Understanding Three Hydration-Dependent Transitions of Zwitterionic Carboxybetaine Hydrogel by Molecular Dynamics Simulations

In this work, molecular dynamics simulations were performed to study a carboxybetaine methacrylate (CBMA) hydrogel under various swelling states. The water content in this study ranged from 28% to 91% of the total weight of the hydrogel. Three transitions of the CBMA hydrogel were observed as the water content increased. The first transition occurs when the water content increases from 33% to 37%. The observed kink in the self-diffusion coefficient of water indicates that the hydration of the polymer network of the hydrogel is saturated; the further added water is in a less confined state. The second transition was found to be related to the physical cross-links of the polymer network. As the water content rises to above 62%, the lifetime of the physical cross-links decreases significantly. This abrupt change in the lifetime indicates that the transition represents the equilibrium swelling state of the hydrogel. Finally, the third transition was observed when the water content goes above 81%. The significant increases in the bond and angle energies of the polymer network indicate that the hydrogel reaches its upper limit swelling state at this transition. These results are comparable to previously published experimental studies of similar zwitterionic hydrogels

A Wideband MIMO Channel Model for Aerial Intelligent Reflecting Surface-Assisted Wireless Communications

Compared to traditional intelligent reflecting surfaces(IRS), aerial IRS (AIRS) has unique advantages, such as more flexible deployment and wider service coverage. However, modeling AIRS in the channel presents new challenges due to their mobility. In this paper, a three-dimensional (3D) wideband channel model for AIRS and IRS joint-assisted multiple-input multiple-output (MIMO) communication system is proposed, where considering the rotational degrees of freedom in three directions and the motion angles of AIRS in space. Based on the proposed model, the channel impulse response (CIR), correlation function, and channel capacity are derived, and several feasible joint phase shifts schemes for AIRS and IRS units are proposed. Simulation results show that the proposed model can capture the channel characteristics accurately, and the proposed phase shifts methods can effectively improve the channel statistical characteristics and increase the system capacity. Additionally, we observe that in certain scenarios, the paths involving the IRS and the line-of-sight (LoS) paths exhibit similar characteristics. These findings provide valuable insights for the future development of intelligent communication systems.Comment: 6 pages, 7 figure

抗がん治療の強化を志向した酵素活性化ナノキャリアの開発

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 カブラル オラシオ, 東京大学教授 鄭 雄一, 東京大学教授 津本 浩平, 東京大学特任准教授 金野 智浩, 東京大学准教授 寺村 裕治, 東京大学准教授 宮田 完二郎University of Tokyo(東京大学

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS

THE DETECTION OF FRAUDULENT FINANCIAL STATEMENTS: AN INTEGRATED LANGUAGE MODEL

Among the growing number of Chinese companies that went public overseas, many have been detected and alleged as conducting financial fraud by market research firms or U.S. Securities and Exchange Commission (SEC). Then investors lost money and even confidence to all overseas-listed Chinese companies. Likewise, these companies suffered serious stock sank or were even delisted from the stock exchange. Conventional auditing practices failed in these cases when misleading financial reports presented. This is partly because existing auditing practices and academic researches primarily focus on statistical analysis of structured financial ratios and market activity data in auditing process, while ignoring large amount of textual information about those companies in financial statements. In this paper, we build integrated language model, which combines statistical language model (SLM) and latent semantic analysis (LSA), to detect the strategic use of deceptive language in financial statements. By integrating SLM with LSA framework, the integrated model not only overcomes SLM’s inability to capture long-span information, but also extracts the semantic patterns which distinguish fraudulent financial statements from non-fraudulent ones. Four different modes of the integrated model are also studied and compared. With application to assess fraud risk in overseas-listed Chinese companies, the integrated model shows high accuracy to flag fraudulent financial statements

ARHI (DIRAS 3), an Imprinted Tumor Suppressor Gene, Binds to Importins, and Blocks Nuclear Translocation of Stat3

ARHI (DIRAS3) is an imprinted tumor suppressor gene whose expression is lost in the majority of breast and ovarian cancers. Unlike its homologs Ras and Rap, ARHI functions as a tumor suppressor. Our previous study showed that ARHI can interact with transcription activator Stat3 and inhibit its nuclear translocation in human breast and ovarian cancer cells. To identify proteins that interact with ARHI in nuclear translocation, we have performed proteomic analysis and identified several importins that can associate with ARHI. To further explore this novel finding, we have purified 10 GST-importin fusion proteins (importin 7, 8, 13, b1, a1, a3, a5, a6, a7 as well as mutant a1). Using a GST-pull down assay, we found that ARHI can bind strongly to most importins; however, its binding is significantly reduced with an importin a1 mutant which contains an altered nuclear localization signal (NLS) domain. In addition, an ARHI N-terminal deletion mutant (NTD) exhibits much less binding to all importins than does wild type ARHI ARHI and NTD proteins were purified and tested for their ability to inhibit nuclear importation of proteins in HeLa cells. ARHI protein inhibits interaction of Ran-importin complexes with GFP fusion proteins that contain an NLS domain and a beta-like import receptor binding domain, blocking their nuclear localization. Addition of ARHI also blocked nuclear localization of phosphorylated Stat3β. By GST-pull down assays, we found that ARHI could compete for Ran-importins binding. Thus, ARHI-induced disruption of importin binding to cargo proteins including Stat3 could serve as an important regulatory mechanism that contributes to the tumor suppressor function of ARHI