Cytoskeleton network participates in the anti-infection responses of macrophage.

During immune responses against invading pathogenic bacteria, the cytoskeleton network enables macrophages to implement multiple essential functions. To protect the host from infection, macrophages initially polarize to adopt different phenotypes in response to distinct signals from the microenvironment. The extracellular stimulus regulates the rearrangement of the cytoskeleton, thereby altering the morphology and migratory properties of macrophages. Subsequently, macrophages degrade the extracellular matrix (ECM) and migrate toward the sites of infection to directly contact invading pathogens, during which the involvement of cytoskeleton-based structures such as podosomes and lamellipodia is indispensable. Ultimately, macrophages execute the function of phagocytosis to engulf and eliminate the invading pathogens. Phagocytosis is a complex process that requires the cooperation of cytoskeleton-enriched super-structures, such as filopodia, lamellipodia, and phagocytic cup. This review presents an overview of cytoskeletal regulations in macrophage polarization, ECM degradation, migration, and phagocytosis, highlighting the pivotal role of the cytoskeleton in host defense against infection.

Towards Trajectory Forecasting From Detection.

Trajectory forecasting for traffic participants (e.g., vehicles) is critical for autonomous platforms to make safe plans. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose to predict trajectories directly based on detection results without relying on explicitly formed trajectories. Different from traditional methods which encode the motion cues of an agent based on its clearly defined trajectory, we extract the motion information only based on the affinity cues among detection results, in which an affinity-aware state update mechanism is designed to manage the state information. In addition, considering that there could be multiple plausible matching candidates, we aggregate the states of them. These designs take the uncertainty of association into account which relax the undesirable effect of noisy trajectory obtained from data association and improve the robustness of the predictor. Extensive experiments validate the effectiveness of our method and its generalization ability to different detectors or forecasting schemes.

Parallel Multistage Wide Neural Network.

Deep learning networks have achieved great success in many areas, such as in large-scale image processing. They usually need large computing resources and time and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gcForest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with radial basis function (RBF) networks, and knowledge transfer (FDRK). In this article, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances and can be trained in one epoch using subsampling and least squares (LS). Second, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data are acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of: 1) optimized computing resources; 2) incremental learning; and 3) parallel testing with stages. The experimental results with the MNIST data, a number of large hyperspectral remote sensing data, and different types of data in different application areas, including many image and nonimage datasets, show that the WRBF and PMWNN can work well on both image and nonimage data and have very competitive accuracy compared to learning models, such as stacked autoencoders, deep belief nets, support vector machine (SVM), multilayer perceptron (MLP), LeNet-5, RBF network, recently proposed CDL, broad learning, gcForest, ERDK, and FDRK.

Progressive Temporal-Spatial-Semantic Analysis of Driving Anomaly Detection and Recounting.

For analyzing the traffic anomaly within dashcam videos from the perspective of ego-vehicles, the agent should spatial-temporally localize the abnormal occasion and regions and give a semantically recounting of what happened. Most existing formulations concentrate on the former spatial-temporal aspect and mainly approach this goal by training normal pattern classifiers/regressors/dictionaries with large-scale availably labeled data. However, anomalies are context-related, and it is difficult to distinguish the margin of abnormal and normal clearly. This paper proposes a progressive unsupervised driving anomaly detection and recounting (D&R) framework. The highlights are three-fold: (1) We formulate driving anomaly D&R as a temporal-spatial-semantic (TSS) model, which achieves a coarse-to-fine focusing and generates convincing driving anomaly D&R. (2) This work contributes an unsupervised D&R without any training data while performing an effective performance. (3) We novelly introduce the traffic saliency, isolation forest, visual semantic causal relations of driving scene to effectively construct the TSS model. Extensive experiments on a driving anomaly dataset with 106 video clips (temporal-spatial-semantically labeled carefully by ourselves) demonstrate superior performance over existing techniques.

Upregulation of CD11b and CD86 through LSD1 inhibition promotes myeloid differentiation and suppresses cell proliferation in human monocytic leukemia cells.

LSD1 (Lysine Specific Demethylase1)/KDM1A (Lysine Demethylase 1A), a flavin adenine dinucleotide (FAD)-dependent histone H3K4/K9 demethylase, sustains oncogenic potential of leukemia stem cells in primary human leukemia cells. However, the pro-differentiation and anti-proliferation effects of LSD1 inhibition in acute myeloid leukemia (AML) are not yet fully understood. Here, we report that small hairpin RNA (shRNA) mediated LSD1 inhibition causes a remarkable transcriptional activation of myeloid lineage marker genes (CD11b/ITGAM and CD86), reduction of cell proliferation and decrease of clonogenic ability of human AML cells. Cell surface expression of CD11b and CD86 is significantly and dynamically increased in human AML cells upon sustained LSD1 inhibition. Chromatin immunoprecipitation and quantitative PCR (ChIP-qPCR) analyses of histone marks revealed that there is a specific increase of H3K4me2 modification and an accompanied increase of H3K4me3 modification at the respective CD11b and CD86 promoter region, whereas the global H3K4me2 level remains constant. Consistently, inhibition of LSD1 in vivo significantly blocks tumor growth and induces a prominent increase of CD11b and CD86. Taken together, our results demonstrate the anti-tumor properties of LSD1 inhibition on human AML cell line and mouse xenograft model. Our findings provide mechanistic insights into the LSD1 functions in controlling both differentiation and proliferation in AML.

Histone Demethylase LSD1 Promotes Adipocyte Differentiation through Repressing Wnt Signaling.

Adipose tissue plays important roles in animals. White fat stores energy in lipids, while brown fat is responsible for nonshivering thermogenesis through UCP1-mediated energy dissipation. Although epigenetic mechanisms modulate differentiation in multiple lineages, the epigenetic regulation of brown adipocyte differentiation is poorly understood. By screening a collection of epigenetic compounds, we found that Lysine-Specific Demethylase 1 (LSD1) inhibitors repress brown adipocyte differentiation. RNAi-mediated Lsd1 knockdown causes a similar effect, which can be rescued by expression of wild-type but not catalytic-inactive LSD1. Mechanistically, LSD1 promotes brown adipogenesis by demethylating H3K4 on promoter regions of Wnt signaling components and repressing the Wnt pathway. Furthermore, deletion of Lsd1 in mice leads to inhibition of brown adipogenesis, validating the pivotal role of LSD1 in brown fat development in vivo. Our work identifies LSD1 as a key epigenetic regulator in brown adipogenesis. The link between LSD1 and the Wnt pathway provides potential opportunities to modulate brown fat differentiation.

Online anomaly detection in crowd scenes via structure analysis.

Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural context descriptor (SCD) for describing the crowd individual, which originally introduces the potential energy function of particle's interforce in solid-state physics to intuitively conduct vision contextual cueing. For computing the crowd SCD variation effectively, we then design a robust multi-object tracker to associate the targets in different frames, which employs the incremental analytical ability of the 3-D discrete cosine transform (DCT). By online spatial-temporal analyzing the SCD variation of the crowd, the abnormality is finally localized. Our contribution mainly lies on three aspects: 1) the new exploration of abnormal detection from structure modeling where the motion difference between individuals is computed by a novel selective histogram of optical flow that makes the proposed method can deal with more kinds of anomalies; 2) the SCD description that can effectively represent the relationship among the individuals; and 3) the 3-D DCT multi-object tracker that can robustly associate the limited number of (instead of all) targets which makes the tracking analysis in high density crowd situation feasible. Experimental results on several publicly available crowd video datasets verify the effectiveness of the proposed method.