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MDVs to save the day: Precisely how autophagy-deficient cancer malignancy tissues adjust to flawed mitophagy.
Surabaya River is one of the lower tributary of the Brantas, which is included as the top 20 plastic polluted rivers worldwide. The main function of Surabaya River is for raw water source for Surabaya City. Besides, this river is used for domestic and industrial waste discharges, and for irrigation. This study aimed to investigate the distribution of microplastics (MPs/MP) and its characteristics in three stratified depths (surface, middle, and bottom) of the river. This study was conducted in eight sampling sites, which were located in the Lower Brantas and the Surabaya Rivers. The MP abundance in the surface, middle, and bottom of the river ranged 1.47-43.11; 0.76-12.56; and 1.43-34.63 particles/m3, respectively. The highest average of MP abundance was 21.16 particles/m3 at the lower end of the river. The MP particles tend to be mainly distributed in the surface than in the other depth levels. The MP particles in each depth were generally dominated by film shaped large MP of 1-5 mm size, and transparent in color. Three main polymer types of the MPs were low density polyethylene, polypropylene, and polyethylene. Due to their small size, nanoplastics (NPLs) possess specific properties which can potentiate their toxicity towards aquatic organisms. As primary producers, microalgae are at the base of aquatic food chains, thus negative impacts of NPLs will likely lead to disturbances in ecosystem productivity. The majority of data available on the toxicity of NPLs is limited to polystyrene and green microalgae, leaving a significant lack of knowledge on impacts of other polymer types across different taxonomic groups. So, the main objective of this study was to evaluate the cell responses of the red microalgae Rhodomonas baltica to plain and carboxylated poly(methyl methacrylate) NPLs (PMMA and PMMA-COOH, 50 nm). Results showed different NPL behaviour in media over time, with PMMA forming micro-scale aggregates and PMMA-COOH maintaining its nominal size range. PMMA caused a higher impact in cellular and physiological parameters than PMMA-COOH, even though a decrease in algal growth was only seen for the later. Overall, PMMA caused a significant decrease in cell viability followed by an increase in cell size and complexity, overproduction of pigments, loss of membrane integrity, hyperpolarization of the mitochondrial membrane, increased production of ROS and LPO, decrease in DNA content and reduced photosynthetic capacity. Conversely, a decrease in algal growth for PMMA-COOH was connected to an impairment in cell cycle and consequent decrease in cell viability, metabolic activity and photosynthetic performance, with negligible effects in ROS formation and pigments content. buy ATG-017 This study provided a first insight into the mechanistic understanding of the toxic impacts of PMMA and PMMA-COOH NPLs in red microalgae. Results obtained suggest an interaction between both NPLs and R. baltica cell surface that is dependent on particle behaviour and surface chemistry. Future experiments focusing on the in-depth characterization of the mode of action of these particles are recommended. Accurate exposure estimate of the air pollutant PM2.5 is required to evaluate its health impacts in epidemiological studies, due to its adverse effects on human's respiratory and cardiovascular systems. However, traditional personal sampling is time and cost consuming. Thus, modeling techniques are needed to accurately predict the personal exposure level to PM2.5. In this study, a total of 117 older adults over 60 were recruited in Tianjin, a heavily polluted city in northern China, for indoor, outdoor and personal PM2.5 sampling. Eighteen variables which may increase the exposure level of older adults were recorded for artificial neural network (ANN) simulation. Four modeling techniques, including time-integrated activity modeling, Monte Carlo simulation, ANN modeling, and combined use of principal component analysis (PCA) and ANN model, were used to evaluate their ability for predicting real exposure values of PM2.5. The results of traditional time-weighted activity modeling showed the lowest correlation with measured values with R2 of 0.57 and 0.42 in winter and summer, respectively. For Monte Carlo simulation, high correlation was obtained (R2 of 0.93 and 0.92 in winter and summer, respectively) between percentiles of the predicted and the real exposure values. Compared with the simple ANN models, the combined use of PCA and ANN produced the most accurate results with R2 of 0.99 and RMSE lower than 15. Since the information of the input variables for the PCA-ANN model can be obtained from the questionnaire and fixed air quality monitoring sites, this technique shows a great potential in predicting personal exposure level to the air pollutant because no additional concentration measurement is needed. Rigorous air pollution managements since 2013 resulted in decreasing trend in fine particulate matter (PM2.5) in China. Regional air pollution prevention measures were extra implemented in the "2 + 26" region since 2017. However, haze pollution dramatically rebounded in the winter of 2018. Both of the observed analyses and the numerical results (basing on a global 3-D chemical transport model) demonstrated that, although intensified prevention measures existed, atmospheric circulation and local meteorological conditions still significantly influenced the variation in haze pollution. The simulated PM2.5 concentrations (with fixed emissions) driven by meteorology in 2018 were 12-15% higher than those with atmospheric circulations in 2017 both under a low and a high emission level, close to the observed 10% PM2.5 rebound. In 2018, positive sea ice anomalies around Beaufort Sea and "triple pattern" anomalies of sea surface temperature in the North Pacific and North Atlantic enhanced the anomalous anticyclonic circulations over the air-polluted region, and thus resulted in minimum surface wind speed during 1979-2018 and 16.8% shallower boundary layer than those in 2017. In the stagnated air of winter 2018, the transported dispersion of pollutant particles was weakened, however more secondary aerosols were produced by enhanced chemical reactions, which jointly contributed to the PM2.5 rebound in 2018.
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