A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research

Genome Sequence Analyses of Pseudomonas savastanoi pv. glycinea and Subtractive Hybridization-Based Comparative Genomics with Nine Pseudomonads

Bacterial blight, caused by Pseudomonas savastanoi pv. glycinea (Psg), is a common disease of soybean. In an effort to compare a current field isolate with one isolated in the early 1960s, the genomes of two Psg strains, race 4 and B076, were sequenced using 454 pyrosequencing. The genomes of both Psg strains share more than 4,900 highly conserved genes, indicating very low genetic diversity between Psg genomes. Though conserved, genome rearrangements and recombination events occur commonly within the two Psg genomes. When compared to each other, 437 and 163 specific genes were identified in B076 and race 4, respectively. Most specific genes are plasmid-borne, indicating that acquisition and maintenance of plasmids may represent a major mechanism to change the genetic composition of the genome and even acquire new virulence factors. Type three secretion gene clusters of Psg strains are near identical with that of P. savastanoi pv. phaseolicola (Pph) strain 1448A and they shared 20 common effector genes. Furthermore, the coronatine biosynthetic cluster is present on a large plasmid in strain B076, but not in race 4. In silico subtractive hybridization-based comparative genomic analyses with nine sequenced phytopathogenic pseudomonads identified dozens of specific islands (SIs), and revealed that the genomes of Psg strains are more similar to those belonging to the same genomospecies such as Pph 1448A than to other phytopathogenic pseudomonads. The number of highly conserved genes (core genome) among them decreased dramatically when more genomes were included in the subtraction, suggesting the diversification of pseudomonads, and further indicating the genome heterogeneity among pseudomonads. However, the number of specific genes did not change significantly, suggesting these genes are indeed specific in Psg genomes. These results reinforce the idea of a species complex of P. syringae and support the reclassification of P. syringae into different species

Evaluation of Duck Egg Hatching Characteristics with a Lightweight Multi-Target Detection Method

Since it is difficult to accurately identify the fertilization and infertility status of multiple duck eggs on an incubation tray, and due to the lack of easy-to-deploy detection models, a novel lightweight detection architecture (LDA) based on the YOLOX-Tiny framework is proposed in this paper to identify sterile duck eggs with the aim of reducing model deployment requirements and improving detection accuracy. Specifically, the method acquires duck egg images through an acquisition device and augments the dataset using rotation, symmetry, and contrast enhancement methods. Then, the traditional convolution is replaced by a depth-wise separable convolution with a smaller number of parameters, while a new CSP structure and backbone network structure are used to reduce the number of parameters of the model. Finally, to improve the accuracy of the network, the method includes an attention mechanism after the backbone network and uses the cosine annealing algorithm in training. An experiment was conducted on 2111 duck eggs, and 6488 duck egg images were obtained after data augmentation. In the test set of 326 duck egg images, the mean average precision (mAP) of the method in this paper was 99.74%, which was better than the 94.92% of the YOLOX-Tiny network before improvement, and better than the reported prediction accuracy of 92.06%. The number of model parameters was only 1.93 M, which was better than the 5.03 M of the YOLOX-Tiny network. Further, by analyzing the concurrent detection of single 3 × 5, 5 × 7 and 7 × 9 grids, the algorithm achieved a single detection number of 7 × 9 = 63 eggs. The method proposed in this paper significantly improves the efficiency and detection accuracy of single-step detection of breeder duck eggs, reduces the network size, and provides a suitable method for identifying sterile duck eggs on hatching egg trays. Therefore, the method has good application prospects

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effectively and accurately track multiple moving targets from a video. First, an entropy-based birth intensity estimation method is incorporated to eliminate the false positives caused by noisy video data. Then, a weight-penalized method with multi-feature fusion is proposed to accurately track the targets in close movement. For targets without occlusion, a weight matrix that contains all updated weights between the predicted target states and the measurements is constructed, and a simple, but effective method based on total weight and predicted target state is proposed to search the ambiguous weights in the weight matrix. The ambiguous weights are then penalized according to the fused target features that include spatial-colour appearance, histogram of oriented gradient and target area and further re-normalized to form a new weight matrix. With this new weight matrix, the tracker can correctly track the targets in close movement without occlusion. For targets with occlusion, a robust game-theoretical method is used. Finally, the experiments conducted on various video scenarios validate the effectiveness of the proposed penalization method and show the superior performance of our tracker over the state of the art

EXPERIMENTAL STUDY OF THE BENDING PERFORMANCE OF HOLLOW GLULAM BEAMS

Hollow glulam beam has some advantages that the traditional solid glulam beam does not have, such as the convenience for wiring construction and comparably light weight. Four-point bending tests of three solid glulam beams and 15 hollow glulam beams with various sizes of rectangular holes produced from small-diameter larch timber were conducted to investigate the influence of the hollow ratio and wall thickness on bending stiffness and load capacity. The midspan deflection, cross-section strain, and ultimate load were obtained from the tests, and the detailed failure modes and apparent MOE for all specimens are reported. Hollow glulam beams with the hollow ratio ranged from 25% to 40%, and the wall thickness greater than 20 mm after the assumption of plane section under bending moment. The apparent bending stiffness and ductility of hollow glulam beam were less than those of solid glulam beam, and the apparent MOE is 0.86 times the elastic modulus value calculated by theory of elasticity. In addition, a calculation formula for the ultimate bending moment is proposed.

Targeting human caseinolytic protease P (ClpP) as a novel therapeutic strategy in ovarian cancer

Abstract Ovarian cancer (OC) is currently one of the most life‐threatening types of gynecological malignancy with limited treatment options and poor clinical outcomes. Human caseinolytic protease P (HsClpP) is located in the mitochondria and plays an important role in several tumors. Moreover, HsClpP is overexpressed in OC and several other tumor cells. Thus, HsClpP modulation is regarded as a potential approach for OC treatment. In this study, we identified and validated a novel boron peptide Compound 43‐8F as a potent HsClpP inhibitor. Upon 43‐8F treatment, mitochondrial damage was observed to be closely correlated with upregulated intracellular reactive oxygen species production, decreasement of membrane potential, and ATP content suppression. Meanwhile, the expression level of SDHB and the ATF4 was increased after 43‐8F treatment, suggesting that 43‐8F treatment induces mitochondrial respiratory disorders and activates the integrated stress response pathway to inhibit tumor cell growth. Further, 43‐8F exhibited a good therapeutic and safety profile in OC xenograft model in nude mice. Together, these results suggest that 43‐8F exerts an anti‐ovarian cancer effect by inhibiting HsClpP pathway