Isolates of Staphylococcus aureus and saprophyticus resistant to antimicrobials isolated from the Lebanese aquatic environment

The indiscriminate use of antimicrobials especially in developing countries has evoked serious bacterial resistance and led to the emergence of new and highly resistant strains of bacteria to commonly used antimicrobials. In Lebanon, pollution levels and bacterial infections are increasing at a high rate as a result of inadequate control measures to limit untreated effluent discharges into the sea or freshwater resources. The aim of this study was to isolate and molecularly characterize various Staphylococcus strains isolated from sea water, fresh water, sediments, and crab samples collected from representative communities along the coast of Lebanon. The results on the antimicrobial resistance indicated that the level of resistance of Staphylococcus aureus varied with various antimicrobials tested. The resistance patterns ranged between 45% in freshwater isolates and 54.8% in seawater ones. Fifty one percent of the tested isolates have shown resistance to at least one of the five tested antimicrobials; with seawater isolates exhibiting the highest rates of antimicrobial resistance.     

Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.     

Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network

A landslide is one of the natural disasters that occur in Malaysia. In addition to the geological factor and the rain as triggering factor, topographic factors such as elevation, slope angle, slope aspect, and curvature are considered as the main causes of landslides. The study in this paper was conducted in three stages. The first stage involved the extraction of extra topographic factors. Previous landslide studies had identified only four of the topographic factors. However, eight new additional factors have also been identified in this study. They are general curvature, longitudinal curvature, tangential curvature, cross-section curvature, surface area, diagonal line length, surface roughness, and rugosity. At this stage, 13 factors were extracted from the digital elevation model. The second stage involved specifying the importance of each factor. The multilayer perceptron network and backpropagation algorithm were used to specify the weight of each factor. Results were verified using the receiver operating characteristics based on the area under the curve method in the third stage. The results indicated 76.07 % accuracy in predicting of landslides, with slope angle as the most important factor while the tangential curvature has the least importance.     

Antimicrobial-resistance of Streptococcus pneumoniae isolated from the Lebanese environment

The aim of this study was to isolate and molecularly characterize Streptococcus pneumoniae in samples collected from the aquatic environment in Lebanon. For this purpose, 57 samples of ground waters, sea water, sediments and crabs were collected. Biochemical tests as well as PCR were used for further identification and molecular characterization of 46 suspected isolates using specific primers for the lytA-gene and protein 2B-gene. The confirmed isolates were evaluated for their antimicrobial resistance, using the minimum inhibitory concentration method. Crab samples exhibited a high contamination level of an average count of 8.2 x 10(4)CFU/g. In water samples, the highest contamination was recorded near a coastal landfill with a bacterial count of 1.8 x 10(3)CFU/ml whereas the lowest bacterial counts (non detectable) were observed in ground water samples. Twenty out of the 46 suspected isolates came from sea water, and 17 out of those were characterized by PCR as S. pneumoniae. Testing the antimicrobial resistance of the 20 isolates to various antimicrobials showed that all sea water isolates were resistant to vancomycin, gentamicin, and oxacillin (100%), whereas 62.5% were resistant to clindamycin, and only 25% were erythromycin resistant. Resistance rates were higher in the case of fresh water samples, with 57% resistance to erythromycin, and 100% resistance to vancomycin, gentamicin, clindamycin and oxacillin. Alternatively, all tested crab isolates were susceptible to erythromycin. However, they showed a high resistance of 80% against vancomycin, gentamicin, clindamycin and 100% against oxacillin.