Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN.

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). METHODS: RS-fMRI data from 35 CAE patients were collected and compared with findings from 35 age and gender matched healthy controls (HCs). The patients were treated with AEDs for 46.03 weeks before undergoing a second RS-fMRI scan. RESULTS: CAE children at baseline showed increased DC in thalamus, postcentral and precentral and reduced DC in medial frontal cortex, superior frontal cortex, middle temporal cortex, angular and precuneus. However, those abnormalities showed a clear renormalization after AEDs treatments. We then explored the viability of graph-theory-based degree centrality to accurately classify effectiveness to AEDs. Support Vector Machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.22% [sensitivity 78.76%, specificity 89.65%, and area under the receiver operating characteristic curve (AUC) 0.96] for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the right thalamus, bilateral middle temporal gyrus, right medial frontal gyrus, right inferior frontal gyrus, left precuneus, bilateral angular right precentral and left postcentral. Furthermore, the DC change within the bilateral angular are positively correlated with the symptom improvements after AEDs treatment. CONCLUSION: These findings suggest that graph-theory-based measures, such as DC, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of AEDs.

Topological Organization Alterations of Whole-Brain Functional Networks in Patients with Childhood Absence Epilepsy: Associations with Treatment Effects.

PURPOSE: The purpose of the current study is to detect changes of topological organization of whole-brain functional networks and their relationship with the clinical treatment effects of antiepileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). Patients and Methods. RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 age- and gender-matched healthy controls (HCs). The patients were treated with first-line AEDs for 46.03 months before undergoing a second RS-fMRI scan. RESULTS: CAE children at baseline showed a reduced clustering coefficient (Cp) and local efficiency (El) than the HC group, implying the reduction of functional segregation. CAE children at baseline also showed smaller characteristic path length (Lp) and higher global efficiency (Eg) compared with the HC group, implying the impairment of functional segregation. However, those metrics showed no significant differences between CAE children at follow-up and the HC group which indicated a clear renormalization of topological organization after AED treatments. CAE at follow-up also showed significantly decreased connectivity between several network regions, with which the thalamus is mainly involved. Furthermore, the reduced connectivity change between the left superior parietal gyrus and the left thalamus is positively correlated with the symptom improvements after AED treatment. CONCLUSION: We highlighted the convergence and divergence of brain functional network dysfunctions in CAE patients and provided crucial insights into pathophysiological mechanisms and the AED effects.

Uniform Distribution and Densification of Jets in Needleless Electrospinning Using Annular Tip Nozzle.

Numerous jets can be generated simultaneously on a nozzle by needleless melt electrospinning technology which has the advantages of solvent-free residues and environmental friendliness; and potential industrial application prospects. In this paper, the linear annular tip nozzle was taken as the research object, and the high-speed image acquisition of the jets generation and distribution process of annular tip nozzle was carried out and compared with that of straight-line tip nozzle. The results showed that the repulsive force between the jets caused a slight adjustment in the position of the jets on the free surface, the force between the jets on the annular closed curve canceled each other and eventually reached the equilibrium state, making the position of the jets stable and the distance between the jets the same, and the distance between the jets was related to the intensity of the induced electric field at the tip of the nozzle. Relevant conclusions can provide scientific and practical guidance for the design of needleless electrospinning nozzles on free surface in order to achieve uniform and efficient preparation of ultrafine fibers.

Altered degree centrality in childhood absence epilepsy: A resting-state fMRI study.

Modern network studies have suggested that the pathology of many neurological diseases is in fact not equally distributed over the brain but preferentially affects the hub regions. This study aims to investigate how hub regions were affected in Children with Childhood absence epilepsy (CAE) using resting-state fMRI (rs-fMRI). As one important measures obtained from rs-fMRI, degree centrality (DC) calculates the number of direct connections between a given node and the rest of the brain within the entire connectivity matrix of the brain. In this study, twenty-five CAE children and 25 healthy controls were recruited to investigate the DC changes in CAE patients. Compared with healthy controls, children with CAE showed significantly decreased DC in default mode network (DMN, medial prefrontal cortex, posterior cingulate cortex, precuneus and middle temporal cortex) and increased DC in thalamus. Importantly, significant negative correlation between the epilepsy duration and DC was found in precuneus. Our results suggested selective and specific disruption of hub nodes, especially thalamus and the highly connected brain regions within DMN, might underlie the pathophysiological mechanism of CAE.