Oral immunotherapy be heated ovomuciod-reduced egg white in a Balb/C mouse model

Resumen del trabajo presentado al Food Allergy and Anaphylaxis Meeting (FAAM-2011) celebrado en Venecia.Peer Reviewe

Kinetics, Kinematics, and Muscle Activity Patterns During Back Squat With Different Contributions of Elastic Resistance

Purpose: Performing back squats with elastic bands has been widely used in resistance training. Although research demonstrated greater training effects obtained from adding elastic bands to the back squat, little is known regarding the optimal elastic resistance and how it affects neuromuscular performance. This study aimed to compare the force, velocity, power, and muscle activity during back squats with different contributions of elastic resistance. Methods: Thirteen basketball players performed 3 repetitions of the back squat at 85% of 1-repetition maximum across 4 conditions: (1) total load from free weight and (2) 20%, (3) 30%, and (4) 40% of the total load from elastic band and the remaining load from free weight. The eccentric and concentric phases of the back squat were divided into upper, middle, and bottom phases. Results: In the eccentric phase, mean velocity progressively increased with increasing elastic resistance, and muscle activity of the vastus medialis and rectus femoris significantly increased with the largest elastic resistance in the upper phase (P ≤ .036). In the concentric phase, mean power (P ≤ .021) and rate of force development (P ≤ .002) significantly increased with increasing elastic resistance. Furthermore, muscle activity of the vastus lateralis and vastus medialis significantly improved with the largest elastic resistance in the upper phases (P ≤ .021). Conclusion: Velocity, power, rate of force development, and selective muscle activity increased as the elastic resistance increased in different phases during the back-squat exercise

Synthesis and properties of a novel highly thermal stable N-propargyl monomer containing benzoxazole ring

© 2017, © The Author(s) 2017. A novel highly thermal stable propargyl functional compound containing benzoxazole ring, N, N, N′, N′-tetra propargyl-5-amino-2-(p-aminophenyl) benzoxazole (TPAPB), was proposed and synthesized using a phase-transfer catalytic method. The cure behavior of TPAPB was investigated by non-isothermal differential scanning calorimetry analysis. The solubility and rheological properties of TPAPB, as well as its broad temperature window from 130°C to 200°C with low viscosity, offered excellent processability for TPAPB to be used as a potential monomer of thermosetting polymer resin. It was found that the glass transition temperature of cured TPAPB was 359°C, and the temperature of 5% weight loss was 418°C in argon with the char residue up to 70% at 700°C. The polymerized resin exhibited high heat resistance and thermal stability, together with its processability, making it good candidate as highly heat-resistant polymer matrix for advanced composite applications

Multi-parametric quantitative microvascular imaging with optical-resolution photoacoustic microscopy in vivo

Many diseases involve either the formation of new blood vessels (e.g., tumor angiogenesis) or the damage of existing ones (e.g., diabetic retinopathy) at the microcirculation level. Optical-resolution photoacoustic microscopy (OR-PAM), capable of imaging microvessels in 3D in vivo down to individual capillaries using endogenous contrast, has the potential to reveal microvascular information critical to the diagnosis and staging of microcirculation-related diseases. In this study, we have developed a dedicated microvascular quantification (MQ) algorithm for OR-PAM to automatically quantify multiple microvascular morphological parameters in parallel, including the vessel diameter distribution, the microvessel density, the vascular tortuosity, and the fractal dimension. The algorithm has been tested on in vivo OR-PAM images of a healthy mouse, demonstrating high accuracy for microvascular segmentation and quantification. The developed MQ algorithm for OR-PAM may greatly facilitate quantitative imaging of tumor angiogenesis and many other microcirculation related diseases in vivo

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management

COLERGs-constrained safe reinforcement learning for realising MASS's risk-informed collision avoidance decision making

Maritime autonomous surface ship (MASS) represents a significant advancement in maritime technology, offering the potential for increased efficiency, reduced operational costs, and enhanced maritime traffic safety. However, MASS navigation in complex maritime traffic and congested water areas presents challenges, especially in Collision Avoidance Decision Making (CADM) during multi-ship encounter scenarios. Through a robust risk assessment design for time-sequential and joint-target ships (TSs) encounter scenarios, a novel risk and reliability critic-enhanced safe hierarchical reinforcement learning (RA-SHRL), constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), is proposed to realize the autonomous navigation and CADM of MASS. Finally, experimental simulations are conducted against a time-sequenced obstacle avoidance scenario and a swarm obstacle avoidance scenario. The experimental results demonstrate that RA-SHRL generates safe, efficient, and reliable collision avoidance strategies in both time-sequential dynamic obstacles and mixed joint-TSs environments. Additionally, the RA-SHRL is capable of assessing risk and avoiding multiple joint-TSs. Compared with Deep Q-network (DQN) and Constrained Policy Optimization (CPO), the search efficiency of the algorithm proposed in this paper is improved by 40% and 12%, respectively. Moreover, it achieved a 91.3% success rate of collision avoidance during training. The methodology could also benefit other autonomous systems in dynamic environments