Keeping the Vimentin Network under Control: Cell–Matrix Adhesion–associated Plectin 1f Affects Cell Shape and Polarity of Fibroblasts

Mature focal adhesions and fibrillar adhesions act as anchorage sites for vimentin filaments, with plectin isoform 1f being the crucial linker protein. Plectin serves as a nucleation and assembly center for the de novo formation of vimentin networks. Anchored vimentin creates a resilient cage-like core structure that affects cell shape

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components

Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants

Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components

Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants

Analytical modeling of mixed-Mode bending behavior of asymmetric adhesively bonded pultruded GFRP joints

This paper presents a semi-analytical methodology for the fracture mechanics assessment of asymmetric adhesively bonded composite joints. The method is based on the classical lamination theory, the simple beam theory and the extended global method. Experimental results obtained from quasi-static mixed-Mode bending (MMB) tests of adhesively bonded glass fiber reinforced polymer (GFRP) laminates were used for the validation of the introduced methodology. The main advantage of the proposed methodology is the ability of taking into account the fiber bridging effects as well as the arbitrariness of the adherend stacking sequence in a distance from the crack propagation path

Contribution of Single Tryptophan Residues to the Fluorescence and Stability of Ribonuclease Sa

Ribonuclease Sa (RNase Sa) contains no tryptophan (Trp) residues. We have added single Trp residues to RNase Sa at sites where Trp is found in four other microbial ribonucleases, yielding the following variants of RNase Sa: Y52W, Y55W, T76W, and Y81W. We have determined crystal structures of T76W and Y81W at 1.1 and 1.0 Å resolution, respectively. We have studied the fluorescence properties and stabilities of the four variants and compared them to wild-type RNase Sa and the other ribonucleases on which they were based. Our results should help others in selecting sites for adding Trp residues to proteins. The most interesting findings are: 1), Y52W is 2.9 kcal/mol less stable than RNase Sa and the fluorescence intensity emission maximum is blue-shifted to 309 nm. Only a Trp in azurin is blue-shifted to a greater extent (308 nm). This blue shift is considerably greater than observed for Trp(71) in barnase, the Trp on which Y52W is based. 2), Y55W is 2.1 kcal/mol less stable than RNase Sa and the tryptophan fluorescence is almost completely quenched. In contrast, Trp(59) in RNase T1, on which Y55W is based, has a 10-fold greater fluorescence emission intensity. 3), T76W is 0.7 kcal/mol more stable than RNase Sa, indicating that the Trp side chain has more favorable interactions with the protein than the threonine side chain. The fluorescence properties of folded Y76W are similar to those of the unfolded protein, showing that the tryptophan side chain in the folded protein is largely exposed to solvent. This is confirmed by the crystal structure of the T76W which shows that the side chain of the Trp is only ∼7% buried. 4), Y81W is 0.4 kcal/mol less stable than RNase Sa. Based on the crystal structure of Y81W, the side chain of the Trp is 87% buried. Although all of the Trp side chains in the variants contribute to the unusual positive circular dichroism band observed near 235 nm for RNase Sa, the contribution is greatest for Y81W