Understanding Population Dynamics in Multi- and Many-Objective Evolutionary Algorithms for High-Resolution Approximations

Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all members of the population is the goal for multi- and many-objective optimization problems, and more so in real-world applications where there is also the desire to extract knowledge about the problem from this set. The task requires not only to reach the Pareto optimal set but also to be able to continue discovering new solutions, even if the population is filled with them. Particularly in many-objective problems where the population may not be able to accommodate the full Pareto optimal set. In this work, our goal is to investigate some tools to understand the behavior of algorithms once they converge and how their population size and particularities of their selection mechanism aid or hinder their ability to keep finding optimal solutions. Through the use of features that look into the population composition during the search process, we will look into the algorithm’s behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi- and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition.

Distance Traveled in Times of Pandemic: An Endogenous Switching Regression Approach

This paper studies the change in the distance traveled by domestic tourists considering the pre- and post-pandemic outbreak summer periods of 2019 and 2020. Using representative monthly microdata involving more than 31,000 trips conducted by Spanish residents, we examine the heterogeneity in behavioral adaptation to COVID-19 based on sociodemographic and trip-related characteristics. To account for selection effects and the potential change in the population composition of travelers between the two periods, we estimate an endogenous switching regression that conducts separate regressions for the pre- and post-pandemic periods in a unified econometric framework. Our results point to heterogeneous shifts in the distance traveled by domestic travelers after COVID-19 outbreak per sociodemographic group, with notable differences by travel purpose and lower relevance of traditional determinants like income.

Communication determines population-level fitness under cation stress by modulating the ratio of motile to sessile B. subtilis cells

Bacterial populations frequently encounter potentially lethal environmental stress factors. Growing Bacillus subtilis populations are comprised of a mixture of "motile" and "sessile" cells but how this affects population-level fitness under stress is poorly understood. Here, we show that, unlike sessile cells, motile cells are readily killed by monovalent cations under conditions of nutrient deprivation - owing to elevated expression of the lytABC operon, which codes for a cell-wall lytic complex. Forced induction of the operon in sessile cells also causes lysis. We demonstrate that population composition is regulated by the quorum sensing regulator ComA, which can favor either the motile or the sessile state. Specifically social interactions by ComX-pheromone signaling enhance population-level fitness under stress. Our study highlights the importance of characterizing population composition and cellular properties for studies of bacterial physiology and functional genomics. Our findings open new perspectives for understanding the functions of autolysins and collective behaviors that are coordinated by chemical and electrical signals, with implications for multicellular development and biotechnology.

Vaccination and partisanship during Covid-19 in England

An emerging literature documents the presence of large partisan differences in the views and behaviours of different voters towards the Covid-19 pandemic. How does this affect vaccination rates? We address this question in the case of England using a cross-sectional regression analysis of constituency level vaccination data in October 2021 matched with the latest General Election results in England. Our results show that partisanship is crucial to account for differences across English constituencies. We find a positive and robust association between the share of Conservative voters and vaccination rates in different constituencies. This effect holds when controlling for differences in house prices and wages, population composition, the health and deprivation of the constituency and past austerity, and when rerunning the analysis on vaccination rates for different age groups. Our results contrast with studies of beliefs about Covid-19 and compliance with national lockdowns in England, where Conservative voter appear more sceptic, but also with the US where Republican States and individuals tend to vaccinate less.

The unequal cycling boom in Germany

Over the last two decades, cycling in Germany has increased by more than 40%. This paper analyses how this overall increase is broken down by group, characterised by residence (rural and smaller towns vs. medium-sized and larger cities) and education (high vs. low). It analyses (1) how the composition of the population changes according to these groups, (2) how cycling behaviour develops within these groups, and (3) how the changes in composition and behaviour shape the overall volume of cycling. Data on mobility behaviour comes from the large-scale, representative German Mobility Panel from 1996 to 2018, and the analytical sample covers information on more than 28,000 persons over approximately 730,000 reported trips. Data on changes in population composition comes from the German Socio-Economic Panel. Results show that the increase in cycling is unbalanced and largely a consequence of highly educated people in cities who now cycle twice as much and whose share of the population has doubled. This reveals that the cycling boom is bypassing important parts of the population, which limits the contribution of cycling to sustainability goals. Furthermore, the uneven evolution of cycling amplifies social inequalities in finances and health. Finally, this paper shows that increased cycling comes not only from changing behaviour within groups, but also from altered population composition. The most impactful compositional shift is the increasing level of education, which will likely continue to boost cycling.

Analysis of Porcine Model of Fecal-Induced Peritonitis Reveals the Tropism of Blood Microbiome

Recent studies have suggested the existence of a blood microbiome in the healthy host. However, changes in the blood microbiome upon bloodstream infection are not known. Here, we analyzed the dynamics of the blood microbiome in a porcine model of polymicrobial bacteremia induced by fecal peritonitis. Surprisingly, we detected bacterial populations in the bloodstream even before the infection, and these populations were maintained over time. The native blood microbiome was notably taxonomically different from the fecal microbiome that was used to induce peritonitis, reflecting microbial tropism for the blood. Although the population composition after the infection was similar to that of the native blood microbiome, new bacterial strains entered the bloodstream upon peritonitis induction as clinical symptoms relevant to sepsis developed. This indicates that the bacteria detected in the blood before peritonitis induction were derived from the blood rather than a contamination. Comparison of the functional pathways enriched in the blood and fecal microbiomes revealed that communication and stress management pathways are essential for the survival of the blood microbiome.

Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016

Tuberculosis (TB) incidence and corresponding mortality rates in S. Korea are unusual and unique compared to other economically developed countries. Korea has the highest TB incidence rate in Organization for Economic Co-operation and Development (OECD) countries. TB is known as a disease reflecting socio-economic and environmental conditions of a society. Besides, TB is an infectious disease spread through the air, naturally forming spatial dependence of its incidence. This study investigates TB incidences in Korea in socio-economic and environmental perspectives. Eigenvector spatial filtering applied accounts for spatial autocorrelation in the TB incidence, and Getis-Ord Gi* statistic tracks the changes of TB clusters at given time. The results show that population composition ratio, population growth rate, health insurance payment, and public health variables are significant throughout the study period. Environmental variables make minor effects on TB incidence. This study argues that unique demographic features of Korea are a potential threat to TB control in the future.

Image3C, a multimodal image-based and label independent integrative method for single-cell analysis

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.