Search

Molecular biomarkers, like gene transcripts or enzyme activities, are potentially powerful tools for early warning assessment of pollution. However, a thorough understanding of response and baseline variation is required to distinguish actual effects from pollution. Here, we assess the freshwater mussel Anodonta anatina as a biomarker model species for freshwater ecosystems, by testing responses of six transcriptional (cat, gst, hsp70, hsp90, mt, and sod) and two biochemical (AChE and GST) biomarkers to environmentally relevant Cu water concentrations. Mussels (n = 20), collected from a stream free from point source pollution, were exposed in the laboratory, for 96 h, to Cu treatments (2 fold-change ≤ 0.80 and Cohen’s f ≤ 0.69, respectively), and no significant treatment effects were observed. In contrast, four out of eight biomarkers (cat, gst, hsp70, and GST) showed a significant sex:tissue interaction, and additionally one (sod) showed significant overall effects from sex. Specifically, three markers in gills (cat, mt, GST) and one in digestive gland (AChE) displayed significant sex differences, independent of treatment. Results suggest that sex or tissue effects might obscure low-magnitude biomarker responses and potential early warnings. Thus, variation in biomarker baselines and response patterns needs to be further addressed for the future use of A. anatina as a biomarker model species.

Halomonads are moderately halophilic bacteria that are studied as models of prokaryotic osmoadaptation and sources of enzymes and chemicals for biotechnological applications. Despite the progress in understanding the diversity of these organisms, our ability to explain ecological, metabolic, and biochemical traits of halomonads at the genomic sequence level remains limited. This study addresses this gap by presenting draft genomes of Salinicola socius SMB35T, Salinicola sp. MH3R3–1 and Chromohalobacter sp. SMB17, which were isolated from potash mine tailings in the Verkhnekamsk salt deposit area of Russia. The analysis of these genomes confirmed the importance of ectoines and quaternary amines to the capacity of halomonads to tolerate osmotic stress and adapt to hypersaline environments. The study also revealed that Chromohalobacter and Salinicola share 75–90% of the predicted proteome, but also harbor a set of genus-specific genes, which in Salinicola amounted to approximately 0.5 Mbp. These genus-specific genome segments may contribute to the phenotypic diversity of the Halomonadaceae and the ability of these organisms to adapt to changing environmental conditions and colonize new ecological niches.

The main objective of this study was to identify and isolate arsenic resistant bacteria that can be used for removing arsenic from the contaminated environment. Here we report a soil borne bacterium, B1-CDA that can serve this purpose. B1-CDA was isolated from the soil of a cultivated land in Chuadanga district located in the southwest region of Bangladesh. The morphological, biochemical and 16S rRNA analysis suggested that the isolate belongs to Lysinibacillus sphaericus. The minimum inhibitory concentration (MIC) value of the isolate is 500 mM (As) as arsenate. TOF-SIMS and ICP-MS analysis confirmed intracellular accumulation and removal of arsenics. Arsenic accumulation in cells amounted to 5.0 mg g(-1) of the cells dry biomass and thus reduced the arsenic concentration in the contaminated liquid medium by as much as 50%. These results indicate that B1-CDA has the potential for remediation of arsenic from the contaminated water. We believe the benefits of implementing this bacterium to efficiently reduce arsenic exposure will not only help to remove one aspect of human arsenic poisoning but will also benefit livestock and native animal species. Therefore, the outcome of this research will be highly significant for people in the affected area and also for human populations in other countries that have credible health concerns as a consequence of arsenic-contaminated water.

Miniature tensile-test specimens of soft-annealed, weakly textured AA3003 aluminum sheet in 0.9 mm thickness were deformed until fracture inside a scanning electron microscope. Tensile strength measured by the miniature tensile test stage agreed well with the tensile strength by regular tensile testing. Strain over the microscope field of view was determined from changes in positions of constituent particles. Slip lines were visible in secondary electron images already at 0.3% strain; activity from secondary slip systems became apparent at 2% strain. Orientation rotation behavior of the tensile load axis with respect to the crystallographic axes agreed well with previously reported trends for other aluminum alloys. Start of the fracture and tensile crack propagation were documented in secondary electron images. The region of fracture nucleation included and was surrounded by many grains that possessed high Schmid factors at zero strain. Crystal lattice rotation angles in the grains surrounding the initial fracture zone were higher than average while rotations inside the initial fracture zone were lower than average for strains from zero to 31%. The orientation rotation behavior of the tensile load axes of the grains around the fracture zone deviated from the average behavior in this material.

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks," since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2-3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices. © the author(s), publisher and licensee Libertas Academica Ltd. CC BY-NC 3.0© the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.