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Zirconium-based metallic glasses (Zr-MGs) are demonstrated to exhibit high mechanical strength, low elastic modulus and excellent biocompatibility, making them promising materials for endosseous implants. Meanwhile, tantalum (Ta) is also well known for its ideal corrosion resistance and biological effects. However, the metal has an elastic modulus as high as 186 GPa which is not comparable to the natural bone (10–30 GPa), and it also has a relative high cost. Here, to fully exploit the advantages of Ta as endosseous implants, a small amount of Ta (as low as 3 at. %) was successfully added into a Zr-MG to generate an advanced functional endosseous implant, Zr58Cu25Al14Ta3MG, with superior comprehensive properties. Upon carefully dissecting the atomic structure and surface chemistry, the results show that amorphization of Ta enables the uniform distribution in material surface, leading to a significantly improved chemical stability and extensive material-cell contact regulation. Systematical analyses on the immunological, angiogenesis and osteogenesis capability of the material are carried out utilizing the next-generation sequencing, revealing that Zr58Cu25Al14Ta3MG can regulate angiogenesis through VEGF signaling pathway and osteogenesis via BMP signaling pathway. Animal experiment further confirms a sound osseointegration of Zr58Cu25Al14Ta3MG in achieving better bone-implant-contact and inducing faster peri-implant bone formation.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

The ever-growing aging population has led to an increasing need for removable partial dentures (RPDs) since they are typically the least expensive treatment options for partial edentulism. However, the digital design of RPDs remains challenging for dental technicians due to the variety of partially edentulous scenarios and complex combinations of denture components. To accelerate the design of RPDs, we propose a U-shape network incorporated with Transformer blocks to automatically generate RPD clasps, one of the most frequently used RPD components. Unlike existing dental restoration design algorithms, we introduce the voxel-based truncated signed distance field (TSDF) as an intermediate representation, which reduces the sensitivity of the network to resolution and contributes to more smooth reconstruction. Besides, a selective insertion scheme is proposed for solving the memory issue caused by Transformer blocks and enables the algorithm to work well in scenarios with insufficient data. We further design two weighted loss functions to filter out the noisy signals generated from the zero-gradient areas in TSDF. Ablation and comparison studies demonstrate that our algorithm outperforms state-of-the-art reconstruction methods by a large margin and can serve as an intelligent auxiliary in denture design.

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From the perspective of collaborative innovation risk, the relationship between different risks and IP is explored based on the BPNN (Back Propagation Neural Network) model. Then, the SE (Synergy Effect) and the DC (Dynamic Capability) are introduced as intermediary and moderating variables. Following specific enterprise data input, the relationship between collaborative innovation risk and IP is analyzed based on deep learning and its endogenous mechanism. Conclusion: the analysis model of enterprise IP based on deep learning BPNN can well process enterprise data; different types of collaborative innovation risks in industrial parks have significantly negative effects on IP; the negative effect of organizational collaborative risk on IP is −0.268; apart from market risk factors, the other collaborative innovation methods further hinder the improvement of IP by inhibiting SE; apart from the risk of benefit distribution, the other collaborative innovation risks and IP are negatively regulated by DC, and the regulation effect on the risk of innovative factor input is the largest.

Bone substitute material implantation has become an important treatment strategy for the repair of oral and maxillofacial bone defects. Recent studies have shown that appropriate inflammatory and immune cells are essential factors in the process of osteoinduction of bone substitute materials. Previous studies have mainly focused on innate immune cells such as macrophages. In our previous work, we found that T lymphocytes, as adaptive immune cells, are also essential in the osteoinduction procedure. As the most important antigen-presenting cell, whether dendritic cells (DCs) can recognize non-antigen biomaterials and participate in osteoinduction was still unclear. In this study, we found that surgical trauma associated with materials implantation induces necrocytosis, and this causes the release of high mobility group protein-1 (HMGB1), which is adsorbed on the surface of bone substitute materials. Subsequently, HMGB1-adsorbed materials were recognized by the TLR4-MYD88-NFκB signal axis of dendritic cells, and the inflammatory response was activated. Finally, activated DCs release regeneration-related chemokines, recruit mesenchymal stem cells, and initiate the osteoinduction process. This study sheds light on the immune-regeneration process after bone substitute materials implantation, points out a potential direction for the development of bone substitute materials, and provides guidance for the development of clinical surgical methods.

ABSTRACTTechnological innovation, which can be classified into major and minor innovation, enables firms to gain competitive and sustainable advantage in the changing environment. R&D investment and financial resources are recognized as two critical factors for fostering technological innovation. Based on a panel dataset of 257 listed Chinese high-tech manufacturing firms over the period of 2008–2015, we explored the effect of R&D intensity on technological innovation and the moderating role of free cash flow using negative binormal regression models. Our results reveal that the relationship between R&D intensity and technological innovation (both major and minor innovation) follows an inverted U pattern in which technological innovation increases with R&D intensity at decreasing rates and falls gradually after R&D intensity exceeds a threshold. Our results also indicate that free cash flow plays a moderating role in the relationship between R&D intensity and major innovation but not in the relationship between R&D intensity and minor innovation. Our study draws attention to the improvement of R&D investment efficiency in high-tech manufacturing firms and sheds light on the importance of holding cash in major innovation-oriented firms.

Reducing the diameter of silver nanowires has been proven to be an effective way to improve their optoelectronic performance by lessening light attenuation. The state-of-the-art silver nanowires are typically around 20 nm in diameter. Herein we report a modified polyol synthesis of silver nanowires with average diameters as thin as 13 nm and aspect ratios up to 3000. The success of this synthesis is based on the employment of benzoin-derived radicals in the polyol approach and does not require high-pressure conditions. The strong reducing power of radicals allows the reduction of silver precursors to occur at relatively low temperatures, wherein the lateral growth of silver nanowires is restrained because of efficient surface passivation. The optoelectronic performance of as-prepared 13 nm silver nanowires presents a sheet resistance of 28 Ω sq–1at a transmittance of 95% with a haze factor of ∼1.2%, comparable to that of commercial indium tin oxide (ITO).

The authors of this study aimed to compare the treatment outcomes and time efficiency between digital and conventional complete denture restorations and propose suggestions for clinical use.

The traditional patterns of ethnic minorities in gansu province are ethnic arts with strong ethnic characteristics. It is the crystallization of the hard work and wisdom of minority nationalities in gansu province. Unique traditional patterns of ethnic minorities in Gansu province with rich ethnic folk arts, is the crystallization of geographical environment in Gansu minority diligence and wisdom. By using the Surf feature point identification algorithm, the feature point extractor in OpenCV is used to extract the feature points. And the feature points are applied to compare the pattern features to find patterns similar to the artistic features. The application of this method can quickly or efficiently extract pattern information in a database.