RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.     

水化硅酸钙纳米压痕分子动力学模拟方法优化

针对水化硅酸钙纳米压痕模型忽视了压头与基底之间相互作用的问题，由尺寸差异引起的金刚石压头难以计算的问题，以及Wittmann模型无法得到实际接触面积的问题，提出了新的模型与计算方法．结合分子动力学方法，采用金刚石压头-Wittmann模型基底的组合方式构建无定形态水化硅酸钙纳米压痕试验模型．在建模阶段，考虑到压头模型与基底模型粒子间尺寸差异，提出了等比例替换模型，通过公式推导并就不同尺寸模拟结果验证了等比例替换模型的可行性．在计算阶段，提出了局部前处理的弛豫方法进行模拟．确定最大荷载位置处的接触面积为546 nm2，进而求出水化硅酸钙模型硬度H为0.84 GPa、折合模量Er为30.52 GPa．并通过纳米压痕试验，验证了模拟结果的准确性，证明了模型的科学性，对今后水化硅酸钙(C-S-H)纳米层面的模拟具有重要借鉴意义．     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Direct visualization of structural defects in 2D semiconductors

Direct visualization of the structural defects in two-dimensional (2D) semiconductors at a large scale plays a significant role in understanding their electrical/optical/magnetic properties, but is challenging. Although traditional atomic resolution imaging techniques, such as transmission electron microscopy and scanning tunneling microscopy, can directly image the structural defects, they provide only local-scale information and require complex setups. Here, we develop a simple, non-invasive wet etching method to directly visualize the structural defects in 2D semiconductors at a large scale, including both point defects and grain boundaries. Utilizing this method, we extract successfully the defects density in several different types of monolayer molybdenum disulfide samples, providing key insights into the device functions. Furthermore, the etching method we developed is anisotropic and tunable, opening up opportunities to obtain exotic edge states on demand.     

Explosive synchronization in a mobile network in the presence of a positive feedback mechanism

Synchronization is a process that describes the coherent dynamics of a large ensemble of interacting units.The study of explosive synchronization transition attracts considerable attention.Here,I report the explosive transition within the framework of a mobile network,while each oscillator is controlled by global-order parameters of the system.Using numerical simulation,I find that the explosive synchronization(ES)transition behavior can be controlled by simply adjusting the fraction of controlled oscillators.The influences of some parameters on explosive synchronization are studied.Moreover,due to the presence of the positive feedback mechanism,I prevent the occurrence of the synchronization of continuous-phase transition and make phase transition of the system a first-order phase transition accompanied by a hysteresis loop.     

Pattern transition and regulation in a subthalamopallidal network under electromagnetic effect

Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied, their influence on Parkinson's disease (PD) has yet to be well explored. By virtue of the magnetic flux variable, this paper studies the transition of firing patterns induced by magnetic induction and the regulation effect of external magnetic radiation on the firing activities of the subthalamopallidal network in basal ganglia. We find: (i) The network reproduces five typical waveforms corresponding to the severity of symptoms: weak cluster, episodic, continuous cluster, episodic, and continuous wave. (ii) Magnetic induction is a double-edged sword for the treatment of PD. Although the increase of magnetic coefficient may lead the physiological firing activity to transfer to pathological firing activity, it also can regulate the pathological intensity firing activity with excessive β-band power transferring to the physiological firing pattern with weak β-band power. (iii) External magnetic radiation could inhibit continuous tremulous firing and β-band power of subthalamic nucleus (STN), which means the severity of symptoms weakened. Especially, the bi-parameter plane of the regulation region shows that a short pulse period of magnetic radiation and a medium level of pulse percentage can well regulate pathological oscillation. This work helps to understand the firing activity of the subthalamopallidal network under electromagnetic effect. It may also provide insights into the mechanisms behind the electromagnetic therapy of PD-related firing activity.     

基于视觉辐条图案识别方法的蓝宝石引晶机制研究

工业生产蓝宝石晶体过程中,引晶步骤有着至关重要的地位。引晶必须在温度梯度较小,温度分布趋于稳定的条件下进行。目前,工业生产蓝宝石主要依靠人工经验操控籽晶杆实现引晶操作,但是人工引晶操作的准确性不高会导致成品品质不佳、资源浪费。为此,本文提出一种基于蓝宝石视觉辐条图案识别方法来检测蓝宝石熔体状态自由液面状态,从而实现一种高效率引晶的机制。此方法利用经典骨架化算法细化辐条图案,Harris算子实现特征信息的提取,提取的特征信息放入运动轨迹模型中判断熔体稳定性,分析液面温度分布稳定性从而实现引晶。结果表明,此算法具有有效性,蓝宝石晶体引晶效率大大提高,生产出的成品良率也有提升,可有效指导蓝宝石的工业生产。     

基于毛竹叶片理化参数的刚竹毒蛾危害检测研究

虫害检测算法研究是开展虫害快速、准确监测，制定精准森防检疫措施的重要基础。以毛竹叶片为研究尺度，基于刚竹毒蛾危害下的寄主外部形态与内部生理现象总结，选择并实测叶损量LL、相对叶绿素含量RCC、相对含水量RWC、原始光谱的733.66~898.56 nm值(ρ_{733.66~898.56})、一阶微分光谱的562.95~585.25 nm值(ρ′_{562.95~585.25})与706.18~725.41 nm值(ρ′_{706.18~725.41})等理化参数，随机划分实验组(63组)和验证组(37组)并设计5次重复实验；分别运用Fisher判别分析、BP神经网络、随机森林等三种方法建立刚竹毒蛾危害等级的检测模型，从检测精度、Kappa系数及R2等指标对模型的检测效果予以分析和比较。结果显示，Fisher判别分析、BP神经网络、随机森林的检测精度分别为69.19%，65.41%，83.78%，Kappa系数分别为0.576 9，0.532 4和0.778 8，R2分别为0.722 2，0.582 6和0.870 9，总体而言，三种方法均具备刚竹毒蛾危害的检测能力，随机森林的检测效果最优，Fisher判别分析次之，再次为BP神经网络；从分等级来看，随机森林的检测精度亦优于Fisher判别分析与BP神经网络，但3种方法对中度危害等级的检测精度均有所不足。该成果可为刚竹毒蛾危害及其他病虫害检测算法的选择提供参考，并为进一步建立冠层、遥感影像像元等尺度的虫害检测模型奠定基础。     

New lower bounds on the radius of spatial analyticity for the KdV equation

The radius of spatial analyticity for solutions of the KdV equation is studied. It is shown that the analyticity radius does not decay faster than $t^{?1/4}$ as time t goes to infinity. This improves the works of Selberg and da Silva (2017) [30] and Tesfahun (2017) [34]. Our strategy mainly relies on a higher order almost conservation law in Gevrey spaces, which is inspired by the I-method.