基于自回归谱外推的全聚焦成像分辨力提升

针对相邻缺陷全聚焦超声成像混叠问题，结合低阶、宽有效频带自回归谱外推方法，压缩超声波时域脉冲宽度，实现亚波长级全聚焦（Total focusing method，TFM）成像分辨力。建立碳钢试块模型，设置两个中心间距1.8 mm，直径1.3 mm圆孔，选用中心频率2.25 MHz，32阵元相控阵探头采集全矩阵数据。针对全矩阵数据，选择自回归阶数为2，信号频谱最大幅值下降14 dB为有效频带，建立自回归模型并外推有效频带外的高频与低频成分，随后对全矩阵数据进行延迟叠加处理和TFM成像。仿真结果表明，低阶、宽有效频带自回归谱外推处理方法具有较高的鲁棒性和准确性，TFM成像后可有效分离中心间距0.7λ（λ为超声波长）圆孔，保留缺陷横向位置信息的同时，定位误差不超过0.73%。对碳钢试块中相同位置及尺寸的圆孔进行试验验证，定位误差不超过1.06%，有效地提高TFM成像分辨力。     

光伏用电致发光缺陷检测仪空间分辨率的量化评估

针对光伏用电致发光缺陷检测仪空间分辨率目视判定重复性、准确性差,并且难以量化等问题,提出一套基于卷积神经网络模型的空间分辨率量化评估方法。参照相关标准JJF(闽)1088-2018,采取不同的拍摄条件拍摄并切割出空间分辨率线对图像,对图像进行人工分类。设计卷积神经网络结构,采用卷积层、池化层和全连接层结构,并使用已分类完成的空间分辨率线对图像对模型进行训练。最终使用测试集对模型进行评估,结果表明,模型在测试集上的判别正确率达到99.2%。该方法满足使用要求,可取代目视判别,提高了光伏用电致发光缺陷检测仪空间分辨率判定的准确性与重复性,并为其量化提出了新的解决思路。由于太阳电池片用光致发光缺陷检测仪与光伏用电致发光缺陷检测仪的成像方式类似,该方法可兼容太阳电池片光致发光检测仪的性能评估。     

一种Chirplet神经网络自动目标识别算法

针对飞机目标的自动识别问题,提出一种联合特征提取与分类的Chirplet神经网络方法,实现一维高分辨率距离像的识别。Chirplet神经网络将Chirplet原子变换用于多层前馈神经网络结构的输入层,替换传统的激励函数对距离像序列进行特征提取;网络的分类部分由隐层和输出层组成。在训练过程中调整神经网络权值的同时,完成对Chirplet原子时频参数的自动调整,协调优化特征参数和分类器参数,使Chirplet神经网络同时实现特征提取和目标分类。对4类飞机目标的仿真测试结果表明,相比时频变换和Gabor原子网络等方法,具有四特征参数的Chirplet神经网络方法具有较高的识别率和抗噪性能。     

NanoPaint: A Tool for Rapid and Dynamic Imaging of Membrane Structural Plasticity at the Nanoscale

Single‐particle tracking with quantum dots (QDs) constitutes a powerful tool to track the nanoscopic dynamics of individual cell membrane components unveiling their membrane diffusion characteristics. Here, the nano‐resolved population dynamics of QDs is exploited to reconstruct the topography and structural changes of the cell membrane surface with high temporal and spatial resolution. For this proof‐of‐concept study, bright, small, and stable biofunctional QD nanoconstructs are utilized recognizing the endogenous neuronal cannabinoid receptor 1, a highly expressed and fast‐diffusing membrane protein, together with a commercial point‐localization microscope. Rapid QD diffusion on the axonal plasma membrane of cultured hippocampal neurons allows precise reconstruction of the membrane surface in less than 1 min with a spatial resolution of tens of nanometers. Access of the QD nanoconstructs to the synaptic cleft enables rapid 3D topological reconstruction of the entire presynaptic component. Successful reconstruction of membrane nano‐topology and deformation at the second time‐scale is also demonstrated for HEK293 cell filopodia and axons. Named “nanoPaint,” this super‐resolution imaging technique amenable to any endogenous transmembrane target represents a versatile platform to rapidly and accurately reconstruct the cell membrane nano‐topography, thereby enabling the study of the rapid dynamic phenomena involved in neuronal membrane plasticity.     

Surfactant-free electrochemical synthesis of fluoridated hydroxyapatite nanorods for biomedical applications

Fluoridated hydroxyapatite (FHA) [Ca₁₀(PO₄)₆F_x(OH)_2−x, x = 0–2] is believed to be a promising calcium phosphate (CaP) to replace pure hydroxyapatite (HA) for next-generation implants, owing to its better biocompatibility, higher antibacterial activity, and lower solubility. Notably, the shape and size of the CaP crystals play key roles in their performance and can influence their applications. One-dimensional (1D) FHA nanorods are important CaP materials which have been widely used in regenerative medicine applications such as restorative dentistry. Unfortunately, the traditional synthesis methods for FHA nanorods either employ surfactants or take a relatively long time. In this study, we aimed to propose a facile synthesis route to fabricate FHA nanorods without any surfactants using an electrochemical deposition method for the first time. This study focused on preparing FHA nanorods without the assistance of any surfactant, unlike the traditional synthesis methods, to avoid chemical impurities. FHA nanorods with lengths of 124–2606 nm, diameters of 28–211 nm, and aspect ratios of 4.4–21.8 were synthesized using the electrochemical method, followed by a heat treatment. For the as-synthesized FHA nanorods, the Ca/P ratio was 1.60 and the atomic concentration of F was 2.06 at.%. An ultrastructure examination revealed that each FHA nanorod possessed long-range order, good crystallinity, and a defect-free lattice with a certain crystallographic plane orientation along the whole rod. In short, we propose a novel, surfactant-free, cost-saving, and more efficient route to synthesize FHA nanorods which can be widely applied in multiple biomedical applications, including drug delivery, bone repair, and restorative dentistry.     

高分辨连续光源原子吸收光谱法测定铀铌铅多金属矿中痕量银

铀铌铅多金属矿组成成分复杂,准确有效的测定其中的痕量银,有利于银的回收利用。采用HCl-HNO₃-HF-HClO₄处理铀铌铅多金属矿样品,以HCl(1+4)-2g/L酒石酸为测定介质,以λ_Ag=328.068nm为分析谱线,建立了高分辨连续光源原子吸收光谱法(HR-CS-AAS)测定铀铌铅多金属矿样品中痕量银的新方法。对仪器参数进行了优化,确定采用迭代基线校正(IBC)背景校正方式,CCD检测器有效像素点选择5个;由银的平均吸收光谱图和吸光度-波长-时间三维吸收光谱图可知,银的CCD检测器分辨率为0.0020nm/pixel,λ_Ag=328.068nm在327.86~328.27nm范围内没有受到溶液中其他共存元素的谱线干扰。在优化的实验条件下,银的吸光度与其质量浓度在0.40~1.60μg/mL范围内运用二次方程最小二乘法拟合校准曲线,相关系数为0.9999,特征浓度为0.0148μg/mL,方法检出限为0.0013μg/mL。将HR-CS-AAS测定样品中银的参数与空心阴极灯原子吸收光谱法(HCL-AAS)相比,其检出限更低,特征浓度更低,标准溶液系列的吸光度更高。按照实验方法对铀铌铅多金属矿样品中的银进行测定,结果与电感耦合等离子体质谱法基本一致,相对标准偏差(RSD,n=9)为0.63%~3.3%。按实验方法在铀铌铅多金属矿样品中加入银标准溶液进行加标回收试验,回收率为97%~104%,满足国家地质矿产行业标准DZ/T 0130—2006的要求。     

Prediction of Long-Term Treatment Response to Selective Serotonin Reuptake Inhibitors (SSRIs) Using Scalp and Source Loudness Dependence of Auditory Evoked Potentials (LDAEP) Analysis in Patients with Major Depressive Disorder

Background: Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD). Methods: Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of >50% between baseline and follow-up. Results: The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017; p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041). Conclusions: The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.