Multistate analysis of multitype recurrent event and failure time data with event feedbacks in biomarkers

From Wiley via Jisc Publications RouterHistory: received 2020-04-07, rev-recd 2021-04-19, accepted 2021-06-05, pub-electronic 2021-07-13Article version: VoRPublication status: PublishedAbstract: In this paper we propose a class of multistate models for the analysis of multitype recurrent event and failure time data when there are past event feedbacks in longitudinal biomarkers. It can well incorporate various effects, including time‐dependent and time‐independent effects, of different event paths or the number of occurrences of events of different types. Asymptotic unbiased estimating equations based on polynomial splines approximation are developed. The consistency and asymptotic normality of the proposed estimators are provided. Simulation studies show that the naive estimators which either ignore the past event feedback or the measurement errors are biased. Our method has a better coverage probability of the time‐varying/constant coefficients, compared to the naive methods. An application to the dataset from the Atherosclerosis Risk in Communities Study, which is also the motivating example to develop the method, is presented

JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery

In this study, we focus on the problem of 3D human mesh recovery from a single image under obscured conditions. Most state-of-the-art methods aim to improve 2D alignment technologies, such as spatial averaging and 2D joint sampling. However, they tend to neglect the crucial aspect of 3D alignment by improving 3D representations. Furthermore, recent methods struggle to separate the target human from occlusion or background in crowded scenes as they optimize the 3D space of target human with 3D joint coordinates as local supervision. To address these issues, a desirable method would involve a framework for fusing 2D and 3D features and a strategy for optimizing the 3D space globally. Therefore, this paper presents 3D JOint contrastive learning with TRansformers (JOTR) framework for handling occluded 3D human mesh recovery. Our method includes an encoder-decoder transformer architecture to fuse 2D and 3D representations for achieving 2D$\&$3D aligned results in a coarse-to-fine manner and a novel 3D joint contrastive learning approach for adding explicitly global supervision for the 3D feature space. The contrastive learning approach includes two contrastive losses: joint-to-joint contrast for enhancing the similarity of semantically similar voxels (i.e., human joints), and joint-to-non-joint contrast for ensuring discrimination from others (e.g., occlusions and background). Qualitative and quantitative analyses demonstrate that our method outperforms state-of-the-art competitors on both occlusion-specific and standard benchmarks, significantly improving the reconstruction of occluded humans.Comment: Camera Ready Version for ICCV 202

Innovation of a Regulatory Mechanism Modulating Semi-determinate Stem Growth through Artificial Selection in Soybean

It has been demonstrated that Terminal Flowering 1 (TFL1) in Arabidopsis and its functional orthologs in other plants specify indeterminate stem growth through their specific expression that represses floral identity genes in shoot apical meristems (SAMs), and that the loss-of-function mutations at these functional counterparts result in the transition of SAMs from the vegetative to reproductive state that is essential for initiation of terminal flowering and thus formation of determinate stems. However, little is known regarding how semi-determinate stems, which produce terminal racemes similar to those observed in determinate plants, are specified in any flowering plants. Here we show that semi-determinacy in soybean is modulated by transcriptional repression of Dt1, the functional ortholog of TFL1, in SAMs. Such repression is fulfilled by recently enabled spatiotemporal expression of Dt2, an ancestral form of the APETALA1/FRUITFULL orthologs, which encodes a MADS-box factor directly binding to the regulatory sequence of Dt1. In addition, Dt2 triggers co-expression of the putative SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (GmSOC1) in SAMs, where GmSOC1 interacts with Dt2, and also directly binds to the Dt1 regulatory sequence. Heterologous expression of Dt2 and Dt1 in determinate (tfl1) Arabidopsis mutants enables creation of semi-determinacy, but the same forms of the two genes in the tfl1 and soc1 background produce indeterminate stems, suggesting that Dt2 and SOC1 both are essential for transcriptional repression of Dt1. Nevertheless, the expression of Dt2 is unable to repress TFL1 in Arabidopsis, further demonstrating the evolutionary novelty of the regulatory mechanism underlying stem growth in soybean