Differential effects of sulforaphane in regulation of angiogenesis in a co-culture model of endothelial cells and pericytes

Aberrant neovascularization supports nutrients and the oxygen microenvironment in tumour growth, invasion and metastasis. Recapitulation of functional microvascular structures in vitro could provide a platform for the study of vascular conditions. Sulforaphane (SFN), an isothiocyanate, has been reported to possess chemopreventive properties. In the present study, the effects of SFN on cell proliferation and tubular formation have been investigated using endothelial cells (ECs) and pericytes in coculture. SFN showed a dose-dependent inhibition on the growth of ECs and pericytes with IC50 values 46.7 and 32.4 µM, respectively. SFN (5-20 µM) inhibited tube formation in a 3D coculture although a lower dose (1.25 µM) promoted 30% more endothelial tube formation than control. Moreover, SFN affected intercellular communication between ECs and pericytes via inhibition of angiogenic factor such as vascular endothelial growth factor (VEGF) expression in pericytes. However, the expression of its receptor (VEGFR-2) was found significantly increased in ECs. These effects were associated with down-regulation of prolyl hydroxylase domain-containing protein 1 and 2 (PHD1/2) and activation of hypoxia-inducible factor-1 (HIF) pathway by SFN. Furthermore, thioredoxin reductase-1 was also up-regulated by SFN treatment, suggesting that anti-oxidant and redox regulation are involved in angiogenesis. Taken together, the results of this study suggest that SFN differentially regulates endothelial cells and pericytes, and disrupting their interplay through the VEGF-VEGFR signalling pathway. Anti-angiogenesis property of SFN indicates it has potential role as anticancer agent

Bootstrap inference for quantile treatment effects in randomized experiments with matched pairs

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). We derive the limit distribution of the QTE estimator under MPDs, highlighting the difficulties that arise in analytical inference due to parameter tuning. It is shown that both the naive multiplier bootstrap and the naive multiplier bootstrap of the pairs fail to approximate the limit distribution of the QTE estimator under MPDs because they do not preserve the dependence structure within the matched pairs. To address this difficulty we propose two bootstrap methods that can consistently approximate the limit distribution: the gradient bootstrap and the multiplier bootstrap of the inverse propensity score weighted (IPW) estimator. The gradient bootstrap is free of tuning parameters but requires knowledge of the pair identities. The multiplier bootstrap of the IPW estimator does not require such knowledge but involves one tuning parameter. Both methods are straightforward to implement and able to provide pointwise confidence intervals and uniform confidence bands that achieve exact limiting coverage rates. We demonstrate their finite sample performance using simulations and provide an empirical application to a well-known dataset in macroinsurance.Comment: 89 page

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202