Human cytosolic NADP-IDH (IDH1) has recently been found to be involved in tumorigenesis. Notably, the tumorderived IDH1 mutations identified so far mainly occur at Arg132, and mutation R132H is the most prevalent one. This mutation impairs the oxidative IDH activity of the enzyme, but renders a new reduction function of converting α-ketoglutarate (αKG) to 2-hydroxyglutarate. Here, we report the structures of the R132H mutant IDH1 with and without isocitrate (ICT) bound. The structural data together with mutagenesis and biochemical data reveal a previously undefined initial ICT-binding state and demonstrate that IDH activity requires a conformational change to a closed pre-transition state. Arg132 plays multiple functional roles in the catalytic reaction; in particular, the R132H mutation hinders the conformational changes from the initial ICT-binding state to the pre-transition state, leading to the impairment of the IDH activity. Our results describe for the first time that there is an intermediate conformation that corresponds to an initial ICT-binding state and that the R132H mutation can trap the enzyme in this conformation, therefore shedding light on the molecular mechanism of the "off switch" of the potentially tumor-suppressive IDH activity. Furthermore, we proved the necessity of Tyr139 for the gained αKG reduction activity and propose that Tyr139 may play a vital role by compensating the increased negative charge on the C2 atom of αKG during the transfer of a hydride anion from NADPH to αKG, which provides new insights into the mechanism of the "on switch" of the hypothetically oncogenic reduction activity of IDH1 by this mutation.

ARL2 is a member of the ADP-ribosylation factor family but has unique biochemical features. BART is an effector of ARL2 that is essential for nuclear retention of STAT3 and may also be involved in mitochondria transport and apoptosis. Here we report the crystal structure and biochemical characterization of human ARL2-GTP-BART complex. ARL2-GTP assumes a typical small GTPase fold with a unique N-terminal alpha helix conformation. BART consists of a six alpha helix bundle. The interactions between ARL2 and BART involve two interfaces: a conserved N-terminal LLXIL motif of ARL2 is embedded in a hydrophobic cleft of BART and the switch regions of ARL2 interact with helix alpha3 of BART. Both interfaces are essential for the binding as verified by mutagenesis study. This novel recognition and binding mode is different from that of other small GTPase-effector interactions and provides molecular basis for the high specificity of ARL2 for BART.

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Lambertian model is a classical illumination model consisting of a surface albedo component and a light intensity component. Some previous researches assume that the light intensity component mainly lies in the large-scale features. They adopt holistic image decompositions to separate it out, but it is difficult to decide the separating point between large-scale and small-scale features. In this paper, we propose to take a logarithm transform, which can change the multiplication of surface albedo and light intensity into an additive model. Then, a difference (substraction) between two pixels in a neighborhood can eliminate most of the light intensity component. By dividing a neighborhood into subregions, edgemaps of multiple scales can be obtained. Then, each edgemap is multiplied by a weight that can be determined by an independent training scheme. Finally, all the weighted edgemaps are combined to form a robust holistic feature map. Extensive experiments on four benchmark data sets in controlled and uncontrolled lighting conditions show that the proposed method has promising results, especially in uncontrolled lighting conditions, even mixed with other complicated variations.

High glucose levels induced by maternal diabetes could lead to defects in neural crest development during embryogenesis, but the cellular mechanism is still not understood. In this study, we observed a defect in chick cranial skeleton, especially parietal bone development in the presence of high glucose levels, which is derived from cranial neural crest cells (CNCC). In early chick embryo, we found that inducing high glucose levels could inhibit the development of CNCC, however, cell proliferation was not significantly involved. Nevertheless, apoptotic CNCC increased in the presence of high levels of glucose. In addition, the expression of apoptosis and autophagy relevant genes were elevated by high glucose treatment. Next, the application of beads soaked in either an autophagy stimulator (Tunicamycin) or inhibitor (Hydroxychloroquine) functionally proved that autophagy was involved in regulating the production of CNCC in the presence of high glucose levels. Our observations suggest that the ERK pathway, rather than the mTOR pathway, most likely participates in mediating the autophagy induced by high glucose. Taken together, our observations indicated that exposure to high levels of glucose could inhibit the survival of CNCC by affecting cell apoptosis, which might result from the dysregulation of the autophagic process.

Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is first transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then, the features with the largest scores are preserved for saving memory requirements. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then, the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation-based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance.

A sparse representation classifier (SRC) and a kernel discriminant analysis (KDA) are two successful methods for face recognition. An SRC is good at dealing with occlusion, while a KDA does well in suppressing intraclass variations. In this paper, we propose kernel extended dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kernel principal components of occlusion variations as an occlusion model, which can represent the possible occlusion variations efficiently. Then, the occlusion model is projected by KDA to get the KED, which can be computed via the same kernel trick as new testing samples. Finally, we use structured SRC for classification, which is fast as only a small number of atoms are appended to the basic dictionary, and the feature dimension is low. We also extend KED to multikernel space to fuse different types of features at kernel level. Experiments are done on several large-scale data sets, demonstrating that not only does KED get impressive results for nonoccluded samples, but it also handles the occlusion well without overfitting, even with a single gallery sample per subject.