Roles of sulfate adsorption and base cation supply in controlling the chemical response of streams of western Virginia to reduced acid deposition

Micro-organisms are vital for the functioning of all food webs and are the major drivers of the global biogeochemical cycles. The microbial community compositions and physicochemical conditions of the different water masses in the North Sea, a biologically productive sea on the northwestern European continental shelf, were studied during two summer cruises, in order to provide detailed baseline data for this region and examine its microbial biogeography. For each cruise the stations were clustered according to their physicochemical characteristics and their microbial community composition. The largest cluster, which covered most of the central and northern North Sea, consisted of stations that were characterized by a thermally stratified water column and had low chlorophyll a autofluorescence and generally low microbial abundances. The second main cluster contained stations that were dominated by picoeukaryotes and showed the influence of influxes of North Atlantic water via the English Channel and south of the Shetland Islands. The third main cluster was formed by stations that were dominated by cyanobacteria and nanoeukaryotes in the reduced salinity Norwegian Coastal and Skagerrak waters, while the fourth cluster represented the German Bight, a region with strong riverine input, high nutrient concentrations, and consequently high heterotrophic bacterial and viral abundances. Despite the complex and dynamic hydrographic nature of the North Sea, the consistent distinctions in microbiology between these different hydrographic regions during both cruises illustrate the strong links between the microbial community and its environment, as well as the possibility to use microorganisms for long-term monitoring of environmental change.     

Characterization and gene mapping of a chlorophyll-deficient mutant clm1 of Triticum monococcum L.

Diploid wheat Triticum monococcum L. is a model plant for wheat functional genomics. Chlorophyll-deficient mutant (clm1) was identified during manual screening of the ethyl methanesulphonate (EMS)-treated M₂ progenies of T. monococcum accession pau14087 in the field. The clm1 mutant, due to significantly decreased chlorophyll content compared with the wild-type (WT), exhibited pale yellow leaves which slowly recovered to green before flowering. The clm1 mutant showed early flowering, reduced number of tillers, trichome length and density, and different shape as compared with the WT. At the same time, clm1 mutant culm had more chlorophyll-containing parenchymatous tissues compared to WT, presumably to absorb more sunlight for photosynthesis. Genetic analysis indicated that the clm1 mutation was monogenic recessive. The clm1 mutant was mapped between Xgwm473 and Xwmc96 SSR markers, with genetic distances of 2.1 and 2.6 cM, respectively, on the 7A^mL chromosome.     

Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco)

Poaceae pollen is one of the most prevalent aeroallergens causing allergenic reactions. The aim of this study was to characterise the grass pollen season in Tetouan during the years 2008–2010, to analyse the effect of some meteorological parameters on the incidence of the airborne Poaceae pollen, and to establish forecasting variables for daily pollen concentrations. Aerobiological sampling was undertaken over three seasons using the volumetric method. The pollen season started in April and showed the highest pollen index in May and June, when the maximum temperature ranged from 23 to 27 °C, respectively. The annual pollen score recorded varied from year to year between 2,588 and 5,404. The main pollen season lasted 114–173 days, with peak days occurring mainly in May; the highest concentration reached 308 pollen grains/m³. Air temperature was the most important meteorological parameter and correlated positively to daily pollen concentration increase. An increase in relative humidity and precipitation was usually related to a decrease in airborne pollen content. External validation of the models performed using data from 2011 showed that Poaceae pollen concentration can be highly predicted (64.2–78.6 %) from the maximum temperature, its mean concentration for the same day in other years, and its concentration recorded on the previous day. Sensitive patients suffering allergy to Poaceae pollen are at moderate to highest risk of manifesting allergic symptoms to grass pollen over 33–42 days. The results obtained provide new information on the quantitative contribution of the Poaceae pollen to the airborne pollen of Tetouan and on its temporal distribution. Airborne pollen can be surveyed and forecast in order to warn the atopic population.     

Discovery of highly potent and selective D4 ligands by interactive SAR study

A series of thienylmethylphenylpiperazins was synthesized and tested for affinity towards the five subtypes of dopaminergic receptors. Compound 5f showed more than 1000 folds selectivity to D4 receptors; analogue 5e showed the highest affinity to D4 receptors with K_i 3.9 nM. An interactive SAR approach was adopted and lead to compound 14a with K_i (D4) as low as 0.03 nM. Molecular docking studies showed a potential, first to report arene cation interaction between the D4 unique residue Arg-186 and the ligands’ arene moiety, explaining the importance of having a strong negative electrostatic potential at this area of the compound structure.     

Two dimensional electrophoresis of the exo-proteome produced from community acquired methicillin resistant Staphylococcus aureus belonging to clonal complex 80

Two-dimensional electrophoresis (2DE) combined with mass spectrometry was used to characterize the exo-proteome secreted by two strains (ER13 and ER21) representing community acquired methicillin resistant Staphylococcus aureus (CA-MRSA) belonging to clonal complex 80 (CC80). Common spots were detected between the 2 gels using the Progenesis SameSpots software. Two hundred and fifty-one and 312 spots from the exo-proteome of ER13 and ER21 were resolved, respectively. 2DE overlap comparison showed that 59 spots were shared. LC–MS/MS analysis identified 57 proteins from these spots comprising about 21% extracellular, 48% cytoplasmic, 2% cytoplasmic membrane, 2% cell wall, and 26% with unknown localization. The identified proteins were classified with respect to their Gene Ontology (GO) annotation as ～24% virulence determinants and toxins, ～17% involved in carbohydrate metabolism, ～14% involved in environmental stress, and ～12% associated with cell division. The identification of the enterotoxin B from the exo-products of both strains used in our study, as belonging to CC80 was interesting.     

Fusarium Species Associated with Mango Malformation in Peninsular Malaysia

Mango malformation has become the most important global disease on mango. Fusarium species previously associated with this disease include F. mangiferae, F. mexicanum, F. sterilihyphosum, F. proliferatum, F. subglutinans and F. tupiense. A few strains of F. proliferatum have been reported from Malaysia, but in this study, we report the results of more extensive sampling. The recovered strains were evaluated with morphology, mating tester strain cross‐fertility, amplified fragment length polymorphisms (AFLPs), and partial DNA sequences of the genes encoding translation elongation factor 1‐α (tef‐1α) and β‐tubulin (tub‐2). Amongst the 43 strains evaluated, three species were identified – F. proliferatum, F. mangiferae and F. subglutinans – with F. proliferatum being the most frequent (69%). None of the Fusarium species that appear to originate in the Americas were recovered in Malaysia, which suggests special measures may be warranted to keep these species from entering the country.     

Application of Adaptive Cluster Sampling with a Data‐Driven Stopping Rule to Plant Disease Incidence

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data‐driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.