Physiology of purple sulfur bacteria forming macroscopic aggregates in Great Sippewissett Salt Marsh, Massachusetts

Abstract Purple bacterial aggregates found in tidal pools of Great Sippewissett Salt Marsh (Falmouth, Cape Cod, MA) were investigated in order to elucidate the ecological significance of cell aggregation. Purple sulfur bacteria were the dominant microorganisms in the aggregates which also contained diatoms and a high number of small rod-shaped bacteria. Urea in concentrations of ≥ 1 M caused disintegration of the aggregates while proteolytic enzymes, surfactants or chaotropic agents did not exhibit this effect. This suggests that polysaccharides in the embedding slime matrix stabilize the aggregate structure. In addition cell surface hydrophobicity is involved in aggregate formation. The concentration of dissolved oxygen decreased rapidly below the surface of aggregates while sulfide was not detected. The apparent respiration rate in the aggregates was high when the purple sulfur bacteria contained intracellular sulfur globules. In the presence of DCMU, respiration remained light-inhibited. Light inhibition disappeared in the presence of KCN. These results demonstrated that respiration in the aggregates is due mainly to purple sulfur bacteria. The concentration of bacteriochlorophyll (Bchl) a in the aggregates (0.205 mg Bchl a cm−3) was much higher than in the pool sediments but comparable to concentrations in microbial mats of adjacent sand flats. Purple aggregates may therefore originate in the microbial mats rather than in the pools themselves. Rapid sedimentation and high respiration rates of Chromatiaceae in the aggregates would prevent the inhibition of Bchl synthesis if aggregates were lifted off the sediment and up into the oxic pool water by tidal currents

Microsensor measurements of hydrogen gas dynamics in cyanobacterial microbial mats

© 2015 Nielsen, Revsbech and Kühl. We used a novel amperometric microsensor for measuring hydrogen gas production and consumption at high spatio-temporal resolution in cyanobacterial biofilms and mats dominated by non-heterocystous filamentous cyanobacteria (Microcoleus chtonoplastes and Oscillatoria sp.). The new microsensor is based on the use of an organic electrolyte and a stable internal reference system and can be equipped with a chemical sulfide trap in the measuring tip; it exhibits very stable and sulfide-insensitive measuring signals and a high sensitivity (1.5-5 pA per μmol L-1 H2). Hydrogen gas measurements were done in combination with microsensor measurements of scalar irradiance, O2, pH, and H2S and showed a pronounced H2 accumulation (of up to 8-10% H2 saturation) within the upper mm of cyanobacterial mats after onset of darkness and O2 depletion. The peak concentration of H2 increased with the irradiance level prior to darkening. After an initial build-up over the first 1-2 h in darkness, H2 was depleted over several hours due to efflux to the overlaying water, and due to biogeochemical processes in the uppermost oxic layers and the anoxic layers of the mats. Depletion could be prevented by addition of molybdate pointing to sulfate reduction as a major sink for H2. Immediately after onset of illumination, a short burst of presumably photo-produced H2 due to direct biophotolysis was observed in the illuminated but anoxic mat layers. As soon as O2 from photosynthesis started to accumulate, the H2 was consumed rapidly and production ceased. Our data give detailed insights into the microscale distribution and dynamics of H2 in cyanobacterial biofilms and mats, and further support that cyanobacterial H2 production can play a significant role in fueling anaerobic processes like e.g., sulfate reduction or anoxygenic photosynthesis in microbial mats

STACCATO: a novel solution to supernova photometric classification with biased training sets

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. – a diffusion map combined with a random forest classifier – to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the ‘gold standard’ of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe

Microenvironment and photosynthesis of zooxanthellae in scleractinian corals studied with microsensors for O2, pH and light

During experimental Light-dark cycles, O-2 in the tissue of the colonial scleractinian corals Favia sp. and Acropora sp. reached >250% of air saturation after a few minutes in Light. Immediately after darkening, O-2 was depleted rapidly, and within 5 min the O-2 concentration at the tissue surface reached 6 times higher at a saturating irradiance of 350 mu Ein m(-2) s(-1) than the dark respiration measured under identical hydrodynamic conditions (flow rate of 5 to 6 cm s(-1))