bluuebunny/crossref_metadata_2025 huggingface dataset:
Bites‘n’Bits
Authors: Biel Joan-Isaac , Martin Nathalie , Labbe David , Gatica-Perez Daniel
Published: January 2018

We collect and analyze mobile data about everyday eating occasions to study eating behavior in relation to its context (time, location, social context, related activities and physical activity). Our contributions are three-fold. First, we deployed a data collection campaign with 122 Swiss university students, resulting in 1208 days of food data, 3414 meal occasions, 1034 snacking occasions, 5097 photos, and 998 days of physical activity. Second, we analyzed the collected data and report findings associated to the compliance, snacks vs. meals patterns, physical activity, and contextual differences between snacks and meals. Third, we addressed a novel ubicomp task, namely the classification of eating occasions (meals vs. snacks) in everyday life. We show that a machine learning method using time of day, time since last intake, and location is able to discriminate eating occasions with 84% accuracy, which significantly outperforms a baseline method based only on time.
SynchroWatch
Authors: Reyes Gabriel , Wu Jason , Juneja Nikita , Goldshtein Maxim , Edwards W. Keith , Abowd Gregory D. , Starner Thad
Published: January 2018

SynchroWatch is a one-handed interaction technique for smartwatches that uses rhythmic correlation between a user's thumb movement and on-screen blinking controls. Our technique uses magnetic sensing to track the synchronous extension and reposition of the thumb, augmented with a passive magnetic ring. The system measures the relative changes in the magnetic field induced by the required thumb movement and uses a time-shifted correlation approach with a reference waveform for detection of synchrony. We evaluated the technique during three distraction tasks with varying degrees of hand and finger movement: active walking, browsing on a computer, and relaxing while watching online videos. Our initial offline results suggest that intentional synchronous gestures can be distinguished from other movement. A second evaluation using a live implementation of the system running on a smartwatch suggests that this technique is viable for gestures used to respond to notifications or issue commands. Finally, we present three demonstration applications that highlight the technique running in real-time on the smartwatch.
ObjectSkin
Authors: Groeger Daniel , Steimle Jürgen
Published: January 2018

Augmenting everyday objects with interactive input and output surfaces is a long-standing topic in ubiquitous computing and HCI research. Existing approaches, however, fail to leverage the objects' full potential, particularly in highly curved organic geometries and in diverse visuo-haptic surface properties. We contribute ObjectSkin, a fabrication technique for adding conformal interactive surfaces to rigid and flexible everyday objects. It enables multi-touch sensing and display output that seamlessly integrates with highly curved and irregular geometries. The approach is based on a novel water-transfer process for interactive surfaces. It leverages off-the-shelf hobbyist equipment to fabricate thin, conformal, and translucent electronic circuits that preserve the surface characteristics of everyday objects. It offers two methods, for rapid low-fidelity and versatile high-fidelity prototyping, and is applicable to a wide variety of materials. Results from a series of technical experiments provide insights into the supported object geometries, compatible object materials, and robustness. Seven example cases demonstrate how ObjectSkin makes it possible to leverage geometries, surface properties, and unconventional objects for prototyping novel interactions for ubiquitous computing.
Improving Input Accuracy on Smartphones for Persons who are Affected by Tremor using Motion Sensors
Authors: Plaumann Katrin , Babic Milos , Drey Tobias , Hepting Witali , Stooss Daniel , Rukzio Enrico
Published: January 2018

Having a hand tremor often complicates interactions with touchscreens on mobile devices. Due to the uncontrollable oscillations of both hands, hitting targets can be hard, and interaction can be slow. Correcting input needs additional time and mental effort. We propose a method for automatically correcting such inputs based on motion data, gathered both with the devices' sensors and a small wearable sensor on the finger used for tapping. The development was informed by interviews with persons with tremor. Two empirical studies showed that our method, involving both smartphone and finger motion sensors without changing the user interface, allows users with tremor to select objects with up to 40 % fewer misses.
Flower-Pop
Authors: Lee Moon-Hwan , Row Yea-Kyung , Son Oosung , Lee Uichin , Kim Jaejeung , Jeong Jungi , Maeng Seungryoul , Nam Tek-Jin
Published: January 2018

We explore the potential use of mobile devices as a collaborative sensing system that can proactively mediate casual group conversations. In this study, we aim to investigate (i) the impacts of a mobile system's passive and active conversation facilitation and (ii) the ways in which sociocultural aspects that affect casual group conversation should be considered in the design of proactive mobile systems. Toward this goal, we developed Flower-Pop, a mobile system that monitors group conversations and visualizes interaction patterns using metaphorical expressions based on blossoms. This system provides passive facilitation as well as active facilitation modes such as proactive conversation visualization and photo sharing. The active modes can encourage inactive participants to share photos and select random people to speak. Focusing on Korea, our field study showed that Flower-Pop's mediation created smooth topic/speaker transitions and encouraged less-active speakers to better engage in group conversation. We also found that the sociocultural aspects of casual group conversation, such as the location's characteristics, social relations, and the group's interests, affected participants' use of the Flower-Pop system. Based on our findings, we discuss methods for designing mobile systems for conversation facilitation and outline how opportune sociocultural factors could be identified based on mobile devices.
Inferring Correlation between User Mobility and App Usage in Massive Coarse-grained Data Traces
Authors: Lu Zheng , Feng Yunhe , Zhou Wenjun , Li Xiaolin , Cao Qing
Published: January 2018

With the rapid growth in smartphone usage, it has been more and more important to understand the patterns of mobile data consumption by users. In this paper, we present an empirical study of the correlation between user mobility and app usage patterns. In particular, we focus on users' moving speed as the key mobility metric, and try to answer the following question: are there any notable relations between moving speed and the app usage patterns? Our study is based on a real-world, large-scale dataset of 2G phone network data request records. A critical challenge was that the raw data records are rather coarse-grained. More specifically, unlike GPS traces, the exact locations of users were not readily available. We inferred users' approximate locations according to their interactions with nearby cell towers, whose locations were known. We proposed a novel method to filter out noises and perform reliable speed estimation. We verify our methodology with out of sample data and show its improvement in speed estimation accuracy. We then examined several aspects of mobile data usage patterns, including the data volume, the access frequency, and the app categories, to reveal the correlation between these patterns and users' moving speed. Experimental results based on our large-scale real-world datasets revealed that users under different mobility categories not only have different smartphone usage motivations but also have different ways of using their smartphones.
Sensei
Authors: Saquib Nazmus , Bose Ayesha , George Dwyane , Kamvar Sepandar
Published: January 2018

We present Sensei, the first system designed to understand social interaction and learning in an early-childhood classroom using a distributed sensor network. Our unobtrusive sensors measure proximity between each node in a dynamic range-based mesh network. The sensors can be worn in the shoes, attached to selected landmarks in the classroom, and placed on Montessori materials. This data, accessible to teachers in a web dashboard, enables teachers to derive deeper insights from their classrooms. Sensei is currently deployed in three Montessori schools and we have evaluated the effectiveness of the system with teachers. Our user studies have shown that the system enhances teachers' capabilities and helps discover insights that would have otherwise been lost. From our evaluation interviews, we have established three major use cases of the system. Sensei augments teachers' manual observations, helps them plan individualized curriculum for each student, and identifies their needs for more interaction with some children. Further, the anonymized data can be used in large-scale research in early childhood development.
Automated Dyadic Data Recorder (ADDR) Framework and Analysis of Facial Cues in Deceptive Communication
Authors: Sen Taylan , Hasan Md Kamrul , Teicher Zach , Hoque Mohammed Ehsan
Published: January 2018

We developed an online framework that can automatically pair two crowd-sourced participants, prompt them to follow a research protocol, and record their audio and video on a remote server. The framework comprises two web applications: an Automatic Quality Gatekeeper for ensuring only high quality crowd-sourced participants are recruited for the study, and a Session Controller which directs participants to play a research protocol, such as an interrogation game. This framework was used to run a research study for analyzing facial expressions during honest and deceptive communication using a novel interrogation protocol. The protocol gathers two sets of nonverbal facial cues in participants: features expressed during questions relating to the interrogation topic and features expressed during control questions. The framework and protocol were used to gather 151 dyadic conversations (1.3 million video frames). Interrogators who were lied to expressed the smile-related lip corner puller cue more often than interrogators who were being told the truth, suggesting that facial cues from interrogators may be useful in evaluating the honesty of witnesses in some contexts. Overall, these results demonstrate that this framework is capable of gathering high quality data which can identify statistically significant results in a communication study.
SafeDrive
Authors: Jiang Landu , Lin Xinye , Liu Xue , Bi Chongguang , Xing Guoliang
Published: January 2018

Distracted driving causes a large number of fatalities every year and is now becoming an important issue in the traffic safety study. In this paper, we present SafeDrive, a driving safety system that leverages wearable wrist sensing techniques to detect and analyze driver distracted behaviors. Existing wrist-worn sensing approaches, however, do not address challenges under real driving environments, such as less distinguishable gesture patterns due to in-vehicle physical constraints, various gesture hallmarks produced by different drivers and significant noise introduced by various driving conditions. In response, SafeDrive adopts a semi-supervised machine learning model for in-vehicle distracting activity detection. To improve the detection accuracy, we provide online updated classifiers by collecting real-time gesture data, while at the same time utilize smartphone sensing to generate soft hints filtering out anomalies and non-distracted hand movements. In the evaluation, we conduct extensive real-road experiments involving 20 participants (10 males and 10 females) and 5 vehicles (a sedan, a minivan and three SUVs). Our approach can achieve an average classification accuracy of over 90% with a error rate of a few percent, which demonstrate that SafeDrive is robust to real driving environments, and has great potential to help drivers shape safe driving habits.
FallDeFi
Authors: Palipana Sameera , Rojas David , Agrawal Piyush , Pesch Dirk
Published: January 2018

Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.
CTS
Authors: Huang Xingyu , Li Yong , Wang Yue , Chen Xinlei , Xiao Yu , Zhang Lin
Published: January 2018

GPS has been widely used for locating mobile devices on the road map. Due to its high power consumption and poor signal penetration, GPS is unfortunately unsuitable to be used for continuously tracking low-power devices. Compared with GPS-based positioning, cellular-infrastructure-based positioning consumes much less energy, and works in any place covered by the cellular networks. However, the challenges of cellular positioning come from the relatively low accuracy and sampling rate. In this paper, we propose a novel cellular-based trajectory tracking system, namely CTS. It achieves GPS-level accuracy by combining trilateration-based cellular positioning, stationary state detection, and Hidden-Markov-Model-based path recovery. In particular, CTS utilizes basic characteristics of cellular sectors to produce more credible inferences for device locations.

To evaluate the performance of CTS, we collaborated with a mobile operator and deployed the system the city of Urumchi, Xinjiang Province of China. We collected the location data of 489,032 anonymous mobile subscribers from cellular networks during 24 hours, and retrieved 201 corresponding GPS trajectories. Our experimental results show that CTS achieves GPS-level accuracy in 95.7% of cases, which significantly outperforms the state-of-the-art solutions.
CoCo
Authors: Samrose Samiha , Zhao Ru , White Jeffery , Li Vivian , Nova Luis , Lu Yichen , Ali Mohammad Rafayet , Hoque Mohammed Ehsan
Published: January 2018

We present and discuss a fully-automated collaboration system, CoCo, that allows multiple participants to video chat and receive feedback through custom video conferencing software. After a conferencing session, a virtual feedback assistant provides insights on the conversation to participants. CoCo automatically pulls audial and visual data during conversations and analyzes the extracted streams for affective features, including smiles, engagement, attention, as well as speech overlap and turn-taking. We validated CoCo with 39 participants split into 10 groups. Participants played two back-to-back team-building games, Lost at Sea and Survival on the Moon, with the system providing feedback between the two. With feedback, we found a statistically significant change in balanced participation---that is, everyone spoke for an equal amount of time. There was also statistically significant improvement in participants' self-evaluations of conversational skills awareness, including how often they let others speak, as well as of teammates' conversational skills. The entire framework is available at https://github.com/ROC-HCI/CollaborationCoach_PostFeedback.
C-FMCW Based Contactless Respiration Detection Using Acoustic Signal
Authors: Wang Tianben , Zhang Daqing , Zheng Yuanqing , Gu Tao , Zhou Xingshe , Dorizzi Bernadette
Published: January 2018

Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.
RF-Copybook
Authors: Chang Liqiong , Xiong Jie , Wang Ju , Chen Xiaojiang , Wang Yu , Tang Zhanyong , Fang Dingyi
Published: January 2018

As one of the best ways to learn and appreciate the Chinese culture, Chinese calligraphy is widely practiced and learned all over the world. Traditional calligraphy learners spend a great amount of time imitating the image templates of reputed calligraphers. In this paper, we propose an RF-based Chinese calligraphy template, named RF-Copybook, to precisely monitor the writing process of the learner and provide detail instructions to improve the learner's imitating behavior. With two RFID tags attached on the brush pen and three antennas equipped at the commercial RFID reader side, RF-Copybook tracks the pen's 3-dimensional movements precisely. The key intuition behind RF-Copybook's idea is that: (i) when there is only direct path signal between the tag and the antenna, the phase measured at the reader changes linearly with the distance, (ii) the reader offers very fine-grained phase readings, thus a millimeter level accuracy of antenna-tag distance can be obtained, (iii) by combing multiple antenna-tag distances, we can quantify the writing process with stroke based feature models. Extensive experiments show that RF-Copybook is robust against the environmental noise and achieves high accuracies across different environments in the estimation of the brush pen's elevation angle, nib's moving speed and position.
Understanding Group Event Scheduling via the OutWithFriendz Mobile Application
Authors: Zhang Shuo , Alanezi Khaled , Gartrell Mike , Han Richard , Lv Qin , Mishra Shivakant
Published: January 2018

The wide adoption of smartphones and mobile applications has brought significant changes to not only how individuals behave in the real world, but also how groups of users interact with each other when organizing group events. Understanding how users make event decisions as a group and identifying the contributing factors can offer important insights for social group studies and more effective system and application design for group event scheduling.

In this work, we have designed a new mobile application called OutWithFriendz, which enables users of our mobile app to organize group events, invite friends, suggest and vote on event time and venue. We have deployed OutWithFriendz at both Apple App Store and Google Play, and conducted a large-scale user study spanning over 500 users and 300 group events. Our analysis has revealed several important observations regarding group event planning process including the importance of user mobility, individual preferences, host preferences, and group voting process.
MoodExplorer
Authors: Zhang Xiao , Li Wenzhong , Chen Xu , Lu Sanglu
Published: January 2018

Social psychology and neuroscience had confirmed that emotion state exerts a significant effect on human communication, perception, social behavior and decision making. With the wide availability of smartphones equipped with microphone, accelerometer, GPS, and other source of sensors, it is worthwhile to explore the possibility of automatic emotion detection via smartphone sensing. Particularly, we focus on a novel research problem that tries to detect the compound emotion (a set of multiple dimensional basic emotions) of smartphone users. We observe that users' self-reported emotional states have high correlation with their smartphone usage patterns and sensing data. Based on the observations, we exploit a feature extraction and selection algorithm to find the most significant features. We further adopt a factor graph model to tackle the correlations between features and emotion labels, and propose a machine learning algorithm for compound emotion detection based on the smartphone sensing data. The proposed mechanism is implemented as an APP called MoodExplorer in Android platform. Extensive experiments conducted on the smartphone data collected from 30 university students show that MoodExplorer can recognize users' compound emotions with 76.0% exact match on average.
Wordometer Systems for Everyday Life
Authors: Augereau Olivier , Sanches Charles Lima , Kise Koichi , Kunze Kai
Published: January 2018

We present in this paper a detailed comparison of different algorithms and devices to determine the number of words read in everyday life. We call our system the “Wordometer”. We used three kinds of eye tracking systems in our experiment: mobile video-oculography (MVoG); stationary video-oculography (SVoG); and electro-oculography (EoG). By analyzing the movement of the eyes we were able to estimate the number of words that a user read. Recently, inexpensive eye trackers have appeared on the market. Thus, we undertook a large-scale experiment that compared three devices that can be used for daily reading on a screen: the Tobii Eye X SVoG; the JINS MEME EoG; and the Pupil MVoG. We found that the accuracy of the everyday life devices and professional devices was similar when used with the Wordometer. We analyzed the robustness of the systems for special reading behaviors: rereading and skipping.

With the MVoG, SVoG and EoG systems, we obtained estimation errors respectively, 7.2%, 13.0%, and 10.6% in our main experiment. In all our experiments, we obtained 300 recordings by 14 participants, which amounted to 109,097 read words.
Monitoring in a DevOps World
Authors: Schlossnagle Theo
Published: January 2018

Monitoring can seem quite overwhelming. The most important thing to remember is that perfect should never be the enemy of better. DevOps enables highly iterative improvement within organizations. If you have no monitoring, get something; get anything. Something is better than nothing, and if you’ve embraced DevOps, you’ve already signed up for making it better over time.
Primary Peritoneal Angiosarcoma Metastatic to Liver and Bone without History of Radiation Therapy
Authors: Chiang Way , Tynski Zofia
Published: January 2018

Angiosarcoma is a rare vascular soft tissue tumor of endothelial origin most commonly seen in the elderly as a primary cutaneous head and neck malignancy. Furthermore, a peritoneal angiosarcoma is an exceedingly rare entity. This is the second case of primary peritoneal angiosarcoma reported in literature that is not associated with prior radiotherapy. Herein, we describe a case of primary peritoneal angiosarcoma metastatic to both the liver and bone in a male patient with metachronous renal cell carcinoma and parathyroid adenoma.
Dynamic Analysis for a Kaldor–Kalecki Model of Business Cycle with Time Delay and Diffusion Effect
Authors: Hu Wenjie , Zhao Hua , Dong Tao
Published: January 2018

The dynamics behaviors of Kaldor–Kalecki business cycle model with diffusion effect and time delay under the Neumann boundary conditions are investigated. First the conditions of time‐independent and time‐dependent stability are investigated. Then, we find that the time delay can give rise to the Hopf bifurcation when the time delay passes a critical value. Moreover, the normal form of Hopf bifurcations is obtained by using the center manifold theorem and normal form theory of the partial differential equation, which can determine the bifurcation direction and the stability of the periodic solutions. Finally, numerical results not only validate the obtained theorems, but also show that the diffusion coefficients play a key role in the spatial pattern. With the diffusion coefficients increasing, different patterns appear.
A Dynamic Time Warping Algorithm Based Analysis of Pedestrian Shockwaves at Bottleneck
Authors: Sun Lishan , Gong Qingsheng , Yao Liya , Luo Wei , Zhang Tianqi
Published: January 2018

Since the quantitative methodology analysis of the high-density pedestrian shockwaves at a bottleneck is limited, this paper proposes a dynamic time warping (DTW) algorithm for identifying, analyzing, and verifying the shockwaves. A set of real-world trajectory data is used to illustrate the proposed algorithm. Results show that the DTW algorithm is capable of depicting the pedestrian shockwaves elaborately and accurately. Results also show that the shockwave velocity is unsteady, as throughout time the gathering wave velocity and the evanescent wave velocity are decreasing and increasing, respectively. The mutual influence between followers and leaders is decreased when the shockwave spreads. There is a linear relationship between the shockwave velocity and density. Furthermore, singularities present a potential match solution to help identify the changing of pedestrian behaviors. The DTW algorithm for evaluating the pedestrian system stability has significant intrinsic features in the pedestrian traffic control and management.
Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid
Authors: Jiang Yujia , Liu Xin
Published: January 2018

Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.
Higher-Order Spectral Analysis to Identify Quadratic Nonlinearities in Fluid-Structure Interaction
Authors: Akhtar Imran , Elyyan Mohammad
Published: January 2018

Hydrodynamic forces on a structure are the manifestation of fluid-structure interaction. Since this interaction is nonlinear, these forces consist of various frequencies: fundamental, harmonics, excitation, sum, and difference of these frequencies. To analyze this phenomenon, we perform numerical simulations of the flow past stationary and oscillating cylinders at low Reynolds numbers. We compute the pressure, integrate it over the surface, and obtain the lift and drag coefficients for the two configurations: stationary and transversely oscillating cylinders. Higher-order spectral analysis is performed to investigate the nonlinear interaction between the forces. We confirmed and investigated the quadratic coupling between the lift and drag coefficients and their phase relationship. We identify additional frequencies and their corresponding energy present in the flow field that appear as the manifestation of quadratic nonlinear interaction.
Gastroprotective Mechanism and Ulcer Resolution Effect of Cyrtocarpa procera Methanolic Extract on Ethanol‐Induced Gastric Injury
Authors: Escobedo-Hinojosa Wendy Itzel , Gomez-Chang Erika , García-Martínez Karina , Guerrero Alquicira Raquel , Cardoso-Taketa Alexandre , Romero Irma
Published: January 2018

Gastric ulcers are a worldwide health problem and their poor healing is one of the most important causes for their recurrence. We have previously reported the remarkable gastroprotective and anti‐Helicobacter pylori activities of the methanolic extract (CpMet) of Cyrtocarpa procera bark. This work investigates, in a murine model, the CpMet gastroprotective mechanism and establishes its preclinical efficacy in the resolution of ethanol‐induced gastric ulcers. The results showed that the gastroprotective activity of CpMet is mainly associated with endogenous NO and prostaglandins, followed by sulfhydryl groups and KATP channels. Furthermore, CpMet (300 mg/kg, twice a day) orally administered during 20 consecutive days promoted an ulcer area reduction of 62.65% at the 20th day of the treatment. The effect was confirmed macroscopically by the alleviation of gastric mucosal erosions and microscopically by an increase in mucin content and a reduction in the inflammatory infiltration at the site of the ulcer. No clinical symptoms or signs of toxicity were observed in the treated animals. The results indicate the safety and efficacy of CpMet in promoting high quality of ulcer healing by different mechanisms, but mostly through cytoprotective and anti‐inflammatory effects, making it a promising phytodrug for ulcer treatment.
Low, rather than High, Body Mass Index Is a Risk Factor for Acute Kidney Injury in Multiethnic Asian Patients: A Retrospective Observational Study
Authors: Liu Allen Yan Lun , Wang Jiexun , Nikam Milind , Lai Boon Cheok , Yeoh Lee Ying
Published: January 2018

Background. Acute kidney injury (AKI) is common in hospitalised patients. The relationship between body mass index (BMI) and the risk of having AKI for patients in the acute hospital setting is not known, particularly in the Asian population.Methods. This was a retrospective, single-centre, observational study conducted in Singapore, a multiethnic population. All patients aged ≥21 years and hospitalised from January to December 2013 were recruited.Results. A total of 12,555 patients were eligible for the analysis. A BMI of <18.5 kg/m2was independently associated with the development of AKI in hospitalised patients (odds ratio (OR): 1.23 [95% confidence interval [CI]: 1.04–1.44,P=0.01]) but not for overweight and obesity. Subgroup analysis further revealed that underweight patients aged ≥75 and repeated hospitalisation posed a higher risk of AKI (OR: 1.25 [CI: 1.01–1.56],P=0.04; OR: 1.23 [CI: 1.04–1.44],P=0.01, resp.). Analyses by interactions between different age groups and BMI using continuous or categorised variables did not affect the overall probability of developing AKI.Conclusions. Underweight Asian patients are susceptible to AKI in acute hospital settings. Identification of this novel risk factor for AKI allows us to optimise patient care by prevention, early detection, and timely intervention.
Intrahepatic Sarcomatous Cholangiocarcinoma: Case Report and Review of the Literature
Authors: Boonsinsukh Thana , Viriyaroj Vichit , Rookkachart Thammanij , Trongwongsa Therdkiat
Published: January 2018

The authors report a case of a patient with intrahepatic sarcomatous cholangiocarcinoma. A 45-year-old Thai man presented with a 3-month history of right upper abdominal pain. CT scan revealed hepatomegaly with a 6.5 cm hypovascular soft tissue density mass in the right lobe and showed mild delayed enhancement. On exploratory laparotomy, the tumor adherent to right diaphragm was found. We performed right hepatectomy, partial resection of right diaphragm, and cholecystectomy. The immunohistological results suggested “sarcomatous intrahepatic cholangiocarcinoma.” The tumor was recurrent in 5 months after operation and unresectable. Therefore, the treatment in this patient was supportive care. He died 11 months after his initial presentation. The literature reviews showed that intrahepatic sarcomatous cholangiocarcinoma is aggressive malignant with poor prognosis. Early detection, radical resection, and careful follow-up would be the treatment for the favorable prognosis.
Increased Micronuclei Frequency in Oral and Lingual Epithelium of Treated Diabetes Mellitus Patients
Authors: Quintero Ojeda Jesús Emilo , Aguilar-Medina Maribel , Olimón-Andalón Vicente , García Jau Rosa Alicia , Ayala Ham Alfredo , Romero Quintana José Geovanni , Silva-Benítez Erika de Lourdes , Sanchez-Schmitz Guzmán , Ramos-Payán Rosalío
Published: January 2018

Diabetes mellitus (DM) is a metabolic disease characterized by persistent high levels of glucose in plasma. Chronic hyperglycemia is thought to increase oxidative stress and the formation of free radicals that in turn damage cells. Thus, we decided to determine the frequency of nuclear abnormalities in epithelial cells from cheek and tongue mucosa of DM patients with type 1 (DM1, treated only with insulin) and type 2 (DM2, treated with metformin) using the buccal micronucleus cytome (BMCyt) assay. Micronuclei frequency in cheek epithelial cells was higher in both DM1 (0.75 ± 0.31, P<0.001) and DM2 (0.52 ± 0.27, P<0.001) patients, as compared to healthy controls (0.07  ±  0.06). Similarly, micronuclei frequency in tongue epithelium was increased in DM1 (0.81  ±  0.22, P<0.001) and DM2 (0.41  ±  0.21, P<0.001) groups, in comparison to controls (0.06  ±  0.05). Besides, we found a positive correlation between micronuclei frequency and the onset time of DM2 in both cheek (ρ = 0.69, P<0.001) and tongue epithelial cells (ρ = 0.71, P<0.001), but not with onset time of DM1 or age of the patients. Considering all this, we pose that BMCyt could serve as a fast and easily accessible test to assess genotoxic damage during dental visits of DM patients, helping to monitor their disease.
Synthesis of CIS (CuInSe2) Based Materials for Solar Applications
Authors: Soydan Ali Murat , Yilmaz Pinar , Tunaboylu Bahadır
Published: January 2018

Nanopowders of copper indium diselenide were produced with five different organic solvents: ethylenediamine, triethanolamine, oleylamine, oleic acid, and polyetheramine. We successfully synthesized pure CIS nanopowders at a temperature of 240°C at three different durations of 10 h, 20 h, and 40 h with a one-step process. This shorter time method offers important cost advantages in manufacturing. Polyetheramine and oleic acid were used for the first time in literature for CIS synthesis in an autoclave.
LSTM-Based Hierarchical Denoising Network for Android Malware Detection
Authors: Yan Jinpei , Qi Yong , Rao Qifan
Published: January 2018

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.
Effects of Ginger (Zingiber officinale Roscoe) on Type 2 Diabetes Mellitus and Components of the Metabolic Syndrome: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Authors: Zhu Jie , Chen Hao , Song Zhixiu , Wang Xudong , Sun Zhenshuang
Published: January 2018

Objective. This article aims to assess the effects of ginger (Zingiber officinale Roscoe) on type 2 diabetes mellitus (T2DM) and/or components of the metabolic syndrome (MetS). Methods. Electronic literature was searched in PubMed, Embase, the Cochrane Library, Chinese Biomedical Database, China National Knowledge Infrastructure, and Wanfang Database from inception of the database to May 19, 2017, and supplemented by browsing reference lists of potentially eligible articles. Randomized controlled trials on research subjects were included. Data were extracted as a mean difference (MD) and 95% confidence interval (CI). Subgroup analysis of fasting blood glucose (FBG) was performed. Results. 10 studies met the inclusion criteria with a total of 490 individuals. Ginger showed a significant beneficial effect in glucose control and insulin sensitivity. The pooled weighted MD of glycosylated hemoglobin (HbA1c) was −1.00, (95% CI: −1.56, −0.44; P < 0.001). Subgroup analysis revealed that ginger obviously reduced FBG in T2DM patients (−21.24; 95% CI: −33.21, −9.26; P < 0.001). Meanwhile, the significant effects of improvement of lipid profile were observed. Most analyses were not statistically heterogeneous. Conclusion. Based on the negligible side effects and obvious ameliorative effects on glucose control, insulin sensitivity, and lipid profile, ginger may be a promising adjuvant therapy for T2DM and MetS.
Structural Motion Grammar for Universal Use of Leap Motion: Amusement and Functional Contents Focused
Authors: Lee Byungseok , Lee Donghwe , Chin Seongah
Published: January 2018

Motions using Leap Motion controller are not standardized while the use of it is spreading in media contents. Each content defines its own motions, thereby creating confusion for users. Therefore, to alleviate user inconvenience, this study categorized the commonly used motion by Amusement and Functional Contents and defined the Structural Motion Grammar that can be universally used based on the classification. To this end, the Motion Lexicon was defined, which is a fundamental motion vocabulary, and an algorithm that enables real-time recognition of Structural Motion Grammar was developed. Moreover, the proposed method was verified by user evaluation and quantitative comparison tests.
The Protective Effect of Total Flavones from Rhododendron simsii Planch. on Myocardial Ischemia/Reperfusion Injury and Its Underlying Mechanism
Authors: Luo Sheng-Yong , Xu Qing-Hua , Peng Gong , Chen Zhi-Wu
Published: January 2018

Objectives. Total flavones from Rhododendron simsii Planch. (TFR) are the effective part extracted from the flowers of Rhododendron simsii Planch. and have obvious protective effects against cerebral ischemic or myocardial injuries in rabbits and rats. However, their mechanism of cardioprotection is still unrevealed. Therefore, the present study was designed to investigate the effect of TFR on myocardial I/R injury and the underlying mechanism. Methods. TFR groups were treated by gavage once a day for 3 days at a dose of 20, 40, and 80 mg/kg, respectively, and then the model of myocardial I/R injury was established. Myocardial infarction, ST‐segment elevation, and the expression of UTR, ROCK1, ROCK2, and p‐MLC protein in rat myocardium were determined at 90 min after reperfusion. UTR siRNA in vivo transfection and competition binding assay method were used to study the relationship between the protective effect of TFR and UTR. Results. The expression of UTR protein markedly decreased in myocardium of UTR siRNA transfection group rats. TFR could significantly reduce the infarct size and inhibit the increase of RhoA activity and ROCK1, ROCK2, and p‐MLC protein expressions both in WT and UTR knockdown rats. The reducing rate of TFR in myocardial infarction area, RhoA activity, and ROCK1, ROCK2, and p‐MLC protein expressions in UTR knockdown rats decreased markedly compared with that in WT rats. In addition, TFR had no obvious effect on the increase of ΣST in UTR knockdown rats in comparison with that in model group. In particular, TFR could significantly inhibit the combination of [ 125I]‐hu‐II and UTR, and IC50 was 0.854 mg/l. Conclusions. The results indicate that the protective effect of TFR on I/R injury may be correlated with its blocking UTR and the subsequent inhibition of RhoA/ROCK signaling pathway.
The Efficacy of Conventionally Fractionated Radiation in the Management of Osseous Metastases from Metastatic Renal Cell Carcinoma
Authors: Ganju Rohit Gunan , TenNapel Mindi , Mahan Nicholas , Zahra Amir , Shen Xinglei
Published: January 2018

Background. There is little data regarding the effectiveness of palliative radiation with conventional fractionation for metastatic renal cell carcinoma (RCC), which has been described as radioresistant. We conducted a retrospective analysis of patients with metastatic bony disease from RCC treated with radiation therapy at our institution. Methods. Forty patients with histologically confirmed RCC with a total of 53 treatment courses were included. Pain response after radiotherapy was recorded and freedom from progression was generated using posttreatment radiographs. Patient data was analyzed to assess influence on local control. Results. Patients had a median age of 63. Median follow-up was 9.3 months. The most common radiation dose was 30 Gy in 10 fractions. Pain control after radiotherapy was achieved in 73.6% of patients. Increasing age was associated with nonresponse at the initial pain assessment post-RT (p=0.02). In lesions with initial pain response, nonclear cell histology was associated with increased pain recurrence (p=0.01) and a shorter duration to pain recurrence (p=0.01). Radiographic control at 1 year was 62%. Conclusions. Pain response and control rates for osseous metastatic disease in RCC are comparable to other histologies when treated with conventional fractionation. These appear to be inferior to reported control rates from stereotactic treatments.
Postrecruitment Function of Yeast Med6 Protein during the Transcriptional Activation by Mediator Complex
Authors: Kim Gwang Sik , Lee Young Chul
Published: January 2018

Med6 protein (Med6p) is a hallmark component of evolutionarily conserved Mediator complexes, and the genuine role of Med6p in Mediator functions remains elusive. For the functional analysis ofSaccharomyces cerevisiaeMed6p (scMed6p), we generated a series of scMed6p mutants harboring a small internal deletion. Genetic analysis of these mutants revealed that three regions (amino acids 33–42 (Δ2), 125–134 (Δ5), and 157–166 (Δ6)) of scMed6p are required for cell viability and are located at highly conserved regions of Med6 homologs. Notably, the Med6p-Δ2 mutant was barely detectable in whole-cell extracts and purified Mediator, suggesting a loss of Mediator association and concurrent rapid degradation. Consistent with this, the recombinant forms of Med6p having these mutations partially (Δ2) restore or fail (Δ5 and Δ6) to restore in vitro transcriptional defects caused by temperature-sensitivemed6mutation. In an artificial recruitment assay, Mediator containing a LexA-fused wild-type Med6p or Med6p-Δ5 was recruited to thelexAoperator region with TBP and activated reporter gene expression. However, the recruitment of Mediator containing LexA-Med6p-Δ6 tolexAoperator region resulted in neither TBP recruitment nor reporter gene expression. This result demonstrates a pivotal role of Med6p in the postrecruitment function of Mediator, which is essential for transcriptional activation by Mediator.
A Proposed Approach for Joint Modeling of the Longitudinal and Time-To-Event Data in Heterogeneous Populations: An Application to HIV/AIDS’s Disease
Authors: Roustaei Narges , Ayatollahi Seyyed Mohammad Taghi , Zare Najaf
Published: January 2018

In recent years, the joint models have been widely used for modeling the longitudinal and time-to-event data simultaneously. In this study, we proposed an approach (PA) to study the longitudinal and survival outcomes simultaneously in heterogeneous populations. PA relaxes the assumption of conditional independence (CI). We also compared PA with joint latent class model (JLCM) and separate approach (SA) for various sample sizes (150, 300, and 600) and different association parameters (0, 0.2, and 0.5). The average bias of parameters estimation (AB-PE), average SE of parameters estimation (ASE-PE), and coverage probability of the 95% confidence interval (CP) among the three approaches were compared. In most cases, when the sample sizes increased, AB-PE and ASE-PE decreased for the three approaches, and CP got closer to the nominal level of 0.95. When there was a considerable association, PA in comparison with SA and JLCM performed better in the sense that PA had the smallest AB-PE and ASE-PE for the longitudinal submodel among the three approaches for the small and moderate sample sizes. Moreover, JLCM was desirable for the none-association and the large sample size. Finally, the evaluated approaches were applied on a real HIV/AIDS dataset for validation, and the results were compared.
D-Tagatose Is a Promising Sweetener to Control Glycaemia: A New Functional Food
Authors: Guerrero-Wyss Marion , Durán Agüero Samuel , Angarita Dávila Lisse
Published: January 2018

The objective of the current research was to review and update evidence on the dietary effect of the consumption of tagatose in type 2 diabetes, as well as to elucidate the current approach that exists on its production and biotechnological utility in functional food for diabetics. Articles published before July 1, 2017, were included in the databases PubMed, EBSCO, Google Scholar, and Scielo, including the terms “Tagatose”, “Sweeteners”, “Diabetes Mellitus type 2”, “Sweeteners”, “D-Tag”. D-Tagatose (D-tag) is an isomer of fructose which is approximately 90% sweeter than sucrose. Preliminary studies in animals and preclinical studies showed that D-tag decreased glucose levels, which generated great interest in the scientific community. Recent studies indicate that tagatose has low glycemic index, a potent hypoglycemic effect, and eventually could be associated with important benefits for the treatment of obesity. The authors concluded that D-tag is promising as a sweetener without major adverse effects observed in these clinical studies.