A spatially explicit analysis to extrapolate carbon fluxes in upland tundra where permafrost is thawing

One of the most important changes in high‐latitude ecosystems in response to climatic warming may be the thawing of permafrost soil. In upland tundra, the thawing of ice‐rich permafrost can create localized surface subsidence called thermokarst, which may change the soil environment and influence ecosystem carbon release and uptake. We established an intermediate scale (a scale in between point chamber measurements and eddy covariance footprint) ecosystem carbon flux study in Alaskan tundra where permafrost thaw and thermokarst development had been occurring for several decades. The main goal of our study was to examine how dynamic ecosystem carbon fluxes [gross primary production (GPP), ecosystem respiration (R_eco), and net ecosystem exchange (NEE)] relate to ecosystem variables that incorporate the structural and edaphic changes that co‐occur with permafrost thaw and thermokarst development. We then examined how these measured ecosystem carbon fluxes responded to upscaling. For both spatially extensive measurements made intermittently during the peak growing season and intensive measurements made over the entire growing season, ecosystem variables including degree of surface subsidence, thaw depth, and aboveground biomass were selected in a mixed model selection procedure as the ‘best’ predictors of GPP, R_eco, and NEE. Variables left out of the model (often as a result of autocorrelation) included soil temperature, moisture, and normalized difference vegetation index. These results suggest that the structural changes (surface subsidence, thaw depth, aboveground biomass) that integrate multiple effects of permafrost thaw can be useful components of models used to estimate ecosystem carbon exchange across thermokarst affected landscapes.     

Population structure and geographical subdivision of the Leishmania major vector Phlebotomus papatasi as revealed by microsatellite variation

Abstract Multi‐locus microsatellite typing (MLMT) has been employed to infer the population structure of Phlebotomus papatasi (Scopoli) (Diptera: Psychodidae) sandflies and assign individuals to populations. Phlebotomus papatasi sandflies were collected from 35 sites in 15 countries. A total of 188 P. papatasi individuals were typed using five microsatellite loci, resulting in 113 different genotypes. Unique microsatellite signatures were observed for some of the populations analysed. Comparable results were obtained when the data were analysed with Bayesian model and distance‐based methods. Bayesian statistic‐based analyses split the dataset into two distinct genetic clusters, A and B, with further substructuring within each. Population A consisted of five subpopulations representing large numbers of alleles that were correlated with the geographical origins of the sandflies. Cluster B comprised individuals collected in the Middle East and the northern Mediterranean area. The subpopulations B1 and B2 did not, however, show any further correlation to geographical origin. The genetic differentiation between subpopulations was supported by F statistics showing statistically significant (Bonferroni‐corrected P < 0.005) values of 0.221 between B2 and B1 and 0.816 between A5 and A4. Identification of the genetic structure of P. papatasi populations is important for understanding the patterns of dispersal of this species and to developing strategies for sandfly control.     

Combination of classical and statistical approaches to enhance the fermentation conditions and increase the yield of Lipopeptide(s) by Pseudomonas sp. OXDC12: its partial purification and determining antifungal property

Around 200 different lipopeptides (LPs) have been identified to date, most of which are produced via Bacillus and Pseudomonas species. The clinical nature of the lipopeptide (LP) has led to a big surge in its research. They show antimicrobial and antitumor activities due to which mass-scale production and purification of LPs are beneficial. Response surface methodology (RSM) approach has emerged as an alternative in the field of computational biology for optimizing the reaction parameters using statistical models. In the present study, Pseudomonas sp. strain OXDC12 was used for production and partial purification of LPs using Thin Layer Chromatography (TLC). The main goal of the study was to increase the overall yield of LPs by optimizing the different variables in the fermentation broth. This was achieved using a combination of one factor at a time (OFAT) and response surface methodology (RSM) approaches. OFAT technique was used to optimize the necessary parameters and was followed by the creation of statistical models (RSM) to optimize the remaining variables. Maximum mycelial growth inhibition (%) against the fungus Mucor sp. was 61.3% for LP. Overall, the combination of both OFAT and RSM helped in increasing the LPs yield by 3 folds from 367mg/L to 1169mg/L.     

Use of a Chemical Sanitizer for Cleaning Fish Boxes

A number of organic, inorganic and surfactant substances were tested for their cleaning efficiency, ability to reduce bacterial load and deodorize fish boxes. A 0.1% iodine solution had good bactericidal and deodorizing properties, whilst a number of surfactants, especially the quaternary compound Quat-LTM, had good cleaning and bactericidal properties. None of the substances used possessed all three desirable properties sought for in this study.