Miracle-dz/UniFAR-FTUs huggingface dataset:
Ubiquitous Mobile Sensing: Behaviour, Mood, and Environment

Our world is increasingly interconnected via a wide variety of computers, IoT, wearable and mobile devices. The information provided collectively through these devices offers insightful information on our everyday lives, daily patterns, mood, behaviour, and surrounding environment. Our workshop brings together researchers interested in collecting and augmenting context to understand device specific behaviour and routines, human behaviour and mood, and changes in the environment. The outcomes of this workshop are new tools, methodologies, and potential collaborations for sensing the outlying world as well as ourselves.
Effect of chitosan coating on surgical sutures to strengthen the colonic anastomosis.

BACKGROUND We evaluated the feasibility of chitosan-coated sutures for intestinal anastomosis strength through wound-healing effect. METHODS Vicryl and PDS sutures were coated with 2% chitosan. While laparotomy was applied to the first group, chitosan was applied in the peritoneal cavity in the second group. Then the following materials were applied to colon anastomosis, in order: Vicryl, PDS, chitosan-coated Vicryl, and chitosan-coated PDS sutures. On the 7th and 14th days, eight rats from each group were euthanized. RESULTS The adhesion scores of chitosan and control groups were lower than the suture groups. The vascularization of Vicryl-chitosan was lower than PDS-chitosan on the 14th day (p=0.038). Fibroblast cells and vascularization of anastomosis with chitosan-coated Vicryl were lower than Vicryl and chitosan-coated PDS on the 14th day (p<0.05). The tensile strength of Vicryl-chitosan increased more than Vicryl in vitro (p<0.05) on the 14th and 7th days, but there was no difference in vivo. The tensile strength of PDS-chitosan decreased more than PDS on the 7th day in vivo (p<0.05). CONCLUSION The chitosan-coating effect on the adhesion and reinforcement of anastomosis in some parts of Vicryl in vitro and PDS in vivo was slightly improved.
Cognitive outcomes following epilepsy in infancy: A longitudinal community‐based study

Onset of epilepsy before 2 years of age is associated with poor cognitive outcome; however, the natural course of the range of epilepsies that occur at this age is unknown. The aim of this prospective community‐based study was to investigate the neuropsychological development of infants with newly diagnosed epilepsy longitudinally and to identify the clinical factors that predict long‐term impairment.
Neural Activation During Tonic Pain and Interaction Between Pain and Emotion in Bipolar Disorder: An fMRI Study

Objective: Pain and affective disorders have clear clinical relevance; however, very few studies have investigated the association between pain and bipolar disorder. This study investigated the brain activity of patients with bipolar disorder (BPs) undergoing tonic pain and assessed the interaction between pain and emotion. Methods: Ten BPs and ten healthy controls (HCs) were exposed to emotional pictures (positive, neutral, or negative), tonic pain only (pain session), and emotional pictures along with tonic pain (combined session). A moderate tonic pain was induced by the infusion of hypertonic saline (5% NaCl) into the right masseter muscle with a computer-controlled system. Whole-brain blood oxygenation level dependent (BOLD) signals were acquired using 3T functional resonance imaging (fMRI). Results: Ten BPs and ten healthy participants were included in the final analysis. During the pain session, BPs accepted more saline, but showed lower pain rating scores than HCs. When experiencing pain, BPs showed a significant decrease in the BOLD signal in the bilateral insula, left inferior frontal gyrus (IFG), and left cerebellum as compared with HCs. In the combined session, the activated regions for positive mood (pain with positive mood > baseline) in BPs were the left cerebellum, right temporal gyrus, and left occipital gyrus; the activated regions for negative mood (pain with negative mood > baseline) were the right occipital gyrus, left insula, left IFG, and bilateral precentral gyrus. Conclusions: This study presents the preliminary finding of the interaction between pain and emotion in BPs. BPs exhibited lower sensitivity to pain, and the activation of insula and IFG may reflect the interaction between emotion and pain stimulus.
Sorting mRNA Molecules for Cytoplasmic Transport and Localization

In eukaryotic cells, gene expression is highly regulated at many layers. Nascent RNA molecules are assembled into ribonucleoprotein complexes that are then released into the nucleoplasmic milieu and transferred to the nuclear pore complex for nuclear export. RNAs are then either translated or transported to the cellular periphery. Emerging evidence indicates that RNA-binding proteins play an essential role throughout RNA biogenesis, from the gene to polyribosomes. However, the sorting mechanisms that regulate whether an RNA molecule is immediately translated or sent to specialized locations for translation are unclear. This question is highly relevant during development and differentiation when cells acquire a specific identity. Here, we focus on the RNA-binding properties of heterogeneous nuclear ribonucleoproteins (hnRNPs) and how these mechanisms are believed to play an essential role in RNA trafficking in polarized cells. Further, by focusing on the specific hnRNP protein CBF-A/hnRNPab and its naturally occurring isoforms, we propose a model on how hnRNP proteins are capable of regulating gene expression both spatially and temporally throughout the RNA biogenesis pathway, impacting both healthy and diseased cells.
Do Chinese Traditional and Modern Cultures Affect Young Adults’ Moral Priorities?

Dramatic cultural change has occurred in Mainland China over the past four decades, yet little is known about how this cultural shift impacts Chinese peoples’ moral values. The present research aims to fill this gap by examining whether Chinese traditional and modern cultures influence young adults’ moral judgments. Study 1 investigated the relation between psychological traditionality/modernity and moral concerns. Results indicated that participants who strongly endorsed Chinese traditional culture prioritize relationship concern rather than justice concern. Study 2 used the cultural priming method and tested the effects of traditional and modern icons on moral concerns. Results suggested that participants who were primed with traditional or modern or neutral icons did not give priority to relationship or justice concern. Together, our findings provide initial empirical evidence on whether Chinese traditional and modern cultures shift the moral mindsets of bicultural young Chinese among alternative (and even competing) moral codes.
AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks

Extreme multi-label text classification (XMTC) is a task for tagging each given text with the most relevant multiple labels from an extremely large-scale label set. This task can be found in many applications, such as product categorization,web page tagging, news annotation and so on. Many methods have been proposed so far for solving XMTC, while most of the existing methods use traditional bag-of-words (BOW) representation, ignoring word context as well as deep semantic information. XML-CNN, a state-of-the-art deep learning-based method, uses convolutional neural network (CNN) with dynamic pooling to process the text, going beyond the BOW-based appraoches but failing to capture 1) the long-distance dependency among words and 2) different levels of importance of a word for each label. We propose a new deep learning-based method, AttentionXML, which uses bidirectional long short-term memory (LSTM) and a multi-label attention mechanism for solving the above 1st and 2nd problems, respectively. We empirically compared AttentionXML with other six state-of-the-art methods over five benchmark datasets. AttentionXML outperformed all competing methods under all experimental settings except only a couple of cases. In addition, a consensus ensemble of AttentionXML with the second best method, Parabel, could further improve the performance over all five benchmark datasets.
Projection-based Localization and Control Method of Robot Swarms for Swarm User Interfaces

The augmented reality systems, in which computer graphics and multiple mobile robots are cooperatively controlled, are useful for an intuitive understanding of complicated information because users can touch and operate information directly through robots [10, 18]. The swarm user interfaces using cooperatively controlled multiple mobile robots are also a useful approach [11]. These systems express various modalities utilizing the assembly and distribution of robots; however, they cannot realize a collaboration between computer graphics and robots. Therefore, the system, in which computer graphics and multiple mobile robots seamlessly cooperate, can be expected to expand the domain of the swarm user interfaces. Two problems remain in terms of ensuring a seamless collaboration between computer graphics and multiple mobile robots. First, previous methods use external measurement systems for localization that use the computer vision technology. However, the camera positions must be corrected and calibrated, and the spatial position of the robot in the camera images must be computed. Localization methods without computer vision may depend on a laser, sonar, or visible light communication [13]. However, these approaches have limited accuracy because of the resolution of each sensor. Augmented Coliseum [10] approaches this issue using a display-based measurement control system (DMCS) [19]. This technology eliminates the need for position-measuring devices and can support multiple robots on display. However, a prior initialization of tracking robots by marker-pattern images is necessary. Therefore, we cannot add or remove robots.
Bushenhuoxue Formula Facilitates Articular Cartilage Repair and Attenuates Matrix Degradation by Activation of TGF-β Signaling Pathway

Objective To investigate the effect and underlying mechanism of Bushenhuoxue (BSHX) formula on articular cartilage repair. Methods Twenty-four full-thickness cartilage defect rats were divided into two groups: model group and BSHX group (treated with BSHX formula). Macroscopic observation and histopathological study were conducted after 4- and 8-week treatment. Additionally, we also evaluated chondrocyte proliferation, extracellular matrix (ECM) deposition, cartilage degradation, and chondrocyte hypertrophy-related genes expression in chondrogenic ATDC5 cells cultured in BSHX formula-mediated serum. Moreover, we assessed aforementioned genes expression and pSMAD2/3 protein level in Tgfβr2 siRNA transfected chondrogenic ATDC5 cells in order to address whether BSHX formula exerts cartilage repairing effect through TGF-β signaling. Results Neocartilage regeneration promotion effect was observed in cartilage defect rats after BSHX formula treatment, with increases in Col2 and pSMAD2 and decreases in Mmp13 and Runx2. Moreover, cell proliferation, the elevated Col2a1, Aggrecan and pSMAD2/3, reduced Mmp13, Adamts5, Col10a1, and Runx2 expression were also observed in chondrogenic ATDC5 cells cultured in BSHX formula-mediated serum. Besides, the expression alteration of ECM deposition, cartilage degradation, chondrocyte hypertrophy-related genes, and pSMAD2/3 protein levels presented in Tgfβr2 downregulated chondrogenic ATDC5 cells couldn't be adjusted by BSHX formula treatment. Conclusion By activation of TGF-β signaling, BSHX formula can promote articular cartilage repair by accelerating chondrocyte proliferation and maintaining chondrocyte phenotype, upregulate ECM accumulation, and inhibit matrix degradation.
Cocktail BPSK: Energy Reused Scheme for High Achievable Data Rates

The present paper proposes a novel transmission strategy, referred to as cocktail BPSK, whereat two independent BPSKs are superposed with the non-orthogonal basis in a parallel transmission. In contrast to the conventional signal superpositions, the proposed scheme avoids the interference between the two symbols, allows the symbol-energy-reuse of each other and gains the extra energy. Based on the formulation of the mutual informations, the theoretical analysis shows that the cocktail BPSK scheme can achieve high data rate beyond the channel capacity at very low SNR, and the numerical results confirm this approach eventually.
Demographic Attributes Prediction Through App Usage Behaviors on Smartphones

Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. App usage behaviors reveal rich clues regarding one's personal attributes. It is possible to predict smartphone users' demographic attributes through their app usage behaviors. In this paper, we predict users' gender and income level on a large-scale dataset of app usage records from 10,000 Android users. More specifically, we first extracted features from app usage behaviors in terms of app, category, and app usage sequence. Then, we accessed the predictive ability of individual features and combinations of different features for gender and income level. We achieved an accuracy of 82.49%, precision of 82.01%, recall of 81.38% and F1 score of 0.82 for gender, with the best set of features. For income level (three classes), we achieved an accuracy of 69.71%, precision of 70.31%, recall of 70.38% and F1 score of 0.70.
Responding to the challenges of international collaborations between the east and the west – report of the first JCOG–EORTC symposium and a perspective from young JCOG and EORTC investigators

International/intercontinental collaboration is necessary to set up new innovative clinical trials for cancer treatment. However, the infrastructure, especially Asia-Europe academic partnerships, to enable such collaboration has not been fully structured and differences and similarities between the research groups have not been well studied. In 2015, collaboration started between the biggest cancer research organizations in Asia and EU, Japan Clinical Oncology Group (JCOG) and European Organisation for Research and Treatment of Cancer (EORTC). Following the first pilot collaboration study, the first scientific symposium took place in December 2017 in Tokyo. Before the symposium, a working visit for EORTC investigators from the Early Career Investigator initiative (ECI), willing to develop projects within the JCOG-EORTC partnership, was held. In addition to the digest of the working visit and symposium, we aimed to describe the differences and similarities between the two groups and to identify key factors for collaboration from the perspective of the young investigators of the networks. These findings are described in this article.
Flexibility and structure of flanking DNA impact transcription factor affinity for its core motif

Abstract Spatial and temporal expression of genes is essential for maintaining phenotype integrity. Transcription factors (TFs) modulate expression patterns by binding to specific DNA sequences in the genome. Along with the core binding motif, the flanking sequence context can play a role in DNA–TF recognition. Here, we employ high-throughput in vitro and in silico analyses to understand the influence of sequences flanking the cognate sites in binding of three most prevalent eukaryotic TF families (zinc finger, homeodomain and bZIP). In vitro binding preferences of each TF toward the entire DNA sequence space were correlated with a wide range of DNA structural parameters, including DNA flexibility. Results demonstrate that conformational plasticity of flanking regions modulates binding affinity of certain TF families. DNA duplex stability and minor groove width also play an important role in DNA–TF recognition but differ in how exactly they influence the binding in each specific case. Our analyses further reveal that the structural features of preferred flanking sequences are not universal, as similar DNA-binding folds can employ distinct DNA recognition modes.
Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection

Clickbaits are catchy headlines that are frequently used by social media outlets in order to allure its viewers into clicking them and thus leading them to dubious content. Such venal schemes thrive on exploiting the curiosity of naive social media users, directing traffic to web pages that won't be visited otherwise. In this paper, we propose a novel, semi-supervised classification based approach, that employs attentions sampled from a Gumbel-Softmax distribution to distill contexts that are fairly important in clickbait detection. An additional loss over the attention weights is used to encode prior knowledge. Furthermore, we propose a confidence network that enables learning over weak labels and improves robustness to noisy labels. We show that with merely 30% of strongly labeled samples we can achieve over 97% of the accuracy, of current state of the art methods in clickbait detection.
A Training-Free Contactless Human Vitality Monitoring Platform Using Commodity Wi-Fi Devices

Human vitality information is pivotal to many sensing applications. By vitality, we mean the status of a human target in a multi-room environment: whether he/she is still and which room he/she is located in. Continuous monitoring of human vitality helps us obtain important high-level contexts like one's emotions, living habits, and physical conditions. Unlike the most existing solutions that require human efforts in offline training or calibration, in this demo, we present WiVit, a training-free contactless Wi-Fi based sensing platform that can capture human vitality information in 7*24 hours. In typical indoor environments, WiVit can achieve 98% accuracy of vitality detection and nearly 100% accuracy of area detection.
Automated Wireless Localization Data Acquisition and Calibration with 6DOF Image Localization

Radio frequency (RF) signals have been used extensively to enable (indoor) localization and proximity detection based on Received Signal Strength Indication (RSSI). However, localization systems often suffer from large data collection and calibration overhead, especially when being deployed in a new environment. RSSI fingerprinting based localization systems require the construction of a fingerprinting database. This localization data acquisition is a hindrance for the proliferation of localization systems in practice. Similarly, RSSI proximity applications require an RSSI calibration for the receiver hardware and the deployment environment. To overcome these problems, we propose the usage of visual 3D models which enable 6DOF localization and distance measurement with high accuracy. We then fuse this physical knowledge with RF data: (1) for automated acquisition of fingerprinting data and (2) easy calibration of a RF propagation model for proximity estimation.
EmoTour: Multimodal Emotion Recognition using Physiological and Audio-Visual Features

To design more context-aware systems for smart environments, especially smart cities, the psychological user status such as emotion should be considered in addition to environmental information. In this study, we focus on the tourism domain as a typical use case, and propose a multimodal tourist emotion recognition method during the sightseeing. We employ behavioural cues (eye and head/body movements) and audio-visual features to recognise emotion. As a result of real-world experiments with tourists, we achieved up to 0.71 of average recall score in 3-class emotion recognition task with feature level fusion.
Bacterioplankton Activity in a Meso-eutrophic Subtropical Coastal Lagoon

The aim of this study was to investigate whether the bacterioplankton activity in the meso-eutrophic Conceição Lagoon would increase significantly under allochthonous inputs of inorganic nutrients and organic carbon. Abundance and biomass of bacterioplankton were evaluated under three treatments: light (14 h light/10 h dark), complete darkness (dark-control), and nutrient (C + N + P—dark, 100 : 10 : 1) enrichments during 72 h. Nutrient enrichments promoted a significant increase in abundance (maximum of 19.0 ×109 cells·L−1 in the first 32 hours) and biomass of the heterotrophic bacterioplankton, which induced the formation of large clusters. Bacterial biomass remained constant in the non-enriched incubations (dark-control and light). Bacterial growth rates were significantly higher after nutrient additions (1.35 d−1), followed by control (0.79 d−1), and light (0.63 d−1) treatments, which were statistically equal (p > 0.05). Bacterial production rates were also significantly higher under nutrient additions (1.28 d−1), compared to the control and light (0.50 d−1 and 0.44 d−1, respectively), demonstrating that bacterial growth and production in this meso-eutrophic lagoon are under an immediate “bottom-up” regulation, followed by a potential top-down effect. These facts reinforce the urgency on improving the local wastewater management plan in order to prevent further expansion of anoxic waters.
Predicting Diabetes Mellitus With Machine Learning Techniques

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used.