Coupled-channel analysis of the possible D(∗)D(∗)D^{(*)}D^{(*)}, Bˉ(∗)Bˉ(∗)\bar{B}^{(*)}\bar{B}^{(*)} and D(∗)Bˉ(∗)D^{(*)}\bar{B}^{(*)} molecular states

We perform a coupled-channel study of the possible deuteron-like molecules with two heavy flavor quarks, including the systems of $D^{(*)}D^{(*)}$ with double charm, $\bar{B}^{(*)}\bar{B}^{(*)}$ with double bottom and $D^{(*)}\bar{B}^{(*)}$ with both charm and bottom, within the one-boson-exchange model. In our study, we take into account the S-D mixing which plays an important role in the formation of the loosely bound deuteron, and particularly, the coupled-channel effect in the flavor space. According to our calculation, the states $D^{(*)}D^{(*)}[I(J^P)=0(1^+)]$ and $(D^{(*)}D^{(*)})_s[J^P=1^+]$ with double charm, the states $\bar{B}^{(*)}\bar{B}^{(*)}[I(J^P)=0(1^+),0(2^+),1(0^+),1(1^+),1(2^+)]$, $(\bar{B}^{(*)}\bar{B}^{(*)})_s[J^P=0^+,1^+,2^+]$ and $(\bar{B}^{(*)}\bar{B}^{(*)})_{ss}[J^P=0^+,1^+,2^+]$ with double bottom, and the states $D^{(*)}\bar{B}^{(*)}[I(J^P)=0(0^+),0(1^+)]$ and $(D^{(*)}\bar{B}^{(*)})_s[J^P=0^+,1^+]$ with both charm and bottom are good molecule candidates. However, the existence of the states $D^{(*)}D^{(*)}[I(J^P)=0(2^+)]$ with double charm and $D^{(*)}\bar{B}^{(*)}[I(J^P)=1(1^+)]$ with both charm and bottom is ruled out.Comment: 1 figure added, published in Physical Review

Sex, sleep duration and the association of cognition: Findings from the china health and retirement longitudinal study

Background: We aimed to examine the association between sleep duration and cognitive impairment among elderly Chinese people. Methods: generalized linear modeling was used to analyze the baseline data for adults aged 65 years and over (n = 4785) selected from the 2011 China Health and Retirement Longitudinal Study (CHARLS). The two aspects of cognitive impairment assessed in the study were mental status and memory. Sex-stratified logistic regression models were conducted to identify the effect of sleep duration in the different gender groups. Results: of all the participants, 49.8% were females and 32.5% aged 75 years and over. Of the participants, 59.7% had short sleep duration (\u3c7 h/day), and 9.0% of them had long sleep duration (\u3e8 h/day). Compared to normal sleep duration, long sleep duration was associated with lower mental status scores (β = −0.43, p = 0.001) and lower memory scores (β = −0.26, p = 0.006). Long sleep duration was associated with lower mental status in both men (β = −0.37, p = 0.033) and women (β = −0.46, p = 0.025), however, only in men was long sleep duration found to be associated with low memory scores (β = −0.26, p = 0.047). Conclusions: Our study showed that long sleep duration was significantly associated with poorer mental status and memory scores in elderly Chinese people. Paying greater attention to the effects of sleep patterns on the risk of cognitive decline may yield practical implications for dementia prevention and health promotion, especially among older women with lower educational attain-ment, living in rural areas, and those who have long sleep duration

Automatic Pavement Crack Recognition Based on BP Neural Network

A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application

Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution

Objective. A phased or a curvilinear array produces ultrasound (US) images with a sector field of view (FOV), which inherently exhibits spatially-varying image resolution with inferior quality in the far zone and towards the two sides azimuthally. Sector US images with improved spatial resolutions are favorable for accurate quantitative analysis of large and dynamic organs, such as the heart. Therefore, this study aims to translate US images with spatially-varying resolution to ones with less spatially-varying resolution. CycleGAN has been a prominent choice for unpaired medical image translation; however, it neither guarantees structural consistency nor preserves backscattering patterns between input and generated images for unpaired US images. Approach. To circumvent this limitation, we propose a constrained CycleGAN (CCycleGAN), which directly performs US image generation with unpaired images acquired by different ultrasound array probes. In addition to conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN introduces an identical loss and a correlation coefficient loss based on intrinsic US backscattered signal properties to constrain structural consistency and backscattering patterns, respectively. Instead of post-processed B-mode images, CCycleGAN uses envelope data directly obtained from beamformed radio-frequency signals without any other non-linear postprocessing. Main Results. In vitro phantom results demonstrate that CCycleGAN successfully generates images with improved spatial resolution as well as higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of the in vivo human beating heart further facilitate higher quality heart wall motion estimation than benchmarks-generated ones, particularly in deep regions

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017

Structure of the substrate-engaged SecA-SecY protein translocation machine.

The Sec61/SecY channel allows the translocation of many proteins across the eukaryotic endoplasmic reticulum membrane or the prokaryotic plasma membrane. In bacteria, most secretory proteins are transported post-translationally through the SecY channel by the SecA ATPase. How a polypeptide is moved through the SecA-SecY complex is poorly understood, as structural information is lacking. Here, we report an electron cryo-microscopy (cryo-EM) structure of a translocating SecA-SecY complex in a lipid environment. The translocating polypeptide chain can be traced through both SecA and SecY. In the captured transition state of ATP hydrolysis, SecAs two-helix finger is close to the polypeptide, while SecAs clamp interacts with the polypeptide in a sequence-independent manner by inducing a short β-strand. Taking into account previous biochemical and biophysical data, our structure is consistent with a model in which the two-helix finger and clamp cooperate during the ATPase cycle to move a polypeptide through the channel

Synonym Detection Using Syntactic Dependency And Neural Embeddings

Recent advances on the Vector Space Model have significantly improved some NLP applications such as neural machine translation and natural language generation. Although word co-occurrences in context have been widely used in counting-/predicting-based distributional models, the role of syntactic dependencies in deriving distributional semantics has not yet been thoroughly investigated. By comparing various Vector Space Models in detecting synonyms in TOEFL, we systematically study the salience of syntactic dependencies in accounting for distributional similarity. We separate syntactic dependencies into different groups according to their various grammatical roles and then use context-counting to construct their corresponding raw and SVD-compressed matrices. Moreover, using the same training hyperparameters and corpora, we study typical neural embeddings in the evaluation. We further study the effectiveness of injecting human-compiled semantic knowledge into neural embeddings on computing distributional similarity. Our results show that the syntactically conditioned contexts can interpret lexical semantics better than the unconditioned ones, whereas retrofitting neural embeddings with semantic knowledge can significantly improve synonym detection