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Durability of high-energy throughput batteries is a prerequisite for electric vehicles to penetrate the market. Despite remarkable progresses in silicon anodes with high energy densities, rapid capacity fading of full cells with silicon–graphite anodes limits their use. In this work, we unveil degradation mechanisms such as Li + crosstalk between silicon and graphite, consequent Li + accumulation in silicon, and capacity depression of graphite due to silicon expansion. The active material properties, i.e. silicon particle size and graphite hardness, are then modified based on these results to reduce Li + accumulation in silicon and the subsequent degradation of the active materials in the anode. Finally, the cycling performance is tailored by designing electrodes to regulate Li + crosstalk. The resultant full cell with an areal capacity of 6 mAh cm −2 has a cycle life of >750 cycles the volumetric energy density of 800 Wh L −1 in a commercial cell format. The degradation in silicon-graphite anodes is originated from Li ion crosstalk between silicon and graphite, and the pressure-induced staging transition of the graphite. Here, the authors demonstrate a prismatic cell with improved volumetric energy density and cycle stability by targeted solving above issues.

Wet and dry depositions of aerosols in WRF‐Chem are revisited and updated based on recent observational findings. Traditionally, in‐cloud scavenging was thought to play a more dominant role in aerosol wet removal than below‐cloud scavenging. However, recent field measurements indicated a considerable contribution of below‐cloud scavenging of 50%–60% to total wet deposition. In contrast, the simulated contribution of in‐cloud scavenging in the previous version of WRF‐Chem was too large, exhibiting 88%–95%, likely due to the binary representation of cloud fraction. To reduce the model bias, this study adopts a continuous‐type cloud fraction and implements a semi‐empirical below‐cloud scavenging parameterization. Simulation results with the new scheme show that the contribution of below‐cloud (in‐cloud) scavenging is increased to 63%–66% (decreased to 34%–37%), well capturing the observational estimates. The magnitude of total wet deposition is increased by 18.2% for SO4, 7.16% for NO3, and 14.8% for NH4, showing better agreements with observations particularly for SO4 and NH4 deposition. The increased wet removal with the new scheme reduces and so better reproduces surface PM2.5 and PM10 concentrations, which is also partly attributed to the increased contribution of below‐cloud scavenging. It is found that dry deposition velocity in the previous version was too high for coarse mode particles when friction velocity is large, which underestimates surface PM10 concentration. The updated dry deposition scheme that is constrained by observations effectively improves PM10 performance by reducing the dry deposition velocity for coarse mode particles. Plain Language Summary Aerosols in the atmosphere are ultimately removed by wet and dry deposition processes. There are two wet scavenging processes: in‐cloud and below‐cloud scavenging. It has been believed that aerosols are scavenged more by in‐cloud processes, but recent field measurements revealed that the contribution of below‐cloud scavenging accounts for 50%–60% of total wet scavenging. A numerical air quality model, WRF‐Chem, was found to underrepresent the contribution of below‐cloud scavenging (∼5%–10%), however. Therefore, we update in‐cloud scavenging processes and implement a below‐cloud scavenging parameterization. The new method shows the below‐cloud scavenging contribution of ∼60%, increases wet deposition fluxes, and hence decreases surface aerosol concentrations. The wet deposition and aerosol concentrations simulated using the new method show better agreements with observations than those using the old one. It is also found that the dry deposition for large particles is overestimated in the previous version of WRF‐Chem, leading to low PM10 concentrations. We implemented a recent dry deposition parameterization constrained by observations, and the results show that PM10 concentration is greatly increased and so agrees better with observed PM10. Key Points Too large (small) contribution of in‐cloud (below‐cloud) scavenging was found in the previous WRF‐Chem due to binary‐type cloud fraction New scheme updating cloud fraction and below‐cloud scavenging better captures observed wet deposition fluxes and surface PM2.5 Too large dry deposition velocities for coarse particles in the previous WRF‐Chem are updated and surface PM10 is better reproduced

Recently, the issue of air quality in South Korea reached an unprecedented level of social concern regarding public health, quality of life, and environmental policies, even as the level of particulate matter less than 10 μm (PM10) showed a decreasing trend. Why have social concerns emerged in recent years, specifically after 2013-2014? This study aims to understand how people perceive air quality apart from the measured levels of airborne pollutants using internet search volume data from Google and NAVER. An empirical model that simulates the air quality perception index (AQPI) is developed by employing the decay theory of forgetting and is trained by PM10, visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public's perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013-2014 increased public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM10 and people slowly forget about air quality. The retrospective AQPI analysis under a low level of awareness confirms that perceived air quality is indeed worst in the year 2013-2014. Our results provide a better understanding of public perception of air quality, and will contribute to the creation of more effective regulatory policies. It should be noted, however, that the proposed model is primarily meant to diagnose historic public perception and that more sophisticated models are needed to reliably predict perception of air quality.

Gram-negative bacteria derived extracellular vesicles (EVs), also known as outer membrane vesicles, have attracted significant attention due to their pathogenic roles in various inflammatory diseases. We recently demonstrated that EVs secreted by the periodontopathogen Aggregatibacter actinomycetemcomitans (Aa) can cross the blood–brain barrier (BBB) and that their extracellular RNA cargo can promote the secretion of proinflammatory cytokines, such as IL-6 and TNF-α, in the brain. To gain more insight into the relationship between periodontal disease (PD) and neuroinflammatory diseases, we investigated the effect of Aa EVs in a mouse model of ligature-induced PD. When EVs were administered through intragingival injection or EV-soaked gel, proinflammatory cytokines were strongly induced in the brains of PD mice. The use of TLR (Toll-like receptor)-reporter cell lines and MyD88 knockout mice confirmed that the increased release of cytokines was triggered by Aa EVs via TLR4 and TLR8 signaling pathways and their downstream MyD88 pathway. Furthermore, the injection of EVs through the epidermis and gingiva resulted in the direct retrograde transfer of Aa EVs from axon terminals to the cell bodies of trigeminal ganglion (TG) neurons and the subsequent activation of TG neurons. We also found that the Aa EVs changed the action potential of TG neurons. These findings suggest that EVs derived from periodontopathogens such as Aa might be involved in pathogenic pathways for neuroinflammatory diseases, neuropathic pain, and other systemic inflammatory symptoms as a comorbidity of periodontitis.

This study identifies causative factors of the urban heat island (UHI) and quantifies their relative contributions to the daytime and nighttime UHI intensities using a mesoscale atmospheric model that includes a single-layer urban canopy model. A midlatitude city and summertime conditions are considered. Three main causative factors are identified: anthropogenic heat, impervious surfaces, and three-dimensional (3D) urban geometry. Furthermore, the 3D urban geometry factor is subdivided into three subfactors: additional heat stored in vertical walls, radiation trapping, and wind speed reduction. To separate the contributions of the factors and interactions between the factors, a factor separation analysis is performed. In the daytime, the impervious surfaces contribute most to the UHI intensity. The anthropogenic heat contributes positively to the UHI intensity, whereas the 3D urban geometry contributes negatively. In the nighttime, the anthropogenic heat itself contributes most to the UHI intensity, although it interacts strongly with other factors. The factor that contributes the second most is the impervious-surfaces factor. The 3D urban geometry contributes positively to the nighttime UHI intensity. Among the 3Durban geometry subfactors, the additional heat stored in vertical walls contributes most to both the daytime and nighttime UHI intensities. Extensive sensitivity experiments to anthropogenic heat intensity and urban surface parameters show that the relative importance and ranking order of the contributions are similar to those in the control experiment.

Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O3 concentrations on some days. The average difference in summertime surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O3 (MDA8 O3) over the CONUS. This represents up to ∼ 40 % of the total MDA8 O3 bias under cloudy conditions in the tested model version. Surface O3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ∼ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOx-limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Molecular classifications of breast cancer (BRC), such as human epidermal growth factor receptor 2 (HER2), luminal A and luminal B, have been developed to reduce unnecessary treatment by dividing patients with BRC into low- and high-risk progression groups. However, these methods do not cover all of the pathological characteristics of BRC, and investigations into novel prognostic/therapeutic markers are thus continually required. In this study, we identified the overexpression of the histone methyltransferase, euchromatic histone-lysine N-methyltransferase 2 (EHMT2) in BRC samples (n=1,222) and normal samples (n=113) derived from the TCGA portal by performing a BRC tissue microarray. EHMT2 overexpression was clearly associated with a poor prognosis in multiple cohorts of patients with BRC (total, n=1,644). Furthermore, the knockdown of EHMT2 expression affected cell apoptosis via the downregulation and re-localization of heat shock protein family D (Hsp60) member 1 (HSPD1). In addition, a statistically significant positive correlation between EHMT2 and HSPD1 expression was revealed in the clinical cohorts. On the whole, the findings of this study may assist the development of novel therapeutic strategies and provide a prognostic marker (EHMT2) for patients with BRC.

Force changes in axially loaded members can be monitored by quantifying variations in impedance signatures. However, statistical damage metrics, which are not physically related to the axial load, often lead to difficulties in accurately estimating the amount of axial force changes. Inspired by the wearable technology, this study proposes a novel wearable piezoelectric interface that can be used to monitor and quantitatively estimate the force changes in axial members. Firstly, an impedance-based force estimation method was developed for axially loaded members. The estimation was based on the relationship between the axial force level and the peak frequencies of impedance signatures, which were obtained from the wearable piezoelectric interface. The estimation of the load transfer capability from the axial member to the wearable interface was found to be an important factor for the accurate prediction of axial force. Secondly, a prototype of the wearable piezoelectric interface was designed to be easily fitted into existing axial members. Finally, the feasibility of the proposed technique was established by assessing tension force changes in a numerical model of an axially loaded cylindrical member and a lab-scale model of a prestressed cable structure.

The human microbiome plays an essential role in the human immune system, food digestion, and protection from harmful bacteria by colonizing the human intestine. Recently, although the human microbiome affects colorectal cancer (CRC) treatment, the mode of action between the microbiome and CRC remains unclear. This study showed that propionate suppressed CRC growth by promoting the proteasomal degradation of euchromatic histone-lysine N-methyltransferase 2 (EHMT2) through HECT domain E3 ubiquitin protein ligase 2 (HECTD2) upregulation. In addition, EHMT2 downregulation reduced the H3K9me2 level on the promoter region of tumor necrosis factor α-induced protein 1 (TNFAIP1) as a novel direct target of EHMT2. Subsequently, TNFAIP1 upregulation induced the apoptosis of CRC cells. Furthermore, using Bacteroides thetaiotaomicron culture medium, we confirmed EHMT2 downregulation via upregulation of HECTD2 and TNFAIP1 upregulation. Finally, we observed the synergistic effect of propionate and an EHMT2 inhibitor (BIX01294) in 3D spheroid culture models. Thus, we suggest the anticancer effects of propionate and EHMT2 as therapeutic targets for colon cancer treatment and may provide the possibility for the synergistic effects of an EHMT2 inhibitor and microbiome in CRC treatment.