Interpreting the negative mood-helping literature via "mega"-analysis: A contrary view.

"Mega"-analysis was developed by M. Carlson and N. Miller (see record 1987-31249-001) as an extension of traditional meta-analytic procedures for conducting integrative reviews of existing research literatures. One such mega-analysis was conducted by Carlson and Miller to synthesize the literature on the relation between negative mood states and helping. That analysis found no support for a theoretical account (negative state relief) that had been confirmed previously by using various experimental approaches. In an attempt to reconcile the discrepancy, the logic and methods used in Carlson and Miller's mega-analysis of the negative mood-helping literature were examined, and several serious problems were found. These problems are discussed, and data are presented to show that the results of that mega-analysis, and perhaps all mega-analyses, should not be viewed with confidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于Shannon熵的飞机交流发电机振动分析

飞机交流发电机是飞机关键部件之一,监测其健康状况具有重要意义。通过设计实验数据获取平台采集交流发电机在转速上升(从6000转/分到8500转/分)、平稳运行(7500转/分)及转速下降(8500转/分到6000转/分)三个阶段的振动信号,利用符号序列Shannon熵的计算方法以及符号化方法设定合适参数对振动信号进行了处理,得出了交流发电机在三个阶段的振动信号的Shannon熵;通过初步的分析表明,交流发电机在正常状态下三个阶段的Shannon熵都比较有规律的在一定范围内变化,一旦发生振动异常将会引起Shannon熵突变;经过分析讨论,Shannon熵的突变与否是可以用来反映交流发电机在机械方面是否存在故障隐患的。     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Model-guided evaluation of consumer products

An evaluation of a consumer product is presented as an example of how ergonomics models can guide data collection and interpretation by non-ergonomists. The product, a new cooking pan, was compared with its earlier counterpart on performance and safety tests for specific features. Model-guided evaluations produced data which could be well-fitted by linear relationships, and measures derived from the data could be interpreted in physical terms by engineers. This is now the standard methodology for pan evaluation in the client company.     

The quantifier structure of sentences that characterize nondeterministic time complexity

For every nondeterministic Turing machineM of time complexityT(n), there is a second-order sentence of a very restricted form, whose set of finite models encodes the set of strings recognized byM. Specifically, has a relational symbol which is interpreted as addition restricted to finite segments of the natural numbers, and a prefix consisting of existentially quantified unary second-order variables followed by a universal-existential first-order part. Here, every input stringx is encoded by a model of sizeT(|x|). Using a closely related encoding of strings as models where the size of the model is the length of the string, a consequence is that ifT(n)=n ^d, then there is a sentence with a similar prefix but whose second-order variables ared-ary and whose finite models encode the strings accepted byM. Potential applications to low-level complexity are discussed.     

Oxytocin Signaling as a Target to Block Social Defeat-Induced Increases in Drug Abuse Reward

There is huge scientific interest in the neuropeptide oxytocin (OXT) due to its putative capacity to modulate a wide spectrum of physiological and cognitive processes including motivation, learning, emotion, and the stress response. The present review seeks to increase the understanding of the role of OXT in an individual’s vulnerability or resilience with regard to developing a substance use disorder. It places specific attention on the role of social stress as a risk factor of addiction, and explores the hypothesis that OXT constitutes a homeostatic response to stress that buffers against its negative impact. For this purpose, the review summarizes preclinical and clinical literature regarding the effects of OXT in different stages of the addiction cycle. The current literature affirms that a well-functioning oxytocinergic system has protective effects such as the modulation of the initial response to drugs of abuse, the attenuation of the development of dependence, the blunting of drug reinstatement and a general anti-stress effect. However, this system is dysregulated if there is continuous drug use or chronic exposure to stress. In this context, OXT is emerging as a promising pharmacotherapy to restore its natural beneficial effects in the organism and to help rebalance the functions of the addicted brain.     

Adenosine Receptor Agonist HE-NECA Enhances Antithrombotic Activities of Cangrelor and Prasugrel in vivo by Decreasing of Fibrinogen Density in Thrombus

Blood platelets’ adenosine receptors (AR) are considered to be a new target for the anti-platelet therapy. This idea is based on in vitro studies which show that signaling mediated by these receptors leads to a decreased platelet response to activating stimuli. In vivo evidence for the antithrombotic activity of AR agonists published to date were limited, however, to the usage of relatively high doses given in bolus. The present study was aimed at verifying if these substances used in lower doses in combination with inhibitors of P2Y₁₂ could serve as components of dual anti-platelet therapy. We have found that a selective A_2A agonist 2-hexynyl-5’-N-ethylcarboxamidoadenosine (HE-NECA) improved the anti-thrombotic properties of either cangrelor or prasugrel in the model of ferric chloride-induced experimental thrombosis in mice. Importantly, HE-NECA was effective not only when applied in bolus as other AR agonists in the up-to-date published studies, but also when given chronically. In vitro thrombus formation under flow conditions revealed that HE-NECA enhanced the ability of P2Y₁₂ inhibitors to decrease fibrinogen content in thrombi, possibly resulting in their lower stability. Adenosine receptor agonists possess a certain hypotensive effect and an ability to increase the blood–brain barrier permeability. Therefore, the effects of anti-thrombotic doses of HE-NECA on blood pressure and the blood–brain barrier permeability in mice were tested. HE-NECA applied in bolus caused a significant hypotension in mice, but the effect was much lower when the substance was given in doses corresponding to that obtained by chronic administration. At the same time, no significant effect of HE-NECA was observed on the blood–brain barrier. We conclude that chronic administration of the A_2A agonist can be considered a potential component of a dual antithrombotic therapy. However, due to the hypotensive effect of the substances, dosage and administration must be elaborated to minimize the side-effects. The total number of animals used in the experiments was 146.     

A blueprint for capturing grid value in a decentralized electricity sector

This study addresses the question of how the electricity grid can best be priced, in the context of disruptive technologies, increasing decentralisation of the power sector and new business models. The emergence of business models can affect the design of tariffs, but at the same time, tariff design can trigger new business models as they can nudge adoption towards certain technologies. Network tariffs have traditionally been established using a cost accounting method. Due to its cost structure, network tariffs are second best constructs, as marginal cost pricing does not lead to cost recovery. Second best solutions, in all probability, will overcompensate some inputs which in turn will be overused (the Averch-Johnson effect). New distributed energy technologies that perform better in these overcompensated inputs will therefore see a higher penetration in the market. Penetration of some technologies over others in a previously monolithic vertically integrated power sector will open up new customer needs and therefore business opportunities. Each resulting business opportunity would make use of the grid differently from which it was originally conceived, and at one extreme, they can bypass it altogether. We provide a framework of analysis that leads to the pricing of the services provided by the electricity grid for each new business opportunity.     

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.