Improved traceability of Shiga-toxin-producing <Emphasis Type="Italic">Escherichia coli</Emphasis> using CRISPRs for detection and typing

Among strains of Shiga-toxin-producing Escherichia coli (STEC), seven serogroups (O26, O45, O103, O111, O121, O145, and O157) are frequently associated with severe clinical illness in humans. The development of methods for their reliable detection from complex samples such as food has been challenging thus far, and is currently based on the PCR detection of the major virulence genes stx1, stx2, and eae, and O-serogroup-specific genes. However, this approach lacks resolution. Moreover, new STEC serotypes are continuously emerging worldwide. For example, in May 2011, strains belonging to the hitherto rarely detected STEC serotype O104:H4 were identified as causative agents of one of the world’s largest outbreak of disease with a high incidence of hemorrhagic colitis and hemolytic uremic syndrome in the infected patients. Discriminant typing of pathogens is crucial for epidemiological surveillance and investigations of outbreaks, and especially for tracking and tracing in case of accidental and deliberate contamination of food and water samples. Clustered regularly interspaced short palindromic repeats (CRISPRs) are composed of short, highly conserved DNA repeats separated by unique sequences of similar length. This distinctive sequence signature of CRISPRs can be used for strain typing in several bacterial species including STEC. This review discusses how CRISPRs have recently been used for STEC identification and typing.     

Use of modeling to protect,plan, and manage water resources in catchment areas

The degradation of water resources by diffuse pollution, mainly due to nitrate and pesticides, is an important matter for public health. Restoration of the quality of natural water catchments by focusing on their catchment areas is therefore a national priority in France. To consider catchment areas as homogeneous and to expend an equal effort on the entire area inevitably leads to a waste of time and money, and restorative actions may not be as efficient as intended. The variability of the pedological and geological properties of the area is actually an opportunity to invest effort on smaller areas, simply because every action is not equally efficient on every kind of pedological or geological surface. Using this approach, it is possible to invest in a few selected zones that will be efficient in terms of environmental results. The contributive hydraulic areas (CHA) concept is different from that of the catchment area. Because the transport of most of the mobile and persistent pollutants is primarily driven by water circulation, the concept of the CHA is based on the water pathway from the surface of the soil in the catchment area to the well. The method uses a three-dimensional hydrogeological model of surface and groundwater integrated with a geographic information system called Watermodel. The model calculates the contribution (m³/h or %) of each point of the soil to the total flow pumped in a well. Application of this model, partially funded by the Seine Normandy Basin Agency, to the catchment of the Dormelles Well in the Cretaceous chalk aquifer in the Orvanne valley, France (catchment area of 23,000 ha at Dormelles, county 77), shows that 95 % of the water pumped at the Dormelles Well comes from only 26 % of the total surface area of the catchment. Consequently, an action plan to protect the water resource will be targeted at the 93 farmers operating in this source area rather than the total number of farmers (250) across the entire 23,000 ha. Another model, developed from Epiclès© software, permits the calculation of the under-root nitrate concentrations for each field based on soil type, climate, and farming practices. When the Watermodel and Epiclès© are coupled, nitrate transfers from the soil to the catchment and the river can be modeled. In this study, the initial pollution due to the actual farming practices was simulated and we were also able to estimate the efficiency of the agronomic action plan by testing several scenarios and calculating the time needed to reach the target nitrate concentration in the well.     

Are seed and dispersal characteristics of plants capable of predicting colonization of post-mining sites?

Seed characteristics play an important role in the colonization and subsequent persistence of species during succession in disturbed sites and thus may contribute to being able to predict restoration success. In the present study, we investigated how various seed characteristics participated in 11 spontaneous successional series running in different mining sites (spoil heaps, extracted sand and sand-gravel pits, extracted peatlands, and stone quarries) in the Czech Republic, Central Europe. Using 1864 samples from 1- to 100-years-old successional stages, we tested whether species optimum along the succession gradient could be predicted using 10 basic species traits connected with diaspores and dispersal. Seed longevity, diaspore mass, endozoochory, and autochory appeared to be the best predictors. The results indicate that seed characteristics can predict to a certain degree spontaneous vegetation succession, i.e., passive restoration, in the mining sites. A screening of species available in the given landscape (regional and local species pools) may help to identify those species which would potentially colonize the disturbed sites. Extensive databases of species traits, nowadays available for the Central European flora, enable such screening.     

Impact of temperature on the dynamics of organic matter and on the soil-to-plant transfer of Cd,Zn and Pb in a contaminated agricultural soil

Predicting the soil-to-plant transfer of metals in the context of global warming has become a major issue for food safety. It requires a better understanding of how the temperature alters the bioavailability of metals in cultivated soils. This study focuses on one agricultural soil contaminated by Cd, Zn and Pb. DGT measurements were performed at 10, 20 and 30 °C to assess how the bioavailability of metals was affected by a rise in soil temperature. A lettuce crop was cultivated in the same conditions to determine if the soil-to-plant transfer of metals increased with a rise in soil temperature. A gradual decline in Cd and Zn bioavailability was observed from 10 to 30 °C, which was attributed to more intense complexation of metals in the pore water at higher temperatures. Together with its aromaticity, the affinity of dissolved organic matter (DOM) for metals was indeed suspected to increase with soil temperature. One main output of the present work is a model which satisfactorily explains the thermal-induced changes in the characteristics of DOM reported in Cornu et al. (Geoderma 162:65–70, 2011) by assuming that the mineralization of initial aliphatic compounds followed a first-order reaction, increased with soil temperature according to the Arrhenius law, and due to a priming effect, led to the appearance of aromatic molecules. The soil-to-plant transfer of Cd and Zn was promoted at higher soil temperatures despite a parallel decrease in Cd and Zn bioavailability. This suggests that plant processes affect the soil-to-plant transfer of Cd and Zn the most when the soil temperature rises.     

Power generation by high head water in a building using micro hydro turbine—a greener approach

Demand for green energy production is arising all over the world. A lot of emphasis is laid in making the buildings green. Even a small amount of energy savings made contribute to saving the environment. In this study, an idea is proposed and studied to extract power from the high head water in the pipelines of a building. A building of height 15 m is considered for this study. Water flowing in the pipe has sufficient energy to run a micro hydro turbine. The feasibility of producing electrical energy from the energy of pipe water is found. The motivation is to find the feasibility of generating power using a low-cost turbine. The experimental setup consists of micro turbine of 135 mm diameter coupled to a 12-V DC generator; LEDs and resistors are employed to validate the results. The theoretical calculations were presented using the fundamental equations of fluid mechanics. The theoretical results are validated using experimental and numerical results using CFD simulation. In addition, exergy analysis has been carried out to quantify the irreversibilities during the process in the system.     

Phytoplankton dynamics with a special emphasis on harmful algal blooms in the Mar Piccolo of Taranto (Ionian Sea,Italy)

The response of phytoplankton assemblages to the closure of urban sewage outfalls (USOs) was examined for the Mar Piccolo of Taranto (Mediterranean Sea), a productive semi-enclosed coastal marine ecosystem devoted to shellfish farming. Phytoplankton dynamics were investigated in relation to environmental variables, with a particular emphasis on harmful algal blooms (HABs). Recent analyses evidenced a general reduction of the inorganic nutrient loads, except for nitrates and silicates. Also phytoplankton biomass (chlorophyll a) and abundances were characterized by a decrease of the values, except for the inner area of the basin (second inlet). The phytoplankton composition changed, with nano-sized species, indicators of oligotrophic conditions, becoming dominant over micro-sized species. If the closure of the USOs affected phytoplankton dynamics, however, it did not preserve the Mar Piccolo from HABs and anoxia crises. About 25 harmful species have been detected throughout the years, such as the potentially domoic acid producers Pseudo-nitzschia cf. galaxiae and P seudo-nitzschia cf. multistriata, identified for the first time in these waters. The presence of HABs represents a threat for human health and aquaculture. Urgent initiatives are needed to improve the communication with authorities responsible for environmental protection, economic development, and public health for a sustainable mussel culture in the Mar Piccolo.     

Depth-dependence and monthly variability of charophyte biomass production: consequences for the precipitation of calcium carbonate in a shallow <Emphasis Type="Italic">Chara</Emphasis>-lake

The month-to-month variability of biomass and CaCO₃ precipitation by dense charophyte beds was studied in a shallow Chara-lake at two depths, 1 and 3 m. Charophyte dry weights (d.w.), the percentage contribution of calcium carbonate to the dry weight and the precipitation of CaCO₃ per 1 m² were analysed from May to October 2011. Physical-chemical parameters of water were also measured for the same sample locations. The mean dry weight and calcium carbonate precipitation were significantly higher at 1 m than at 3 m. The highest measured charophyte dry weight (exceeding 2000 g m^?2) was noted at 1 m depth in September, and the highest CaCO₃ content in the d.w. (exceeding 80 % of d.w.) was observed at 3 m depth in August. The highest CaCO₃ precipitation per 1 m² exceeded 1695 g at 1 m depth in August. Significant differences in photosynthetically active radiation (PAR) were found between 1 and 3 m depths; there were no significant differences between depths for other water properties. At both sampling depths, there were distinct correlations between the d.w., CaCO₃ content and precipitation and water properties. In addition to PAR, the water temperature and magnesium and calcium ion concentrations were among the most significant determinants of CaCO₃ content and d.w. The results show that light availability seems to be the major factor in determining charophyte biomass in a typical, undisturbed Chara-lake. The study results are discussed in light of the role of charophyte vegetation in whole ecosystem functioning, with a particular focus on sedimentary processes and the biogeochemical cycle within the littoral zone.     

Research and application of method of oxygen isotope of inorganic phosphate in Beijing agricultural soils

Phosphorus (P) in agricultural ecosystems is an essential and limited element for plants and microorganisms. However, environmental problems caused by P accumulation as well as by P loss have become more and more serious. Oxygen isotopes of phosphate can trace the sources, migration, and transformation of P in agricultural soils. In order to use the isotopes of phosphate oxygen, appropriate extraction and purification methods for inorganic phosphate from soils are necessary. Here, we combined two different methods to analyze the oxygen isotopic composition of inorganic phosphate (δ¹⁸O_P) from chemical fertilizers and different fractions (Milli-Q water, 0.5 mol L^?1 NaHCO₃ (pH = 8.5), 0.1 mol L^?1 NaOH and 1 mol L^?1 HCl) of agricultural soils from the Beijing area. The δ¹⁸O_P results of the water extracts and NaHCO₃ extracts in most samples were close to the calculated equilibrium value. These phenomena can be explained by rapid P cycling in soils and the influence of chemical fertilizers. The δ¹⁸O_P value of the water extracts and NaHCO₃ extracts in some soil samples below the equilibrium value may be caused by the hydrolysis of organic P fractions mediated by extracellular enzymes. The δ¹⁸O_P values of the NaOH extracts were above the calculated equilibrium value reflecting the balance state between microbial uptake of phosphate and the release of intracellular phosphate back to the soil. The HCl extracts with the lowest δ¹⁸O_P values and highest phosphate concentrations indicated that the HCl fraction was affected by microbial activity. Hence, these δ¹⁸O_p values likely reflected the oxygen isotopic values of the parent materials. The results suggested that phosphate oxygen isotope analyses could be an effective tool in order to trace phosphate sources, transformation processes, and its utilization by microorganisms in agricultural soils.     

Wastewater reuse: modeling chloroform formation

The chloroform is a substance that presents a significant risk to or via the aquatic environment. Thus, the emissions, discharges and losses of this substance need to be controlled during wastewater disinfection for reclamation and reuse purposes. Due to its carcinogenetic potential, multiple studies have been carried out on drinking and surface/natural waters but less consideration has been directed to the wastewater disinfection. The focus of this work studied the formation of chloroform during chlorination in prepared waters or artificial matrices that intended to simulate wastewaters stored in landscape ponds for green areas irrigation. The relation between reaction time, chlorine dose, and chloroform formation and the variation of the dissolved organic carbon (DOC) content during the reaction was assessed. A two-variant model was proposed to simulate breakpoint chlorination practices (when chlorine dose is equal or lower than chlorine demand) and super chlorination techniques (when chlorine dose tends to surpass chlorine demand). The model was validated by the application of actual data from working conditions of six wastewater treatment plants located in Algarve, Portugal, including other data obtained in previous research studies that were not used in the model development, and by comparing the predicted values with real measured ones.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.