Shared Environment Research Articles

An out-of-hours cell culture autopilot proof of concept based on a modular architecture using the SiLA2 open standard.

There has been much talk and substantial progress in automated and flexible smart lab concepts in biopharma R&D. This is acknowledged to be important in enabling the acceleration of innovation and digitization of R&D operations. However, many proposals stop short of full end-to-end automation - limiting out-of-hours operation, which is particularly important in tasks such as cell culture - or are locked to a particular vendor's offering in a dedicated system - which can limit the flexibility and shared use access so important in R&D. In this contribution we describe a proof-of-concept of a fully integrated automated adherent cell culture system based on a modular architecture that allows integration of the most recent developments on the market (cell imaging, collaborative cloud robotics, mobile robots) as well as reuse of existing legacy devices (incubators and refrigerators). This creates a "cell culture autopilot" for small-scale cell culture, with repetitive media exchange, confluency checking, and splitting steps which are typically labor-intensive and must take place at times outside the working day. The system is built around the open lab communication standard SiLA2 and various other open-source resources to create three ways in which the SiLA2 standard can be leveraged. This choice of connectivity options provides freedom to integrate the most appropriate device while minimizing undesired vendor-lock in This paper provides sufficient details for the reader to access the resources to build on such a system for cell culture and other applications. We believe this to be the first report of a true vendor-agnostic system operating in a commercial environment. This paper corresponds to the special issue on Robotics in Laboratory Automation as it describes robotics for labware transportation within a shared environment, and an automation framework supporting physical and logical interoperability.

Disentangling non-random assortment, indirect effects, and joint plasticity as causes of phenotypic (dis)similarity between social partners.

Social partners frequently resemble each other. These correlations between the phenotypes of interacting individuals (e.g. social partners, group members, etc.) can be caused by multiple processes. These processes include joint plasticity in response to shared environments, plasticity in response to partner phenotype, and genetic similarity arising from non-random assortment due to clustered relatives, spatiotemporal stratification, and partner choice. Although social plasticity and non-random assortment can influence evolutionary dynamics, these two processes have most often been studied separately and disentangling the causes of partner resemblance in observational datasets can be challenging. Furthermore, standard statistical models of social plasticity do not allow for potential social feedback between partners' phenotypes and estimating joint plasticity to shared environmental effects requires environmental data that is rarely available. In this study, we investigated the performance of various statistical models to estimate non-random assortment and social plasticity in observational datasets. We simulated data for a socially monogamous species in which non-random mating, social plasticity (with or without feedback) and joint plastic responses to unknown environmental factors occurred alone or simultaneously. Commonly used 'variance-partitioning approaches' retrieved biased estimates except when the process they aimed to estimate occurred on its own. By contrast, a recently proposed statistical model explicitly including social plasticity as a dynamic process generating feedback between partners' phenotypes (the so-called social animal model) performed best even in scenarios with multiple co-occurring processes. While we recommend empiricists use this latter approach, we also highlight the importance of appropriate sampling designs given the study question and system, and using simulations to assess model performance in realistic scenarios.

Assessing environmental factors and human practices on the welfare of working donkeys in Kenya

Donkeys play a crucial role in supporting the livelihoods of low-income households in Kenya by providing complementary services in transportation, agriculture, and other economic activities. Their importance not withstanding, working donkeys frequently face poor welfare conditions, often due to mistreatment by owners, limited resources, poor environmental conditions, and a lack of recognition in policy frameworks like the national livestock master plan. Donkeys, due to their shared environment with humans are constantly exposed to welfare challenges, whose extent has not been quantified. This study aimed to assess the role of environmental and human factors on the welfare of working donkeys in Kenya, surveying 1059 donkey owners and their donkeys across 20 out of 47 counties. The Equine Welfare and Owner Behaviour tool, developed based on the Five Domains Model of animal welfare, was used to assess factors related to nutrition, environment, health, behaviour, and mental state. Data were collected through stratified random sampling, with responses from 26 questions in the tool categorized into three areas namely; (1) owner behaviour, practices and livelihood income outcomes, (2) environmental factors and (3) donkey welfare. Robust ethical guidelines ensured respectful and unbiased data collection. Overall, 28% of donkey owners demonstrated positive welfare practices while 72% required improvement, based on the set threshold of 0.85. Owner behaviour and practices scored 0.70 on average, with compassionate handling (0.75) and food quality (0.73) achieving relatively high ratings. Livelihood outcomes, reflecting socio-economic challenges, averaged 0.65, with 79% of owners reporting satisfactory outcomes and 21% requiring improvement. Environmental parameter scores averaged 0.64, with notable challenges in water access (0.59) and shelter provision (0.55), which are essential for sustaining donkey health and welfare. The overall welfare score for donkeys was 0.80, meeting acceptable standards in general health (0.86) and non-lameness (0.90), though ectoparasite presence (0.84) and body lesion occurrence (0.76) highlighted persistent welfare concerns. Improving environmental conditions, enhanced owner practices, and diversified income streams, are essential for better donkey welfare. Results of this study will inform evidence-based interventions aimed at improving the welfare of working donkeys in Kenya.

Organization efficiency enhancement by DNA Technologies

This paper explores the innovative application of DNA technologies in enhancing organizational efficiency. By analyzing genetic traits and Single Nucleotide Polymorphisms (SNPs), DNA testing can reveal individual talents, personality traits, cognitive abilities, and behavioral patterns. These insights help in aligning people with suitable roles, improving career planning, and optimizing team dynamics. Furthermore, regional DNA-based traits—shaped by shared environments, culture, and lifestyle—can influence collective behavior and preferences. Integrating these traits into organizational culture through architecture, design, and daily practices can significantly boost employee productivity, stress tolerance, and workplace satisfaction, offering a potential 20–30% increase in efficiency

Assessing Wine Grape Cultivar Susceptibility to Spotted Wing Drosophila and Melanogaster-Type Drosophila in Hungarian Vineyards: Effects of Berry Integrity and Insights into Larval Interactions.

The invasive spotted wing Drosophila (SWD) represents new challenges for European and North American fruit producers. The aim of our study was to examine wine grape cultivar susceptibility to this pest and melanogaster-type Drosophila (MTD) by surveying drosophilid populations using field traps and conducting emergence tests. We assessed fly development from intact and artificially injured berries collected from four cultivars. Berries were incubated individually and in pooled samples to evaluate infestation patterns and potential larval interactions. Although grapes are generally considered less favorable hosts for SWD, the pest was consistently present across all vineyard plots. Infestation levels differed significantly among cultivars, with the Hungarian white cultivar Furmint being the most susceptible, while French-origin red cultivars Cabernet Franc and Cabernet Sauvignon, along with the other Hungarian cultivar Rózsakő, were less susceptible. Berry integrity played a crucial role: intact berries showed minimal infestation, whereas physical injuries led to a substantial and significant increase in infestation rates and fly emergence. In contrast to SWD-dominated trap catches and the nearly equal proportions of SWD and MTD observed in intact berries, injured berries were predominantly colonized by MTD. This dominance became even more pronounced in pooled samples, suggesting that larval competition in shared environments favors MTD over SWD. These findings underscore the importance of grape cultivar traits and berry condition in shaping Drosophila infestation dynamics. Further research into the chemical and ecological drivers of host selection and interspecific interactions is warranted to improve vineyard pest management strategies.

Identifying and Managing Noisy Neighbors in Multi-Tenant PostgreSQL Deployments (On-Premise)

In on-premise multi-tenant PostgreSQL deployments, multiple tenants share the same physical infrastructure to maximize resource utilization and reduce operational costs. However, this shared environment can give rise to significant resource contention, particularly when one tenant exhibits the “noisy neighbor” effect—where its workload consumes a disproportionate amount of CPU, memory, or disk I/O relative to others. This unbalanced resource consumption can lead to widespread performance degradation, manifesting as increased query latency, reduced throughput, and overall service instability. The present article investigates the challenges inherent in multi-tenant setups, focusing on the detection and management of noisy neighbors. It explores both native PostgreSQL monitoring techniques (such as system views and performance statistics) and external solutions including Linux control groups (cgroups) for isolating and limiting resource usage. Additionally, the article outlines best practices for proactive monitoring, query optimization, and resource allocation to ensure a balanced and efficient multi-tenant environment. By providing a comprehensive framework for understanding and mitigating the noisy neighbor phenomenon, this work aims to equip database administrators and system architects with effective strategies for maintaining robust performance, even under heavy and unevenly distributed workloads.

Advancing Multi-Agent Pathfinding in Gaming: A Review of Cooperative, Dynamic, and Real-Time Algorithmic Optimizations

Multi-agent pathfinding (MAPF) is a problem focused on coordinating multiple agents to navigate from starting positions to goals in shared environments while avoiding collisions. This capability is important for applications in areas such as computer gaming and robotics, where efficient and safe navigation in complex environments is required. Although advancements have been made, challenges remain in areas such as multi-agents cooperation, MAPFunderdynamicenvironments, and real-time MAPF. This paper reviews three advanced MAPF algorithms: Cooperative Conflict-Based Search (Co-CBS), which extends the traditional CBS algorithm by introducing cooperative planning; Dynamic Incremental Conflict Based Search (DI-CBS), which adapts to environmental changes through integration with ECTand SLPA* algorithms; and Bounded Multi-Agent A* (BMAA*), which enables agents to independently plan paths using real-time heuristic search without explicit coordination. Experimental results highlight the distinct characteristics and advantages of each algorithm across different scenarios

Optimizing Cost-Effective gene expression phenotyping approaches in cattle using 3′ mRNA sequencing

BackgroundGenetic and genomic selection programs require large numbers of phenotypes observed for animals in shared environments. Direct measurements of phenotypes like meat quality, methane emission, and disease susceptibility are difficult and expensive to measure at scale but are critically important to livestock production. Our work leans on our understanding of the “Central Dogma” of molecular genetics to leverage molecular intermediates as cheaply-measured proxies of organism-level phenotypes. The rapidly declining cost of next-generation sequencing presents opportunities for population-level molecular phenotyping. While the cost of whole transcriptome sequencing has declined recently, its required sequencing depth still makes it an expensive choice for wide-scale molecular phenotyping. We aim to optimize 3′ mRNA sequencing (3′ mRNA-Seq) approaches for collecting cost-effective proxy molecular phenotypes for cattle from easy-to-collect tissue samples (i.e., whole blood). We used matched 3′ mRNA-Seq samples for 15 Holstein male calves in a heat stress trail to identify the (1) best library preparation kit (Takara SMART-Seq v4 3′ DE and Lexogen QuantSeq) and (2) optimal sequencing depth (0.5 to 20 million reads/sample) to capture gene expression phenotypes most cost-effectively.ResultsTakara SMART-Seq v4 3′ DE outperformed Lexogen QuantSeq libraries across all metrics: number of quality reads, expressed genes, informative genes, differentially expressed genes, and 3′ biased intragenic variants. Serial downsampling analyses identified that as few as 8.0 million reads per sample could effectively capture most of the between-sample variation in gene expression. However, progressively more reads did provide marginal increases in recall across metrics. These 3′ mRNA-Seq reads can also capture animal genotypes that could be used as the basis for downstream imputation. The 10 million read downsampled groups called an average of 109,700 SNPs and 11,367 INDELs, many of which segregate at moderate minor allele frequencies in the population.ConclusionThis work demonstrates that 3′ mRNA-Seq with Takara SMART-Seq v4 3′ DE can provide an incredibly cost-effective (< 25 USD/sample) approach to quantifying molecular phenotypes (gene expression) while discovering sufficient variation for use in genotype imputation. Ongoing work is evaluating the accuracy of imputation and the ability of much larger datasets to predict individual animal phenotypes.

Performance optimization of multilink laser powered UAV relay systems

Unmanned Aerial Vehicles (UAVs) can provide flexible wireless connectivity in temporary hotspots and disaster-stricken areas, but their energy constraints remain a challenge. In this paper, we propose a laser-powered UAV relay system where UAVs harvest energy from ground-based laser transmitters and operate in a multi-link shared spectrum environment. With the goal of maximizing the minimum system link throughput, we formulate a joint optimization problem for UAV trajectory and power control and propose an iterative algorithm to efficiently manage energy and mitigate co-channel interference while solving this non-convex problem. Simulation results validate the effectiveness of the proposed scheme and highlight significant performance gains achieved through joint optimization, underscoring the potential of laser-powered UAVs to overcome energy limitations and enhance overall system performance.

Sequential Order Adjustment of Action Decisions for Multi-Agent Transformer (Student Abstract)

Multi-agent reinforcement learning (MARL) trains multiple agents in shared environments. Recently, MARL models have significantly improved performance by leveraging sequential decision-making processes. Although these models can enhance performance, they do not explicitly con-sider the importance of the order in which agents make decisions. We propose AOAD-MAT, a novel model incorporating action decision sequence into learning. AOAD-MAT uses a Transformer-based actor-critic architecture to dynamically adjust agent action order. It introduces a subtask predicting the next agent to act, integrated into a PPO-based loss function. Experiments on StarCraft Multi-Agent Challenge and Multi-Agent MuJoCo benchmarks show AOAD-MAT out-performs existing models, demonstrating the effectiveness of adjusting agent order in MARL.

Dynamics of the canine gut microbiota of a military dog birth cohort.

We systematically tracked early life stages in a military dog birth cohort to investigate canine gut microbiota dynamics related to environmental exposure during growth. This study utilized 16s rRNA amplicon sequencing-based analysis with molecular epidemiology of Enterococcus faecalis within a controlled environment at a military dog training center. We examined shifts in gut microbiota diversity and taxonomic composition across four growth stages (lactation, weaning, starter, puppy) in three littermate groups. Additionally, E. faecalis dynamics was analyzed to confirm strain sharing among littermate groups. Gut microbiota changed rapidly during early growth, stabilizing at the puppy stage. This is supported by increased similarity in taxonomic composition among littermate groups, as they experienced an increased shared external environment and consumed the identical diets. E. faecalis strain sharing among littermate groups increased as dogs aged. Nine E. faecalis cluster types were identified; three specific types (type I, II, and IX) dominated in each littermate group during lactation. With greater exposure to the shared external environment, cluster type I gradually assumed dominance across all groups. Despite the dynamic shifts in microbiota, we found five genera within the core microbiota, Bacteroides, Peptoclostridium, Fusobacterium, Lactobacillus, and Blautia. This study is the first to explore the dynamic nature of early-life canine gut microbiota, illustrating its transition to stability and its resilience to environmental perturbations within the controlled training environment of a military dog birth cohort.

Family-based genome-wide association study designs for increased power and robustness

Family-based genome-wide association studies (FGWASs) use random, within-family genetic variation to remove confounding from estimates of direct genetic effects (DGEs). Here we introduce a ‘unified estimator’ that includes individuals without genotyped relatives, unifying standard and FGWAS while increasing power for DGE estimation. We also introduce a ‘robust estimator’ that is not biased in structured and/or admixed populations. In an analysis of 19 phenotypes in the UK Biobank, the unified estimator in the White British subsample and the robust estimator (applied without ancestry restrictions) increased the effective sample size for DGEs by 46.9% to 106.5% and 10.3% to 21.0%, respectively, compared to using genetic differences between siblings. Polygenic predictors derived from the unified estimator demonstrated superior out-of-sample prediction ability compared to other family-based methods. We implemented the methods in the software package snipar in an efficient linear mixed model that accounts for sample relatedness and sibling shared environment.

Place effects on adult obesity and cardiometabolic health: Evidence from a natural experiment.

Place effects on adult obesity and cardiometabolic health: Evidence from a natural experiment.

Maternal Anemia as a Predictor of Childhood Anemia: Evidence from Gambian Health Data.

Iron deficiency anemia (IDA) is a significant global health problem affecting close to 2 billion people worldwide. The prevalence of IDA is higher among children younger than five years and women of reproductive age, indicating an intergenerational correlation between maternal and child anemia. This study aims to analyze the association between maternal and child anemia in The Gambia. A nationally representative dataset comprising 3249 children under the age of five, obtained from The Gambia Demographic and Health Survey (2019-2020), was utilized for empirical analyses. Multivariate linear regression models were employed to assess the association between maternal and child anemia. The multivariate models were adjusted for various confounding variables, including birth order, age, and the gender of the child, as well as maternal education, religion, wealth quintiles, rural residence, and region-fixed effects. Fifty-three percent of children and 52% of mothers are anemic. Children from poorer households show a higher rate of anemia compared to those from wealthier households. Maternal anemia was significantly associated with the anemia status of the children. Children born to anemic mothers were 13.5% more likely to be also anemic (p < 0.001). The correlation coefficient between mother and child hemoglobin levels is 0.165 (p < 0.001). The correlation coefficient between maternal and child anemia is higher among the bottom three wealth quintiles than the top two wealth quintiles. The significant intergenerational association between maternal and child anemia status highlights the need for targeted, multi-pronged strategies to combat the adverse impacts of anemia. Maternal anemia, in general, appears to influence childhood anemia beyond just the pregnancy period. Shared socioeconomic environment, dietary patterns, and exposure to infections likely contribute to this intergenerational association.

Shared environments complicate the use of strain-resolved metagenomics to infer microbiome transmission

BackgroundIn humans and other social animals, social partners have more similar microbiomes than expected by chance, suggesting that social contact transfers microorganisms. Yet, social microbiome transmission can be difficult to identify based on compositional data alone. To overcome this challenge, recent studies have used information about microbial strain sharing (i.e., the shared presence of highly similar microbial sequences) to infer transmission. However, the degree to which strain sharing is influenced by shared traits and environments among social partners, rather than transmission per se, is not well understood.ResultsHere, we first use a fecal microbiota transplant dataset to show that strain sharing can recapitulate true transmission networks under ideal settings when donor-recipient pairs are unambiguous and recipients are sampled shortly after transmission. In contrast, in gut metagenomes from a wild baboon population, we find that demographic and environmental factors can override signals of strain sharing among social partners.ConclusionsWe conclude that strain-level analyses provide useful information about microbiome similarity, but other facets of study design, especially longitudinal sampling and careful consideration of host characteristics, are essential for inferring the underlying mechanisms of strain sharing and resolving true social transmission network.6nNWceJPFrQM44UU9RL6LyVideo

Lessons for creating shared value from ESG and RBC: toward indicators and operationalisation

Creating Shared Value (CSV), Environmental, Social and Governance (ESG) investment, and Responsible Business Conduct (RBC), are three frameworks used to assess the sustainability impacts of businesses. As a relative newcomer, we assess the role of CSV developed in strategic management studies by Porter and Kramer in the light of the latest advances in ESG and RBC. Each framework provides its own perspective, but they all focus on the challenge of measuring and demonstrating practical impact. Each contributes insight and metrics to assess business activity on wider issues than just shareholder value, narrowly defined and measured. This article points to the remarkable speed and evolution in ESG and RBC. The conclusion reached is that CSV should draw on the materiality and due diligence principles underlying ESG and RBC respectively, while contributing its unique insights in terms of business models, innovation and contribution to societal needs. We see the merits of an integrative approach wherein the three perspectives deliver an updated and nuanced understanding of shareholder, and more broadly stakeholder value. This analysis will be relevant to scholars and practitioners with an interest in corporate sustainability and sustainable finance, from both a managerial practice as well as public policy perspectives.

Virtual elasto-plastic robot compliance to active environments.

Humans exhibit a particular compliant behavior in interactions with their environment. Facilitated by fast physical reasoning, humans are able to rapidly alter their compliance, enhancing robustness and safety in active environments. Transferring these capabilities to robotics is of utmost importance particularly as major space agencies begin investigating the potential of cooperative robotic teams in space. In this scenario, robots in orbit or on planetary surfaces are meant to support astronauts in exploration, maintenance, and habitat building to reduce costs and risks of space missions. A major challenge for interactive robot teams is establishing the capability to act in and interact with dynamic environments. Analogous to humans, the robot should be not only particularly compliant in case of unexpected collisions with other systems but also able to cooperatively handle objects requiring accurate pose estimation and fast trajectory planning. Here, we show that these challenges can be attenuated through an enhancement of active robot compliance introducing a virtual plastic first-order impedance component. We present how elasto-plastic compliance can be realized via energy-based detection of active environments and how evasive motions can be enabled through adaptive plastic compliance. Two space teleoperation experiments using different robotic assets confirm the potential of the method to enhance robustness in interaction with articulated objects and facilitate robot cooperation. An experiment in a health care facility presents how the same method analogously solidifies robotic interactions in human-robot shared environments by giving the robot a subordinate role.

Gene expression variation across genetically identical individuals predicts reproductive traits.

In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult Caenorhabditis elegans roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on individuals' historical environments and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that gene expression variation may be driven in part by chromatin structure. Together, this work shows that individual, non-genetic differences in gene expression are both highly predictive and causal in shaping reproductive traits.

Reproductive and Metabolic Health Following Exposure to Environmental Chemicals: Mechanistic Insights from Mammalian Models.

The decline in human reproductive and metabolic health over the past 50years is associated with exposure to complex mixtures of anthropogenic environmental chemicals (ECs). Real-life EC exposure has varied over time and differs across geographical locations. Health-related issues include declining sperm quality, advanced puberty onset, premature ovarian insufficiency, cancer, obesity, and metabolic syndrome. Prospective animal studies with individual and limited EC mixtures support these observations and provide a means to investigate underlying physiological and molecular mechanisms. The greatest impacts of EC exposure are through programming of the developing embryo and/or fetus, with additional placental effects reported in eutherian mammals. Single-chemical effects and mechanistic studies, including transgenerational epigenetic inheritance, have been undertaken in rodents. Important translational models of human exposure are provided by companion animals, due to a shared environment, and sheep exposed to anthropogenic chemical mixtures present in pastures treated with sewage sludge (biosolids). Future animal research should prioritize EC mixtures that extend beyond a single developmental stage and/or generation. This would provide a more representative platform to investigate genetic and underlying mechanisms that explain sexually dimorphic and individual effects that could facilitate mitigation strategies.

Biological control of bacterial wilt (Ralstonia pseudosolanacearum) in tomato (Solanum lycopersicum) using an avirulent strain, Bacillus sp., and Pseudomonas fluorescens

Bacterial wilt disease, caused by Ralstonia pseudosolanacearum, threatens tomato cultivation due to the pathogen’s soil-borne nature, high genetic variability, wide host range, and potential for severe yield loss. Utilizing avirulent strains of R. pseudosolanacearum and antagonistic bacteria emerges as a promising biocontrol strategy for managing R. pseudosolanacearum, given their shared environment in soil. This research used a completely randomized design with 4 treatments, involving 3 different species of biocontrol agents: Pseudomonas fluorescens, Bacillus sp., and avirulent strain of R. pseudosolanacearum. The results showed that all biocontrol agent treatments inhibited the growth of R. pseudosolanacearum and reduce the development of bacterial wilt disease in tomatoes. Avirulent strains of R. pseudosolanacearum showed the most significant results in delaying the development of bacterial wilt disease with an incubation period of up to 13 days, suppressing the disease severity to 18.67%, disease incidence to 26.67%, infection rate of 0.0037 unit/day, and lowest AUDPC value. Furthermore, based on plant growth parameters, all antagonistic bacteria showed promising results in promoting tomato height and number of leaves. This comprehensive exploration underscores the potential of avirulent strains and antagonistic bacteria, not only in inhibiting bacterial wilt but also in enhancing the overall growth and resilience of tomato plants.