过敏原肌质钙结合蛋白在毕赤酵母中的表达、优化及免疫反应性研究

目的 探究天然肌质钙结合蛋白(sarcoplasmic calcium binding protein, SCP)的可替代物,为蟹类过敏原的检测提供基础材料,本研究首次利用毕赤酵母(Pichia pastoris, P. pastoris)高效表达表达三疣梭子蟹(Portunus trituberculatus)重要过敏原SCP,并检验其免疫反应性。方法 根据毕赤酵母的密码子偏好性优化SCP基因并构建重组质粒。将其热激转化至P. pastoris GS115菌株后经遗传霉素(Geneticin, G418)筛选获得阳性高拷贝子。最后通过甲醇诱导表达重组SCP并结合免疫印记(Western blotting, WB)和间接酶联免疫吸附实验(enzyme-linked immunosorbent assay, ELISA)验证其免疫反应性。结果 SCP在P. pastoris GS115中实现了可溶性高效表达,其表观分子量约为28 kDa。在摇瓶水平下,最佳诱导条件为pH为6.0、每24 h添加1.0%(v/v)甲醇,于28℃发酵144 h,在此条件下,纯度为91.6%的SCP产量可达15 mg/L。WB和间接ELISA结果表明,重组SCP具有IgG结合能力。结论 毕赤酵母表达系统可以得到纯度较高且免疫反应性良好的重组SCP。本研究为SCP的理化研究及产业化应用奠定了基础,并有望促进特异性甲壳类过敏原检测的发展。     

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Magnetic Resonance Materials in Physics, Biology and Medicine - This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated...     

Minimizing Energy Barrier in Intermediate Connection Layer for Monolithic Tandem WPeLEDs with Wide Color Gamut

Perovskite light-emitting diodes (PeLEDs) show promising prospects in the wide color gamut display owing to their ultra-narrow full width at half maximum (FWHM). However, up to now, all perovskite white LEDs integrated by standard red, green, and blue perovskite emitters, namely, monolithic white PeLEDs (WPeLEDs), have been rarely reported, owing to facing some issues, e.g., solvent incompatibility in solution technique, ion exchange, and energy transfer between different emission centers. Herein, centered on these issues, an optimal intermediate connection layer (ICL) of Po-T2T/LiF/Ag/HAT-CN/MoO₃ is adopted to successfully develop monolithic tandem multicolor PeLEDs and WPeLEDs for the first time. The multicolor PeLEDs can achieve the best external quantum efficiency of 1.8% and the highest luminance of 4844 cd m⁻². Besides, the red/green/blue (R/G/B) monolithic tandem WPeLED shows a standard white International Commission on Illumination coordinate of (0.33, 0.33) and achieves an extremely wide color gamut reaching  National Television Standards Committee of 130%. This study is the first to realize the standard R/G/B co-electroluminescence in a monolithic perovskite device and offers a feasible strategy for developing wide-color gamut perovskite displays.     

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Neural Computing and Applications - Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation...     

Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

2D Bi2O2Te Semiconductor with Single-Crystal Native Oxide Layer

Following logic in the silicon semiconductor industry, the existence of native oxide and suitable fabrication technology is essential for 2D semiconductors in planar integronics, which are surface-sensitive to typical coating technologies. To date, very few types of integronics are found to possess this feature. Herein, the 2D Bi₂O₂Te developed recently is reported to possess large-area synthesis and controllable thermal oxidation behavior toward single-crystal native oxides. This shows that surface-adsorbed oxygen atoms are inclined to penetrate across [Bi₂O₂]_n²ⁿ⁺ layers and bond with the underlying [Te]_n²ⁿ⁻ at elevated temperatures, transforming directly into [TeO₄]_n²ⁿ⁻ with the basic architecture remaining stable. The oxide can be adjusted to form in an accurate layer-by-layer manner with a low-stress sharp interface. The native oxide Bi₂TeO₆ layer (bandgap of ≈2.9 eV) exhibits visible-light transparency and is compatible with wet-chemical selective etching technology. These advances demonstrate the potential of Bi₂O₂Te in planar-integrated functional nanoelectronics such as tunnel junction devices, field-effect transistors, and memristors.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -     

Confidence regions of two-stage stochastic linear complementarity problems

Two-stage stochastic linear complementarity problems (TSLCP) model a large class of equilibrium problems subject to data uncertainty, and are closely related to two-stage stochastic optimization problems. The sample average approximation (SAA) method is one of the basic approaches for solving TSLCP and the consistency of the SAA solutions has been well studied. This paper focuses on building confidence regions of the solution to TSLCP when SAA is implemented. We first establish the error-bound condition of TSLCP and then build the asymptotic and nonasymptotic confidence regions of the solutions to TSLCP by error-bound approach, which is to combine the error-bound condition with central limit theory, empirical likelihood theory, and large deviation theory.