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Background Inflammatory breast cancer (IBC) and triple-negative breast cancer (TNBC) are aggressive breast cancer subtypes with poor clinical outcomes due to the lack of well-validated and actionable targets and the onset of chemo-resistant metastasis. TNBC and IBC account for 15-20% and 2-4% of all breast cancer diagnoses but result in 30% and 8%–10% of breast cancer-related deaths, respectively. MELK (maternal embryonic leucine zipper kinase) is a potential therapeutic target in both TNBC and triple-negative IBC (TN-IBC). We have shown that 1) MELK expression is higher in basal tumors (mostly TNBC) and in IBC than in non-IBC, 2) high MELK expression is an independent prognostic factor for poor overall survival (P=0.0002) and poor distant metastasis-free survival (P=0.008) in breast cancer, and 3) MELK knockout leads to significantly lower metastatic burden and prolonged survival in a xenograft model of TNBC (unpublished data). MELK inhibitor (MELKi) OTS167 is in clinical trials but it lacks specificity and cross-reacts with other essential kinases. It is critical to discover novel, selective inhibitors with good bioavailability that target MELK and minimize adverse effects. We have developed a second-generation small-molecule MELK inhibitor 30e that is a highly potent and slow-binding ATP-competitive inhibitor of MELK. 30e exhibits potent cellular inhibition of MELK (IC50< 10 nM), as assessed using a live cell NanoBRET assay, and is highly selective as assessed by live-cell KiNativ profiling experiments in MDA-MB-231 TNBC cells. Methods We screened a panel of TNBC and IBC cell lines for MELK expression by western blot and qPCR analysis. We assessed the effects of MELK inhibition with the 30e on migration and invasion. The role of MELK in regulating cancer stem cell phenotypes was assessed using the mammosphere assay and flow cytometry (CD44+/CD24-/low surface marker). MELK’s protumorigenic role was determined in vitro using a soft agar assay, and the anti-tumor activity of 30e was assessed in vivo using a TN-IBC orthotopic xenograft mouse model. Results We found that TNBC had high mRNA levels of MELK (62%) compared to non-TNBC cell lines (25%), which correlated with MELK protein levels. 30e inhibited cell viability in TNBC and IBC cells in a dose-dependent manner and with IC50s ranging from 0.45 to 1.76 μM (P≤0.05). However, no significant effect was observed when normal MCF-10A breast cells were treated with 30e (IC50>20 μM). 30e also significantly reduced colony formation ability. Further, we observed that 30e reduced mammosphere formation efficiency and the CD44+/CD24-/low subpopulation in both TNBC and IBC cells in a dose-dependent manner (P≤0.05), suggesting the potential of 30e to inhibit the stem cell population in vitro. The soft agar assay also showed a significant decrease in colony formation in the TNBC and IBC cells after treatment with 30e, an indirect indicator of in vivo tumorigenicity. To assess the efficacy of 30e treatment on tumor growth, we evaluated this inhibitor as a single agent in a SUM-149 (TN-IBC) orthotopic mouse model. The mice were treated with intraperitoneal injections daily with 3 doses (2.5, 5, and 10 mg/kg) of 30e. Our data show that 30e suppressed tumor growth in a dose-dependent manner (P< 0.007). Conclusions and Future Directions Our data demonstrate that a novel, potent and specific MELK inhibitor inhibits tumor growth by suppressing the cancer stem cell phenotype. MELK is a promising target for aggressive cancers such as TNBC and IBC. We are evaluating how MELK inhibition modulates the tumor microenvironment, which is a critical component of breast cancer response to treatment. We performed a cytokine antibody array and are validating the top targets. Further, we will evaluate 30e in combination with standard-of-care treatment for toxicity and inhibition of metastasis. Citation Format: Mohd Mughees, Moises Tacam, Alex Tan, Hector Gonzalez, Emilly S. Villodre, Bisrat Debeb, Debu Tripathy, Geoffrey Bartholomeusz, Juhyeon Lee, Kevin Dalby, Chandra Bartholomeusz. Evaluation of potent novel second-generation MELK-selective inhibitor in aggressive breast cancers [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-10-08.

Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia.A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features.Estimated total BARS showed strong agreement with clinician-evaluated scores with rThe proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task.Our work could support fine-grained monitoring of disease progression and patients' responses to medical/clinical interventions.

A classic routing protocol Dynamic Source Routing (DSR) can quickly establish a routing path but it is prone to cause a congestion and load unbalance under a presence of high amount of traffic. To improve the performance of DSR in large data networks and to solve various problems caused by network congestion, an improved DSR(DSR-p) is proposed. DSR-p has the congestion control ability by using Hello messages to find out the congestion status of links and establishing a ``bypass“ based on the congestion status. In addition, a congestion parameter γ is introduced to define the status of a link, whether the link is congested(red) or not(green).Simulations with Truetime, a network simulation library,were performed to evaluate the performance of DSR-p. The simulation results show that the DRS-p can effectively improve packet delivery rate (PDR) and reduce end-to-end delay (ED), routing overhead (EO) and energy consumption. Furthermore, the experimental tests were conducted with Telos B Mote TPR2420 on TinyOS platform. The test results demonstrate DSR-p outperforms DSR on PDR performance as the amount of transmitted data is larger, increasing by 5.6% as the packet sending rate reaches 900 packets/h. Therefore, both simulation and experimental results prove that the proposed DSR-p performs more efficiently than the conventional DSR.

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

Backgrounds/Aims Intrahepatic recurrence is frequent result after hepatectomy for hepatocellular carcinoma (HCC). We analyzed the clinical results of patients who had the intrahepatic recurrences of HCC after curative surgical resections. Methods From January 2009 to December 2016, 320 patients underwent curative surgical resection for HCC in department of Surgery, Korea Cancer Center Hospital. After surgical resection, 155 patients had suffered HCC recurrence during follow-up period. Among them, 122 patients had only intrahepatic recurrence initially, who were included in this retrospective study. We analyzed about the period of the recurrence after surgery, treatment methods for the recurred tumors, and poor prognostic factors for survival after intrahepatic recurrences. Results Among the 122 patients, 83 patients had recurrence within 24 months after surgery. Thirty-eight patients underwent curative treatment for the recurred tumors (re-resection in 18, radiofrequency ablation in 20 patients). Non-curative treatments were performed in 77 patients (TACE in 68 patients, radiotherapy in 9 patients) and conservative management in 7 patients. Five-year survival rate of patients who underwent curative treatment is 86.4% (p≤0.001). Five-year survival rate of non-curative treatment is 55.7% (p≤0.001), conservative management is 0% (p=0.021). Among the clinical factors, non-curative treatment for recurred tumor, AFP level at the time of recurrence, size of recurred tumor were independent poor prognostic factors for survival after intrahepatic recurrences (p

In this paper, we propose a distributed solution to design a multi-hop ad hoc Internet of Things (IoT) network where mobile IoT devices strategically determine their wireless transmission ranges based on a deep reinforcement learning approach. We consider scenarios where only a limited networking infrastructure is available but a large number of IoT devices are deployed in building a multi-hop ad hoc network to deliver source data to the destination. An IoT device is considered as a decision-making agent that strategically determines its transmission range in a way that maximizes network throughput while minimizing the corresponding transmission power consumption. Each IoT device collects information from its partial observations and learns its environment through a sequence of experiences. Hence, the proposed solution requires only a minimal amount of information from the system. We show that the actions that the IoT devices take from its policy are determined as to activate or inactivate its transmission, i.e., only necessary relay nodes are activated with the maximum transmit power, and nonessential nodes are deactivated to minimize power consumption. Using extensive experiments, we confirm that the proposed solution builds a network with higher network performance than the current state-of-the-art solutions in terms of system goodput and connectivity ratio.

Background The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients’ functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients’ arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice. Objective We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders. Methods We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors’ arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach. Results Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs’ envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice. Conclusions These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Stroke is a leading cause of permanent impairments worldwide. Stroke survivors can improve their motor function through intensive and longitudinal rehabilitation therapies. However, rehabilitation therapy in outpatient settings often does not provide a sufficient amount of rehabilitation for meaningful patients’ recovery. Hence, patients need to perform therapeutic exercises in their home settings. However, despite the widely acknowledged importance of executing exercise movements in therapeutically desirable ways, stroke patients often practice exercise movements in a therapeutically undesirable manner, especially when therapists’ supervision is not available. While wearable sensors have been investigated to monitor patients’ exercise movements, it remains as a challenge to accurately and objectively estimate the quality of at-home exercise movements. This study proposes an analytic method to estimate the quality of individual reaching movements in stroke survivors using a wrist-worn wearable inertial sensor. We extracted time/frequency-domain features from stroke survivor’s horizontal movements in reaching and trained supervised machine learning models to classify if each reaching movement is executed in a therapeutically desirable way or not. The estimation results show that acceptable performance with the Area Under the Receiver Operating Characteristic Curve of 0.94. The extracted temporal and frequency features demonstrate that significant differences exist between the two classes and reflect the level of impairments in reaching movements. We envision these results enable monitoring the quality of at-home exercise movements and personalizing rehabilitation therapy.

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and China on the issues of trade were collected and analytically examined for US-China gaps in major foreign policies, such as bilateral trade and the Belt and Road Initiative. In this paper, a term frequency-inverse document frequency word cloud, a network similarities index, machine learning-processed latent Dirichlet allocation (LDA), and structural equivalence are applied to examine the meanings of the speeches. The main arguments in this paper are as follows. First, the document similarity between the speeches of Chinese and US leaders appears to be completely different. Also, while the documents from Chinese leaders are considerably similar, the documents from US leaders differ by far. Secondly, LDA topic analysis indicates that China concentrates more on international and collaborative relationships, while the U.S. has more focus on domestic and economic interests. Third, from a word hierarchy analysis, the basic words used by American and Chinese leaders are also completely different. Agriculture, farmers, automobiles, and negotiations are the basic words for American leaders, but for Chinese leaders, the basic words are planning, markets, and education.

Selective flame retardant decomposition and polymer recycling in a polystyrene (PS) material containing hexabromocyclododecane (HBCD) were studied using a paint-type copper phthalocyanine modified TiO2 (CuPcTiO2)/polyethylene oxide (PEO) photocatalyst system with three kinds of hindered amine light stabilizer (HALS) loadings. They were performed using visible light irradiation and additional thermal hybrid treatments. Mobility of the HALS with lightest weight was highest and caused the bleeding from the matrix during the irradiation treatment, leading to a lower decomposition ratio and larger changes of dynamic mechanical properties of the recycled PS. On the other hand, the higher molecular weight HALS loadings provided the higher ratios and blocked the changes. In particular, the properties in the HALS loading with a dormant (TEMPO) bond were hardly changed by such hybrid treatment. The py-GC/MS spectra of the extraction residue showed that the HALS adequately worked depending on the treatment situation as radical scavenger and initiator. The tensile strength and strain at break values of the recycled PS kept 90% of the pristine one. In addition, there were no significant differences between the SEM images. The results showed that it had sufficient performance to be used as the post-consumer material.

Leaf senescence is an integrated response of the cells to develop age information and various environmental signals. Thus, some of the genes involved in the response to environmental changes are expected to regulate leaf senescence. Light acts not only as the primary source of energy for photosynthesis but also as an essential environmental cue that directly control plant growth and development including leaf senescence. The molecular mechanisms linking light signaling to leaf senescence have recently emerged, exploring the role of Phytochrome-Interacting Factors (PIFs) as a central player leading to diverse senescence responses, senescence-promoting gene regulatory networks (GRNs) involving PIFs, and structural features of transcription modules in GRNs. The circadian clock is an endogenous time-keeping system for the adaptation of organisms to changing environmental signals and coordinates developmental events throughout the life of the plant. Circadian rhythms can be reset by environmental signals, such as light-dark or temperature cycles, to match the environmental cycle. Research advances have led to the discovery of the role of core clock components as senescence regulators and their underlying signaling pathways, as well as the age-dependent shortening of the circadian clock period. These discoveries highlight the close relationship between the circadian system and leaf senescence. Key issues remain to be elucidated, including the effect of light on leaf senescence in relation to the circadian clock, and the identification of key molecules linking aging, light, and the circadian clock, and integration mechanisms of various senescence-affecting signals at the multi-regulation levels in dynamics point of view.

PURPOSE: Although Common Variable Immunodeficiency (CVID) is considered the most prevalent symptomatic primary antibody deficiency (PAD), there is a population with symptomatic PADs that do not meet criteria for CVID. We analyzed clinical and immunological profiles of patients with different PADs to better understand the differences and similarities between CVID and other PADs. METHODS: We extracted clinical and laboratory data of patients with PADs from electronic medical records. Patients were categorized into CVID, IgG subclass 2 deficiency (IgG2D), IgG deficiency (IgGD) and selective antibody deficiency (sAbD) based on basal immunoglobulin levels and pneumococcal vaccine responses. We compared clinical and immunological characteristics in these groups. RESULTS: All patients, regardless of PAD types, showed similar frequencies of infections, bronchiectasis, and interstitial lung disease (ILD). Hematopoietic malignancies were more frequently found in the CVID than in the IgG2D, IgGD and sAbD groups, while the latter groups trended towards an increased frequency of connective tissue diseases (CTD). Low counts of natural killer (NK) cells were associated with malignancy, autoimmunity, and ILD in CVID but not in other PAD groups. CONCLUSIONS: Higher frequency of hematopoietic malignancy in CVID than in the other PADs and association of lower NK cell counts with non-infectious complications in CVID suggest a relationship between immune alterations and the development of non-infectious manifestations in PADs.

In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.

The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. Sixty healthy mindfulness-naïve males participated in an MRI session consisting of two non-rtfMRI-runs, followed by two rtfMRI-NF runs and one transfer run. Once the regions-of-interest of each of the triple networks were defined using the non-rtfMRI-runs, the slope level was calculated by mediation analysis and used as neurofeedback information, in the form of a thermometer bar, to assist with participant mindfulness during the rtfMRI-NF runs. The participants were asked to increase the level of the thermometer bar while deploying a mindfulness strategy, which consisted of focusing attention on the physical sensations of breathing. rtfMRI-NF training was conducted as part of a randomized controlled trial design, in which participants were randomly assigned to either an experimental group or a control group. The participants in the experimental group received contingent neurofeedback information, which was obtained from their own brain signals, whereas the participants in the control group received non-contingent neurofeedback information that originated from matched participants in the experimental group. Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training.

As cyber attacks on national critical infrastructure have increased, researches are actively conducted to protect industrial control systems(ICS). Compared with existing IT systems, research on special attributes of ICS has been performed mainly, and based on this, many security requirements for ICSs have been derived. However, there is insufficient research on security control to understand and improve the security status of target ICS. To the best of our knowledge, only a comprehensive guide to applying existing security controls to ICSs is being presented. Therefore, this study suggests implications for the current security control items from the ICS point of view. For this purpose, each viewpoint for categorizing and analyzing information security control items was established and based on this, an integrated matrix of ICS security control was derived. Through the ICS security control matrix, the information security control items currently being guided are analyzed from the viewpoint of the ICS, and the present status and implications are derived. The findings can be used as an indicator to effectively apply appropriate controls to appropriate locations where security is required. It is also expected to be a useful reference for improving and supplementing security control items currently being used and used.

Red, green, and blue (RGB)-infrared technology for imaging systems is developing in many research areas including surgery and monitoring systems. This article proposes a driver fatigue monitoring system that detects the face and eye regions (especially the pupil area) of the driver and measures the opened–closed eye status using RGB/infrared cameras. In the proposed system, the mean values of the eye region are used to enhance the accuracy of the extracted pupil area. The results show that the accuracy rate of classification for the eye opened/closed states is more than 93%. Therefore, we have confirmed that our proposed system is practical and accurate. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:2452–2455, 2015

Nowadays, as IT becomes a key element in most business models, it is very important for countries and corporations to create effective business / IT value from IT-related investments. To do so, the process and structure for business / IT value creation must be designed appropriately for the organization's goals and context. For this, the IT governance models that organizations can use typically include COBIT(Control Objectives for Information and Related Technologies) 5, ITIL (IT Infrastructure Library), and TOGAF (The Open Group Architecture Framework). This research helps to use the COBIT 5 Goals Cascade when companies use COBIT 5 to design processes for business / IT value creation. The COBIT 5 Goals Cascade is a mechanism that translates the needs of stakeholders into actionable and customized enterprise goals, IT-related goals and enabler goals. However, this mechanism does not give priority to what goals should be prioritized. According to the COBIT 5 Enterprise Goals to IT-related Goals Mapping table, multiple IT-related goals must be fulfilled to achieve a single enterprise goal. As a result, the IT-related goals for some enterprise goals will be much larger, confusing which goals should be undertaken. To contribute to this problem, this research presents a prioritized methodology that determines the IT-related goals that are more relevant to the organization's goals in realizing IT-related goals. This prioritization methodology is based on the AHP(Analytic Hierarchy Process) using the 'Balanced Scorecard' and 'Governance Objectives' as indicators.

Mild cognitive impairment is status between healthy people and dementia patients. In order to preventdementia or slow its progression, cognitive interventions could be effective. In this paper, review of the cognitiveintervention with computer software was undertaken. Previous reports showed that the cognitive intervention withadditional equipment could practical to the mild cognitive impairment.Key words: Cognitive intervention with computer software, cognitive intervention, mild cognitive impairment I. 서 론 현대 사회는 의료기술의 발달로 평균 수명이 늘어났지만,양육비 증가와 여성의 사회적 진출 등으로 출산율은 감소하고 있다. 이로 인해 현대 인구는 점점 더 고령화돼 가고 있다. 그림 1에서 보여 지듯이, 우리나라 총인구 대비 연령 별고령 인구 구성을 보면 65세 이상의 인구들이 1960년 73만명(2.9%)에서 지속적으로 증가하여 10명 중 1명 꼴인 것을볼 수 있다. 2010년에는 545만 명(11%)이였으며, 2030년에는 1,269만 명(24.3%), 2060년 1,762만 명(40.1%)수준으로증가하여, 10명 중 4명 꼴이 될 것으로 추정된다[1,2].연령이 증가하면서 유기체에 나타나는 점진적인 변화를노화라고 한다. 노화의 한 증상으로 신체기관의 활동력 저하, 기억력 장애, 학습 능력 감소 등과 같은 인지적 기능 장애가있다. 이러한인지기능의장애는노인들의독립적인일상 생활 활동을 어렵게 한다.여러 가지 인지 장애들이 있지만, Poon(1985)은 노인들이 호소하는 인지적인 문제 중 약 80%가 기억과 관련이 있다고 했을 정도로 기억 장애에 대한 문제가 심각하다[4]. 기억 장애의 대표적인 예로는 치매가 있다. 대부분의 치매 환자는 인지 기능이 서서히 저하되면서 치매로 발전을 하는데,이때 치매와 정상의 중간 단계를 경도 인지 장애라고 부른다. 따라서 경도 인지 장애는 정상 노인에 비해서는 인지기능이 저하되었지만 임상적으로는 치매의 기준에는 맞지 않는 상태를 말한다. 정상적인 사람이 치매를 앓게 되는 경우는 매년 1-2%의비율로 미미하지만 경도 인지 장애를 가진 사람이 치매를앓게 되는 경우는 10-15%로 높다[5,6]. 2008년 우리나라의경도 인지 장애 유병률은 그림 2에서와 같이 24.1%로 65세 이상 전체 노인 인구의 약 1/4을 차지한다[3].경도 인지 장애란 병적인 기억력 저하 상태를 말하며, 다음과 같은 증상을 보인다[7,8].·단순한 사실을 기억하는 것이 점점 어려워 진다. ·이전부터 잘 알고 있던 것들에 대해 기억하기 힘들어 진다. ·대화의 내용이나 기본적인 지시내용을 따라가지 못한다.

Spectrum handoff is the process of exchanging progressing data transmission from the current channel to another idle channel. The essential goal of spectrum handoff in CR(Cognitive Radio) networks is to perform consistent data transmission while sustaining performance of ongoing transmission of secondary users. This handoff procedure can cause additional latency that eventually affects on the performance of CR transmission. Channel prediction method is expected to avoid the disruption to primary users and to reduce the handoff latency. In this paper, adaptive channel prediction is proposed to cope with time-varying channel and an adaptive channel prediction based proactive handoff procedure is designed to enhance data transmission performance.

Cognitive radio (CR) has been proposed to solve the spectrum utilization problem by dynamically exploiting the unused spectrum. In CR networks, a spectrum selection scheme is an important process to efficiently exploit the spectrum holes, and an efficient channel allocation scheme must be designed to minimize interference to the primary network as well as to achieve better spectrum utilization. In this paper, we propose a multichannel selection algorithm that uses spectrum hole prediction to limit the interference to the primary network and to exploit channel characteristics in order to enhance channel utilization. The proposed scheme considers both the interference length and the channel capacity to limit the interference to primary users and to enhance system performance. By using the proposed scheme, channel utilization is improved whereas the system limits the collision rate of the CR packets.

Recently, multimedia data transmission becomes important issues in the wireless sensor network. For the multimedia data transmission in wireless sensor network, QoS (Quality of service) is an important factor to determine the system performance. AODV algorithm is one of the widely used algorithm for wireless ad-hoc environment. In AODV, the destination node selects the route with the smallest hop count. Since AODV only considers the hop count, the selected route could have low channel condition, and degrade QoS. In this paper, we propose a modified AODV algorithm that considers the successful transmission rate of each link. The proposed algorithm regards the number of retransmission which can be obtained from the successful transmission rate as the hop count, and applies the modified hop count to AODV. The advantage of the proposed algorithm is that the modified AODV can support QoS without significant change of the conventional algorithm.

Cognitive radio has been recently proposed to dynamically access unused-spectrum. The CR users can share the same frequency band with the primary user without interference to each other. Usually each CR user needs to determine spectrum availability by itself depending only on its local observations. But uncertainty communication environment effects can be mitigated so that the detection probability is improved in a heavily shadowed environment. Soft detection is a primary user detection method of cooperative cognitive radio networks. In our research, we will improve system detection probability by using optimal cooperative node selection algorithm. New algorithm can find optimal number of cooperative sensing nodes for cooperative soft detection by using maximum ratio combining (MRC) method. Through analysis, proposed cooperative node selection algorithm can select optimal node for cooperative sensing according to the system requirement and improve the system detection probability.

Cognitive radio technology enables us to utilize the extra spectrum which is not used by the primary users by sensing the channel condition. To use such an extra spectrum, spectrum allocation is one of the important issues in the cognitive radio networks. The network is dynamic and the available channels are changeable, and the opportunistic channel allocation is required to use the resource efficiently without interference to the primary networks. In this paper, modified proportional fairness scheduling is proposed for cognitive radio networks to satisfy the both fairness and system throughput, and the modified scheduling was designed to reduce the interference to the primary users.

Mobile WiMAX system provides broadband wireless access with a variety of services such as voice, video and data communications and providing QoS for each of these services become an important issue. In Mobile WiMAX system, it is important to allocate resources appropriately in order to support the efficient utilization of resources among various real-time and non real-time services. Although many packet scheduling schemes for real-time services in OFDMA system have been proposed, it need to be modified to apply to Mobile WiMAX system. Since Mobile WiMAX supports five types of service classes, QoS constraints of each class should be taken into consideration. In this paper, we propose an efficient packet scheduling scheme to support various services by considering the QoS constraints of each class.

Wireless Sensor Networks are widely applied for organizing real-time monitoring systems. Unlike general kinds of sensor networks, it is important to preferential send emergency data to the control server when urgent events are occurred. But IEEE 802.15.4 MAC protocol which is generally deployed for wireless sensor network does not support algorithm for urgency data handling. Therefore, we proposed a MAC protocol which consider the priority for the urgency data handling.

A unique synthesis method was developed, which is called solution combustion method (SCM). TiO2 nanoparticles were synthesized by this method. These SCM TiO2 nanoparticles (~35 nm) were added to the dielectric layers of AC powder electroluminescence (EL) devices. The dielectric layers were made of commercial BaTiO3 particles (~1.2 μm) and binding polymer. 0, 5, 10 and 15 wt% of SCM TiO2 nanoparticles were added to the dielectric layers during fabrication of AC powder EL devices respectively. Dielectric constants of these four kinds of dielectric layers were measured. The brightnesses and current densities of AC powder EL devices were also measured. When 10 wt% of SCM TiO2 nanoparticles was added, dielectric constant and brightness were increased by 30% and 60% respectively. Furthermore, the current density was decreased by 60%. This means that the addition of SCM TiO2 nanoparticles resulted in four fold higher luminous efficiency.

Dynamically exploiting unused-spectrum, cognitive radio has been proposed to solve spectrum utilization problem. In cognitive radio, it is important to minimize the interference to primary service as well as to provide efficient channel allocation. In this paper, we propose a multi-channel allocation scheme based on spectrum hole prediction. Proposed scheme considered both interference length and channel capacity to limit the interference to primary user as well as to enhance system performance. Simulation results show the proposed scheme improves the system throughput.

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU`s are satisfied.

In Cognitive Radio network, spectrum selection scheme is one of a important part to manage idle spectrums efficiently. However, in CR networks, they have to adopt time-varying channel availability to minimize the interference to primary users (PU), and be able to manage spectrum resources efficiently. In this paper, we proposed a modified PF scheduler which can be appropriate to schedule downlink CR users and channels, by considering the fairness and the throughput as well as the primary user characteristics of each channel.

This study analysed modern fashion designs which show influences of Asian ethnic dresses focusing on China, Indonesia, Japan, Korea and Vietnam. A total of 190 designs presented during 1982–97 were collected from seven magazines. There were 68 designs with Chinese influences, 49 for Japan, 35 for Korea, 21 for Indonesia and 5 for Vietnam. Twelve designs showed the influences of more than one country. Each design was content analysed in terms of eleven aspects (shape, garment type, silhouette, colour, feature of fabric, surface pattern, trimming, how to wear, accessories, hair-style and make-up). The degree of influence was measured on a 3-point scale ranging from 1 (symbolic resemblance) to 2 (modification) and 3 (replication). The results confirmed that China and Japan have been the most well-known sources of Oriental culture in fashion world. Influence of ethnic dresses were most evident in the aspects related to materials (surface pattern, feature of fabric and colour). The Indonesian sarong and the way to wrap skirts around the waist were also frequently adopted in modern fashion.

In this paper, we suggest multiple kernel learning with hierarchical feature representations. Recently, deep learning represents excellent performance to extract hierarchical feature representations in unsupervised manner. However, since fine-tuning step of deep learning only considers global level of features for classification problems, it makes each layers hierarchical features intractable. Therefore, we propose a method to employ the combined multiple levels of pre-trained features via Multiple Kernel Learning (MKL). MKL is lately proposed optimization problem in classification and is applied to various machine learning problems. MKL automatically finds the best combination of kernels. By applying multiple kernel learning to hierarchical features pre-trained by deep learning, we obtain the optimal combinations of multiple levels of features for the classification task. Also, MKL is applied to analyze the contribution of each layer of features for classification by obtained weight of each kernel.

Channel selection is one of important issues in Cognitive Radio (CR) networks and it is closely related to the channel characteristics and primary user's behavior. In this paper, we proposed a modified proportional fairness scheduling which not only consider the fairness and the throughput but also take the primary user characteristics of each channel into account.

Volitional movement is a hallmark for human behavior. How such well-intended concatenation of behaviors is achieved remains, however, elusive. In the present study, we hypothesized that visual memory of past motion trajectories may be used for selecting future behavior. Based on our memory prediction hypothesis, we designed motor planning experiments that generate new path when given a fixed goal by using only visual memories of past motor trajectories. We conducted simulation experiments and applied the motion planning algorithm for a humanoid robot. The results of our studies suggest that new motor trajectory for a fixed goal can be generated on learned visual memories of past behaviors.

Publisher Summary This chapter begins with the description of the chemical and pharmacological properties of asiaticoside. Asiaticoside, a saponin component that has been isolated from C. asiatica, induces type I collagen synthesis in human dermal fibroblast cells. C. asiatica contains triterpenoids, essential oils, amino acids, and other compounds, such as vellarin. The terpenoids include asiaticoside, centelloside, madecassoside, brahmoside, brahminoside, thankuniside, and centellose, and asiatic, brahmic, centellic, and madecassic acids. Asiaticoside produces no side effects such as localized erythema or any kind of discomfort. Following this, the antibiotic character of asiaticoside, anti-herpes simplex virus activities of asiaticoside, wound-healing activity of asiaticoside, anxiolytic properties of asiaticoside, and the anti-wrinkle activity of asiaticoside are described. Asiaticoside induced the phosphorylation of both Smad2 and Smad3. In addition, the asiaticoside-induced binding of Smad3 and Smad4 has also been detected. It is also demonstrated that treatment with asiaticoside induces the synthesis of type I collagen, and the mechanisms underlying its action may be mediated via a TGFβ receptor I kinase (TβRI kinase)-independent Smad activation pathway in cultured human dermal fibroblast cells. It is concluded that asiaticoside can induce the synthesis of type I collagen and that the mechanisms underlying its action are mediated via a TβRI kinase-independent Smad activation pathway.