Nudging and the Ecological and Social Roots of Human Agency

List of Stress Shared Gene

Offspring Educational Attainment and Older Parents\u27 Cognition in Mexico

Population-level disparities in later-life cognitive health point to the importance of family resources. Although the bulk of prior work establishes the directional flow of resources from parents to offspring, the linked lives perspective raises the question of how offspring resources could affect parental health as well. This paper examines whether adult children\u27s education influences older parents\u27 (aged 50+) cognitive health in Mexico, where schooling reforms have contributed to significant gains in the educational achievements of recent birth cohorts. Harnessing a change in compulsory school laws and applying an instrumental variables approach, we found that each year of offspring schooling was associated with higher overall cognition among parents, but was less predictive across different cognitive functioning domains. More offspring schooling improved parents\u27 cognitive abilities in verbal learning, verbal fluency, and orientation, but not in visual scanning, visuo-spatial ability, or visual memory. The beneficial effects of offspring schooling on those cognitive domains are more salient for mothers compared to fathers, suggesting potential gendered effects in the influence of offspring schooling. The results remained robust to controls for parent-child contact and geographic proximity, suggesting other avenues through which offspring education could affect parental health and a pathway for future research. Our findings contribute to growing research which stresses the causal influence of familial educational attainment on population health

An empirical study on predicting defect numbers

Abstract-Defect prediction is an important activity to make software testing processes more targeted and efficient. Many methods have been proposed to predict the defect-proneness of software components using supervised classification techniques in within-and cross-project scenarios. However, very few prior studies address the above issue from the perspective of predictive analytics. How to make an appropriate decision among different prediction approaches in a given scenario remains unclear. In this paper, we empirically investigate the feasibility of defect numbers prediction with typical regression models in different scenarios. The experiments on six open-source software projects in PROMISE repository show that the prediction model built with Decision Tree Regression seems to be the best estimator in both of the scenarios, and that for all the prediction models, the results yielded in the cross-project scenario can be comparable to (or sometimes better than) those in the within-project scenario when choosing suitable training data. Therefore, the findings provide a useful insight into defect numbers prediction for those new and inactive projects

Regulation of Irregular Neuronal Firing by Autaptic Transmission

The importance of self-feedback autaptic transmission in modulating spike-time irregularity is still poorly understood. By using a biophysical model that incorporates autaptic coupling, we here show that self-innervation of neurons participates in the modulation of irregular neuronal firing, primarily by regulating the occurrence frequency of burst firing. In particular, we find that both excitatory and electrical autapses increase the occurrence of burst firing, thus reducing neuronal firing regularity. In contrast, inhibitory autapses suppress burst firing and therefore tend to improve the regularity of neuronal firing. Importantly, we show that these findings are independent of the firing properties of individual neurons, and as such can be observed for neurons operating in different modes. Our results provide an insightful mechanistic understanding of how different types of autapses shape irregular firing at the single-neuron level, and they highlight the functional importance of autaptic self-innervation in taming and modulating neurodynamics.Comment: 27 pages, 8 figure

An improved differential evolution algorithm and its applications to orbit design

Differential Evolution (DE) is a basic and robust evolutionary strategy that has been applied to determining the global optimum for complex optimization problems[1–5]. It was introduced in 1995 by Storn and Price [1] and has been successfully applied to optimization problems including nonlinear, non-differentiable, non-convex, and multi-model functions. DE algorithms show good convergence, high-reliability, simplicity, and a reduced number of controllable parameters [2]. Olds and Kluever [3] applied DE to an interplanetary trajectory optimization problem and demonstrated the effectiveness of DE to produce rapid solutions. Madavan [4] discussed various modifications to the DE algorithm, improved its computational efficiency, and applied it to aerodynamic shape optimization problems. DE algorithms are easy to use, as they require only a few robust control variables, which can be drawn from a well-defined numerical interval. However, the existing various DE algorithms also have limitations, being susceptible to instability and getting trapped into local optima[2]. Notable effort has been spent addressing this by coupling DE algorithms with other optimization algorithms (for example, Self Organizing Maps (SOM) [6], Dynamic Hill Climbing (DHC) [7], Neural Networks (NN) [7], Particle Swarm Optimization (PSO) [8]). In these cases, the additional algorithm is used as an additional loop within the optimization process, creating a hybrid system with an inner and outer loop. Such hybrid algorithms are inherently more complex and so the computation cost is increased. Attempting to address this, a self-adaptive DE was designed and applied to the orbit design problem for prioritized multiple targets by Chen[5]. However, the self-adaptive feature is somewhat limited as it relates only to the number of generations within the optimization. A Self-adaptive DE which can automatically adapt its learning strategies and the associated parameters during the evolving procedure was proposed by Qin and Suganthan[9] and 25 test functions were used to verify the algorithm

Comparison of the efficacy of half ticagrelor loading doses and clopidogrel in elderly acute coronary syndrome patients in China

Purpose: To evaluate the effects of half-load doses (HLD) of ticagrelor and clopidogrel on elderly acute coronary syndrome patients (ACS) over a period of 90 days. Methods: Seventy-four patients diagnosed as ACS were included in this trial. The patients were randomly distributed into group 1 (treated with HLD ticagrelor, 90 mg LD) and group 2 (treated with clopidogrel, 300 mg LD). The interaction of treatment effect was evaluated using Multivariate Cox proportional hazards regression models. Results: Within three months, a total of 12 patients (16.21 %) died of myocardial infarction or stroke. The endpoint of HLD ticagrelor-treated elderly ACS patients was 20 %, and the incidence of clopidogreltreated endpoints was 14.81 %. Conclusion: In the first 45 patients treated with HLD ticagrelor, their cumulative incidence of cardiac events was relatively high. However, there were no considerable changes in the therapeutic benefits of these two drugs in elderly ACS patients. Keywords: Elder patients, Acute coronary syndrome, Ticagrelor, Clopidogre

Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer

The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion, which ignore the implicit diversities existed in style features and thus fail for generating better stylised results. In this paper, we propose a Retinex theory guided, channel-grouping based patch swap technique to solve the above challenges. Channel-grouping strategy groups the style feature maps into surface and texture channels, which prevents the winner-takes-all problem. Retinex theory based decomposition controls a more stable channel code rate generation. In addition, we provide complementary fusion and multi-scale generation strategy to prevent unexpected black area and over-stylised results respectively. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity