Defoliation of strawberry mother plants for the production of runner tips

The objective of this work was to determine the sink-source relationships and their effects on the number and growth of runner tips of 'Camino Real' strawberry stock plants. Three types of sources were evaluated: one defoliation at 96 days after planting (DAP), two defoliations at 50 and 96 DAP, and mother plants without defoliation. Four types of sink were accessed: runner tips collected weekly and monthly, four stolons with rooted runner tips in pots, and four freely-grown stolons. A completely randomized experimental design was used in a split-plot arrangement, with four replicates. The source types were placed in the plots, and sink types in the subplots. The number of runner tips, the crown diameter, and the dry matter mass were determined. Number and growth of tips were higher on plants without defoliation, and decreased 44.7% on twice-defoliated mother plants. The two-defoliation management did not reduce runner tip dry matter mass only on plants with rooted stolons, which produced runner tips 50% heavier. Defoliation of mother plants bearing rooting stolons can be used to reduce their growth, without reducing the emission and growth of runner tips

Clustering with hypergraphs: the case for large hyperedges

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many grouping problems require an affinity measure that must involve a subset of data of size more than two, i.e., a hyperedge. Almost all previous works, however, have considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both theoretical and empirical standpoints. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate pure large hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. In the important applications of face clustering and motion segmentation, our method demonstrates substantially better accuracy and efficiency.Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Sute

Assessing anxious features in depressed outpatients

10.1002/mpr.353International Journal of Methods in Psychiatric Research204e69-e82IPSR