In several pedigrees of early onset familial Alzheimer's disease (FAD), point mutations in the beta-amyloid precursor protein (APP) gene are genetically linked to the disease. This finding implicates APP in the pathogenesis of Alzheimer's disease in these individuals. To understand the in vivo function of APP and its processing, we have generated an APP-null mutation in mice. Homozygous APP-deficient mice were viable and fertile. However, the mutant animals weighed 15%-20% less than age-matched wild-type controls. Neurological evaluation showed that the APP-deficient mice exhibited a decreased locomotor activity and forelimb grip strength, indicating a compromised neuronal or muscular function. In addition, four out of six homozygous mice showed reactive gliosis at 14 weeks of age, suggesting an impaired neuronal function as a result of the APP-null mutation.

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Steel strip surface defects recognition is very important to steel strip production and quality control, which needs further improvement. In this paper, an end‐to‐end surface defects recognition system is proposed for steel strip surface inspection. This system is based on the symmetric surround saliency map for surface defects detection and deep convolutional neural networks (CNNs) which directly use the defect image as input and defect category as output for seven classes of steel strip defects classification. The CNNs are trained purely on raw defect images and learned defect features from the training of network, which avoiding the separation between feature extraction and image classification, so that forms an end‐to‐end defects recognition pipeline. To further illustrate the superiority of the defect recognition methods with CNNs, an authoritative and standard steel strip surface defect dataset − NEU is also used to evaluate the defect recognition effect using CNNs. Experimental results demonstrate that the proposed methods perform well in steel strip surface defect detection of different types and achieve a high recognition rate for defect images. In addition, a series of data augmentation methods are discussed to analyze its effect on avoiding over‐fitting for defects recognition.

Although laboratory studies have implicated the high mobility group box 1 (HMGB1) in melanoma, its clinical relevance remains unclear. We analyzed nearly 100 cases of human melanoma and found that HMGB1 was highly overexpressed in melanoma samples relative to normal skin and nevi tissues. Significantly, higher levels of HMGB1 correlated with more advanced disease stages and with poorer survival in melanoma patients. Unlike the well-documented pro-inflammatory role of the extracellular HMGB1, we found that its intracellular activity is necessary for melanoma cell proliferation. An absolute dependency of melanoma cell proliferation on HMGB1 was underscored by the marked response of cell cycle arrest and senescence to HMGB1 knockdown. We demonstrated that HMGB1 deficiency-induced inhibition of cell proliferation was mediated by p21, which was induced via a Sp1-dependent mechanism. Taken together, our data demonstrate a novel oncogenic role of HMGB1 in promoting human melanoma cell proliferation and have important implications in melanoma patient care.

To understand the in vivo function of the amyloid precursor protein (APP) we generated an APP null mutation in mice by homologous recombination in embryonic stem (ES) cells. We show here that homozygous APP deficient mice were produced at expected frequencies. Neither APP mRNA nor protein could be detected in these animals. Yet the homozygous APP mutant mice are fertile and do not show overt abnormalities at up to 12 weeks of age. Neuroanatomical studies of the brain did not reveal significant differences in the knockout mice as compared to the wild-type controls. These results argue against an essential function of APP in mouse embryonic and early neuronal development.