Effect of pruning and mechanical fruit thinning on crop load and berry and wine composition of Tempranillo in Texas

Tempranillo is the second most planted variety in Texas. However, over-cropping can be an issue. Crop load can be managed by pruning and mechanical fruit thinning. Mechanizing fruit thinning provides three benefits: yield reduction, berry thinning to decrease cluster compactness and reduce fungal disease and lower production costs than fruit thinning by hand (Tardaguila et al., 2008). In this study, crop load was manipulated by pruning and mechanical fruit thinning and its effect was determined on berry and wine quality.

Unsupervised random forest for affinity estimation

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

Modifying Shoot Tip Management to Reduce Cluster Compactness and Lateral Emergence in ‘Cabernet franc’ Grapevines

To manage excessive vine vigor, Vitis vinifera L. ‘Cabernet franc’ grapevines were subjected to shoot wrap, shoot tuck, and hedge (control) techniques at one of two growth stages (shoot tips at 30 cm or at 90 cm above the top catch wire) in the Finger Lakes region of New York from 2016 to 2019. Shoot tuck and shoot wrap both reduced fruit zone lateral counts, with reductions up to 33% and 56% compared with the control, respectively. Shoot wrap reduced fruit zone lateral lengths by up to 50% and cluster compactness by up to 2.4 fewer berries per centimeter rachis. Although shoot wrap improved spray penetration to the clusters by up to 28% in one year of the study, enhanced point quadrat analysis indicated that occlusion layer number was not affected by the treatments. Shoot tip management treatments did not affect yield or fruit composition consistently. Phenological timing of shoot tip management had little impact on vine growth. Although the impacts of these modified shoot tip management practices on lateral emergence and cluster morphology were generally positive, the required hand labor to apply the treatments on a large scale may discourage the use of these management practices.

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

A new weighted fuzzy C-means clustering approach considering between-cluster separability

Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance.

Gibberellin Application Improved Bunch Rot Control of Vignoles Grape, but Response to Mechanical Defoliation Varied between Training Systems

Late-season bunch rot causes significant crop loss for grape growers in wet and humid climates. For 3 years (2016 to 2018), we integrated prebloom mechanized defoliation (MD) in the fruit zone and bloom gibberellin (GA) applications, either alone or in combination, into the bunch rot control program of Vignoles, a commercially valuable grape variety that is highly susceptible to bunch rot. We hypothesized that both treatments would decrease bunch rot through modification of cluster architecture or fruit zone microclimate compared with vines treated with the standard chemical control program. Grapevines were trained to two popular training systems, four-arm Kniffin (4AK) and high-wire bilateral cordon (HWC). Treatment responses varied between training systems. MD, alone or in combination with GA, reduced bunch rot incidence and severity every year on 4AK-trained vines, an effect attributed mainly to fruit zone improvements. Conversely, MD alone did not reduce bunch rot incidence on HWC-trained vines, despite significant improvements in cluster architecture (reduced number of berries per cluster and cluster compactness). GA applications were more effective than MD at reducing cluster compactness, regardless of training system. As a result, GA reduced bunch rot incidence and severity when applied alone or with MD on 4AK- and HWC-trained vines. All treatments positively improved fruit-soluble sugar concentration on both training systems, while positive effects on titratable acidity were more consistent across training systems with MD.

Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia

Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.

Image-based Phenotyping Identifies Quantitative Trait Loci for Cluster Compactness in Grape

Grape (Vitis vinifera) cluster compactness is an important trait due to its effect on disease susceptibility, but visual evaluation of compactness relies on human judgement and an ordinal scale that is not appropriate for all populations. We developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape (Vitis sp.) population for 2 years. Images were collected from grape clusters immediately after harvest, segmented by color, and analyzed using a custom script. Both automated and conventional phenotyping methods were used, and comparisons were made between each method. A partial least squares (PLS) model was constructed to evaluate the prediction of physical cluster compactness using image-derived measurements. Quantitative trait loci (QTL) on chromosomes 4, 9, 12, 16, and 17 were associated with both image-derived and conventionally phenotyped traits within years, which demonstrated the ability of image-derived traits to identify loci related to cluster morphology and cluster compactness. QTL for 20-berry weight were observed between years on chromosomes 11 and 17. Additionally, the automated method of cluster length measurement was highly accurate, with a deviation of less than 10 mm (r = 0.95) compared with measurements obtained with a hand caliper. A remaining challenge is the utilization of color-based image segmentation in a population that segregates for fruit color, which leads to difficulty in differentiating the stem from the fruit when the two are similarly colored in non-noir fruit. Overall, this research demonstrates the validity of image-based phenotyping for quantifying cluster compactness and for identifying QTL for the advancement of grape breeding efforts.

Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great opportunity to study gene expression at cellular resolution, and the scRNA-seq data has been routinely conducted to unfold cell heterogeneity and diversity. A critical step for the scRNA-seq analyses is to cluster the same type of cells, and many methods have been developed for cell clustering. However, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed a new method (GraphSCC) to cluster cells based on scRNA-seq data by accounting structural relations between cells through a graph convolutional network. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. Extensive experiments indicate that GraphSCC model outperforms state-of-the-art methods in various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability.