CIA in Laos: A Secret Collaboration of CIA and Hmong

Senior Project submitted to The Division of Social Studies of Bard College

Phenotypic connections in surprising places

Connections have been revealed between very different human diseases using phenotype associations in other specie

P53 and p73 differ in their ability to inhibit glucocorticoid receptor (GR) transcriptional activity

BACKGROUND: p53 is a tumor suppressor and potent inhibitor of cell growth. P73 is highly similar to p53 at both the amino acid sequence and structural levels. Given their similarities, it is important to determine whether p53 and p73 function in similar or distinct pathways. There is abundant evidence for negative cross-talk between glucocorticoid receptor (GR) and p53. Neither physical nor functional interactions between GR and p73 have been reported. In this study, we examined the ability of p53 and p73 to interact with and inhibit GR transcriptional activity. RESULTS: We show that both p53 and p73 can bind GR, and that p53 and p73-mediated transcriptional activity is inhibited by GR co-expression. Wild-type p53 efficiently inhibited GR transcriptional activity in cells expressing both proteins. Surprisingly, however, p73 was either unable to efficiently inhibit GR, or increased GR activity slightly. To examine the basis for this difference, a series of p53:p73 chimeric proteins were generated in which corresponding regions of either protein have been swapped. Replacing N- and C-terminal sequences in p53 with the corresponding sequences from p73 prevented it from inhibiting GR. In contrast, replacing p73 N- and C-terminal sequences with the corresponding sequences from p53 allowed it to efficiently inhibit GR. Differences in GR inhibition were not related to differences in transcriptional activity of the p53:p73 chimeras or their ability to bind GR. CONCLUSION: Our results indicate that both N- and C-terminal regions of p53 and p73 contribute to their regulation of GR. The differential ability of p53 and p73 to inhibit GR is due, in part, to differences in their N-terminal and C-terminal sequences

Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertainty of heterogeneous materials are considered within the proposed self-optimization wavelet-learning framework. Firstly, meso- and micro-structural modeling of random heterogeneous materials are achieved by the proposed computer representation method, whose simulated hierarchical configurations have relatively high volume ratio of material inclusions. Moreover, temperature-dependent nonlinearity and material property uncertainties of random heterogeneous materials are modeled by a polynomial nonlinear model and Weibull probabilistic model, which can closely resemble actual material properties of heterogeneous materials. Secondly, an innovative stochastic three-scale homogenized method (STSHM) is developed to compute the macroscopic nonlinear thermal conductivity of random heterogeneous materials. Background meshing and filling techniques are devised to extract geometry and material features of random heterogeneous materials for establishing material databases. Thirdly, high-dimensional and highly nonlinear material features of material databases are preprocessed and reduced by wavelet decomposition technique. The neural networks are further employed to excavate the predictive models from dimension-reduced low-dimensional data

Can financial capability improve entrepreneurial performance? Evidence from rural China

The capability of individuals to manage their finances is essential to the outcomes of their entrepreneurial activities. Using panel data from the China Household Finance Survey (C.H.F.S.) in 2013, 2015 and 2017, this article examines how financial capability affects entrepreneurial performance in rural China. The results demonstrate that financial capability is positively correlated with the scale, profitability and sustainability of entrepreneurship, which is robust in consideration of endogeneity. The effects of financial capability are heterogeneous for different entrepreneurs. Furthermore, technology, labour and land act as the mediating variables through which financial capability improves entrepreneurial performance. Therefore, to facilitate entrepreneurial success, it is important to provide entrepreneurs with financial education. Meanwhile, improvements to the financial environment should also be considered. Additionally, financial institutions should combine financial services with factors, such as technology, land and labour, to improve entrepreneurial performance

Higher-order multi-scale deep Ritz method for multi-scale problems of authentic composite materials

The direct deep learning simulation for multi-scale problems remains a challenging issue. In this work, a novel higher-order multi-scale deep Ritz method (HOMS-DRM) is developed for thermal transfer equation of authentic composite materials with highly oscillatory and discontinuous coefficients. In this novel HOMS-DRM, higher-order multi-scale analysis and modeling are first employed to overcome limitations of prohibitive computation and Frequency Principle when direct deep learning simulation. Then, improved deep Ritz method are designed to high-accuracy and mesh-free simulation for macroscopic homogenized equation without multi-scale property and microscopic lower-order and higher-order cell problems with highly discontinuous coefficients. Moreover, the theoretical convergence of the proposed HOMS-DRM is rigorously demonstrated under appropriate assumptions. Finally, extensive numerical experiments are presented to show the computational accuracy of the proposed HOMS-DRM. This study offers a robust and high-accuracy multi-scale deep learning framework that enables the effective simulation and analysis of multi-scale problems of authentic composite materials

Towards Structural Stability of Social Networks

The structural stability of a social network indicates the ability of the network to maintain a sustainable service, which is important for both the network holders and the participants. Graphs are widely used to model social networks, where the coreness of a vertex (node) has been validated as the "best practice" for capturing a user's engagement. Based on this argument, we study the following problems: 1) reinforcing the network structural stability by detecting critical users, with its efficient solution in distributed computation environment; 2) monitoring each user's influence on the network structural stability. Firstly, we aim to reinforce a social network in a global manner, instead of focusing on a local view as existing works, e.g., the anchored k-core problem aims to enlarge the size of k-core with a fixed input k. We propose a new model so-called the anchored coreness problem: anchoring a small number of users to maximize the coreness gain (the total increment of coreness) of all the users in the network. We prove the problem is NP-hard and show it is more challenging than the existing local-view problems. An efficient greedy algorithm is proposed with novel techniques on pruning search space and reusing the intermediate results. The algorithm is also extended to distributed environment with a novel graph partition strategy to ensure the computing independency of each machine. Extensive experiments on real-life data demonstrate that our model is effective for reinforcing social networks and our algorithms are efficient. Secondly, although the static engagement of a user is well estimated by its coreness, each user's influence on other users is not well monitored when its engagement is weakened or strengthened. Besides, the dynamic of user engagement has not been well captured for evolving networks. We systematically study the network dynamic against the engagement change of each user. The influence of a user is monitored via two novel concepts: the collapsed power to measure the effect of user weakening, and the anchored power to measure the effect of user strengthening. The two concepts can be naturally integrated such that a unified offline algorithm is proposed to compute both the collapsed and anchored followers for each user. When the network structure evolves, online techniques are designed to maintain the users' followers, which is faster than redoing the offline algorithm by around 3 orders of magnitude

Research on the carbon emission reduction effects of green finance in the context of environment regulations

The rise in carbon emissions has significantly aggravated issues related to climate change. In light of this background, there has been a strong focus on using financial methods to reduce carbon emissions. Based on panel data for China for the period 2003– 2019, we examine the effects of green finance on carbon emissions and the moderating effects of environmental regulations. The results indicate that green finance development alleviates carbon emissions. Meanwhile, our findings on the effects of green finance policies suggest that the implementation of such policies will strengthen the carbon-emission reduction effects of green finance. Additionally, the impacts of green finance on carbon emissions are moderated by administration and public-oriented environmental regulations rather than market-oriented environmental regulations. As the biggest emitter of carbon emissions in the world, China should prioritise the consistent and steady development of green finance and facilitate the green finance legislation. Furthermore, China should enhance the role of marketoriented environmental regulations while considering the synergy between environmental regulations and green finance