Predicting daily concentrations of nitrogen dioxide, particulate matter and ozone at fine spatial scale in Great Britain

Short-term exposure studies have often relied on time-series of air pollution measurements from monitoring sites. However, this approach does not capture short-term changes in spatial contrasts in air pollution. To address this, models representing both the spatial and temporal variability in air pollution have emerged in recent years. Here, we modelled daily average concentrations of nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) and ozone (O3) on a 25 m grid for Great Britain from 2011 to 2015 using a generalised additive mixed model, with penalised spline smooth functions for covariates. The models included local-scale predictors derived using a Geographic Information System (GIS), daily estimates from a chemical transport model, and daily meteorological characteristics. The models performed well in explaining the variability in daily averaged measured concentrations at 48–85 sites: 63% for NO2, 77% for PM2.5, 80% for PM10 and 85% for O3. Outputs of the study include daily air pollution maps that can be applied in epidemiological studies across Great Britain. Daily concentration values can also be predicted for specific locations, such as residential addresses or schools, and aggregated to other exposure time periods (including weeks, months, or pregnancy trimesters) to facilitate the needs of different health analyses

Predicting daily concentrations of nitrogen dioxide, particulate matter and ozone at fine spatial scale in Great Britain

Short-term exposure studies have often relied on time-series of air pollution measurements from monitoring sites. However, this approach does not capture short-term changes in spatial contrasts in air pollution. To address this, models representing both the spatial and temporal variability in air pollution have emerged in recent years. Here, we modelled daily average concentrations of nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) and ozone (O3) on a 25 m grid for Great Britain from 2011 to 2015 using a generalised additive mixed model, with penalised spline smooth functions for covariates. The models included local-scale predictors derived using a Geographic Information System (GIS), daily estimates from a chemical transport model, and daily meteorological characteristics. The models performed well in explaining the variability in daily averaged measured concentrations at 48–85 sites: 63% for NO2, 77% for PM2.5, 80% for PM10 and 85% for O3. Outputs of the study include daily air pollution maps that can be applied in epidemiological studies across Great Britain. Daily concentration values can also be predicted for specific locations, such as residential addresses or schools, and aggregated to other exposure time periods (including weeks, months, or pregnancy trimesters) to facilitate the needs of different health analyses

Predicting daily concentrations of nitrogen dioxide, particulate matter and ozone at fine spatial scale in Great Britain

Short-term exposure studies have often relied on time-series of air pollution measurements from monitoring sites. However, this approach does not capture short-term changes in spatial contrasts in air pollution. To address this, models representing both the spatial and temporal variability in air pollution have emerged in recent years. Here, we modelled daily average concentrations of nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) and ozone (O3) on a 25 m grid for Great Britain from 2011 to 2015 using a generalised additive mixed model, with penalised spline smooth functions for covariates. The models included local-scale predictors derived using a Geographic Information System (GIS), daily estimates from a chemical transport model, and daily meteorological characteristics. The models performed well in explaining the variability in daily averaged measured concentrations at 48–85 sites: 63% for NO2, 77% for PM2.5, 80% for PM10 and 85% for O3. Outputs of the study include daily air pollution maps that can be applied in epidemiological studies across Great Britain. Daily concentration values can also be predicted for specific locations, such as residential addresses or schools, and aggregated to other exposure time periods (including weeks, months, or pregnancy trimesters) to facilitate the needs of different health analyses

Two new benzoate derivatives and one new phenylacetate derivative from a marine-derived fungus <i>Engyodontium album</i>

Two new benzoate derivatives, ethyl 3,5-dimethoxy-2-propionylbenzoate (1) and ethyl 3,5-dihydroxy-2-propionylbenzoate (2), and one new phenylacetate derivative, ethyl 3,5-dimethoxy-2-propionylphenylacetate (3), together with 9 known compounds, were isolated from the fermentation of Engyodontium album derived from deep sea sediment. Their structures were elucidated by spectroscopic techniques, such as NMR, IR and HRESIMS. Compound 3 exhibited inhibitory activities against methicillin-resistant Staphylococcus aureus ATCC 43300 (MRSA) and Vibrio vulnificus, with MIC values of 7.8 and 15.6 μg/mL, respectively.</p

A Novel Nomogram for predicting coronary vulnerable plaques risk in patients with coronary artery disease - Supplementary Figure 1

Objective: To develop and validate a nomogram for predicting coronary vulnerable plaques (VPs) in coronary artery disease (CAD) patients. Methods: One hundred seventy-seven CAD patients were enrolled in the training group. Another 60 patients were included for validation. Based on the identified independent risk factors, a nomogram model was developed and then validated. Results: Type 2 diabetes, hypertension, neutrophil-to-lymphocyte ratio, low-density lipoprotein cholesterol, MCP-1 and MMP-9 were found to be independent risk factors for coronary VPs. Both internal and external validation showed this nomogram had satisfactory discrimination via receiver operating characteristic curves, calibration via calibration plots and clinical application values via decision curve analysis. Conclusion: The authors established a nomogram model predicting coronary VP risk in CAD patients with promising clinical application value. Plain language summary: Vulnerability to coronary atherosclerotic plaques is the important initiating cause of major adverse cardiovascular events in coronary artery disease (CAD) patients. Early detection of high-risk CAD patients with vulnerable plaques (VPs) could prevent the occurrence of major adverse cardiovascular events and improve patients’ clinical outcomes. The present study aimed to investigate the risk factors for coronary VPs and then develop a model for predicting VP risk in CAD patients. The authors found that Type 2 diabetes, hypertension, neutrophil-to-lymphocyte ratio, low-density lipoprotein cholesterol, MCP-1 and MMP-9 were independently associated with coronary VPs in CAD patients. Based on these variables, the authors constructed a nomogram to estimate the individualized risk of VPs and validated the nomogram internally and externally with good accuracy and discrimination. These demonstrated that this nomogram model could achieve individualized prediction of coronary VP risk and would aid physicians in identifying high-risk patients and optimizing a timely treatment strategy with potential clinical application value.</p

Synthesis of 4,7-Difunctionalized Indoles via Imino Exchange and Sulfinyl Migration

A process that rapidly assembles 4,7-difunctionalized indoles from 2-alkynycyclohexadienimines, sulfinamides, and nucleophiles (amines or alcohols) was developed. The process involves imino exchange, cascade cyclization/1,4-nucleophilic addition/aromatization, and 1,3-migration of the sulfinyl group. The 7-sulfinyl group is easy to convert into the sulfonyl or the thioether group through a simple oxidation and reduction reaction

High-Throughput Computational Screening of Novel Two-Dimensional Covalent Organic Frameworks for Efficient Photocatalytic Overall Water Splitting

The pursuit of efficient photocatalysts toward photocatalytic water splitting has attracted wide attention. However, the low efficiency of photocatalytic reactions due to the rapid electron–hole recombination and the time-consuming searching process hinder the development of high-performance photocatalysts. Here, we proposed a data-driven screening procedure for covalent organic frameworks (COFs) as overall solar water-splitting photocatalysts. Based on a COF database through assembling different Cores and Linkers, three COFs are predicted to be efficient photocatalysts for overall solar water splitting after high-throughput computational screening. We found that the photogenerated electrons and holes are well separated on single COF photocatalysts without material engineering, and both hydrogen and oxygen evolution reactions can occur spontaneously on the three screened COFs under visible light radiation. This kind of novel COF screened by a data-driven screening procedure offers new perspectives for advancing efficient photocatalysts

Density-based clustering for data containing two types of points

<div><p>When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day.</p></div

The rs41291957 polymorphism of miR-143/145 and cancer risk: a case-control study and meta-analysis

Recently, the rs41291957 polymorphism in the promoter region of miR-143/145 has been repeatedly investigated for its contribution to cancer susceptibility. However, the results remain conflicting rather than conclusive, which calls for further investigations. Therefore, we here conducted a case-control study and meta-analysis to explore the association between rs41291957 and cancer risk. In the case-control study, a total of 2277 cancer patients (lung, liver, gastric and colorectal cancers) and 800 normal controls were recruited, the genotyping of rs41291957 was performed with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and Sanger sequencing. In the meta-analysis, 5 previously published studies and our present study were included, the STATA 14.0 software was applied to conduct all statistical analyses. The results of case-control study showed that rs41291957 was significantly associated with the risk of gastric cancer, colon cancer, rectal cancer, and colorectal cancer in Hubei Han Chinese population. The results of meta-analysis demonstrated that rs41291957 was significantly associated with overall cancer risk, especially colorectal cancer risk and lung cancer risk. Collectively, the rs41291957 polymorphism of miR-143/145 may be a plausible susceptible locus for cancer risk, which should be validated in future studies with larger samples in different ethnic populations.</p

Data_Sheet_1_Secondary metabolites from the deep-sea derived fungus Aspergillus terreus MCCC M28183.PDF

Aspergillus fungi are renowned for producing a diverse range of natural products with promising biological activities. These include lovastatin, itaconic acid, terrin, and geodin, known for their cholesterol-regulating, anti-inflammatory, antitumor, and antibiotic properties. In our current study, we isolated three dimeric nitrophenyl trans-epoxyamides (1–3), along with fifteen known compounds (4–18), from the culture of Aspergillus terreus MCCC M28183, a deep-sea-derived fungus. The structures of compounds 1–3 were elucidated using a combination of NMR, MS, NMR calculation, and ECD calculation. Compound 1 exhibited moderate inhibitory activity against human gastric cancer cells MKN28, while compound 7 showed similar activity against MGC803 cells, with both showing IC50 values below 10 μM. Furthermore, compound 16 exhibited moderate potency against Vibrio parahaemolyticus ATCC 17802, with a minimum inhibitory concentration (MIC) value of 7.8 μg/mL. This promising research suggests potential avenues for developing new pharmaceuticals, particularly in targeting specific cancer cell lines and combating bacterial infections, leveraging the unique properties of these Aspergillus-derived compounds.</p