Human Parainfluenza Virus 3 Phosphoprotein Is a Tetramer and Shares Structural and Interaction Features with Ebola Phosphoprotein VP35

The human parainfluenza virus 3 (HPIV3) poses a risk for pneumonia development in young children and immunocompromised patients. To investigate mechanisms of HPIV3 pathogenesis, we characterized the association state and host protein interactions of HPIV3 phosphoprotein (HPIV3 P), an indispensable viral polymerase cofactor. Sequence analysis and homology modeling predict that HPIV3 P possesses a long, disordered N-terminal tail (PTAIL) a coiled-coil multimerization domain (PMD), similar to the well-characterized paramyxovirus phosphoproteins from measles and Sendai viruses. Using a recombinantly expressed and purified construct of PMD and PTAIL, we show that HPIV3 P in solution is primarily an alpha-helical tetramer that is stable up to 60 °C. Pulldown and isothermal titration calorimetry experiments revealed that HPIV3 P binds the host hub protein LC8, and turbidity experiments demonstrated a new role for LC8 in increasing the solubility of HPIV3 P in the presence of crowding agents such as RNA. For comparison, we show that the multimerization domain of the Zaire Ebola virus phosphoprotein VP35 is also a tetramer and binds LC8 but with significantly higher affinity. Comparative analysis of the domain architecture of various virus phosphoproteins in the order Mononegavirales show multiple predicted and verified LC8 binding motifs, suggesting its prevalence and importance in regulating viral phosphoprotein structures. Our work provides evidence for LC8 binding to phosphoproteins with multiple association states, either tetrameric, as in the HPIV3 and Ebola phosphoproteins shown here, or dimeric as in rabies virus phosphoprotein. Taken together the data suggest that the association states of a virus-specific phosphoprotein and the complex formed by binding of the phosphoprotein to host LC8 are important regulators of viral function.

Extended Graphical Lasso for Multiple Interaction Networks for High Dimensional Compositional Data

Compositional data are quantitative descriptions of the parts of some whole, conveying relative information, which are ubiquitous in many fields. There has been a spate of interest in association networks for such data in biological and medical research, for example, microbial interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional compositional data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem using log ratios of compositional data and solve it with an alternating direction method of multipliers (ADMM) algorithm. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease.Author summaryReconstruction of multiple association networks from high dimensional compositional data is an important topic, especially in biology. Previous studies focused on estimating different networks and detecting common hubs among all classes. Integration of information over different classes of data while allowing difference in the hub nodes is also biologically plausible. Therefore, we propose a method, EDOHA, to jointly construct multiple interaction networks with capacity in finding different hub networks for each class of data. Simulation studies show the better performance over conventional methods. The method has been demonstrated in three real world data.

Mining and analysis of multiple association rules between the Xining loess collapsibility and physical parameters

Collapsibility determination in loess area is expensive, and it also requires a large amount of experimentation. This paper aims to find the association rules between physical parameters and collapsibility of the loess in Xining through the method of data mining, so to help researchers predict the collapsibility of loess. Related physical parameters of loess collapsibility, collected from 1039 samples, involve 13 potential influence factors. According to Grey Relational Analysis, the key influence factors that lead to collapsing are identified from these potential influence factors. Subsequently, take the key influence factors, δs (coefficient of collapsibility) and δzs (coefficient of collapsibility under overburden pressure) as input items, and use the Apriori algorithm to find multiple association rules between them. Then, through analysing the results of association rules between these key influence factors and collapsibility, the evaluation criteria for collapsibility in this area is proposed, which can be used to simplify the workload of determining collapsibility. Finally, based on these research results, recommendations for projects construction were made to ensure the safety of construction in the area.

Efficient Rule Generation for Associative Classification

Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

The structure of developing semantic networks: Evidence from single and multiple nominal word associations in young monolingual and bilingual readers

ABSTRACTThe present study focuses on the effect of an important methodological choice in word association studies in children: the elicitation of single versus multiple responses. This choice has been shown to affect the numbers and types of associations adults produce, however, little is known about how it affects children’s word associations. A total of 11,725 associations to 80 nouns from 207 monolingual and bilingual minority children were classified according to a detailed coding system, and differences between the semantic characteristics of first, second, and third responses were examined. We show that in children as well, the multiple association task elicits more and qualitatively different responses, resulting in more diversified semantic networks surrounding the stimulus nouns. On the speaker level, reading comprehension scores were related differently to initial and later responses, suggesting a more complex measure of semantic knowledge emerges from the multiple word association task. No differences were found between monolingual and bilingual children’s associative preferences. We argue that the multiple association task produces more detailed data on language users’ semantic networks than the single association task, and suggest a number of ways in which this task could be employed in future research.