Activity

Most trackers focus solely on robustness and accuracy. Visual tracking, however, is a long-term problem with a high time limitation. A tracker that is robust, accurate, with long-term sustainability and real-time processing, is of high research value and practical significance. In this paper, we comprehensively consider these requirements in order to propose a new, state-of-the-art tracker with an excellent performance. EfficientNet-B0 is adopted for the first time via neural architecture search technology as the backbone network for the tracking task. This improves the network feature extraction ability and significantly reduces the number of parameters required for the tracker backbone network. In addition, maximal Distance Intersection-over-Union is set as the target estimation method, enhancing network stability and increasing the offline training convergence rate. selleck compound Channel and spatial dual attention mechanisms are employed in the target classification module to improve the discrimination of the trackers. Furthermore, the conjugate gradient optimization strategy increases the speed of the online learning target classification module. A two-stage search method combined with a screening module is proposed to enable the tracker to cope with sudden target movement and reappearance following a brief disappearance. Our proposed method has an obvious speed advantage compared with pure global searching and achieves an optimal performance on OTB2015, VOT2016, VOT2018-LT, UAV-123 and LaSOT while running at over 50 FPS.Cells recovering from the G2/M DNA damage checkpoint rely more on Aurora A-PLK1 signaling than cells progressing through an unperturbed G2 phase, but the reason for this discrepancy is not known. Here, we devised a method based on a FRET reporter for PLK1 activity to sort cells in distinct populations within G2 phase. We employed mass spectroscopy to characterize changes in protein levels through an unperturbed G2 phase and validated that ATAD2 levels decrease in a proteasome-dependent manner. Comparing unperturbed cells with cells recovering from DNA damage, we note that at similar PLK1 activities, recovering cells contain higher levels of Cyclin B1 and increased phosphorylation of CDK1 targets. The increased Cyclin B1 levels are due to continuous Cyclin B1 production during a DNA damage response and are sustained until mitosis. Whereas partial inhibition of PLK1 suppresses mitotic entry more efficiently when cells recover from a checkpoint, partial inhibition of CDK1 suppresses mitotic entry more efficiently in unperturbed cells. Our findings provide a resource for proteome changes during G2 phase, show that the mitotic entry network is rewired during a DNA damage response, and suggest that the bottleneck for mitotic entry shifts from CDK1 to PLK1 after DNA damage.Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.The bacterial community profile of cricket powder highlighted the presence of four main genera Bacteroides spp., Parabacteroides spp., Lactococcus spp., and Enterococcus spp. The spontaneous fermentation of cricket powder allowed for the isolation and characterization of seven lactic acid bacteria strains belonging to six species Latilactobacillus curvatus, Lactiplantibacillus plantarum, Latilactobacillus sakei, Lactococcus garvieae, Weissella confusa, and Enterococcus durans. The strains were characterized and selected according to different technological properties. L. plantarum CR L1 and L. curvatus CR L13 showed the best performance in terms of general aminopeptidase activity, acidification, and growth rate in MRS broth and in dough with cricket powder and wheat flour, as well as robustness during consecutive backslopping. Thus, they were used as starter-mixed to produce sourdough to manufacture bread fortified with 20% cricket powder. The addition of cricket powder led to a significant increase of protein (up to 94%) and lipid content, from 0.