Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

Background: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. Methods: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. Results: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. Conclusions: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Inhibition of transforming growth factor-β signals suppresses tumor formation by regulation of tumor microenvironment networks

The tumor microenvironment (TME) consists of cancer cells surrounded by stromal components including tumor vessels. Transforming growth factor-β (TGF-β) promotes tumor progression by inducing epithelial-mesenchymal transition (EMT) in cancer cells and stimulating tumor angiogenesis in the tumor stroma. We previously developed an Fc chimeric TGF-β receptor containing both TGF-β type I (TβRI) and type II (TβRII) receptors (TβRI-TβRII-Fc), which trapped all TGF-β isoforms and suppressed tumor growth. However, the precise mechanisms underlying this action have not yet been elucidated. In the present study, we showed that the recombinant TβRI-TβRII-Fc protein effectively suppressed in vitro EMT of oral cancer cells and in vivo tumor growth in a human oral cancer cell xenograft mouse model. Tumor cell proliferation and angiogenesis were suppressed in tumors treated with TβRI-TβRII-Fc. Molecular profiling of human cancer cells and mouse stroma revealed that K-Ras signaling and angiogenesis were suppressed. Administration of TβRI-TβRII-Fc protein decreased the expression of heparin-binding epidermal growth factor-like growth factor (HB-EGF), interleukin-1β (IL-1β) and epiregulin (EREG) in the TME of oral cancer tumor xenografts. HB-EGF increased proliferation of human oral cancer cells and mouse endothelial cells by activating ERK1/2 phosphorylation. HB-EGF also promoted oral cancer cell-derived tumor formation by enhancing cancer cell proliferation and tumor angiogenesis. In addition, increased expressions of IL-1β and EREG in oral cancer cells significantly enhanced tumor formation. These results suggest that TGF-β signaling in the TME controls cancer cell proliferation and angiogenesis by activating HB-EGF/IL-1β/EREG pathways and that TβRI-TβRII-Fc protein is a promising tool for targeting the TME networks

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

Background: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis. Methods: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value. Results: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies. Conclusions: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm