Efficient test for nonlinear dependence of two continuous variables

Integrating meta-analysis with a quantitative microbial risk assessment model to investigate Campylobacter contamination of broiler carcasses

This study investigated the prevalence and associated risk factors of Campylobacter in South Korean broilers using a random-effects meta-analysis. Subsequently, to facilitate the design of preventive measures, the prevalence estimate from the meta-analysis was incorporated into a stochastic risk assessment model to quantify the Campylobacter contamination levels on broiler carcasses. The baseline model was developed based on the most common practices along the South Korean broiler processing line, with no interventions. Meta-analysis results revealed Campylobacter prevalence across the chicken supply chain in the following order: farms (60.6 % [57.3-63.4]), retail markets (43.90 % [24.81-64.99]), slaughterhouses (27.71 % [18.56-39.21]), and processing plants (14.50 % [3.96-41.09]). The model estimated a 52 % (36.1-70.8) Campylobacter prevalence at the end of chilling, with an average contamination level of 4.62 (2.50-6.74) log CFU/carcass. Sensitivity analysis indicated that Campylobacter fecal shedding (r = 0.95) and the amount of feces on bird exteriors (r = 0.17) at pre-harvest were the main factors for carcass contamination, while soft scalding (r = -0.22) and air chilling (r = -0.12) can serve as critical control points (CCPs) at harvest. Scenario analysis indicated that a combination of hard scalding, inside-outside bird washing, spray washing, and chlorinated water immersion chilling can offer a 30.9 % reduction in prevalence and a reduction of 2.23 log CFU/carcass in contamination levels compared to the baseline model. Apart from disinfection and sanitation interventions carried out during meat processing, the implementation of robust control measures is indispensable to mitigate Campylobacter prevalence and concentration at broiler farms, thereby enhancing meat safety and public health. Furthermore, given the high Campylobacter prevalence in the retail markets, future studies should explore the potential risk of cross-contamination at post-harvest stage.

Pseudolaminar chaos from on-off intermittency

In finite-dimensional, chaotic, Lorenz-like wave-particle dynamical systems one can find diffusive trajectories, which share their appearance with that of laminar chaotic diffusion [Phys. Rev. Lett. 128, 074101 (2022)0031-900710.1103/PhysRevLett.128.074101] known from delay systems with lag-time modulation. Applying, however, to such systems a test for laminar chaos, as proposed in [Phys. Rev. E 101, 032213 (2020)2470-004510.1103/PhysRevE.101.032213], these signals fail such a test, thus leading to the notion of pseudolaminar chaos. The latter can be interpreted as integrated periodically driven on-off intermittency. We demonstrate that, on a signal level, true laminar and pseudolaminar chaos are hardly distinguishable in systems with and without dynamical noise. However, very pronounced differences become apparent when correlations of signals and increments are considered. We compare and contrast these properties of pseudolaminar chaos with true laminar chaos.

Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis

The nature of dependence between random variables has always been the subject of many statistical problems for over a century. Yet today, there is a great deal of research on this topic, especially focusing on the analysis of nonlinearity. Shannon mutual information has been considered to be the most comprehensive measure of dependence for evaluating total dependence, and several methods have been suggested for discerning the linear and nonlinear components of dependence between two variables. We, in this study, propose employing the Rényi and Tsallis mutual information measures for measuring total dependence because of their parametric nature. We first use a residual analysis in order to remove linear dependence between the variables, and then we compare the Rényi and Tsallis mutual information measures of the original data with that the lacking linear component to determine the degree of nonlinearity. A comparison against the values of the Shannon mutual information measure is also provided. Finally, we apply our method to the environmental Kuznets curve (EKC) and demonstrate the validity of the EKC hypothesis for Eastern Asian and Asia-Pacific countries.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

Background

Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles.

Methods

We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision.

Results

The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences.

Conclusions

This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08437-4.

Influence of speciation distribution and particle size on heavy metal leaching from MSWI fly ash

Fly ash from municipal solid waste incineration (MSWI-FA) contains leachable heavy metals. In the present study the correlations between heavy metal content, particle size, speciation distribution with respect to water leaching are investigated, using a combination of solid-state bulk analytical techniques, leaching treatments, sequential extractions and thermodynamic geochemical modelling. Among the analyzed heavy metals, Zn and Pb are the most abundant in any grain size class, followed by Cu, Cr, Cd and Ni, with concentration that tends to increase with a decrease of the grain size. The phase composition is constituted of salt (halite, sylvite, anhydrite and syngenite), which provide the main minerals regardless of the particle size class; calcite, quartz and gehlenite occur in comparatively lower amounts, while 50% wt is composed of amorphous fraction. Heavy metal leaching is strongly correlated to speciation distribution, and in particular to the fraction (F1) associated with salt, carbonate and weak surface sorption. Leaching from speciation due to surface complexation on Al/Fe (hydr)oxide becomes relevant at acidic regime. Particle size and heavy metal content, in turn, moderately correlate with leaching. The F1-speciation as a function of particle size does not exhibit a definite trend shared by all heavy metals under investigation. This suggests that i) differences in speciation distribution, rather than bare heavy metal content or particle size, govern leaching from MSWI-FA; ii) F1 can be regarded as a marker of the potential heavy metal leaching; iii) a comparatively modest efficiency in managing MSWI-FA is expected from grain size separation strategies.

An empirical test of the relationship between the bootstrap and likelihood ratio support in maximum likelihood phylogenetic analysis

In maximum likelihood (ML), the support for a clade can be calculated directly as the likelihood ratio (LR) or log-likelihood difference (S, LLD) of the best trees with and without the clade of interest. However, bootstrap (BS) clade frequencies are more pervasive in ML phylogenetics and are almost universally interpreted as measuring support. In addition to theoretical arguments against that interpretation, BS has several undesirable attributes for a support measure. For example, it does not vary in proportion to optimality or identify clades that are rejected by the evidence and can be overestimated due to missing data. Nevertheless, if BS is a reliable predictor of S, then it might be an efficient indirect method of measuring support-an attractive possibility, given the speed of many BS implementations. To assess the relationship between S and BS, we analyzed 106 empirical datasets retrieved from TreeBASE. Also, to evaluate the degree to which S and BS are affected by the number of replicates during suboptimal tree searches for S and pseudoreplicates during BS estimation, we randomly selected 5 of the 106 datasets and analyzed them using variable numbers of replicates and pseudoreplicates, respectively. The correlation between S and BS was extremely weak in the datasets we analyzed. Increasing the number of replicates during tree search decreased the estimated values of S for most clades, but the magnitude of change was small. In contrast, although increasing pseudoreplicates affected BS values for only approximately 40% of clades, values both increased and decreased, and they did so at much greater magnitudes. Increasing replicates/pseudoreplicates affected the rank order of clades in each tree for both S and BS. Our findings show decisively that BS is not an efficient indirect method of measuring support and suggest that even quite superficial searches to calculate S provide better estimates of support.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

Correlation-Based Framework for Extraction of Insights from Quantum Chemistry Databases: Applications for Nanoclusters

The amount of quantum chemistry (QC) data is increasing year by year due to the continuous increase of computational power and development of new algorithms. However, in most cases, our atom-level knowledge of molecular systems has been obtained by manual data analyses based on selected descriptors. In this work, we introduce a data mining framework to accelerate the extraction of insights from QC datasets, which starts with a featurization process that converts atomic features into molecular properties (AtoMF). Then, it employs correlation coefficients (Pearson, Spearman, and Kendall) to investigate the AtoMF features relationship with a target property. We applied our framework to investigate three nanocluster systems, namely, Pt_nTM_55-n, Ce_nZr_15-nO₃₀, and (CH_n + mH)/TM₁₃. We found several interesting and consistent insights using Spearman and Kendall correlation coefficients, indicating that they are suitable for our approach; however, our results indicate that the Pearson coefficient is very sensitive to outliers and should not be used. Moreover, we highlight problems that can occur during this analysis and discuss how to handle them. Finally, we make available a new Python package that implements the proposed QC data mining framework, which can be used as is or modified to include new features.

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.

Scale-Free Features in Collective Robot Foraging

In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm—such as number of communication links or time spent in resting state—spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items.

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

BACKGROUND

Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1).

RESULTS

We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets).

CONCLUSIONS

We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.

A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models

Murine transplantation models are used extensively to research immunological rejection and tolerance. Here we studied both murine heart and liver allograft models using microarray technology. We had difficulty in identifying genes related to acute rejections expressed in both heart and liver transplantation models using two standard methodologies: Student's t test and linear models for microarray data (Limma). Here we describe a new method, standardized fold change (SFC), for differential analysis of microarray data. We estimated the performance of SFC, the t test and Limma by generating simulated microarray data 100 times. SFC performed better than the t test and showed a higher sensitivity than Limma where there is a larger value for fold change of expression. SFC gave better reproducibility than Limma and the t test with real experimental data from the MicroArray Quality Control platform and expression data from a mouse cardiac allograft. Eventually, a group of significant overlapping genes was detected by SFC in the expression data of mouse cardiac and hepatic allografts and further validated with the quantitative RT‐PCR assay. The group included genes for important reactions of transplantation rejection and revealed functional changes of the immune system in both heart and liver of the mouse model. We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.

Bagging Nearest-Neighbor Prediction independence Test: an efficient method for nonlinear dependence of two continuous variables

Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for nonlinear dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction independence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test nonlinear correlation in real world applications.

DTW-MIC Coexpression Networks from Time-Course Data

When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy.