Breenmartinussen0550

Thus, the effect of Zn and Mn concentrations up to 5 mg L-1and above 50 mg L-1on B. minor and L. incisa has a different character. At the same time, the response of the studied algae species to the action of Zn and Mn has individual differences. In general, B. minor is more resistant to Mn, while L. incisa is more resistant to Zn.Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used to construct a new retrieval model, namely, point-centered regression convolutional neural network (PSRCNN) suitable for Sentinel 2 and Landsat 8 images. The impact of input feature variables on the accuracy of the inversion model was examined, and the performance of an optimized PSRCNN model was also assessed. This model was applied to remote sensing images of three shallow lakes in the eastern China plain acquired in summer. The PSRCNN model, constructed using five identical bands from Landsat 8 and Sentinel 2 images and 20 band combinations as the input variables, the input window size of 5 × 5 pixels, proves a good predictive ability, with a verification accuracy of R2 = 0.85, the root mean squared error (RMSE) = 13.0 cm, and the relative predictive deviation (RPD) = 2.58. After the sensitive spectral analysis of water transparency, the band combinations that had correlation coefficients higher than 0.6 were selected as the new input feature variables to construct an optimized PSRCNN model (PSRCNNopt) for water transparency. The PSRCNNopt model has an excellent predictive ability, with a verification accuracy of R2 = 0.89, RMSE = 11.48 cm, and RPD =3.0. It outperforms the commonly retrieval models (band ratios, random forest, support vector machine, etc.), with higher accuracy and robustness. Spatial variations in water transparency of three lakes from the retrieval results by PSRCNNopt model are consistent with the field observations.The sorption processes of persistent organic pollutants on microplastics particles are poorly understood. Therefore, the present study investigated the sorption processes of perfluorooctanesulfonate (PFOS) on polyethylene (PE) microplastic particles (MPs) which are representing a prominent environmental pollutant and one of the most abundant microplastic polymers in the aquatic environment, respectively. The focus was set on the investigation of the impact of the particle size on PFOS sorption using four different PE MPs size ranges. The sorption kinetics for 6 months was studied with one selected size range of PE MPs. Besides, the desorption of PFOS from PE MPs under simulated digestive conditions was carried out by using artificial gut fluid mimicking the intestinal juice of fish. The investigation of the size effects of particles over 6 months demonstrated a linear increase of PFOS concentration sorbed onto PE with a decrease of the particle size. Thus, our findings implicate efficient sorption of PFOS onto PE MPs of different sizes. The results showed that PFOS desorbed from the PE MPs into the artificial gut fluid with a rate of 70 to 80%. Besides, a longer exposure of PE MPs to PFOS leads to a higher concentration adsorbed by PE MPs, which may favor the ingestion of higher concentration of PFOS, and thus represents a higher risk to transfer relevant concentrations of PFOS during digestion.We use input-output analysis and Levinson's structural decomposition method to measure China's CO2 emissions under the no-trade hypothesis, to calculate how international trade affects China's emissions. We also analyze the driving factors of the difference between hypothetical no-trade CO2 emissions and actual emissions and discuss the existence of "Pollution Haven Hypothesis" (PHH) in China. The results show that (1) from 2000 to 2017, the hypothetical no-trade CO2 emissions are 2.43-14.67% lower than actual emissions. The scale effect is the main cause of this difference, while the composition effect fluctuates and has little impact. (2) Although exports make other economies' CO2 emissions transfer to China, imports also help avoid China's emissions from some carbon-intensive sectors. (3) International trade has little impact on the cleanliness of China's industry composition. The no-trade industry composition is slightly cleaner than the actual one before 2010, after which trade improves the cleanliness of industry composition to a small extent. PHH is invalid for China in recent years, and results for most developing countries do not support PHH. (4) The relationship between no-trade effects and income per capita for all the economies does not also support PHH. Most economies reduce emissions, and their industry compositions are cleaner because of trade, regardless of their development degree. Trade will not severely influence China's future emission reduction, and improving the cleanliness of carbon-intensive sectors should be paid more attention to.The impacts of Ni toxicity on growth behaviors, photochemical, and antioxidant enzymes activities of wild (Carthamus oxyacantha M. Bieb.) and cultivated (Carthamus tinctorius L.) safflower species were investigated in this study. Fourteen-day-old seedlings were treated with excessive Ni levels [control, 0.5, 0.75, and 1.0 mM NiCl2·6H2O] for 7 days. The results of chlorophyll a fluorescence indicated that toxic nickel exposure led to changes in specific, phenomenological energy fluxes and quantum yields in thylakoid membranes, and activities of donor and acceptor sides of photosystems. These changes resulted in a significant decrease in the photosynthetic activities by about 50% in both species, but these negative effects of Ni were not in a level to destroy the functionality of the photosystems. At the same time, toxic Ni affected membrane integrity and the amount of photosynthetic pigments in the antenna and active reaction centers. AG-14361 inhibitor Additionally, the accumulation of Ni was higher in roots than in stem and le species as phytostabilizers of soils contaminated with nickel, because of their roots accumulating more nickel.Nowadays, most of the country switched to generate their power by renewable energy sources as well as the power industries also mainly focused on the renewable resources for power generation. The renewable resources are solar, wind, biomass, and hydroelectric; out of these, the solar market is developing due to shortage of non-renewable resources. The solar energy is freely obtainable during the year; also, it provides a clean and noiseless environment. Most of the large- and small-scale industries and household consumers moved to generate the power through a PV solar cell. Most of the research work includes the modelling of the PV solar cell based on their requirement in a one-diode model. In this article, a detailed study is provided about the circuit-based single-diode solar cell (SCSC) model and double-diode solar cell (DDSC) with different conditions done in MATLAB/Simulink. Both the SDSC and DDSC models are tested with different values of temperature, irradiation, and shunt resistance. This work helps the researchers study V-I and P-V characteristics of the PV solar cell clearly with different conditions. The outputs of SDSC and DDSC models are compared with simulation outputs. Effect of partial shading is also discussed to get a better idea.Unplanned anthropogenic activities and erratic climate events pose serious threats to groundwater contamination. Therefore, the vulnerability assessment model becomes an essential tool for proper planning and protection of this precious resource. DRASTIC is an extensively adopted groundwater vulnerability assessment model that suffers from several shortcomings in its assessment due to the subjectivity of its rates and weights. In this paper, a new framework was developed to address the subjectivity of DRASTIC model using a bivariate, multi-criteria decision-making approach coupled with a metaheuristic algorithm. Shannon entropy (SE) and stepwise weight assessment ratio analysis (SWARA) methods were coupled with biogeography-based optimization (BBO) to modify rates and weights. The performance of developed models was assessed using area under the receiver operating characteristic (AU-ROC) curve and weighted F1 score. The Shannon-MH model yields better results with an AUC value of 0.8249, whereas other models resulted in an AUC value of 0.8186, 0.7714, 0.7672, and 0.7378 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. It is also evident from weighted F1 score that Shannon-MH model produced maximum accuracy with a value of 0.452 followed by 0.437, 0.419, 0.370, and 0.234 for SWARA-MH, SWARA, SE, and original DRASTIC models, respectively. The results indicated that Shannon model coupled with metaheuristic algorithm outperforms other developed models in groundwater vulnerability assessment.

To identify the genetic causes for acephalic spermatozoa syndrome.

Whole-exome sequencing was performed on the proband from a non-consanguineous to identify pathogenic mutations for acephalic spermatozoa syndrome. Quantitative real-time polymerase chain reaction and whole genome sequencing were subjected to detect deletion. The functional effect of the identified splicing mutation was investigated by minigene assay. Western blot and immunofluorescence were performed to detect the expression level and localization of mutant TSGA10 protein.

Here, we identified a novel heterozygous splicing mutation in TSGA10 (NM_025244 c.1108-1G > T), while we confirmed that there was a de novo large deletion in the proband. The splicing mutation led to the skipping of the exon15 of TSGA10, which resulted in a truncated protein (p. A370Efs*293). Therefore, we speculated that the splicing mutation might affect transcription and translation without the dosage compensation of a normal allele, which possesses a large deletion including intact TSGA10. Western blot and immunofluorescence demonstrated that the very low expression level of truncated TSGA10 protein led the proband to present the acephalic spermatozoa phenotype.

Our finding expands the spectrum of pathogenic TSGA10 mutations that are responsible for ASS and male infertility. It is also important to remind us of paying attention to the compound heterozygous deletion in patients from non-consanguineous families, so that we can provide more precise genetic counseling for patients.

Our finding expands the spectrum of pathogenic TSGA10 mutations that are responsible for ASS and male infertility. It is also important to remind us of paying attention to the compound heterozygous deletion in patients from non-consanguineous families, so that we can provide more precise genetic counseling for patients.Duchenne muscular dystrophy (DMD) is a relatively widespread genetic disease which develops as a result of a mutation in the gene DMD encoding dystrophin. In this review, animal models of DMD are described. These models are used in preclinical studies to elucidate the pathogenesis of the disease or to develop effective treatments; each animal model has its own advantages and disadvantages. For instance, Caenorhabditis elegans, Drosophila melanogaster, and zebrafish (sapje) are suitable for large-scale chemical screening of large numbers of small molecules, but their disease phenotype differs from that of mammals. The use of larger animals is important for understanding of the potential efficacy of various treatments for DMD. While mdx mice have their advantages, they exhibit a milder disease phenotype compared to humans or dogs, making it difficult to evaluate the efficacy of new treatment for DMD. The disease in dogs and pigs is more severe and progresses faster than in mice, but it is more difficult to breed and obtain sufficient numbers of specimens in order to achieve statistically significant results.