Comparison of Probabilistic Boolean Network and Dynamic Bayesian Network Approaches for Inferring Gene Regulatory Networks

Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency. Results: In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches. Conclusions: The comparison indicates that both approaches had good performance in modeling the gene regulatory networks. The accuracy in terms of recall and precision can be improved if a smaller subset of genes is selected for inferring GRNs. The accuracy of both approaches is dependent upon the number of selected genes and time points of gene samples. In all tested cases, DBN identified more gene interactions and gave better recall than PBN

Investigation of Haemophilus parasuis from healthy pigs in China

Haemophilus parasuis is a common colonizer of the upper respiratory tract of swine and frequently causes disease, especially in weaner pigs. To date, limited epidemiological data was available for H. parasuis from healthy pigs, which might be carriers of potential pathogenic strains. In this study, from September 2016 to October 2017, we investigated the prevalence and characteristics of H. parasuis from healthy pigs in China. Totally, we obtained 244 isolates from 1675 nasal samples from 6 provinces. H. parasuis isolation was more successful in weaner pigs (22.6%, 192/849), followed by finisher pigs (9.3%, 43/463), and sows (2.5%, 9/363). The most prevalent serovars were 7 (20.1%, 49/244), followed by 3 (14.8%, 36/244), 2 (14.3%, 35/244), 11 (12.7%, 31/244), 5/12 (5.7%, 14/244) and 4 (2.5%, 6/244). Bimodal or multimodal distributions of MICs were observed for most of the tested drugs, which suggested the presence of non-wild type populations. It was noted that the MIC90 values of tilmicosin (64 μg/ml) was relatively higher than that reported in previous studies. Our results suggest that: 1) potentially pathogenic serovars of H. parasuis are identified in healthy pigs, and 2) elevated MICs and presence of mechanisms of resistance not yet described for clinically important antimicrobial agents would increase the burden of disease caused by H. parasuis.info:eu-repo/semantics/acceptedVersio

State Space Model with hidden variables for reconstruction of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.</p> <p>Method</p> <p>True GRNs and synthetic gene expression datasets were generated by using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.</p> <p>Results</p> <p>Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.</p> <p>Conclusion</p> <p>This study provides useful information in handling the hidden variables and improving the inference precision.</p

SeqAssist: A Novel Toolkit For Preliminary Analysis of Next-Generation Sequencing Data

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ( Sequencing Assistant or SA). Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: (1) the SA_RunStats workflow generates basic statistics about an NGS dataset, including numbers of raw, cleaned, redundant and unique reads, redundancy rate, and a list of unique sequences with length and read count; (2) the SA_Run2Ref workflow estimates the breadth, depth and evenness of genome-wide coverage of the NGS dataset at a nucleotide resolution; and (3) the SA_Run2Run workflow compares two NGS datasets to determine the redundancy (overlapping rate) between the two NGS runs. Statistics produced by SeqAssist or derived from SeqAssist output files are designed to inform the user: whether, what percentage, how many times and how evenly a genomic locus (i.e., gene, scaffold, chromosome or genome) is covered by sequencing reads, how redundant the sequencing reads are in a single run or between two runs. These statistics can guide the user in evaluating the quality of a DNA library prepared for RNA-Seq or genome (re-)sequencing and in deciding the number of sequencing runs required for the library. We have tested SeqAssist using a synthetic dataset and demonstrated its main features using multiple NGS datasets generated from genome re-sequencing experiments. Conclusions: SeqAssist is a useful and informative tool that can serve as a valuable assistant to a broad range of investigators who conduct genome re-sequencing, RNA-Seq, or de novo genome sequencing and assembly experiments

The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data

Terrestrial ecosystems greatly contribute to carbon (C) emission reduction targets through photosynthetic C uptake.Net primary production (NPP) represents the amount of atmospheric C fixed by plants and accumulated as biomass. The Three-North Shelterbelt Program (TNSP) zone accounts for more than 40% of China’s landmass. This zone has been the scene of several large-scale ecological restoration efforts since the late 1990s, and has witnessed significant changes in climate and human activities.Assessing the relative roles of different causal factors on NPP variability in TNSP zone is very important for establishing reasonable local policies to realize the emission reduction targets for central government. In this study, we examined the relative roles of drought and land cover conversion(LCC) on inter-annual changes of TNSP zone for 2001–2010. We applied integrated correlation and decomposition analyses to a Standardized Evapotranspiration Index (SPEI) and MODIS land cover dataset. Our results show that the 10-year average NPP within this region was about 420 Tg C. We found that about 60% of total annual NPP over the study area was significantly correlated with SPEI (p<0.05). The LCC-NPP relationship, which is especially evident for forests in the south-central area, indicates that ecological programs have a positive impact on C sequestration in the TNSP zone. Decomposition analysis generally indicated that the contributions of LCC, drought, and other Natural or Anthropogenic activities (ONA) to changes in NPP generally had a consistent distribution pattern for consecutive years. Drought and ONA contributed about 74% and 23% to the total changes in NPP, respectively, and the remaining 3% was attributed to LCC. Our results highlight the importance of rainfall supply on NPP variability in the TNSP zone

Nonlinear and threshold effects of built environment on older adults’ walking duration: do age and retirement status matter?

IntroductionWalking plays a crucial role in promoting physical activity among older adults. Understanding how the built environment influences older adults’ walking behavior is vital for promoting physical activity and healthy aging. Among voluminous literature investigating the environmental correlates of walking behaviors of older adults, few have focused on walking duration across different age groups and life stages, let alone examined the potential nonlinearities and thresholds of the built environment.MethodsThis study employs travel diary from Zhongshan, China and the gradient boosting decision trees (GBDT) approach to disentangle the age and retirement status differences in the nonlinear and threshold effects of the built environment on older adults’ walking duration.ResultsThe results showed built environment attributes collectively contribute 57.37% for predicting older adults’ walking duration, with a higher predicting power for the old-old (70+ years) or the retired. The most influencing built environment attribute for the young-old (60–70 years) is bus stop density, whereas the relative importance of population density, bus stop density, and accessibility to green space or commercial facilities is close for the old-old. The retired tend to walk longer in denser-populated neighborhoods with better bus service, but the non-retired are more active in walking in mixed-developed environments with accessible commercial facilities. The thresholds of bus stop density to encourage walking among the young-old is 7.8 counts/km2, comparing to 6 counts/km2 among the old-old. Regarding the green space accessibility, the effective range for the non-retired (4 to 30%) is smaller than that of the retired (12 to 45%).DiscussionOverall, the findings provide nuanced and diverse interventions for creating walking-friendly neighborhoods to promote walking across different sub-groups of older adults

Entering the Era of Earth Observation-Based Landslide Warning Systems: A novel and exciting framework

Landslide early warning remains a grand challenge due to the high human cost of catastrophic landslides globally and the difficulty of identifying a diverse range of landslide triggering factors. There have been only a very limited number of success stories to date. However, recent advances in earth observation (EO) from ground, aircraft and space have dramatically improved our ability to detect and monitor active landslides and a growing body of geotechnical theory suggests that prefailure behavior can provide clues to the location and timing of impending catastrophic failures. In this paper, we use two recent landslides in China as case studies, to demonstrate that (i) satellite radar observations can be used to detect deformation precursors to catastrophic landslide occurrence, and (ii) early warning can be achieved with real-time in-situ observations. A novel and exciting framework is then proposed to employ EO technologies to build an operational landslide early warning system.This work was supported by the National Natural Science Foundation of China under grants 41801391, 41874005, and 41929001; the National Science Fund for Outstanding Young Scholars of China under grant 41622206; the Fund for International Cooperation under grant NSFCRCUK_NERC; Resilience to Earthquake-Induced Landslide Risk in China under grant 41661134010; the open fund of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2018-5-3-E); Sichuan Science and Technology Plan Project under grant 2019YJ0404; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under grant SKLGP2018Z019; the Spanish Ministry of Economy and Competitiveness, the State Agency of Research, and the European Funds for Regional Development under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P; and the Spanish Ministry of Education, Culture, and Sport under project PRX17/00439. This work was also partially supported by the U.K. Natural Environment Research Council through the Center for the Observation and Modeling of Earthquakes, Volcanoes, and Tectonics under come30001 and the Looking Inside the Continents From Space and Community Earthquake Disaster Risk Reduction in China projects under NE/K010794/1 and NE/N012151/1, respectively, and by the European Space Agency through the ESA-MOST DRAGON-4 project (32244 [4]). Roland Bürgmann acknowledges support by the NASA Earth Surface and Interior focus area

Laser-based defect characterization and removal process for manufacturing fused silica optic with high ultraviolet laser damage threshold

Residual processing defects during the contact processing processes greatly reduce the anti-ultraviolet (UV) laser damage performance of fused silica optics, which significantly limited development of high-energy laser systems. In this study, we demonstrate the manufacturing of fused silica optics with a high damage threshold using a CO2 laser process chain. Based on theoretical and experimental studies, the proposed uniform layer-by-layer laser ablation technique can be used to characterize the subsurface mechanical damage in three-dimensional full aperture. Longitudinal ablation resolutions ranging from nanometers to micrometers can be realized; the minimum longitudinal resolution is < 5 nm. This technique can also be used as a crack-free grinding tool to completely remove subsurface mechanical damage, and as a cleaning tool to effectively clean surface/subsurface contamination. Through effective control of defects in the entire chain, the laser-induced damage thresholds of samples fabricated by the CO2 laser process chain were 41% (0% probability) and 65.7% (100% probability) higher than those of samples fabricated using the conventional process chain. This laser-based defect characterization and removal process provides a new tool to guide optimization of the conventional finishing process and represents a new direction for fabrication of highly damage-resistant fused silica optics for high-energy laser applications