Interpretation of SPECT wall motion with deep learning.

OBJECTIVES: We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion. BACKGROUND: Wall motion assessment with SPECT is limited by image temporal and spatial resolution. Visual interpretation of wall motion can be subjective and prone to error. Artificial intelligence (AI) may improve accuracy of wall motion assessment. METHODS: A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included. Using echocardiography as truth, a DL-model (DL-model 1) was trained to predict the probability of abnormal wall motion. Of the 1038 patients, 317 were used to train a DL-model (DL-model 2) to assess regional wall motion. A 10-fold cross-validation was adopted. Diagnostic performance of DL was compared with human readers and quantitative parameters. RESULTS: The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of DL model (AUC: .82 [95% CI: .79-.85]; ACC: .88) were higher than human (AUC: .77 [95% CI: .73-.81]; ACC: .82; P < .001) and quantitative parameter (AUC: .74 [95% CI: .66-.81]; ACC: .78; P < .05). The net reclassification index (NRI) was 7.7%. The AUC and accuracy of DL model for per-segment and per-vessel territory diagnosis were also higher than human reader. The DL model generated results within 30 seconds with operable guided user interface (GUI) and therefore could provide preliminary interpretation. CONCLUSIONS: DL can be used to improve interpretation of rest SPECT wall motion as compared with current human readers and quantitative parameter diagnosis.

Longitudinal albuminuria patterns predict coronary artery calcification progression: Findings from the Coronary Artery Risk Development in Young Adults study.

BACKGROUND AND AIMS: Albuminuria is an established risk factor for adverse cardiovascular outcomes. However, few studies have characterized longitudinal albuminuria patterns based on long-term measurement of urine albumin-to-creatinine ratio (UACR) levels. We aimed to evaluate the association between longitudinal albuminuria patterns in midlife adults and subsequent CAC progression. METHODS: We included 1919 participants with CAC assessment by computed tomography from CARDIA (Coronary Artery Risk Development in Young Adults) study. CAC progression was determined for each individual as the difference of logarithmic CAC scores at follow-up and baseline. Albuminuria patterns across a 10-year span were estimated by longitudinal UACR levels. Multivariable general linear models were used to evaluate the association of long-term albuminuria patterns with CAC progression. RESULTS: Of the 1919 included participants, 583 (30.4 %) participants experienced CAC progression, and the mean (SD) age was 50.4 (3.5) years at year 25. A total of four dynamic albuminuria patterns were identified. After multivariable adjustment, there were significant differences in CAC progression by albuminuria patterns as demonstrated as percent change in CAC with 36.0 % (SE, 1.5) progression for mid- and late-life normoalbuminuria group, 46.0 % (SE, 7.6) for midlife normoalbuminuria and late-life high albuminuria group, 52.4 % (SE, 7.1) for midlife high albuminuria and late-life normoalbuminuria group, and 54.5 % (SE, 8.7) for mid- and late-life high albuminuria group (p = 0.019). Similar findings were also observed in CAC volume score changes. CONCLUSIONS: Longitudinal albuminuria patterns across a 10-year span were associated with worse CAC progression independent of baseline CAC level and albuminuria changes, suggesting that it may provide early identification of high-risk individuals with dynamic rises in albuminuria who may benefit from aggressive risk factor modification.

Comparison of ANN and LR models for predicting Carbapenem-resistant Klebsiella pneumoniae isolates from a southern province of China's RNSS data.

OBJECTIVES: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious threat to public health due to its limited treatment options and high mortality rate. This study aims to identify the risk factors of carbapenem resistance in patients with K. pneumoniae isolates and develop CRKP prediction models using logistic regression (LR) and artificial neural network (ANN) methods. METHODS: We retrospectively analysed the data of 49,774 patients with Klebsiella pneumoniae isolates from a regional nosocomial infection surveillance system (RNSS) between 2018 and 2021. We performed logistic regression analyses to determine the independent predictors for CRKP. We then built and evaluated LR and ANN models based on these predictors using calibration curves, ROC curves, and decision curve analysis (DCA). We also applied the Synthetic Minority Over-Sampling Technique (SMOTE) to balance the data of CRKP and non-CRKP groups. RESULTS: The LR model showed good discrimination and calibration in both training and validation sets, with areas under the ROC curve (AUROC) of 0.824 and 0.825, respectively. The DCA indicated that the LR model had clinical usefulness for decision making. The ANN model outperformed the LR model both in the training set and validation set. The SMOTE technique improved the performance of both models for CRKP detection in training set, but not in the validation set. CONCLUSION: We developed and validated LR and ANN models for predicting CRKP based on RNSS data. Both models were feasible and reliable for CRKP inference and could potentially assist clinicians in selecting appropriate empirical antibiotics and reducing unnecessary medical resource utilization.

Risk exploration and prediction model construction for linezolid-resistant Enterococcus faecalis based on big data in a province in southern China.

BACKGROUND: Enterococcus faecalis is a common cause of healthcare-associated infections. Its resistance to linezolid, the antibiotic of last resort for vancomycin-resistant enterococci, has become a growing threat in healthcare settings. METHODS: We analyzed the data of E. faecalis isolates from 26 medical institutions between 2018 and 2020 and performed univariate and multivariate logistic regression analyses to determine the independent predictors for linezolid-resistant E. faecalis (LREFs). Then, we used the artificial neural network (ANN) and logistic regression (LR) to build a prediction model for linezolid resistance and performed a performance evaluation and comparison. RESULTS: Of 12,089 E. faecalis strains, 755 (6.25%) were resistant to linezolid. Among vancomycin-resistant E. faecalis, the linezolid-resistant rate was 24.44%, higher than that of vancomycin-susceptible E. faecalis (p < 0.0001). Univariate and multivariate regression analyses showed that gender, age, specimen type, length of stay before culture, season, region, GDP (gross domestic product), number of beds, and hospital level were predictors of linezolid resistance. Both the ANN and LR models constructed in the study performed well in predicting linezolid resistance in E. faecalis, with AUCs of 0.754 and 0.741 in the validation set, respectively. However, synthetic minority oversampling technique (SMOTE) did not improve the prediction ability of the models. CONCLUSION: E. faecalis linezolid-resistant rates varied by specimen site, geographic region, GDP level, facility level, and the number of beds. At the same time, community-acquired E. faecalis with linezolid resistance should be monitored closely. We can use the prediction model to guide clinical medication and take timely prevention and control measures.

Biomimetic Grapefruit-Derived Extracellular Vesicles for Safe and Targeted Delivery of Sodium Thiosulfate against Vascular Calcification.

As the prevalence of vascular calcification (VC), a strong contributor to cardiovascular morbidity and mortality, continues to increase, the need for pharmacologic therapies becomes urgent. Sodium thiosulfate (STS) is a clinically approved drug for therapy against VC; however, its efficacy is hampered by poor bioavailability and severe adverse effects. Plant-derived extracellular vesicles have provided options for VC treatment since they can be used as biomimetic drug carriers with higher biosafety and targeting abilities than artificial carriers. Inspired by natural grapefruit-derived extracellular vesicles (EVs), we fabricated a biomimetic nanocarrier comprising EVs loaded with STS and further modified with hydroxyapatite crystal binding peptide (ESTP) for VC-targeted delivery of STS. In vitro, the ESTP nanodrug exhibited excellent cellular uptake capacity by calcified vascular smooth muscle cells (VSMCs) and subsequently inhibited VSMCs calcification. In the VC mice model, the ESTP nanodrug showed preferentially the highest accumulation in the calcified arteries compared to other treatment groups. Mechanistically, the ESTP nanodrug significantly prevented VC via driving M2 macrophage polarization, reducing inflammation, and suppressing bone-vascular axis as demonstrated by inhibiting osteogenic phenotype trans-differentiation of VSMCs while enhancing bone quality. In addition, the ESTP nanodrug did not induce hemolysis or cause any damage to other organs. These results suggest that the ESTP nanodrug can prove to be a promising agent against VC without the concern of systemic toxicity.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration.

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

Dynamic Changes of Composition of Particulate Matter Emissions during Residential Biomass Combustion.

Residential biomass combustion in developing countries produces significant primary particulate matter (PM) emissions. Highly time-resolved aerosol mass spectrometry and aethalometer measurements were used to investigate the dynamic changes of emitted PM chemical composition from a typical improved stove burning with wood and crop straw in China. Combustion temperature and organic aerosol (OA) concentration increased quickly during the ignition stage. The flaming stage was characterized by high combustion temperature and high pollutant [including OA, black carbon (BC), inorganic salts, and polycyclic aromatic hydrocarbons (PAHs)] emissions, while the burnout stage is characterized by low combustion temperature and lower pollutant emissions. OA was the primary emitted species; emission factors of OA in the flaming stage were generally higher (24.5-792%) than those in the burnout stage. Mass spectral signatures of OA were obtained. The ratio of Cl-/OA for wood combustion (0.05 ± 0.01) is much lower than that from burning crop straw (0.32 ± 0.19). Hydrocarbon OA emissions dominated during the ignition and flaming stages. A high percentage of oxidized OA was emitted during the burnout stage. The relationship between PAHs and BC/OA emissions under different burning conditions was investigated, and PAHs may act as intermediate products in the conversion of OA to BC.

Volatility distribution of primary organic aerosol emissions from household crop waste combustion in China.

Biomass-burning emissions are a significant source of primary organic aerosol (POA). Volatility is one of the most important physical properties of organic aerosol (OA). Dilution and thermodenuder (TD) measurements were used to investigate the volatility of POA from household crop waste combustion in China. Between 10% and 30% of the POA desorbed when diluted from 20:1 to 120:1, while 10%-40% of POA evaporated in the TD when heated to 150 °C. Thus, a considerable proportion of the POA emissions were volatile. A dynamic mass transfer model was applied to derived volatility distributions of POA based on TD data. A best fit volatility distributions for POA and associated mass accommodation coefficients (α), and the enthalpy of vaporization (ΔHvap) were presented. The emissions factors and volatility distribution of POA emission from household crop waste combustion in this study can be used to improve emission inventories and simulate gas-particle partitioning of organic aerosol in chemical transport models.

Source characterization of volatile organic compounds in urban Beijing and its links to secondary organic aerosol formation.

Volatile organic compounds (VOCs) are precursors for ozone and secondary organic aerosol (SOA) formation, thereby playing a vital role in atmospheric chemistry and urban air quality. To characterize the relationship between VOCs and SOA, organics both in gas and particulate phases were concurrently measured in urban Beijing. The VOCs and organic aerosol (OA) were apportioned into factors with different oxidation levels by applying the factorization analysis on their detailed mass spectra. Six factors of VOCs were identified, including four primary VOCs (PVOC) factors and two secondary VOCs (SVOC) factors. The PVOC factors dominated the total VOCs when the air mass originated in the cleaner northern areas, while SVOC factors dominated for polluted southern air masses. The normalized concentrations of PVOC and primary OA factors showed consistent diurnal variations regardless of air mass directions, owing to the relatively stable local emissions during the experimental period. This contrasted with the secondary factors due to more complex transformation processes. The traffic-related VOCs and solid fuel combustion VOCs negatively correlated with SOA, implying that they may have contributed to the SOA formation through photooxidation. The VOCs in lower oxidation levels were found to have poor correlations with the less oxidized SOA, whereas they correlated strongly to the more oxidized SOA. This implied that the less oxidized SOA may be in a transition state, where its production and loss rates were balanced. These served as products of VOCs oxidation and reactants of more oxidized SOA formation, playing important roles on the VOC to SOA transformation. The identified VOC emission sources and their photochemical production of SOA should be considered in air quality policy planning.

An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging.

OBJECTIVES: We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated 13N-NH3 PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment. METHODS: Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD. RESULTS: Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 (P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth (r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair (r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194. CONCLUSION: The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.

Effects of hygroscopicity on aerosol optical properties and direct radiative forcing in Beijing: Based on two-year observations.

The influence of relative humidity on aerosol properties and the direct radiative forcing of PM10 and PM1 were investigated in Beijing from January 2018 to December 2019. The annual mean scattering hygroscopic growth factor at RH = 80 % [f(80 %)] of PM10 and PM1 were 1.60 ± 0.24 and 1.58 ± 0.22, respectively. The variation of aerosol hygroscopic growth factors of PM10 and PM1 aerosols was similar, which is mainly due to the fact that aerosol scattering in Beijing is dominated by fine particles. The seasonal mean f(80 %) of PM10 from spring to winter were 1.66 ± 0.23, 1.71 ± 0.25, 1.51 ± 0.20, 1.49 ± 0.16, respectively, which were higher in spring and summer, and lower in autumn and winter. The diurnal variation of f(80 %) was relatively higher from 12:00 to 18:00, which could be related to the formation of secondary aerosols by photochemical reactions. f(80 %) shows a strong positive relationship with both the scattering Angström exponent (SAE) and the single scattering albedo (ω0) under dry conditions; therefore, the scattering hygroscopic growth factor could be estimated using these two parameters. The upscatter fraction (ß) and single scattering albedo, which are the key aerosol optical properties for the calculation of direct radiative forcing, are also RH-dependent. As RH increases, the upscatter fraction (backscatter fraction) decreases and ω0 increases. The aerosol radiative forcing at RH 80 % was 1.48 times as that in the dry state. The sensitivity experiment showed that the variation in the scattering coefficient with relative humidity had the greatest influence on radiation forcing, followed by ß and ω0. The seasonal variation of ΔF(80 %)/ΔF(dry) coincides with that of the aerosol hygroscopic growth factor. Our study suggests that understanding the influence of relative humidity on aerosol properties and direct radiative forcing is important for accurately estimating the radiative forcing of aerosols.

Atmospheric Particle Hygroscopicity and the Influence by Oxidation State of Organic Aerosols in Urban Beijing.

A hygroscopic tandem differential mobility analyser (H-TDMA) was used to observe the size-resolved hygroscopic characteristics of submicron particles in January and April 2018 in urban Beijing. The probability distribution of the hygroscopic growth factor (HGF-PDF) in winter and spring usually showed a bimodal pattern, with more hygroscopic mode (MH) being more dominant. The seasonal variation in particle hygroscopicity was related to the origin of air mass, which received polluted southerly air masses in spring and clean northwesterly air masses in winter. Particles showed stronger hygroscopic behaviour during heavy pollution episodes (HPEs) with elevated concentrations of secondary aerosols, especially higher mass fraction of nitrate, which were indicated using the PM2.5 (particulate matter with diameter below 2.5 µm) mass concentration normalised by CO mass concentration. The hygroscopic parameter (κ) values were calculated using H-TDMA (κhtdma) and chemical composition (κchem). The closure study showed that κchem was overestimated in winter afternoon when compared with κhtdma, because the organic particle hygroscopic parameter (κorg) was overestimated in the calculations. It was influenced by the presence of a high concentration of hydrocarbon-like organic aerosol (HOA) with a weak water uptake ability. A positive relationship was observed between κorg and the ratio of oxygenated organic aerosol (OOA) and HOA, thereby indicating that the strong oxidation state enhanced the hygroscopicity of the particles. This study revealed the effect of local emission sources and secondary aerosol formation processes on particle hygroscopicity, which is of great significance for understanding the pollution formation mechanism in the North China Plain.

Grape exosome-like nanoparticles: A potential therapeutic strategy for vascular calcification.

Vascular calcification (VC) is prevalent in hypertension, diabetes mellitus, chronic kidney disease, and aging and has been identified as an important predictor of adverse cardiovascular events. With the complicated mechanisms involved in VC, there is no effective therapy. Thus, a strategy for attenuating the development of VC is of clinical importance. Recent studies suggest that grape exosome-like nanoparticles (GENs) are involved in cell-cell communication as a means of regulating oxidative stress, inflammation, and apoptosis, which are known to modulate VC development. In this review, we discuss the roles of GENs and their potential mechanisms in the development of VC.

PRKAA1, stabilized by FTO in an m6A-YTHDF2-dependent manner, promotes cell proliferation and glycolysis of gastric cancer by regulating the redox balance.

Gastric carcinoma (GC) is the fourth most common malignancy worldwide and the second cause of death of all malignancies worldwide. AMPK catalytic subunit α1 (PRKAA1) is involved in various types of cancer and PRKAA1 knockdown significantly decreased the invasiveness of GC cells. Fat mass and obesity-associated protein (FTO)-regulation of m6A has been shown to be involved in different diseases including cancer. However, the role of PRKAA1 and m6A modification in GC remains to be elucidated. PRKAA1 was silenced or overexpressed to study the role of PRKAA1 in regulating cell viability, colony formation, and glycolysis. Levels of lactic acid, GSH, and NADP+/NADPH were measured using commercial kits. Extracellular acidification rates were determined by an extracellular flux analyzer. RNA immunoprecipitation was performed to test m6A levels and the interaction between PRKAA1-3'-UTR and YTHDF2. Quantitative RT-PCR and immunoblots were applied to measure mRNA or protein levels, respectively. PRKAA1 silencing inhibited cell growth, colony formation, and glycolysis but enhanced apoptosis, while the PRKAA1 overexpression promoted cell growth, colony formation, and glycolysis but inhibited apoptosis of GC cells. Data also indicated that PRKAA1 regulated cell growth and apoptosis by regulating the redox balance. Mechanism study demonstrated that FTO regulated PRKAA1 mRNA m6A modification and stability. Clinical samples analysis demonstrated that PRKAA1 and FTO expression were increased in GC patients and positively correlated with each other. FTO increased levels of PRKAA1 by regulating its mRNA m6A modification and stability. PRKAA1, in turn, promoted cell viability, colony formation, and glycolysis but inhibited apoptosis of GC cells by promoting the redox balance.

Micro ribonucleic acid-448 regulates zinc finger e-box binding homeobox 1 to inhibit the growth of breast cancer cells and increase their sensitivity to chemotherapy.

OBJECTIVE: This study aimed to investigate the effect of Zinc Finger E-box Binding Homeobox 1 (ZEB1) regulation by Micro Ribonucleic acid (miR)-448 on Breast Cancer (BC) cells and their sensitivity to chemotherapy. METHODS: miR-448 and ZEB1 mRNA levels in BC and normal tissues were detected by qPCR, and ZEB1 protein was detected by Western Blotting (WB). The correlation between miR-448 and tumor metastasis, clinical staging, and ZEB1 expression was analyzed. MCF-7 cells were transfected or co-transfected with the miR-448 mimic, oe-ZEB1, or their negative controls. Changes in miR-448 and ZEB1 expression were detected by qPCR and WB. Cell proliferation was determined by CCK-8 assays, invasion changes were analyzed by Transwell assays, and apoptosis was detected by flow cytometry. RESULTS: miR-448 expression in BC tissues was lower than that in normal tissues, while ZEB1 expression was increased in the former. ZEB1 expression was lower in BC patients with lymph node metastasis than in those without. In patients with clinical stage I-III BC, miR-448 expression decreased with an increase in tumor stage, which was negatively correlated with ZEB1 expression. Upregulation of miR-448 expression can suppress MCF-7 cell proliferation and invasion and promote apoptosis. Upregulation of ZEB1 expression in cells overexpressing miR-448 can partially reverse the inhibition of BC cell growth induced by miR-448. miR-448 can enhance the sensitivity of cells toward paclitaxel and 5-fluorouracil. CONCLUSIONS: miR-448 suppresses cell proliferation and invasion and promotes apoptosis by targeting ZEB1. Moreover, it can increase the sensitivity of cells toward paclitaxel and 5-fluorouracil.

Incorporation and improvement of a heterogeneous chemistry mechanism in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on secondary inorganic aerosol and PM2.5 simulations in Middle-Eastern China.

Heterogeneous chemistry is considered one of the critical pathways of secondary inorganic aerosol (SIA) productions. In this study, a heterogeneous chemistry mechanism is incorporated into the atmospheric chemistry model GRAPES_Meso5.1/CUACE. Varying uptake coefficient schemes of SO2 and NO2 are compared and the equivalent ratio of inorganic aerosol (ER)-dependent scheme for SO2 and relative humidity (RH)/ER-dependent scheme for NO2 are used to form the improved heterogeneous chemistry. Focusing on a severe haze episode in Middle-Eastern China, the impacts of heterogeneous mechanism on SIA and PM2.5 composition are investigated based on the updated model. Study results show that the differences in RH or ER uptake coefficients result in obvious differences in sulfate and nitrate concentrations, especially during the severe pollution period, because the ER schemes restrict the excessive production of sulfate and nitrate under high RH effectively by including the self-limitation of heterogeneous reactions, which shows better performance in capturing the magnitude and temporal variations of surface SIA and PM2.5. Normalized mean bias of sulfate, nitrate, ammonium, and PM2.5 in megacity Beijing decreases from -27.0, -28.3, -58.2, and -26.3 to 1.0, -2.2, -47.2, and -16.5 %, respectively. And the fractions of sulfate, nitrate, ammonium, and organics during the polluted period change from 13.7, 19.3, 6.9, and 60.1 to 16.5, 23.0, 7.6, and 52.9 %, respectively, which is more consistent with the observation (16.0, 23.2, 14.1, and 46.7 %). SIA and PM2.5 simulations in another megacity Shanghai have the similar improvements. The modeled SIA by heterogeneous processes contributes 11.7 % of total PM2.5 in Beijing and 22.5 % in Shanghai. That is 13.5 % in the Chinese megalopolis Beijing-Tianjin-Hebei and 19.8 % in Yangtze-River-Delta, indicating a considerable contribution of heterogeneous pathways to haze pollution. This work indicates the importance of detailed and reasonable heterogeneous schemes for better SIA and haze/fog prediction in the atmospheric chemistry model.

On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain.

Regional transport is a key source of carbonaceous aerosol in many Chinese megacities including Beijing. The sources of carbonaceous aerosol in urban areas have been studied extensively but are poorly known in upwind rural areas. This work aims to quantify the contributions of fossil and non-fossil fuel emissions to carbonaceous aerosols at a rural site in North China Plain in winter 2016. We integrated online high resolution-time of flight-aerosol mass spectrometer (HR-TOF-AMS) observations and radiocarbon (14C) measurements of fine particles with Positive Matrix Factorization (PMF) analysis as well as Extended Gelencsér (EG) method. We found that fine particle concentration is much higher at the rural site than in Beijing during the campaign (Dec 7, 2016 to Jan 8, 2017). PMF analysis of the AMS data showed that coal-combustion related organic aerosol (CCOA + Oxidized CCOA) and more oxidized oxygenated organic aerosol (MO-OOA) contributed 48% and 30% of organic matter to non-refractory PM1 (NR-PM1) mass. About 2/3 of the OC and EC were from fossil-fuel combustion. The EG method, combining AMS-PMF and 14C data, showed that primary and secondary OC from fossil fuel contribute 35% and 22% to total carbon (TC), coal combustion emission dominates the fossil fuel sources, and biomass burning accounted for 21% of carbonaceous aerosol. In summary, our results confirm that fossil fuel combustion was the dominant source of carbonaceous aerosol during heavy pollution events in the rural areas. Significant emissions of solid fuel carbonaceous aerosols at rural areas can affect air quality in downwind cities such as Beijing and Tianjin, highlighting the benefits of energy transition from solid fuels to cleaner energy in rural areas.

Long-term trend of new particle formation events in the Yangtze River Delta, China and its influencing factors: 7-year dataset analysis.

To evaluate the influence of anthropogenic emission reductions since 2013 in China, a long-term trend analysis of the particle number size distribution (PNSD) and new particle formation (NPF) events in the Yangtze River Delta (YRD) region was conducted based on the PNSD measurement (diameter ranging from 3 to 850 nm) at the Lin'an (LAN) regional background station from 2013 to 2019. A modified Mann-Kendall test and a Theil-Sen estimator were used to calculate the overall trend of particle number concentrations in different modes and the relevant influencing factors. We observed a significant decreasing trend in the Aitken and accumulation mode number concentrations, with annual decrease rates of approximately 5.6% and 8.2%, respectively, resulting in an approximately 6.0% decline in total particles annually. However, the nucleation mode particle number concentration showed no significant trend from 2013 to 2016, but an increasing trend from 2016 to 2019, which was related to the NPF events occurrence frequency. The regional NPF events of "banana shape" accounted for an increasing fraction of all NPF events. As a key parameter influencing the NPF event, the condensation sink decreased by approximately 63% from 2013 to 2019. Moreover, the estimated sulfuric acid concentration decreased by approximately 50%, with a higher reduction rate occurring during 2013-2016 as result of the effective SO2 reduction. Surface meteorological factors (including the air temperature, relative humidity, air pressure, and wind) and the air masses origin were found to played minor roles in the long-term trend of NPF events. As PNSD and NPF events are closely related to changes in the particle emissions and regional air pollution levels, studies concerning PNSD and NPF are necessary to provide important information regarding air quality improvements and evaluating the efficacy of climate change mitigation strategies.

Micro ribonucleic acid-448 regulates zinc finger e-box binding homeobox 1 to inhibit the growth of breast cancer cells and increase their sensitivity to chemotherapy

Abstract Objective: This study aimed to investigate the effect of Zinc Finger E-box Binding Homeobox 1 (ZEB1) regulation by Micro Ribonucleic acid (miR)-448 on Breast Cancer (BC) cells and their sensitivity to chemotherapy. Methods: miR-448 and ZEB1 mRNA levels in BC and normal tissues were detected by qPCR, and ZEB1 protein was detected by Western Blotting (WB). The correlation between miR-448 and tumor metastasis, clinical staging, and ZEB1 expression was analyzed. MCF-7 cells were transfected or co-transfected with the miR-448 mimic, oe-ZEB1, or their negative controls. Changes in miR-448 and ZEB1 expression were detected by qPCR and WB. Cell proliferation was determined by CCK-8 assays, invasion changes were analyzed by Transwell assays, and apoptosis was detected by flow cytometry. Results: miR-448 expression in BC tissues was lower than that in normal tissues, while ZEB1 expression was increased in the former. ZEB1 expression was lower in BC patients with lymph node metastasis than in those without. In patients with clinical stage I-III BC, miR-448 expression decreased with an increase in tumor stage, which was negatively correlated with ZEB1 expression. Upregulation of miR-448 expression can suppress MCF-7 cell proliferation and invasion and promote apoptosis. Upregulation of ZEB1 expression in cells overexpressing miR-448 can partially reverse the inhibition of BC cell growth induced by miR-448. miR-448 can enhance the sensitivity of cells toward paclitaxel and 5-fluorouracil. Conclusions: miR-448 suppresses cell proliferation and invasion and promotes apoptosis by targeting ZEB1. Moreover, it can increase the sensitivity of cells toward paclitaxel and 5-fluorouracil.

Robust prediction of hourly PM2.5 from meteorological data using LightGBM.

Retrieving historical fine particulate matter (PM2.5) data is key for evaluating the long-term impacts of PM2.5 on the environment, human health and climate change. Satellite-based aerosol optical depth has been used to estimate PM2.5, but estimations have largely been undermined by massive missing values, low sampling frequency and weak predictive capability. Here, using a novel feature engineering approach to incorporate spatial effects from meteorological data, we developed a robust LightGBM model that predicts PM2.5 at an unprecedented predictive capacity on hourly (R2 = 0.75), daily (R2 = 0.84), monthly (R2 = 0.88) and annual (R2 = 0.87) timescales. By taking advantage of spatial features, our model can also construct hourly gridded networks of PM2.5. This capability would be further enhanced if meteorological observations from regional stations were incorporated. Our results show that this model has great potential in reconstructing historical PM2.5 datasets and real-time gridded networks at high spatial-temporal resolutions. The resulting datasets can be assimilated into models to produce long-term re-analysis that incorporates interactions between aerosols and physical processes.