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Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Background NOAA’s Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000-98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67-90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000-11,000) each year, valued at 3.2 (95% CI: 0.49-7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.

The year 2020 brought unimaginable challenges in public health, with the confluence of the COVID-19 pandemic and wildfires across the western United States. Wildfires produce high levels of fine particulate matter (PM2.5). Recent studies reported that short-term exposure to PM2.5 is associated with increased risk of COVID-19 cases and deaths. We acquired and linked publicly available daily data on PM2.5, the number of COVID-19 cases and deaths, and other confounders for 92 western U.S. counties that were affected by the 2020 wildfires. We estimated the association between short-term exposure to PM2.5 during the wildfires and the epidemiological dynamics of COVID-19 cases and deaths. We adjusted for several time-varying confounding factors (e.g., weather, seasonality, long-term trends, mobility, and population size). We found strong evidence that wildfires amplified the effect of short-term exposure to PM2.5 on COVID-19 cases and deaths, although with substantial heterogeneity across counties.

Emissions of particulate matter from fires associated with land management practices in Indonesia contribute to regional air pollution and mortality. We assess the public health benefits in Indonesia, Malaysia, and Singapore from policies to reduce fires by integrating information on fire emissions, atmospheric transport patterns, and population exposure to fine particulate matter (PM2.5). We use adjoint sensitivities to relate fire emissions to PM2.5 for a range of meteorological conditions and find that a Business-As-Usual scenario of land use change leads, on average, to 36,000 excess deaths per year into the foreseeable future (the next several decades) across the region. These deaths are largely preventable with fire reduction strategies, such as blocking fires in peatlands, industrial concessions, or protected areas, which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different strategies in mitigating human health impacts depends on the location of fires relative to the population distribution. For example, protecting peatlands through eliminating all fires on such lands would prevent on average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This scientific framework is publicly available through an online decision support tool that allows stakeholders to readily determine the public health benefits of different land management strategies.

Gene editing is a growing gene engineering technique that allows accurate editing of a broad spectrum of gene‐regulated diseases to achieve curative treatment and also has the potential to be used as an adjunct to the conventional treatment of diseases. Gene editing technology, mainly based on clustered regularly interspaced palindromic repeats (CRISPR)–CRISPR‐associated protein systems, which is capable of generating genetic modifications in somatic cells, provides a promising new strategy for gene therapy for a wide range of human diseases. Currently, gene editing technology shows great application prospects in a variety of human diseases, not only in therapeutic potential but also in the construction of animal models of human diseases. This paper describes the application of gene editing technology in hematological diseases, solid tumors, immune disorders, ophthalmological diseases, and metabolic diseases; focuses on the therapeutic strategies of gene editing technology in sickle cell disease; provides an overview of the role of gene editing technology in the construction of animal models of human diseases; and discusses the limitations of gene editing technology in the treatment of diseases, which is intended to provide an important reference for the applications of gene editing technology in the human disease. [ABSTRACT FROM AUTHOR]

The hydrothermal dolomitization, facilitated by basement fault activities, had an important impact on the Permian Maokou Formation dolomite in the Sichuan Basin, which experienced complex diagenesis and presented strong reservoir heterogeneity. The source and age of diagenetic fluids in this succession remain controversial. In this study, various analyses were implemented on samples collected from outcrops and wells near the No. 15 fault in the eastern Sichuan Basin to reconstruct the multi-stage fluid activity and analyze the impact on reservoir development, including petrology, micro-domain isotopes, rare earth elements, homogenization temperature of fluid inclusions, and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) U-Pb dating. The homogenization temperature of primary brine inclusions in fine-grained matrix dolomite and saddle dolomite is concentrated between 100 and 150 °C, which indicates that the impacts of abnormally high temperatures of other geological bodies. The δ13C and δ18O value and low 87Sr/86Sr value indicate that the diagenetic fluid of fine-grained matrix dolomite is mainly Permian seawater. The U-Pb ages of fine-grained matrix dolomite are ~260 Ma, which coincides with the age of the main magmatism of Emeishan Large Igneous Province (ELIP), and hydrothermal fluid provided a favorable high-temperature environment in the penecontemporaneous stage. While highly radiogenic 87Sr/86Sr compositions suggests those of saddle dolomite, the high-temperature Sr-rich formation fluid. The U-Pb ages of saddle dolomite are 245–250 Ma, which coincides with the age of the 255~251 Ma magmatism of ELIP. This indicates that those should be the diagenetic products of the ELIP hydrothermal fluid in the shallow burial stage. The U-Pb age of coarse-grained calcite is 190–220 Ma, and it should be the diagenetic product of the deep burial stage. Brine inclusions associated with primary methane inclusions were developed in coarse-grained calcite, with a homogenization temperature range of 140.8–199.8 °C, which indicates that the formation fluid activities were related to hydrocarbon charging. The Permian Maokou Formation dolomite was firstly formed in the penecontemporaneous shallow burial stage, and then it was subjected to further hydrothermal dolomitization due to the basement faulting and the abnormally high heat flow during the active period of ELIP. Hydrothermal dolomitization contributed to the formation and maintenance of intercrystalline and dissolution pores, whereas it also formed saddle dolomite to fill the pores, and reduce the pore space. The influence of deep fluid activities on reservoir evolution is further distinguished. [ABSTRACT FROM AUTHOR]

Nuclear factor Ys (NF-Ys) are heterotrimeric transcription factors that specifically bind to CCAAT boxes present in numerous eukaryotic promoters. In plants, NF-Y proteins consist of the following three subunits: NF-YA, NF-YB, and NF-YC, each encoded by a gene family. Accumulating evidence underscores the crucial roles of NF-Y proteins in various plant development processes and stress responses, such as embryogenesis, flowering time control, drought tolerance, and heat tolerance. Despite this, a comprehensive genome-wide overview of the NF-Y gene family in strawberries is lacking. To bridge this gap, this study was conducted to identify and characterize the NF-Ys in Fragaria vesca. The investigation revealed the presence of six NF-YA, twelve NF-YB, and five NF-YC members in F. vesca. Furthermore, a comprehensive analysis of the FveNF-Ys was performed, including their phylogenetic relationships, gene structures, chromosomal locations, and conserved domains. MiRNA target site prediction found that there were 30 miRNA target sites in 12 (52.2%) FveNF-Y genes. Additionally, the expression profiles of different tissues and developmental stages demonstrated tissue-specific expression patterns among certain members of each NF-Y subfamily. This observation suggests that specific NF-Y subfamily members may play unique roles in different tissues or stages of development. Furthermore, the transient expression assay demonstrated that three selected FveNF-Ys were localized in the nucleus. Our study represents a pioneering effort in the systemic analyses of FveNF-Y genes and will be useful in understanding the functional characterization of NF-Y genes in Fragaria species. [ABSTRACT FROM AUTHOR]

The upper member of the Lower Ganchaigou Formation (UMoLGF) is a highpotential hydrocarbon exploration area in the North margin of the Qaidam Basin (NMoQB). It represents a typical low-permeability sandstone reservoir. The current understanding of reservoir characteristics of the UMoLGF is poor, and the main controlling factors of high-quality reservoir development remaining unclear. This study, for the first time, integrated various factors to investigate the formation mechanism of high-quality reservoirs in the UMoLGF's low-permeability sandstone reservoirs. Results show three provenance systems developed in the study area: northwest, northeast, and east. The northwestern and northeastern areas share similar reservoir characteristics. The rock type is predominantly feldspar, with relatively poor particle sorting and rounding. Pore types are dominated by secondary dissolution pores. However, the northwestern area has more developed fractures and poorer pore structures than the northeastern. Meanwhile, in the eastern area, the rock fragment content was high, the rock type was mainly litharentie and lithic arkose, particles were well-sorted and well-rounded. Residual intergranular pores, with good structures, dominated the pore type. The UMoLGF has entered the eo-diagenesis B stage with minor progression into the meso-diagenesis A stage. Based on quantitive calculations, this study established porosity evolution models for the different study areas. The initial porosities in the northwestern, northeastern, and eastern areas were 30.8%, 30.4%, and 34.8%, respectively. Compaction and cementation are the major factors contributing to porosity reduction in the three areas, with the most significant impact in the northwestern area. Dissolution significantly improved the reservoir properties in the northwestern area, with little effect on the northeastern and eastern areas. The formation of high-quality reservoir in the UMoLGF was affected by provenance, diagenesis, and fractures, with the primary controlling factors varying by area. In the northwestern area, the formation of high-quality reservoirs benefited from strong dissolution and well-developed fractures. In the northeastern area, the high-quality reservoir was relied upon favorable provenance and dissolution. In the eastern area, provenance provided an excellent material basis for developing high-quality reservoirs, with dissolution and chlorite cementation further improving reservoir properties. This study provides a theoretical foundation for further exploration and development of UMoLGF and offers insights for exploring and developing similar low-permeability sandstone reservoirs. [ABSTRACT FROM AUTHOR]

Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM 2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM 2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM 2.5 , especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88 , normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM 2.5 in several key cities near the wildfire source regions, with smoke PM 2.5 accounting for 5 %–52 % of total PM 2.5 during fire seasons from 2009 to 2020. [ABSTRACT FROM AUTHOR]

The Emeishan large igneous province (ELIP) is among the prominent large igneous provinces in the world and has been associated with mantle plume activity. In this study we investigated the impact of the Emeishan mantle plume on generating the sedimentary rock suites of the eastern Sichuan Basin. We present petrology, detrital zircon U – Pb ages, trace elements, and Hf isotopic compositions of the Middle-Late Permian sedimentary sequences in the Huayingshan region to constrain the provenance and characterize the depositional response to the Emeishan mantle plume activity. The detrital zircon U – Pb ages of the Middle Permian Maokou Formation indicate that terrigenous detritus was mainly sourced from the Kangdian paleoland in the southwestern margin of South China. Detrital zircon grains from the bottom of the Late Permian Longtan Formation yield a lower intercept age of 257 ± 28 Ma, interpreted as the contemporaneous metamorphism related to the ELIP magma. The detrital zircon U – Pb ages of the middle part of the Longtan Formation are characterized by unimodal age spectra with a peak at 259 Ma. Provenance analysis indicates that the Longtan Formation was mainly sourced from the Emeishan volcanic rocks in the Huayingshan region. We propose that the Kangdian paleohigh may have been regionally uplifted as the result of the pre-eruption rise of the Emeishan mantle plume in the late Middle Permian. Moreover, this paleohigh was translated into a denuded zone and provided voluminous detritus into the eastern Sichuan Basin. In the early Late Permian, the ELIP magmas upwelled and outpoured from the Huayingshan fault, and flowed past the peripheral rocks to the low terrain in the Huayingshan region. The ELIP volcanic rocks in the Huayingshan region underwent intense post-eruption erosion, resulting in the deposition of voluminous volcanic detritus in the eastern Sichuan Basin. [ABSTRACT FROM AUTHOR]

Ongoing research is gradually broadening the idea of cancer treatment, with attention being focused on nanoparticles to improve the stability, therapeutic efficacy, targeting, and other important metrics of conventional drugs and traditional drug delivery methods. Studies have demonstrated that drug delivery carriers based on biomaterials (e.g., protein nanoparticles and lipids) and inorganic materials (e.g., metal nanoparticles) have potential anticancer effects. Among these carriers, self-assembled proteins and peptides, which are highly biocompatible and easy to standardize and produce, are strong candidates for the preparation of anticancer drugs. Breast cancer (BC) and cervical cancer (CC) are two of the most common and deadly cancers in women. These cancers not only threaten lives globally but also put a heavy burden on the healthcare system. Despite advances in medical care, the incidence of these two cancers, particularly CC, which is almost entirely preventable, continues to rise, and the mortality rate remains steady. Therefore, there is still a need for in-depth research on these two cancers to develop more targeted, efficacious, and safe therapies. This paper reviews the types of self-assembling proteins and peptides (e.g., ferritin, albumin, and virus-like particles) and natural products (e.g., soy and paclitaxel) commonly used in the treatment of BC and CC and describes the types of drugs that can be delivered using self-assembling proteins and peptides as carriers (e.g., siRNAs, DNA, plasmids, and mRNAs). The mechanisms (including self-assembly) by which the natural products act on CC and BC are discussed. The mechanism of action of natural products on CC and BC and the mechanism of action of self-assembled proteins and peptides have many similarities (e.g., NF-KB and Wnt). Thus, natural products using self-assembled proteins and peptides as carriers show potential for the treatment of BC and CC. [ABSTRACT FROM AUTHOR]

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities. Plain Language Summary: Communities in the United States with lower socioeconomic status and larger racial minority populations more often face higher exposure to air pollution and are therefore more vulnerable to negative health effects. Government officials rely on regulatory networks and low‐cost sensors to monitor air pollution in urban areas, but these networks are typically located in wealthier or predominantly White communities. This disparity in coverage can lead to inaccuracies in exposure assessments and health impact evaluations for nonwhite and/or low‐income communities. To address this issue, we develop a data‐driven algorithm that identifies the most representative and equitable placement of fine particulate matter (PM2.5) air pollution sensors in four U.S. cities with long histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. Our algorithm places a larger number of sensors in low‐income and nonwhite neighborhoods with known environmental pollution issues, while also capturing air pollution extremes compared to networks that only consider air pollution data. Our work provides guidance for creating fair sensor networks in U.S. cities and can help increase access to air pollution data in underprivileged communities. Key Points: A method using pollution data to diagnose the optimal and equitable placement of PM2.5 sensors is presentedBy incorporating race and income information, the method shifts the sensor distribution to more nonwhite and low‐income neighborhoodsThe approach provides a roadmap for urban areas to create low‐cost sensor networks that address America's legacy of environmental racism [ABSTRACT FROM AUTHOR]