Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT17 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.

Neuritic plaques, a pathological hallmark in Alzheimer’s disease (AD) brains, comprise extracellular aggregates of amyloid-beta (Aβ) peptide and degenerating neurites that accumulate autolysosomes. We found that, in the brains of patients with AD and in AD mouse models, Aβ plaque-associated Olig2- and NG2-expressing oligodendrocyte progenitor cells (OPCs), but not astrocytes, microglia, or oligodendrocytes, exhibit a senescence-like phenotype characterized by the upregulation of p21/CDKN1A, p16/INK4/CDKN2A proteins, and senescence-associated β-galactosidase activity. Molecular interrogation of the Aβ plaque environment revealed elevated levels of transcripts encoding proteins involved in OPC function, replicative senescence, and inflammation. Direct exposure of cultured OPCs to aggregating Aβ triggered cell senescence. Senolytic treatment of AD mice selectively removed senescent cells from the plaque environment, reduced neuroinflammation, lessened Aβ load, and ameliorated cognitive deficits. Our findings suggest a role for Aβ-induced OPC cell senescence in neuroinflammation and cognitive deficits in AD, and a potential therapeutic benefit of senolytic treatments.

Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods.

An F-actin–enriched protrusion resembling an invasive podosome promotes fusion pore formation between muscle founder cells and fusion-competent myoblasts.