https://github. com/timflutre/rutilstimflutre) according to the following equation: r 2 RH = cov g i , g j 2 var g i × var g j where g i and g j are the genotypes at SNP i and j, respectively, coded as 0 for the reference allele present in CDC Bethune and 1 for the alternative allele.
Statistical Analysis of the Phenotypic Data The powdery mildew phenotyping data collected in 2018 and 2019 were analyzed after first checking for residual normality and error variance homogeneity at each date used for the calculation of AUDPC. We explored spatial and scoring effects on AUDPC through a mixed-model including genotype, row, and column factors modeled as random effects and the order of plot phenotyping in the field (modeling the bias in decreasing attentiveness) as a fixed effect. To correct for spatial heterogeneities, each year was analyzed separately using the SpATS v1. 0-11 R-package  (Rodríguez-Álvarez et al. , 2018). Spatial effects were modeled on a row and column basis by specifying the P-spline ANOVA algorithm  (Lee et al. , 2013) , with the number of segments set to the respective number of rows and columns in our RCBD experimental design.
Genome-Wide Association Study Analysis Genome-wide association study analysis was done using the genotyping of 1,693,910 SNPs of 286 genotypes and the AUDPC phenotypic data corrected for spatial heterogeneities, separately for the years 2018 and 2019. Wild flax, the CDC Bethune variety used for mapping reads, and genotypes belonging to the AS-ME cluster (group of small size, poorly related to the other groups and which exhibited long distance LD, cf results on panel structure and LD extent) were excluded from GWAS. Analyses were performed by applying a mixed linear association model (MLM) with a leave-one-chromosome-out (LOCO) approach as implemented in GCTA software v1. 93  (Yang et al. , 2011 (Yang et al. , , 2014)) , taking into consideration the panel structure by the kinship matrix and by including the DAPC clustering as quantitative covariates in the model.
The genetic relationships among all of the 286 individuals were estimated in GCTA with the following equation: A jk = 1 N i A ijk =    1 N i (x ij -2p i )(x ik -2p i ) 2p i (1-pi) , j = k 1 + 1 N i x 2 ij -(1+2p i )xij+2p 2 i 2p i (1-pi) , j = k where A jk is the calculated genetic relationship value for individuals j and k; N is the number of SNPs shared between two individuals on all the chromosomes except the one of the tested SNP; x ij is the number of copies of the reference allele for the i th SNP of the j th individual; p i is the frequency of the reference allele for locus i. The quantile-quantile (QQ) and Manhattan plots were visualized using the "qqman" v0. 1. 4 R-package  (Turner, 2014). We applied the Benjamini-Hochberg false discovery rate (FDR) approach  (Benjamini and Hochberg, 1995) , which controls the expected proportion of false positives among all signals with a FDR value below a fixed threshold, to determine significance.
Candidate Gene Identification Candidate loci were explored using a combination of GWAS p-values, local linkage disequilibrium, and gene annotation using the GFF3 file of the Linum usitatissimum genome. This information was summarized for each candidate loci using the LocusZooms R-package (https://github. com/Geeketics/ LocusZooms) and the LDBlockShow tool (https://github. com/ BGI-shenzhen/LDBlockShow). An approximate flanking region of 20 to 100 kb was explored for each significant SNP and adjusted according to the extent of local linkage disequilibrium with the candidate SNP (considering regions with r 2 ≥ 0. 2 with the significant SNP).
Powdery Mildew Phenotyping Under Natural Infection Very significant disease development occurred over the 2 years, with maximum severity reaching to a score of 9 for the most susceptible lines and to a score of 1 for the most resistant lines. Statistical analysis of disease scores obtained in 2018 and 2019 showed that the local environment had a significant effect on powdery mildew resistance in the panel, in particular, the date of phenotyping (Supplementary Figure  S6 ) and the position (row and column) of the genotype in the block. To integrate these effects along the plant cycle, we therefore decided to calculate the AUDPC based on the phenotyping dates carried out during the year and to adjust them with the R-package SpATS to correct for spatial heterogeneities in natural infection of powdery mildew in the field (Supplementary Figure  S7 ).
ACKNOWLEDGMENTS We thank all  Terre de Lin  breeding team for their advices and in particular  Géraldine Dambry  and  Céline Souc  for their help with seed management and DNA extractions. We are grateful to  GeT-PlaGe Genotoul  platform for performing the NovaSeq sequencing. We thank  Timothée Flutre  for his recommendations on DL analysis. IPS2 and  GQE-Le Moulon  benefit from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ) and  GQE-Le Moulon has been supported by LabEx BASC  ( ANR-11-LABX-0034 ).
Methods and Analysis In this retrospective study, UWF-CFP images of patients with retinal vascular disease (DR, RVO, and SCR) and healthy controls were included. The images were used to train a multilayer deep convolutional neural network to differentiate on UWF-CFP between different vascular diseases and healthy controls. A total of 224 UWF-CFP images were included, of which 169 images were of retinal vascular diseases and 55 were healthy controls. A cross-validation technique was used to ensure that every image from the dataset was tested once. Established augmentation techniques were applied to enhance performances, along with an Adam optimiser for training. The visualisation method was integrated gradient visualisation.
Datasets Patients with retinal vascular disease (DR, RVO and SCR) presenting to the department of ophthalmology at the Creteil University Hospital between January 2018 and January 2021, and having undergone UWF-CFP were retrospectively included in this study. A group of healthy controls having undergone UWF-CFP was randomly selected. This study was conducted in accordance with the tenets of the Declaration of Helsinki. Written consent was waived because of the retrospective nature of the study. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. High-resolution (2133×1048 pixels), 200° field-of-view images were captured on the Optos California imaging system. All UWF-CFP images were deidentified then cropped to a size of 700×500 pixels with the macula at the centre and were labelled as either DR, RVO, SCR, or healthy by a retina specialist and a retina fellow (AM, EA).
Online supplemental figure 1 illustrates the UWF-CFP for each of the four classes used in this study. A four-class classification system (DR, RVO, SCR, healthy control) was implemented with a dataset of 224 images uniformly distributed among the classes: 65 DR (29. 0%), 47 RVO (21. 0%), 57 SCR (25. 4%), and 55 healthy controls (24. 6%).
Development of a DL classifier For this study, the DL framework TensorFlow (Google, Mountain View, USA) was used. We used the DenseNet121  29 30  convolutional neural network (CNN) to perform the classification task. Transfer learning from the ImageNet dataset (http:// www. image-net. org/) was used to provide base knowledge to the CNN before fine-tuning it. To fit our task, we reduced the number of output neurons in the last fully connected layer to 4. Data augmentation was used to increase the original dataset and to reduce the overfitting of the final model. This was achieved through a combination of image translation, cropping and rotation. Moreover, Gaussian noise augmentation was used to mimic low-quality noisy images. Gaussian noise, also called white noise, has a mean of 0 and an SD of 1.
The addition of Gaussian noise to the inputs of our CNN during training was meant to increase the robustness of our model, as due to the added noise the CNN was ultimately less able to memorise training samples. The images were normalised using the mean and SD of the ImageNet dataset to match the model initialisation. An integral fine-tuning was performed, meaning that all trainable weights were optimised using Adam Optimization Algorithm 31 with a learning rate of 0. 001 and a batch size of 24. An early stopping procedure has been applied to minimise overfitting. It consists in stopping training when there is no improvement after a certain number of epochs (30 in our case). For the same purpose, learning rate reduction was used as well, meaning that the learning rate was decreased by a factor of 10 when no improvement was observed for 10 epochs.
Performances were evaluated using fivefold cross-validation. Therefore, the entire dataset was divided into five groups (20%). At each step, one group was selected as the test set, while the rest of the dataset, named train-val test (the 80% of observations not in the test set), was also randomly divided into two subsets called training set (90% of the train-val set) and validation set (10% of the train-val set). The DL model learnt on the training set and optimised its hyperparameters on the validation set. Then performances were assessed on the unused test set. This procedure of training-validation-testing was repeated five times since we used a fivefold cross-validation. Therefore, it allowed to test every image from the whole dataset once in an unbiased manner. This repeated training-validation-testing is a common machine learning procedure that maximises performances while minimising bias.
32 Furthermore, data augmentation was separately done on both training and validation, but not on the final test set. Smoothed saliency map  33  was used to better capture model attention. GradCAM++  34  was also applied to the last convolutional layer before the output. We preferred to use both methods as they each have their advantages and downsides. While the saliency map techniques offer a more global insight about the image's important areas in the prediction (yet can suffer from noise generated by first layers, which might or might not have a significant impact on the final decision), GradCAM++ is less global since it focuses only on the last convolutional layer, as this latter is considered to be crucial in the decision, especially in dense networks, where it has access to Open access information from past blocks.
RESULTS The data used to train and test the algorithm were composed of 55 UWF images from healthy patients, and 169 images from participants with retinal vascular diseases distributed as follows: 65 UWF images of DR including 48 non-proliferative DRs (24 mild, 17 moderate, 7 severe) and 17 proliferative DRs, 47 UWF images of RVO including 28 central RVOs and 19 branch RVOs, 57 UWF images of SCR, including 47 non-proliferative SCRs and 10 proliferative SCRs. The degree of agreement between the two expert readers to classify the images was 96. 87%. Of these, 179 UWF images were used for training and validation and the remaining 45 UWF images were used for testing. The overall accuracy for the classification was 88. 4%. These results are summarised in tables 1 and 2 and figure  1. Figures  2  and 3  illustrate saliency maps and GradCAM++ visualisation for correct and incorrect attributions, respectively.
DISCUSSION In this study, we showed the feasibility of an automated DL classification for the detection of several retinal vascular diseases using UWF-CFP, with an overall accuracy of 88. 4%. The cross-validation technique used in our study allowed taking into account the whole dataset since we were able to make a prediction for every image within the dataset and minimising bias. In our study, SCR Open access was the best-identified category on UWF-CFP with an accuracy of 93. 8%, and RVO is the second-best detected class, with an accuracy of 88. 4%. The reliability of our model was confirmed by the high AUC-ROC obtained. Consequently, the SCR category had the highest AUC-ROC with 96. 7% followed by the RVO class with 91. 2%. Other classes had an AUC-ROC of about 90%. Moreover, our model obtained a specificity of more than 90% for all four classes and high sensitivity for the SCR class (94.
26 Nagasato et al  28  used a dataset of 125 central RVOs and 238 healthy controls to train and test both a DL model and support vector machine (SVM) model. The authors obtained a higher sensitivity and specificity for the DL model (sensitivity: 98. 4%, specificity: 97. 9%, AUC-ROC of 98%) than for the SVM model (sensitivity: 84%, specificity: 87. 5%, AUC-ROC of 89. 5%) for this binary classification. 28 Cai et al gathered 1182 UWF-CFP images from 190 patients with SCR to build their DL model (Inception V4 architecture), aiming to automatically detect sea fan neovascularisation. The authors used two visualisation methods, both Grad-CAM and SmoothGrad. The model obtained a sensitivity of 97. 4%, a specificity of 97% and an AUC-ROC of 98. 8% for detecting sea fan neovascularisation. 35 evertheless, these studies used binary classifications (healthy vs retinal disease), while our model used a fourclass classification system.
Moreover, sickle cell disease is an inherited disorder. Most of the cases are concentrated in referral centres for patients in Europe and the USA. As ophthalmology clinics are not always available in all of these referral centres, an automated artificial intelligence (AI) detection could be of great interest for the diagnosis of retinal involvement. Our study has several limitations. First, our dataset was rather small, given that 224 images were available for model construction and testing across four classes. In comparison, Nagasawa et al  26  or Nagasato et al  28  used comparable datasets for binary classifications. Second, another limitation of our study may be the use of the Optos pseudocolour UWF-CFP. Using the Optos system, pseudocolour images are obtained using red and green scanning lasers, and different magnification between the central and peripheral retina.
37 This may artefactually enhance certain features while diminishing others. In our dataset, the pseudocolour UWF images of the retina were not individually balanced for the green and red laser images by a grader before export. Moreover, UWF-CFP images can have some artefacts limiting the discriminating power for models, such as eye contour elements like eyelid or eyelash. Third, no objective quality assessment metrics have been used. Last but not least, the lack of an external test dataset counts among the limitations of this study. The analysis of the prediction distribution showed that there was a difference in the model's confidence when making a correct or incorrect prediction (online supplemental figure  2 ). Nevertheless, this difference in confidence might not be enough to clearly identify Open access a correct prediction without prior knowledge of the ground truth.
The monolithic solution scheme is based on the weighted residuals method, with the boundary-fitted finite element method used for the discretization in space, and the generalized- method for discretization in time. The proposed framework is evaluated against reference data of two popular FSI benchmark problems. Two additional numerical examples of flow-driven thin-walled piezoelectric energy harvesters demonstrate the feasibility of the framework to predict controlled cyclic response and limit-cycle oscillations and thus the power output in typical operational states of this class of energy harvesting devices.
With this choice, stable discretization of the fluid field is achieved using Taylor-Hood  2∕ 1 elements  [44] , and shear-locking effects are avoid for the beam model  [45]. All simulations are performed using the open-source computing platform FEniCS  [46]  and the extension Multiphenics. FEniCS can discretize mathematical models and generate efficient finite element code. To solve the nonlinear weak form with the N-R method, the automatic functional differentiation (AD) in the Unified Form Language (UFL) is employed. The resulting monolithic system of algebraic equations within each Newton's step can be solved with a variety of techniques available through the linear algebra backend PETSc  [47]. A parallel direct solver (MUMPS  [48] ) is used in the following numerical examples, where the degrees of freedom are between 1×10 5 and 2×10 5.
A vineyard trial was established in winter 2017, and repeated in winter 2018, in McLaren Vale, South Australia, to evaluate whether the addition of spray adjuvants (di-1-p-menthene or trisiloxane ethoxylate) to fungicide treatments (tebuconazole or fluazinam) could improve wound coverage and protection against infection by E. lata. Optimal spray output to achieve adequate coverage of pruning wounds is at least 600 L ha -1 , but to ascertain any benefits from addition of adjuvants, a recycle sprayer applied treatments at 200 L ha -1 , prior to inoculation of wounds with E. lata. A novel technique for evaluating wound coverage was developed, using fluorescent pigment added to treatments, and was compared with the use of water-sensitive papers (WSPs). Digital images of fluorescent pigment on wounds, captured under UV light, and from WSPs, were assessed using Image J image analysis software.
To determine the diversity of "Cylindrocarpon"-like fungi associated with black foot, grapevines of different cultivars grafted onto Gravesac, 1103P, SO4, 101-14 and 3309C rootstocks and with black foot symptoms, and ready to be sold to growers, were evaluated from 2017 to 2019. A total of 77 "Cylindrocarpon"-like strains were isolated and identified by DNA sequence analysis of the partial histone H3 gene. The BLAST search was conducted against type specimens in GenBank, and analysed phylogenetically by the Maximum Likelihood method. Five species of Dactylonectria, and three of Ilyonectria, were found. The most prevalent species was D. macrodidyma (32 isolates), followed by D. novozelandica (15), D. torresensis (ten), I. liriodendri (nine), D. pauciseptata (five), D. valentina (one), I. robusta (one) and Ilyonectria sp.