Privacy-preserving framework for context-aware mobile applications

In recent years, the pervasiveness of mobile devices, especially mobile phones and personal digital assistants (PDAs), has increased rapidly. At the same time, wireless communication networks have improved considerably and the usage of mobile devices to access the internet is, with decreasing costs, possible almost everywhere and at any time in industrialized countries. However, the usage of mobile technology and mobile applications to support business processes, trans- actions, and personal tasks is still low compared to their potential. The improved capabilities resulted in the introduction of many applications for mobile devices by network operators and software vendors. These services were meant to increase the average revenue per user (ARPU) on top of the voice call income. But many of these services have failed and none of them has led to an improved usage of mobile services today, besides e-mail. A new kind of application, the context-aware application, exploits the ubiquity of the mobile devices in order to fit the personal need or task the user is about to execute satisfactorily. Context-aware systems try to improve the communication with the user by adding information about the current context to the explicit user input and by adapting the output to the current setting of the user. While those applications are seen as important steps to a widespread usage, there are strong factors inhibiting their development and adoption. First of all, the lack of common frameworks handling context data and improv- ing software development increases the cost to build context-aware applications. Each application currently implements its own sensors and logic to handle its data. Furthermore, service providers need to offer tailored services for every con- text of the user. Since no single provider is able to be an expert for all kinds of applications and will not have the necessary number of developers, a common service which finds services of multiple providers for the current situation of the user is needed. All services need to utilize the context attributes which are locally determined by the user’s situation. Development costs are further boosted by the difficulty of developing applications for multiple devices with varying input/output (IO) capabilities like speech output, small and big screens, full qwerty-keyboards, touchscreens, or numeric keypads. From the user’s perspective, privacy also endangers the adoption of mobile services. Context information may include very private data and expose the user’s preferences and habits. While the user may trust a single, well-known, provider to secure the private data and to respect the user’s privacy concerns, the problem increases with more and more smaller service providers

Developing Genetic Algorithms and Mixed Integer Linear Programs for Finding Optimal Strategies for a Student’s “Sports” Activity

An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, and their good performance for a wide range of different problems. GAs are able to find good solutions for many problems even if the problem is complicated and its properties are not well known. In contrast, classical optimization approaches like linear programming or mixed integer linear programs (MILP) can only be applied to restricted types of problems as non-linearities of a problem that occur in many real-world applications can not appropriately modeled. This paper illustrates for an entertaining student “sports” game that GAs can easily be adapted to a problem where only limited knowledge about its properties and complexity are available and are able to solve the problem easily. Modeling the problem as a MILP and trying to solve it by using a standard MILP solver reveals that it is not solvable within reasonable time whereas GAs can solve it in a few seconds. The game studied is known to students as the so-called “beer-run”. There are different teams that have to walk a certain distance and to carry a case of beer. When reaching the goal all beer must have been consumed by the group and the winner of the game is the fastest team. The goal of optimization algorithms is to determine a strategy that minimizes the time necessary to reach the goal. This problem was chosen as it is not well studied and allows to demonstrate the advantages of using metaheuristics like GAs in comparison to standard optimization methods like MILP solvers for problems of unknown structure and complexity

Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model

Background: With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (Pomphorhynchus laevis, Neoechinorhynchus agilis, Neoechinorhynchus buttnerae) from four fish species (common barbel, European eel, thinlip mullet, tambaqui).Results: The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel.Conclusions: The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7

PU.1 controls fibroblast polarization and tissue fibrosis

Fibroblasts are polymorphic cells with pleiotropic roles in organ morphogenesis, tissue homeostasis and immune responses. In fibrotic diseases, fibroblasts synthesize abundant amounts of extracellular matrix, which induces scarring and organ failure. By contrast, a hallmark feature of fibroblasts in arthritis is degradation of the extracellular matrix because of the release of metalloproteinases and degrading enzymes, and subsequent tissue destruction. The mechanisms that drive these functionally opposing pro-fibrotic and pro-inflammatory phenotypes of fibroblasts remain unknown. Here we identify the transcription factor PU.1 as an essential regulator of the pro-fibrotic gene expression program. The interplay between transcriptional and post-transcriptional mechanisms that normally control the expression of PU.1 expression is perturbed in various fibrotic diseases, resulting in the upregulation of PU.1, induction of fibrosis-associated gene sets and a phenotypic switch in extracellular matrix-producing pro-fibrotic fibroblasts. By contrast, pharmacological and genetic inactivation of PU.1 disrupts the fibrotic network and enables reprogramming of fibrotic fibroblasts into resting fibroblasts, leading to regression of fibrosis in several organs

Testing both affordability-availability and psychological-coping mechanisms underlying changes in alcohol use during the COVID-19 pandemic

Two theoretical perspectives have been proffered to explain changes in alcohol use during the pandemic: the ‘affordability-availability’ mechanism (i.e., drinking decreases due to changes in physical availability and/or reduced disposable income) and the ‘psychological-coping’ mechanism (i.e., drinking increases as adults attempt to cope with pandemic-related distress). We tested these alternative perspectives via longitudinal analyses of the COVID-19 Psychological Consortium (C19PRC) Study data (spanning three timepoints during March to July 2020). Respondents provided data on psychological measures (e.g., anxiety, depression, posttraumatic stress, paranoia, extraversion, neuroticism, death anxiety, COVID-19 anxiety, intolerance of uncertainty, resilience), changes in socio-economic circumstances (e.g., income loss, reduced working hours), drinking motives, solitary drinking, and ‘at-risk’ drinking (assessed using a modified version of the AUDIT-C). Structural equation modelling was used to determine (i) whether ‘at-risk’ drinking during the pandemic differed from that recalled before the pandemic, (ii) dimensions of drinking motives and the psychosocial correlates of these dimensions, (iii) if increased alcohol consumption was predicted by drinking motives, solitary drinking, and socio-economic changes. The proportion of adults who recalled engaging in ‘at-risk’ drinking decreased significantly from 35.9% pre-pandemic to 32.0% during the pandemic. Drinking to cope was uniquely predicted by experiences of anxiety and/or depression and low resilience levels. Income loss or reduced working hours were not associated with coping, social enhancement, or conformity drinking motives, nor changes in drinking during lockdown. In the earliest stage of the pandemic, psychological-coping mechanisms may have been a stronger driver to changes in adults’ alcohol use than ‘affordability-availability’ alone

Predicting resilience during the COVID-19 Pandemic in the United Kingdom: Cross-sectional and longitudinal results

Although the COVID-19 pandemic has impacted the psychological wellbeing of some people, there is evidence that many have been much less affected. The Ecological Model of Resilience (EMR) may explain why some individuals are not resilient whilst others are. In this study we test the EMR in a comparison of UK survey data collected from the COVID-19 Psychological Research Consortium (C19PRC) longitudinal study of a representative sample of the United Kingdom (UK) adult population and data from an Italian arm of the study. We first compare data from the third wave of the UK arm of the study, collected in July/August 2020, with data from an equivalent sample and stage of the pandemic in Italy in July 2020. Next, using UK longitudinal data collected from C19PRC Waves 1, 3 and 5, collected between March 2020 and April 2021 we identify the proportion of people who were resilient. Finally, we examine which factors, drawn from the EMR, predict resilient and non-resilient outcomes. We find that the 72% of the UK sample was resilient, in line with the Italian study. In the cross-sectional logistic regression model, age and self-esteem were significantly associated with resilience whilst death anxiety thoughts, neuroticism, loneliness, and Post Traumatic Stress Disorder (PTSD) symptoms related to COVID-19 were significantly associated with Non-Resilient outcomes. In the longitudinal UK analysis, at Wave 5, 80% of the sample was Resilient. Service use, belonging to wider neighbourhood, self-rated health, self-esteem, openness, and externally generated death anxiety were associated with Resilient outcomes. In contrast, PTSD symptoms and loneliness were associated with Non-Resilient outcomes. The EMR effectively explained the results. There were some variables which are amenable to intervention which could increase resilience in the face of similar future challenges

The emotional consequences of novel political identities: Brexit and mental health in the United Kingdom

AbstractFollowing the 2016 EU referendum on Britain's membership in the European Union, many people described themselves as “Leavers” or “Remainers.” Here, we examine the emotional responses associated with Brexit identities using survey data collected from two nationally representative samples of the British public in 2019 (N = 638) and 2021 (N = 2,058). Confirmatory factor analysis indicated that many in both samples had coherent Leave or Remain identities. Remain and, to a lesser extent, Leave identities (regardless of how people actually voted in the referendum) predicted distress about Brexit‐related events and clinical symptoms of depression and anxiety at both time points. Structural equation models suggested that the effect of identities on symptoms was largely mediated by distress about Brexit‐related events. We demonstrate a lasting impact of Brexit on the mental health of UK citizens and show that the formation of novel political identities has been more important in this process than voting behavior.</jats:p

Psychological Factors Influencing Protective Behaviours during the COVID-19 Pandemic: Capability, Opportunity and Motivation

This chapter will explore psychological and demographic influences on citizens’ ability to enact protective health behaviours during the COVID-19 pandemic. Such behaviours include social distancing and hygienic practices that have been recommended across the globe to reduce the spread of infection from the coronavirus. Such behaviours represent a seismic change in usual social behaviour and have been particularly difficult to adopt under urgent circumstances. However, human behaviour is the essential driver of the rate and spread of infection. Using evidence from a large-scale longitudinal survey conducted throughout the pandemic in the UK, this chapter explores protective behaviours in relation to the Capability, Opportunity, Motivation-Behaviour (COM-B) model of behaviour change, which presents a framework for understanding the influences on behaviour. We will illustrate how the components of the COM-B model can inform behaviour change interventions and the importance of the role of anxiety in shaping behavioural responses to the pandemic