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Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship(DDR) for the human MADs, and whether there are consistent differences in the taxonomic compositionof microbiomes sampled from healthy versusdiseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versusdiseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease.

Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship(DDR) for the human MADs, and whether there are consistent differences in the taxonomic compositionof microbiomes sampled from healthy versusdiseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versusdiseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease.

Cell-penetrating peptides (CPPs), have been proven as important drug-delivery vehicles, demonstrating the potential as therapeutic candidates. The past decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine-learning-based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and, for the first time, construct a two-layer prediction framework based on the random forest algorithm. The jackknife results on benchmark data sets show that the proposed CPPred-RF is at least competitive with the state-of-the-art predictors. Moreover, we establish the first online Web server in terms of predicting CPPs and their uptake efficiency simultaneously. It is freely available at http://server.malab.cn/CPPred-RF.

We propose a unified definition for reliability, survivability and resilience, which aims to synthesise the elements from existing definitions for survivability and also takes inspirations from the handicap principle and ecological stability. The unified definition is a four-tuple in the form of Survivability = [ Resistance , Resilience , Persistence and Failure-Counter ]. Resistance is largely equivalent to reliability and can be modelled with survival analysis (including competing risk analysis and multivariate survival analysis), which has some unique advantages over traditional reliability analysis such as dealing with censoring. Resilience can be formulated as a first passage time (FPT) problem; persistence can be defined as the minimum resistance level that a survivable system must maintain to be survivable. The unified definition offers a conceptual framework in which reliability and survivability can be quantitatively analysed with the approaches that we previously developed such as dynamic hybrid fault models, three-layer survivability analysis and extended evolutionary game theory.

In a classic monograph titled Ecological Theatre and Evolutionary Play, Hutchinson (1966) tried to ‘break’ the genetics dominance over the study of evolution and emphasised that natural selection happens in the context of environment. His vision clearly indicated the inseparability between evolution and ecology. In other words, without ecological ‘theatre’, natural selection has no place to act and evolution cannot happen. Perhaps following a similar development trajectory of evolutionary study in biology, much of the evolutionary computing (EC) has been inspired by genetics-centred evolutionary theory. The major objective of this paper is to suggest and review some inspirations from several fields of population ecology and evolutionary ecology to evolutionary computing (EC). A minor one is to demonstrate that, at least in the case of EC population size setting, the dynamic population, which is inspired by the natural population dynamics, indeed outperforms the traditional fixed-size population in the genetic algorithms (GA).

The common usage of the term frailty , which literally means the condition (quality) of being frail or a fault, may be too ambiguous to be very useful for characterising network security. Nevertheless, the mathematical concept of frailty , which originatedin the study of aging and demography, may offer a powerful approach for analysing the risks in network security and survivability. This is because network security analysis is essentially a risk analysis problem, and identifying risks, especially those that are uncertain, latent, unobserved or unobservable (UUUR) (Ma, 2008), and further analysing the potential dependence between those risks are two critical challenges for risk analysis in any field. Frailty modelling excels in addressing these two challenges. When it is integrated with dynamic hybrid fault models (DHF) and extended evolutionary game theory (EEGT) (Ma and Krings, 2011), frailty modelling should provide powerful approaches for analysing risks in network security and survivability.

In a classic monograph titled Ecological Theatre and Evolutionary Play, Hutchinson (1966) tried to 'break' the genetics dominance over the study of evolution and emphasised that natural selection happens in the context of environment. His vision clearly indicated the inseparability between evolution and ecology. In other words, without ecological 'theatre', natural selection has no place to act and evolution cannot happen. Perhaps following a similar development trajectory of evolutionary study in biology, much of the evolutionary computing (EC) has been inspired by genetics-centred evolutionary theory. The major objective of this paper is to suggest and review some inspirations from several fields of population ecology and evolutionary ecology to evolutionary computing (EC). A minor one is to demonstrate that, at least in the case of EC population size setting, the dynamic population, which is inspired by the natural population dynamics, indeed outperforms the traditional fixed-size population in the genetic algorithms (GA).

The objective of this study is to develop an integrated modelling approach to network intrusion detection with three multivariate statistical methods: Block Clustering (BC) Analysis, Generalised Logistic Regression (GLR) and Linear Discriminant Analysis (LDA). A pipeline processing strategy with BC followed by either GLR or LDA is attempted in order to automate the intrusion detection process. The preliminary testing results show that the integration of BC and LDA is very promising, but that of BC and GLR is uncertain. Essentially, BC offers a classification algorithm, and LDA or GLR further assesses the results pipelined from BC and enables a judgement to be made (e.g., intrusive, suspicious, or normal). Although clustering techniques have been widely utilised for intrusion detection from the very beginning of the field, to the best of our knowledge, BC has not been applied in intrusion detection or computer science previously. The two-way joining strategy of BC in cluster detection is especially desirable for intrusion detection since information from both data cases and variables (features) are synthesised to form block clusters, while other clustering methods often only consider information from either data cases or variables. The paper also discusses the justification for our choice of the three statistical methods. The choice is largely determined by two of the most obvious properties of intrusion audit data: most variables in intrusion detection data are categorical, rather than continuous; the probability distributions of these variables usually are not normally distributed. In perspective, we suggest that the integration of BC with Independent Component Analysis (ICA) (which has been successfully utilised in speech recognition, brain imaging and intrusion detection in combination with other statistical methods) is likely to offer a mutually complementary approach. We further suggest that the integration of the approach developed in this paper with Multidimensional Scaling (MDS) may produce an effective technology for building visualised real-time intrusion detection systems.

The vaginal microbiome is dynamic, varying over time in composition and function with implications for women’s health.

We applied 14 insect development models, both deterministic and distributed, to describe Russian wheat aphid, Diuraphis noxia (Mordvilko) (Hemiptera: Aphididae), development and phenology. The Russian wheat aphid developmental data were from a laboratory experiment of 25 combinatorial treatments of five temperatures and five spring barley, Hordeum vulgare L., plant growth stages. The developmental times of 1,800 individual Russian wheat aphids at various stages were recorded in the experiment. We first compared 11 deterministic development models and discussed some problems associated with the fitting of these models. Not all nonlinear models could be fitted to every Russian wheat aphid stage. The results show that Stinner’s model overall best fit Russian wheat aphid developmental rate data, as judged by mean square error (MSE) and successful convergence. However, we observed a seemingly inescapable dilemma: when one introduces more complex nonlinear models to increase the descriptive power of models (with the hope that model parameters have some biological meanings), the more difficult it is to successfully fit the model. Even if the model is fitted successfully, the values of the model parameters may well be beyond biologically meaningful ranges. Furthermore, we believe that a potentially more serious trap associated with complex nonlinear model is the extreme low tolerance of model parameters. Our results show that the extreme low tolerance associated with the parameter estimate may occur even if the MSE of model fitting is very small. The second part of our results presented same-shape distribution models for Russian wheat aphid development. Stinner’s model performed well and successfully fitted all 22 data sets, whereas the Weibull distribution and logistic model only succeeded in 17 and six of 22 data sets, respectively. Finally, Population Model Design System software was used to simulate Russian wheat aphid phenology based on the integration of deterministic nonlinear developmental rate models and same-shape distributions models.