Identification of a novel submergence response gene regulated by the Sub1A gene

Submergence is one of the major constraints to rice production in many rice growing areas in the world. The Sub1A gene has been demonstrated to dramatically improve submergence tolerance in rice. Here, we report the identification of a novel submergence response (RS1) gene that is specifically induced in the Sub1A-mediated submergence tolerance response. Under submergence, RS1 was upregulated in M202 (Sub1A) but downregulated in M202 in RNA-seq and microarray assays. Expression analyses of various tissues and developmental stages show that RS1 mRNA levels are high in leaves and sheaths, but low in roots, stems, and panicles. Our results also show that RS1 is highly expressed under submergence, drought, and NaCl stresses, but not under cold or dehydration stress. Hormone ABA treatment induces, whereas GA treatment decreases, RS1 expression. The RS1 and Sub1A genes are co-regulated under submergence. Overexpression of RS1 in transgenic Kitaake (without Sub1A) and M202(Sub1A)×Kitaake do not result in enhanced submergence tolerance. Conversely, down-regulation of RS1 in M202(Sub1A)×Kitaake lead to weaken submergence tolerance. We hypothesize that RS1 may play a role in the Sub1A-mediated submergence tolerance pathway.Key word: Rice (Oryza sativa L.), submergence, RNA-seq, Sub1A, abiotic stress

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu

TRIM9-Mediated Resolution of Neuroinflammation Confers Neuroprotection upon Ischemic Stroke in Mice

Excessive and unresolved neuroinflammation is a key component of the pathological cascade in brain injuries such as ischemic stroke. Here, we report that TRIM9, a brain-specific tripartite motif (TRIM) protein, was highly expressed in the peri-infarct areas shortly after ischemic insults in mice, but expression was decreased in aged mice, which are known to have increased neuroinflammation after stroke. Mechanistically, TRIM9 sequestered β-transducin repeat-containing protein (β-TrCP) from the Skp-Cullin-F-box ubiquitin ligase complex, blocking IκBα degradation and thereby dampening nuclear factor κB (NF-κB)-dependent proinflammatory mediator production and immune cell infiltration to limit neuroinflammation. Consequently, Trim9-deficient mice were highly vulnerable to ischemia, manifesting uncontrolled neuroinflammation and exacerbated neuropathological outcomes. Systemic administration of a recombinant TRIM9 adeno-associated virus that drove brain-wide TRIM9 expression effectively resolved neuroinflammation and alleviated neuronal death, especially in aged mice. These findings reveal that TRIM9 is essential for resolving NF-κB-dependent neuroinflammation to promote recovery and repair after brain injury and may represent an attractive therapeutic target

Spatial-temporal clustering of an outbreak of SARS-CoV-2 Delta VOC in Guangzhou, China in 2021

BackgroundIn May 2021, the SARS-CoV-2 Delta variant led to the first local outbreak in China in Guangzhou City. We explored the epidemiological characteristics and spatial-temporal clustering of this outbreak.MethodsBased on the 153 cases in the SARS-CoV-2 Delta variant outbreak, the Knox test was used to analyze the spatial-temporal clustering of the outbreak. We further explored the spatial-temporal clustering by gender and age groups, as well as compared the changes of clustering strength (S) value between the two outbreaks in Guangzhou.ResultsThe result of the Knox analysis showed that the areas at short distances and brief periods presented a relatively high risk. The strength of clustering of male-male pairs was higher. Age groups showed that clustering was concentrated in cases aged ≤ 18 years matched to 18–59 years and cases aged 60+ years. The strength of clustering of the outbreak declined after the implementation of public health measures. The change of strength of clustering at time intervals of 1–5 days decreased greater in 2021 (S = 129.19, change rate 38.87%) than that in 2020 (S = 83.81, change rate 30.02%).ConclusionsThe outbreak of SARS-CoV-2 Delta VOC in Guangzhou has obvious spatial-temporal clustering. The timely intervention measures are essential role to contain this outbreak of high transmission