Thiomicrospira kuenenii sp. nov., and Thiomicrospira frisia sp. nov., two mesophilic obligately chemolithoautotrophic sulfur-oxidizing bacteria isolated from an intertidal mud flat

Two new members of the genus Thiomicrospira were isolated from an intertidal mud flat sample with thiosulfate as the electron donor and CO2 as carbon source. On the basis of differences in genotypic and phenotypic characteristics, it is proposed that strain JB-A1(T) (= DSM 12350(T)) and strain JB-A2(T) (= DSM 12351(T)) are members of two new species, Thiomicrospira kuenenii and Thiomicrospira frisia, respectively. The cells were Gram-negative vibrios or slightly bent rods. Strain JB-A1(T) was highly motile, whereas strain JB-A2(T) showed a much lower degree of motility combined with a strong tendency to form aggregates. Both organisms were obligately autotrophic and strictly aerobic. Nitrate was not used as electron acceptor. Chemolithoautotrophic growth was observed with thiosulfate, tetrathionate, sulfur and sulfide. Neither isolate was able to grow heterotrophically. For strain JB-A1(T), growth was observed between pH values of 4.0 and 7.5 with an optimum at pH 6.0, whereas for strain JB-A2(T), growth was observed between pH 4.2 and 8.5 with an optimum at pH 6.5. The temperature limits for growth were between 3.5 and 42 degrees C and 3.5 and 39 degrees C, respectively. The optimum growth temperature for strain JB-A1(T) was between 29 and 33.5 degrees C, whereas strain JB-A2(T) showed optimal growth between 32 and 35 degrees C. The mean maximum growth rate on thiosulfate was 0.35 h(-1) for strain JB-A1(T) and 0.45 h(-1) for strain JB-A2(T)

Block-level macadamia yield forecasting using spatio-temporal datasets

Early crop yield forecasts provide valuable information for growers and industry to base decisions on. This work considers early forecasting of macadamia nut yield at the individual orchard block level with input variables derived from spatio-temporal datasets including remote sensing, weather and elevation. Yield data from 2012–2019, for 101 blocks belonging to 10 orchards, was obtained. We forecast yield on each test year from 2014–2019 using models trained on data from years prior to the test year. Forecasts are generated in January, for the coming harvest in March–September. A linear model using ridge regularized regression produced consistently good predictions compared with other machine learning algorithms including lasso, support vector regression and random forest. Adding meteorological variables offered little improvement over using only remote sensing variables. The 2019 forecast root mean square error at the block level was 0.8 t/ha, and mean absolute percentage error was 20.9%. When block level predictions were aggregated across the multiple orchards per region, production prediction errors were between 0–15% from 2016–2019. The ridge regression model can be easily implemented in GIS platforms to deliver block-level yield forecast maps to end users

Macadamia Orchard Planting Year and Area Estimation at a National Scale

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold

Thiomicrospira arctica sp nov and Thiomicrospira psychrophila sp nov., psychrophilic, obligately chemolithoautotrophic, sulfur-oxidizing bacteria isolated from marine Arctic sediments

Two psychrophilic, chemolithoautotrophic, sulfur-oxidizing bacteria were isolated from marine Arctic sediments sampled off the coast of Svalbard with thiosulfate as the electron donor and CO(2) as carbon source. Comparative analysis of 16S rRNA gene sequences suggested that the novel strains, designated SVAL-D(T) and SVAL-E(T), represent members of the genus Thiomicrospira. Further genotypic (DNA-DNA relatedness, DNA G+C content) and phenotypic characterization revealed that the strains represent members of two novel species. Both organisms are obligately autotrophic and strictly aerobic. Nitrate was not used as an electron acceptor. Chemolithoautotrophic growth was observed with thiosulfate, tetrathionate and sulfur. The temperature limits for growth of both strains were between -2 degrees C and 20.8 degrees C, with optima of 11.5-13.2 degrees C (SVAL-E(T)) and 14.6-15.4 degrees C (SVAL-D(T)), which is about 13-15 degrees C lower than the optima of all other recognized Thiomicrospira species. The maximum growth rate on thiosulfate at 14 degrees C was 0.14 h(-1) for strain SVAL-E(T) and 0.2 h(-1) for strain SVAL-D(T). Major fatty acids of SVAL-D(T) are C(16 : 1), C(18 : 0) and C(16 : 0), and those of SVAL-E(T) are C(16 : 1), C(18 : 1), C(16 : 0) and C(14 : 1). Cells of SVAL-D(T) and SVAL-E(T) are rods, like those of their closest relatives. To our knowledge the novel strains are the first psychrophilic, chemolithoautotrophic, sulfur-oxidizing bacteria so far described. The names Thiomicrospira arctica sp. nov. and Thiomicrospira psychrophila sp. nov. are proposed for SVAL-E(T) (=ATCC 700955(T)=DSM 13458(T)) and SVAL-D(T) (=ATCC 700954(T)=DSM 13453(T)), respectively

Filling some black holes: modeling the connection between urbanization, infrastructure, and global service intensity

This empirical article combines insights from previous research on the level of knowledge-intensive service in metropolitan areas with the aim to develop an understanding of the spatial structure of the global service economy. We use a stepwise regression model with the Globalization and World Cities research network's measure of globalized service provisioning as the dependent variable and a range of variables focusing on population, infrastructure, urban primacy, and national regulation as independent variables. The discussion of the results focuses on model parameters as well as the meaning of outliers and is used to explore some avenues for future research

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km² area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently

Diversity of thiosulfate-oxidizing bacteria from marine sediments and hydrothermal vents

Species diversity, phylogenetic affiliations, and environmental occurrence patterns of thiosulfate-oxidizing marine bacteria were investigated by using new isolates from serially diluted continental slope and deep-sea abyssal plain sediments collected off the coast of New England and strains cultured previously from Galapagos hydrothermal vent samples. The most frequently obtained new isolates, mostly from 103- and 104-fold dilutions of the continental slope sediment, oxidized thiosulfate to sulfate and fell into a distinct phylogenetic cluster of marine alpha-Proteobacteria. Phylogenetically and physiologically, these sediment strains resembled the sulfate-producing thiosulfate oxidizers from the Galapagos hydrothermal vents while showing habitat-related differences in growth temperature, rate and extent of thiosulfate utilization, and carbon substrate patterns. The abyssal deep-sea sediments yielded predominantly base-producing thiosulfate-oxidizing isolates related to Antarctic marine Psychroflexus species and other cold-water marine strains of the Cytophaga-Flavobacterium-Bacteroides phylum, in addition to gamma-proteobacterial isolates of the genera Pseudoalteromonas and Halomonas-Deleya. Bacterial thiosulfate oxidation is found in a wide phylogenetic spectrum of Flavobacteria and Proteobacteria

Speed Partitioning for Indexing Moving Objects

Indexing moving objects has been extensively studied in the past decades. Moving objects, such as vehicles and mobile device users, usually exhibit some patterns on their velocities, which can be utilized for velocity-based partitioning to improve performance of the indexes. Existing velocity-based partitioning techniques rely on some kinds of heuristics rather than analytically calculate the optimal solution. In this paper, we propose a novel speed partitioning technique based on a formal analysis over speed values of the moving objects. We first show that speed partitioning will significantly reduce the search space expansion which has direct impacts on query performance of the indexes. Next we formulate the optimal speed partitioning problem based on search space expansion analysis and then compute the optimal solution using dynamic programming. We then build the partitioned indexing system where queries are duplicated and processed in each index partition. Extensive experiments demonstrate that our method dramatically improves the performance of indexes for moving objects and outperforms other state-of-the-art velocity-based partitioning approaches

Forecasting carrot yield with optimal timing of Sentinel 2 image acquisition

Accurate, non-destructive forecasting of carrot yield is difcult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from 'days after sowing' (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at diferent PhS were established. Although the average root yield (t ha−1) did not signifcantly difer across the regions, the temporal VI signatures, indicating diferent regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhSR2max) with two of the regions producing a delayed PhSR2max (i.e. 90–130 DAS). The best multivariate model was identifed at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha−1 (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions ofering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods

Molecular analysis of the distribution and phylogeny of the soxB gene among sulfur-oxidizing bacteria - evolution of the Sox sulfur-oxidizing enzyme system

The soxB gene encodes the SoxB component of the periplasmic thiosulfate-oxidizing Sox enzyme complex, which has been proposed to be widespread among the various phylogenetic groups of sulfur-oxidizing bacteria (SOB) that convert thiosulfate to sulfate with and without the formation of sulfur globules as intermediate. Indeed, the comprehensive genetic and genomic analyses presented in the present study identified the soxB gene in 121 phylogenetically and physiologically divergent SOB, including several species for which thiosulfate utilization has not been reported yet. In first support of the previously postulated general involvement of components of the Sox enzyme complex in the thiosulfate oxidation process of sulfur-storing SOB, the soxB gene was detected in all investigated photo- and chemotrophic species that form sulfur globules during thiosulfate oxidation (Chromatiaceae, Chlorobiaceae, Ectothiorhodospiraceae, Thiothrix, Beggiatoa, Thiobacillus, invertebrate symbionts and free-living relatives). The SoxB phylogeny reflected the major 16S rRNA gene-based phylogenetic lineages of the investigated SOB, although topological discrepancies indicated several events of lateral soxB gene transfer among the SOB, e.g. its independent acquisition by the anaerobic anoxygenic phototrophic lineages from different chemotrophic donor lineages. A putative scenario for the proteobacterial origin and evolution of the Sox enzyme system in SOB is presented considering the phylogenetic, genomic (sox gene cluster composition) and geochemical data