A robust defect detection method for syringe scale without positive samples.

With the worldwide spread of the COVID-19 pandemic, the demand for medical syringes has increased dramatically. Scale defect, one of the most common defects on syringes, has become a major barrier to boosting syringe production. Existing methods for scale defect detection suffer from large volumes of data requirements and the inability to handle diverse and uncertain defects. In this paper, we propose a robust scale defects detection method with only negative samples and favorable detection performance to solve this problem. Different from conventional methods that work in a batch-mode defects detection manner, we propose to locate the defects on syringes with a two-stage framework, which consists of two components, that is, the scale extraction network and the scale defect discriminator. Concretely, the SeNet is first built to utilize the convolutional neural network to extract the main structure of the scale. After that, the scale defect discriminator is designed to detect and label the scale defects. To evaluate the performance of our method, we conduct experiments on one real-world syringe dataset. The competitive results, that is, 99.7% on F1, prove the effectiveness of our method.

[Association of JMJD3, MMP-2 and VEGF expressions with clinicopathological features of invasive ductal breast carcinoma].

OBJECTIVE: To examine the expressions of JMJD3, matrix metalloproteinase-2 (MMP-2) and vascular endothelial growth factor (VEGF) in invasive ductal breast carcinoma, their association with the clinicopathological features of the patients and the effect of JMJD3 overexpression on proliferation and MMP-2 and VEGF expressions in breast cancer cells. METHODS: The protein and mRNA expressions of JMJD3, MMP-2, and VEGF in invasive ductal breast carcinoma and paired adjacent tissues were detected by immunohistochemistry and RT-PCR, respectively, and their correlation with the clinicopathological characteristics of the patients was analyzed. Kaplan-Meier survival analysis was used to evaluate the correlation of JMJD3, MMP-2 and VEGF expression levels with the survival of the patients. In breast cancer MDA-MB-231 cells transfected with a JMJD3-expression plasmid, the expression of Ki67 was examined immunohistochemically, the cell proliferation was assessed with CCK8 assay, and the mRNA expressions of MMP-2 and VEGF were detected with RT-PCR. RESULTS: Breast cancer tissues had significantly lower JMJD3 expression and higher MMP-2 and VEGF expressions at both the mRNA and protein levels than the adjacent tissue (P < 0.05). The positivity rates of JMJD3, MMP-2 and VEGF in breast cancer tissues were significantly correlated with tumor diameter, differentiation, TNM stage, lymph node metastasis, and molecular subtypes (P < 0.05). KaplanMeier analysis showed that JMJD3 expression level was positively while MMP-2 and VEGF were inversely correlated with the disease-free survival time of the patients (P < 0.05). Cox regression analysis identified JMJD3, MMP-2, VEGF and tumor differentiation as independent prognostic factors of breast cancer. Spearman correlation analysis suggested a negative correlation of JMJD3 with MMP2 (r=-0.569, P < 0.05) and VEGF (r=-0.533, P < 0.05) and a positive correlation between MMP2 and VEGF (r=0.923, P < 0.05). In MDA-MB-231 cells, overexpression of JMJD3 inhibited the proliferation of MDA-MB-231 cells and the expression of MMP-2 and VEGF. CONCLUSIONS: The expressions of JMJD3, MMP-2 and VEGF in invasive ductal breast carcinoma are closely correlated to tumor proliferation, invasion, metastasis and prognosis and can be used for prognostic evaluation of breast cancer.

OSRI: a rotationally invariant binary descriptor.

Binary descriptors are becoming widely used in computer vision field because of their high matching efficiency and low memory requirements. Since conventional approaches, which first compute a floating-point descriptor then binarize it, are computationally expensive, some recent efforts have focused on directly computing binary descriptors from local image patches. Although these binary descriptors enable a significant speedup in processing time, their performances usually drop a lot due to orientation estimation errors and limited description abilities. To address these issues, we propose a novel binary descriptor based on the ordinal and spatial information of regional invariants (OSRIs) over a rotation invariant sampling pattern. Our main contributions are twofold: 1) each bit in OSRI is computed based on difference tests of regional invariants over pairwise sampling-regions instead of difference tests of pixel intensities commonly used in existing binary descriptors, which can significantly enhance the discriminative ability and 2) rotation and illumination changes are handled well by ordering pixels according to their intensities and gradient orientations, meanwhile, which is also more reliable than those methods that resort to a reference orientation for rotation invariance. Besides, a statistical analysis of discriminative abilities of different parts in the descriptor is conducted to design a cascade filter which can reject nonmatching descriptors at early stages by comparing just a small portion of the whole descriptor, further reducing the matching time. Extensive experiments on four challenging data sets (Oxford, 53 Objects, ZuBuD, and Kentucky) show that OSRI significantly outperforms two state-of-the-art binary descriptors (FREAK and ORB). The matching performance of OSRI with only 512 bits is also better than the well-known floating-point descriptor SIFT (4K bits) and is comparable with the state-of-the-art floating-point descriptor MROGH (6K bits), while it is two orders of magnitude faster to match than SIFT and MROGH.

Acupuncture to point Baihui prevents ischemia-induced functional impairment of cortical GABAergic neurons.

Ischemia impairs brain function and networks, in which the vulnerability of GABAergic neurons causes neural excitotoxicity and nerve cell death. Acupuncture presumably improves the outcome of stroke patients; however, cellular mechanisms underlying this improvement remain to be elusive. We have investigated whether electrical stimuli to acupoint Baihui prevent ischemia- induced impairment of cortical GABAergic neurons. After acupuncture to a Baihui-point of mice for a week, we examined the responses of cortical GABAergic neurons to ischemia by whole-cell recording. Compared with the data from a group of ischemia only, the acupuncture prevents the impairments of spike encoding and synaptic transmission at GABAergic neurons from ischemia. This prevention is associated with the resistance of these cells to ischemia-induced changes in spike threshold potentials and refractory periods Therefore, acupuncture to Baihui-point improves ischemic stroke via preventing the impairment of cortical GABAergic neurons.