Endoplasmic Reticulum Stress in the β-Cell Pathogenesis of Type 2 Diabetes

Type 2 diabetes is a complex metabolic disorder characterized by high blood glucose in the context of insulin resistance and relative insulin deficiency by β-cell failure. Even if the mechanisms underlying the pathogenesis of β-cell failure are still under investigation, recent increasing genetic, experimental, and clinical evidence indicate that hyperactivation of the unfolded protein response (UPR) to counteract metabolic stresses is closely related to β-cell dysfunction and apoptosis. Signaling pathways of the UPR are “a double-edged sword” that can promote adaptation or apoptosis depending on the nature of the ER stress condition. In this paper, we summarized our current understanding of the mechanisms and components related to ER stress in the β-cell pathogenesis of type 2 diabetes

Loss of Distal Femur Combined with Popliteal Artery Occlusion: Reconstructive Arthroplasty Using Modular Segmental Endoprosthesis: A Case Report

Severe injury to the knee and the surrounding area is frequently associated with injury to ligaments of the knee joint and structures in the popliteal fossa. This case involved a popliteal artery occlusion, severe bone loss of distal femur, loss of collateral ligaments, and extensor mechanism destruction of the knee. Initially, prompt recognition and correction of associated popliteal artery injury are important for good results after treatment. After successful revascularization, treatment for severe bone loss of distal femur and injury of the knee joint must be followed. We treated this case by delayed reconstruction using modular segmental endoprosthesis after revascularization of the popliteal artery. This allowed early ambulation. At 36 months after surgery, the patient had good circulation of the lower limb and was ambulating independently

A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

A new obstacle avoidance method for autonomous vehicles called obstacle-dependent Gaussian potential field (ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases

Two distinct domains of Flo8 activator mediates its role in transcriptional activation and the physical interaction with Mss11

Flo8 is a transcriptional activator essential for the inducible expression of a set of target genes such as STA1, FLO11, and FLO1 encoding an extracellular glucoamylase and two cell surface proteins, respectively. However, the molecular mechanism of Flo8-mediated transcriptional activation remains largely elusive. By generating serial deletion constructs, we revealed here that a novel transcriptional activation domain on its extreme C-terminal region plays a crucial role in activating transcription. On the other hand, the N-terminal LisH motif of Flo8 appears to be required for its physical interaction with another transcriptional activator, Mss11, for their cooperative transcriptional regulation of the shared targets. Additionally, GST pull-down experiments uncovered that Flo8 and Mss11 can directly form either a heterodimer or a homodimer capable of binding to DNA, and we also showed that this formed complex of two activators interacts functionally and physically with the Swi/Snf complex. Collectively, our findings provide valuable clues for understanding the molecular mechanism of Flo8-mediated transcriptional control of multiple targets. © 2014 Elsevier Inc. All rights reserved.

Multiple Object Tracking Using Re-Identification Model with Attention Module

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system

Unsupervised Semantic Segmentation Inpainting Network Using a Generative Adversarial Network with Preprocessing

The generative adversarial neural network has shown a novel result in the image generation area. However, applying it to a semantic segmentation inpainting task exhibits instability due to the different data distribution. To solve this problem, we propose an unsupervised semantic segmentation inpainting method using an adversarial deep neural network with a newly introduced preprocessing method and loss function. For stabilizing the adversarial training for semantic segmentation inpainting, we match the probability distribution of the segmentation maps with the developed preprocessing method. In addition, a new cross-entropy total variation loss for the probability map is introduced to improve the segmentation inpainting work by smoothing the segmentation map. The experimental results demonstrate the proposed algorithm’s effectiveness on both synthetic and real datasets