Multi-Scale Analysis of the European Airspace Using Network Community Detection

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.     

Multi-Scale Identification of Urban Landscape Structure Based on Two-Dimensional Wavelet Analysis: The Case of Metropolitan Beijing,China

Scale dependence is one of the major characteristics of landscape. Urban landscape is highly affected by human activities with a multi-scale structure, which makes the multi-scale identification of urban structure an obligation for urban spatial studies. Although there have been many previous studies on urban landscape structure, most of them have been conducted on a single scale, and the multi-scale effects of landscape patterns were rarely involved. Two-dimensional wavelet transforms can link spatial structures to scale and spatial locations, and maybe an effective method for the multi-scale analysis of landscape. In this paper, we applied two-dimensional discrete wavelet transform and wavelet variance to analyze the multi-scale spatial structure characteristics and the nested hierarchical structure of the metropolitan Beijing area. The results indicated that the spatial distribution and configuration of the patches were highly scattered at small scales, and the urban landscape exhibited a relatively complicated structure. At medium scales, a combination of the polycentric and sectorial structure was identified due to the prominence of dominant patches within each administrative district. At larger scales, the urban landscape pattern exhibits typical concentric ring characteristics. Two characteristic scales were detected by the wavelet variance in the south-north direction of the main urban zones, scale 4 (112m) and 8 (1792m) in Dongcheng District, scale 3 (56m) and 6 (448m) in Xicheng District, which were corresponding to the extent of middle-small blocks and large blocks respectively. One characteristic scale was detected in each of the suburb areas (Chaoyang, Haidian, and Fengtai District). The spatial structure of the main urban zones is more complex than that of the suburb areas, and it presents a typical hierarchical structure in the south-north direction. In general, the spatial structure of Beijing metropolitan area appears polycentric and concentric ring structure at large scales, the main urban area has nested hierarchies at different characteristic scales, and the wavelet method can effectively identify multi-scale characteristics of urban spatial structure.     

Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.     

Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network

Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from to . Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the network. Although marginal improvements in favor of interdisciplinarity are inferable during the 70s and 80s, the overall trends during the past 20 years are constant and indicative of stalemate. We conclude that the flow of knowledge between different fields of research in Slovenia is in need of further stimulation.     

Clinic Network Collaboration and Patient Tracing to Maximize Retention in HIV Care

BackgroundUnderstanding retention and loss to follow up in HIV care, in particular the number of people with unknown outcomes, is critical to maximise the benefits of antiretroviral therapy. Individual-level data are not available for these outcomes in Australia, which has an HIV epidemic predominantly focused amongst men who have sex with men.ConclusionsThe network demonstrated low numbers of people with unknown outcomes and high levels of retention in care. Increased levels of retention in care and reductions in unknown outcomes identified after the intervention largely reflected confirmation of clinic transfers while a smaller number were successfully re-engaged in care. Factors associated with disengagement from care were identified. Systems to monitor patient retention, care transfer and minimize disengagement will maximise individual and population-level outcomes for populations with HIV.     

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.     

The Oligomerization Landscape of Histones

In eukaryotes, DNA is packaged within nucleosomes. The DNA of each nucleosome is typically centered around an octameric histone protein core: one central tetramer plus two separate dimers. Studying the assembly mechanisms of histones is essential for understanding the dynamics of entire nucleosomes and higher-order DNA packaging. Here, we investigate canonical histone assembly and that of the centromere-specific histone variant, centromere protein A (CENP-A), using molecular dynamics simulations. We quantitatively characterize their thermodynamical and dynamical features, showing that two H3/H4 dimers form a structurally floppy, weakly bound complex, the latter exhibiting large instability around the central interface manifested via a swiveling motion of two halves. This finding is consistent with the recently observed DNA handedness flipping of the tetrasome. In contrast, the variant CENP-A encodes distinctive stability to its tetramer with a rigid but twisted interface compared to the crystal structure, implying diverse structural possibilities of the histone variant. Interestingly, the observed tetramer dynamics alter significantly and appear to reach a new balance when H2A/H2B dimers are present. Furthermore, we found that the preferred structure for the (CENP-A/H4)₂ tetramer is incongruent with the octameric structure, explaining many of the unusual dynamical behaviors of the CENP-A nucleosome. In all, these data reveal key mechanistic insights and structural details for the assembly of canonical and variant histone tetramers and octamers, providing theoretical quantifications and physical interpretations for longstanding and recent experimental observations. Based on these findings, we propose different chaperone-assisted binding and nucleosome assembly mechanisms for the canonical and CENP-A histone oligomers.     

The Epigenomic Landscape of Prokaryotes

DNA methylation acts in concert with restriction enzymes to protect the integrity of prokaryotic genomes. Studies in a limited number of organisms suggest that methylation also contributes to prokaryotic genome regulation, but the prevalence and properties of such non-restriction-associated methylation systems remain poorly understood. Here, we used single molecule, real-time sequencing to map DNA modifications including m6A, m4C, and m5C across the genomes of 230 diverse bacterial and archaeal species. We observed DNA methylation in nearly all (93%) organisms examined, and identified a total of 834 distinct reproducibly methylated motifs. This data enabled annotation of the DNA binding specificities of 620 DNA Methyltransferases (MTases), doubling known specificities for previously hard to study Type I, IIG and III MTases, and revealing their extraordinary diversity. Strikingly, 48% of organisms harbor active Type II MTases with no apparent cognate restriction enzyme. These active ‘orphan’ MTases are present in diverse bacterial and archaeal phyla and show motif specificities and methylation patterns consistent with functions in gene regulation and DNA replication. Our results reveal the pervasive presence of DNA methylation throughout the prokaryotic kingdoms, as well as the diversity of sequence specificities and potential functions of DNA methylation systems.     

Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

Background

This study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process.

Methodology and Principal Findings

The method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing consistency, especially for smaller datasets. The latter is especially important as reference data acquisition is always an issue in remote sensing applications. In the regression case, MSRBF provided improved accuracy and consistency when contrasted with a multi kernel RBF network.

Conclusion and Significance

Results highlight the potential of a novel training methodology that is not restricted to a specific algorithmic type, therefore significantly advancing machine learning algorithms for classification and regression tasks. The MSRBF is expected to find numerous applications within and outside the remote sensing field.     

基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.