Bisacurone gel ameliorated burn wounds in experimental rats via its anti-inflammatory, antioxidant, and angiogenic properties

ABSTRACT Purpose: To investigate putative mechanism of wound healing for chitosan-based bisacurone gel against secondary burn wounds in rats. Methods: A second-degree burn wound with an open flame using mixed fuel (2 mL, 20 seconds) was induced in Sprague Dawley rats (male, 180-220 g, n = 15, each) followed by topical treatments with either vehicle control (white petroleum gel, 1%), silver sulfadiazine (1%) or bisacurone gel (2.5, 5, or 10%) for 20 days. Wound contraction rate and paw withdrawal threshold were monitored on various days. Oxidative stress (superoxide dismutase, glutathione, malondialdehyde, and nitric oxide), pro-inflammatory cytokines (tumour necrosis factor-alpha, interleukins by enzyme-linked immunosorbent assay), growth factors (transforming growth factor-β, vascular endothelial growth factor C using real time polymerase chain reaction and Western blot assay) levels, and histology of wound skin were assessed at the end. Results: Bisacurone gel showed 98.72% drug release with a 420.90–442.70 cps viscosity. Bisacurone gel (5 and 10%) significantly (p < 0.05) improved wound contraction rate and paw withdrawal threshold. Bisacurone gel attenuated oxidative stress, pro-inflammatory cytokines, and water content. It also enhanced angiogenesis (hydroxyproline and growth factor) and granulation in wound tissue than vehicle control. Conclusions: These findings suggested that bisacurone gel can be a potential candidate to treat burn wounds via its anti-inflammatory, antioxidant, and angiogenic properties

High performance position-sensitive-detector based on graphene-silicon heterojunction

Position-sensitive-detectors (PSDs) based on lateral photoeffect have been widely used in diverse applications, including optical engineering, aerospace and military fields. With increasing demands in long working distance, low energy consumption, and weak signal sensing systems, the poor responsivity of conventional Silicon-based PSDs has become a bottleneck limiting their applications. Herein, we propose a high-performance passive PSD based on graphene-Si heterostructure. The graphene is adapted as a photon absorbing and charge separation layer working together with Si as a junction, while the high mobility provides promising ultra-long carrier diffusion length and facilitates large active area of the device. A PSD with working area of 8 mm x 8 mm is demonstrated to present excellent position sensitivity to weak light at nWs level (much better than the limit of ~{\mu}Ws of Si p-i-n PSDs). More importantly, it shows very fast response and low degree of non-linearity of ~3%, and extends the operating wavelength to the near infrared (IR) region (1319 and 1550 nm). This work therefore provides a new strategy for high performance and broadband PSDs.Comment: 25 pages, 13 figures, to appear in Optic

Dynamics of Instruction Tuning: Each Ability of Large Language Models Has Its Own Growth Pace

Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models (LLMs). However, the creation of instruction data is still largely heuristic, leading to significant variation in quality and distribution across existing datasets. Experimental conclusions drawn from these datasets are also inconsistent, with some studies emphasizing the importance of scaling instruction numbers, while others argue that a limited number of samples suffice. To better understand data construction guidelines, we deepen our focus from the overall model performance to the growth of each underlying ability, such as creative writing, code generation, and logical reasoning. We systematically investigate the effects of data volume, parameter size, and data construction methods on the development of various abilities, using hundreds of model checkpoints (7b to 33b) fully instruction-tuned on a new collection of over 40k human-curated instruction data. This proposed dataset is stringently quality-controlled and categorized into ten distinct LLM abilities. Our study reveals three primary findings: (i) Despite data volume and parameter scale directly impacting models' overall performance, some abilities are more responsive to their increases and can be effectively trained using limited data, while some are highly resistant to these changes. (ii) Human-curated data strongly outperforms synthetic data from GPT-4 in efficiency and can constantly enhance model performance with volume increases, but is unachievable with synthetic data. (iii) Instruction data brings powerful cross-ability generalization, with evaluation results on out-of-domain data mirroring the first two observations. Furthermore, we demonstrate how these findings can guide more efficient data constructions, leading to practical performance improvements on public benchmarks

Unveiling the Secrets of Engaging Conversations: Factors that Keep Users Hooked on Role-Playing Dialog Agents

With the growing humanlike nature of dialog agents, people are now engaging in extended conversations that can stretch from brief moments to substantial periods of time. Understanding the factors that contribute to sustaining these interactions is crucial, yet existing studies primarily focusing on short-term simulations that rarely explore such prolonged and real conversations. In this paper, we investigate the factors influencing retention rates in real interactions with roleplaying models. By analyzing a large dataset of interactions between real users and thousands of characters, we systematically examine multiple factors and assess their impact on user retention rate. Surprisingly, we find that the degree to which the bot embodies the roles it plays has limited influence on retention rates, while the length of each turn it speaks significantly affects retention rates. This study sheds light on the critical aspects of user engagement with role-playing models and provides valuable insights for future improvements in the development of large language models for role-playing purposes

Integrated Positioning for Coal Mining Machinery in Enclosed Underground Mine Based on SINS/WSN

To realize dynamic positioning of the shearer, a new method based on SINS/WSN is studied in this paper. Firstly, the shearer movement model is built and running regularity of the shearer in coal mining face has been mastered. Secondly, as external calibration of SINS using GPS is infeasible in enclosed underground mine, WSN positioning strategy is proposed to eliminate accumulative error produced by SINS; then the corresponding coupling model is established. Finally, positioning performance is analyzed by simulation and experiment. Results show that attitude angle and position of the shearer can be real-timely tracked by integrated positioning strategy based on SINS/WSN, and positioning precision meet the demand of actual working condition

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem