Phylogenetic congruence between subtropical trees and their associated fungi.

Recent studies have detected phylogenetic signals in pathogen-host networks for both soil-borne and leaf-infecting fungi, suggesting that pathogenic fungi may track or coevolve with their preferred hosts. However, a phylogenetically concordant relationship between multiple hosts and multiple fungi in has rarely been investigated. Using next-generation high-throughput DNA sequencing techniques, we analyzed fungal taxa associated with diseased leaves, rotten seeds, and infected seedlings of subtropical trees. We compared the topologies of the phylogenetic trees of the soil and foliar fungi based on the internal transcribed spacer (ITS) region with the phylogeny of host tree species based on matK, rbcL, atpB, and 5.8S genes. We identified 37 foliar and 103 soil pathogenic fungi belonging to the Ascomycota and Basidiomycota phyla and detected significantly nonrandom host-fungus combinations, which clustered on both the fungus phylogeny and the host phylogeny. The explicit evidence of congruent phylogenies between tree hosts and their potential fungal pathogens suggests either diffuse coevolution among the plant-fungal interaction networks or that the distribution of fungal species tracked spatially associated hosts with phylogenetically conserved traits and habitat preferences. Phylogenetic conservatism in plant-fungal interactions within a local community promotes host and parasite specificity, which is integral to the important role of fungi in promoting species coexistence and maintaining biodiversity of forest communities

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Topological Gauge Structure and Phase Diagram for Weakly Doped Antiferromagnets

We show that the topological gauge structure in the phase string theory of the {\rm t-J} model gives rise to a global phase diagram of antiferromagnetic (AF) and superconducting (SC) phases in a weakly doped regime. Dual confinement and deconfinement of holons and spinons play essential roles here, with a quantum critical point at a doping concentration $x_c\simeq 0.043$. The complex experimental phase diagram at low doping is well described within such a framework.Comment: 4 pages, 2 figures, modified version, to appear in Phys. Rev. Let

Next-to-leading order QCD predictions for A0γA^{0}\gamma associated production at the CERN Large Hadron Collider

We calculate the complete next-to-leading-order (NLO) QCD corrections (including SUSY QCD corrections) to the inclusive total cross sections of the associated production processes $pp\rightarrow A^{0}\gamma+X$ in the minimal supersymmetric standard model (MSSM) at the CERN Large Hadron Collider (LHC). Our results show that the enhancement of the total cross sections from the NLO QCD corrections can reach $15$ for 200 GeV$<m_{A}<500$ GeV and $\tan\beta=50$. The scale dependence of the total cross section is improved by the NLO corrections, which is less than 5%. We also show the Monte Carlo simulation results for the $\tau^{+}\tau^{-}+\gamma$ signature including the complete NLO QCD effects, and find an observable signature above the standard model (SM) background for a normal luminosity of 100 fb$^{-1}$ at the LHC.Comment: Published version in Phys.Rev.

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Biokinetic Mechanisms Linked With Musculoskeletal Health Disparities: Stochastic Models Applying Tikhonov\u27s Theorem to Biomolecule Homeostasis.

Multiscale technology and advanced mathematical models have been developed to control and characterize physicochemical interactions, respectively, enhancing cellular and molecular engineering progress. Ongoing tissue engineering development studies have provided experimental input for biokinetic models examining the influence of static or dynamic mechanical stimuli (Saha, A. K., and Kohles, S. S., 2010, “A Distinct Catabolic to Anabolic Threshold Due to Single-Cell Nanomechanical Stimulation in a Cartilage Biokinetics Model,” J. Nanotechnol. Eng. Med., 1(3) p. 031005; 2010, “Periodic Nanomechanical Stimulation in a Biokinetics Model Identifying Anabolic and Catabolic Pathways Associated With Cartilage Matrix Homeostasis,” J. Nanotechnol. Eng. Med., 1(4), p. 041001). In the current study, molecular regulatory thresholds associated with specific disease disparities are further examined through applications of stochastic mechanical stimuli. The results indicate that chondrocyte bioregulation initiates the catabolic pathway as a secondary response to control anabolic processes. In addition, high magnitude loading produced as a result of stochastic input creates a destabilized balance in homeostasis. This latter modeled result may be reflective of an injurious state or disease progression. These mathematical constructs provide a framework for single-cell mechanotransduction and may characterize transitions between healthy and disease states