Noninvasive prenatal testing of trisomies 21 and 18 by massively parallel sequencing of maternal plasma DNA in twin pregnancies.

OBJECTIVE: The objective of this study is to assess the performance of noninvasive prenatal testing for trisomies 21 and 18 on the basis of massively parallel sequencing of cell-free DNA from maternal plasma in twin pregnancies. METHOD: A double-blind study was performed over 12 months. A total of 189 pregnant women carrying twins were recruited from seven hospitals. Maternal plasma DNA sequencing was performed to detect trisomies 21 and 18. The fetal karyotype was used as gold standard to estimate the sensitivity and specificity of sequencing-based noninvasive prenatal test. RESULTS: There were nine cases of trisomy 21 and two cases of trisomy 18 confirmed by karyotyping. Plasma DNA sequencing correctly identified nine cases of trisomy 21 and one case of trisomy 18. The discordant case of trisomy 18 was an unusual case of monozygotic twin with discordant fetal karyotype (one normal and the other trisomy 18). The sensitivity and specificity of maternal plasma DNA sequencing for fetal trisomy 21 were both 100% and for fetal trisomy 18 were 50% and 100%, respectively. CONCLUSION: Our study further supported that sequencing-based noninvasive prenatal testing of trisomy 21 in twin pregnancies could be achieved with a high accuracy, which could effectively avoid almost 95% of invasive prenatal diagnosis procedures.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm.

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices - Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) - indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.

QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors.

By partial least square regression, simple quantitative structure-activity relationship (QSAR) models were developed for the toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Quantum chemical descriptors computed by semi-empirical PM3 method were used as predictor variables. Three optimal QSAR models are developed for 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together, respectively. The cross-validated Q (cum) (2) values for the three QSAR models of 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together are 0.816, 0.629 and 0.603, respectively, indicating good predictive capabilities for the biological toxicity of these PCDD/Fs. The present study suggests that quantum chemical descriptors of POPs indeed govern the binding affinity of these chemicals for aryl hydrocarbon receptors. Moreover, different models contain different molecular descriptors to define respective equation, which suggests that the relationship between molecular structure and the binding affinity of these chemicals for aryl hydrocarbon receptors is complex.

Quantitative structure-activity relationships for prediction of the toxicity of hydroxylated and quinoid PCB metabolites.

Quantitative structure-activity relationship (QSAR) models were developed for the in vitro potencies to downregulate gap junctional intercellular communication (GJIC) of hydroxylated polychlorinated biphenyls (OH-PCBs) and PCB quinines using partial least squares (PLS) regression. Quantum chemical descriptors computed by the semiempirical AM1, PM3 and MNDO methods were used as predictor variables. The cross-validated Q2cum values for the three optimal QSAR models are 0.784, 0.789 and 0.755, respectively, indicating good predictive capabilities for the acute inhibition of GJIC (IC(50)) of oxygenated PCB derivatives. The slightly higher Q2cum value of the model using computed molecular descriptors from the PM3 Hamiltonian suggested a slightly better predictive power than the models developed using AM1 or MNDO. However, given these dispersion parameters in these three optimal models, there would not be a significant difference between the Q2cum values. Results from this study showed that the logarithmic scale of IC(50) is affected by different molecular structural descriptors.

[Research on quantitative structure-property relationships for n-octanol/water partition coefficients of phthalic acid esters].

Quantum chemical parameters based on the PM3 method were calculated for phthalic acid esters (PAEs). Using partial least squares (PLS) algorithm, a quantitative structure-property relationships (QSAR) model for the n-octanol/water partition coefficients (K(ow)) of PAEs was developed. The result indicates that the model has a good stability and predicting ability. It can be concluded from this study that the main factors affecting IgK(ow) of PAEs are total energy (TE), relative molecular weight (M(r)), average molecular polarizability (alpha) and standard heat of formation (deltaH(f)). The IgK(ow) values increase with increasing M(r) and a values. In contrast, increasing TE and deltaH(f) values leads to the decrease of IgK(ow).