Active Learning based Structural Inference

peer reviewedU-AGR-6052 - IAS-AUDACITY Generic (01/07/2022 - 30/06/2026) - PANG Ju

Structural Inference with Dynamics Encoding and Partial Correlation Coefficients

peer reviewe

Effective and Efficient Structural Inference with Reservoir Computing

peer reviewedU-AGR-6052 - IAS-AUDACITY Generic (01/07/2022 - 30/06/2026) - PANG Ju

Feasibility study on a robot-assisted procedure for tumor localization using needle-rotation force signals

Accurate tumor localization is critical to early-stage cancer diagnosis and therapy. The recent force-guided technique allows to determine the depth of a suspicious tumor on the insertion path, while the spatial localization is still a great challenge. In this paper, a novel force-guided procedure was proposed to identify spatial tumor location using force signals during needle rotation. When there is a harder tumorous tissue around the needle rotation, an abnormal force signal will point to the location of the suspicious tissue. Finite element simulation and phantom experiment were conducted to test the feasibility of the procedure for the tumor localization. The simulation results showed that the harder tumorous tissue made a significant difference on the stress and deformation distributions for the surroundings, changing the needle-rotation force signals when the needle rotated towards the harder tissue. The experimental results indicated that the direction of the tumor location can be identified by the rotation-needle force signals. The intersection point of the two identified directions, derived from force signals of twice needle rotations, determined the tumor location ultimately. Also, parametric sensitivity tests were performed to examine the effective distance of the tumor location centre and the needle insertion point for the tumor localization. This procedure is expected to be used in robot-assisted system for cancer biopsy and brachytherapy

Impact of cytotoxic T lymphocytes immunotherapy on prognosis of colorectal cancer patients

BackgroundExpansion and activation of cytotoxic T lymphocytes (CTLs) in vitro represents a promising immunotherapeutic strategy, and CTLs can be primed by dendritic cells (DCs) loaded with tumor-associated antigens (TAAs) transformed by recombinant adeno-associated virus (rAAV). This study aimed to explore the impact of rAAV-DC-induced CTLs on prognosis of CRC and to explore factors associated with prognosis.MethodsThis prospective observational study included patients operated for CRC at Yan’an Hospital Affiliated to Kunming Medical University between 2016 and 2019. The primary outcome was progression-free survival (PFS), secondary outcomes were overall survival (OS) and adverse events. Totally 49 cases were included, with 29 and 20 administered rAAV-DC-induced CTL and chemotherapy, respectively.ResultsAfter 37-69 months of follow-up (median, 54 months), OS (P=0.0596) and PFS (P=0.0788) were comparable between two groups. Mild fever occurred in 2 (6.9%) patients administered CTL infusion. All the chemotherapy group experienced mild-to-moderate adverse effects, including vasculitis (n=20, 100%), vomiting (n=5, 25%), nausea (n=17, 85%) and fatigue (n=17, 85%).ConclusionsLymphatic metastasis (hazard ratio [HR]=4.498, 95% confidence interval [CI]: 1.290-15.676; P=0.018) and lower HLA-I expression (HR=0.294, 95%CI: 0.089-0.965; P=0.044) were associated with poor OS in the CTL group. CTLs induced by rAAV-DCs might achieve comparable effectiveness in CRC patients compare to chemotherapy, cases with high tumor-associated HLA-I expression and no lymphatic metastasis were more likely to benefit from CTLs

Granular avalanche statistics in rotating drum with varied particle roughness

We experimentally investigate the avalanche statistics of dry granular materials in a slowly rotating drum for five types of beads with varied surface roughness. For all beads, two distinct angles, i.e., repose angle θr and maximal angle θm, can be clearly defined, and the avalanche size distributions P(δθ) are Gaussian-like. θr, θm, and the span in P(δθ) are all positively correlated with bead surface roughness. This observation thus contrasts with a power-law P(δθ) predicted by self-organized criticality, but is reminiscent of a first-order phase transition. We speculate that both the inertia effect and the velocity-weakening mechanism during an avalanche process can enhance the first-order features, which are however absent in plasticity of sheared amorphous solids. We also discuss the dependence between θr and θm for various particles, as well as the correlation between starting and stopping angles for an individual avalanche

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods

Application of the YOLOv6 Combining CBAM and CIoU in Forest Fire and Smoke Detection

Forest fires are a vulnerable and devastating disaster that pose a major threat to human property and life. Smoke is easier to detect than flames due to the vastness of the wildland scene and the obscuring vegetation. However, the shape of wind-blown smoke is constantly changing, and the color of smoke varies greatly from one combustion chamber to another. Therefore, the widely used sensor-based smoke and fire detection systems have the disadvantages of untimely detection and a high false detection rate in the middle of an open environment. Deep learning-based smoke and fire object detection can recognize objects in the form of video streams and images in milliseconds. To this end, this paper innovatively employs CBAM based on YOLOv6 to increase the extraction of smoke and fire features. In addition, the CIoU loss function was used to ensure that training time is reduced while extracting the feature effects. Automatic mixed-accuracy training is used to train the model. The proposed model has been validated on a self-built dataset containing multiple scenes. The experiments demonstrated that our model has a high response speed and accuracy in real-field smoke and fire detection, which provides intelligent support for forest fire safety work in social life