Effects of antipsychotics on bone mineral density and prolactin levels in patients with schizophrenia: a 12-month prospective study

Objective: Effects of conventional and atypical antipsychotics on bone mineral density (BMD) and serum prolactin levels (PRL) were examined in patients with schizophrenia.Methods: One hundred and sixty-three first-episode inpatients with schizophrenia were recruited, to whom one of three conventional antipsychotics (perphenazine, sulpiride, and chlorpromazine) or one of three atypical antipsychotics (clozapine, quetiapine, and aripiprazole)was prescribed for 12 months as appropriate. BMD and PRL were tested before and after treatment. Same measures were conducted in 90 matched healthy controls.Results Baseline BMD of postero-anterior L1–L4 range from 1.04 ± 0.17 to 1.42 ± 1.23, and there was no significant difference between the patients group and healthy control group. However, post-treatment BMD values in patients (ranging from 1.02 ± 0.15 to 1.23 ± 0.10) were significantly lower than that in healthy controls (ranging from 1.15 ± 0.12 to 1.42 ± 1.36). The BMD values after conventional antipsychotics were significantly lower than that after atypical antipsychotics. The PRL level after conventional antipsychotics (53.05 ± 30.25 ng/ml) was significantly higher than that after atypical antipsychotics (32.81 ± 17.42 ng/ml). Conditioned relevance analysis revealed significant negative correlations between the PRL level and the BMD values after conventional antipsychotics.Conclusion The increase of PRL might be an important risk factor leading to a high prevalence of osteoporosis in patients with schizophrenia on long-term conventional antipsychotic medication.<br/

Exploring the factors influencing business model innovation using grounded theory: the case of a Chinese high-end equipment manufacturer

Business model innovation is vitally important for firms to gain competitive advantages and improve their performance. While it has attracted much attention recently, considerable work is still needed to properly understand business model innovation. This study aims to examine the factors influencing business model innovation through a case study of Shaanxi Blower, a high-end equipment manufacturer in China. Using grounded theory in terms of open coding, axial coding and selective coding, this case study found seven main factors that influenced business model innovation, namely, market pressure, government policy, entrepreneurship, culture and strategy, technology, human resources, and organizational capabilities. Market pressure, government policy and information technology are external factors, whereas, entrepreneurship and technological innovation are internal factors. Culture and strategy, human resources, and organizational capabilities are the guarantee factors. This study’s findings add to the growing literature by developing a more holistic understanding of the factors that influence business model innovation in the Chinese context, which indicates a possibility for Chinese high-end equipment manufacturers to improve their competitiveness and performance through better management of their business model innovation. View Full-Tex

Analysis on the aerodynamic performance of vertical axis wind turbine subjected to the change of wind velocity

AbstractReynolds averaged Navier-Stokes equations and Realizable kɛ− model were used in this paper, and the two dimensional unsteady flow field of the vertical axis wind turbine was simulated numerically at different wind velocity. The calculation results showed that the velocity in the region of wind turbine's rotation was much larger than the air flow of the upstream. The length of the wind turbine's downstream wake dispersion region was increased with the increase of the wind velocity. There is a much larger value of the eddy in the rear region of the wind turbine's rotational blades. And eddy existed in the downstream region of the wind turbine, and the larger velocity of cross flow, the larger value of the downstream flow's eddy. When the rotational speed was constant, with the increase in wind velocity, the variation of the wind turbine's total torque coefficient tended to smooth. The calculation results pointed out the direction for the follow-up study

Analysis on the influence of rotational speed to aerodynamic performance of vertical axis wind turbine

AbstractA two dimensional vertical axis wind turbine's model was established in this paper, and two dimensional unsteady incompressible N-S equations and Realizable kɛ− turbulence model were solved with software FLUENT. SIMPLC algorithm was applied, combined with the sliding grid technology; the influence of rotational speed to the flow structure of vertical axis wind turbine was discussed. The results showed that, the rotation of wind turbine had significant influence on wake, and higher the rotational speed, the greater reduction of the wake velocity. The wake velocity restored gradually away from the rotational part. There was much larger turbulent kinetic energy near the tail of the wind turbine's blade. The value of turbulent kinetic energy reduced gradually away from the rotational part, and the flow restored the stratospheric state gradually. With the increase of wind turbine's rotational speed, the value of turbulent kinetic energy in calculation domain increased too. The results showed that the flow structure of vertical axis wind turbine's rotational process could be revealed effectively by numerical simulation, provided theoretical reference for the engineering design of the vertical axis wind turbine

A dynamic performance-based payment mechanism for Public-Private Partnership projects: An integrated model for principal-agent and multi-objective optimization models

Performance-based payment mechanism is one of the key issues to ensure all stakeholders’ benefits in infrastructure Public-Private Partnership (PPP) projects. However, most existing research on performance-based payment with a fixed incentive coefficient can’t play a good incentive role. This study aims to the intrinsic mechanism between the performance appraisal score and the performance-based payment structure, so as to design the dynamic performance-based payment mechanism for infrastructure PPP projects. Firstly, the multi-objective optimal method is used to calculate the unit-payment. Second, principal-agent theory is used to construct the performance-based payment model with a changeable incentive coefficient. The findings of this study show that, the performance-based payment mechanism can effectively motivate participants to provide high-quality and efficient services, because their remuneration directly depends on their performance. When the outcome does not meet expectations, the amount paid can be adjusted accordingly, thus ensuring the maximum protection of public resources as well as the private sector’s profits. They serve a dual purpose, on one hand, they offer insights to rectify the shortcomings in the current unsatisfactory payment structure. On the other hand, the study provides a theoretical reference for the public sector to effectively incentivize the private sector in enhancing project performance

Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework

Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification.

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework

Inducible and Selective Erasure of Memories in the Mouse Brain via Chemical-Genetic Manipulation

SummaryRapid and selective erasures of certain types of memories in the brain would be desirable under certain clinical circumstances. By employing an inducible and reversible chemical-genetic technique, we find that transient αCaMKII overexpression at the time of recall impairs the retrieval of both newly formed one-hour object recognition memory and fear memories, as well as 1-month-old fear memories. Systematic analyses suggest that excessive αCaMKII activity-induced recall deficits are not caused by disrupting the retrieval access to the stored information but are, rather, due to the active erasure of the stored memories. Further experiments show that the recall-induced erasure of fear memories is highly restricted to the memory being retrieved while leaving other memories intact. Therefore, our study reveals a molecular genetic paradigm through which a given memory, such as new or old fear memory, can be rapidly and specifically erased in a controlled and inducible manner in the brain