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Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

With cities facing increasing challenges due to climate change, we developed a deep learning‐based surrogate modeling framework to optimize urban roofing strategies for climate risk mitigation. Applied to Seoul, South Korea, the framework utilized the Weather Research and Forecasting model coupled with an Urban Canopy Model (WRF‐UCM) to generate objective indices for heat stress, flash floods, and wind circulation projected to the end of this century under four roof schemes: business‐as‐usual, 25% and 100% cool roofs (CR25 and CR100), and 100% green roofs (GR100). These indices were used to test four deep learning algorithms: UNet, UNet++, UNet3+, and Multi‐ResUNet. Multi‐ResUNet demonstrated superior performance, thus it was employed to develop the surrogate model, which was applied to 262,144 multi‐type roofing scenarios. Two optimal roofing scenarios were identified using the Pareto method, balancing the three climate objectives and economic costs: the first with CR100 covering 95.9% of urban areas, reducing heat stress by over 50% in 34.3% of regions and wind circulation by 10% in 27.7% of regions, and the second with CR100 covering 60.2% of urban areas, achieving a similar heat stress reduction in 21.6% of regions but a stronger reduction in wind circulation. Both scenarios had minimal impact on flash flood mitigation. This study highlights the importance of spatial configuration in maximizing the benefits of urban roofing strategies due to the heterogeneous effects across urban areas. Furthermore, the considerably lower computational time increases the practical utility of the proposed surrogate modeling framework for use in a diverse range of urban contexts, advancing global efforts to mitigate urban climate risks. Plain Language Summary This study focused on improving urban adaptation to climate change by developing a deep‐learning surrogate modeling framework to optimize urban roofing strategies. Applied to Seoul in South Korea, this framework assessed the impacts of 262,144 roofing scenarios that combined four roof types (traditional roofs, partially and fully covered by cool roofs, and green roofs) on heat stress, flash flooding, wind circulation, and construction costs. Two optimal solutions were identified: one with cool roofs covering nearly all urban areas that considerably reduced heat stress while decreasing wind circulation and another with less cool roof coverage that achieved moderate heat reduction while improving wind circulation benefits. The results highlighted the importance of spatial configuration in maximizing the benefits of urban roofing strategies because their effects vary across urban areas. The efficiency of the proposed framework in analyzing numerous scenarios and its dramatically lower computational time position it as a practical tool for cities worldwide that are seeking to minimize climate risks. Key Points A deep learning‐based surrogate modeling framework was developed to optimize urban roofing strategies and mitigate climate risks in Seoul Two optimal roofing scenarios were identified with noticeable trade‐offs between heat stress reduction and wind circulation benefits This framework cut evaluation time by 94%, demonstrating its potential as a practical tool to address global urban climate risks

Wildfires influence not only ecosystems but also carbon and water fluxes on Earth. Yet, the fire processes including the occurrence and consequences of fires are still limitedly represented in land surface models (LSMs). In particular, the performance of LSMs in estimating burned areas across high northern latitudes is poor. In this study, we employed the daily burned areas from the satellite-based Global Fire Emissions Database (version 4) (GFED4) into the Community Land Model (version 5.0) with a biogeochemistry module (CLM5-BGC) to identify the effects of accurate fire simulation on carbon and water fluxes over Alaska and Eastern Siberia. The results showed that the simulated carbon emissions with burned areas from GFED4 (i.e., experimental run) were significantly improved in comparison to the default CLM5-BGC simulation, which resulted in opposite signs of the net ecosystem exchange for 2004, 2005, and 2009 over Alaska between the default and experimental runs. Also, we identified that carbon emissions were more sensitive to the wildfires in Alaska than in Eastern Siberia, which could be explained by the vegetation distribution (i.e., tree cover ratio). In terms of water fluxes, canopy transpiration in Eastern Siberia was relatively insensitive to the size of the burned area due to the interaction between leaf area and soil moisture. This study uses CLM5-BGC to improve our understanding of the role of burned areas in ecohydrological processes at high latitudes. Furthermore, we suggest that the improved approach will be required for better predicting future carbon fluxes and climate change.

Fire plays an important role in terrestrial ecosystems. The burning of biomass affects carbon and water fluxes and vegetation distribution. To understand the effect of interactive processes of fire and ecological succession on surface carbon and water fluxes, this study employed the Community Land Model version 4.5 to conduct a series of experiments that included and excluded fire and dynamic vegetation processes. Results of the experiments that excluded the vegetation dynamics showed a global increase in net ecosystem production (NEP) in post-fire regions, whereas the inclusion of vegetation dynamics revealed a fire-induced decrease in NEP in some regions, which was depicted when the dominant vegetation type was changed from trees to grass. Carbon emissions from fires are enhanced by reduction in NEP when vegetation dynamics are considered; however, this effect is somewhat mitigated by the increase in NEP when vegetation dynamics are not considered. Fire-induced changes in vegetation modify the soil moisture profile because grasslands are more dominant in post-fire regions. This results in less moisture within the top soil layer than that in unburned regions, even though transpiration is reduced overall. These findings are different from those of previous fire model evaluations that ignored vegetation dynamics and thus highlight the importance of interactive processes between fires and vegetation dynamics in evaluating recent model developments.

Matrix metalloproteinases (MMPs) are calcium-dependent zinc-containing endopeptidases involved in multiple cellular processes. Among the MMP isoforms, MMP-9 regulates cancer invasion, rheumatoid arthritis, and osteoarthritis by degrading extracellular matrix proteins present in the tumor microenvironment and cartilage and promoting angiogenesis. Here, we identified two potent natural product inhibitors of the non-catalytic hemopexin domain of MMP-9 using a novel quantum mechanical fragment molecular orbital (FMO)-based virtual screening workflow. The workflow integrates qualitative pharmacophore modeling, quantitative binding affinity prediction, and a raw material search of natural product inhibitors with the BMDMS-NP library. In binding affinity prediction, we made a scoring function with the FMO method and applied the function to two protein targets (acetylcholinesterase and fibroblast growth factor 1 receptor) from DUD-E benchmark sets. In the two targets, the FMO method outperformed the Glide docking score and MM/PBSA methods. By applying this workflow to MMP-9, we proposed two potent natural product inhibitors (laetanine 9 and genkwanin 10) that interact with hotspot residues of the hemopexin domain of MMP-9. Laetanine 9 and genkwanin 10 bind to MMP-9 with a dissociation constant (KD) of 21.6 and 0.614 μM, respectively. Overall, we present laetanine 9 and genkwanin 10 for MMP-9 and demonstrate that the novel FMO-based workflow with a quantum mechanical approach is promising to discover potent natural product inhibitors of MMP-9, satisfying the pharmacophore model and good binding affinity.

The most effective method of limiting the coronavirus disease pandemic of 2019 (COVID-19) is vaccination. For the determination of the comparative efficacy and safety of COVID-19 vaccines and their platforms during the pre-Delta era, a systematic review and network meta-analysis was conducted. The MEDLINE, Embase, and MedRxiv databases were searched, and the gray literature was manually searched up to 8 July 2021. The review includes the phase II and III randomized controlled trials (RCTs) that assessed the efficacy, immunogenicity, and safety of the COVID-19 vaccines. The network meta-analysis used a Bayesian model and used the surface under the cumulative ranking to rank the comparisons between the vaccines. All included studies were quality appraised according to their design, and the heterogeneity of the analyses was assessed using I2. In terms of vaccine efficacy, the mRNA-1273 vaccine ranked the highest, and the CoronaVac vaccine ranked the lowest. The mRNA-1273 ranked the highest for neutralizing antibody responses to live SARS-CoV-2. The WIV04 vaccine was associated with the lowest incidence of both local and systemic adverse reactions. All studies except one had a low to moderate risk of bias. The mRNA platform vaccines showed higher efficacy and more adverse reactions than the other vaccines.

We report the complete plastid genome of Cumathamnion serrulatum, also known as Delesseria serrulata. The plastid genome was 174,192 bp in size. Annotation showed there were 193 protein coding genes, three ribosomal RNAs, and 29 transfer RNAs. One intron was found, and the GC content was 27.2%. The maximum likelihood tree with the concatenated 177 plastid coding genes showed a strong monophyletic relationship to Membranoptera spp. within the Ceramiales.

The reliability and performance of a steam generator (SG) is one of the serious concerns in the operation of pressurized water nuclear power plants. Because of high levels of radiation, robotic systems have been used to inspect and repair SG tubes. In this paper, we present a mobile robotic system that positions the inspection and repair tools while hanging down from the tube sheets where the tubes are fixed. All of the driving mechanisms of the mobile robot are actuated by electric motors to start its works, providing that the electric power is prepared without the additional need for an on-site air services. A special tube-holding mechanism with a high holding force has been developed to prevent falling from the tube sheets, even in the case of an electric power failure. We have also developed a quick installation guide device that guides the mobile robot to desired initial positions in the tube sheet exactly and quickly, which helps to reduce the radiation exposure of human workers during the installation work. This paper also provides on-site experimental results and lessons learned.