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Rhododendron, with its high ornamental value and ecological benefits, is severely impacted by the azalea lace bug (Stephanitis pyrioides), one of its primary pests. This study utilized three Rhododendron cultivars, 'Zihe', 'Yanzhimi', and 'Taile', to conduct a non-targeted metabolomic analysis of leaf samples before and after azalea lace bug stress using headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME/GCMS) and liquid chromatography–mass spectrometry (LCMS). A total of 81 volatile metabolites across 11 categories and 448 nonvolatile metabolites across 55 categories were detected. Significant differences in metabolic profiles were observed among the different cultivars after pest stress. A total of 47 volatile compounds and 49 nonvolatile metabolites were upregulated in the most susceptible cultivar 'Zihe', including terpenes, alcohols, nucleotides, amino acids, and carbohydrates, which are involved in energy production and secondary metabolism. Conversely, 'Yanzhimi' showed a downtrend in both the differential volatiles and metabolites related to purine metabolism and zeatin biosynthesis under pest stress. The resistant cultivar 'Taile' exhibited moderate changes, with 17 volatile compounds and 17 nonvolatile compounds being upregulated and enriched in the biosynthesis of amino acids, pentose, glucuronate interconversions, carbon metabolism, etc. The phenylalanine metabolic pathway played an important role in the pest resistance of different susceptible cultivars, and relevant metabolites such as phenylethyl alcohol, methyl salicylate, and apigenin may be involved in the plant's resistance response. The results of this study provide a new perspective on the metabolomics of Rhododendron–insect interactions and offer references for the development of pest control strategies. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

Avasimibe is a bioavailable acetyl-CoA acetyltransferase (ACAT) inhibitor and shows a good antitumor effect in various human solid tumors, but its therapeutic value in cholangiocarcinoma (CCA) and underlying mechanisms are largely unknown. In the study, we proved that avasimibe retard cell proliferation and tumor growth of CCAs and identified FoxM1/AKR1C1 axis as the potential novel targets of avasimibe. Aldo-keto reductase 1 family member C1 (AKR1C1) is gradually increased along with the disease progression and highly expressed in human CCAs. From survival analysis, AKR1C1 could be a vital predictor of tumor recurrence and prognostic factor. Enforced Forkhead box protein M1 (FoxM1) expression results in the upregulation of AKR1C1, whereas silencing FoxM1 do the opposite. FoxM1 directly binds to promoter of AKR1C1 and triggers its transcription, while FoxM1-binding site mutation decreases AKR1C1 promoter activity. Moreover, over-expressing exogenous FoxM1 reverses the growth retardation of CCA cells induced by avasimibe administration, while silencing AKR1C1 in FoxM1-overexpressing again retard cell growth. Furthermore, FoxM1 expression significantly correlates with the AKR1C1 expression in human CCA specimens. Our study demonstrates a novel positive regulatory between FoxM1 and AKR1C1 contributing cell growth and tumor progression of CCA and avasimibe may be an alternative therapeutic option for CCA by targeting this FoxM1/AKR1C1 signaling pathway. [ABSTRACT FROM AUTHOR]

Soil quality in alpine ecosystems requires regular monitoring to assess its dynamics under changes in climate and land use. Visible near‐infrared (vis‐NIR) spectroscopy could offer an option, as sampling and transporting large numbers of soil samples in the Qinghai‐Tibet Plateau is extremely difficult. However, the potential for in situ vis‐NIR spectra and the optimal algorithms need to be defined in this region. We have therefore evaluated the performance of a deep learning method, multilayer perceptron (MLP), for in situ spectral measurement of soil organic carbon (SOC) with in situ vis‐NIR spectroscopy in southeastern Tibet, China. A total of 39 soil cores (maximum depth 1 m), including 547 soil samples taken from each 5‐cm depth interval, were collected. The spectra were also measured at each 5‐cm depth interval accordingly. After spectral preprocessing, 4,096 MLP models were generated by taking all the combinations from six parameters defined in the MLP. The 10‐fold‐core cross‐validation showed that MLP had a good performance for in situ SOC prediction, and the best MLP model had an R2 of.92, which were much better than those of the partial least squares regression model (R2 =.80). The results also suggested that the number of epochs, number of neurons, and dropout rate were the most important parameters in the MLP model. We concluded that in situ vis‐NIR spectroscopy coupled with an MLP model has high potential for large‐scale SOC monitoring in the Qinghai‐Tibet Plateau. Our results also provide a reference for rapid hyperparameter optimization using MLP for future soil spectroscopic modeling. Highlights: We evaluated the in situ measurement of SOC using vis‐NIR spectra.A multilayer perceptron was used to predict SOC in alpine soils.Hyperparameter optimization was conducted by grid searching.A multilayer perceptron had good performance for in situ SOC prediction.The most vital parameters for a multilayer perceptron model were identified. [ABSTRACT FROM AUTHOR]

The inorganic pyrophosphatase gene (PPA1) encodes inorganic pyrophosphatase, an enzyme that catalyzes the hydrolysis of inorganic pyrophosphate to orthophosphate, and has been revealed to be dysregulated in several types of human cancer. However, the role of PPA1 in intrahepatic cholangiocarcinoma (ICC) has not yet been determined. The present study detected PPA1 expression and investigated its clinical significance in ICC. Tissue microarray blocks containing 93 ICC specimens were constructed. The protein expression of PPA1 in these specimens was detected by immunohistochemistry. PPA1 was overexpressed in 49.5% of the ICC specimens and was significantly associated with large tumor size, positive margins, T stage, lymph nodal metastases, poorly differentiated tumors and advanced disease stage. Furthermore, PPA1 expression was an indicator of future recurrence and poor survival in patients with ICC. Increased expression of PPA1 is a common event in human ICC and is significantly associated with a poor outcome in patients with ICC, suggesting a potential role for PPA1 in the development and progression of ICC. [ABSTRACT FROM AUTHOR]

Rhododendron spp. is an important ornamental species that is widely cultivated for landscape worldwide. Heat stress is a major obstacle for its cultivation in south China. Previous studies on rhododendron principally focused on its physiological and biochemical processes, which are involved in a series of stress tolerance. However, molecular or genetic properties of rhododendron’s response to heat stress are still poorly understood. The phenotype and chlorophyll fluorescence kinetics parameters of four rhododendron cultivars were compared under normal or heat stress conditions, and a cultivar with highest heat tolerance, “Yanzhimi” (R. obtusum) was selected for transcriptome sequencing. A total of 325,429,240 high quality reads were obtained and assembled into 395,561 transcripts and 92,463 unigenes. Functional annotation showed that 38,724 unigenes had sequence similarity to known genes in at least one of the proteins or nucleotide databases used in this study. These 38,724 unigenes were categorized into 51 functional groups based on Gene Ontology classification and were blasted to 24 known cluster of orthologous groups. A total of 973 identified unigenes belonged to 57 transcription factor families, including the stress-related HSF, DREB, ZNF, and NAC genes. Photosynthesis was significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway, and the changed expression pattern was illustrated. The key pathways and signaling components that contribute to heat tolerance in rhododendron were revealed. These results provide a potentially valuable resource that can be used for heat-tolerance breeding. [ABSTRACT FROM AUTHOR]

The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce). The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce). Figures and Tables Graph: Figure 9 Progression of loss values (A) and training accuracy (B) for four loss functions used with Nested SE-Deeplab during training. [Extracted from the article]

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination. [ABSTRACT FROM AUTHOR]

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model's training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet. [ABSTRACT FROM AUTHOR]

Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms' health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger–Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin's concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC <0.20 and RPIQ <0.75. These results confirm the effectiveness of using portable spectrometers to determine the contents of several potentially toxic elements in soils. Image 1 • pXRF and vis-NIR spectra used to measure contents of 8 potentially toxic elements in soil. • Three levels of data fusion were employed and compared. • Cu and Zn could be measured by pXRF spectra alone; neither spectra could measure Hg. • Other potentially toxic elements were best measured by high-level fusion of both spectra. Capsule: Soil potentially toxic elements could be estimated rapidly with good accuracy by portable spectrometers and sensor fusion could further improve estimation accuracy. [ABSTRACT FROM AUTHOR]

The cover image is based on the Research Article I Monitoring soil organic carbon in alpine soils using in situ vis-NIR spectroscopy and a multilayer perceptron i by Songchao Chen et al., https://doi.org/10.1002/ldr.3497 GLO:KQ7/15may20:ldr3648-toc-0001.jpg PHOTO (COLOR): . gl. [Extracted from the article]

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification. [ABSTRACT FROM AUTHOR]

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin's concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain. [ABSTRACT FROM AUTHOR]

Background: Annexin-1 contributes to the pathological consequence and sequelae of most serious human diseases including cardiovascular disease and cancer. Although diverse roles in carcinogenesis have been postulated, its role in human gastrointestinal cancers still remains controversial.Methods: The mRNA and protein expression profiles of ANXA1 were studied in human esophageal, gastric, pancreatic, colorectal, liver, and bile duct cancers using Real-Time PCR, western blotting, and immunohistochemistry. Gain/loss-of-function by pcDNA3.1-ANXA1 and ANXA1-shRNA was performed in gastric cancer cells.Results: ANXA1 was widely expressed in adult gastrointestinal tissue. All methods showed that ANXA1 was down-regulated in esophageal, gastric, and bile duct cancers, but up-regulated in pancreatic cancer. Forced ANXA1 expression in gastric cancer cells leads to cell growth inhibition and concomitantly modulates COX-2 expression. We confirm loss of ANXA1 and overexpression of COX-2 in clinical gastric cancer, suggesting that the anti-proliferative function of ANXA1 against COX-2 production might be lost.Conclusions: ANXA1 expression is "tumor-specific" and might play a multifaceted role in cancer development and progression. ANXA1 was widely expressed in normal gastrointestinal epithelium, suggesting its role in the maintenance of cellular boundaries. Furthermore, ANXA1 regulates GC cell viability via the COX-2 pathway. [ABSTRACT FROM AUTHOR]

Mercury is a highly toxic element that is widely present in all types of environmental media and can accumulate in living organisms. Prolonged exposure to high levels of mercury can lead to brain damage and death, so the detection of mercury is of great importance. In this study, a cost-effective and easy-to-operate electrochemical sensing method was successfully developed based on an amino-functionalized titanium-based MXene (NH 2 -Ti 3 C 2 T x ) for the rapid and selective detection of Hg 2+ that could have a coordination effect with the -NH 2 group of NH 2 -Ti 3 C 2 T x to promote the efficient accumulation of Hg 2+ . In this strategy, the NH 2 -Ti 3 C 2 T x was first modified on glassy carbon electrodes (GCE) to fabricate the electrochemical sensor. Benefiting from the excellent electrical conductivity, abundant active sites, and strong adsorption capacity performance of the NH 2 -Ti 3 C 2 T x , the NH 2 -Ti 3 C 2 T x modified GCE (NH 2 -Ti 3 C 2 T x /GCE) exhibited satisfactory selectivity and enhanced square wave anodic stripping voltammetry (SWASV) measurement for the rapid detection of trace amounts of Hg 2+ in aqueous solutions. The electrochemical sensor was found to be capable of detecting Hg 2+ with a low detection limit of 8.27 nmol L -1 and a linear range of 0.5 μmol L -1 to 50 μmol L -1 . The response time of the electrochemical sensing method was 308 s. In addition, the electrochemical sensing method has good selectivity, repeatability and stability, and multiple heavy metal ions have no effect on its detection, with repeatability and stability RSDs of 1.68% and 1.43%, respectively. Furthermore, the analysis of practical water samples demonstrated that the developed method was highly practical for the actual determination of Hg 2+ with recoveries in the range of 99.22-101.90%.

A simple dual-signal assay that combined colorimetric and fluorometric strategy for uric acid (UA) rapid detection was designed based on the versatility of facile synthesized MnO 2 nanosheet. The oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) and the fluorescence quenching of quantum dots (QDs) occurred simultaneously in the presence of MnO 2 nanosheet. UA could decompose MnO 2 nanosheet into Mn 2+ , resulting in the fluorescence recovery of QDs, along with the fading of the blue color of ox TMB. Based on the principles above, the detection of UA could be realized by the change of the dual signals (colorimetric and fluorometric). The linear range of the colorimetric mode was 5-60 μmol L -1 , and the limit of detection (LOD) was 2.65 μmol L -1 ; the linear range of the fluorescence mode was wide at 5-120 μmol L -1 , and the LOD could be as low as 1.33 μmol L -1 . The method was successfully used for analyzing UA levels in human serum samples, indicating that this new dual-signal method could be applied in clinical diagnosis.

Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the toxicity, abundant sources, non-biodegradable properties, and accumulative behaviour of HMs. The degree of soil HM contamination in China, especially in the Yangtze River Delta, is prominent. In this study, 1822 pairs of soil and crop samples at corresponding locations were collected from the southern Yangtze River Delta of China, and the contents of Ni, Cr, Zn, Cd, As, Cu, Hg, and Pb were measured. The single pollution index in soil (SPI) and Nemerow composite pollution index (NCPI) were used to assess the degree of HM pollution in soil, and the crop pollution index (CPI) was used to explore the degree of HM accumulation in crops. The bioaccumulation factor (BAF) was used to investigate the translocation of heavy metals in the soil-crop system. The health risks caused by HMs were calculated based on the model released by the U.S. Environmental Protection Agency. The SPIs of all elements were at the unpolluted level. The mean NCPI was at the alert level. The mean CPIs were in the following decreasing order: Ni (1.007) > Cr (0.483) > Zn (0.335) > Cd (0.314) > As (0.232) > Cu (0.187) > Hg (0.118) > Pb (0.105). Only the mean content of Ni in the crops exceeded the national standard value. The standard exceeding rates were used to represent the percentage of samples whose heavy metal content is higher than the corresponding national standard values. The standard exceeding rates of Cu, Hg, and Cd in soil were significantly higher than corresponding values in crops. Meanwhile, the standard exceeding rates of Ni, As, and Cr in crops were significantly higher than corresponding values in soil. The chronic daily intake (CDI) of children (13.8 × 10 -3 ) was the largest among three age groups, followed by adults (6.998 × 10 -4 ) and seniors (5.488 × 10 -4 ). The bioaccumulation factors (BAFs) of all crops followed the order Cd (0.249) > Zn (0.133) > As (0.076) > Cu (0.064) > Ni (0.018) > Hg (0.011) > Cr (0.010) > Pb (0.001). Therefore, Cd was most easily absorbed by crops, and different crops had different capacities to absorb HMs. The hazard quotient (HQ) represents the potential non-carcinogenic risk for an individual HM and it is an estimation of daily exposure to the human population that is not likely to represent an appreciable risk of deleterious effects during a lifetime. All the HQs of the HMs for the different age groups were significantly less than the alert value of 1.0 and were at a safe level. This indicated that citizens in the study area face low potential non-carcinogenic risk caused by HMs. The total carcinogens risks (TCRs) for children, adults, and seniors were 5.24 × 10 -5 , 2.65 × 10 -5 , and 2.08 × 10 -5 , respectively, all of which were less than the guideline value but at the alert level. Ingestion was the main pathway of carcinogen risk to human health., Competing Interests: The authors have declared that no competing interests exist. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Background: The therapeutic and prognostic value of the glycolytic enzymes hexokinase, phosphofructokinase, and pyruvate kinase (PK) has been implicated in a variety of cancers, while their roles in treatment of and prognosis for hilar cholangiocarcinoma (HC) remain unclear. In this study, we determined the expression of PKM2 in and its impact on biology and clinical outcome of human HC., Methods: The regulation and function of PKM2 in HC pathogenesis was evaluated using human tissues, molecular and cell biology, and animal models, and its prognostic significance was determined according to its impact on patient survival., Results: We found that expression of hexokinase 1 and the M2 splice isoform of PK (PKM2) was upregulated in HC tissues and that this expression correlated with tumor recurrence and outcome. PKM2 expression was increased in HC cases with chronic cholangitis as demonstrated by isobaric tags for relative and absolute quantification. High PKM2 expression was highly correlated with high syndecan 2 (SDC2) expression and neural invasion. PKM2 downregulation led to a decrease in SDC2 expression. Treatment with metformin markedly suppressed PKM2 and SDC2 expression at both the transcriptional and posttranscriptional levels and inhibited HC cell proliferation and tumor growth., Conclusions: PKM2 regulates neural invasion of HC cells at least in part via regulation of SDC2. Inhibition of PKM2 and SDC2 expression contributes to the therapeutic effect of metformin on HC. Therefore, PKM2 is an independent prognostic factor and potential therapeutic target for human HC.

Hexokinase 1 (HK1) and pyruvate kinase M2 (PKM2) are two key regulators in glycosis and oncogenic markers in cancers. In the present study, we investigated the expression profile by Western blotting and immunohistochemistry and determined their prognostic values in the gastric cancer. Expression of HK1 and PKM2 was remarkably increased in gastric cancer tissues and was significantly associated lymphatic metastasis and advanced TNM staging. In the COX regression model, HK1 and TNM stage were analyzed as adverse prognostic indicators in gastric cancer. Furthermore, patients with HK1 expression showed remarkable shorter survival duration in both lymphatic metastasis cohort and advanced staging cohort. Our results suggest that overexpression of PKM2 and HK1, especially the latter, significantly associates with lymphatic metastasis, advanced clinical staging and unfavorable prognosis in gastric cancer.