Analysis of genetic structure and function of clustered regularly interspaced short palindromic repeats loci in 110 Enterococcus strains

Clustered regularly interspaced short palindromic repeats (CRISPR) and their CRISPR-associated proteins (Cas) are an adaptive immune system involved in specific defenses against the invasion of foreign mobile genetic elements, such as plasmids and phages. This study aims to analyze the gene structure and to explore the function of the CRISPR system in the Enterococcus genome, especially with regard to drug resistance. The whole genome information of 110 enterococci was downloaded from the NCBI database to analyze the distribution and the structure of the CRISPR-Cas system including the Cas gene, repeat sequences, and spacer sequence of the CRISPR-Cas system by bioinformatics methods, and to find drug resistance-related genes and analyze the relationship between them and the CRISPR-Cas system. Multilocus sequence typing (MLST) of enterococci was performed against the reference MLST database. Information on the drug resistance of Enterococcus was retrieved from the CARD database, and its relationship to the presence or absence of CRISPR was statistically analyzed. Among the 110 Enterococcus strains, 39 strains (35.45%) contained a complete CRISPR-Cas system, 87 CRISPR arrays were identified, and 62 strains contained Cas gene clusters. The CRISPR system in the Enterococcus genome was mainly type II-A (59.68%), followed by type II-C (33.87%). The phylogenetic analysis of the cas1 gene sequence was basically consistent with the typing of the CRISPR-Cas system. Of the 74 strains included in the study for MLST typing, only 19 (25.68%) were related to CRISPR-Cas typing, while the majority of the strains (74.32%) of MLST typing were associated with the untyped CRISPR system. Additionally, the CRISPR-Cas system may only be related to the carrying rate of some drug-resistant genes and the drug-resistant phenotype. In conclusion, the distribution of the enterococcus CRISPR-Cas system varies greatly among different species and the presence of CRISPR loci reduces the horizontal transfer of some drug resistance genes

(E)-1-[2-Hy­droxy-4,6-bis­(meth­oxy­meth­oxy)phen­yl]-3-[3-meth­oxy-4-(meth­oxy­meth­oxy)phen­yl]prop-2-en-1-one

The title compound, C22H26O9, crystallizes with two independent mol­ecules in the asymmetric unit in which the dihedral angles between the two benzene rings are 21.4 (2) and 5.1 (2)°. An intra­molecular O—H⋯O hydrogen bond occurs in each mol­ecule. Inter­molecular C—H⋯O hydrogen bonds stabilize the crystal structure

Disentangling the complex gene interaction networks between rice and the blast fungus identifies a new pathogen effector

Studies focused solely on single organisms can fail to identify the networks underlying host–pathogen gene-for-gene interactions. Here, we integrate genetic analyses of rice (Oryza sativa, host) and rice blast fungus (Magnaporthe oryzae, pathogen) and uncover a new pathogen recognition specificity of the rice nucleotide-binding domain and leucine-rich repeat protein (NLR) immune receptor Pik, which mediates resistance to M. oryzae expressing the avirulence effector gene AVR-Pik. Rice Piks-1, encoded by an allele of Pik-1, recognizes a previously unidentified effector encoded by the M. oryzae avirulence gene AVR-Mgk1, which is found on a mini-chromosome. AVR-Mgk1 has no sequence similarity to known AVR-Pik effectors and is prone to deletion from the mini-chromosome mediated by repeated Inago2 retrotransposon sequences. AVR-Mgk1 is detected by Piks-1 and by other Pik-1 alleles known to recognize AVR-Pik effectors; recognition is mediated by AVR-Mgk1 binding to the integrated heavy metal-associated (HMA) domain of Piks-1 and other Pik-1 alleles. Our findings highlight how complex gene-for-gene interaction networks can be disentangled by applying forward genetics approaches simultaneously to the host and pathogen. We demonstrate dynamic coevolution between an NLR integrated domain and multiple families of effector proteins

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks

An Unsupervised Learning Approach to Social Circles Detection in Ego Bluetooth Proximity Network

Understanding a user's social interactions in the physical world proves important in building context-aware ubiquitous applications. A good way towards that objective is to categorize people to whom a user is socially related into what we call as social circles. In this note, we propose a novel unsupervised approach that learns from the Bluetooth (BT) sensed data recording one's dynamic proximity relations with others to identify her social circles, each of which is formed along a semantically coherent aspect. For each circle we learn its members as well as the temporal dimensions along which it is formed. Our method is innovative in that it well over- comes data sparsity by information sharing, and allows for circle overlaps which is common in reality. Experiments on real data demonstrate the effectiveness of our method, and also show the potentials of relational mobile data in sensing personal behaviors beyond personal data. Copyright © 2013 AC

Time-dependent trajectory regression on road networks via multi-task learning

Road travel costs are important knowledge hidden in largescale GPS trajectory data sets, the discovery of which can benefit many applications such as intelligent route planning and automatic driving navigation. While there are previous studies which tackled this task by modeling it as a regression problem with spatial smoothness taken into account, they unreasonably assumed that the latent cost of each road remains unchanged over time. Other works on route planning and recommendation that have considered temporal factors simply assumed that the temporal dynamics be known in advance as a parametric function over time, which is not faithful to reality. To overcome these limitations, in this paper, we propose an extension to a previous static trajectory regression framework by learning the temporal dynamics of road travel costs in an innovative non-parametric manner which can effectively overcome the temporal sparsity problem. In particular, we unify multiple different trajectory regression problems in a multi-task framework by introducing a novel crosstask regularization which encourages temporal smoothness on the change of road travel costs. We then propose an efficient block coordinate descent method to solve the resulting problem by exploiting its separable structures and prove its convergence to global optimum. Experiments conducted on both synthetic and real data sets demonstrate the effectiveness of our method and its improved accuracy on travel time prediction. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Investigation on the Mathematical Relation Model of Structural Reliability and Structural Robustness

Structural reliability and structural robustness, from different research fields, are usually employed for the evaluative analysis of building and civil engineering structures. Structural reliability has been widely used for structural analysis and optimization design, while structural robustness is still in rapid development. Several dimensionless evaluation indexes have been defined for structural robustness so far, such as the structural reliability-based redundancy index. However, these different evaluation indexes are usually based on subjective definitions, and they are also difficult to put into engineering practice. The mathematical relational model between structural reliability and structural robustness has not been established yet. This paper is a quantitative study, focusing on the mathematical relation between structural reliability and structural robustness so as to further develop the theory of structural robustness. A strain energy evaluation index for structural robustness is introduced firstly by considering the energy principle. The mathematical relation model of structural reliability and structural robustness is then derived followed by a further comparative study on sensitivity, structural damage, and random variation factor. A cantilever beam and a truss beam are also presented as two case studies. In this study, a parabolic curve mathematical model between structural reliability and structural robustness is established. A significant variation trend for their sensitivities is also observed. The complex interaction mechanism of the joint effect of structural damage and random variation factor is also reflected. With consideration of the variation trend of the structural reliability index that is affected by different degrees of structural damage (mild impairment, moderate impairment, and severe impairment), a three-stage framework for structural life-cycle maintenance management is also proposed. This study can help us gain a better understanding of structural robustness and structural reliability. Some practical references are also provided for the better decision-making of maintenance and management departments

Robust bayesian inverse reinforcement learning with sparse behavior noise

Inverse reinforcement learning (1RL) aims to recover the reward function underlying a Markov Decision Process from behaviors of experts in support of decision-making. Most recent work on IRL assumes the same level of trustworthiness of all expert behaviors, and frames IRL as a process of seeking reward function that makes those behaviors appear (near)-optimal. However, it is common in reality that noisy expert behaviors disobeying the optimal policy exist, which may degrade the IRL performance significantly. To address this issue, in this paper, we develop a robust IRL framework that can accurately estimate the reward function in the presence of behavior noise. In particular, we focus on a special type of behavior noise referred to as sparse noise due to its wide popularity in real-world behavior data. To model such noise, we introduce a novel latent variable characterizing the reliability of each expert action and use Laplace distribution as its prior. We then devise an EM algorithm with a novel variational inference procedure in the E-step, which can automatically identify and remove behavior noise in reward learning. Experiments on both synthetic data and real vehicle routing data with noticeable behavior noise show significant improvement of our method over previous approaches in learning accuracy, and also show its power in de-noising behavior data

Influence of Freeze-Thaw Cycles on Mechanical Response of Levee Pavement

The freeze-thaw cycles cause deterioration in mechanical properties of levee soil and further endanger the pavement structure on the embankment. This study attempts to comprehensively understand the mechanical response of pavement after freeze-thaw cycles. In this paper, the freeze-thaw cycles test under an open system was carried out, and then the triaxial compression test was conducted. Based on the test results, the effects of freeze-thaw cycles, temperature range, initial dry density, and initial moisture content of embankment soil on the mechanical response of road structure after freeze-thaw were calculated and analyzed. Finally, the stability of the slope of the levee was evaluated. The results show that the number of freeze-thaw cycles has the most significant impact on the mechanical response of pavement, the stress and strain of the structural layers vary in different ranges, and the pavement deflection increases by 5 times after 7 freeze-thaw cycles. However, the initial dry density and initial moisture content of the soil have little influence on the pavement structure, and the temperature range will exert an influence when it exceeds a certain threshold