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Current visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to classification or neglecting less abundant reads. Snowflake displays every observed OTU/ASV in the microbiome abundance table and provides a solution to include the data’s hierarchical structure and additional information obtained from downstream analysis (e.g., alpha- and beta-diversity) and metadata. Based on the value-driven ICE-T evaluation methodology, Snowflake was positively received. Experts in microbiome research found the visualizations to be user-friendly and detailed and liked the possibility of including and relating additional information to the microbiome’s composition. Exploring the topological structure of the microbiome abundance table allows them to quickly identify which taxa are unique to specific samples and which are shared among multiple samples (i.e., separating sample-specific taxa from the core microbiome), and see the compositional differences between samples. An R package for constructing and visualizing Snowflake microbiome composition graphs is available at https://gitlab.com/vda-lab/snowflake.

Smartphones and other mobile devices have proliferated in the past five years. The expectation of mobile device users to always be online has led to Wi-Fi networks being offered by a variety of providers. Using these networks introduces multiple security risks. In this work, we assess to what extent the privacy stance of mobile device users corresponds with their actual behavior by conducting a study with 108 participants. Our methodology consists of monitoring Wi-Fi networks that the participants’ devices connect to and the connections made by apps on these devices, for a period of 30 days. Afterwards, participants are surveyed about their awareness and privacy sensitiveness. We show that while a higher expertise in computer networks corresponds to more awareness about the connections made by apps, neither this expertise nor the actual privacy stance of the participant translates to better security habits. Moreover, participants in general were unaware about a significant part of connections made by apps on their devices, a matter that is worsened by the fact that one third of Wi-Fi networks that participants connect to do not have any security enabled. Based on our results, we provide recommendations to network providers, developers and users on how to improve Wi-Fi security for mobile devices.

The study of dialectal microvariation in spoken Spanish faces challenges due to the absence of an adequate morpho-syntactically annotated and parsed corpus. Therefore, this article introduces a novel technique, a game-based approach, for creating resources for non-standard Spanish language varieties. The article provides an overview of the progress in designing three Games With A Purpose (GWAPs) prototypes, to wit, Agentes, Tesoros, and Anotatlón. These games aim to facilitate the confirmation and correction of the morpho-syntactic tagging task of the COSER-AP (Corpus Oral y Sonoro del Español Rural-Anotado y Parseado, 'Annotated and Parsed Audible Corpus of Spoken Rural Spanish'). First, the article presents the methodology used to build the games. Second, it offers a detailed description of the implemented Game Design Elements (GDEs). Finally, the article discusses the results of a pilot evaluation that assesses player enjoyment and the linguistic accuracy. Findings are promising, with Tesoros and Anotatlón demonstrating high levels of enjoyment. Additionally, Agentes proves to be effective in collecting a large number of annotations. The linguistic accuracy also shows potential benefits of gamified approaches in linguistic annotation tasks. However, it also emphasizes the importance of considering regional in player assessment and training them in multidialectal contexts. [ABSTRACT FROM AUTHOR]

Whereas exercise training is key in the management of patients with cardiovascular disease (CVD) risk (obesity, diabetes, dyslipidaemia, hypertension), clinicians experience difficulties in how to optimally prescribe exercise in patients with different CVD risk factors. Therefore, a consensus statement for state-of-the-art exercise prescription in patients with combinations of CVD risk factors as integrated into a digital training and decision support system (the EXercise Prescription in Everyday practice & Rehabilitative Training (EXPERT) tool) needed to be established. EXPERT working group members systematically reviewed the literature for meta-analyses, systematic reviews and/or clinical studies addressing exercise prescriptions in specific CVD risk factors and formulated exercise recommendations (exercise training intensity, frequency, volume and type, session and programme duration) and exercise safety precautions, for obesity, arterial hypertension, type 1 and 2 diabetes, and dyslipidaemia. The impact of physical fitness, CVD risk altering medications and adverse events during exercise testing was further taken into account to fine-tune this exercise prescription. An algorithm, supported by the interactive EXPERT tool, was developed by Hasselt University based on these data. Specific exercise recommendations were formulated with the aim to decrease adipose tissue mass, improve glycaemic control and blood lipid profile, and lower blood pressure. The impact of medications to improve CVD risk, adverse events during exercise testing and physical fitness was also taken into account. Simulations were made of how the EXPERT tool provides exercise prescriptions according to the variables provided. In this paper, state-of-the-art exercise prescription to patients with combinations of CVD risk factors is formulated, and it is shown how the EXPERT tool may assist clinicians. This contributes to an appropriately tailored exercise regimen for every CVD risk patient. [ABSTRACT FROM AUTHOR]