Complications such as thromboembolism and bacterial infection, arising from the widespread use of implanted medical devices, are serious problems for current clinical treatment. In this work, ...dual-functional anticoagulant and antibacterial blend coatings were designed and fabricated from gemini quaternary ammonium salt waterborne polyurethane (GWPU) emulsion and heparin aqueous solution. The results demonstrate that the
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-lysine-derivative gemini quaternary ammonium salt (GQAS) in GWPU endows the coating with excellent non-releasing antibacterial ability and simultaneously the release of heparin provides superior anticoagulant activity. Moreover, the controlled release of heparin is achieved by the ionic interaction between the positive charge in GQAS and the negative charge in heparin. On the basis of the dual-functional anticoagulant and antibacterial properties of GWPU/heparin blend coatings, this work proposes a facile strategy to achieve syncretic performances in biomaterials, and these coatings would have the potential to improve the application of implanted devices.
A simple design of the dual-functional anticoagulant and antibacterial blend coatings with controlled release of heparin.
Large quantities of indium nitride (InN) nanowires are synthesized by the in situ nitriding of indium oxide (In2O3) powders in an ammonia (NH3) flux. Tens of milligrams of nanowires are obtained in ...one batch. Every 100 mg of In2O3 starting powder can produce up to 65 mg of InN nanowires under the optimized conditions. The synthesized nanowires grow along the 001 direction with excellent crystallinity. They are of high purity and are 30–50 μm in length with an almost uniform diameter of about 100 nm. Photoluminescence measurements of the nanowires exhibit a strong peak at 707 nm. An optical bandgap of about 1.7 eV is estimated based on the absorption spectrum. The experimental results also demonstrate that the approach of nitriding In2O3 powders in situ is feasible for the synthesis of high‐purity InN nanowires in large quantities, with good reproducibility and without catalyst materials. The synthesis of InN nanowires in large quantities would be of benefit to the further study and understanding of their intrinsic properties, as well as being advantageous for their potential application in nanodevices.
InN nanowires are synthesized in large quantities by nitriding indium oxide powder with a conversion ratio up to 65 %. The as‐synthesized nanowires have uniform diameters of about 100 nm (see SEM image), are 30–50 μm in length, and possess excellent crystallinity. The synthesis of large quantities of these relatively long nanowires with high purity enables further studies on their intrinsic properties and is of benefit for their potential application in nanodevices.
A layer of carbonaceous deposit with spectacular morphologies was obtained on the anode surface in a hydrogen DC arc-discharge. Pure graphite rods were used in the synthesis process without use of ...catalytic materials. Along the rod, the deposit product presented many different morphologies such as nanofibers, microfibers, pillars and trees, and was approximately divided into five regions with distinct features. The temperature gradient along the anode was thought to be responsible for the orderly change in deposit morphology.
In J. Optim. Theory Appl. 66 (1) (1990) 37 seven kinds of monotone are considered. In this paper, point monotonicity of maps is proposed and its applications in numerical computation are presented. ...Especially, with the point monotonicity the global convergence of continuous Levenberg–Marquardt method is dissused and then monotonicity of maps is extended in a new way.
The adsorption behavior of human fibrinogen (Hfg) on single-walled carbon nanotube (SWNT) films was investigated using scanning electron microscopy (SEM) and near edge X-ray absorption fine structure ...(NEXAFS) spectroscopy. It was shown in the SEM images that fibrinogen was adsorbed strongly on the surface of SWNT when the samples were incubated in the Hfg solutions for 10
min. The dependence of adsorption on the concentration of fibrinogen was also investigated and it was found that adsorption increased with increasing concentration. In order to further explore the adsorption of fibrinogen on SWNT surface, NEXAFS spectra were obtained at the N K-edge and the C K-edge. The results confirmed the conclusion regarding the dependence of adsorption on fibrinogen concentration. It is demonstrated that, due to its high sensitivity to the surface elements, NEXAFS spectroscopy is a powerful tool to investigate the adsorption of fibrinogen on SWNT films.
Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to ...achieve better model performance. However, conventional VFL methods may run into data deficiency as they exploit only aligned and labeled samples (belonging to different parties), leaving often the majority of unaligned and unlabeled samples unused. The data deficiency hampers the effort of the federation. In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i.e., dispersed features) of samples aligned among parties and local views (i.e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model. FedHSSL further exploits invariant features across parties to boost the performance of the joint model through partial model aggregation. FedHSSL, as a framework, can work with various representative SSL methods. We empirically demonstrate that FedHSSL methods outperform baselines by large margins. We provide an in-depth analysis of FedHSSL regarding label leakage, which is rarely investigated in existing self-supervised VFL works. The experimental results show that, with proper protection, FedHSSL achieves the best privacy-utility trade-off against the state-of-the-art label inference attack compared with baselines. Code is available at \url{https://github.com/jorghyq2016/FedHSSL}.
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large ...language models (LLMs) and the intricacies of the target task. However, automating the generation of such expert-level prompts remains elusive. Existing prompt optimization methods tend to overlook the depth of domain knowledge and struggle to efficiently explore the vast space of expert-level prompts. Addressing this, we present PromptAgent, an optimization method that autonomously crafts prompts equivalent in quality to those handcrafted by experts. At its core, PromptAgent views prompt optimization as a strategic planning problem and employs a principled planning algorithm, rooted in Monte Carlo tree search, to strategically navigate the expert-level prompt space. Inspired by human-like trial-and-error exploration, PromptAgent induces precise expert-level insights and in-depth instructions by reflecting on model errors and generating constructive error feedback. Such a novel framework allows the agent to iteratively examine intermediate prompts (states), refine them based on error feedbacks (actions), simulate future rewards, and search for high-reward paths leading to expert prompts. We apply PromptAgent to 12 tasks spanning three practical domains: BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing it significantly outperforms strong Chain-of-Thought and recent prompt optimization baselines. Extensive analyses emphasize its capability to craft expert-level, detailed, and domain-insightful prompts with great efficiency and generalizability.
