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Gorlin syndrome is a rare autosomal dominant hereditary disease with high incidence of tumors such as basal cell carcinoma and medulloblastoma. Disease-specific induced pluripotent stem cells (iPSCs) have now been used as a model to analyze disease pathogenesis as well as an animal model. In this study, we generated iPSCs derived from fibroblasts of four patients with Gorlin syndrome (Gln-iPSCs) with a heterozygous mutation of the PTCH1 gene. Gln-iPSCs from the four patients developed medulloblastoma in 100% (four out of four), a manifestation of Gorlin syndrome, in the teratomas after implantation into immunodeficient mice, but none (0/584) of the other iPSC-teratomas. One of the medulloblastomas had loss of heterozygosity in the PTCH1 gene while benign teratoma, i.e. non-medulloblastoma part, did not, indicating a close clinical correlation between tumorigenesis in Gorlin syndrome patients and Gln-iPSCs.

Heart failure is caused by various factors, making its underlying pathogenic mechanisms difficult to identify, and tends to worsen over time. Early diagnosis of cardiovascular disease is the key for treatment to promote healthy life. To detect the structural and functional molecular changes associated with cardiovascular disease, we focused on glycans, which reflect the type and state of cells. We investigated glycan localization in the cardiac tissue of normal mice and their alterations during aging using an evanescent-filed lectin microarray, a technique based on lectin-glycan interaction, and lectin staining. The glycan profiles in the left ventricle showed differences between the luminal side (medial) and the wall side (lateral) region. The former area was characterized by the presence of sialic acid residues.Moreover, age-related changes in glycan profiles were observed earlier in the medial region. The difference in the age-related decrease of a-galactose stained with griffonia simplicifolia lectin-IB4 in different region of the leftventricle suggested spatiotemporal changes in microvessels. The glycan profile, which retains diverse glycan structures, is supported by many cell populations and maintains cardiac function. Glycan localization and changes are expected to be developed as a marker of the signs and symptoms of heart failure in the future.

Liver cirrhosis is a critical health problem associated with several complications, including skeletal muscle atrophy, which adversely affects the clinical outcome of patients independent of their liver functions. However, the precise mechanism underlying liver cirrhosis-induced muscle atrophy has not been elucidated. Here we show that serum factor induced by liver fibrosis leads to skeletal muscle atrophy. Using bile duct ligation (BDL) model of liver injury, we induced liver fibrosis in mice and observed subsequent muscle atrophy and weakness. We developed culture system of human primary myotubes that enables an evaluation of the effects of soluble factors on muscle atrophy and found that serum from BDL mice contains atrophy-inducing factors. This atrophy-inducing effect of BDL mouse serum was mitigated upon inhibition of TNFα signalling but not inhibition of myostatin/activin signalling. The BDL mice exhibited significantly up-regulated serum levels of TNFα when compared with the control mice. Furthermore, the mRNA expression levels of Tnf were markedly up-regulated in the fibrotic liver but not in the skeletal muscles of BDL mice. The gene expression analysis of isolated nuclei revealed that Tnf is exclusively expressed in the non-fibrogenic diploid cell population of the fibrotic liver. These findings reveal the mechanism through which circulating TNFα produced in the damaged liver mediates skeletal muscle atrophy. Additionally, this study demonstrated the importance of inter-organ communication that underlies the pathogenesis of liver cirrhosis.

Previous chapters of this textbook have mainly focused on introducing different types of periodic patterns and their mining algorithms. Some chapters have also focused on evaluating the algorithms. In this chapter, we will present three real-world applications of periodic patterns. The first case study is traffic congestion analytics, where periodic-frequent pattern mining was employed to identify the road segments in which users have regularly encountered traffic congestion in the transportation network. The second case study is flight incidents data analytics, where partial periodic pattern mining was employed to identify factors that are regularly causing flight incidents in the data. The third case study is air pollution analytics, where fuzzy periodic pattern mining was employed to identify the geographical regions where people were exposed to harmful levels of air pollution.

Large events with many attendees cause congestion in the traffic network around the venue. To avoid accidents or delays due to this kind of unexpected congestion, it is important to predict the level of congestion in advance of the event. This study aimed to forecast congestion triggered by large events. However, historical congestion information alone is insufficient to forecast congestion at large venues when non-recurrent events are held there. To address this problem, we utilize microblog posts that refer to future events as an indicator of event attendance. We propose a regression model that is trained with microblog posts and historical congestion information to accurately forecast congestion at large venues. Experiments on next 24-hour congestion forecasting using real-world traffic and Twitter data demonstrate that our model reduces the prediction errors over those of the baseline models (autoregressive and long short term memory) by 20% – 50%.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, this model’s prosperous industrial use has been hindered by the problem of combinatorial explosion of patterns, which is the generation of too many redundant patterns, most of which may be useless to the user. We propose a novel model of closed periodic-frequent patterns that may exist in a temporal database to address this problem. Closed periodic-frequent patterns represent a concise lossless subset that uniquely preserves the complete information of all periodic-frequent patterns in a database. An efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern Miner (CPFP-Miner), has been introduced to find all the database’s desired patterns. Experimental results demonstrate that CPFP-Miner is not only memory, runtime, and energy-efficient, but also highly scalable. The usefulness of our model has also been shown with a case study on traffic congestion analytics.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, the successful industrial application of this model has been hindered by the problem of combinatorial explosion of patterns, that is the generation of too many redundant patterns, most of which may be useless to the user. To address this problem, this paper proposes a novel model of maximal periodic- frequent pattern that may exist in a temporal database. A new pattern-growth algorithm, called Maximum Periodic-Frequent Pattern-growth (maxPFP-growth), has also been introduced to efficiently find all desired patterns in the data. Experimental results demonstrate that maxPFP-growth is not only memory and runtime efficient, but also highly scalable as well. The usefulness of our model has also been demonstrated with a case study on traffic congestion analytics.

Inner and middle ear disorders are the leading cause of hearing loss, and are said to be among the greatest risk factors of dementia. The use of regenerative medicine for the treatment of inner ear disorders may offer a potential alternative to cochlear implants for hearing recovery. In this paper, we reviewed recent research and clinical applications in middle and inner ear regeneration and cell therapy. Recently, the mechanism of inner ear regeneration has gradually been elucidated. “Inner ear stem cells,” which may be considered the precursors of various cells in the inner ear, have been discovered in the cochlea and vestibule. Research indicates that cells such as hair cells, neurons, and spiral ligaments may form promising targets for inner ear regenerative therapies by the transplantation of stem cells, including mesenchymal stem cells. In addition, it is necessary to develop tests for the clinical monitoring of cell transplantation. Real-time imaging techniques and hearing rehabilitation techniques are also being investigated, and cell therapy has found clinical application in cochlear implant techniques.

Because open-domain dialogues allow diverse responses, basic reference-based metrics such as BLEU do not work well unless we prepare a massive reference set of high-quality responses for input utterances. To reduce this burden, a human-aided, uncertainty-aware metric, ΔBLEU, has been proposed; it embeds human judgment on the quality of reference outputs into the computation of multiple-reference BLEU. In this study, we instead propose a fully automatic, uncertainty-aware evaluation method for open-domain dialogue systems, υBLEU. This method first collects diverse reference responses from massive dialogue data and then annotates their quality judgments by using a neural network trained on automatically collected training data. Experimental results on massive Twitter data confirmed that υBLEU is comparable to ΔBLEU in terms of its correlation with human judgment and that the state of the art automatic evaluation method, RUBER, is improved by integrating υBLEU.

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories., Comment: Accepted to EMNLP 2020 Workshop NLP-COVID

Partial Periodic itemsets are an important class of regularities that exist in a temporal database. A Partial Periodic itemset is something persistent and predictable that appears in the data. Past studies on Partial Periodic itemsets have been primarily focused on centralized databases and are not scalable for Big Data environments. One cannot ignore the advantage of scalability by using more resources. This is because we deal with large databases in a real-time environment and using more resources can increase the performance. To address the issue we have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in a number of machines.

Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.

Vascular insulin resistance induced by inflammatory cytokines leads to the initiation and development of vascular diseases. In humans, circulating TNFα levels are increased during aging, suggesting a correlation between vascular insulin resistance and plasma TNFα levels. Currently, the precise molecular mechanisms of vascular insulin resistance mediated by TNFα are not well characterized. We aimed at clarifying whether glycosphingolipids contribute to vascular insulin resistance after inflammatory stimulation. In this study, we examined vascular insulin resistance using human aortic endothelial cells after treatment with different concentrations of TNFα for different time intervals for mimicking in vivo acute or chronic inflammatory situations. We show that ganglioside GM1 levels on cell membranes change depending on time of exposure to TNFα and its concentration and that the GM1 expression is associated with specific extracellular/intracellular regulation of the insulin signaling cascade. Furthermore, we provide evidence that factors such as aging and senescence affect the regulation of insulin resistance. Our data suggest that GM1 is a key player in the induction of vascular insulin resistance after short- or long-term exposure to TNFα and is a good extracellular target for prevention and cure of vascular diseases.

Insulin in physiological concentrations is important to maintain vascular function. Moreover, vascular insulin resistance contributes to vascular impairment. In the elderly, other factors including hypertension, dyslipidemia, and chronic inflammation amplify senescence of vascular endothelial and smooth muscle cells. In turn, senescence increases the risk for vascular-related diseases such as arteriosclerosis, diabetes, and Alzheimer’s disease. Recently, it was found that GM1 ganglioside, one of the glycolipids localized on the cell membrane, mediates vascular insulin resistance by promoting senescence and/or inflammatory stimulation. First, it was shown that increased GM1 levels associated with aging/senescence contribute to insulin resistance in human aortic endothelial cells (HAECs). Second, the expression levels of gangliosides were monitored in HAECs treated with different concentrations of tumor necrosis factor-alpha (TNFα) for different time intervals to mimic in vivo acute or chronic inflammatory conditions. Third, the levels of insulin signaling-related molecules were monitored in HAECs after TNFα treatment with or without inhibitors of ganglioside synthesis. In this review, we summarize the molecular mechanisms of insulin resistance in aged/senescent and TNFα-stimulated endothelial cells mediated by gangliosides and highlight the possible roles of gangliosides in vascular insulin resistance-related diseases.

There exist biases in individual’s language use; the same word (e.g., cool) is used for expressing different meanings (e.g., temperature range) or different words (e.g., cloudy, hazy) are used for describing the same meaning. In this study, we propose a method of modeling such personal biases in word meanings (hereafter, semantic variations) with personalized word embeddings obtained by solving a task on subjective text while regarding words used by different individuals as different words. To prevent personalized word embeddings from being contaminated by other irrelevant biases, we solve a task of identifying a review-target (objective output) from a given review. To stabilize the training of this extreme multi-class classification, we perform a multi-task learning with metadata identification. Experimental results with reviews retrieved from RateBeer confirmed that the obtained personalized word embeddings improved the accuracy of sentiment analysis as well as the target task. Analysis of the obtained personalized word embeddings revealed trends in semantic variations related to frequent and adjective words.

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers’ behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies and domain experts’ feedbacks about various kinds of events.

Vascular diseases, such as myocardial infarction and cerebral infarction, are most commonly caused by atherosclerosis, one of the leading causes of death worldwide. Risk factors for atherosclerosis include lifestyle and aging. It has been reported that lifespan could be extended in mice by targeting senescent cells, which led to the suppression of aging-related diseases, such as vascular diseases. However, the molecular mechanisms underlying the contribution of aging to vascular diseases are still not well understood. Several types of cells, such as vascular (endothelial cell), vascular-associated (smooth muscle cell and fibroblast) and inflammatory cells, are involved in plaque formation, plaque rupture and thrombus formation, which result in atherosclerosis. Gangliosides, a group of glycosphingolipids, are expressed on the surface of vascular, vascular-associated and inflammatory cells, where they play functional roles. Clarifying the role of gangliosides in atherosclerosis and their relationship with aging is fundamental to develop novel prevention and treatment methods for vascular diseases based on targeting gangliosides. In this review, we highlight the involvement and possible contribution of gangliosides to vascular diseases and further discuss their relationship with aging.

We investigated whether mesenchymal stem cell (MSC)-based treatment could inhibit neointimal hyperplasia in a rat model of carotid arterial injury and explored potential mechanisms underlying the positive effects of MSC therapy on vascular remodeling/repair. Sprague-Dawley rats underwent balloon injury to their right carotid arteries. After 2 days, we administered cultured MSCs from bone marrow of GFP-transgenic rats (0.8 × 106 cells, n = 10) or vehicle (controls, n = 10) to adventitial sites of the injured arteries. As an additional control, some rats received a higher dose of MSCs by systemic infusion (3 × 106 cells, tail vein; n = 4). Local vascular MSC administration significantly prevented neointimal hyperplasia (intima/media ratio) and reduced the percentage of Ki67 + proliferating cells in arterial walls by 14 days after treatment, despite little evidence of long-term MSC engraftment. Notably, systemic MSC infusion did not alter neointimal formation. By immunohistochemistry, compared with neointimal cells of controls, cells in MSC-treated arteries expressed reduced levels of embryonic myosin heavy chain and RM-4, an inflammatory cell marker. In the presence of platelet-derived growth factor (PDGF-BB), conditioned medium from MSCs increased p27 protein levels and significantly attenuated VSMC proliferation in culture. Furthermore, MSC-conditioned medium suppressed the expression of inflammatory cytokines and RM-4 in PDGF-BB-treated VSMCs. Thus, perivascular administration of MSCs may improve restenosis after vascular injury through paracrine effects that modulate VSMC inflammatory phenotype., Highlights • Local MSC therapy suppressed neointimal formation after arterial injury in rats. • Local MSC therapy modulated VSMC phenotype after arterial injury. • MSC-secreted factors prevented VSMC proliferation and altered the phenotype.

In statistical machine translation (smt), differences between domains of training and test data result in poor translations. Although there have been many studies on domain adaptation of language models and translation models, most require supervised in-domain language resources such as parallel corpora for training and tuning the models. The necessity of supervised data has made such methods difficult to adapt to practical smt systems. We thus propose a novel method that adapts translation models without in-domain parallel corpora. Our method infers translation candidates of unseen words by nearest-neighbor search after projecting their vector-based semantic representations to the semantic space of the target language. In our experiment of out-of-domain translation from Japanese to English, our method improved bleu score by 0.5–1.5.

Traffic accidents are still troubling our society. The number of drive recorders sold has increased, and therefore we can collect large-scale vehicle recorder data to be used to support traffic safety. We have developed a system for detecting potentially risky crossroads on the basis of vehicle recorder data, road shapes, and weather information. Visualization combining space and time in a single display called a “space time cube (STC)” helps us to understand and analyze spatio-temporal mobility data on caution crossroads. The STC enables us to simultaneously explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find good viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoint selection method for visualizing spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. Major contributions of this paper are as follows: (1) We provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of a viewpoint quality on a projected 2D image. (2) We demonstrate our solution can be adapted to crossroads with different trajectory shapes. We also extend the proposed method to find an optimal viewpoint for multiple crossroads. (3) We verify the proposed method through users' evaluations. (4) We construct an overviewing catalog of potentially risky crossroads detected from real vehicle recorder big data to discuss and analyze them with stakeholders.

We introduce our developing system which can analyze drivers' driving behavior collected from vehicle recorder and other datasources such as weather reports and road maps. We show some performance issues while (pre-)processing large-scale data and discuss the requirements for the practical system.

Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.

A large number of myocytes is necessary to treat intractable muscular disorders such as Duchenne muscular dystrophy with cell-based therapies. However, starting materials for cellular therapy products such as myoblasts, marrow stromal cells, menstrual blood-derived cells and placenta-derived cells have a limited lifespan and cease to proliferatein vitro. From the viewpoints of manufacturing and quality control, cells with a long lifespan are more suitable as a starting material. In this study, we generated stromal cells for future myoblast therapy from a working cell bank of human embryonic stem cells (ESCs). The ESC-derived CD105+cells with extensivein vitroproliferation capability exhibited myogenesis and genetic stabilityin vitro. These results imply that ESC-derived CD105+cells are another cell source for myoblasts in cell-based therapy for patients with genetic muscular disorders. Since ESCs are immortal, mesenchymal stromal cells generated from ESCs can be manufactured at a large scale in one lot for pharmaceutical purposes.

Vascular endothelial cells (ECs) form the inner lining of blood vessels. They are critically involved in many physiological functions, including control of vasomotor tone, blood cell trafficking, hemostatic balance, permeability, proliferation, survival, and immunity. It is considered that impairment of EC functions leads to the development of vascular diseases. The carbohydrate antigens carried by glycoconjugates (e.g., glycoproteins, glycosphingolipids, and proteoglycans) mainly present on the cell surface serve not only as marker molecules but also as functional molecules. Recent studies have revealed that the carbohydrate composition of the EC surface is critical for these cells to perform their physiological functions. In this paper, we consider the expression and functional roles of endogenous glycoconjugates and related molecules (galectins and glycan-degrading enzymes) in human ECs.

Introduction. Amniotic membrane contains a multipotential stem cell population and is expected to possess the machinery to regulate immunological reactions. We investigated the safety and efficacy of allogeneic amniotic membrane-derived mesenchymal stromal cell (AMSC) transplantation in a porcine model of chronic myocardial ischemia as a preclinical trial. Methods. Porcine AMSCs were isolated from amniotic membranes obtained by cesarean section just before delivery and were cultured to increase their numbers before transplantation. Chronic myocardial ischemia was induced by implantation of an ameroid constrictor around the left circumflex coronary artery. Four weeks after ischemia induction, nine swine were assigned to undergo either allogeneic AMSC transplantation or normal saline injection. Functional analysis was performed by echocardiography, and histological examinations were carried out by immunohistochemistry 4 weeks after AMSC transplantation. Results. Echocardiography demonstrated that left ventricular ejection fraction was significantly improved and left ventricular dilatation was well attenuated 4 weeks after AMSC transplantation. Histological assessment showed a significant reduction in percentage of fibrosis in the AMSC transplantation group. Injected allogeneic green fluorescent protein (GFP)-expressing AMSCs were identified in the immunocompetent host heart without the use of any immunosuppressants 4 weeks after transplantation. Immunohistochemistry revealed that GFP colocalized with cardiac troponin T and cardiac troponin I. Conclusions. We have demonstrated that allogeneic AMSC transplantation produced histological and functional improvement in the impaired myocardium in a porcine model of chronic myocardial ischemia. The transplanted allogeneic AMSCs survived without the use of any immunosuppressants and gained cardiac phenotype through either their transdifferentiation or cell fusion.

Nanoparticle technology is being incorporated into many areas of molecular science and biomedicine. Because nanoparticles are small enough to enter almost all areas of the body, including the circulatory system and cells, they have been and continue to be exploited for basic biomedical research as well as clinical diagnostic and therapeutic applications. For example, nanoparticles hold great promise for enabling gene therapy to reach its full potential by facilitating targeted delivery of DNA into tissues and cells. Substantial progress has been made in binding DNA to nanoparticles and controlling the behavior of these complexes. In this article, we review research on binding DNAs to nanoparticles as well as our latest study on non-viral gene delivery using polyethylenimine-coated magnetic nanoparticles.

Web spamming has emerged to deceive search engines and obtain a higher ranking in search result lists which brings more traffic and profits to web sites. Link farm is one of the major spamming techniques, which creates a large set of densely inter-linked spam pages to deceive link-based ranking algorithms that regard incoming links to a page as endorsements to it. Those link farms need to be eliminated when we are searching, analyzing and mining the Web, but they are also interesting social activities in the cyberspace. Our purpose is to understand dynamics of link farms, such as, how much they are growing or shrinking, and how their topics change over time. Such information is helpful in developing new spam detection techniques and tracking spam sites for observing their topics. Especially, we are interested in where we can find emerging spam sites that is useful for updating spam classifiers. In this paper, we study overall size/topic distribution and evolution of link farms in large-scale Japanese web archives for three years containing four million hosts and 83 million links. As far as we know, the overall characteristics of link farms in a time-series of web snapshots of this scale have never been explored. We propose a method for extracting link farms and investigate their size distribution and topics. We observe the evolution of link farms from the perspective of size growth and change in topic distribution. We recursively decomposed host graphs into link farms and found that from 4% to 7% of hosts were members of link farms. This implies we can remove quite a number of spam hosts without contents analysis. We also found the two dominant topics, “Adult” and “Travel”, accounted for over 60% of spam hosts in link farms. The size evolution of link farms showed that many link farms maintained for years, but most of them did not grow. The distribution of topics in link farms was not significantly changed, but hosts and keywords related to each topic dynamically changed. These results suggest that we can observe topic changes in each link farm, but we cannot efficiently find emerging spam sites by monitoring link farms. This implies that to detect newly created spam sites, monitoring current link farm is not enough. Detecting sites that generate links to spam sites would be an effective approach.

ATP-binding cassette subfamily G member 2 (ABCG2), an important multidrug resistance transporter, can mediate the efflux of various chemotherapy drugs and contribute to drug resistance in cancer cells. The correlation between ABCG2 expression and cancer stem cell (CSC) phenotypes has been examined in hepatocellular carcinoma, as well as in glioma, breast, prostate, and colon cancer; however, the results remain controversial. In this study, we compared the characteristics of low- (ABCG2-) and high-expressing (ABCG2+) pancreatic ductal adenocarcinoma (PDAC) cells using a human pancreatic cancerous cell line (PANC-1) because the role of ABCG2 in CSC-related malignant characteristics in PDAC is not well elucidated. ABCG2- and ABCG2+ PDAC cells were separated using flow cytometric cell sorting. In adherent cell culture conditions, 10% of all PANC-1 cells were ABCG2+. Using transmission electron microscopy, we found that ABCG2+ cells showed more abundant and longer microvilli on the cell surface than ABCG2- cells. Unexpectedly, ABCG2+ cells did not demonstrate significantly greater drug resistance against 5-FU, gemcitabine, and vincristine than ABCG2- cells, as assessed using a WST-8 assay. Furthermore, ABCG2- cells exhibited better sphere formation ability and higher stemness marker expression, including that of Sox2, Oct4, ALDH1, CD44v9, and Nestin, than ABCG2+ cells, as observed using qRT-PCR. Cell growth rates and motilities, examined using the Boyden chamber and scratch assays, were higher in ABCG2- cells than in ABCG2+ cells. However, the epithelial-mesenchymal transition (EMT) ability, assessed by examining the alteration of E-cadherin, N-cadherin, Snail, and Vimentin expression after TGFβ addition, was comparable between ABCG2- and ABCG2+ cells. In 3D-culture conditions using ultra low-attachment plates, ABCG2- cells formed spheres containing a large number of ABCG2+ cells, and expression of stemness markers in these spheres was higher than that of spheres derived from ABCG2+ cells. Furthermore, spheres derived from ABCG2- cells included large populations of ABCG2+ cells and exhibited high resistance against anti-cancer drugs, presumably depending on ABCG2 expression. We found that in adherent culture conditions, ABCG2+ PDAC cells do not exhibit stemness and malignant behaviors, but ABCG2+ cells derived from ABCG2- cells after sphere formation have high stemness and anti-cancer drug resistance. This suggests that ABCG2- cells have the capacity to generate ABCG2+ cells, and the malignant potential of ABCG2+ cells in PDAC depends on the environment. The 3D-culture system was expected to mimic in vivo environments. Therefore, ABCG2+ cells in pancreatic cancer tumors may exhibit high stemness and anti-cancer drug resistance. Thus, ABCG2+ cells should be considered as novel therapeutic targets for pancreatic cancer. Citation Format: Norihiko Sasaki, Fumio Hasegawa, Masaki Michishita, Yoko Matsuda, Tomio Arai, Naoshi Ishikawa, Yoko Itakura, Junko Aida, Kaiyo Takubo, Masashi Toyoda, Toshiyuki Ishiwata. ABCG2-positive cells derived from ABCG2-negative pancreatic cancer cells in 3D-culture conditions show high stemness and anti-cancer drug resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3057.

Introduction Bone marrow mesenchymal stem cells (BMMSCs) ameliorate tissue damage after ischemic injury. Erythropoietin (Epo) has pleiotropic effects in addition to hematopoietic activity. The aim of this study was to investigate whether Epo enhanced cell survival and angiogenic effect of BMMSC implantation in rat limb ischemia model. Methods and results MSCs were isolated from BM in GFP-transgenic rats. In a culture study, Epo promoted BMMSC proliferation in normoxia and enhanced cell survival under the culture condition mimicking ischemia (1% oxygen and nutrient deprivation). BMMSCs with and without 48 h of pretreatment by Epo (80 IU/ml) were locally administered to rat hindlimb ischemia models in vivo. At 3 days after implantation, BMMSC engraftment in the perivascular area of the injured muscle was significantly higher in the cells preconditioned with Epo than in the cells without preconditioning. Stromal derived factor-1α and fibroblast growth factor-2 expressions were detected in the engrafted BMMSCs. At 14 days after implantation, the Epo-preconditioned BMMSCs significantly promoted blood perfusion and capillary growth compared to the controls in laser Doppler and histological studies. In addition to promoting neovascularization, the Epo-preconditioned BMMSCs significantly inhibited macrophage infiltration in the perivascular area. Conclusion Epo elicited pro-survival potential in the BMMSCs. Pharmacological cell modification with Epo before implantation may become a feasible and promising strategy for improving current therapeutic angiogenesis with BMMSCs., Highlights • Erythropoietin rescued the BMMSCs against the culture condition mimicking ischemia. • Erythropoietin promoted cellular engraftment of the BMMSCs in rat ischemic limbs. • Preconditioning with erythropoietin enhanced angiogenic effects of the BMMSC implantation.

It is vital for the transportation industry, which performs most of their work by automobiles, to reduce its number of traffic accidents. Many local governments in Japan have made potential risk maps of traffic accident spots. However, making such maps in wide areas and with the time information had been difficult because most of them are made based on an investigation. Utilizing long-term driving records can extract wide area spatio-temporal caution spots. This paper proposes a visual interaction method for exploring caution spots from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations using various combinations of attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution spots. We demonstrate the usefulness of our novel visual exploration environment using real data given by one of the biggest transportation companies in Japan. Exploration results show our environments can extract caution spots where some accidents have actually occurred or that are on very narrow roads with bad visibility.

We propose a method that learns a crosslingual projection of word representations from one language into another. Our method utilizes translatable context pairs as bonus terms of the objective function. In the experiments, our method outperformed existing methods in three language pairs, (English, Spanish), (Japanese, Chinese) and (English, Japanese), without using any additional supervisions.

Transportation systems in mega-cities play a very important role in social and economic activities. Especially in Japan, the Tokyo city is strongly required to increase resiliency and safety of its transportation systems, because of the 2020 Olympic games and big earthquakes predicted to occur. Our research group is trying to address those challenges utilizing mobility big data. We have been archiving several mobility data sets, such as smart card data and vehicle recorder data, and developed platforms for processing, analyzing, and visualizing them. In this paper I briefly introduce our analysis and visualization of passenger flows in public transportation systems and behaviors of vehicle drivers.

This paper describes the history and the current challenges of archiving massive and extremely diverse amounts of user-generated data in an international environment on the World Wide Web and the technologies required for interoperability between service providers and for preserving their contents in the future.

Wolfram syndrome is a genetic disorder characterized by diabetes and neurodegeneration and considered as an endoplasmic reticulum (ER) disease. Despite the underlying importance of ER dysfunction in Wolfram syndrome and the identification of two causative genes, Wolfram syndrome 1 (WFS1) and Wolfram syndrome 2 (WFS2), a molecular mechanism linking the ER to death of neurons and β cells has not been elucidated. Here we implicate calpain 2 in the mechanism of cell death in Wolfram syndrome. Calpain 2 is negatively regulated by WFS2, and elevated activation of calpain 2 by WFS2-knockdown correlates with cell death. Calpain activation is also induced by high cytosolic calcium mediated by the loss of function of WFS1. Calpain hyperactivation is observed in the WFS1 knockout mouse as well as in neural progenitor cells derived from induced pluripotent stem (iPS) cells of Wolfram syndrome patients. A small-scale small-molecule screen targeting ER calcium homeostasis reveals that dantrolene can prevent cell death in neural progenitor cells derived from Wolfram syndrome iPS cells. Our results demonstrate that calpain and the pathway leading its activation provides potential therapeutic targets for Wolfram syndrome and other ER diseases.

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies about various kinds of events.

The Web has involved diverse media services, such as blogs, photo/video/link sharing, social networks, and microblogs. These Web media react to and affect realworld events, while the mass media still has big influence on social activities. The Web and mass media now affect each other. Our use of media has evolved dynamically in the last decade, and this affects our societal behavior. For instance, the first photo of a plane crash landing during the "Miracle on the Hudson" on January 15, 2009 appeared and spread on Twitter and was then used in TV news. During the "Chelyabinsk Meteor" incident on February 15, 2013, many people reported videos of the incident on YouTube then mass media reused them on TV programs. Large scale collection, analysis, and visualization of those multiple media are strongly required for sociology, linguistics, risk management, and marketing researches. We are building a huge scale Japanese web archive, and various analytics engines with a large-scale display wall. Our archive consists of 30 billion web pages crawled for 14 years, 1 billion blog posts for 7 years, and 15 billion tweets for 3 years. In this talk, I present several analysis and visualization systems based on network analysis, natural language processing, image processing, and 3 dimensional visualization.

To understand recent societal behavior, it is important to compare how multiple media are affected by real-world events and how each medium affects other media. This paper proposes a novel framework for inter-media comparison through visualizing images extracted from different types of media. We extract blog image clusters from our six-year blog archive and search for similar TV shots in each cluster from a broadcast news video archive by using image similarities. We then visualize such image flows on a timeline in 3D space to visually and interactively explore time sequential changes in influences among media resources and differences and/or similarities between them such as topics that become popular on only blogs or that become popular on blogs earlier than on TV.

Online social networking services (SNSs) involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest SNSs. We define a network of user pairs that send reciprocal messages. Based on the mixing pattern observed in this network, we argue that this network differs from conventional understandings in the sense that there is a small number of distinctive users that we call outsiders. Outsiders do not belong to any user groups but they are connected with different groups, while not being well connected with each other. We identify outsiders by maximizing the degree assortativity coefficient of the network via node removal, thereby confirming that local structural properties of outsiders identified are consistent with our hypothesis. Our findings suggest that the existence of outsiders should be considered when using Twitter communication networks for social network analysis., Comment: 40 pages, 11 figures, 1 table

We have constructed a framework for analyzing passenger behaviors in public transportation systems as understanding these variables is a key to improving the efficiency of public transportation. It uses a large-scale dataset of trip records created from smart card data to estimate passenger flows in a complex metro network. Its interactive flow visualization function enables various unusual phenomena to be observed. We propose a predictive model of passenger behavior after a train accident. Evaluation showed that it can accurately predict passenger flows after a major train accident. The proposed framework is the first step towards real-time observation and prediction for public transportation systems.

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them. © 2013 Springer Science+Business Media New York.

This paper presents a scalable visualization technique for automatic animation of data-flow visual program execution, and a software architecture to provide a scalable interface for debugging programs, which exploits a multi-focus fisheye viewing algorithm in conjunction with a semantic zooming interface to show various kinds of information at runtime. The architecture also supports users' browsing with the interface by automatically assigning proper focal points, based on information embedded in the debugged programs. Thus, it is possible to provide scalable views and intelligent assistance for browsing dynamically created data-flow networks. We have incorporated these ideas into the visual tracer of the KLIEG visual parallel programming environment.

This paper proposes a system for analyzing societal behaviors by visualizing time-varying topics in multiple media. Various types of content such as text, images, and videos have spread throughout multiple media, such as TV and the Web, that have complementary information and influence one another. It is important to compare how these media react to real world events to understand recent societal behaviors and how each medium reacts to other media. Our system visualizes flows of content in multiple media in 3D space enabling us to simultaneously explore them. We present two example applications using our system. The first involves the visualization of inter-media events comparing the exposure of topics in TV news and the activities of bloggers. The second example application is a system for visualizing visual trends on social media that chronologically displays extracted clusters of images on blogs. The proposed systems enable users to visually monitor changes in thought, activities, and interests of people, and differences between media through interactively exploring flows of texts and images extracted from the media.

Online social media are increasingly facilitating our social interactions, thereby making available a massive "digital fossil" of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of "collective attention" on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or "tweets." Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. "Retweet" networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era. Language: en

Mass and social media provide flows of images for real world events. It is sometimes difficult to represent realities and impressions of events using only text. However, even a single photo might remind us complex events. Along with events in the real world, there are representative images, such as design of products and commercial pictures. We can therefore recognize changes in trends of people's ideas, experiences, and interests through observing the flows of such representative images. This paper presents a novel 3D visualization system to explore temporal changes in trends using images associating with different topics, called Image Bricks. We show case studies using images extracted from our six-year blog archive. We first extract clusters of images as topics related to given keywords. We then visualize them on multiple timelines in a 3D space. Users can visually read stories of topics through exploring visualized images.

Nowadays people can share useful information on social networking sites such as Facebook and Twitter. The information is spread over the networks when it is forwarded or copied repeatedly from friends to friends. This phenomenon is so called "information cascade", and has been studied long time since it sometimes has an impact on the real world. Various social activities tends to have different ways of cascade on the social networks. Our focus in this study is on characterizing the cascade patterns according to users' influence and posting behaviors in various topics. The cascade patterns could be useful for various organizations to consider the strategy of public relations activities. We explore four measures which are cascade ratio, tweet ratio, time of tweet, and exposure curve. Our results show that hashtags in different topics have different cascade patterns in term of these measures. However, some hashtags even in the same topic have different cascade patterns. We discover that such kind of hidden relationship between topics can be surprisingly revealed by using only our four measures rather than considering tweet contents. Finally, our results also show that cascade ratio and time of tweet are the most effective measures to distinguish cascade patterns in different topics.