Antimicrobial and antiviral activities of an actinomycete (Streptomyces sp.) isolated from a Brazilian tropical forest soil

A promising producer of bioactive compounds isolated from a Brazilian tropical soil was tested for its range of antimicrobial activities. Strain 606, classified as Streptomyces sp., could not be identified up to species level, suggesting a possible new taxon. The supernatant and 10 extracts and fractions, obtained by extraction and chromatographic techniques, presented antimicrobial activity using antibiograms. The methanolic fraction was highly active against pathogenic bacteria, phytopathogenic fungi and the human pathogenic yeast Candida albicans. It also possessed high antiviral activity inhibiting the propagation of an acyclovir-resistant herpes simplex virus type 1 strain on HEp-2 cells at non-cytotoxic concentration. The strong cytotoxic effect suggests an antitumour action. This revised version was published online in August 2006 with corrections to the Cover Date.     

Biosensors based on peptide exposure show single molecule conformations in live cells

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Environment versus dispersal in the assembly of western Amazonian palm communities

Aim It is a central issue in ecology and biogeography to understand what governs community assembly and the maintenance of biodiversity in tropical rain forest ecosystems. A key question is the relative importance of environmental species sorting (niche assembly) and dispersal limitation (dispersal assembly), which we investigate using a large dataset from diverse palm communities. Location Lowland rain forest, western Amazon River Basin, Peru. Methods We inventoried palm communities, registering all palm individuals and recording environmental conditions in 149 transects of 5 m × 500 m. We used ordination, Mantel tests and indicator species analysis (ISA) to assess compositional patterns, species responses to geographical location and environmental factors. Mantel tests were used to assess the relative importance of geographical distance (as a proxy for dispersal limitation) and environmental differences as possible drivers of dissimilarity in palm species composition. We repeated the Mantel tests for subsets of species that differ in traits of likely importance for habitat specialization and dispersal (height and range size). Results We found a strong relationship between compositional dissimilarity and environmental distance and a weaker but also significant relationship between compositional dissimilarity and geographical distance. Consistent with expectations, relationships with environmental and geographical distance were stronger for understorey species than for canopy species. Geographical distance had a higher correlation with compositional dissimilarity for small‐ranged species compared with large‐ranged species, whereas the opposite was true for environmental distance. The main environmental correlates were inundation and soil nutrient levels. Main conclusions The assembly of palm communities in the western Amazon appears to be driven primarily by species sorting according to hydrology and soil, but with dispersal limitation also playing an important role. The importance of environmental characteristics and geographical distance varies depending on plant height and geographical range size in agreement with functional predictions, increasing our confidence in the inferred assembly mechanisms.     

Determination of trace levels of fatty acid metal salts by high-performance liquid chromatography with fluorescence prelabeling

A simple method is described for picomole determinations of fatty acid metal salts. Fatty acid salts are directly labeled with 4-bromomethyl-7-methoxycoumarin in the presence of excess ethylenediaminetetraacetic acid tripotassium salt without any solvent extractions. The fluorescence derivatives of fatty acids are separated by reverse-phase high-performance liquid chromatography followed by fluorometric detection. The response of each fatty acid (C8-C18) calcium salt is linear from 1 to 50 micrograms/ml of samples. The detection limit is about 7 pmol. Good recoveries are obtained for the calcium salts of myrystic acid and soap (C8-C18, C18:1,2). The new method is successfully applied to the study on biodegradation of fatty acids in river water.     

Measurement of 7-dehydrocholesterol and cholesterol in hair can be used in the diagnosis of Smith-Lemli-Opitz syndrome

7-dehydrocholesterol (7-DHC) and cholesterol (CHOL) are biomarkers of Smith-Lemli-Opitz Syndrome (SLOS), a congenital autosomal recessive disorder characterized by elevated 7-DHC level in patients. Hair samples have been shown to have great diagnostic and research value, which has long been neglected in the SLOS field. In this study, we sought to investigate the feasibility of using hair for SLOS diagnosis. In the presence of antioxidants (2,6-ditert-butyl-4-methylphenol and triphenylphosphine), hair samples were completely pulverized and extracted by micro-pulverized extraction in alkaline solution or in n-hexane. After microwave-assisted derivatization with N,O-Bis(trimethylsilyl)trifluoroacetamide, the analytes were measured by GC-MS. We found that the limits of determination for 7-DHC and CHOL were 10 ng/mg and 8 ng/mg, respectively. In addition, good linearity was obtained in the range of 50–4000 ng/mg and 30–6000 ng/mg for 7-DHC and CHOL, respectively, which fully meets the requirement for SLOS diagnosis and related research. Finally, by applying the proposed method to real hair samples collected from 14 healthy infants and two suspected SLOS patients, we confirmed the feasibility of hair analysis as a diagnostic tool for SLOS. In conclusion, we present an optimized and validated analytical method for the simultaneous determination of two SLOS biomarkers using human hair.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.