NotiMind: responses to smartphone notifications as affective sensors

Today's mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users’ emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users’ phones collected thousands of social and system notifications along with affect data collected via self-reported PANAS tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of Post and Remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different classifiers "in the wild" (F-measure 74-78% within-subject model, 72-76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication

Exploring the Touch and Motion Features in Game-Based Cognitive Assessments

Early detection of cognitive decline is important for timely intervention and treatment strategies to prevent further deterioration or development of more severe cognitive impairment, as well as identify at risk individuals for research. In this paper, we explore the feasibility of using data collected from built-in sensors of mobile phone and gameplay performance in mobile-game-based cognitive assessments. Twenty-two healthy participants took part in the two-session experiment where they were asked to take a series of standard cognitive assessments followed by playing three popular mobile games in which user-game interaction data were passively collected. The results from bivariate analysis reveal correlations between our proposed features and scores obtained from paper-based cognitive assessments. Our results show that touch gestural interaction and device motion patterns can be used as supplementary features on mobile game-based cognitive measurement. This study provides initial evidence that game related metrics on existing off-the-shelf games have potential to be used as proxies for conventional cognitive measures, specifically for visuospatial function, visual search capability, mental flexibility, memory and attention

Psychometric Properties of the Training Parenting Style Scale in a Malaysian Sample of Adolescents: Factor Analysis, Internal Consistency, and Measurement Invariance

The importance of parenting styles on children’s outcomes, including cognitive, social, academic, and values makes this topic a central concern to social researchers and psychologists. However, past research has reported controversial evidence on the relationship between authoritarian parenting and children’s outcomes in non-Western cultural contexts. This raises awareness on the implication of cultural differences in parenting styles. As a result, the training parenting style scale (TPSS) was proposed based on the Confucian concept of ‘Guan’ and ‘Chiao Shu.’ This scale is allegedly more reflective of the Asian parenting style. The present study examined the psychometric properties and measurement invariance of the Malay version of the TPSS across adolescents’ perceived maternal and paternal training and by adolescent gender. Of the 8 items in the original TPSS, confirmatory factor analysis supported 6-item scale with error correlations was the best-fitting model. Internal consistency was also good for the 6-item scale. Furthermore, support for configural, metric, scalar, residual, and structural invariance emerged across adolescents’ perceived maternal and paternal training and across adolescent gender. Results of this study supported the psychometric properties of the 6-item TPSS after taking into account several cautiously considered limitations

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a need for an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes

Manipulation of the phenolic quality of assam green tea through thermal regulation and utilization of microwave and ultrasonic extraction techniques

The aim of this study was to investigate the catechin levels and antioxidant activities as manipulated by roasting temperature and roasting time of green tea. Roasting temperature and time varied between 100–300 ºC and 60–240 s in green tea production. The main interactions measured were effects on the antioxidant activities, total phenolic content, DPPH, ABTS, FRAP and catechin content (catechin (C), epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG) and epicatechin (EC)). Optimum roasting conditions were determined as 270 ºC for 240 s, since this enabled high catechin contents, antioxidant activities and production yield. The extraction methods for green tea including traditional extraction (TDE), microwave-assisted extraction (MAE) and ultrasonic-assisted extraction (UAE) using 60% ethanol as solvent were investigated to evaluate the highest bioactive compound and yield of extraction. MAE was found to be more efficient in green tea extraction compared to UAE and TDE. The extracts showed significant cytotoxic potential against the Huh-7 cell line, in concentrations ranging from 31.25 to 1000 µg/mL. The results are useful in understanding the relationship between thermal treatment and extraction conditions on the chemical and nutritional properties of tea catechins, making it possible to select the production and extraction conditions that maximize the levels of beneficial tea ingredients

Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than later born neurons. This indicates that the longer time frame available to them might underlie high connectivity. Both outcomes are not observed for random connection formation. The study finds that around one-third of electrically coupled long-range connections are late forming, raising the question of what mechanisms are involved in ensuring their accuracy, particularly in light of the extremely invariant connectivity observed in C. elegans. In conclusion, the sequence of neural network development highlights the possibility of early contact or interaction in securing long-distance and high-degree connectivity

Sialylation of campylobacter jejuni lipo-oligosaccharides: impact on phagocytosis and cytokine production in mice

&lt;p&gt;Background: Guillain-Barré syndrome (GBS) is a post-infectious polyradiculoneuropathy, frequently associated with antecedent Campylobacter jejuni (C. jejuni) infection. The presence of sialic acid on C. jejuni lipo-oligosaccharide (LOS) is considered a risk factor for development of GBS as it crucially determines the structural homology between LOS and gangliosides, explaining the induction of cross-reactive neurotoxic antibodies. Sialylated C. jejuni are recognised by TLR4 and sialoadhesin; however, the functional implications of these interactions in vivo are unknown.&lt;/p&gt; &lt;p&gt;Methodology/Principal Findings: In this study we investigated the effects of bacterial sialylation on phagocytosis and cytokine secretion by mouse myeloid cells in vitro and in vivo. Using fluorescently labelled GM1a/GD1a ganglioside-mimicking C. jejuni strains and corresponding (Cst-II-mutant) control strains lacking sialic acid, we show that sialylated C. jejuni was more efficiently phagocytosed in vitro by BM-MΦ, but not by BM-DC. In addition, LOS sialylation increased the production of IL-10, IL-6 and IFN-β by both BM-MΦ and BM-DC. Subsequent in vivo experiments revealed that sialylation augmented the deposition of fluorescent bacteria in splenic DC, but not macrophages. In addition, sialylation significantly amplified the production of type I interferons, which was independent of pDC.&lt;/p&gt; &lt;p&gt;Conclusions/Significance: These results identify novel immune stimulatory effects of C. jejuni sialylation, which may be important in inducing cross-reactive humoral responses that cause GBS&lt;/p&gt

State history and economic development: evidence from six millennia

The presence of a state is one of the most reliable historical predictors of social and economic development. In this article, we complete the coding of an extant indicator of state presence from 3500 BCE forward for almost all but the smallest countries of the world today. We outline a theoretical framework where accumulated state experience increases aggregate productivity in individual countries but where newer or relatively inexperienced states can reach a higher productivity maximum by learning from the experience of older states. The predicted pattern of comparative development is tested in an empirical analysis where we introduce our extended state history variable. Our key finding is that the current level of economic development across countries has a hump-shaped relationship with accumulated state history