Research on China’s Risk of Housing Price Contagion Based on Multilayer Networks

The major issue in the evolution of housing prices is risk of housing price contagion. To model this issue, we constructed housing multilayer networks using transfer entropy, generalized variance decomposition, directed minimum spanning trees, and directed planar maximally filtered graph methods, as well as China’s comprehensive indices of housing price and urban real housing prices from 2012 to 2021. The results of our housing multilayer networks show that the topological indices (degree, PageRank, eigenvector, etc.) of new first-tier cities (Tianjin, Qingdao, and Shenyang) rank higher than those of conventional first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzheng).     

Improvement of Contact Tracing with Citizen’s Distributed Risk Maps

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society’s resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project’s impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants.     

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.     

碳碳双键法研究萝卜不同部位的β-胡萝卜素含量分布

β-胡萝卜素广泛存在于植物体中,是典型的线性多稀分子,具有重要的生物功能。由于β-胡萝卜素是碳碳单、双键(C-C,C=C)交替的短链共轭多稀分子,含有大量离域的π电子,具有重要的光电特性。根据Andreas等对拉曼散射强度的研究,当激发光波长落在分子的电子吸收带时,会产生共振拉曼效应,能使拉曼光谱强度提高106倍。利用共振拉曼光谱技术,测量了β胡萝卜素分子及胡萝卜、青萝卜、白萝卜肉质直根不同部位其拉曼光谱,发现含β-胡萝卜素较高的胡萝卜的拉曼光谱与β-胡萝卜素的吻合很好。Gellerman等研究表明,样品浓度与拉曼峰强成正比关系,从拉曼光谱中容易发现三种萝卜的光谱强度纵向根头到主根及横向表皮到根芯逐渐降低,且青萝卜和白萝卜拉曼光谱强度都很低,并在碳碳单键的振动峰处发生峰劈裂。分别计算了碳碳单键和碳碳双键与碳氢键拉曼强度比,三种萝卜的I_CC/I_C-H随着测量部位(横向和纵向)的不同变化幅度接近：胡萝卜的表皮和根芯纵向的变化率分别为A₁=0.213 3和A₂=0.215 9,青萝卜表皮外和里的变化率分别为B₁=0.219 1和B₂=0.211 4,白萝卜表皮外和里分别为D₁=0.223 9和D₂=0.224 1;而对于I_C-C/I_C-H随着测量部位不同其变化率相差很大：胡萝卜的变化率a₁=0.212 1和a₂=0.232 4,青萝卜的变化率b₁=0.263 5和b₂=0.268 7,白萝卜的变化率d₁=0.369 0和d₂=0.304 9。对比发现三种萝卜的碳碳单键与碳氢键振动强度比随着测量部位的不同变化幅度相差很大,而从碳碳双键与碳氢键振动强度比发现三种萝卜中不同部位的β胡萝卜含量有相似的分布。这是由于青萝卜和白萝卜中β-胡萝卜素的含量少, 随着测量部位的不同C-C伸缩振动峰发生峰劈裂, 即在1 130和1 156 cm-1处出现两个振动峰, 经过计算和分析这两个峰都属于碳碳单键的伸缩振动峰, 且随着β-胡萝卜素含量的减少C-C整体的强度降低, 劈裂的新峰峰强度却有增加的趋势, 这使得原峰位的峰强度大幅度降低, 这与计算I_C-C/I_C-H的结果一致,不同品种的萝卜中β-胡萝卜素含量随测量部位的不同变化幅度截然不同。因此, 当样品中β-胡萝卜含量较少时,利用C=C振动峰峰强度同时分析样品不同部位的β-胡萝卜素含量分布变化会更准确。同时,研究和了解萝卜中不同部位β-胡萝卜素的含量为日常消费和膳食营养提供了很好的理论依据。     

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.