Sequential formation and resolution of multiple rosettes drive embryo remodelling after implantation

The morphogenetic remodelling of embryo architecture after implantation culminates in pro-amniotic cavity formation. Despite its key importance, how this transformation occurs remains unknown. Here, we apply high-resolution imaging of embryos developing in vivo and in vitro, spatial RNA sequencing and 3D trophoblast stem cell models to determine the sequence and mechanisms of these remodelling events. We show that cavitation of the embryonic tissue is followed by folding of extra-embryonic tissue to mediate the formation of a second extra-embryonic cavity. Concomitantly, at the boundary between embryonic and extra-embryonic tissues, a hybrid 3D rosette forms. Resolution of this rosette enables the embryonic cavity to invade the extra-embryonic tissue. Subsequently, β1-integrin signalling mediates the formation of multiple extra-embryonic 3D rosettes. Podocalyxin exocytosis leads to their polarized resolution, permitting the extension of embryonic and extra-embryonic cavities and their fusion into a unified pro-amniotic cavity. These morphogenetic transformations of embryogenesis reveal a previously unappreciated mechanism for lumen expansion and fusionThe M.Z.G lab is supported by grants from the European Research Council (669198) and the Welcome Trust (098287/Z/12/Z) and the EU Horizon 2020 Marie Sklodowska-Curie actions (ImageInLife,721537). C.K is supported by BBSRC Doctoral training studentship

Analysis of Large-Strain Consolidation Behavior of Soil with High Water Content in Consideration of Self-Weight

Based on the axisymmetric large-strain consolidation (ALSC) model with the void ratio as the variable under equal strain condition, difference schemes of model’s equation, initial condition, and boundary condition were given. Taking phosphatic clay in Florida as a research object, the consolidation behaviors of soil with high water content by axisymmetric large-strain theory and one-dimensional large-strain theory were analyzed. The effect of different kinds of consolidation theories and self-weight stress on an average degree of consolidation was evaluated. The development of the void ratio and excess pore water pressure along the soil layer was clarified. The results show that the theoretical value of Terzaghi’s consolidation degree is always less than that of ALSC (Us, the average degree of consolidation defined by strain)-vertical drainage in the consolidation process. Terzaghi’s solution overestimates the dissipation rate of excess pore water pressure during the earlier consolidation period but underestimates it during the later consolidation period. The degree of consolidation calculated by Hansbo develops faster than ALSC (Up, the average degree of consolidation defined by stress)-radial drainage, but slower than ALSC (Us)-radial drainage. In the ALSC model, Us is always been faster than Up. The effect of self-weight on the consolidation degree of axisymmetric large-strain consolidation theory is relatively small (maximum error is less than 16%), while it can accelerate the consolidation rate of soil in one-dimensional large-strain consolidation theory largely. When only the vertical drainage occurs, the consolidation rate in the middle of the soil is obviously lagging the upper and lower parts, while the radial drainage can reduce the void ratio and the excess pore water pressure along the soil layer uniformly and more rapidly

Accurate salient object detection via dense recurrent connections and residual-based hierarchical feature integration

Recently, the convolutional neural network (CNN) has achieved great progress in many computer vision tasks including object detection, image restoration, and scene understanding. In this paper, we propose a novel CNN-based saliency detection method through dense recurrent connections and residual-based hierarchical feature integration. Inspired by the recent neurobiological finding that abundant recurrent connections exist in the human visual system, we firstly propose a novel dense recurrent CNN module (D-RCNN) to learn informative saliency cues by incorporating dense recurrent connections into sub-layers of convolutional stages. Then we present a residual-based architecture with short connections for deep supervision which hierarchically combines both coarse-level and fine-level feature representations. Our end-to-end method takes raw RGB images as input and directly outputs saliency maps without relying on any time-consuming pre/post-processing techniques. Extensive qualitative and quantitative evaluation results on four widely tested benchmark datasets demonstrate that our method can achieve more accurate saliency detection results solutions with significantly fewer model parameters

The plaintiff qualification on environmental public litigation of village committee in China

The establishment of the environmental protection law reflects the Chinese government's determination to achieve ecological civilization. Since January 1, 2015, when the revised Environmental Protection Law of the People's Republic of China came into effect, environmental public interest litigation in China has been rapidly growing. In the implementation of environmental law, environmental public interest litigation plays an irreplaceable role. The mechanism of environmental public interest litigation will ideally enhance the effectiveness and prevention of environmental protection and become a major contributor to public awareness of environmental protection. Despite the fact that environmental public interest litigation in China has made great progress, there are still many shortcomings and defects. One of the major difficulties facing environmental public interest litigation in China is the lack of standing of plaintiffs in environmental public interest litigation. This paper will explore the necessity of plaintiff status for environmental public interest litigation in China's grassroots self-governance organizations based on the situation of environmental pollution in rural China, using village committees as an example

A deep learning-based surface defect inspection system using multi-scale and channel-compressed features

In machine vision-based surface inspection tasks, defects are typically considered as local anomalies in homogeneous background. However, industrial workpieces commonly contain complex structures, including hallow regions, welding joints, or rivet holes. Such obvious structural interference will inevitably cause cluttered background and mislead the classification results. Moreover, the sizes of various surface defects might change significantly. Last but not the least, it is extremely time-consuming and not scalable to capture large-scale defect datasets to train deep CNN models. To address the challenges mentioned above, we firstly proposed to incorporate multiple convolutional layers with different kernel sizes to increase the receptive field and to generate multi-scale features. As a result, the proposed model can better handle cluttered background and defects of various sizes. Also, we purposely compress the size of parameters in the newly added convolutional layers for better learning of defect-related features using a limited number of training samples. Evaluated in a newly constructed surface defect dataset (images contain complex structures and defects of various sizes), our proposed model achieves more accurate recognition results compared with the state-of-the-art surface defect classifiers. Moreover, it is a light-weight model and can deliver real-time processing speed (>100fps) on a computer equipped with a single NVIDIA TITAN X Graphics Processing Unit (12G memory

Fusion of multi-light source illuminated images for effective defect inspection on highly reflective surfaces

It is observed that a human inspector can obtain better visual observations of surface defects via changing the lighting/viewing directions from time to time. Accordingly, we first build a multi-light source illumination/acquisition system to capture images of workpieces under individual lighting directions and then propose a multi-stream CNN model to process multi-light source illuminated images for high-accuracy surface defect classification on highly reflective metal. Moreover, we present two effective techniques including individual stream deep supervision and channel attention (CA) based feature re-calibration to generate and select the most discriminative features on multi-light source illuminated images for the subsequent defect classification task. Comparative evaluation results demonstrate that our proposed method is capable of generating more accurate recognition results via the fusion of complementary features extracted on images illuminated by multi-light sources. Furthermore, our proposed light-weight CNN model can process more than 20 input frames per second on a single NVIDIA Quadro P6000 GPU (24G RAM) and is faster than a human inspector. Source codes and the newly constructed multi-light source illuminated dataset will be accessible to the public

Does Subacromial Osteolysis Affect Shoulder Function after Clavicle Hook Plating?

Purpose. To evaluate whether subacromial osteolysis, one of the major complications of the clavicle hook plate procedure, affects shoulder function. Methods. We had performed a retrospective study of 72 patients diagnosed with a Neer II lateral clavicle fracture or Degree-III acromioclavicular joint dislocation in our hospital from July 2012 to December 2013. All these patients had undergone surgery with clavicle hook plate and were divided into two groups based on the occurrence of subacromial osteolysis. By using the Constant-Murley at the first follow-up visit after plates removal, we evaluated patients’ shoulder function to judge if it has been affected by subacromial osteolysis. Results. We have analyzed clinical data for these 72 patients, which shows that there is no significant difference between group A (39 patients) and group B (33 patients) in age, gender, injury types or side, and shoulder function (the Constant-Murley scores are 93.38±3.56 versus 94.24±3.60, P>0.05). Conclusion. The occurrence of subacromial osteolysis is not rare, and also it does not significantly affect shoulder function