Molecular identification and nanoremediation of microbial contaminants in algal systems using untreated wastewater

Wastewater-algal biomass is a promising option to biofuel production. However, microbial contaminants constitute a substantial barrier to algal biofuel yield. A series of algal strains, Nannochloris oculata and Chlorella vulgaris samples (n = 30), were purchased from the University of Texas, and were used for both stock flask cultures and flat-panel vertical bioreactors. A number of media were used for isolation and differentiation of potential contaminants according to laboratory standards (CLSI). Conventional PCR amplification was performed followed by 16S rDNA sequencing to identify isolates at the species level. Nanotherapeutics involving a nanomicellar combination of natural chitosan and zinc oxide (CZNPs) were tested against the microbial lytic groups through Minimum Inhibitory Concentration (MIC) tests and Transmission Electronic Microscopy (TEM). Results indicated the presence of Pseudomonas spp., Bacillus pumilus/ safensis, Cellulosimicrobium cellulans, Micrococcus luteus and Staphylococcus epidermidis strains at a substantial level in the wastewater-fed algal reactors. TEM confirmed the effectiveness of CZNPs on the lytic group while the average MICs (mg/mL) detected for the strains, Pseudomonas spp, Micrococcus luteus, and Bacillus pumilus were 0.417, 3.33, and 1.458, respectively. Conclusively, CZNP antimicrobials proved to be effective as inhibitory agents against currently identified lytic microbial group, did not impact algae cells, and shows promise for in situ interventions.     

Fate of sex hormones in two pilot-scale municipal wastewater treatment plants: conventional treatment

The fate of seven sex hormones (estrone (E1), estradiol (E2), estriol (E3), ethinylestradiol (EE2), testosterone, androstenedione, and progesterone) was determined in two pilot-scale wastewater treatment plants operated under conventional loading conditions. The levels of hormones in both the liquid and the solid matrixes of the plants were determined. Each of the two 20-l/h pilot-scale plants consisted of a primary clarifier followed by a three-stage aeration tank and a final clarifier. The primary sludge and the waste activated sludge (WAS) were digested anaerobically in one pilot plant and aerobically in the other. The pilot plants were fed a complex synthetic wastewater spiked with the hormones. Levels of testosterone, androstenedione and progesterone were close to method detection limit (MDL) concentrations in the final and digester effluents (both liquid and solid phases) and were considered as completely removed. Average mass flux removals from the liquid streams (plant influent minus secondary clarifier effluent) for the natural estrogens were 82% for E1, 99% for E2, and 89% for (E1+E2). An average overall removal of only 42% was achieved for EE2. These values reflect removals averaged for the two pilot plants.     

Derivation of ozone flux-yield relationships for lettuce: a key horticultural crop

Ozone flux-response relationships were derived for lettuce, employing a multiplicative approach to model the manner in which stomatal conductance is influenced by key environmental variables, using a dataset collected during field experimentation in Crete and yield-response relationships derived from parallel open-top chamber experiments. Regional agronomic practices were adopted throughout. Computed versus measured data revealed that the derived model explained 51% (P<0.001) of the observed variation in stomatal conductance. Concentration-based indices were compared with flux-based indices. Analyses revealed a significant relationship between accumulated stomatal ozone flux and yield employing flux threshold cut-offs up to 4 nmol m(-2) s(-1). Regressions employing very low or zero flux thresholds resulted in the strongest yield-flux relationships (explaining approximately 80% (P<0.05) of the variation in the dataset).     

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.     

Immunoassay-based screening of polychlorinated biphenyls (PCB) in sediments: requirements for a new generation of test kits

Polychlorinated biphenyls (PCBs) have been proposed for the inclusion in the European Water Framework Directive (WFD) priority list, currently under revision. Various screening methods have been employed for PCB determination in different environmental matrixes in the last decades, immunoassays being one of the most employed. A literature review reveals that the enzyme-linked immunosorbent assay (ELISA) is the most commonly applied immunoassay for PCB determination in the environment. However, its application to sediments is very limited. A suitability assessment of immunoassay-based analysis for PCB screening in sediments is presented in this work. The significance of available immunoassay-based test kits under the current environmental pollution scenario and their performance against the sensitivity and specificity requirements dictated by the WFD for PCB analysis in sediments is discussed. For example, current detection limits of available test kits for PCB determination in sediments may not be enough for compliance checking under the WFD. In addition, concentration expressed as Aroclor equivalents does not seem to be the way forward. A proposal for adapting available test kits in order to become more suitable tools for PCB screening in sediments is also presented in this study.     

Seeds of promise: Developing a sustainable agricultural biotechnology industry in sub–Saharan Africa

In a continent ravaged by poverty, disease and malnutrition, agricultural practices have changed little over millennia. Of all the new technologies recently arisen, molecular biotechnology is one of the few that could signi ficantly improve the livelihoods of the large numbers of people in sub–Saharan Africa. Its impact could exceed that in the developed world, where a multi–billion dollar a year agricultural biotechnology industry has emerged, secured on the back of a highly skilled research and educational sector, extensive biosafety measures, and a strong corporate sector. For much of sub–Saharan Africa, the absence of these foundations constitutes a substantial hurdle to the development of a sustainable biotechnology industry. A critical element is the development of an indigenous and innovative agricultural biotechnology community — one that is responsive to African crops, farming practices and economic imperatives, yet sensitive to local concerns and biosafety issues surrounding use of the controversial GM technology.     

Robinsonecio gerberifolius as a sentinel organism for atmospheric pollution by heavy metals in several sites of Mexico city and its metropolitan area

Environmental Science and Pollution Research - Evaluate the effect of heavy metals (HM) on sentinel organisms such as vascular plants represent a model to estimate toxic hazard due to environmental...     

Using selection functions to describe changes in environmental variables

The selection function (which shows how the frequency of sampling units with the value X = x at one point in time must change in order to produce the distribution that occurs at a later point in time) is proposed for describing the changes over time in an environmentally important variable X. It is shown that the theory of selection functions as used in the study of natural selection and resource selection by animals requires some modifications in this new application and that a selection function is a useful tool in long-term monitoring studies because all changes in a distribution can be examined (rather than just changes in single parameters such as the mean), and because graphical presentations of the selection function are easy for non-statisticians to understand. Estimation of the selection function is discussed using a method appropriate for normal distributions and bootstrapping is suggested as a method for assessing the precision of estimates and for testing for significant differences between samples taken at different times. Methods are illustrated using data on water chemical variables from a study of the effects of acid precipitation in Norway.