Flow-diverter stents combined with flow-T stenting-assisted coiling for the treatment of a large basilar apex aneurysm: a case report with a 9-month follow-up

BackgroundEndovascular or surgical treatment of wide-neck, large basilar apex aneurysms is challenging. We present a novel concept for the treatment of complex basilar apex aneurysms using flow-diverter devices combined with the flow-T stenting-assisted coiling technique. Assess the efficacy and safety profile of the technique in this complex aneurysm.Case descriptionA patient with multiple unruptured intracranial aneurysms underwent staged treatment. A large basilar apex aneurysm was treated with a flow-diverter stent combined with a flow-T stenting-assisted coiling technique in the first stage, and a giant supraclinoid aneurysm was treated with a flow-diverter stent applied in the second stage. Clinical presentations, technical details, intra- and perioperative complications, and clinical and angiographic outcomes were recorded, with a 9-month follow-up.ResultsThe patient achieved full neurologic recovery postoperatively. Cerebral angiography performed postoperatively showed revascularization, good laminar flow, and no in-stent or adjacent stenosis.ConclusionFlow-diverter stents combined with flow-T stenting-assisted coiling for the treatment of giant basilar apex aneurysms is a feasible technique with efficacy demonstrated at a 9-month follow-up. Staged endovascular treatment of multiple intracranial aneurysms may be a safe and viable option

HBV infection-induced liver cirrhosis development in dual-humanized mice with human bone mesenchymal stem cell transplantation

疾病动物模型是现代医学发展的基石，尤其是重大、突发传染病暴发时，适宜的疾病动物模型可为及时发现病原体、制定防控策略提供强大保障，原创的疾病动物模型已成为衡量一个国家生物医药科研水平的标志。我校夏宁邵教授团队和浙江大学附属第一医院李君教授团队历经5年的协同攻关，终于建立了国际上首个高度模拟人类乙肝病毒（HBV）自然感染诱发的慢乙肝肝硬化小鼠模型。厦门大学公共卫生学院袁伦志博士生、浙江大学医学院附属第一医院江静博士和厦门大学公共卫生学院刘旋博士生为该论文共同第一作者。厦门大学夏宁邵教授、浙江大学附属第一医院李君教授和厦门大学程通副教授为该论文共同通讯作者。【Abstract】Objective： Developing a small animal model that accurately delineates the natural history of hepatitis B virus (HBV) infection and immunopathophysiology is necessary to clarify the mechanisms of host-virus interactions and to identify intervention strategies for HBV-related liver diseases. This study aimed to develop an HBV-induced chronic hepatitis and cirrhosis mouse model through transplantation of human bone marrow mesenchymal stem cells (hBMSCs). Design： Transplantation of hBMSCs into Fah -/- Rag2 -/- IL-2Rγc -/- SCID (FRGS) mice with fulminant hepatic failure (FHF) induced by hamster-anti-mouse CD95 antibody JO2 generated a liver and immune cell dual-humanized (hBMSC-FRGS) mouse. The generated hBMSC-FRGS mice were subjected to assessments of sustained viremia, specific immune and inflammatory responses and liver pathophysiological injury to characterize the progression of chronic hepatitis and cirrhosis after HBV infection. Results： The implantation of hBMSCs rescued FHF mice, as demonstrated by robust proliferation and transdifferentiation of functional human hepatocytes and multiple immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and viremia and specific immune and inflammatory responses and showed progression to chronic hepatitis and liver cirrhosis at a frequency of 55% after 54 weeks. Conclusion： This new humanized mouse model recapitulates the liver cirrhosis induced by human HBV infection, thus providing research opportunities for understanding viral immune pathophysiology and testing antiviral therapies in vivo.this work was supported by the national Science and technology Major Project (grant nos. 2017ZX10304402, 2017ZX10203201 and 2018ZX09711003-005-003), the national natural Science Foundation of china(grant nos. 81672023, 81571818 and 81771996), the Scientific research Foundation of the State Key laboratory of Molecular Vaccinology and Molecular Diagnostics (grant no 2016ZY005), Zhejiang Province and State's Key Project of the research and Development Plan of china (grant nos 2017c01026 and 2016YFc1101304/3).该研究获得了传染病防治国家科技重大专项、新药创制国家科技重大专项和国家自然科学基金的资助

A Data-Driven Smart Evaluation Framework for Teaching Effect Based on Fuzzy Comprehensive Analysis

In recent years, the epidemic of communicable diseases has boosted the prevalence of online teaching activities. But how to make smart evaluation towards teaching effect has always been a technical barrier. As consequence, this paper utilizes fuzzy comprehensive analysis to deal with this problem from the perspective of big data mining. In particular, it proposes a data-driven smart evaluation framework for teaching effect based on fuzzy comprehensive analysis. Firstly, business data is timely collected from online courses as the basis, including teacher performance, teaching contents, student feedback, etc. Specifically, the initial data is encoded into structured format, from which characteristics of students behaviors can be analyzed. Then, the fuzzy comprehensive analysis is utilized to calculate evaluation results of teaching effect. Some simulation experiments are conducted based on the computer programming design, in which the proposal technical framework is implemented on a developed Web platform. The experiments reflect that the proposal can well realize evaluation of teaching effect

Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting from the low threshold is taken as the marker image, and that from the high threshold is taken as mask image, and mathematical morphology reconstruction is performed on the two images, with the resulting image being the preliminary result of the wheel defect segmentation. Finally, with area and diameter parameters as the conditions, the preliminary segmentation result is analyzed, and the defect regions satisfying the conditions are taken as the ultimate result of the whole solution. Experiments proved the feasibility of the above solution, which is found capable of extracting different types of wheel defects satisfactorily

Heterogeneous Urban Thermal Contribution of Functional Construction Land Zones: A Case Study in Shenzhen, China

Anthropogenic interferences through various intensive social-economic activities within construction land have induced and strengthened the Urban Heat Island (UHI) effects in global cities. Focused on the relative heat effect produced by different social-economic functions, this study established a general framework for functional construction land zones (FCLZs) mapping and investigated their heterogeneous contribution to the urban thermal environment, and then the thermal responses in FCLZs with 12 environmental indicators were analyzed. Taking Shenzhen as an example city, the results show that the total contribution and thermal effects within FCLZs are significantly different. Specifically, the FCLZs contribution to UHI regions highly exceeds the corresponding proportions of their area. The median warming capacity order of FCLZs is: Manufacture function (3.99 °C) > Warehousing and logistics function (3.69 °C) > Street and transportation function (3.61 °C) > Business services function (3.06 °C) > Administration and public services function (2.54 °C) > Green spaces and squares function (2.40 °C) > Residential function (2.21 °C). Both difference and consistency coexist in the responses of differential surface temperature (DST) to environmental indicators in FCLZs. The thermal responses of DST to biophysical and building indicators in groups of FCLZs are approximately consistent linear relationships with different intercepts, while the saturation effects shown in location and social-economic indicators indicate that distance and social-economic development control UHI effects in a non-linear way. This study could extend the understanding of urban thermal warming mechanisms and help to scientifically adjust environmental indicators in urban planning

Evaluation of Medical Carrying Capacity for Megacities from a Traffic Analysis Zone View: A Case Study in Shenzhen, China

Sustainable Development Goals propose to build inclusive, safe, resilient, and sustainable cities and human settlements, which requires us to scientifically evaluate the carrying capacity of current urban public service facilities, but there is still a lack of in-depth exploration of urban public medical service facilities. Therefore, this paper, within the mobile phone signaling data, improved the potential model and carrying capacity evaluation model of public medical facilities, explored the spatial pattern distribution of public medical resources in Shenzhen, and analyzed the current situation of carrying capacity of public medical resources. The study showed that: (1) the overall spatial distribution of public medical resources in Shenzhen is uneven, showing a pattern of multicenter aggregation and multilevel development; (2) the service potential of public medical facilities has obvious spatial variations, with Futian District, Dapeng New District, and Nanshan District showing more obvious high-gravitational-value aggregation centers; (3) medical facilities in Shenzhen are never empty, but the problems of medical underloading and overloading are severe, and spatial allocation and utilization efficiency need to be further optimized. The research results can provide a scientific basis for the research on the allocation and sustainable construction of medical resources in megacities

Identifying Spatial Matching between the Supply and Demand of Medical Resource and Accessing Carrying Capacity: A Case Study of Shenzhen, China

Previous Studies, such as the evaluation of the supply of and demand for regional medical resources and carrying capacity assessments, require further development. This paper aims to evaluate the carrying capacity and spatial distribution of medical resources in Shenzhen from the perspective of supply and demand, and to conduct a time-series variation of the coupling coordination degree from 1986 to 2019. The two-step floating catchment area method was employed to quantify the carrying capacity and coupling coordination degree method and spatial autocorrelation analysis were applied to analyze spatial distribution between supply and demand. The results were as follows. (1) The carrying capacity index in more than 50% of the districts was classified as low-grade. The percentage of regions with good grades was 8.27%. The regions with a high carrying capacity were distributed in the central and southeastern areas. (2) The coupling coordination continued to rise, increasing from 0.03397 in 1986 to 0.33627 in 2019. (3) The level of supply and demand for medical resources in Shenzhen increased from 1986 to 2019, and the highest degree of compatibility between the supply and the population size was largely concentrated in the western and eastern regions. This research can provide a theoretical reference for Shenzhen to rationally plan medical resources and improve the carrying capacity of medical resources

A multimodal approach for improving market price estimation in online advertising

Learning the distribution of market prices is an important and challenging issue for demand-side platforms (DSPs) that serve as advertisers’ agents to compete for online advertising placements in real-time bidding (RTB) systems. Many existing approaches make an assumption that the market prices follow an unimodal distribution. However, based on analytical insights from real-world datasets, we found the distinct multimodal characteristics underlying the distribution of market prices. Moreover, the impression-level features for each ad are also ignored by these approaches in prediction, reducing the accuracy further. To address these problems, a Gaussian Mixture Model (GMM) is proposed in this paper to describe and discriminate the multimodal distribution of market price by utilizing the impression-level features. To further improve its robustness, GMM is extended into a censored version (CGMM) to handle the right-censored challenge in RTB systems (i.e., the market price is only visible to the winner of the ad auction. Thus, the dataset is always biased). Extensive experiments on two real-world public datasets demonstrate that GMM and CGMM significantly outperform 10 state-of-the-art baselines in terms of Wasserstein distance, KL-divergence, ANLP and MSE. To the best of our knowledge, this paper is the first work to simultaneously deal with the multimodal nature of market price distribution and the right-censored challenge in existing RTB systems. It will enable future RTB systems to develop more realistic bidding strategies to enhance the efficiency of online advertising placement auctioning.Nanyang Technological UniversityNational Research Foundation (NRF)This work was supported, in part, by the National Natural Science Foundation of China [grant numbers 71671069]; the National Key Research and Development Program of China [grant numbers 2018YFC0830900]; the Pre-Research Foundation of China [grant numbers 61400010205]; the National Research Foundation, Singapore under its the AI Singapore Programme [grant numbers AISG2-RP-2020-019]; the Joint NTU-WeBank Research Centre on Fintech, Singapore [grant numbers NWJ-2020-008]; the RIE 2020 Advanced Manufacturing and Engineering Programmatic Fund, Singapore [grant numbers A20G8b0102]; the Nanyang Assistant/Associate Professorships (NAP) and the Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Singapore; the Future Communications Research & Development Programme, Singapore [grant numbers FCP-NTU-RG-2021-014]

A revenue-maximizing bidding strategy for demand-side platforms

In real-time bidding (RTB) systems for display advertising, a demand-side platform (DSP) serves as an agent for advertisers and plays an important role in competing for online advertising spaces by placing proper bidding prices. A critical function of the DSP is formulating proper bidding strategies to maximize key performance indicators, such as the number of clicks and conversions. However, many small and medium-sized advertisers' main goal is to maximize revenue with an acceptable return on investment (ROI), rather than simply increase clicks or conversions. Most existing approaches are inapplicable of satisfying the revenue-maximizing goals directly. To solve this problem, we first theoretically analyze the relationships among the conversion rate, ROI, and ad cost, and how they affect revenue. By doing so, we reveal that it is a challenge to increase revenue by relying solely on improving ROI without considering the impact of the ad cost. Based on this insight, the maximal revenue (MR) bidding strategy is proposed to maximize revenue by maximizing the ad cost with a desirable ROI constraint. Unlike previous studies, the proposed MR first distinguishes bid prices from ad costs explicitly, which makes it more applicable to the real second-price auction (GSP) auction mechanism in RTB systems. Then, the winning function is empirically defined in the form of tanh that provides a promising solution for estimating ad costs by jointly considering ad costs with the winning function. The experimental results based on two real-world public datasets demonstrate that the MR significantly outperforms five state-of-the-art models in terms of both revenue and ROI.Published versio