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Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R2 values of resulting ANN models range from 0.54 to 0.75 (median R2 = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals.

Direct air capture (DAC) is critical for achieving stringent climate targets, yet the environmental implications of its large-scale deployment have not been evaluated in this context. Performing a prospective life cycle assessment for two promising technologies in a series of climate change mitigation scenarios, we find that electricity sector decarbonization and DAC technology improvements are both indispensable to avoid environmental problem-shifting. Decarbonizing the electricity sector improves the sequestration efficiency, but also increases the terrestrial ecotoxicity and metal depletion levels per tonne of CO2 sequestered via DAC. These increases can be reduced by improvements in DAC material and energy use efficiencies. DAC exhibits regional environmental impact variations, highlighting the importance of smart siting related to energy system planning and integration. DAC deployment aids the achievement of long-term climate targets, its environmental and climate performance however depend on sectoral mitigation actions, and thus should not suggest a relaxation of sectoral decarbonization targets.

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating division rates of microbial populations by mechanistically describing changes in microbial cell size distributions over time. Motivated by the mechanistic structure of these models, we employ a Bayesian approach to extend size-structured MPMs to capture additional biological processes describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework is able to take prior scientific knowledge into account and generate biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we isolate respiratory and exudative carbon losses as critical parameters for the modeling of their population dynamics. The results suggest that this modeling framework can provide deeper insights into microbial population dynamics provided by size distribution time-series data.

With the growing complexity of design technology and the emergence of intelligent design assistance, architectural studio classes are facing a new pedagogical paradigm. The digital literacy of younger generations and availability of scientific simulation have the potential to transform the traditional master-apprentice model. In our experiment, we had students perform a museum layout task and observed their behaviors from three perspectives: (1) how students utilize an assistance tool and whether we can group their behavior, (2) how the new simulation-aided design process is different from a traditional one, particularly in terms of the evolution of solution over iteration, and (3) whether students' behavior is affected by the type of design problem given. Protocol analysis on design processes and interviews revealed that individual's characteristic design processes in terms of iteration ranged from distinct iterations guided by simulation to monotonous progress with little simulation. When comparing between an instructor and software, it was not the given environment but the designer's subjectivity that determined their attitudes toward either type of feedback. Lastly, the challenge of integrating design concepts with performance requirements stemmed from a misalignment between their true evaluative measures. We propose that a versatile design platform implement real-time, non-intrusive mechanisms for performance reporting and solution branching, and include social and psychological measures as well as physical ones in order to expand designers' concept choices.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

RATIONALE: Vascular smooth muscle cells (SMCs) exhibit remarkable plasticity and can undergo dedifferentiation upon pathological stimuli associated with disease and interventions. OBJECTIVE: Although epigenetic changes are critical in SMC phenotype switching, a fundamental regulator that governs the epigenetic machineries regulating the fate of SMC phenotype has not been elucidated. METHODS AND RESULTS: Using SMCs, mouse models, and human atherosclerosis specimens, we found that FAK (focal adhesion kinase) activation elicits SMC dedifferentiation by stabilizing DNMT3A (DNA methyltransferase 3A). FAK in SMCs is activated in the cytoplasm upon serum stimulation in vitro or vessel injury and active FAK prevents DNMT3A from nuclear FAK-mediated degradation. However, pharmacological or genetic FAK catalytic inhibition forced FAK nuclear localization, which reduced DNMT3A protein via enhanced ubiquitination and proteasomal degradation. Reduced DNMT3A protein led to DNA hypomethylation in contractile gene promoters, which increased SMC contractile protein expression. RNA-sequencing identified SMC contractile genes as a foremost upregulated group by FAK inhibition from injured femoral artery samples compared with vehicle group. DNMT3A knockdown in injured arteries reduced DNA methylation and enhanced contractile gene expression supports the notion that nuclear FAK-mediated DNMT3A degradation via E3 ligase TRAF6 (TNF [tumor necrosis factor] receptor-associated factor 6) drives differentiation of SMCs. Furthermore, we observed that SMCs of human atherosclerotic lesions exhibited decreased nuclear FAK, which was associated with increased DNMT3A levels and decreased contractile gene expression. CONCLUSIONS: This study reveals that nuclear FAK induced by FAK catalytic inhibition specifically suppresses DNMT3A expression in injured vessels resulting in maintaining SMC differentiation by promoting the contractile gene expression. Thus, FAK inhibitors may provide a new treatment option to block SMC phenotypic switching during vascular remodeling and atherosclerosis.

Capacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180-$730 and 110-570 kgCO2 per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%-67%, while impact on emissions is mixed (-20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

Objectives: To compare the effectiveness of bivalent and monovalent COVID-19 vaccines throughout the 2022-2023 winter season based on real-world data. Methods: This retrospective observational matched cohort study used the national vaccination program and a surveillance dataset from the Republic of Korea, and included adults aged >18 years who received bivalent or monovalent COVID-19 vaccines between October 11, 2022, and December 17, 2022. Cox proportional hazard models were used to estimate the hazard ratio for COVID-19 infection between the groups. Results: We included 29,245 matched individuals in the bivalent and monovalent vaccine groups, respectively. The bivalent vaccine recipients showed 12.2% (95% confidence interval [CI] 6.5-17.7%) additional protection against COVID-19 infection compared with the monovalent vaccine recipients. The additional protection provided by bivalent vaccines was significantly higher among residents of long-term care facilities (39.4%, 95% CI 21.6-53.1%). Maximum additional protection was observed 3 to 4 months after completing the vaccination (17.6%, 95% CI 6.6-27.3%). Conclusion: Bivalent COVID-19 vaccines showed significantly better protection against infection than monovalent vaccines among adults during the 2022-2023 winter season. Our results highlight that immunization programs with bivalent vaccines comprising recent variants can be an effective measure to prepare for seasonal COVID-19 circulation.