Optimization of Fermentation Medium for Indole Acetic Acid Production by Pseudarthrobacter sp. NIBRBAC000502770.

Indole acetic acid (IAA) has been an important compound for plant growth and is widely known to be produced by plant growth-promoting rhizobacteria (PGPR). The isolate producing the maximum amount of IAA from the Korea shooting range soil was identified as Pseudarthrobacter sp. NIBRBAC000502770, using 16S rRNA gene sequencing. IAA production was determined in Luria-Bertani (LB) broth and optimized using different temperatures, agitation rates, L-tryptophan concentrations, carbon and nitrogen sources, and inorganic salts. The strain NIBRBAC000502770 showed better production of IAA at temperature 30 °C (29.47 mg·L-1) and at an agitation rate of 200 rpm (32.65 mg·L-1). Maltose (0.5%) was found to be the best carbon source for the strain (yielding 36.48 mg·L-1 IAA). IAA yield was 19.17 mg·L-1 and 24.73 mg·L-1 at 1% yeast extract and 1% tryptone as nitrogen sources, respectively. qRT-PCR showed the transcript levels of amiE and aldH genes, which had been predicted to encode indole-3-acetamide hydrolase and indole-3-acetaldehyde dehydrogenase, to be significantly upregulated in response to tryptophan. This study has examined that NIBRBAC000502770 has significant effects as a biological agent such as plant growth promotion, and development of optimal medium could significantly reduce the cost of mass production of microorganisms.

Examining Impacts of Mass-Diameter (m-D) and Area-Diameter (A-D) Relationships of Ice Particles on Retrievals of Effective Radius and Ice Water Content from Radar and Lidar Measurements.

Mass-diameter (m-D) and projected area-diameter (A-D) relations are often used to describe the shape of nonspherical ice particles. This study analytically investigates how retrieved effective radius (r eff ) and ice water content (IWC) from radar and lidar measurements depend on the assumption of m-D [m(D) = a D b ] and A-D [A(D) = γD s ] relationships. We assume that unattenuated reflectivity factor (Z) and visible extinction coefficient (k ext ) by cloud particles are available from the radar and lidar measurements, respectively. A sensitivity test shows that r eff increases with increasing a, decreasing b, decreasing γ, and increasing δ. It also shows that a 10% variation of a, b, γ, and δ induces more than a 100% change of r eff . In addition, we consider both gamma and lognormal particle size distributions (PSDs), and examine the sensitivity of r eff to the assumption of PSD. It is shown that r eff increases by up to 10% with increasing dispersion (µ) of the gamma PSD by 2, when large ice particles are predominant. Moreover, r eff decreases by up to 20% with increasing the width parameter (ω) of the lognormal PSD by 0.1. We also derive an analytic conversion equation between two effective radii when different particle shapes and PSD assumptions are used. When applying the conversion equation to nine types of m-D and A-D relationships, r eff easily changes up to 30%. The proposed r eff -convertion method can be used to eliminate the inconsistency of assumptions that made in a cloud retrieval algorithm and a forward radiative transfer model.

Cloud Occurrences and Cloud Radiative Effects (CREs) from CCCM and CloudSat Radar-Lidar Products.

Two kinds of radar-lidar synergy cloud products are compared and analyzed in this study; CERES-CALIPSO-CloudSat-MODIS (CCCM) product and CloudSat radar-lidar (RL) product such as GEOPROF-LIDAR and FLXHR-LIDAR. Compared to GEOPROF-LIDAR, CCCM has more low-level (< 1 km) clouds over tropical oceans because CCCM uses a more relaxed threshold of Cloud-Aerosol Discrimination (CAD) score for Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) product. In contrast, GEOPROF-LIDAR has more mid-level (1-8 km) clouds than CCCM at high latitudes (> 40°). The difference occurs when hydrometeors are detected by CALIPSO lidar but are undetected by CloudSat radar, which may be related to precipitation. In the comparison of cloud radiative effects (CREs), global mean differences between CCCM and FLXHR-LIDAR are mostly smaller than 5 W m-2, while noticeable regional differences are found over three regions. First, CCCM has larger shortwave (SW) and longwave (LW) CREs than FXLHR-LIDAR along the west coasts of Africa and America. This might be caused by missing small-scale marine boundary layer clouds in FLXHR-LIDAR. Second, over tropical oceans where precipitation frequently occurs, SW and LW CREs from FLXHR-LIDAR are larger than those from CCCM partly because FLXHR-LIDAR algorithm includes the contribution of rainwater to total liquid water path. Third, over midlatitude storm-track regions, CCCM shows larger SW and LW CREs than FLXHR-LIDAR, due to CCCM biases caused by larger cloud optical depth or higher cloud effective height.