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We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a k-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.

Polyhydroxyalkanoate (PHA), with a long chain length and high poly(4–hydroxybutyric acid) (P4HB) ratio, can be used as a base polymer for eco-friendly and biodegradable adhesives owing to its high elasticity, elongation at break, flexibility, and processability; however, its molecular structures must be adjusted for adhesive applications. In this study, surface-modified cellulose nanofibers (CNFs) were used as a hydrophobic additive for the PHA-based adhesive. For the surface modification of CNFs, double silanization using tetraethyl orthosilicate (TEOS) and methyltrimethoxysilane (MTMS) was performed, and the thermal and structural properties were evaluated. The hydrophobicity of the TEOS- and MTMS-treated CNFs (TMCNFs) was confirmed by FT-IR and water contact angle analysis, with hydrophobic CNFs well dispersed in the PHA. The PHA–CNFs composite was prepared with TMCNFs, and its morphological analysis verified the good dispersion of TMCNFs in the PHA. The tensile strength of the composite was enhanced when 10% TMCNFs were added; however, the viscosity decreased as the TMCNFs acted as a thixotropic agent. Adding TMCNFs to PHA enhanced the flowability and infiltration ability of the PHA–TMCNFs-based adhesive, and an increase in the loss tangent (Tan δ) and adjustment of viscosity without reducing the adhesive strength was also observed. These changes in properties can improve the flowability and dispersibility of the PHA–TMCNFs adhesive on a rough adhesive surface at low stress. Thus, it is expected that double-silanized CNFs effectively improve their interfacial adhesion in PHA and the adhesive properties of the PHA–CNFs composites, which can be utilized for more suitable adhesive applications.

Intrusion detection and prevention systems (IDPSs) are at the core of protecting an enterprise's network. In general, IDPSs use pre-defined rules to detect potential attacks. As the size of an organization grows and new types of intrusions appear, the quantity and complexity of the rules also increase. Moreover, IDPSs generate an overwhelming number of logs that are challenging to handle and analyze. For a more effective and integrative analysis and management of the rules and logs, we propose a novel visual analytics tool, Hyperion. Hyperion interactively visualizes rules to help users understand how the IDPS rules are managed and applied to the enterprise's network entities. Hyperion also provides effective visualizations to enable users to visually analyze the type, period, traffic, and frequency of attacks in addition to a traditional count-based timeline visualization. Finally, Hyperion enables users to interactively simulate the effect of a change in parameters of a detection rule. These features can help streamline the security control cycle consisting of rule application, information collection, log analysis, and rule revision.

In this study, chemical and enzymatic hydrolysis of rice bran protein were conducted to produce polyphenolic compounds and functional peptides with anti-oxidant effects. In order to produce functional peptides, rice bran protein was hydrolyzed using chemicals, commercial enzymes as well as Bacillus natto protease. The results showed that major peptides were hydrolyzed into smaller than 10 kDa when using chemicals, commercial enzymes, and B. natto protease. The protein hydrolysis potential of B. natto protease was equal or higher than that of commercial enzymes. Total phenolic compound concentrations of 9.91 in 1 M NaOH, 9,02 in 2% (v/v) Alcalase and 14.5% in B. natto were measured. Radical scavenging activities of 67.9% in 1 M NaOH, 36.1% in Alcalase and 50.0% in B. natto protease were achieved. The results concluded that B. natto protease isolated chenonggkjang comparable to that of commercial enzymes in the production of functional peptides with anti-oxidant effect and can be used for the value added materials for food and cosmetics industries.

We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document's styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9\% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab.com/., Comment: ACL 2021 (system demonstration)

We propose Steadiness and Cohesiveness, two novel metrics to measure the inter-cluster reliability of multidimensional projection (MDP), specifically how well the inter-cluster structures are preserved between the original high-dimensional space and the low-dimensional projection space. Measuring inter-cluster reliability is crucial as it directly affects how well inter-cluster tasks (e.g., identifying cluster relationships in the original space from a projected view) can be conducted; however, despite the importance of inter-cluster tasks, we found that previous metrics, such as Trustworthiness and Continuity, fail to measure inter-cluster reliability. Our metrics consider two aspects of the inter-cluster reliability: Steadiness measures the extent to which clusters in the projected space form clusters in the original space, and Cohesiveness measures the opposite. They extract random clusters with arbitrary shapes and positions in one space and evaluate how much the clusters are stretched or dispersed in the other space. Furthermore, our metrics can quantify pointwise distortions, allowing for the visualization of inter-cluster reliability in a projection, which we call a reliability map. Through quantitative experiments, we verify that our metrics precisely capture the distortions that harm inter-cluster reliability while previous metrics have difficulty capturing the distortions. A case study also demonstrates that our metrics and the reliability map 1) support users in selecting the proper projection techniques or hyperparameters and 2) prevent misinterpretation while performing inter-cluster tasks, thus allow an adequate identification of inter-cluster structure., Comment: IEEE Transactions of Visualization and Computer Graphics (TVCG, Proc. VIS 2021), to appear

Anticancer effects of L-ascorbic acid (Vitamin C, L-AA) have been reported in various types of cancers. L-AA intake reduces breast cancer recurrence and mortality, however, the role of L-AA in the treatment of breast cancer remains poorly understood. In this study, we investigated the effect and mechanism action of L-AA on breast cancer growth. L-AA inhibited the growth of breast cancer cells by inducing apoptotic cell death at the evaluated treatment concentrations without affecting normal cells. Moreover, L-AA induces autophagosome formation via regulation of mammalian target of rapamycin (mTOR), Beclin1, and autophagy-related genes (ATGs) and increased autophagic flux. Notably, we observed that L-AA increased p62/SQSTM1 (sequestosome 1) protein levels. Accumulation of p62 protein in cancer cells in response to stress has been reported, but its role in cancer regulation remains controversial. Here, we demonstrated that L-AA-induced p62 accumulation is related to L-AA-induced breast cancer growth inhibition. Furthermore, L-AA induced endoplasmic reticulum (ER) stress via the IRE&ndash, JNK&ndash, CHOP (inositol-requiring endonuclease&ndash, c-Jun N-terminal kinase&ndash, C/EBP homologous protein) signaling pathways, which increased the nuclear levels of p62/SQSTM1. These findings provide evidence that L-AA-induced ER stress could be crucial for p62 accumulation-dependent cell death, and L-AA can be useful in breast cancer treatment.

Background: Traditionally believed to be an integral part of multiple myeloma (MM) treatment, the role of hematopoietic stem-cell transplantation (HSCT) is being challenged. As such, we sought to evaluate the impact of HSCT in the era of novel agents. Methods: A multicenter, retrospective, longitudinal cohort study was carried out between January 2016 and December 2018. A total of 55 patients who received VTD (bortezomib-thalidomide-dexamethasone) as first-line treatment and KRd (carfilzomib-lenalidomide-dexamethasone) as second-line treatment were analyzed for outcomes. Results: The enrolled patients were divided into Group 1, defined as those who continued KRd treatment until progression ( n = 41), versus Group 2, defined as those who underwent HSCT after a certain number of cycles of KRd ( n = 14). Both groups showed a generally favorable response to KRd, with overall response rate (ORR) of 87.9% and clinical benefit rate of 92.8% after a median of seven cycles in Group 1, and ORR 92.8% and clinical benefit rate 100% after median of five cycles in Group 2. However, significantly poorer progression-free survival (PFS) ( p = 0.004) was observed in Group 1 (median 12 months) compared with Group 2 (median not reached). Multivariate analyses identified HSCT after KRd as potential risk factors associated with PFS. Also, in Group 1, bortezomib refractoriness was associated with significantly shorter PFS compared with those who were responsive (median 12 months versus 14 months, respectively, p = 0.039). Conclusions: In conclusion, even with the advent of novel agents, HSCT still remains a valuable treatment modality with additive efficacy.

We present a systematic review on three comparative layouts (i.e., juxtaposition, superposition, and explicit-encoding) which are information visualization (InfoVis) layouts designed to support comparison tasks. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented. The goal of our research is to distill the results from previous studies into a consistent and reusable framework. We review 127 research papers, including 15 papers with quantitative user studies, which employed comparative layouts. We first alleviate the ambiguous boundaries in the design space of comparative layouts by suggesting lucid terminology (e.g., chart-wise and item-wise juxtaposition). We then identify the diverse aspects of comparative layouts, such as the advantages and concerns of using each layout in the real-world scenarios and researchers' approaches to overcome the concerns. Building our knowledge on top of the initial insights gained from the Gleicher et al.'s survey, we elaborate on relevant empirical evidence that we distilled from our survey (e.g., the actual effectiveness of the layouts in different study settings) and identify novel facets that the original work did not cover (e.g., the familiarity of the layouts to people). Finally, we show the consistent and contradictory results on the performance of comparative layouts and offer practical implications for using the layouts by suggesting trade-offs and seven actionable guidelines., Comment: IEEE VIS InfoVis 2020, ACM 2012 CCS - Human-centered computing, Visualization, Visualization theory, concepts, and paradigms

Audit logs are different from other software logs in that they record the most primitive events (i.e., system calls) in modern operating systems. Audit logs contain a detailed trace of an operating system, and thus have received great attention from security experts and system administrators. However, the complexity and size of audit logs, which increase in real time, have hindered analysts from understanding and analyzing them. In this paper, we present a novel visual analytics system, LongLine, which enables interactive visual analyses of large-scale audit logs. LongLine lowers the interpretation barrier of audit logs by employing human-understandable representations (e.g., file paths and commands) instead of abstract indicators of operating systems (e.g., file descriptors) as well as revealing the temporal patterns of the logs in a multi-scale fashion with meaningful granularity of time in mind (e.g., hourly, daily, and weekly). LongLine also streamlines comparative analysis between interesting subsets of logs, which is essential in detecting anomalous behaviors of systems. In addition, LongLine allows analysts to monitor the system state in a streaming fashion, keeping the latency between log creation and visualization less than one minute. Finally, we evaluate our system through a case study and a scenario analysis with security experts.

PURPOSE Previous retrospective studies suggest that anaplastic lymphoma kinase (ALK) mutation-positive (ALK+) non-small cell lung cancer (NSCLC) patients are sensitive to pemetrexed. To determine its efficacy, we retrospectively evaluated clinical outcomes of pemetrexed-based chemotherapy in patients with ALK+ NSCLC. MATERIALS AND METHODS We identified 126 patients with advanced, ALK+ NSCLC who received first-line cytotoxic chemotherapy. We compared response, progression-free survival (PFS), and overall survival (OS) rates according to chemotherapy regimens. Furthermore, we evaluated intracranial time to tumor progression (TTP) and proportion of ALK+ cells as prognostic factors. RESULTS Forty-eight patients received pemetrexed-based chemotherapy, while 78 received other regimens as first-line treatment. The pemetrexed-based chemotherapy group showed superior overall response (44.7% vs. 14.3%, p

Each individual’s usage behavior on mobile devices depends on a variety of factors, such as time, location, and previous actions. Hence, context-awareness provides great opportunities to make the networking and computing capabilities of mobile systems more personalized and more efficient in managing their resources. To this end, we first reveal new findings from our own Android user experiment: 1) the launching probabilities of applications follow Zipf’s law and 2) inter-running and running times of applications conform to log-normal distributions. We also find contextual dependencies between application usage patterns, for which we classify contexts autonomously with unsupervised learning methods. Using the knowledge acquired, we develop a context-aware application scheduling framework, context-aware application scheduler (CAS), that adaptively unloads and preloads background applications for a joint optimization in which the energy saving is maximized and the user discomfort from the scheduling is minimized. Our trace-driven simulations with 96 user traces demonstrate that the context-aware design of the CAS enables it to outperform existing process scheduling algorithms. Our implementation of the CAS over Android platforms and its end-to-end evaluations verify that its human-involved design indeed provides substantial user-experience gains in both energy and application launching latency.

We present a new visual exploration concept—Progressive Visual Analytics with Safeguards—that helps people manage the uncertainty arising from progressive data exploration. Despite its potential benefits, intermediate knowledge from progressive analytics can be incorrect due to various machine and human factors, such as a sampling bias or misinterpretation of uncertainty. To alleviate this problem, we introduce PVA-Guards , safeguards people can leave on uncertain intermediate knowledge that needs to be verified, and derive seven PVA-Guards based on previous visualization task taxonomies. PVA-Guards provide a means of ensuring the correctness of the conclusion and understanding the reason when intermediate knowledge becomes invalid. We also present ProReveal, a proof-of-concept system designed and developed to integrate the seven safeguards into progressive data exploration. Finally, we report a user study with 14 participants, which shows people voluntarily employed PVA-Guards to safeguard their findings and ProReveal’s PVA-Guard view provides an overview of uncertain intermediate knowledge. We believe our new concept can also offer better consistency in progressive data exploration, alleviating people’s heterogeneous interpretation of uncertainty.

Background/aim Daratumumab is a promising novel agent for relapsed/refractory multiple myeloma (RRMM). However, there are limited data on its efficacy and toxicity profiles in real-world patients, especially in the Asian population. Patients and methods This was a multicenter, retrospective, longitudinal cohort study set between January 2017 and April 2019. We collected and analyzed clinical and survival data of 21 patients treated with daratumumab monotherapy. All patients were previously exposed to proteasome inhibitors and immunomodulatory drugs. Results The overall response rate was 42.1%, including one complete remission (4.8%) and three very good partial responses (14.3%). The cycles of daratumumab delivered were three (range=1-10 cycles) and the median progression-free survival was 6 months, while the overall survival was not reached. Infusion reaction was observed in nine patients (42.9%), and one discontinued permanently. Fatigue was the most common adverse event (52.4%), and there were five cases of documented infection during daratumumab treatment, two of them leading to the death of the patient. Conclusion Daratumumab monotherapy showed fairly promising activity with modest tolerance in heavily treated Asian RRMM patients.

International audience; Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model—a simple yet expressive JSON grammar associated with visual semantics—that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.

Polyhydroxyalkanoate (PHA) is a biodegradable plastic with great potential for tackling plastic waste and marine pollution issues, but its commercial applications have been limited due to its poor processability. In this study, surface-modified cellulose nanocrystals were used to improve the mechanical properties of PHA composites produced via a melt-extrusion process. Double silanization was conducted to obtain hydrophobically treated CNC-based fillers, using tetraethyl orthosilicate (TEOS) and methyltrimethoxysilane (MTMS). The morphology, particle size distributions, and surface characteristics of the silanized CNCs and their compatibility with a PHA polymer matrix differed by the organosiloxane treatment and drying method. It was confirmed that the double silanized CNCs had hydrophobic surface characteristics and narrow particle size distributions, and thereby showed excellent dispersibility in a PHA matrix. Adding hydrophobically treated CNCs to form a PHA composite, the elongation at break of the PHA composites was improved up to 301%, with little reduction of Young’s modulus, compared to pure PHA. Seemingly, the double silanized CNCs added played a similar role to a nucleation agent in the PHA composite. It is expected that such high ductility can improve the mechanical properties of PHA composites, making them more suitable for commercial applications.

Diffuse alveolar hemorrhage (DAH) is a life-threatening syndrome caused by various etiologies. DAH has rarely been reported to be associated with metastatic angiosarcoma. However, due to the rarity of complications, it may not be well-recognized by most clinicians. Herein, we report the case of a 70-year-old man with newly diagnosed metastatic cardiac angiosarcoma presenting with DAH. After an immediate bronchoalveolar lavage study and bronchoscopic biopsy, the patient was successfully treated with paclitaxel chemotherapy. Although most patients with this phenomenon have an extremely grave prognosis in previous literature, our experience showed that appropriate evaluation and treatment may be beneficial.

미세조류 세포벽은 셀룰로오스가 주요 구성성분으로 리그닌을 포함하지 않아 낮은 온도의 전처리 조건에서도 효과적으로 셀룰로오스와 헤미셀룰로오스 분해가 가능하다. 차세대 바이오매스로 주목 받는 미세조류(Tetraselmis KCTC12236BP)로부터 120 oC 이하의 낮은 온도 조건에서 열수전처리를 이용한 발효당 생산 증대를 위해 공정조건을 최적화하였다. 주요 공정조건인 추출온도, 황산농도와 추출시간에 따른 당화율 변화를 확인하였을 때, 온도와 황산농도가 글루코오스 생산에 큰 영향을 컸으며 당화율이 비례하여 증가하는 경향을 보였다. 경제성을 고려한 열수전처리 최적조건은 120 oC, 2 mol 황산, 40분으로 95.9%의 당화율을 얻을 수 있었다. 탈지미세조류의 황산 열수전처리와 효소당화를 비교했을 때, 황산 열수전처리의 당화율이 2.1배 이상 높고 전처리 시간이 짧아 황산 열수전처리가 효소당화에 비해 효과적인 공정임을 확인하였다.

합성 항산화제에 대한 대체제로 천연 항산화제에 대한 연구가 활발히 진행되고 있으며 미세조류는 천연 항산화제의 원료로 많은 관심을 받고 있다. 본 연구에서는 탈지미세조류에서 총 폴리페놀(TPC) 추출증대를 위해 추출용매, 온도, 시간, 고액비율과 에탄올 첨가 농도 최적화를 수행하였다. 열수와 유기용매 추출성능을 비교했을 때, 열수추출이 유기용매 보다 우수한 성능을 보였으며 온도 증가에 따라 추출성능도 비례하여 증가함을 보였다. 열수에 의한 추출이 에탄올용액 추출(>98%)에 비해 우수한 성능을 보였으며40% 에탄올 용액을 이용한 열수 추출이 가장 우수한 추출 효과를 보였다. 추출조건10 min, 100 oC, 40% 에탄올 열수추출에서 최대 폴리페놀 농도인 3.35 mg GAE (gallic acid equivalent)/gDM을 얻을 수 있었다. 지질 추출을 위한 유기용매 전처리 공정이 선수행 되었음에도 불구하고 탈지미세조류(TetraselmisKCTC 12236BP)의 폴리페놀 농도가 다른 탈지이전 미세조류와 동등한 수준임을 확인할 수 있어 탈지미세조류가 천연 폴리페놀의 원료로서 적합함을 확인 할 수 있었다. 또한, 고액추출을 모사하기 위해 Peleg 모델을 이용해 예측한 폴리페놀 농도가 실험에 의해 얻어진 값과 높은 일치도를 보임으로 모델을 이용한 모사가 폴리페놀 추출 모사에 유용함을 증명할 수 있었다.

The effect of drying on the enzymatic hydrolysis of cellulose was determined by analysis of porosity and crystallinity. Fiber hornification induced by drying produced an irreversible reduction in pore volume due to shrinkage and pore collapse, and the decrease in porosity inhibited enzymatic hydrolysis. The drying effect index (DEI) was defined as the difference in enzymatic digestibility between oven- and never-dried pulp, and it was determined that more enzymes caused a higher DEI at the initial stage of enzymatic hydrolysis and the highest DEI was also observed at the earlier stages with higher enzyme dosage. However, there was no significant difference in the DEI with less enzymes because cellulose conversion to sugars during hydrolysis did not enhance enzymatic hydrolysis due to the decrease in enzyme activity. The water retention value (WRV) and Simons&rsquo, staining were used to measure pore volume and to investigate the cause of the decrease in enzymatic hydrolysis. A decrease in enzyme accessibility induced by the collapse of enzymes&rsquo, accessible larger pores was determined and this decreased the enzymatic hydrolysis. However, drying once did not cause any irreversible change in the crystalline structure, thus it seems there is no correlation between enzymatic digestibility and crystalline structure.

We present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k -nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e., they are not online . Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and cannot satisfy the latency requirements of progressive systems. This long latency has significantly limited the use of many machine learning methods, such as $t$ t -SNE, in interactive visual analytics. PANENE is a novel algorithm for Progressive Approximate $k$ k -NEarest NEighbors, enabling fast KNN queries while continuously indexing new batches of data. Following the progressive computation paradigm, PANENE operations can be bounded in time, allowing analysts to access running results within an interactive latency. PANENE can also incrementally build and maintain a cache data structure, a KNN lookup table, to enable constant-time lookups for KNN queries. Finally, we present three progressive applications of PANENE, such as regression, density estimation, and responsive $t$ t -SNE, opening up new opportunities to use complex algorithms in interactive systems.

The accuracy and precision of video-based remote gaze trackers is affected by numerous factors (e.g. the head movement of the participant). However, it is challenging to control all factors that have an influence, and doing so (e.g., using a chin-rest to control geometry) could lead to losing the benefit of using gaze trackers, i.e., the ecological validity of their unobtrusive nature. We propose an adaptive zoom-based gaze tracking technique, ZoomTrack that addresses this problem by improving the resolution of the gaze tracking results. Our approach magnifies a region-of-interest (ROI) and retrieves gaze points at a higher resolution under two different zooming modes: only when the gaze reaches the ROI (temporary) or whenever a participant stares at the stimuli (omnipresent). We compared these against the base case without magnification in a user study. The results are then used to summarize the advantages and limitations of our technique.

International audience; We present a progressive algorithm for approximate k-nearest neighbor search. Although the use of k-nearest neighbor libraries (KNN) is common in many data analysis methods, most KNN algorithms can only be run when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and can exceed the latency required by progressive systems. Exceeding this latency significantly impacts the interactivity of visualization systems especially when dealing with large-scale data. We improve traditional k-d trees for progressive approximate k-nearest neighbor search, enabling fast KNN queries while continuously indexing new batches of data when necessary. Following the progressive computation paradigm, our progressive k-d tree is bounded in time, allowing analysts to access ongoing results within an interactive latency. We also present performance benchmarks to compare online and progressive k-d trees.

Syphilis is chronic and systemic inflammation caused by T. pallidum infection. Because the diagnosis is exclusively dependedon histology, its diagnosis could be very difficult in the patients without positive biopsy finding. We experienced a case ofgastric syphilis diagnosed by polymerase chain reaction (PCR) in case of biopsy negative patients. So we report this simplemethod to diagnose gastric syphilis that failed to show spirochetes on histology. A 70 year-old man visited our hospital viaprivate clinic with suspicious gastric malignancy finding on gastroscopy. Our gastroscopy showed the characteristic shape ofmultiple nodular erosions and ulcers of gastric syphilis. Warthin-Starry silver stain failed to find spirochetes. Repeatedgastroscopy was done to take biopsy specimen for T. pallidum PCR and proved positive. We confirmed gastric syphilis andtreated with intravenous antibiotics. After 4 months of treatment, all gastric lesions were disappeared.

No effective systemic chemotherapy is well-established in basal cell carcinoma. We report a case with three simultaneous malignancies: colon cancer, basal cell carcinoma, and smoldering multiple myeloma. The patient was treated with capecitabine and oxaliplatin after surgery for colon cancer. Surprisingly, he achieved a complete response for basal cell carcinoma. This is the first report of this chemotherapy regimen in basal cell carcinoma. This finding suggests that combination capecitabine and oxaliplatin can be a treatment option for patients unable to receive local therapy.

Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.

In this paper, we introduce LiveGantt as a novel interactive schedule visualization tool that helps users explore highly-concurrent large schedules from various perspectives. Although a Gantt chart is the most common approach to illustrate schedules, currently available Gantt chart visualization tools suffer from limited scalability and lack of interactions. LiveGantt is built with newly designed algorithms and interactions to improve conventional charts with better scalability, explorability, and reschedulability. It employs resource reordering and task aggregation to display the schedules in a scalable way. LiveGantt provides four coordinated views and filtering techniques to help users explore and interact with the schedules in more flexible ways. In addition, LiveGantt is equipped with an efficient rescheduler to allow users to instantaneously modify their schedules based on their scheduling experience in the fields. To assess the usefulness of the application of LiveGantt, we conducted a case study on manufacturing schedule data with four industrial engineering researchers. Participants not only grasped an overview of a schedule but also explored the schedule from multiple perspectives to make enhancements.

261 Background: Tumor metabolism measured by [18F] fluorodeoxy-D-glucose (18F-FDG) positron emission tomography (PET) has a diagnostic and prognostic role in several cancers. The clinical implication of tumor metabolism in biliary tract cancer (BTC) has not been studied well. Therefore, we prospectively evaluated the prognostic value of tumor metabolism and chemotherapy-related changes in advanced BTC patients. Methods: We prospectively enrolled advanced BTC patients before the initiation of palliative chemotherapy. Using 18F-FDG PET, we assessed the baseline maximum standardized uptake value (SUVmax) and monitored the changes of SUVmax during chemotherapy. We analyzed the associations between SUVmax, and clinicopathologic factors and clinical outcomes. Results: A total of 75 patients were enrolled. All patients received gemcitabine/cisplatin as first-line chemotherapy. Primary tumor site, histologic differentiation, molecular characteristics, laboratory findings, and disease extent were associated with the metabolic characteristics. The high metabolism group showed worse survival outcome [Hazard ratio (HR)=4.09, p=0.001 for progression-free survival (PFS); HR=2.61, p=0.019 for overall survival (OS)] than the low metabolism group. The lesser reduction of SUVmax during chemotherapy was also associated with worse outcome (HR=3.35, p=0.002 for PFS; HR=1.96, p=0.082 for OS). Considering both baseline tumor metabolic activity and chemotherapy-related changes in the tumor metabolism, patients with a low metabolism and a more reduction in metabolism obtained the best OS (20.7 months, p=0.013). Conclusions: Tumor metabolic activity and the chemotherapy-related changes in the metabolism were associated with prognosis in advanced BTC patients.