Weighted Chebyshev Distance Algorithms for Hyperspectral Target Detection and Classification Applications

In this study, an efficient spectral similarity method referred to as Weighted Chebyshev Distance (WCD) is introduced for supervised classification of hyperspectral imagery (HSI) and target detection applications. The WCD is based on a simple spectral similarity based decision rule using limited amount of reference data. The estimation of upper and lower spectral boundaries of spectral signatures for all classes across spectral bands is referred to as a vector tunnel (VT). To obtain the reference information, the training signatures are provided randomly from existing data for a known class. After determination of the parameters of the WCD algorithm with the training set, classification or detection procedures are accomplished at each pixel. The comparative performances of the algorithms are tested under various cases. The decision criterion for classification of an input vector is based on choosing its class corresponding to the narrowest VT that the input vector fits in to. This is also shown to be approximated by the WCD in which the weights are chosen as an inverse power of the generalized standard deviation per spectral band. In computer experiments, the WCD classifier is compared with the Euclidian Distance (ED) classifier and the Spectral Angle Map (SAM) classifier. The WCD algorithm is also used for HSI target detection purpose. Target detection problem is considered as a two-class classification problem. The WCD is characterized only by the target class spectral information. Then, this method is compared with ED, SAM, Spectral Matched Filter (SMF), Adaptive Cosine Estimator (ACE) and Support Vector Machine (SVM) algorithms. During these studies, threshold levels are evaluated based on the Receiver Operating Characteristic Curves (ROC)

Enhancing acetic acid and 5-hydroxymethyl furfural tolerance of C. saccharoperbutylacetonicum through adaptive laboratory evolution

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.procbio.2020.11.013.In this study, adaptive laboratory evolution (ALE) was applied to isolate four strains of Clostridium saccharoperbutylacetonicum able to grow in the presence of hemicellulosic hydrolysate inhibitors unsupported by the parental strain. Among them, isolate RAC-25 presented the best fermentative performance, producing 22.1g/L of ABE and 16.7g/L of butanol. Genome sequencing revealed a deletion in the arabinose transcriptional repressor gene (araR) and a mutation in the anti-sigma factor I that promoted a downregulation of sigI. Gene expression analysis indicated high expression of genes related to H+-pumps (ATP synthases), proline biosynthesis (gamma phosphate reductase) and chaperonins (Grol), suggesting an integrated mechanism that is probably coordinated by the repression of sigI. Therefore, in addition to highlighting the power of ALE for selecting robust strains, our results suggest that sigI and araR may be interesting gene targets for increased tolerance toward inhibitor compounds relevant for lignocellulosic biofuels production.The authors would like to thank the Brazilian Center for Research in Energy and Materials (CNPEM) for providing access to the bioprocess facility of the Brazilian Biorenewables National Laboratory, and CNPq (400803/2013-5), FCT (UID/BIO/04469), BioTecNorte Operation (NORTE-01-0145-FEDER-000004) and Portuguese Biological Data Network” (ref. LISBOA-01-0145-FEDER-022231) for financial support.info:eu-repo/semantics/publishedVersio