Understanding Convolution for Semantic Segmentation

Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC .Comment: WACV 2018. Updated acknowledgements. Source code: https://github.com/TuSimple/TuSimple-DU

Electrospun Fibrous Architectures for Drug Delivery, Tissue Engineering and Cancer Therapy

The versatile electrospinning technique is recognized as an efficient strategy to deliver active pharmaceutical ingredients and has gained tremendous progress in drug delivery, tissue engineering, cancer therapy, and disease diagnosis. Numerous drug delivery systems fabricated through electrospinning regarding the carrier compositions, drug incorporation techniques, release kinetics, and the subsequent therapeutic efficacy are presented herein. Targeting for distinct applications, the composition of drug carriers vary from natural/synthetic polymers/blends, inorganic materials, and even hybrids. Various drug incorporation approaches through electrospinning are thoroughly discussed with respect to the principles, benefits, and limitations. To meet the various requirements in actual sophisticated in vivo environments and to overcome the limitations of a single carrier system, feasible combinations of multiple drug-inclusion processes via electrospinning could be employed to achieve programmed, multi-staged, or stimuli-triggered release of multiple drugs. The therapeutic efficacy of the designed electrospun drug-eluting systems is further verified in multiple biomedical applications and is comprehensively overviewed, demonstrating promising potential to address a variety of clinical challenges.Peer reviewe

Tailoring Porous Silicon for Biomedical Applications : From Drug Delivery to Cancer Immunotherapy

In the past two decades, porous silicon (PSi) has attracted increasing attention for its potential biomedical applications. With its controllable geometry, tunable nanoporous structure, large pore volume/high specific surface area, and versatile surface chemistry, PSi shows significant advantages over conventional drug carriers. Here, an overview of recent progress in the use of PSi in drug delivery and cancer immunotherapy is presented. First, an overview of the fabrication of PSi with various geometric structures is provided, with particular focus on how the unique geometry of PSi facilitates its biomedical applications, especially for drug delivery. Second, surface chemistry and modification of PSi are discussed in relation to the strengthening of its performance in drug delivery and bioimaging. Emerging technologies for engineering PSi-based composites are then summarized. Emerging PSi advances in the context of cancer immunotherapy are also highlighted. Overall, very promising research results encourage further exploration of PSi for biomedical applications, particularly in drug delivery and cancer immunotherapy, and future translation of PSi into clinical applications.Peer reviewe

An intelligent analysis method of security and stability control strategy based on the knowledge graph

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection

The gene regulatory molecule GLIS3 in gastric cancer as a prognostic marker and be involved in the immune infiltration mechanism

BackgroundGastric cancer is the most prevalent solid tumor form. Even after standard treatment, recurrence and malignant progression are nearly unavoidable in some cases of stomach cancer. GLIS Family Zinc Finger 3 (GLIS3) has received scant attention in gastric cancer research. Therefore, we sought to examine the prognostic significance of GLIS3 and its association with immune infiltration in gastric cancer.MethodUsing public data from The Cancer Genome Atlas (TCGA), we investigated whether GLIS3 gene expression was linked with prognosis in patients with stomach cancer (STAD). The following analyses were performed: functional enrichment analysis (GSEA), quantitative real-time PCR, immune infiltration analysis, immunological checkpoint analysis, and clinicopathological analysis. We performed functional validation of GLIS3 in vitro by plate cloning and CCK8 assay. Using univariate and multivariate Cox regression analyses, independent prognostic variables were identified. Additionally, a nomogram model was built. The link between OS and subgroup with GLIS3 expression was estimated using Kaplan-Meier survival analysis. Gene set enrichment analysis utilized the TCGA dataset.ResultGLIS3 was significantly upregulated in STAD. An examination of functional enrichment revealed that GLIS3 is related to immunological responses. The majority of immune cells and immunological checkpoints had a positive correlation with GLIS3 expression. According to a Kaplan-Meier analysis, greater GLIS3 expression was related to adverse outcomes in STAD. GLIS3 was an independent predictive factor in STAD patients, as determined by Cox regression (HR = 1.478, 95%CI = 1.478 (1.062-2.055), P=0.02)ConclusionGLIS3 is considered a novel STAD patient predictive biomarker. In addition, our research identifies possible genetic regulatory loci in the therapy of STAD

Large manipulative experiments revealed variations of insect abundance and trophic levels in response to the cumulative effects of sheep grazing

This study was supported by the National Natural Science Foundation of China, 31672485, the Earmarked Fund for China Agriculture Research System, CARS-34-07, and the Innovation Project of Chinese Academy of Agricultural Sciences.Livestock grazing can affect insects by altering habitat quality; however, the effects of grazing years and intensities on insect abundance and trophic level during manipulative sheep grazing are not well understood. Therefore, we investigated these effects in a large manipulative experiment from 2014 to 2016 in the eastern Eurasian steppe, China. Insect abundance decreased as sheep grazing intensities increased, with a significant cumulative effect occurring during grazing years. The largest families, Acrididae and Cicadellidae, were susceptible to sheep grazing, but Formicidae was tolerant. Trophic primary and secondary consumer insects were negatively impacted by increased grazing intensities, while secondary consumers were limited by the decreased primary consumers. Poor vegetation conditions caused by heavy sheep grazing were detrimental to the existence of Acrididae, Cicadellidae, primary and secondary consumer insects, but were beneficial to Formicidae. This study revealed variations in insect abundance and trophic level in response to continuous sheep grazing in steppe grasslands. Overall, our results indicate that continuous years of heavy- and over- sheep grazing should be eliminated. Moreover, our findings highlight the importance of more flexible sheep grazing management and will be useful for developing guidelines to optimize livestock production while maintaining species diversity and ecosystem health.Publisher PDFPeer reviewe

Advance on the Application of Magnetic Field-assisted Freezing Technology in Food

Freezing is one of the most common and effective method of preserving food. However, the formation of large ice crystals during traditional freezing process will destroy food tissues and lead to quality deterioration. Therefore, how to improve the quality of frozen food by new freezing technology has become a research hotspot. Magnetic field-assisted freezing is a novel method for controlling ice crystal nucleation. The mechanism of magnetic field-regulated ice crystal nucleation and its applications in the fields of fruits and vegetables, livestock and poultry meat, cereals and other food products are reviewed in the present paper. According to the review results, although magnetic field freezing technology has been applied in many food fields, the current research mainly focuses on the effect of magnetic field on frozen food quality and freezing parameters, while there are few consensus on the mechanism of magnetic field-assisted freezing to regulate ice crystal nucleation. Therefore, more systematic research is required to reveal the mechanism of magnetic field-assisted freezing and promote the application of magnetic field-assisted freezing technology in the food field, to promote the quality of frozen food

Application of stimuli-responsive nanomedicines for the treatment of ischemic stroke

Ischemic stroke (IS) refers to local brain tissue necrosis which is caused by impaired blood supply to the carotid artery or vertebrobasilar artery system. As the second leading cause of death in the world, IS has a high incidence and brings a heavy economic burden to all countries and regions because of its high disability rate. In order to effectively treat IS, a large number of drugs have been designed and developed. However, most drugs with good therapeutic effects confirmed in preclinical experiments have not been successfully applied to clinical treatment due to the low accumulation efficiency of drugs in IS areas after systematic administration. As an emerging strategy for the treatment of IS, stimuli-responsive nanomedicines have made great progress by precisely delivering drugs to the local site of IS. By response to the specific signals, stimuli-responsive nanomedicines change their particle size, shape, surface charge or structural integrity, which enables the enhanced drug delivery and controlled drug release within the IS tissue. This breakthrough approach not only enhances therapeutic efficiency but also mitigates the side effects commonly associated with thrombolytic and neuroprotective drugs. This review aims to comprehensively summarize the recent progress of stimuli-responsive nanomedicines for the treatment of IS. Furthermore, prospect is provided to look forward for the better development of this field