k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

A Molecular Mechanism for Sequential Activation of a G Protein-Coupled Receptor

Ligands targeting G protein-coupled receptors (GPCRs) are currently classified as either orthosteric, allosteric, or dualsteric/bitopic. Here, we introduce a new pharmacological concept for GPCR functional modulation: sequential receptor activation. A hallmark feature of this is a stepwise ligand binding mode with transient activation of a first receptor site followed by sustained activation of a second topographically distinct site. We identify 4-CMTB (2-(4-chlorophenyl)-3-methyl-N-(thiazol-2-yl)butanamide), previously classified as a pure allosteric agonist of the free fatty acid receptor 2, as the first sequential activator and corroborate its two-step activation in living cells by tracking integrated responses with innovative label-free biosensors that visualize multiple signaling inputs in real time. We validate this unique pharmacology with traditional cellular readouts, including mutational and pharmacological perturbations along with computational methods, and propose a kinetic model applicable to the analysis of sequential receptor activation. We envision this form of dynamic agonism as a common principle of nature to spatiotemporally encode cellular information.

Sediment metal bioavailability in Lake Taihu, China: evaluation of sequential extraction, DGT, and PBET techniques

The European “Community Bureau of Reference” (BCR) sequential extraction procedure, diffusive gradient in thin-films technique (DGT), and physiologically based extraction test were applied to assess metal bioavailability in sediments of Lake Taihu (n = 13). Findings from the three methods showed that Cd was a significant problem in the western lake whereas Cu, Zn, and Ni pollution was most severe in the northern lake. Results from the sequential extraction revealed that more than 50 % of the Cu and Zn were highly mobile and defined within the extractable fraction (AS1 + FM2 + OS3) in the majority of the sediments, in contrast extractable fractions of Ni and Cd were lower than 50 % in most of the sampling sites. Average Cu, Zn, Ni, and Cd bioaccessibilities were <50 % in the gastric phase. Zn and Cd bioaccessibility in the intestinal phase was ∼50 % lower than the gastric phase while bioaccessibilities of Cu and Ni were 47–57 % greater than the gastric phase. Linear regression analysis between DGT and BCR measurements indicated that the extractable fractions (AS1 + FM2 + OS3) in the reducing environment were the main source of DGT uptake, suggesting that DGT is a good in situ evaluation tool for metal bioavailability in sediments.