Cigarette smoke-induced bronchoconstriction: causative agents and role of thromboxane receptors

Inhalation of cigarette smoke induces a biphasic bronchoconstriction in guinea pigs: the first phase is induced by a combination of cholinergic reflex and tachykinins, whereas the second phase involves cyclooxygenase metabolites (J.-L. Hong, I. W. Rodger, and L.-Y. Lee. J. Appl. Physiol. 78: 2260-2266, 1995). This study was carried out to further determine the causative agents in the smoke and the types of prostanoid receptors and endogenous prostanoids mediating the bronchoconstriction. Inhalation of 10 ml of high-nicotine cigarette smoke consistently elicited the biphasic bronchoconstriction in anesthetized and artificially ventilated guinea pigs. Pretreatment with hexamethonium (10 mg/kg iv) significantly reduced the first-phase bronchoconstriction but did not have any measurable effect on the second-phase response. In sharp contrast, gas-phase smoke did not elicit any bronchoconstrictive effect. Furthermore, when the animals were challenged with low-nicotine cigarette smoke, only a single second-phase response was evoked, accompanied by increases in thromboxane (Tx) B2 (a stable metabolite of TxA2), prostaglandin (PG) D2, PGF2 alpha in the bronchoalveolar lavage fluid. The bronchoconstrictive response induced by low-nicotine smoke was completely prevented by pretreatment with SQ-29548 (0.3 mg/kg iv), a TxA2-receptor antagonist. These results indicate that 1) nicotine is the primary causative agent responsible for the first-phase bronchoconstriction and 2) nonnicotine smoke particulates evoke the release of TxA2, PGD2, and PGF2 alpha, which act on TxA2 receptors on airway smooth muscles and induce the second-phase response to cigarette smoke.     

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.     

Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling

International Journal of Computer Vision - The “interpretation through synthesis” approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of...     

Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

International Journal of Computer Vision - This paper presents a novel subject-dependent deep aging path (SDAP), which inherits the merits of both generative probabilistic modeling and inverse...     

Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks

Wireless Networks - Congestion control is necessary for enhancing the quality of service in wireless sensor networks (WSNs). With advances in sensing technology, a substantial amount of data...     

A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks

Coverage is a key metric in evaluating the monitoring capacity and quality of services in wireless sensor networks. The energy consumption of self-contained sensors is also a challenging problem for energy-efficient use while still achieving better coverage performance. Although techniques have been developed to mitigate the problem of area coverage, particularly together with efficient clustering methods, none focuses intensively on the sensor activation stage, which is used to maintain coverage while optimizing energy usage. In this research, we thus propose a cover set to find the minimum set of sensors that completely cover the sensing ranges within an interest area as a criterion for sensor activation. Our main goal is to select an optimal number of active sensors considering residual energy and the cover set and to keep alive the important sensors for the sensing coverage task as long as possible. Additionally, this research proposes an area coverage-aware clustering protocol (ACACP) with energy consumption optimization with respect to the activation sensor, network clustering, and multi-hop communication to improve overall network lifetime while preserving coverage. Throughout the intensive simulation, given a diversity of deployments with scalability concern, the results demonstrate the effectiveness of ACACP when compared with other competitive approaches such as ECDC and DECAR, including state-of-the-art clustering protocols such as LEACH, in terms of coverage ratio and overall network lifetime.     

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and the predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and the predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.      

A frequent activated smoothened mutation in sporadic basal cell carcinomas

Basal-cell carcinomas (BCCs) are the most common cancer in Caucasians. It has been reported that the patched gene is inactivated in 30-40% sporadic BCCs and 20% sporadic medulloblastomas via loss of heterozygosity and nonsense mutations. Recently, two activating smoothened mutations have been found in the sporadic basal cell carcinomas. One, at base pair 1604 (G-to-T transversion) of exon 9, changes codon 535 from tryptophan to leucine, and the other, at base pair 1685 (G-to-A transition) of exon 10, changes codon 562 from arginine to glutamine (Xie et al., 1998). In our study, 1604G-->T was found in 20 out of 97 (20.6%) sporadic BCCs. The high prevalence indicates that 1604G is the mutation hot spot in our tumor samples. This mutation was detected in all three histological subtypes of BCCs, suggesting that smoothened mutation is an early event during the development of the tumor. Our finding of a high smoothened mutation rate, together with high frequent patched gene mutations reported recently, indicates that activation of the hedgehog signal transduction pathway is the most common and early event in the development of sporadic BCCs. Additionally, to determine whether smoothened, like patched, is also involved in the carcinogenesis of medulloblastomas, we screened medulloblastoma samples for these two mutations by restriction analysis. We have found the 1604G-->T mutation in 1 out of 21 medulloblastomas. This result confirmed smoothened gene involvement in the carcinogenesis of medulloblastoma.     

3-甲基吡啶与4-甲基吡啶多级解离萃取分离

通过对加入萃取剂后的两相多元混合物体系进行详细地研究,建立能合理描述装置内的浓度分布的多级解离萃取模型.以正交实验所得的最优实验条件设计工艺参数,结合数学模型进行逐级计算,结果表明对于含3-甲基吡啶较多的混合物 (yAf=0.68),两相产品纯度要求≥99%时需要14级操作.