ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and the variable tumor shape and size bring challenges to the accurate segmentation of the breast lesion. Motivated by the selective kernel convolution, we introduce an enhanced selective kernel convolution for breast tumor segmentation, which integrates multiple feature map region representations and adaptively recalibrates the weights of these feature map regions from the channel and spatial dimensions. This region recalibration strategy enables the network to focus more on high-contributing region features and mitigate the perturbation of less useful regions. Finally, the enhanced selective kernel convolution is integrated into U-net with deep supervision constraints to adaptively capture the robust representation of breast tumors. Extensive experiments with twelve state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure

Melatonin Protects MCAO-Induced Neuronal Loss via NR2A Mediated Prosurvival Pathways

Stroke is the significant cause of human mortality and sufferings depending upon race and demographic location. Melatonin is a potent antioxidant that exerts protective effects in differential experimental stroke models. Several mechanisms have been previously suggested for the neuroprotective effects of melatonin in ischemic brain injury. The aim of this study is to investigate whether melatonin treatment affects the glutamate N-methyl-D-aspartate (NMDA) and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor signaling in cerebral cortex and striatum 24 h after permanent middle cerebral artery occlusion (MCAO). Melatonin (5 mg/kg) attenuated ischemia-induced down regulation of NMDA receptor 2 (NR2a), postsynaptic density-95 (PSD95) and increases NR2a/PSD95 complex association, which further activates the pro-survival PI3K/Akt/GSK3β pathway with mitigated collapsin response mediator protein 2 (CRMP2) phosphorylation. Furthermore, melatonin increases the expression of γ-enolase, a neurotrophic factor in ischemic cortex and striatum, and preserve the expression of presynaptic (synaptophysin and SNAP25) and postsynaptic (p-GluR1845) protein. Our study demonstrated a novel neuroprotective mechanism for melatonin in ischemic brain injury which could be a promising neuroprotective agent for the treatment of ischemic stroke

Pathological Comparisons of the Hippocampal Changes in the Transient and Permanent Middle Cerebral Artery Occlusion Rat Models

© Copyright © 2019 Shah, Li, Kury, Zeb, Khatoon, Liu, Yang, Liu, Yao, Khan, Koh, Jiang and Li. Ischemic strokes are categorized by permanent or transient obstruction of blood flow, which impedes delivery of oxygen and essential nutrients to brain. In the last decade, the therapeutic window for tPA has increased from 3 to 5–6 h, and a new technique, involving the mechanical removal of the clot (endovascular thrombectomy) to allow reperfusion of the injured area, is being used more often. This last therapeutic approach can be done until 24 h after stroke onset. Due to this fact, more acute ischemic stroke patients are now being recanalized, and so tMCAO is probably the “best” model to address these patients that have a potential good outcome in terms of survival and functional recovery. However, permanent occlusion patients are also important, not only to increase survival rate but also to improve functional outcomes, although these are more difficult to achieve. So, both models are important, and which target different stroke patients in the clinical scenario. Hippocampus has a vital role in memory and cognition, is prone to ischemic induced neurodegeneration. This study was designed to delineate the molecular, pathological, and neurological changes in rat models of t-MCAO, permanent MCAO (pMCAO), and pMCAO with diabetic conditions in hippocampal tissue. Our results showed that these three models showed distinct discrepancies at numerous pathological process, including key signaling molecules involved in neuronal apoptosis, glutamate induced excitotoxicity, neuroinflammation, oxidative stress, and neurotrophic changes. Our result suggests that the two commonly used MCAO models exhibited tremendous differences in terms of neuronal cell loss, glutamate excitotoxic related signaling, synaptic transmission markers, neuron inflammatory and oxidative stress molecules. These differences may reflect the variations in different models, which may provide valuable information for mechanistic and therapeutic inconsistences as experienced in both preclinical models and clinical trials

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

Large-scale traffic flow simulation based on intelligent PSO

With the rapid development of urban traffic, a large number of vehicles in cities not only bring convenience to people, but also bring a series of traffic problems, including traffic congestion and high traffic accident rates. Driving speed and waiting time of vehicles are two important factors of traffic problems. To simulate the real urban road traffic flow, a one-dimensional traffic flow grid model was proposed, which considered the nearest and next neighbour car at the same time, and connected the front and rear neighbour cars to optimize the traffic flow. The experiment results showed that our traffic flow grid model can simulate the real urban road traffic flow. In addition, we tried to optimize the urban traffic network model and improved the traffic speed of vehicles and reduced the waiting time