[Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy].

Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.

Genetic variation of circHIBADH enhances prostate cancer risk through regulating HNRNPA1-related RNA splicing.

The current study aimed to investigate associations of circRNAs and related genetic variants with the risk of prostate cancer (PCa) as well as to elucidate biological mechanisms underlying the associations. We first compared expression levels of circRNAs between 25 paired PCa and adjacent normal tissues to identify risk-associated circRNAs by using the MiOncoCirc database. We then used logistic regression models to evaluate associations between genetic variants in candidate circRNAs and PCa risk among 4662 prostate cancer patients and 3114 healthy controls, and identified circHIBADH rs11973492 T>C as a significant risk-associated variant (odds ratio = 1.20, 95% confidence interval: 1.08-1.34, P = 7.06 × 10 -4) in a dominant genetic model, which altered the secondary structure of the corresponding RNA chain. In the in silico analysis, we found that circHIBADH sponged and silenced 21 RNA-binding proteins (RBPs) enriched in the RNA splicing pathway, among which HNRNPA1 was identified and validated as a hub RBP using an external RNA-sequencing data as well as the in-house (four tissue samples) and publicly available single-cell transcriptomes. Additionally, we demonstrated that HNRNPA1 influenced hallmarks including MYC target, DNA repair, and E2F target signaling pathways, thereby promoting carcinogenesis. In conclusion, genetic variants in circHIBADH may act as sponges and inhibitors of RNA splicing-associated RBPs including HNRNPA1, playing an oncogenic role in PCa.

[Accumulative levels of organochlorine pesticides of HCHs, DDTs in breast milk and risk factors analysis in Shenzhen].

OBJECTIVE: To first investigate the accumulative levels of DDTs and HCHs in human breast milk of primipara in Shenzhen area, analyze influencing factors and assess exposure risk of infant in Shenzhen. METHODS: From April in 2011 to April in 2012, 85 primiparas who have lived in Shenzhen over 3 years after parturition 4 - 6 weeks were recruited, and their breast milk were collected and questionnaires were filled out simultaneously. The samples were extracted five times with hexane, cleaned up by gel permeation chromatograph (GPC) and SPE, and quantified by gas chromatography-election capture detection (GC-ECD). Correlations between DDTs, HCHs and maternal age, weight, dietary, living time as well as infant birth weight and length were also analyzed with the SPSS 13.0 statistical software respectively. RESULTS: Among the several groups of DDTs and HCHs metabolites, p,p'-DDE were detected in total 85 samples and beta-HCH were detected in 58 samples, which accounted for 68.2% of the breast milk. The median levels of sigmaHCHs and sigma DDTs were 2.980 ng/g whole weight (80.200 ng/g fat) and 9.610 ng/g whole weight (268.390 ng/g fat). Both levels of sigmaHCHs and sigmaDDTs in the human milk had a positive association with maternal age among the demographic characteristics of primiparas. Furthermore, levels of sigma HCHs were positively correlated with freshwater fish consumption. However, sigma DDTs levels which were calculated in fat had a positive association only with the amount of poultry meat intake. CONCLUSION: Beta-HCH and p,p'-DDE were detected in human breast milk, and correlated with age and dietary intake. The average estimated daily intakes of HCHs and DDTs by infants are 0.468, 1.842 microg/(kg x BW x d) respectively, lower than acceptable daily intakes (ADI) proposed by the Ministry of Health of China and (the WHO/FAO) Joint Meeting of Pesticide Residues (JMPR). The average levels of HCHs, DDTs in the breast milk in Shenzhen general population are lower than those of Chinese average level.

[Predicting soil salinity based on spectral symmetry under wet soil condition].

There has been a growing interest in using spectral reflectance as a rapid and inexpensive tool for soil salinity monitoring in recent years. However, since soil moisture often exerts a tremendous influence on soil reflectance, the monitoring accuracy under various moisture conditions cannot fully satisfy the requirements of agricultural practice. In the present paper, a linear model was built to relate the spectral symmetry in the band of 1 370 - 1 610 nm with the salt content and moisture content of the saline soil based on regularly measured data of reflectance, soil moisture and salt content of the surface of 5 soil columns during the simulated evaporation process in laboratory. The results showed that the model was good with r greater than 0.8. By inversing the model, soil salt content then was predicted after moisture content was determined. The results showed that the prediction accuracy was acceptable with a root mean square error (RMSE) of 2.059 g x kg(-1) and an r of 0.656. The results demonstrated the feasibility of using spectral symmetry to predict soil salt content under various moisture conditions.

Protective effects of the notoginsenoside R1 on acute lung injury by regulating the miR-128-2-5p/Tollip signaling pathway in rats with severe acute pancreatitis.

Notoginsenoside R1 (NG-R1), the extract and the main ingredient of Panax notoginseng, has anti-inflammatory effects and can be used in treating acute lung injury (ALI). In this study, we explored the pulmonary protective effect and the underlying mechanism of the NG-R1 on rats with ALI induced by severe acute pancreatitis (SAP). MiR-128-2-5p, ERK1, Tollip, HMGB1, TLR4, IκB, and NF-κB mRNA expression levels were measured using real-time qPCR, and TLR4, Tollip, HMGB1, IRAK1, MyD88, ERK1, NF-κB65, and P-IκB-α protein expression levels using Western blot. The NF-κB and the TLR4 activities were determined using immunohistochemistry, and TNF-α, IL-6, IL-1ß, and ICAM-1 levels in the bronchoalveolar lavage fluid (BALF) using ELISA. Lung histopathological changes were observed in each group. NG-R1 treatment reduced miR-128-2-5p expression in the lung tissue, increased Tollip expression, inhibited HMGB1, TLR4, TRAF6, IRAK1, MyD88, NF-κB65, and p-IκB-α expression levels, suppressed NF-κB65 and the TLR4 expression levels, reduced MPO activity, reduced TNF-α, IL-1ß, IL-6, and ICAM-1 levels in BALF, and alleviated SAP-induced ALI. NG-R1 can attenuate SAP-induced ALI. The mechanism of action may be due to a decreased expression of miR-128-2-5p, increased activity of the Tollip signaling pathway, decreased activity of HMGB1/TLR4 and ERK1 signaling pathways, and decreased inflammatory response to SAP-induced ALI. Tollip was the regulatory target of miR-128-2-5p.

Reliable and stable fundus image registration based on brain-inspired spatially-varying adaptive pyramid context aggregation network.

The task of fundus image registration aims to find matching keypoints between an image pair. Traditional methods detect the keypoint by hand-designed features, which fail to cope with complex application scenarios. Due to the strong feature learning ability of deep neural network, current image registration methods based on deep learning directly learn to align the geometric transformation between the reference image and test image in an end-to-end manner. Another mainstream of this task aims to learn the displacement vector field between the image pair. In this way, the image registration has achieved significant advances. However, due to the complicated vascular morphology of retinal image, such as texture and shape, current widely used image registration methods based on deep learning fail to achieve reliable and stable keypoint detection and registration results. To this end, in this paper, we aim to bridge this gap. Concretely, since the vessel crossing and branching points can reliably and stably characterize the key components of fundus image, we propose to learn to detect and match all the crossing and branching points of the input images based on a single deep neural network. Moreover, in order to accurately locate the keypoints and learn discriminative feature embedding, a brain-inspired spatially-varying adaptive pyramid context aggregation network is proposed to incorporate the contextual cues under the supervision of structured triplet ranking loss. Experimental results show that the proposed method achieves more accurate registration results with significant speed advantage.

Tumor necrosis factor α reduces gonadotropin-releasing hormone release through increase of forkhead box protein O1 activity.

It has been found that hypothalamus helps to control aging, and hypothalamus-driven programmatic aging is associated with nuclear factor-κB (NF-κB)-mediated decrease of gonadotropin-releasing hormone (GnRH). However, the molecular mechanism(s) underlying aging-associated hypothalamic GnRH decline are largely unknown. Forkhead box O (FOXO), a family of transcription factors, has been demonstrated to be associated with aging. GnRH neuronal cell line GT1-7 was used in this study to determine whether FOXO1 was involved in tumor necrosis factor α (TNF-α)-induced decrease of GnRH. Our data showed that FOXO1 activity was increased by TNF-α through inhibition of its phosphorylation. Increased FOXO1 activity inhibited gnrh1 gene and activated NF-κB, thereby impairing the secretion of GnRH from GT1-7 cells. The increase of FOXO1 activity contributes to TNF-α-induced decrease of GnRH release, which may underscore the significance of this event to the development of aging and therapeutic interventions against age-dependent pathologies.

ß-amyloid-induced gonadotropin-releasing hormone decline involving Forkhead transcription factor FOXO3a and nuclear factor-κB.

Previously, it has been demonstrated that aging is controlled by the hypothalamus, and that hypothalamus-driven programmatic aging is associated with nuclear factor-κB (NF-κB)-mediated gonadotropin-releasing hormone (GnRH) decrease. Abundant accumulation of ß-amyloid (Aß) has been observed in brains of cognitively normal elderly. However, it is unclear whether Aß neurotoxicity is involved in aging-associated hypothalamic GnRH decline. GT1-7 cells, which are a cell line of hypothalamic GnRH neurons, were used in the current study to investigate whether and how Aß decreased GnRH release. The results of the current study demonstrated that Aß impaired the release of GnRH through activation of NF-κB. Mechanistic studies revealed that Aß activated NF-κB via Forkhead box protein O3a, thereby inhibiting gnrh1 gene. The results of the present study provided novel insights into the mechanisms underlying aging-dependent hypothalamic GnRH decline.

A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.

The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple types of lesions which provided strong evidence for the ophthalmologists to make diagnosis. It is particularly significant to figure out an efficient method to not only accurately classify DR fundus images but also recognize all kinds of lesions on them. In this paper, a deep learning-based multi-label classification model with Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed, which can both make DR classification and automatically locate the regions of different lesions. To reducing laborious annotation work and improve the efficiency of labeling, this paper innovatively considered different types of lesions as different labels for a fundus image so that this paper changed the task of lesion detection into that of image classification. A total of five labels were pre-defined and 3228 fundus images were collected for developing our model. The architecture of deep learning model was designed by ourselves based on ResNet. Through experiments on the test images, this method acquired a sensitive of 93.9% and a specificity of 94.4% on DR classification. Moreover, the corresponding regions of lesions were reasonably outlined on the DR fundus images.

Predicting Soil Salinity with Vis-NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization.

Robust models for predicting soil salinity that use visible and near-infrared (vis-NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis-NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future.