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Arsenic has been identified as a carcinogen, although the molecular mechanism underlying itscarcinogenesis has not been fully elucidated. To date, only a few studies have attempted to confirm a direct link between oxidative stress and the Warburg effect . This study demonstrated that 0.2 μmol/L As3+ induced the Warburg effect to contribute to abnormal proliferation of L-02 cells, that was mediated by upregulation of hexokinase 2 (HK2), a key enzyme in glycolysis. Further study indicated that arsenic-induced accumulation of reactive oxygen species (ROS) activated the nuclear factor kappa B (NF-κB) signaling pathway by phosphorylation of p65 at the Ser536 and Ser276 sites, leading to upregulated expression of HK2. We therefore concluded that the ROS/NF-κB/HK2 axis contributes to the Warburg effect and cell proliferation induced by low doses of arsenic. AbbreviationsROS, Reactive oxygen species; NAC, N-acetyl-L-cysteine; 2-DG, 2-deoxy-D-glucose; 2-NBDG, 2-Deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose

Arsenic is an identified carcinogen for humans.In this study, chronic exposure of human hepatocyte L-02 to low-doses of inorganic arsenic caused cell malignant proliferation. Meanwhile, compared with normal L-02 cells, arsenic-transformed malignant cells, L-02-As displayed more ROS and significantly higher Cyclin D1 expression as well as aerobic glycolysis. Moreover, Akt activation is followed by the upregulation of Cyclin D1 and HK2 expression in L-02-As cells, since inhibition of Akt activity by Ly294002 attenuated the colony formation in soft agar and decreased the levels of Cyclin D1 and HK2. In addition, scavenging of ROS by NAC resulted in a decreased expression of phospho-Akt, HK2 and Cyclin D1, and attenuates the ability of anchorage-independent growth ofL-02-As cells, suggested that ROS mediated the Akt activation in L-02-As cells. In summary, our results demonstrated that ROS contributes to the malignant phenotype of arsenic-transformed human hepatocyte L-02-As via the activation of Akt pathway.

Exposure to inorganic or organic arsenic compounds continues to pose substantial public health concerns for hundreds of millions of people around the globe. Highly exposed individuals are susceptible to various illnesses, including impairments and cancers of the lung, liver, skin and bladder. Long-term exposure to low-dose arsenic has been identified to induce aerobic glycolysis, which contributes to cells aberrant proliferation. However, the mechanism underlying arsenic-induced aerobic glycolysis is still unclear. Here, mtDNA copy number is enhanced in arsenic-exposed populations and a positive correlation between serum HK2 and urinary total arsenic was observed in the individuals with high urine arsenic (≥ 0.032 mg/L). In a rat model of trivalent arsenic (iAs

Few children of immigrant families are fully proficient in their parents’ first language (PL1), even if it was the only language they spoke before they started school. This has been the story of co...

A Bayesian model was proposed for daily predictions of the probability of water quality standard violation of enterococci (ENT) levels in recreational beach waters. The Bayesian model consisted of a prior distribution and a likelihood function, which were constructed using seven years of environmental and bacteriological data collected at six recreational beach sites along the U.S. Gulf coast. The likelihood function followed best a normal distribution while the prior distribution was found to be best fitted with a Nakagami distribution. Modelling results showed that the Bayesian model was capable of explaining 86.13% of recreational water quality advisories issued by the U.S. Louisiana Beach Monitoring Program with a false positive rate of 7.24% and a false negative rate of 6.23%, indicating an excellent performance of the Bayesian model. The Bayesian model, presented in this paper, is unique and novel in terms of (1) the integration of a deterministic model and a probabilistic model for the prediction of recreation beach water quality; (2) the identification of important hydrodynamic processes controlling the source and transport of bacteria in coastal beach waters; and (3) the identification of key sinks of bacteria in coastal beach waters. It was found that the tidal washing process plays the most important role in causing the violation of ENT water quality standard for coastal beach waters, followed by the wash-off process of up to four-day antecedent rainfall.

This paper presents a new modeling system for nowcasting and forecasting enterococci levels in coastal recreation waters at any time during the day. The modeling system consists of (1) an artificial neural network (ANN) model for predicting the enterococci level at sunrise time, (2) a clear-sky solar radiation and turbidity correction to the ANN model, (3) remote sensing algorithms for turbidity, and (4) nowcasting/forecasting data. The first three components are also unique features of the new modeling system. While the component (1) is useful to beach monitoring programs requiring enterococci levels in early morning, the component (2) in combination with the component (1) makes it possible to predict the bacterial level in beach waters at any time during the day if the data from the components (3) and (4) are available. Therefore, predictions from the component (2) are of primary interest to beachgoers. The modeling system was developed using three years of swimming season data and validated using additional four years of independent data. Testing results showed that (1) the sunrise-time model correctly reproduced 82.63% of the advisories issued in seven years with a false positive rate of 2.65% and a false negative rate of 14.72%, and (2) the new modeling system was capable of predicting the temporal variability in enterococci levels in beach waters, ranging from hourly changes to daily cycles. The results demonstrate the efficacy of the new modeling system in predicting enterococci levels in coastal beach waters. Applications of the modeling system will improve the management of recreational beaches and protection of public health.

This study presents and compares two models for predicting fecal coliform levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB toolbox and the other one was developed based on the multiple linear regression method (MLR). A total of six independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with eight different combinations of the independent variables are capable of explaining about 76 % of variation in fecal coliform levels for model training data and 44 % for independent data. The findings are obtained from the ANN model and the MLR model using six years of bacteriological and environmental monitoring data. The results show that the ANN model performs consistently better than the MLR model. Applications of the ANN model can significantly reduce potential health risks of fecal pollution to beachgoers. The paper provides new insights into environmental processes responsible for the variation in levels of fecal coliform bacteria in coastal beach waters as well.

The paper presents a decision support system (DSS) for managing and using recreational beaches. The DSS consists of (1) a sensor-assisted water quality monitoring system, (2) a multiple linear regression (MLR) model, developed with the Virtual Beach (VB) program, for predicting enterococci levels, and (3) a web-enabled Geographic Information System (GIS) platform for displaying beach water quality conditions. The MLR model was tested using a total of 945 sets of environmental and bacteriological data collected over 6 swimming seasons. It is found that the MLR model is capable of correctly predicting over 88% of beach advisories and over 80% of no advisory events. The web-enabled GIS platform has nowcasting and forecasting functions. The nowcasting function reduces the decision-making time from current 2–3 days to near real-time while the forecasting function changes the decision-making time from current 2–3 days behind to 1–3 days ahead of actual use of beaches, greatly improving the decision-making in beach management and reducing potential health risks of fecal pollution to beachgoers. While the DSS was specifically developed for the Holly Beach, USA, the methods used in this paper are generally applicable to other coastal beaches.

The US EPA BEACH Act requires beach managers to issue swimming advisories when water quality standards are exceeded. While a number of methods/models have been proposed to meet the BEACH Act requirement, no systematic comparisons of different methods against the same data series are available in terms of relative performance of existing methods. This study presents and compares three models for nowcasting and forecasting enterococci levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB Toolbox and the other two were based on the US EPA Virtual Beach (VB) Program. A total of 944 sets of environmental and bacteriological data were utilized. The data were collected and analyzed weekly during the swimming season (May-October) at six sites of the Holly Beach by Louisiana Beach Monitoring Program in the six year period of May 2005-October 2010. The ANN model includes 15 readily available environmental variables such as salinity, water temperature, wind speed and direction, tide level and type, weather type, and various combinations of antecedent rainfalls. The ANN model was trained, validated, and tested using 308, 103, and 103 data sets (collected in 2007, 2008, and 2009) with an average linear correlation coefficient (LCC) of 0.857 and a Root Mean Square Error (RMSE) of 0.336. The two VB models, including a linear transformation-based model and a nonlinear transformation-based model, were constructed using the same data sets. The linear VB model with 6 input variables achieved an LCC of 0.230 and an RMSE of 1.302 while the nonlinear VB model with 5 input variables produced an LCC of 0.337 and an RMSE of 1.205. In order to assess the predictive performance of the ANN and VB models, hindcasting was conducted using a total of 430 sets of independent environmental and bacteriological data collected at six Holly Beach sites in 2005, 2006, and 2010. The hindcasting results show that the ANN model is capable of predicting enterococci levels at the Holly Beach sites with an adjusted RMSE of 0.803 and LCC of 0.320 while the adjusted RMSE and LCC values are 1.815 and 0.354 for the linear VB model and 1.961 and 0.521 for the nonlinear VB model. The results indicate that the ANN model with 15 parameters performs better than the VB models with 6 or 5 parameters in terms of RMSE while VB models perform better than the ANN model in terms of LCC. The predictive models (especially the ANN and the nonlinear VB models) developed in this study in combination with readily available real-time environmental and weather forecast data can be utilized to nowcast and forecast beach water quality, greatly reducing the potential risk of contaminated beach waters to human health and improving beach management. While the models were developed specifically for the Holly Beach, Louisiana, the methods used in this paper are generally applicable to other coastal beaches.

A series of novel oligorhamnoside derivatives ( 1 – 10 ) and naturally occurring cleistrioside- 5 were synthesized and evaluated for their in vitro antibacterial activities. Among them, dirhamnoside derivative 7 and cleistrioside- 5 displayed similar antibacterial profiles and exhibited moderate to good inhibitory activities on bacterial growth against a panel of Gram-positive bacteria (MICs ⩽ 4–32 μg/mL). The results revealed that these two compounds showed selectivity towards bacterial species strictly, without being affected by the antibiotic-resistant/susceptible properties of one species, which suggested that they might have the potential to avoid antibiotic cross-resistance. In addition, the preliminary SARs of this type of oligorhamnoside derivatives on the antibacterial activities were determined.

The first total synthesis of caminoside B, a novel marine antimicrobial glycolipid isolated from the marine sponge Caminus sphaeroconia, was developed. This marine small molecule inhibitor (IC50 = 20 μM) targeting type III secretory pathway of bacterial pathogenesis was assembled in good yield via a ‘2+2+1’ strategy based on stereocontrolled construction of the four glycosidic linkages.

The first synthesis of three natural glycolipid simplexides 1a–1c, which were isolated from the marine sponge Plakortis simplex and claimed to inhibit T cell proliferation, has been concisely accomplished by a reactivity-based one-pot synthetic strategy under the use of p-toluenethioglycoside (STol) donors.

The occurrence of highly pathogenic avian influenza virus H5N1 highlights the urgent need for new classes of antiviral drugs. Inhibition of H5N1 entry into cells may be an effective strategy. We report the first three small molecule inhibitors saponins with 3-O-beta-chacotriosyl residue, which showed potent inhibitory activity with IC(50) of 7.22-9.25 microM. The subsequent SAR studies showed the 3-O-beta-chacotriosyl residue was essential for the activity, and the aglycone structure also affected the activity.

The total synthesis of 2′,3′-di-O-acetylfrangulin A (1) and prinoidin (2), which were isolated from Rhamnus nepalensis and showed significant cytotoxicity to KB cell line, was achieved for the first time. The structures of the two compounds were confirmed by 1H NMR, 13C NMR, 1H-1H COSY, HMQC and HMBC.

A tetrasaccharide, alpha-l-Rhap-(1-->3)-beta-d-GalpNAc-(1-->6)-alpha-d-Galp-(1-->2)-alpha-d-Galp, the carbohydrate moiety of clarhamnoside isolated from the marine sponge Agelas clathrodes, was synthesized as its propyl glycoside via a convergent approach. The key steps to the synthetic strategy were the stereoselective construction of the reducing-end disaccharide alpha-d-Galp-(1-->2)-d-Galp (5) and efficient coupling with the terminal disaccharide alpha-l-Rhap-(1-->3)-d-GalpNAc building block, in which the N-phthalimido-protected trifluoroacetimidate 13 was proved to be an effective donor.

The first total synthesis of a novel α-galactoglycosphingolipid clarhamnoside has been achieved through a straightforward strategy. A thiogalactosyl donor with a benzylidene group at C-4 and C-6 and nonparticipating p -methoxybenzyl group at C-2 was successfully employed in the stereocontrolled syntheses of α-GalGSLs. The N -Phth-protected trifluoroacetimidate donor for terminal disaccharide was successfully applied in constructing the [GalNAcβ-(1→6)-Gal] glycosidic linkage.

The total synthesis of a partially acetylated dodecanyl tetrarhamnoside derivative, cleistetroside-2, which was isolated from Cleistopholis patens and Cleistopholis glauca and showed significant in vitro antibacterial activity against the Gram-positive bacteria, was achieved for the first time.