富空穴CoSi合金无溶剂无碱醇氧化

醇类化合物选择性氧化为醛、酸和酯在工业应用中是一类重要的化学反应.按照绿色化学的要求,无溶剂、无外加碱源,以分子氧为氧化剂,对醇类氧化反应提出了重要挑战,特别是无溶剂氧化醇制备酯类化学品.因此开发高效催化剂应用于无溶剂醇氧化成为研究热点.目前Pd基贵金属催化剂应用于醇的无溶剂氧化已取得一定进展,但贵金属的稀缺性限制了其工业应用.由于碱性环境可以有效促进醇的氧化,但碱性物质的添加可能导致反应体系的二次污染.此外,Co基非贵金属催化剂可以有效无溶剂氧化醇,但稳定性一般.因此,合成高活性高稳定性催化剂用于醇类化合物的无溶剂选择性氧化是重要的科学问题.本文采用电弧熔炼法可控制备半金属CoSi合金(AM-CoSi),在无溶剂无碱条件下将其应用于六种芳香醇的选择性氧化.采用X射线吸收精细结构谱、电子顺磁共振和球差扫描透射电镜对AM-CoSi催化剂进行结构表征,发现其以钴硅合金的形式存在,具有Co空穴和Si空穴,并且以Si空穴为主.反应结果表明,使用AM-CoSi催化剂在220℃可完全转化苯甲醇,并以70%的产率得到苯甲酸苄酯(BBE),使用无缺陷CoSi合金,只得到9.5%的BBE,说明Si空穴存在可以高效的转化苯甲醇并高产率得到BBE.此外,通过调控反应温度,使用AM-CoSi无溶剂氧化六种芳香醇(带有不同吸电子基团和供电子基团)可高选择性得到对应的醛,选择性高达84%以上.中间体反应实验结果表明,AM-CoSi催化剂具有很好的酯化催化活性,苯甲醇首先氧化成苯甲醛,再氧化成苯甲酸,然后苯甲酸与苯甲醇发生酯化反应生成BBE.采用单组分合金成分和无空穴CoSi合金(S-CoSi)进行催化剂对比实验,发现合金结构和空穴有利于苯甲醇的氧化.设计无空穴CoSi合金、Co空穴CoSi合金和Si空穴CoSi合金三种催化剂模型,并利用密度泛函理论计算模拟苯甲醇在这三种模型上的氧化路径,结果表明,CoSi合金结构促进醇的转化,Si空穴有利于BBE的产生.综上,本文将为其他半金属合金催化剂的合成和应用提供了新思路.

Unveiling clinical significance and tumor immune landscape of CXCL12 in bladder cancer: Insights from multiple omics analysis

Objective: The interplay between chemokine C-X-C motif ligand 12(CXCL12) and its specific receptors is known to trigger various signaling pathways, contributing to tumor proliferation and metastasis. Consequently,targeting this signaling axis has emerged as a potential strategy in cancer therapy. However, the precise role of CXCL12 in clinical therapy, especially in immunotherapy for bladder cancer(BCa), remains poorly elucidated.Methods: We gathered multiple omics data from public databases to unveil the clinical relevance and tumor immune landscape associated with CXCL12 in BCa patients. Univariate and multivariate Cox regression analyses were employed to assess the independent prognostic significance of CXCL12 expression and formulate a nomogram. The expression of CXCL12 in BCa cell lines and clinical tissue samples was validated using enzymelinked immunosorbent assays(ELISA) and immunohistochemistry(IHC).Results: While transcriptional expression of CXCL12 exhibited a decrease in nearly all tumor tissues, CXCL12 methylation expression was notably increased in BCa tissues. Single-cell RNA analysis highlighted tissue stem cells and endothelial cells as the primary sources expressing CXCL12. Abnormal CXCL12 expression, based on transcriptional and methylation levels, correlated with various clinical characteristics in BCa patients. Functional analysis indicated enrichment of CXCL12 and its co-expression genes in immune regulation and cell adhesion. The immune landscape analysis unveiled a significant association between CXCL12 expression and M2 macrophages(CD163~+ cells) in BCa tissues. Notably, CXCL12 expression emerged as a potential predictor of immunotherapy response and chemotherapy drug sensitivity in BCa patients.Conclusions: Taken together, these findings suggest aberrant production of CXCL12 in BCa tissues,potentially influencing the treatment responses of affected individuals.

Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

A Generative Adversarial Neural(GAN)network is designed based on deep learning for the Super-Resolution(SR)reconstruction task of temperaturefields(comparable to downscaling in the meteorologicalfield),which is limited by the small number of ground stations and the sparse distribution of observations,resulting in a lack offineness of data.To improve the network’s generalization performance,the residual structure,and batch normalization are used.Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer visionfield.Sub-pixel convolution is used instead of transposed convolution or interpolation methods for up-sampling to speed up network inference.The experimental dataset is the European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)with a bidirectional resolution of 0:1°×0:1°.On the other hand,the task aims to scale up the size by a factor of 8,which is rare compared to conventional methods.The comparison methods include traditional interpolation methods and a more widely used GAN-based network such as the SRGAN.Thefinal experimental results show that the proposed scheme advances the performance of Root Mean Square Error(RMSE)by 37.25%,the Peak Signal-to-noise Ratio(PNSR)by 14.4%,and the Structural Similarity(SSIM)by 10.3%compared to the Bicubic Interpolation.For the traditional SRGAN network,a relatively obvious performance improvement is observed by experimental demonstration.Meanwhile,the GAN network can converge stably and reach the approximate Nash equilibrium for various initialization parameters to empirically illustrate the effectiveness of the method in the temperature fields.

异氰脲酸酯改性聚氯乙烯泡沫的制备与耐热性能

以甲苯二异氰酸酯(TDI)为原料,硬脂酸钾为三聚催化剂,采用本体聚合法合成了三聚甲苯二异氰酸酯(tTDI),然后将不同tTDI含量的TDI溶液作为交联剂制备交联聚氯乙烯泡沫材料。通过红外光谱、核磁碳谱分析研究了泡沫制备过程中化学结构的变化以及最终的交联网络。结果表明,在最终泡沫的交联网络中实现了异氰脲酸酯结构的定量引入。动态力学热分析对泡沫玻璃化温度的研究表明,无苯酐的体系中三聚体含量在10%左右出现拐点;有苯酐改性的体系中,泡沫的玻璃化转变温度随着三聚体含量的增加,呈线性升高趋势。

Surveillance Video Portrait Recognition Preprocessing Technology for Police Actual Combat

With the continuous development of technology, face recognition technology has played an important role in police work. In order to get a clear face image, image preprocessing technology is needed. This paper mainly proposes illumination compensation technology and reconstruction technology based on symmetry.

共价有机框架材料用于光催化产过氧化氢

过氧化氢(H_(2)O_(2))是一种重要的绿色氧化剂,然而其主流生产方法蒽醌法具有耗能高、安全隐患大等缺点。以水和氧气为原料,通过人工光合作用合成H_(2)O_(2)具有安全、环保和节能等特点,已成为当前研究热点。共价有机框架(COFs)因其结构可调性、高比表面积、良好光催化性能等优点被广泛应用于光催化生产H_(2)O_(2)中。本文归纳了近年来COFs光催化产H_(2)O_(2)领域研究进展,分别论述了通过氧还原、水氧化以及双通道过程产生H_(2)O_(2)的反应机理。综述了通过结构设计、官能团修饰等调控COFs光学带隙、提升电荷分离能力和载流子迁移率,从而提高光催化产H_(2)O_(2)性能的方法,有助于设计出高效、稳定、可持续生产的COFs应用于光催化产H_(2)O_(2)。

Application of silk fibroin coatings for biomaterial surface modification:a silk road for biomedicine

Silk fibroin(SF)as a natural biopolymer has become a popular material for biomedical applications due to its minimal immunogenicity,tunable biodegradability,and high biocompatibility.Nowadays,various techniques have been developed for the applications of SF in bioengineering.Most of the literature reviews focus on the SF-based biomaterials and their different forms of applications such as films,hydrogels,and scaffolds.SF is also valuable as a coating on other substrate materials for biomedicine;however,there are few reviews related to SF-coated biomaterials.Thus,in this review,we focused on the surface modification of biomaterials using SF coatings,demonstrated their various preparation methods on substrate materials,and introduced the latest procedures.The diverse applications of SF coatings for biomedicine are discussed,including bone,ligament,skin,mucosa,and nerve regeneration,and dental implant surface modification.SF coating is conducive to inducing cell adhesion and migration,promoting hydroxyapatite(HA)deposition and matrix mineralization,and inhibiting the Notch signaling pathway,making it a promising strategy for bone regeneration.In addition,SF-coated composite scaffolds can be considered prospective candidates for ligament regeneration after injury.SF coating has been proven to enhance the mechanical properties of the substrate material,and render integral stability to the dressing material during the regeneration of skin and mucosa.Moreover,SF coating is a potential strategy to accelerate nerve regeneration due to its dielectric properties,mechanical flexibility,and angiogenesis promotion effect.In addition,SF coating is an effective and popular means for dental implant surface modification to promote osteogenesis around implants made of different materials.Thus,this review can be of great benefit for further improvements in SF-coated biomaterials,and will undoubtedly contribute to clinical transformation in the future.

Safety and immunogenicity of a mosaic vaccine booster against Omicron and other SARS-CoV-2 variants:a randomized phase 2 trial

An ongoing randomized,double-blind,controlled phase 2 trial was conducted to evaluate the safety and immunogenicity of a mosaic-type recombinant vaccine candidate,named NVSI-06-09,as a booster dose in subjects aged 18 years and older from the United Arab Emirates(UAE),who had administered two or three doses of inactivated vaccine BBIBP-CorV at least 6 months prior to enrollment.The participants were randomly assigned with 1:1 to receive a booster dose of NVSI-06-09 or BBIBP-CorV.The primary outcomes were immunogenicity and safety against severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)Omicron variant,and the exploratory outcome was cross-immunogenicity against other circulating strains.Between May 25 and 30,2022,516 adults received booster vaccination with 260 in NVSI-06-09 group and 256 in BBIBP-CorV group.Interim results showed a similar safety profile between two booster groups,with low incidence of adverse reactions of grade 1 or 2.For immunogenicity,by day 14 post-booster,the fold rises in neutralizing antibody geometric mean titers(GMTs)from baseline elicited by NVSI-06-09 were remarkably higher than those by BBIBP-CorV against the prototype strain(19.67 vs 4.47-fold),Omicron BA.1.1(42.35 vs 3.78-fold),BA.2(25.09 vs 2.91-fold),BA.4(22.42 vs 2.69-fold),and BA.5 variants(27.06 vs 4.73-fold).Similarly,the neutralizing GMTs boosted by NVSI-06-09 against Beta and Delta variants were also 6.60-fold and 7.17-fold higher than those by BBIBP-CorV.Our findings indicated that a booster dose of NVSI-06-09 was well-tolerated and elicited broad-spectrum neutralizing responses against divergent SARS-CoV-2 variants,including Omicron and its sub-lineages.

Immunogenicity and safety of NVSI-06-07 as a heterologous booster after priming with BBIBP-CorV:a phase 2 trial

The increased coronavirus disease 2019(COVID-19)breakthrough cases pose the need of booster vaccination.We conducted a randomised,double-blinded,controlled,phase 2 trial to assess the immunogenicity and safety of the heterologous prime-boost vaccination with an inactivated COVID-19 vaccine(BBIBP-CorV)followed by a recombinant protein-based vaccine(NVSI-06-07),using homologous boost with BBIBP-CorV as control.Three groups of healthy adults(600 individuals per group)who had completed two-dose BBIBP-CorV vaccinations 1–3 months,4–6 months and≥6 months earlier,respectively,were randomly assigned in a 1:1 ratio to receive either NVSI-06-07 or BBIBP-CorV boost.Immunogenicity assays showed that in NVSI-06-07 groups,neutralizing antibody geometric mean titers(GMTs)against the prototype SARS-CoV-2 increased by 21.01–63.85 folds on day 28 after vaccination,whereas only 4.20–16.78 folds of increases were observed in control groups.For Omicron variant,the neutralizing antibody GMT elicited by homologous boost was 37.91 on day 14,however,a significantly higher neutralizing GMT of 292.53 was induced by heterologous booster.Similar results were obtained for other SARS-CoV-2 variants of concerns(VOCs),including Alpha,Beta and Delta.Both heterologous and homologous boosters have a good safety profile.Local and systemic adverse reactions were absent,mild or moderate in most participants,and the overall safety was quite similar between two booster schemes.Our findings indicated that NVSI-06-07 is safe and immunogenic as a heterologous booster in BBIBP-CorV recipients and was immunogenically superior to the homologous booster against not only SARS-CoV-2 prototype strain but also VOCs,including Omicron.

Partially Distributed Channel and Power Management Based on Reinforcement Learning

This paper studies a dynamic multi-user wireless network,where users have no knowledge of the arrival rate and size of data block and suffer from a constraint on long-term average power consumption.Considering such a network,we address the problem of dynamically optimizing channel/power allocation,so as to minimize the long-term average data backlog.The design problem is shown to be a constrained Markov decision process.In order to solve the problem without knowledge on dynamics of the system,we introduce post-decision states and propose a resource allocation algorithm based on reinforcement learning.Since the channel/power allocation problem is coupled,the multiuser decision problem suffers from curses of dimensions(of state/action/outcome space).This makes centralized decision-making and optimization on channel/power allocation suffer from a long convergence time.As a countermeasure,a partially distributed resource allocation framework is proposed.The multiuser power allocation problem is decoupled into single-user decision problems,while channel allocation optimization is performed in a centralized manner.In order to further reduce computational complexity,we propose a low-complexity reinforcement learning method.Simulation results reveal that the proposed algorithm outperforms the state-of-the-art myopic optimizations in terms of energy efficiency and the backlog performance.