GANet: Goal Area Network for Motion Forecasting

Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate goal coordinates are hard to predict and evaluate. In addition, the point representation of the destination limits the utilization of a rich road context, leading to inaccurate prediction results in many cases. Goal area, i.e., the possible destination area, rather than goal coordinate, could provide a more soft constraint for searching potential trajectories by involving more tolerance and guidance. In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately. Specifically, we propose a GoICrop (Goal Area of Interest) operator to effectively extract semantic lane features in goal areas and model actors' future interactions, which benefits a lot for future trajectory estimations. GANet ranks the 1st on the leaderboard of Argoverse Challenge among all public literature (till the paper submission), and its source codes will be released

CLOCK 3111T/C Variant Correlates with Motor Fluctuation and Sleep Disorders in Chinese Patients with Parkinson’s Disease

Background. The clock genes controlling biological rhythm play an important role in the pathophysiology of aging. The purpose of this study was to determine whether there is an association between a variant of the circadian locomotor output cycles kaput (CLOCK) gene and circadian dysfunction of Parkinson’s disease (PD). Methods. Six hundred and forty-six cases of Parkinson’s disease from consecutive outpatients and inpatients ward from our hospital were included in this study. Kompetitive allele-specific PCR was used to determine the frequency distribution of genotypes and alleles. The examinations for the PD group were assessed in person in order to evaluate motor symptoms, cognitive function, sleep, and depression, including the Unified Parkinson’s Disease Rating Scale (UPDRS), Mini-Mental State Examination (MMSE), Pittsburgh Sleep Quality Index (PSQI), and 17-item Hamilton Rating Scale for Depression (HAMD-17). Results. Motor fluctuation (P<0.001) and sleep disorders (P=0.007) were significantly different between the two groups. These correlations persisted after adjusting for confounding risk factors by further binary logistic regression analysis, suggesting that the CLOCK 3111T/C variant was associated with motor fluctuation (OR = 1.080, P<0.001) and a subjective sleep disorder (OR = 1.130, P=0.037). Conclusion. The CLOCK 3111T/C variant can be an independent risk factor for motor fluctuation and sleep disorder in Parkinson’s disease

Bioinspired cilia arrays with programmable nonreciprocal motion and metachronal coordination

Coordinated nonreciprocal dynamics in biological cilia is essential to many living systems, where the emergentmetachronal waves of cilia have been hypothesized to enhance net fluid flows at low Reynolds numbers (Re). Experimental investigation of this hypothesis is critical but remains challenging. Here, we report soft miniature devices with both ciliary nonreciprocal motion and metachronal coordination and use them to investigate the quantitative relationship between metachronal coordination and the induced fluid flow. We found that only antiplectic metachronal waves with specific wave vectors could enhance fluid flows compared with the synchronized case. These findings further enable various bioinspired cilia arrays with unique functionalities of pumping and mixing viscous synthetic and biological complex fluids at low Re. Our design method and developed soft miniature devices provide unprecedented opportunities for studying ciliary biomechanics and creating cilia-inspired wireless microfluidic pumping, object manipulation and lab- and organ-on-a-chip devices, mobile microrobots, and bioengineering systems.ISSN:2375-254

Survey on Video Object Tracking Algorithms

Video object tracking is an important research content in the field of computer vision, mainly studying the tracking of objects with interest in video streams or image sequences. Video object tracking has been widely used in cameras and surveillance, driverless, precision guidance and other fields. Therefore, a comprehensive review on video object tracking algorithms is of great significance. Firstly, according to different sources of challenges, the challenges faced by video object tracking are classified into two aspects, the objects&#x02019; factors and the backgrounds&#x02019; factors, and summed up respectively. Secondly, the typical video object tracking algorithms in recent years are classified into correlation filtering video object tracking algorithms and deep learning video object tracking algorithms. And further the correlation filtering video object tracking algorithms are classified into three categories: kernel correlation filtering algorithms, scale adaptive correlation filtering algorithms and multi-feature fusion corre-lation filtering algorithms. The deep learning video object tracking algorithms are classified into two categories: video object tracking algorithms based on siamese network and based on convolutional neural network. This paper analyzes various algorithms from the aspects of research motivation, algorithm ideas, advantages and disadvantages. Then, the widely used datasets and evaluation indicators are introduced. Finally, this paper sums up the research and looks forward to the development trends of video object tracking in the future

Chemotherapy-induced differential cell cycle arrest in B-cell lymphomas affects their sensitivity to Wee1 inhibition

Chemotherapeutic agents, e.g., cytarabine and doxorubicin, cause DNA damage. However, it remains unknown whether such agents differentially regulate cell cycle arrest in distinct types of B-cell lymphomas, and whether this phenotype can be exploited for developing new therapies. We treated various types of B cells, including primary and B lymphoma cells, with cytarabine or doxorubicin, and determined DNA damage responses, cell cycle regulation and sensitivity to a Wee1 inhibitor. We found that cyclin A2/B1 upregulation appears to be an intrinsic programmed response to DNA damage; however, different types of B cells arrest in distinct phases of the cell cycle. The Wee1 inhibitor significantly enhanced the apoptosis of G2 phase-arrested B-cell lymphomas by inducing premature entry into mitosis and mitotic catastrophe, whereas it did not affect G1/S-phase-arrested lymphomas. Cytarabine-induced G1-arrest can be converted to G2-arrest by doxorubicin treatment in certain B-cell lymphomas, which correlates with newly acquired sensitivity to the Wee1 inhibitor. Consequently, the Wee1 inhibitor together with cytarabine or doxorubicin inhibited tumor growth in vitro and in vivo more effectively, providing a potential new therapy for treating B-cell lymphomas. We propose that the differential cell cycle arrest can be exploited to enhance the chemosensitivity of B-cell lymphomas

A novel heteromorphic ensemble algorithm for hand pose recognition

Imagining recognition of behaviors from video sequences for a machine is full of challenges but meaningful. This work aims to predict students’ behavior in an experimental class, which relies on the symmetry idea from reality to annotated reality centered on the feature space. A heteromorphic ensemble algorithm is proposed to make the obtained features more aggregated and reduce the computational burden. Namely, the deep learning models are improved to obtain feature vectors representing gestures from video frames and the classification algorithm is optimized for behavior recognition. So, the symmetric idea is realized by decomposing the task into three schemas including hand detection and cropping, hand joints feature extraction, and gesture classification. Firstly, a new detector method named YOLOv4-specific tiny detection (STD) is proposed by reconstituting the YOLOv4-tiny model, which could produce two outputs with some attention mechanism leveraging context information. Secondly, the efficient pyramid squeeze attention (EPSA) net is integrated into EvoNorm-S0 and the spatial pyramid pool (SPP) layer to obtain the hand joint position information. Lastly, the D–S theory is used to fuse two classifiers, support vector machine (SVM) and random forest (RF), to produce a mixed classifier named S–R. Eventually, the synergetic effects of our algorithm are shown by experiments on self-created datasets with a high average recognition accuracy of 89.6%