The Study on Main Issues of Chinese and Italian Historic Centers’ Conservation Based on A Comparative Perspective

In this research, we can know Chinese historic conservation system from an overall view, to know its evolution, related aspects, its problems and the solutions following Italian successful reference experience. At the outset, we can retrospect how this research comes out, its orientation, subject, purpose and frame. This research was brought out as a result of my awareness of that “National apathy to the large-scale destruction in historic center; misunderstanding of related concepts, principles and tools, aberrant implementation; and my intention to redevelop the current conservation system. It is mainly refers to historic conservation system of Historic Centers, embodying in study of its various aspects in Italy and China The whole research has four parts. The first and second part carry out a comprehensive study on various issues of Chinese and Italian historic conservation separately. In the third part, the dual comparative study of Chinese and Italian historic conservation issues highlight gains and loose, advantages and disadvantages; merits and defects in the conservation work, especially referring to the Chinese part. The fourth part proposes a synthesis of the physical intervention methods; new historic conservation subjects types system and a governance model

Promising molecular mechanisms responsible for gemcitabine resistance in cancer

Gemcitabine is the first-line treatment for pancreatic ductual adenocarcinoma (PDAC) as well as acts against a wide range of other solid tumors. Patients usually have a good initial response to gemcitabine-based chemotherapy but would eventually develop resistance. To improve survival and prognosis of cancer patients, better understanding of the mechanisms responsible for gemcitabine resistance and discovery of new therapeutic strategies are in great need. Amounting evidence indicate that the developmental pathways, such as Hedgehog (Hh), Wnt and Notch, become reactivated in gemcitabine-resistant cancer cells. Thus, the strategies for targeting these pathways may sensitize cancer cells to gemcitabine treatment. In this review, we will summarize recent development in this area of research and discuss strategies to overcome gemcitabine resistance. Given the cross-talk between these three developmental signaling pathways, designing clinical trials using a cocktail of inhibitory agents targeting all these pathways may be more effective. Ultimately, our hope is that targeting these developmental pathways may be an effective way to improve the gemcitabine treatment outcome in cancer patients

The Hedgehog pathway: role in cell differentiation, polarity and proliferation.

Hedgehog (Hh) is first described as a genetic mutation that has "spiked" phenotype in the cuticles of Drosophila in later 1970s. Since then, Hh signaling has been implicated in regulation of differentiation, proliferation, tissue polarity, stem cell population and carcinogenesis. The first link of Hh signaling to cancer was established through discovery of genetic mutations of Hh receptor gene PTCH1 being responsible for Gorlin syndrome in 1996. It was later shown that Hh signaling is associated with many types of cancer, including skin, leukemia, lung, brain and gastrointestinal cancers. Another important milestone for the Hh research field is the FDA approval for the clinical use of Hh inhibitor Erivedge/Vismodegib for treatment of locally advanced and metastatic basal cell carcinomas. However, recent clinical trials of Hh signaling inhibitors in pancreatic, colon and ovarian cancer all failed, indicating a real need for further understanding of Hh signaling in cancer. In this review, we will summarize recent progress in the Hh signaling mechanism and its role in human cancer

V-Cache: Towards Flexible Resource Provisioning for Multi-tier Applications in IaaS Clouds

Abstract—Although the resource elasticity offered by Infrastructure-as-a-Service (IaaS) clouds opens up opportunities for elastic application performance, it also poses challenges to application management. Cluster applications, such as multi-tier websites, further complicates the management requiring not only accurate capacity planning but also proper partitioning of the resources into a number of virtual machines. Instead of burdening cloud users with complex management, we move the task of determining the optimal resource configuration for cluster applications to cloud providers. We find that a structural reorganization of multi-tier websites, by adding a caching tier which runs on resources debited from the original resource budget, significantly boosts application performance and reduces resource usage. We propose V-Cache, a machine learning based approach to flexible provisioning of resources for multi-tier applications in clouds. V-Cache transparently places a caching proxy in front of the application. It uses a genetic algorithm to identify the incoming requests that benefit most from caching and dynamically resizes the cache space to accommodate these requests. We develop a reinforcement learning algorithm to optimally allocate the remaining capacity to other tiers. We have implemented V-Cache on a VMware-based cloud testbed. Exper-iment results with the RUBiS and WikiBench benchmarks show that V-Cache outperforms a representative capacity management scheme and a cloud-cache based resource provisioning approach by at least 15 % in performance, and achieves at least 11 % and 21 % savings on CPU and memory resources, respectively. I

Real-time object detection method based on improved YOLOv4-tiny

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.Comment: 14pages,7figures,2table

A hybrid ensemble method with negative correlation learning for regression

Hybrid ensemble, an essential branch of ensembles, has flourished in numerous machine learning problems, especially regression. Several studies have confirmed the importance of diversity; however, previous ensembles only consider diversity in the sub-model training stage, with limited improvement compared to single models. In contrast, this study selects and weights sub-models from a heterogeneous model pool automatically. It solves an optimization problem using an interior-point filtering linear-search algorithm. This optimization problem innovatively incorporates negative correlation learning as a penalty term, with which a diverse model subset can be selected. Experimental results show some meaningful points. Model pool construction requires different classes of models, with all possible parameter sets for each class as sub-models. The best sub-models from each class are selected to construct an NCL-based ensemble, which is far more better than the average of the sub-models. Furthermore, comparing with classical constant and non-constant weighting methods, NCL-based ensemble has a significant advantage in several prediction metrics. In practice, it is difficult to conclude the optimal sub-model for a dataset prior due to the model uncertainty. However, our method would achieve comparable accuracy as the potential optimal sub-models on RMSE metric. In conclusion, the value of this study lies in its ease of use and effectiveness, allowing the hybrid ensemble to embrace both diversity and accuracy.Comment: 37 pages, 14 figures, 11 table

Case report: Anti-IgLON5 disease and anti-LGI1 encephalitis following COVID-19

Anti-IgLON family member 5 (IgLON5) disease is a rare autoimmune encephalitis, characterized by sleep problems, cognitive decline, gait abnormalities, and bulbar dysfunction. Anti–leucine-rich glioma-inactivated 1 (LGI1) autoimmune encephalitis is characterized by cognitive dysfunction, mental disorders, faciobrachial dystonic seizures (FBDS), and hyponatremia. Various studies report that coronavirus disease 2019 (COVID-19) have an effect on the nervous system and induce a wide range of neurological symptoms. Autoimmune encephalitis is one of the neurological complications in severe acute respiratory syndrome coronavirus 2 infection. Until now, autoimmune encephalitis with both anti-IgLON5 and anti-LGI1 receptor antibodies following COVID-19 is rarely reported. The case report described a 40-year-old man who presented with sleep behavior disorder, daytime sleepiness, paramnesia, cognitive decline, FBDS, and anxiety following COVID-19. Anti-IgLON5 and anti-LGI1 receptor antibodies were positive in serum, and anti-LGI1 receptor antibodies were positive in cerebrospinal fluid. The patient presented with typical symptoms of anti-IgLON5 disease such as sleep behavior disorder, obstructive sleep apnea, and daytime sleepiness. Moreover, he presented with FBDS, which is common in anti-LGI1 encephalitis. Therefore, the patient was diagnosed with anti-IgLON5 disease and anti-LGI1 autoimmune encephalitis. The patient turned better after high-dose steroid and mycophenolate mofetil therapy. The case serves to increase the awareness of rare autoimmune encephalitis after COVID-19

A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference

Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals