Multiple Environmental Factors Research Articles

Toxic interactions between dopamine, α-synuclein, monoamine oxidase, and genes in mitochondria of Parkinson's disease.

Parkinson's disease is characterized by its distinct pathological features; loss of dopamine neurons in the substantia nigra pars compacta and accumulation of Lewy bodies and Lewy neurites containing modified α-synuclein. Beneficial effects of L-DOPA and dopamine replacement therapy indicate dopamine deficit as one of the main pathogenic factors. Dopamine and its oxidation products are proposed to induce selective vulnerability in dopamine neurons. However, Parkinson's disease is now considered as a generalized disease with dysfunction of several neurotransmitter systems caused by multiple genetic and environmental factors. The pathogenic factors include oxidative stress, mitochondrial dysfunction, α-synuclein accumulation, programmed cell death, impaired proteolytic systems, neuroinflammation, and decline of neurotrophic factors. This paper presents interactions among dopamine, α-synuclein, monoamine oxidase, its inhibitors, and related genes in mitochondria. α-Synuclein inhibits dopamine synthesis and function. Vice versa, dopamine oxidation by monoamine oxidase produces toxic aldehydes, reactive oxygen species, and quinones, which modify α-synuclein, and promote its fibril production and accumulation in mitochondria. Excessive dopamine in experimental models modifies proteins in the mitochondrial electron transport chain and inhibits the function. α-Synuclein and familiar Parkinson's disease-related gene products modify the expression and activity of monoamine oxidase. Type A monoamine oxidase is associated with neuroprotection by an unspecific dose of inhibitors of type B monoamine oxidase, rasagiline and selegiline. Rasagiline and selegiline prevent α-synuclein fibrillization, modulate this toxic collaboration, and exert neuroprotection in experimental studies. Complex interactions between these pathogenic factors play a decisive role in neurodegeneration in PD and should be further defined to develop new therapies for Parkinson's disease.

The effect of cave ventilation on carbon and oxygen isotopic fractionation between calcite and drip water

Rapid CO2 degassing and calcite precipitation driven by cave ventilation influence the speleothem δ18O and δ13C. However, the drivers of cave ventilation are not completely understood due to the lack of monitoring of multiple environmental factors. Furthermore, the understanding of isotope fractionation caused by the dissolution of speleothem in undersaturated drip water is limited during the cave air stagnation. In this study, we displayed four years of cave microenvironment monitoring in Shawan Cave, Southwestern China, and analyzed the δ13CDIC and δ18O of drip water, and calcite precipitation δ18O and δ13C. The results show that the ventilation process is attributed to buoyancy airflow between external atmosphere, fissure air, and cave air. This causes that the higher (lower) cave air pCO2 in the summer (winter) is associated with upward airflow mode (downward airflow mode). Furthermore, cave ventilation could control the isotopic fractionation. Specifically, when cave air pCO2 is lower, the carbon isotopic disequilibrium between calcite and dissolved inorganic carbon (DIC) is controlled by the degassing of CO2 associated with calcite precipitation. The disequilibrium fractionation in carbon isotopes is less pronounced at slower drip-rate sites. The oxygen isotope fractionation between calcite and the drip water is found to be close to equilibrium. However, the high cave air pCO2 (exceeding 10,000 ppm) may result in drip water undersaturation to drive the dissolution of speleothem calcite. The δ18O values of drip water are pulled away from their original values to disequilibrate to the calcite because the exchange time of oxygen in the dissolved carbonates with the oxygen in the water is sufficiently long. Hence, the dissolution of speleothems may be a new mechanism to explain the oxygen isotopic disequilibrium between the calcite and drip water during the cave air stagnation. The carbon isotope fractionation between calcite and drip water is close to equilibrium.

The influence of socioenvironmental risk factors on risk-taking behaviors among Bahamian adolescents: a structural equation modeling analysis

ABSTRACT Background Adolescents’ risk-taking behaviors can have profound impacts on their future health. Few studies have established a relationship between multiple social environmental factors and adolescent risk behaviors. We used structural equation modeling to examine the role of parental monitoring and environmental risks on adolescents’ behavioral intentions and risk behaviors. Methods Data were collected through the baseline survey of a national implementation project among 2205 Grade 6 students in 24 government schools in The Bahamas in 2019. Structural equation modeling examined relations among parental monitoring, environmental risk factors, behavioral intentions, and risk behaviors. Results Students had engaged in various delinquent, substance use, and sexual risks. In the structural equation model, parental monitoring demonstrated direct negative (protective) effects on behavioral intentions and risk behaviors, whereas environmental risk factors had a direct positive effect on adolescent behavioral intentions and risk behaviors. The model had an R2 value of 0.57 for adolescent risk behaviors. Conclusion Parental monitoring and environmental risk factors had strong influences on risk-taking behaviors of early adolescents. Future adolescent health behavior interventions should consider offering additional prevention resources to early adolescents who are exposed to multiple environmental risk factors.

Enhanced Photocatalytic Activity of O‐deficient Modified CuAl2O4/MoS2 Composite Photocatalysts in Wastewater Treatment Applications

AbstractThe O‐deficient modified CuAl2O4/MoS2 composite photocatalysts (ODMCAOMSCP) are fabricated by a wet chemical route and low‐temperature treatment for the degradation of tetracycline hydrochloride (TC). The fine particles dotted on the large sheet are revealed to be comprised of large sheets with rich oxygen defects and nanoparticles, which endows it with a high optical absorption coefficient, available reactive species, excellent charge carrier separation, and high photocatalytic activity. The finely designed ODMCAOMSCP achieve a TC degradation first‐order rate constant of 0.01436 min−1, which is 3.99 and 2.70 times higher than that of MoS2 and CuAl2O4, respectively. Response surface analysis reveals that multiple environmental factors including initial TC concentration, catalyst content, and initial pH affect the photocatalytic activity of the catalyst on the basis of backpropagation (BP) neural network optimized by genetic algorithm and Box‐Behnken design. The degradation intermediates and degradation pathways are determined by a liquid chromatograph‐mass spectrometer (LC‐MS). The toxicity of TC over ‐ ODMCAOMSCP is studied by the toxicity analysis software and the cultivation of mung bean. Mechanism investigation demonstrates that the major active species of ODMCAOMSCP for the degradation TC are attributed to oxygen vacancy, hydroxyl radical (∙OH), and superoxide radical (∙O2−).

Influence of perinatal and childhood exposure to tobacco and mercury in children's gut microbiota.

Early life determinants of the development of gut microbiome composition in infants have been widely investigated; however, if early life pollutant exposures, such as tobacco or mercury, have a persistent influence on the gut microbial community, its stabilization at later childhood remains largely unknown. In this exposome-wide study, we aimed at identifying the contribution of exposure to tobacco and mercury from the prenatal period to childhood, to individual differences in the fecal microbiome composition of 7-year-old children, considering co-exposure to a width of established lifestyle and clinical determinants. Gut microbiome was studied by 16S rRNA amplicon sequencing in 151 children at the genus level. Exposure to tobacco was quantified during pregnancy through questionnaire (active tobacco consumption, second-hand smoking -SHS) and biomonitoring (urinary cotinine) at 4 years (urinary cotinine, SHS) and 7 years (SHS). Exposure to mercury was quantified during pregnancy (cord blood) and at 4 years (hair). Forty nine other potential environmental determinants (12 at pregnancy/birth/infancy, 15 at 4 years and 22 at 7 years, such as diet, demographics, quality of living/social environment, and clinical records) were registered. We used multiple models to determine microbiome associations with pollutants including multi-determinant multivariate analysis of variance and linear correlations (wUnifrac, Bray-Curtis and Aitchison ß-diversity distances), single-pollutant permutational multivariate analysis of variance adjusting for co-variates (Aitchison), and multivariable association model with single taxa (MaAsLin2; genus). Sensitivity analysis was performed including genetic data in a subset of 107 children. Active smoking in pregnancy was systematically associated with microbiome composition and ß-diversity (R2 2-4%, p < 0.05, Aitchison), independently of other co-determinants. However, in the adjusted single pollutant models (PERMANOVA), we did not find any significant association. An increased relative abundance of Dorea and decreased relative abundance of Akkermansia were associated with smoking during pregnancy (q < 0.05). Our findings suggest a long-term sustainable effect of prenatal tobacco exposure on the children's gut microbiota. This effect was not found for mercury exposure or tobacco exposure during childhood. Assessing the role of these exposures on the children's microbiota, considering multiple environmental factors, should be further investigated.

Genome assembly and population genomic data of a pulmonate snail Ellobium chinense

Ellobium chinense is an airbreathing, pulmonate gastropod species that inhabits saltmarshes in estuaries of the northwestern Pacific. Due to a rapid population decline and their unique ecological niche in estuarine ecosystems, this species has attracted special attention regarding their conservation and the genomic basis of adaptation to frequently changing environments. Here we report a draft genome assembly of E. chinense with a total size of 949.470 Mb and a scaffold N50 of 1.465 Mb. Comparative genomic analysis revealed that the GO terms enriched among four gastropod species are related to signal transduction involved in maintaining electrochemical gradients across the cell membrane. Population genomic analysis using the MSMC model for 14 re-sequenced individuals revealed a drastic decline in Korean and Japanese populations during the last glacial period, while the southern Chinese population retained a much larger effective population size (Ne). These contrasting demographic changes might be attributed to multiple environmental factors during the glacial–interglacial cycles. This study provides valuable genomic resources for understanding adaptation and historical demographic responses to climate change.

Optimal lane-changing and collision avoidance strategy for intelligent vehicles in high-speed driving environments

This paper proposes an optimal lane-changing and control strategy to solve the collision avoidance of intelligent vehicles in highway driving. A quintic polynomial lane-changing trajectory model is established in the Frenet frame, and the trajectory planning problem is transformed into the optimisation problem of lane-changing target coordinates. To consider the multiple environmental factors on vehicle lane-changing, the feasible region of the lane-changing target point was generated based on the lateral acceleration limitation and the safe distance constraints. The performance evaluation indexes were established, considering efficiency, comfort, lateral slip, and lane offset. The multi-objective optimisation problem is solved in the feasible region, and the optimisation results were applied to the collision detection process. In addition, considering the nonlinear characteristics of tire force caused by sudden lateral movements of vehicles at high-speed, a vehicle dynamics model based on the Magic Formula is established, and Model Predictive Control is applied to track the expected trajectory. Simulation results show that this strategy can plan suitable lane-changing trajectories in high-speed working conditions and achieve vehicle collision avoidance.

Light-enhanced oxygen degradation of MAPbBr3 single crystal.

Organometal halide perovskites are promising materials for optoelectronic applications, whose commercial realization depends critically on their stability under multiple environmental factors. In this study, a methylammonium lead bromide (MAPbBr3) single crystal was cleaved and exposed to simultaneous oxygen and light illumination under ultrahigh vacuum (UHV). The exposure process was monitored using X-ray photoelectron spectroscopy (XPS) with precise control of the exposure time and oxygen pressure. It was found that the combination of oxygen and light accelerated the degradation of MAPbBr3, which could not be viewed as a simple addition of that by oxygen-only and light-only exposures. The XPS spectra showed significant loss of carbon, bromine, and nitrogen at an oxygen exposure of 1010 Langmuir with light illumination, approximately 17 times of the additive effects of oxygen-only and light-only exposures. It was also found that the photoluminescence (PL) emission was much weakened by oxygen and light co-exposure, while previous reports had shown that PL was substantially enhanced by oxygen-only exposure. Measurements using a scanning electron microscope (SEM) and focused ion beam (FIB) demonstrated that the crystal surface was much roughened by the co-exposure. Density functional theory (DFT) calculations revealed the formation of superoxide and oxygen induced gap state, suggesting the creation of oxygen radicals by light illumination as a possible microscopic driving force for enhanced degradation.

Clinico-etiological profile of atopic dermatitis in Northeast India

Objectives: Atopic dermatitis (AD) is a chronic, inflammatory skin disorder with relapsing and remitting course affecting all age groups and has multifactorial pathogenesis. As multiple genetic and environmental factors contribute to the pathogenesis of AD, its prevalence and presentation vary among different ethnic groups residing in different geographical areas. This study was conducted to study the clinical profile and aggravating factors in cases of AD attending a tertiary hospital in Meghalaya, India, and to find a correlation of disease with serum immunoglobulin E (IgE) levels. Material and Methods: Patients suspected of AD were diagnosed on the basis of Hanifin and Rajka criteria and included in this study. All relevant demographic, anthropometric, clinical, and biochemical data were collected as per a preset pro forma. Serum IgE levels were tested for every patient using electrochemiluminescence immunoassay. Results: A total of 50 diagnosed cases of AD were included in our study with ages ranging from 5 months to 61 years, and the median age for the study population was 7.9 years. Male-to-female ratio was 1:1.17. Urban patients (66%) outnumbered rural patients and the majority of these patients (90%) belonged to the upper middle class (class II) according to the modified Kuppuswamy scale. The disease was aggravated in winters in 70%, in summer in 10%, due to wool in 48%, and due to food items in 22% of patients. Allergic rhinitis was found to be coexisting in 8% of patients, whereas asthma was found in 2% of patients. The predominant site of involvement was the face (91.9%) in children and the flexor surface (92.3%) among adults. The most common clinical presentation included pruritus (100%) and xerosis (98%). Serum IgE was raised in 58% of patients. Conclusion: Atopic dermatitis of chronic type with predominant facial involvement in children and predominant flexural involvement in adults was common in our study population. Seasonal changes, food items, and woolen clothes were common causative and exacerbating factors. Some atypical presentations included posterior thigh eczema, infra-auricular fissures, retroauricular fissures, eyelid eczema, genital dermatitis, juvenile plantar dermatoses, infranasal fissure, and follicular variant of the disease.

SAR-Transformer-based decomposition and geophysical interpretation of InSAR time-series deformations for the Hong Kong-Zhuhai-Macao Bridge

Time-series interferometric synthetic aperture radar (InSAR) provides a unique tool for measuring large-scale and long-term land surface deformation. Under the assumption of a single linear deformation model in conventional InSAR, it is difficult to quantify and interpret the impacts of multiple environmental factors that presumably induce nonlinear deformations. In this paper, we propose a SAR-Transformer method to decompose InSAR time-series signals into various physics-related components and apply the method to evaluate the deformation of the world's longest cross-sea bridge, the Hong Kong-Zhuhai-Macao Bridge (HZMB). We first developed an improved bridge geometry-based InSAR network to monitor the deformation of the HZMB using Sentinel-1 and COSMO-SkyMed images from 2019 to 2022, which were validated using the leveling and GPS data. The SAR-Transformer model was trained using synthetic InSAR time-series samples and applied to decompose the monitored InSAR measurements. Compared with that of conventional curve-fitting and seasonal-trend decomposition using LOESS, SAR-Transformer reduced the mean absolute error at least by 58.32% and mean absolute percentage error at least by 8.84% for time-series signal reconstruction. We evaluated the decomposed patterns according to the geotechnical, meteorological, and marine processes, and found that: 1) Seasonal thermal expansion owing to temperature changes was significant in all parts of the bridge, and deflection due to concrete shrinkage and creep was observed on cable-stayed bridges. 2) The artificial islands experienced evident ground subsidence with a decelerating trend. In particular, the newly adopted non-dredged reclamation method resulted in a lower decelerated settlement than that of fully-dredged reclamation areas. 3) The seawall showed linear horizontal movement from the outward stretching of the reclaimed soil consolidation and periodic displacement related to sea tidal loading. Furthermore, typhoons and coastal earthquakes had limited effects on the permanent movement of the bridge. These results improve the understanding of the interactions between artificial super-infrastructures and environmental factors, and provide valuable guidelines for the maintenance and management of the HZMB.

ANTIOXIDANTS AS POTENTIAL BIOMARKERS OF NONALCOHOLIC FATTY LIVER DISEASE STAGES

Nonalcoholic fatty liver disease is a complex and multifactorial pathology associated with multiple epigenetic, genetic and environmental factors, and its pathogenesis is still not fully understood. Nonalcoholic fatty liver disease begins with simple steatosis and progresses to nonalcoholic steatohepatitis, which is characterized by signs of oxidative stress, increased lipid peroxidation activity, inflammation, fibrosis, and apoptosis. Antioxidant deficiency is important in the pathogenesis of nonalcoholic fatty liver disease, as it is they that cause the transition of steatosis to steatohepatitis in nonalcoholic fatty liver disease. Markers of antioxidant defense are used to assess disease progression and pathologic state. This article reviews several key biomarkers of antioxidant defense that can be investigated in clinical practice. Determination of these biomarkers in nonalcoholic fatty liver disease can be used for diagnostic purposes.

Inborn errors of immunity present with neuropsychiatric symptoms overlapping with autistic behavioral symptoms

Autism spectrum disorder (ASD) is a behaviorally defined syndrome affected by multiple genetic and environmental factors. A wide variety of risk factors for ASD have been identified and many of these affect immune functions. This may not be surprising, since the immune system and the nervous system share common signaling mechanisms and affect each other as a part of the neuroimmune network. The ever-expanding scope of inborn errors of immunity (IEIs) has revealed multiple pathogenic gene variants that manifest overlapping clinical features of common neuropsychiatric diseases, including ASD. These IEIs often cause dysregulated immune activation and resultant chronic inflammation affecting multiple organs. Some IEIs also cause changes in morphogenesis and plasticity of the central nervous system. Such patients often present with a puzzling array of clinical features and some of them may be diagnosed with ASD or other neuropsychiatric conditions. The progress of our understanding of disease mechanisms for IEIs at the molecular levels has led to gene-specific treatment measures in some diseases. In addition, some ASD patients are found to have laboratory findings of neuroinflammation that resemble those seen in IEI patients. This may pave the way for applying specific treatment measures used for IEI patients in such ASD patients. This review focuses on describing IEIs that have overlapping features of ASD. Emphasis is also on IEIs that can be treated by targeting identified disease mechanisms. Such information may be helpful for clinicians who are considering genetic/metabolic workup in ASD patients.

Climate (im)mobilities in the Eastern Hindu Kush: The case of Lotkuh Valley, Pakistan

The relationship between climate, environment, and human mobility is complex as (im)mobility outcomes are influenced by multiple socioeconomic, political, and environmental factors. The current debate is focused on migration as an adaptation strategy in the face of climate change but largely ignores the immobility aspect, particularly in the Eastern Hindu Kush where mountain livelihoods are strongly dependent on local environmental conditions. In this study, we examine the interrelations between climate change and the environment as drivers of human mobility and immobility in the mountain communities of Lotkuh valley, Chitral, in north Pakistan. We employed a mixed methods approach grounded in migration theory to describe the relationship between climate change, environment, and (im)mobility outcomes. The study reveals that climate (im)mobilities are the outcome of a complex interplay between climate change, extreme events, and local livelihoods. The primary drivers of (im)mobility are socioeconomic factors. Forced displacement is driven by a multitude of extreme events in the area. Three critical aspects of livelihoods—land resources, crop productivity, and livestock farming—are identified as significant factors influencing mobility and immobility outcomes. Recurring extreme events such as floods and landslides exacerbate soil erosion and the loss of fertile farmlands, leading to food insecurity and compelling households to resort to labor migration as an adaptation strategy. Conversely, for households facing severe income stress and depleted economic assets, immobility becomes the only viable option due to insufficient resources for migration. Moreover, the study reveals that some households adopt a mixed strategy by sending select members to other areas while others remain in their places of origin to sustain their livelihoods. The study has implications for policymakers, government, and development organizations in the region suggesting sustainable livelihoods and adaptation measures to address the specific challenges faced by mountain communities in the Lotkuh valley and the wider region.

Lactic acid bacteria and spoilage bacteria: Their interactions in Escherichia coliO157:H7 biofilms on food contact surfaces and implications for beef contamination

AbstractThis research explores the interaction between Shiga toxin‐producing Escherichia coli (STEC) O157:H7 and bacteria species commonly found in beef processing environments, specifically Carnobacterium, Lactobacillus, Comamonas, Raoultella, and Pseudomonas. The study investigated how various environmental conditions impact the formation of biofilms and the ability of O157:H7 to transfer from multispecies biofilm onto beef surfaces. For this purpose, a mixture of lactic acid bacteria (LAB), spoilage bacteria (106 CFU/mL), and E. coli O157 (103 CFU/mL) were combined as follows: LAB (T1): Carnobacterium piscicola + Lactobacillus bulgaricus + O157:H7, an spoilage bacteria (T2): Comamonas koreensis + Raoultella terrigena + O157:H7, an spoilage bacteria (T3): Pseudomonas aeruginosa + C. koreensis strain + O157:H7 and only O157:H7 as control (T4). Multispecies biofilms were developed on thermoplastic polyurethane (TPU) and stainless steel (SS) coupons at 10 and 25°C for 6 days, washed and stored for 6, 30, and 60 days at wet (60%–90% RH) and dry (20%–50%, RH) conditions. To evaluate O157:H7 transfer, beef cubes (3 × 3 × 1 cm) were placed on the coupons, followed by a 50‐g weight (7.35 kPa). The experiment was repeated three times in triplicate for each strain combination. Results demonstrate that biofilms formed at 10°C were generally weaker (less biomass) than those at 25°C. Regardless of temperature, more viable O157:H7 cells were transferred to beef from moist biofilms on TPU surfaces. At 25°C, T3 biofilm exhibited the lowest O157:H7 transfer to beef by 1.44 log10 CFU/cm2 (p &lt; 0.01). At 10°C, none of the multispecies biofilm (T1–T3) affected the number of O157:H7 transfers to beef (p &gt; 0.05). Notably, O157:H7 was not detected on food contact surfaces with 30 and 60‐day‐old dry biofilms (T1–T4). Through enrichment, E. coli O157:H7 was recovered from multispecies biofilms T1, T2, and T3. Findings from this study imply that multispecies biofilms contribute to the persistence of O157:H7 under dry conditions, regardless of temperature. These results underscore the intricate influence of multiple environmental factors—including surface type, biofilm age, humidity, temperature, and the presence of other bacterial species—on the risk of beef contamination facilitated by biofilms.

The effects of multiple environmental factors on global carbon allocation

BackgroundThe allocation of photosynthate among the parts of plants (e.g., leaves, wood tissues and roots) strongly regulates their growth, and this conditions the terrestrial carbon cycle. Recent studies have shown that atmospheric CO2 and climate change dominate the changes in carbon allocation in plants, but the magnitude and mechanism of its effects remain unclear.MethodsThe Community Atmosphere Biosphere Land Exchange (CABLE) model can accurately simulate the responses of carbon allocation to environmental changes. This study quantifies the contributions of four environmental factors—atmospheric CO2, temperature, precipitation, and radiation—on resource availability and carbon allocation from 1979 to 2014 by using the CABLE model.ResultsThe results of the CABLE model showed that rising CO2 significantly reduced carbon allocation to the leaves of plants at a global scale, but the other three environmental factors exhibited contrasting effects that dominated the rise in carbon allocation to the leaves. The increased precipitation and CO2 significantly reduced the light availability and increased carbon allocation to the wooden parts of plants. By contrast, the rising temperature reduced the water availability, resulting in a decrease in carbon allocation to the wooden parts. All four environmental factors consistently exhibited negative effects on carbon allocation to the roots, with rising precipitation causing the largest reduction in carbon allocation to them. Moreover, except for CO2, the effects of the other three environmental factors were heterogeneous owing to their variable interactions in different regions.ConclusionsThe CABLE model can accurately represent the mechanisms of response of resource availability and carbon allocation to environmental changes. Our study highlights the substantial environmental regulation of global carbon allocation. The responses of carbon allocation to global environmental changes need to be extensively studied through ecosystem models based on different hypotheses.

Predicting Pressure from Environmental Factors in London based on ARIMAX Model

Pressure is an important index in atmospheric science as it influences various aspects of both the natural environment and everyday life. Although the significance of pressure is widely recognized, accurately measuring and recording pressure data remains a crucial issue. This is due to the fact that weather prediction involves fitting a large number of nonlinear factors, which poses significant challenges in establishing precise predicting models. Therefore, the current focus of most research on pressure lies in the innovation and improvement of algorithms for pressure sensors and other related devices. This paper makes a statistical analysis on the raw climate data of London over a period of 13 years starting from 2008, applying an ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) dynamic regression model to fit 4 exogenous variables to the pressure data as linear factors. The fitting results showed that the variation in pressure is associated with multiple environmental factors. Furthermore, it is observed that the pressure in London is projected to be slightly higher than normal one in the upcoming months. Due to the numerous parameters in the ARIMAX model, overfitting and underfitting phenomena are prone to occur, therefore, for parameter adjustments, further processing is required.

Identification of QTLs and allelic effect controlling lignan content in sesame (Sesamum indicum L.) using QTL-seq approach.

Sesame (Sesamum indicum L.), an oilseed crop, is gaining worldwide recognition for its healthy functional ingredients as consumption increases. The content of lignans, known for their antioxidant and anti-inflammatory effects, is a key agronomic trait that determines the industrialization of sesame. However, the study of the genetics and physiology of lignans in sesame is challenging, as they are influenced by multiple genes and environmental factors, therefore, the understanding of gene function and synthetic pathways related to lignan in sesame is still limited. To address these knowledge gaps, we conducted genetic analyses using F7 recombinant inbred line (RIL) populations derived from Goenbaek and Gomazou as low and high lignin content variants, respectively. Using the QTL-seq approach, we identified three loci, qLignan1-1, qLignan6-1, and qLignan11-1, that control lignan content, specifically sesamin and sesamolin. The allelic effect between loci was evaluated using the RIL population. qLignan6-1 had an additive effect that increased lignan content when combined with the other two loci, suggesting that it could be an important factor in gene pyramiding for the development of high-lignan varieties. This study not only highlights the value of sesame lignan, but also provides valuable insights for the development of high-lignan varieties through the use of DNA markers in breeding strategies. Overall, this research contributes to our understanding of the importance of sesame oil and facilitates progress in sesame breeding for improved lignan content.

Lead aggravates Alzheimer's disease pathology via mitochondrial copper accumulation regulated by COX17

Alzheimer's disease (AD) is a common neurodegenerative disease that is associated with multiple environmental risk factors, including heavy metals. Lead (Pb) is a heavy metal contaminant, which is closely related to the incidence of AD. However, the research on the role of microglia in Pb-induced AD-like pathology is limited. To determine the mechanism by which Pb exposure aggravates AD progression and the role of microglial activation, we exposed APP/PS1 mice and Aβ1-42-treated BV-2 cells to Pb. Our results suggested that chronic Pb exposure exacerbated learning and memory impairments in APP/PS1 mice. Pb exposure increased the activation of microglia in the hippocampus of APP/PS1 mice, which was associated with increased deposition of Aβ1-42, and induced hippocampal neuron damage. Pb exposure upregulated copper transporter 1 (CTR1) and downregulated copper P-type ATPase transporter (ATP7A) in the hippocampus of APP/PS1 mice and Aβ1-42-treated BV-2 cells. Moreover, Pb enhanced mitochondrial translocation of the mitochondrial copper transporter COX17, leading to an increase in mitochondrial copper concentration and mitochondrial damage. This could be reversed by copper-chelating agents or by inhibiting the mitochondrial translocation of COX17. The increased mitochondrial copper concentration caused by increased mitochondrial translocation of COX17 after Pb exposure may be related to the enhanced mitochondrial import pathway of AIF/CHCHD4. These results indicate that Pb induces the activation of microglia by increasing the concentration of copper in the mitochondria of microglia, and microglia release inflammatory factors to promote neuroinflammation, thus aggravating the pathology of AD. The present study provides new ideas for the prevention of Pb-induced AD.

On-road high-emitting vehicle identification by an automatic hyperparameter optimization model based on a remote sensing system

Optical remote sensing systems (RSSs) are ideal for monitoring and identifying high-emitting vehicles on roads, as they can be installed on any road for non-contact measurements. In general, an on-road vehicle is considered a high-emitting vehicle when its instantaneous emissions exceed the specified cut-points, as monitored by RSSs. However, RSS measurements of vehicle emissions are easily influenced by transient operating conditions of passing vehicles and multiple environmental factors, resulting in variable results for the same vehicle, further interfering with the screening of high-emitting vehicles. In this paper, an automatic hyperparametric optimization model is established in an RSS to identify high-emitting vehicles by fusing multi-feature data on environmental factors and vehicle operating conditions obtained from the RSS with the chassis and engine dynamometer test results provided by vehicle inspection stations (VISs). Qualitative and quantitative experimental results show that our model exhibits better recognition performance for high-emitting vehicles in RSSs of different times and sites, which reflects the good self-adaptability of the model. Moreover, the hyperparameters of the model do not need to be manually adjusted, so the model can be automatically trained to meet the requirements of real-time recognition scenarios for high-emitting vehicles on the road.

Classifying Alzheimer’s disease and normal subjects using machine learning techniques and genetic-environmental features

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics. The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel). The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.