Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

Heterotopic space characteristics of urban village in China: Take Guandongdian district in Beijing as an example

[EN] For the first time in the history of China, more of its mainland population are living in cities than in rural villages. The land acquisition and real estate development have caused rapid disappearance and decline of a large number of traditional villages, resulting in "urban villages" in China. They seem chaotic, but contain rich and colorful social life. The living environment is really harsh, but people always maintain close relationship with each other. They are different from neither the modern urban nor traditional villages, but they have their own unique vitality. Such heterogeneous space is always a symbol of historical change and cultural collision which, according to the French philosopher Michel Foucault, can be called Heterotopias. In order to study this heterotopic phenomenon, the triangular area of Guandongdian district in Beijing has been chosen as the object of this case study. With the in-depth investigation of interviews, observation, statistics and sketches, this paper is trying to interpret the characteristics of the heterotopic state of the urban village from three aspects of social form, urban morphology and architectural feature. Eventually, in order to keep the complexity and diversification of urban village, several strategies are put forward for reference to future transforming practice.Lu, T.; Li, J.; Peng, N. (2018). Heterotopic space characteristics of urban village in China: Take Guandongdian district in Beijing as an example. En 24th ISUF International Conference. Book of Papers. Editorial Universitat Politècnica de València. 385-394. https://doi.org/10.4995/ISUF2017.2017.6034OCS38539

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

Ruthenium atomically dispersed in carbon outperforms platinum toward hydrogen evolution in alkaline media.

Hydrogen evolution reaction is an important process in electrochemical energy technologies. Herein, ruthenium and nitrogen codoped carbon nanowires are prepared as effective hydrogen evolution catalysts. The catalytic performance is markedly better than that of commercial platinum catalyst, with an overpotential of only -12 mV to reach the current density of 10 mV cm-2 in 1 M KOH and -47 mV in 0.1 M KOH. Comparisons with control experiments suggest that the remarkable activity is mainly ascribed to individual ruthenium atoms embedded within the carbon matrix, with minimal contributions from ruthenium nanoparticles. Consistent results are obtained in first-principles calculations, where RuCxNy moieties are found to show a much lower hydrogen binding energy than ruthenium nanoparticles, and a lower kinetic barrier for water dissociation than platinum. Among these, RuC2N2 stands out as the most active catalytic center, where both ruthenium and adjacent carbon atoms are the possible active sites

Incommensurate Phase of a Triangular Frustrated Heisenberg Model Studied via Schwinger-Boson Mean-Field Theory

We study a triangular frustrated antiferromagnetic Heisenberg model with nearest-neighbor interaction $J_{1}$ and third-nearest-neighbor interactions $J_{3}$ by means of Schwinger-boson mean-field theory. It is shown that an incommensurate phase exists in a finite region in the parameter space for an antiferromagnetic $J_{3}$ while $J_{1}$ can be either positive or negtaive. A detailed solution is presented to disclose the main features of this incommensurate phase. A gapless dispersion of quasiparticles leads to the intrinsic $T^{2}$-law of specific heat. The local magnetization is significantly reduced by quantum fluctuations (for S=1 case, a local magnetization is estimated as $m= \approx0.6223$). The magnetic susceptibility is linear in temperature at low temperatures. We address possible relevance of these results to the low-temperature properties of NiGa$_{2}$S$_{4}$. From a careful analysis of the incommensurate spin wave vector, the interaction parameters for NiGa$_{2}$S$_{4}$ are estimated as, $J_{1}\approx-3.8755$K and $J_{3}\approx14.0628$K, in order to account for the experimental data.Comment: 9pages, 3figure