Surface energy-mediated fibronectin adsorption and osteoblast responses on nanostructured diamond

Nanostructured diamond have potential applications in many biomedical related fields and demonstrated extraordinary capacity to influence cellular responses. Studying the surface property of nanodiamond and its influence to protein adsorption and subsequent cellular responses along with the mechanism behind such capacity becomes more important. Here the role of surface energy associated with nanostructured diamond in modulating fibronectin and osteoblast(OB, bone forming cells) responses was investigated. Nanocrystalline diamond(NCD) and submicron crystalline diamond(SMCD) films with controllable surface energy were prepared by microwave-enhanced plasma chemical vapor deposition(MPCVD) techniques. Fibronectin adsorption on the diamond films with varied surface energy values was measured via the enzyme-linked immunosorbent assay(ELISA) and the relationship between the surface energy and fibronectin adsorption was studied. The result indicated that fibronectin adsorption on nanostructured surfaces was closely related to both surface energy and material microstructures. The spreading and migration of OB aggregates(each containing 30–50 cells) on the NCD with varied surface energy values were also studied. The result indicates a correlation between the cell spreading and migration on nanodiamond and the surface energy of nanostructured surface.     

Numerical and experimental investigations of laser shock forming aluminum alloy sheet with mold

Laser shock forming (LSF) technology employs shock waves to form sheet metal into three-dimensional complex parts, and has application potential in manufacturing sheet metal parts. In this paper, the forming of 2024 aluminum alloy sheet with LSF was investigated through numerical and experimental methods. The numerical model was established with the commercial code ABAQUS/Explicit. The formed conical cup was obtained from the simulation, and validated by the experiment. With the verified numerical model, the deformation behaviors, including deformation velocity, sheet thickness variation and strain distribution, were studied. In addition, the influence of different shock wave pressures on the forming precision was also investigated. The experimental and numerical results show that the metal sheet loaded by shock wave can take the shape of the mold, and the non-uniform thickness is distributed in the formed cup. The investigations also display that there exists reverse deformation at the central region of deforming sheet owing to severe collision during LSF. In order to obtain formed part with better quality, an appropriate pressure of applied shock waves is required.     

Graphene oxide-decorated Fe<Subscript>2</Subscript>(MoO<Subscript>4</Subscript>)<Subscript>3</Subscript> microflowers as a promising anode for lithium and sodium storage

Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe₂(MoO₄)₃ (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g^–1 at 200 mA·g^–1 after 50 cycles and a capacity of 685 mAh·g^–1 at a high current density of 10 A·g^–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g^–1 at 100 mA·g^–1, maintaining a discharge capacity of 307 mAh·g^–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.

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Entrapping multifunctional dendritic nanoparticles into a hydrogel for local therapeutic delivery and synergetic immunochemotherapy

Nano Research - Developing multifunctional nanoparticles to support new therapy models is a promising and challenging task to address the current dilemma on antitumor treatment. Herein, we...     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

舒曼钢琴音乐的双重性格特征

德国作曲家罗伯特·舒曼是十九世纪上半叶欧洲音乐文化史上最突出的人物之一。在他的钢琴音乐创作中,深刻地反映出浪漫主义音乐的特点。舒曼设计了一个想象中的“大卫同盟”,其中有两名核心盟员,一个叫弗洛列斯坦,另一个叫埃塞比乌斯。前者性格热情奔放,锋芒毕露;后者则文雅娴静,富于幻想,实际上这就是舒曼本人性格中的两个侧面。这种双重性格特征在舒曼的钢琴音乐中得到充分展示,并成为舒曼钢琴音乐的艺术特征之一。     

浅析艺术类专业《计算机基础》的教学方法

近年来,高校的计算机基础教学在不断发展,教学方法也在不断地改进。如何根据学生特点,使用各种行之有效的教学方法是提高教学质量的关键。文章结合教学实际,分析探讨了如何针对艺术类专业学生的特点,进行《计算机基础》教学方法改革。     

Semi-supervised locally discriminant projection for classification and recognition

Semi-supervised dimensional reduction methods play an important role in pattern recognition, which are likely to be more suitable for plant leaf and palmprint classification, since labeling plant leaf and palmprint often requires expensive human labor, whereas unlabeled plant leaf and palmprint is far easier to obtain at very low cost. In this paper, we attempt to utilize the unlabeled data to aid plant leaf and palmprint classification task with the limited number of the labeled plant leaf or palmprint data, and propose a semi-supervised locally discriminant projection (SSLDP) algorithm for plant leaf and palmprint classification. By making use of both labeled and unlabeled data in learning a transformation for dimensionality reduction, the proposed method can overcome the small-sample-size (SSS) problem under the situation where labeled data are scant. In SSLDP, the labeled data points, combined with the unlabeled data ones, are used to construct the within-class and between-class weight matrices incorporating the neighborhood information of the data set. The experiments on plant leaf and palmprint databases demonstrate that SSLDP is effective and feasible for plant leaf and palmprint classification.