Origin of the Scaling Law in Human Mobility: Hierarchical Organization of Traffic Systems

Uncovering the mechanism leading to the scaling law in human trajectories is of fundamental importance in understanding many spatiotemporal phenomena. We propose a hierarchical geographical model to mimic the real traffic system, upon which a random walker will generate a power-law travel displacement distribution with exponent -2. When considering the inhomogeneities of cities' locations and attractions, this model reproduces a power-law displacement distribution with an exponential cutoff, as well as a scaling behavior in the probability density of having traveled a certain distance at a certain time. Our results agree very well with the empirical observations reported in [D. Brockmann et al., Nature 439, 462 (2006)].Comment: 6 figures, 4 page

TUMK-ELM: A fast unsupervised heterogeneous data learning approach

© 2013 IEEE. Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness

A convenient tandem one-pot synthesis of donor-acceptor-type triphenylene 2,3-dicarboxylic esters from diarylacetylene

A tandem one-pot method for the direct synthesis of polysubstituted triphenylene 2,3-dicarboxylic esters with different substitution patterns was developed by enyne metathesis of diarylacetylene, followed by Diels–Alder, aromatization and a cyclization cascade

Lotus-leaf-inspired hierarchical structured surface with non-fouling and mechanical bactericidal performances

Antibiotics, a power tool to combat pathogenic bacterial infection, have experienced their inability to kill drug-resistant bacteria due to the development of antibiotic resistance. As an alternative, nanostructured, mechanical bactericidal surfaces may hold promise in killing bacteria without triggering antimicrobial resistance; however, accumulation of dead bacteria would greatly reduce their antimicrobial activity. In this study, for the first time we report a surprising discovery that the lotus leaf, well known for its superhydrophobicity, has demonstrated not only strong repelling effect against bacteria but also bactericidal activity via a cell-rupturing mechanism. Inspired by this unexpected finding, we subsequently designed and prepared a hierarchically structured surface, comprising microscale cylinders with superimposed nanoneedles on top, which was rendered superhydrophobic (water contact angle: 174°; roll-off angle: 99%) were repelled from the surface (non-fouling), those tenacious bacteria that managed to be in touch of the surface were physically killed completely. Compared to a conventional superhydrophobic surface (non-fouling to some extent, but no bacteria-killing) or a mechanical bactericidal surface (bacteria-killing but not bacteria-repelling), our new structured surface has the great advantage in maintaining long-term effectiveness in antimicrobial activity. We envisage that this study will help develop long-term effective antimicrobial strategies based entirely on physical bactericidal mechanism (thus, avoiding risks of triggering antimicrobial resistance)

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni

NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference.

MOTIVATION: Reconstruction of gene regulatory networks (GRNs) is of utmost interest to biologists and is vital for understanding the complex regulatory mechanisms within the cell. Despite various methods developed for reconstruction of GRNs from gene expression profiles, they are notorious for high false positive rate owing to the noise inherited in the data, especially for the dataset with a large number of genes but a small number of samples. RESULTS: In this work, we present a novel method, namely NARROMI, to improve the accuracy of GRN inference by combining ordinary differential equation-based recursive optimization (RO) and information theory-based mutual information (MI). In the proposed algorithm, the noisy regulations with low pairwise correlations are first removed by using MI, and the redundant regulations from indirect regulators are further excluded by RO to improve the accuracy of inferred GRNs. In particular, the RO step can help to determine regulatory directions without prior knowledge of regulators. The results on benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and experimentally determined GRN of Escherichia coli show that NARROMI significantly outperforms other popular methods in terms of false positive rates and accuracy. AVAILABILITY: All the source data and code are available at: http://csb.shu.edu.cn/narromi.htm