Therapeutic potential of anti-VEGF receptor 2 therapy targeting for M2-tumor-associated macrophages in colorectal cancer

博士（医学）福島県立医科大

Epithelial-mesenchymal transition-converted tumor cells can induce T-cell apoptosis through upregulation of programmed death ligand 1 expression in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is an aggressive tumor, and it is urgently needed to develop novel therapeutic strategies including immunotherapy. In this study, we investigated the upregulation of the programmed death ligand 1 (PD-L1) due to epithelial-mesenchymal transition (EMT) in ESCC using an in vitro treatment system with the EMT inducer, glycogen synthase kinase (GSK)-3 inhibitor, and we also analyzed the correlation of EMT and PD-L1 expression in the clinical tumor samples of both tissue microarray (TMA) samples (n = 177) and whole tissue samples (n = 21). As a result, the inhibition of GSK-3β induces EMT phenotype with upregulated vimentin and downregulated E-cadherin as well as increased Snail and Zinc finger E box-binding homeobox (ZEB)-1 gene expression. Simultaneously, we showed that EMT-converted ESCC indicated the upregulation of PD-L1 at both protein (total and surface) and mRNA levels. Of importance, we showed that EMT-converted tumor cells have a capability to induce T-cell apoptosis to a greater extent in comparison to original epithelial type tumor cells. Furthermore, the immunohistochemical stains of ESCC showed that PD-L1 expression on tumor cells was positively correlated with EMT status in TMA samples (P = .0004) and whole tissue samples (P = .0029). In conclusion, our in vitro and in vivo study clearly demonstrated that PD-L1 expression was upregulated in mesenchymal type tumors of ESCC. These findings provide a strong rationale for the clinical use of anti-PD- 1/ anti-PD- L1 monoclonal antibodies for advanced ESCC patients

A PEKS-Based NDN Strategy for Name Privacy

Named Data Networking (NDN), where addressable content name is used, is considered as a candidate of next-generation Internet architectures. NDN routers use In-Network cache to replicate and store passing packets to make faster content delivery. Because NDN uses a human-readable name, it is easy for an adversary to guess what kind of content is requested. To solve this issue, we develop a PEKS-based strategy for forwarding packets, where PEKS stands for public key encryption with keyword search. We implement the PEKS-based strategy based on the best route strategy and multicast strategy of NDN and show the performance of the PEKS-based NDN strategy. We also discuss the issues of the PEKS-based NDN strategy

A Study on the selected Tipitaka catalogue formats of old manuscripts (palm-leaf)

The Thar-lay (South) Monastery library preserves a small but significant collection of palmleaves for the use of Researchers, scholars. The collection, which is in the process of cataloging, need to be, added the class number for individual item. So, there is appeared some effort to produce the propose class number for the collection. The Thar-lay (South) Monastery library includes (19) subject headings in group of the manuscripts. The presentation gives an overview of the The Thar-lay (South) Monastery library collection of manuscripts and propose the class number. . So, there is appeared some effort to produce the propose class number for Tipitaka catalog formats especially manuscripts (palm-leaf)

A Study on the Preservation of Name Privacy in Named Data Networking

13301甲第1920号博士（工学）金沢大学博士論文要旨Abstract 要約Outline 以下に掲載：Future Internet 12(8) 2020. MDPI. 共著者：Kyi Thar Ko, Htet Htet Hlaing, Masahiro Mamb

Facility Location Problem Approach for Distributed Drones

Currently, industry and academia are undergoing an evolution in developing the next generation of drone applications. Including the development of autonomous drones that can carry out tasks without the assistance of a human operator. In spite of this, there are still problems left unanswered related to the placement of drone take-off, landing and charging areas. Future policies by governments and aviation agencies are inevitably going to restrict the operational area where drones can take-off and land. Hence, there is a need to develop a system to manage landing and take-off areas for drones. Additionally, we proposed this approach due to the lack of justification for the initial location of drones in current research. Therefore, to provide a foundation for future research, we give a justified reason that allows predetermined location of drones with the use of drone ports. Furthermore, we propose an algorithm to optimally place these drone ports to minimize the average distance drones must travel based on a set of potential drone port locations and tasks generated in a given area. Our approach is derived from the Facility Location problem which produces an efficient near optimal solution to place drone ports that reduces the overall drone energy consumption. Secondly, we apply various traveling salesman algorithms to determine the shortest route the drone must travel to visit all the tasks

A Deep Learning Model Generation Framework for Virtualized Multi-Access Edge Cache Management

To reduce the network traffic and service delay in next-generation networks, popular contents (videos and music) are proposed to be temporarily stored in the cache located at the edge nodes such as base stations. The challenging issue in the caching process is to correctly predict the popular contents to store, since the more popular the contents, the more reduction in the network traffic and the service delay occurs. Furthermore, network virtualization proposes an existing cellular network to decouple into infrastructure providers (InPs) and mobile virtual network operators (MVNOs) to reduce capital and operation costs. In this architecture, MVNOs lease the physical resources (network capacity and cache storage) from InPs, the owner of the resources, to provide services to their users. On the one hand, if an MVNO leases more resources than necessary, they will be wasted. On the other hand, if an MVNO leases fewer resources than necessary, the traffic and service delay will increase. Our objective is to lease enough resources without going under or over the required amount and store the most popular contents. Thus, we propose a deep learning-based prediction scheme to intelligently manage the resource leasing and caching process to improve MVNO's profit. The main challenging issue in utilizing the deep-learning is searching for the problem specific best-suited prediction model. Hence, we also propose a reinforcement learning-based model searching scheme to find the best suited deep-learning model. We implement the prediction models using the Keras and Tensorflow libraries and the performance of the cache leasing and caching schemes are tested with a Python-based simulator. In terms of utility, simulation results present that the proposed scheme outperforms 46% compared with the randomized caching with optimal cache leasing scheme.</p

Distributed and Democratized Learning:Philosophy and Research Challenges

Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective at solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains largely open. Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks. In this paper, we propose a novel design philosophy called democratized learning (Dem-AI) whose goal is to build large-scale distributed learning systems that rely on the self-organization of distributed learning agents that are well-connected, but limited in learning capabilities. Correspondingly, inspired from the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are selforganized in a hierarchical structure to collectively perform learning tasks more efficiently. As such, the Dem-AI learning system can evolve and regulate itself based on the underlying duality of two processes that we call specialized and generalized processes. In this regard, we present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields. Accordingly, we introduce four underlying mechanisms in the design such as plasticity-stability transition mechanism, self-organizing hierarchical structuring, specialized learning, and generalization. Finally, we establish possible extensions and new challenges for the existing learning approaches to provide better scalable, flexible, and more powerful learning systems with the new setting of Dem-AI