DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

Sensor is the core module in signal perception and measurement applications. Due to the harsh external environment, aging, and so forth, sensor easily causes failure and unreliability. In this paper, three kinds of common faults of single sensor, bias, drift, and stuck-at, are investigated. And a fault diagnosis method based on wavelet permutation entropy is proposed. It takes advantage of the multiresolution ability of wavelet and the internal structure complexity measure of permutation entropy to extract fault feature. Multicluster feature selection (MCFS) is used to reduce the dimension of feature vector, and a three-layer back-propagation neural network classifier is designed for fault recognition. The experimental results show that the proposed method can effectively identify the different sensor faults and has good classification and recognition performance

Idiopathic atrial fibrillation in dogs: Electrophysiologic determinants and mechanisms of antiarrhythmic action of flecainide

Objectives.This study sought to determine the mechanisms of idiopathic atrial fibrillation and the atrial antifibrillatory action of flecainide in dogs.Background.In a small subset of dogs, sustained atrial fibrillation can be readily induced in the absence of vagal tone. The electrophysiologic mechanisms underlying this ability to sustain atrial fibrillation, and of flecainide action on the arrhythmia, are unknown.Methods.Six dogs with inducible sustained atrial fibrillation were studied before and after flecainide administration and compared with a control group of 10 dogs.Results.Dogs with atrial fibrillation differed in displaying more shortening of the atrial refractory period with increased rate, resulting in a significantly shorter refractory period and wavelength for reentry at rapid rates, and in increased regional dispersion in refractoriness. Activation maps during sustained fibrillation showed a mean (± SE) of 6.3 ± 0.4 coexistent zones of reentry, compatible with short wavelengths, whereas in control dogs activation during self-limited atrial fibrillation was better organized, and the number of reentrant circuits was smaller. Quantitative analysis demonstrated significantly greater inhomogeneity of activation during atrial fibrillation in dogs with atrial fibrillation than in control animals. Flecainide terminated atrial fibrillation by increasing the duration and homogeneity of atrial refractoriness at rapid rates, thereby reducing the number of reentry circuits and the heterogeneity of activation.Conclusions.The ability of atrial fibrillation to sustain itself resulted from enhanced rate-dependent shortening of atrial refractoriness and increased regional heterogeneity. Flecainide reversed these changes and restored sinus rhythm. These results suggest potential mechanisms of idiopathic atrial fibrillation and are pertinent to understanding the clinical actions of flecainide

The Effect of Initial Creep Damage on Unloading Failure Properties of Sandstone from Macro-mesoscopic Perspective

The aim of this research was to show the effect of initial creep damage on unloading failure of rock from macro-mesoscopic perspective. A series of triaxial creep tests were carried out on sandstone to simulate initial creep damage and then unloading confining pressure tests were performed, respectively. A creep damage variable was proposed to analyze the degree of initial creep damage and the relationship of it with the macroscopic strength parameters was established. The results showed that the unloading amount of confining pressure and residual strength all tend to decrease when the degree of initial creep damage increases. The critical challenge was how to describe the effect of initial creep damage from mesoscopic perspective. This aim was achieved through two steps. In the first step, the mesoscopic properties were analyzed using experimentally obtained SEM images of the rock samples with different levels of initial creep damage. By comparative analysis of porosities in different magnifications, it can be concluded that porosity can’t reflect the effect of initial creep damage very well, thus, other pore parameters are further proposed. In the second step, three pore parameters were calculated by using the Matlab and IPP software, then, the average value of mean pore diameter is determined as the proper evaluation parameter and, finally, the agreement was verified between the mesoscopic pore parameter and creep damage variable

SMOQ: A Tool for Predicting the Absolute Residue-Specific Quality of a Single Protein Model with Support Vector Machine

Background: It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results: We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusions: SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/

Bilingual sentence alignment of pre-Qin history literature for digital humanities study

Sentence aligned bilingual text of history literature provides support of digital resources for related digital humanities studies, but existing studies have done little work on sentence alignment of ancient Chinese and English. In this study, we made a preliminary attempt to align the sentence of ancient Chinese and English. We used the bilingual text of the Analects of Confucius and Zuo's Commentaries of the Spring and Autumn Annals, extracted features and adopted the classification method to divide the bilingual candidate sentence pairs based on probability scores. The bilingual sentence alignment model based on SVM had the best performance on a larger amount of data when using three features and confirmed the impact of candidate dataset

Designing and Evaluating the MULTICOM Protein Local and Global Model Quality Prediction Methods in the CASP10 Experiment

Background: Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results: MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions: Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy

The Landscape of Histone Modification in Cancer Metastasis

Metastasis represents one of the most devastating aspects of cancer. Epithelial to mesenchymal transition (EMT) has been shown to play a critical role in tumorigenic metastasis. During metastatic progression, both genetic and epigenetic modifications endow cancer cells with properties that modulate the capacity for metastatic success. Histone modification is profoundly altered in cancer cells and contributes to cancer metastasis by controlling different metastatic phenotypes. Here, we first review histone modifications and discuss their roles in EMT and metastasis, with a particular focus on histone methylation and acetylation. Second, we review the major histone modification enzymes that control chromatin in cancer metastasis. Third, we discuss the transcriptional regulation concerted by these enzymes with EMT transcription factors at different molecular layers. Finally, we discuss pharmacologic manipulation of histone modification enzymes for metastasis treatment. A comprehensive understanding of histone modification in metastasis will not only provide new insights into our knowledge of cancer progression and metastasis, but also offer a novel approach for the development of innovative therapeutic strategies