[This corrects the article DOI: 10.1371/journal.pone.0295971.].

[This corrects the article DOI: 10.1371/journal.pone.0320309.].

There has been a drastic increase of pasteurellosis cases in Atlantic salmon aquaculture in Norway since 2018, caused by the bacterium Phocoenobacter atlanticus subsp. atlanticus, resulting in reduced animal welfare and increased mortalities. Early detection and identification of pathogens are crucial in intensive aquaculture to enable implementation of effective management and mitigation strategies. The increased application of environmental DNA (eDNA) monitoring in aquatic environments demonstrates the potential use for early detection of pathogens in aquaculture. In this study we investigated the usage of eDNA monitoring of Ph. atlanticus in Atlantic salmon aquaculture. We conducted a trial to investigate the effect of storage conditions and transport time on eDNA detection of Ph. atlanticus from water samples. We then conducted three field trials: a) to investigate the optimal sampling location for eDNA samples from farms with known Ph. atlanticus infection status, b) to screen farms presumed non-infected with Ph. atlanticus and c) to evaluate the eDNA detection in samples collected from anaesthetic tanks during lice counting. The storage trial revealed a reduction in Ph. atlanticus detection after one week at 4 °C or after two days at 22 °C. At farms with known Ph. atlanticus infection status we experienced poor detection of Ph. atlanticus from water samples collected in the net pen. However, samples collected from treatment water during thermal delousing revealed concentration of Ph. atlanticus during treatment, even in farms with negative net pen samples. Subsequent eDNA screening (sampling treatment water) of farms with presumed non-infected status, identified the presence of Ph. atlanticus in ~55% of the farms tested. Ph. atlanticus was also detected in water samples collected from anaesthetic tanks following lice counting. Our results demonstrate early detection of Ph. atlanticus, even in lowly prevalent populations, demonstrating the potential use of eDNA monitoring of pathogens in Atlantic salmon aquaculture.

Occupational diseases affect many workers in the United States, with Latinos disproportionately affected. Small businesses face barriers to implementing workplace health protections that community health workers (CHWs) may help overcome. The objective of this study was to determine whether a CHW-led industrial hygiene intervention could reduce volatile organic compound (VOC) exposure in small auto repair and beauty shops that primarily employ marginalized workers. In this two-arm, parallel, cluster randomized trial, small business (≤25 employees) auto repair and beauty shops in Tucson, AZ were randomized to immediate or delayed intervention, stratified by sector. CHWs assessed shops and provided knowledge of controls and $300 for new ones. Total VOCs (TVOCs) were measured using photoionization detectors placed on or near participants. The primary outcome was the change in TVOCs at the shop level after the intervention, assessed across three timepoints with four workshift measurements per assessment. Mixed-effects models accounted for clustering by shop. We enrolled 38 auto repair shops and 46 beauty shops (73% Latino workers) and analyzed 846 workshift measurements at 236 shop assessments. Adjusted models showed a non-statistically significant intervention effect: auto shops experienced on average an estimated 28% TVOC increase (95% CI: -46% to 203%); beauty shops experienced on average an estimated 27% reduction (95% CI: -55% to 19%). Beauty shops had TVOC concentrations about 10 times higher than auto shops, and 87% of their assessments had ventilation rates below the recommended minimum. Although not statistically significant, the CHW-led intervention may meaningfully reduce VOC exposure in beauty shops. High TVOC concentrations and inadequate ventilation in beauty shops highlight the need for targeted interventions and policy changes to improve the air quality in these underserved small businesses. This trial was registered with clinicaltrials.gov (NCT03455530) on March 6, 2018.

[This corrects the article DOI: 10.1371/journal.pone.0338997.].

This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an objective assessment tool for clinical practice. A retrospective analysis was conducted on patients treated at Shanghai Parkway Health Ophthalmology Department as the training set, and myopic individuals from the Optometry Center of Peking University People's Hospital as the validation set. Demographic and biometric data were collected, including central corneal thickness (CCT), axial length (AL), corneal curvature (K-value), anterior chamber depth (ACD), corneal diameter (WTW), and pupil size (PS). Seven machine learning models (e.g., XGBoost, random forest, support vector machine) were employed for modeling, with performance optimized via 5-fold cross-validation. Model accuracy was evaluated using mean squared error (MSE) and the coefficient of determination (R²), and variable importance was analyzed. No statistically significant differences were observed in baseline characteristics between the training and validation sets (all P > 0.05). The XGBoost model demonstrated the best performance, achieving R² = 0.913 (MSE = 0.005) on the training set and R² = 0.766 (MSE = 0.016) on the test set. Variable importance analysis revealed pupil size (score 100) and corneal thickness (40.88) as the key predictors of axial elongation rate, followed by age of onset (17.96). The machine learning-based prediction model effectively utilizes ocular biometric data to assess myopia progression risk, with pupil size and corneal thickness identified as core predictive factors. This model provides a quantitative tool for early clinical intervention. Future studies should expand the sample size and incorporate additional biomarkers to optimize performance.

The deep integration of artificial intelligence (AI) into organizational settings has significantly transformed employees' work patterns, underscoring the need to investigate the mechanisms through which employee-AI collaboration influences creativity. Grounded in social cognitive theory, this study examines how employee-AI collaboration affects creativity via internal (self-efficacy) and external (performance pressure) mechanisms, and explores the moderating role of proactive behavior. Using convenience sampling, 733 valid responses were collected from six technology-driven enterprises. The theoretical model was tested through confirmatory factor analysis and regression analyses using AMOS and the Process macro for SPSS. Our results reveal that employee-AI collaboration is positively associated with creativity through two statistically supported indirect pathways: parallel indirect effects via self-efficacy (internal pathway) and performance pressure (external pathway), and a sequential indirect association in which higher self-efficacy is related to higher performance pressure, which in turn is positively related to creativity. Moreover, proactive behavior appears to moderate these relationships, such that the association between employee-AI collaboration and self-efficacy is more pronounced at higher levels of proactive behavior, whereas the association between employee-AI collaboration and performance pressure is less pronounced at higher levels of proactive behavior, yielding a pattern consistent with moderated indirect effects on creativity through both routes. These findings provide theoretical insights into the complex psychological processes underlying creativity in AI-enabled work environments and offer practical implications for optimizing AI applications and developing targeted interventions to foster employee creativity.

Climate change poses increasing risks to outdoor occupations, including construction work. This study explores how vulnerability is constructed in narratives of manual labour within the Swedish construction sector, particularly under extreme weather conditions. Drawing on 16 qualitative interviews with Swedish construction workers, the study adopts a social constructionist lens to explore how vulnerability is shaped and experienced. The findings identify multiple, intersecting forms of vulnerability-bodily, hierarchical, material, social, and market-driven-exacerbated by climate-related challenges such as high temperatures, heavy rainfall, and strong winds. Crucially, the analysis highlights how extreme weather disrupts temporal rhythms, widening the gap between scheduled work plans and the actual time needed to complete tasks. The paper concludes that vulnerability arises not only from direct exposure to adverse weather, but also through indirect social, material, and temporal dynamics inherent in the construction sector and exacerbated by climate change.

The Internet is evolving rapidly, and billions of devices are being connected to it at the edge. Performance at the edge matters, and the role of the congestion control mechanism is important. Since the emergence of the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm by Google, active research has been going on between BBR and TCP Cubic. BBR-v3 is the third version since its inception, and it has tried to address many of the shortcomings of its earlier versions. The issue of fairness with Cubic and Reno flows, the bandwidth overestimation issue in multi-flow scenarios, high packet transmission rate in shallow buffers, and queuing delays and packet losses during/after its startup phase. Not much research work is yet available on BBR-v3 evaluation with Cubic, especially in a variety of connectivity scenarios, such as wired and wireless together. In this paper, we evaluate BBR-v3 with Cubic and have proposed BBR-n+ (BBR Smart Exit) that refines the generic BBR's (BBR-v3) startup exit by detecting receive window limitations and empirically evaluate its performance with the generic BBR (BBR-v3). The early exit issue of BBR-v3 from the startup phase when the congestion window is receiver-limited is probed, and the Smart Exit algorithm has been proposed. It is the continuation of our work on BBR-n, and we have evaluated it using various performance metrics. The role of modern AQMs has been explored with our testing on Common Applications Kept Enhanced (CAKE) and Flow Queue Controlled Delay (FQ_CoDel) AQMs. Through the experiments, we conclude that BBR-n+ in the receive window limitation test provides a 15-20% median throughput gain over BBR-v3 under different receiver window sizes. A 150 ms reduction in HTTP delay when compared with BBR-v3 and a ~ 300 ms reduction versus Cubic. 17% improvement in ping latency compared to BBR-v3 and 45% with Cubic in the wired scenario with a strenuous load of multiple streams. BBR-n+ outperforms BBR-v3 and Cubic in most of the tested scenarios, though it still exhibits limitations, particularly in achieving fairness when competing against Cubic flows when the number of concurrent streams is eight or more.