Structure-Preserved Unsupervised Domain Adaptation

Domain adaptation has been a primal approach to addressing the issues by lack of labels in many data mining tasks. Although considerable efforts have been devoted to domain adaptation with promising results, most existing work learns a classifier on a source domain and then predicts the labels for target data, where only the instances near the boundary determine the hyperplane and the whole structure information is ignored. Moreover, little work has been done regarding to multi-source domain adaptation. To that end, we develop a novel unsupervised domain adaptation framework, which ensures the whole structure of source domains is preserved to guide the target structure learning in a semi-supervised clustering fashion. To our knowledge, this is the first time when the domain adaptation problem is re-formulated as a semi-supervised clustering problem with target labels as missing values. Furthermore, by introducing an augmented matrix, a non-trivial solution is designed, which can be exactly mapped into a K-means-like optimization problem with modified distance function and update rule for centroids in an efficient way. Extensive experiments on several widely-used databases show the substantial improvements of our proposed approach over the state-of-the-art methods

Robust Discriminative Metric Learning for Image Representation

Metric learning has attracted significant attentions in the past decades, for the appealing advances in various realworld applications such as person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm (RDML) via fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, fast low-rank representation is implemented to mitigate the computational burden and make sure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, and image clustering. The experimental results verify the effectiveness of the proposed algorithm by comparing to many metric learning algorithms, even deep learning ones

Transplantation of Human Umbilical Mesenchymal Stem Cells from Wharton's Jelly after Complete Transection of the Rat Spinal Cord

BACKGROUND: Human umbilical mesenchymal stem cells (HUMSCs) isolated from Wharton's jelly of the umbilical cord can be easily obtained and processed compared with embryonic or bone marrow stem cells. These cells may be a valuable source in the repair of spinal cord injury. METHODOLOGY/PRINCIPAL FINDINGS: We examine the effects of HUMSC transplantation after complete spinal cord transection in rats. Approximately 5x10(5) HUMSCs were transplanted into the lesion site. Three groups of rats were implanted with either untreated HUMSCs (referred to as the stem cell group), or HUMSCs treated with neuronal conditioned medium (NCM) for either three days or six days (referred to as NCM-3 and NCM-6 days, respectively). The control group received no HUMSCs in the transected spinal cord. Three weeks after transplantation, significant improvements in locomotion were observed in all the three groups receiving HUMSCs (stem cell, NCM-3 and NCM-6 days groups). This recovery was accompanied by increased numbers of regenerated axons in the corticospinal tract and neurofilament-positive fibers around the lesion site. There were fewer microglia and reactive astrocytes in both the rostral and caudal stumps of the spinal cord in the stem cell group than in the control group. Transplanted HUMSCs survived for 16 weeks and produced large amounts of human neutrophil-activating protein-2, neurotrophin-3, basic fibroblast growth factor, glucocorticoid induced tumor necrosis factor receptor, and vascular endothelial growth factor receptor 3 in the host spinal cord, which may help spinal cord repair. CONCLUSIONS/SIGNIFICANCE: Transplantation of HUMSCs is beneficial to wound healing after spinal cord injury in rats

Antibacterial Bisabolane-Type Sesquiterpenoids from the Sponge-Derived Fungus Aspergillus sp.

Four new bisabolane-type sesquiterpenoids, aspergiterpenoid A (1), (−)-sydonol (2), (−)-sydonic acid (3), and (−)-5-(hydroxymethyl)-2-(2′,6′,6′-trimethyltetrahydro-2H- pyran-2-yl)phenol (4) together with one known fungal metabolite (5) were isolated from the fermentation broth of a marine-derived fungus Aspergillus sp., which was isolated from the sponge Xestospongia testudinaria collected from the South China Sea. Four of them (1–4) are optically active compounds. Their structures and absolute configurations were elucidated by using NMR spectroscopic techniques and mass spectrometric analysis, and by comparing their optical rotations with those related known analogues. Compounds 1–5 showed selective antibacterial activity against eight bacterial strains with the MIC (minimum inhibiting concentrations) values between 1.25 and 20.0 µM. The cytotoxic, antifouling, and acetylcholinesterase inhibitory activities of these compounds were also examined