Serum Adiponectin and Leptin Among Ghanaian Migrants in Amsterdam and Their Compatriots in Rural and Urban Ghana: The RODAM Study

Background

The rapidly rising cardiometabolic disease (CMD) burden in urbanizing sub-Saharan African populations and among sub-Saharan African migrants in Europe likely affects serum adiponectin and leptin levels, but this has not yet been quantified.

Objectives

To compare the serum levels of adiponectin and leptin among migrant, and non-migrant (urban and rural) populations of Ghanaian descent.

Methods

Cross-sectional analysis of serum leptin and adiponectin in the multi-centre Research on Obesity and Diabetes among African Migrants (RODAM) study. Logistic-regression models were used to examine the association between these adipocyte-derived hormones after stratification (sex, geographic area) and adjustments for potential confounders.

Results

A total of 2518 Ghanaians were included. Rural participants had the highest serum adiponectin and lowest leptin levels compared to Amsterdam and urban Ghanaians (P < .001). In fully adjusted models, participants living in urban Ghana had significantly higher odds of hyperleptinemia compared to rural participants (women-odds ratio 2.88; 95% CI, 1.12-7.38, P = .028 and men 43.52, 95% CI, 4.84-391.25, P < .001). Urban Ghanaian men also had higher odds of elevated leptin: adiponectin ratio (6.29, 95% CI, 1.43-27.62, P = .015). The odds of hyperleptinemia were only higher in Amsterdam Ghanaian men (10.56; 95% CI, 1.11-100.85, P = .041), but not in women (0.85; 95% CI, 0.30-2.41, P = .759). There was no significant association between hypoadiponectinemia and geographical location in both sexes.

Conclusion

Urbanization is associated with serum adiponectin and leptin levels after adjusting for confounding covariates in sub-Saharan Africans. These findings serve as a backdrop for further research on the role adipokines play in CMD epidemiology among Africans.

Planning for social distancing: How the legacy of historical epidemics shaped COVID-19's spread in Madrid

This paper combines historical and contemporary sources to examine 'epidemic urban planning' from the first decades of the 20th century through to the present day. It considers how infamous early 20th-century epidemics triggered the development of several urban regulations that profoundly shaped the city's future. To reduce the risk of contagion in bourgeois space, the city began displacing and spatially segregating the urban poor, leading to deprived neighbourhoods in the city's suburbs. The social and urban structure of these deprived, 'vulnerable' neighbourhoods remains to this day. Madrid was also greatly impacted by the COVID-19 crisis, and the initial distribution of COVID geographies seemed to reflect these historical legacies. Epidemic-influenced segregation kept wealthy neighbourhoods relatively safe during the first waves of the COVID-19 pandemic, concentrating the disease in poorer areas.

The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving

OBJECTIVE

We experimentally test the effect of cognitive load on auditory susceptibility during automated driving.

BACKGROUND

In automated vehicles, auditory alerts are frequently used to request human intervention. To ensure safe operation, human drivers need to be susceptible to auditory information. Previous work found reduced susceptibility during manual driving and in a lesser amount during automated driving. However, in practice, drivers also perform nondriving tasks during automated driving, of which the associated cognitive load may further reduce susceptibility to auditory information. We therefore study the effect of cognitive load during automated driving on auditory susceptibility.

METHOD

Twenty-four participants were driven in a simulated automated car. Concurrently, they performed a task with two levels of cognitive load: repeat a noun or generate a verb that expresses the use of this noun. Every noun was followed by a probe stimulus to elicit a neurophysiological response: the frontal P3 (fP3), which is a known indicator for the level of auditory susceptibility.

RESULTS

The fP3 was significantly lower during automated driving with cognitive load compared with without. The difficulty level of the cognitive task (repeat or generate) showed no effect.

CONCLUSION

Engaging in other tasks during automated driving decreases auditory susceptibility as indicated by a reduced fP3.

APPLICATION

Nondriving task can create additional cognitive load. Our study shows that performing such tasks during automated driving reduces the susceptibility for auditory alerts. This can inform designers of semi-automated vehicles (SAE levels 3 and 4), where human intervention might be needed.

Unconventional monetary policies in an agent-based model with mark-to-market standards

We employ an agent-based model to shed light on the macroeconomic effects of accounting principles, unconventional monetary policies, and of their possible interactions. If mark-to-market accounting standards may entail positive feedbacks which amplify economic or financial shocks, unconventional policies may introduce negative feedbacks that might dampen instabilities in financial and real markets. For these reasons, we jointly study these two sets of policies by employing a modified version of the Schumpeter meeting Keynes (K+S) macroeconomic agent-based model. Our results confirm that, due to its pro-cyclical nature, the mark-to-market accounting standard amplifies credit cycles, generating more instability with respect to a simulated economy wherein the historical accounting principle is employed. In contrast, unconventional monetary policy is counter-cyclical and it improves macroeconomic indicators. Finally, we study a scenario wherein mark-to-market accounting and unconventional monetary policy interact. We find that unconventional monetary policy can counterbalance the negative effects brought about by the application of mark-to-market accounting. Our results suggest that unconventional monetary policy instruments should not be considered as temporary interventions to be employed only during crisis periods. They should be part of the toolbox of central banks also in normal times.

Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election

Online Social Networks (OSNs) offer new means for political communications that have quickly begun to play crucial roles in political campaigns, due to their pervasiveness and communication speed. However, the OSN environment is quite slippery and hides potential risks: many studies presented evidence about the presence of d/misinformation campaigns and malicious activities by genuine or automated users, putting at severe risk the efficiency of online and offline political campaigns. This phenomenon is particularly evident during crucial political events, as political elections. In the present paper, we provide a comprehensive description of the networks of interactions among users and bots during the UK elections of 2019. In particular, we focus on the polarised discussion about Brexit on Twitter, analysing a data set made of more than 10 millions tweets posted for over a month. We found that the presence of automated accounts infected the debate particularly in the days before the UK national elections, in which we find a steep increase of bots in the discussion; in the days after the election day, their incidence returned to values similar to the ones observed few weeks before the elections. On the other hand, we found that the number of suspended users (i.e. accounts that were removed by the platform for some violation of the Twitter policy) remained constant until the election day, after which it reached significantly higher values. Remarkably, after the TV debate between Boris Johnson and Jeremy Corbyn, we observed the injection of a large number of novel bots whose behaviour is markedly different from that of pre-existing ones. Finally, we explored the bots' political orientation, finding that their activity is spread across the whole political spectrum, although in different proportions, and we studied the different usage of hashtags and URLs by automated accounts and suspended users, targeting the formation of common narratives in different sides of the debate.

The economics of platform liability

Public authorities in many jurisdictions are concerned about the proliferation of illegal content and products on online platforms. One often discussed solution is to make the platform liable for third parties' misconduct. In this paper, we first identify platform incentives to stop online misconduct in the absence of liability. Then, we provide an economic appraisal of platform liability that highlights the intended and unintended effects of a more stringent liability rule on several key variables such as prices, terms and conditions, business models, and investments. Specifically, we discuss the impact of the liability regime applying to online platforms on competition between them and the incentives of third parties relying on them. Finally, we analyze the potential costs and benefits of measures that have received much attention in recent policy discussions.

A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

Prioritizing health information for national health reporting - a Delphi study of the Joint Action on Health Information (InfAct)

BACKGROUND

Health information (HI) strategies exist in several EU Member States, however, they mainly focus on technical issues and improving governance rather than on content-related priority setting. There is also little research available about national prioritization processes underlying HI development for policy support in the EU. The aim of this study was to broaden the knowledge base on HI prioritization strategies and to encourage expert exchange towards good practice models. A specific focus was put on HI produced for national health reporting, this being a crucial tool for policy advice.

METHODS

We conducted a literature search to identify published and grey literature on national HI prioritization. This was followed by a two-round Policy Delphi study, where we explored which processes and methods exist in EU Member States and associated countries for the prioritization of HI collection. In the first round, information about these processes was gathered in semi-structured questions; in the second round, participants were asked to rank the identified approaches for desirability and feasibility. The survey was conducted online; participants were recruited from the membership of the Joint Action on Health Information (InfAct - Information for Action).

RESULTS

119 experts were contacted, representing 40 InfAct partner institutions in 28 EU Member States and associated countries. Of these, 28 experts responded fully or partially to the first round, and six to the second round. In the first round, more than half of the respondents reported the existence of structured HI prioritization processes in their countries. To prioritize HI, a clear preference was given in the second round for a formal, horizontal process which includes different experts and stakeholders. National public health institutes were named desirable key stakeholders in this process, and also desirable and feasible coordinators for stakeholder coordination.

CONCLUSION

Health information prioritization methods and procedures reflect the heterogeneity of national public health systems in European countries. Mapping, sharing and ranking prioritization methods and procedures for "good practices" provides a meaningful basis for expert knowledge exchange on HI development. We recommend to make this process part of a future sustainable EU health information system and to use the information gathered in this project to initiate the development of a guidance "Good Practice HI Prioritization" among EU Member States and associated countries.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

Targetable Conformationally Restricted Cyanines Enable Photon-Count-Limited Applications*

Cyanine dyes are exceptionally useful probes for a range of fluorescence-based applications, but their photon output can be limited by trans-to-cis photoisomerization. We recently demonstrated that appending a ring system to the pentamethine cyanine ring system improves the quantum yield and extends the fluorescence lifetime. Here, we report an optimized synthesis of persulfonated variants that enable efficient labeling of nucleic acids and proteins. We demonstrate that a bifunctional sulfonated tertiary amide significantly improves the optical properties of the resulting bioconjugates. These new conformationally restricted cyanines are compared to the parent cyanine derivatives in a range of contexts. These include their use in the plasmonic hotspot of a DNA-nanoantenna, in single-molecule Förster-resonance energy transfer (FRET) applications, far-red fluorescence-lifetime imaging microscopy (FLIM), and single-molecule localization microscopy (SMLM). These efforts define contexts in which eliminating cyanine isomerization provides meaningful benefits to imaging performance.