Context-Aware Prediction of User Engagement on Online Social Platforms

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior (R2=0.345) and that the integration of context information substantially improves predictive performance compared to the behavioral baseline model (R2=0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context information is considered (R2=0.44). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, underscoring the value of contextualized representations of user behavior for predicting user engagement on social platforms

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

\ua9 2021, The Author(s). Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare

Transcriptional response of rice flag leaves to restricted external phosphorus supply during grain filling in rice cv IR64

Plant phosphorus (P) remobilisation during leaf senescence has fundamental implications for global P cycle fluxes. Hypothesising that genes involved in remobilisation of P from leaves during grain filling would show altered expression in response to P deprivation, we investigated gene expression in rice flag leaves at 8 days after anthesis (DAA) and 16 DAA in plants that received a continuous supply of P in the nutrient solution vs plants where P was omitted from the nutrient solution for 8 consecutive days prior to measurement. The transcriptional response to growth in the absence of P differed between the early stage (8 DAA) and the later stage (16 DAA) of grain filling. At 8 DAA, rice plants maintained production of energy substrates through upregulation of genes involved in photosynthesis. In contrast, at 16 DAA carbon substrates were produced by degradation of structural polysaccharides and over 50% of highly upregulated genes in P-deprived plants were associated with protein degradation and nitrogen/amino acid transport, suggesting withdrawal of P from the nutrient solution led to accelerated senescence. Genes involved in liberating inorganic P from the organic P compounds and vacuolar P transporters displayed differential expression depending on the stage of grain filling stage and timing of P withdrawal

Perspective-Taking and Perspective-Sharing in Pediatric Education: Exploring Connections Between Strategies of Medical Students and Patients' Caregivers

INTRODUCTION: In pediatric education, caregivers are increasingly involved to share their perspective. Yet, an in-depth understanding of the perspective-taking process between medical students and caregivers is lacking. This study explored: 1) Which strategies do medical students use to take a caregiver's perspective and which facilitators and constraints do they perceive? 2) Which strategies do caregivers use to share their perspective with students? and 3) How do students' perspective-taking strategies relate to caregivers' perspective-sharing strategies? METHODS: In an online lesson: two caregivers of pediatric patients, shared their story with 27 fourth-year Dutch medical students. After the session, students undertook an assignment where they individually reflected on how they took perspective. Students' reflections were collected via audio recordings. Caregivers were individually interviewed. Data were analyzed through thematic and cross-case analysis. RESULTS: Students used eight perspective-taking strategies, in various combinations. Students used inferential strategies, where they made inferences from available information, and cultivating strategies, where they attempted to elicit more information about the caregiver. Students perceived individual-, contextual- and caregiver-related facilitators and constraints for taking perspective. Caregivers shared their perspective by adopting multiple strategies to share their story and create a trusting learning environment. We visualized connections between students' perspective-taking strategies, facilitators/constraints, and caregivers' perspective-sharing strategies. DISCUSSION: By combining data from both perspective-takers (students) and perspective-sharers (caregivers), this study provides a foundation for future research to study perspective-taking between students and patients in an educational context. On a practical level, our findings provide tools for students, patients, and educators to enhance perspective-taking processes

Fast splice site detection using information content and feature reduction

Background: Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data. Results: In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon\u27s information theory, Shapiro\u27s score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data. Conclusion: In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known method

Daily-life tele-monitoring of motor performance in stroke survivors

The objective of the EU project INTERACTION is to develop an unobtrusive and modular sensing system for objective monitoring of daily-life motor performance of stroke survivors. This will enable clinical professionals to advise their patients about their continued daily-life activity profile and home training, and evaluate and optimize rehabilitation programs.A modular textile-integrated sensing system was developed and performance and capacity measures were proposed and clinically tested in stroke subject.Telemonitoring facilities were developed and tested. In the last stage of the project, the system will be tested during daily-life