Zhanshan (Sam) Ma

Temporal Dynamics of the Human Vaginal Microbiota

The vaginal microbiome is dynamic, varying over time in composition and function with implications for women’s health. What’s Up with Vaginal Microbes? The ability to properly identify women at risk of acquiring sexually transmitted infectious diseases or who might suffer from adverse obstetric sequelae is a critical first step in reducing their incidence and the unnecessary use of antibiotics. Currently, patients undergo a clinical examination of the vagina that includes measuring the pH and evaluating the amount and type of discharge and the presence of odor. These criteria are thought to be surrogates for the presence of an “abnormal” vaginal microbiota. Although these kinds of tests, done only once, could be used to diagnose conditions such as bacterial vaginosis, it is debatable whether they are accurate predictors of risk because little is known about how the composition and function of the vaginal microbiome changes over time. Previous studies have established that in healthy asymptomatic women, five types of vaginal microbiota exist that differ in the kinds of microbes they contain. It was thought that each type carries its own risks and particular response to environmental disturbances, such as sexual activity or hygiene practices. In an exciting new study, Gajer and colleagues now describe changes in the identity and abundance of bacteria in the vaginal communities of 32 women by analyzing vaginal samples obtained twice weekly over a 16-week period. The kinds of bacteria present in the samples were identified by classifying thousands of 16S rRNA gene sequences in each sample using high-throughput next-generation sequencing. The authors further characterized vaginal community function by determining the metabolites produced throughout the 16-week period. Gajer and colleagues found that there were five longitudinal patterns of change in vaginal microbial community composition. Moreover, in some women, the vaginal microbial community composition changed markedly and rapidly over time, whereas in others it was relatively stable. Using statistical modeling, the authors showed that the menstrual cycle influenced the stability of the vaginal communities. In many cases, the metabolite profiles indicated that vaginal community function was maintained despite changes in bacterial composition. Intervals of increased susceptibility to disease may occur because the vaginal microbiota varies over time. The authors envision that better knowledge of the causes and consequences of these changes to the host will lead to the development of new strategies to manage vaginal microbiomes in ways that promote health and minimize the use of antibiotics. Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women’s health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.

Spatial heterogeneity and co-occurrence patterns of human mucosal-associated intestinal microbiota.

Human gut microbiota shows high inter-subject variations, but the actual spatial distribution and co-occurrence patterns of gut mucosa microbiota that occur within a healthy human instestinal tract remain poorly understood. In this study, we illustrated a model of this mucosa bacterial communities’ biogeography, based on the largest data set so far, obtained via 454-pyrosequencing of bacterial 16S rDNAs associated with 77 matched biopsy tissue samples taken from terminal ileum, ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum of 11 healthy adult subjects. Borrowing from macro-ecology, we used both Taylor’s power law analysis and phylogeny-based beta-diversity metrics to uncover a highly heterogeneous distribution pattern of mucosa microbial inhabitants along the length of the intestinal tract. We then developed a spatial dispersion model with an R-squared value greater than 0.950 to map out the gut mucosa-associated flora’s non-linear spatial distribution pattern for 51.60% of the 188 most abundant gut bacterial species. Furthermore, spatial co-occurring network analysis of mucosa microbial inhabitants together with occupancy (that is habitat generalists, specialists and opportunist) analyses implies that ecological relationships (both oppositional and symbiotic) between mucosa microbial inhabitants may be important contributors to the observed spatial heterogeneity of mucosa microbiota along the human intestine and may even potentially be associated with mutual cooperation within and functional stability of the gut ecosystem.

DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies.

The highly anticipated transition from next generation sequencing (NGS) to third generation sequencing (3GS) has been difficult primarily due to high error rates and excessive sequencing cost. The high error rates make the assembly of long erroneous reads of large genomes challenging because existing software solutions are often overwhelmed by error correction tasks. Here we report a hybrid assembly approach that simultaneously utilizes NGS and 3GS data to address both issues. We gain advantages from three general and basic design principles: (i) Compact representation of the long reads leads to efficient alignments. (ii) Base-level errors can be skipped; structural errors need to be detected and corrected. (iii) Structurally correct 3GS reads are assembled and polished. In our implementation, preassembled NGS contigs are used to derive the compact representation of the long reads, motivating an algorithmic conversion from a de Bruijn graph to an overlap graph, the two major assembly paradigms. Moreover, since NGS and 3GS data can compensate for each other, our hybrid assembly approach reduces both of their sequencing requirements. Experiments show that our software is able to assemble mammalian-sized genomes orders of magnitude more quickly than existing methods without consuming a lot of memory, while saving about half of the sequencing cost.

Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling

The latest advances in survival analysis have been centered on multivariate systems. Multivariate survival analysis has two major categories of models: one is multi-state modeling; the other is shared frailty modeling. Multi-state models, although formulated differently in both fields, have been extensively studied in reliability analysis in the context of Markov chain analysis. In contrast, shared frailty modeling seems little known in reliability analysis and computer science. In this article, we focus exclusively on shared frailty modeling. Shared frailty refers to the often-unobserved factors or risks responsible for the common risks dependence between multiple events. It is well recognized as the most effective modeling approach to address common risks dependence and, more recently, the event-related dependence. The only exclusion of dependence modeling for the frailty approach is the common events type, which is best addressed by multi-state modeling. We argue that shared frailty modeling not only is perfectly applicable for engineering reliability, but also is of significant potential in other fields of computer science, such as networking and software reliability and survivability, machine learning, and prognostics and health management (PHM).

Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM)

Survival analysis, also known as failure time analysis or time-to-event analysis, is one of the most significant advancements of mathematical statistics in the last quarter of the 20th century. It has become the de facto standard in biomedical data analysis. Although reliability was conceived as a major application field by the mathematicians who pioneered survival analysis, survival analysis failed to establish itself as a major tool for reliability analysis. In this paper, we attempt to demonstrate, by reviewing and comparing the major mathematical models of both fields, that survival analysis and reliability theory essentially address the same mathematical problems. Therefore, survival analysis should become a major mathematical tool for reliability analysis and related fields such as Prognostics and Health Management (PHM). This paper is the first in a four part series in which we review state-of-the-art studies in survival (univariate) analysis, competing risks analysis, and multivariate survival analysis, with focusing on their applications to reliability and computer science. The present article discusses the univariate survival analysis (survival analysis hereafter).

Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs)

In this paper, we introduce a new concept termed dynamic hybrid fault models together with the mathematic models and approaches for applying the new concept to reliability and fault tolerance analyses. It extends the traditional hybrid fault models and their relevant constraints in agreement algorithms with survival analysis and evolutionary game theory. The new dynamic hybrid fault models (i) transform hybrid fault models into time and covariate dependent models; (ii) make real-time prediction of reliability more realistic and allows for real-time prediction of fault-tolerance; (iii) set the foundations for integrating hybrid fault models with reliability and survivability analyses by introducing evolutionary game modeling; (iv) extend evolutionary game theory in its modeling of the survivals of game players. The application domain is wireless sensor network (WSN), but the large part of the modeling architecture also applies to general engineering reliability and network survivability.

Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets

The human microbiome project (HMP) has made it possible to test important ecological theories for arguably the most important ecosystem to human health—the human microbiome. Existing limited number of studies have reported conflicting evidence in the case of the neutral theory; the present study aims to comprehensively test the neutral theory with extensive HMP datasets covering all five major body sites inhabited by the human microbiome. Utilizing 7437 datasets of bacterial community samples, we discovered that only 49 communities (less than 1%) satisfied the neutral theory, and concluded that human microbial communities are not neutral in general. The 49 positive cases, although only a tiny minority, do demonstrate the existence of neutral processes. We realize that the traditional doctrine of microbial biogeography “Everything is everywhere, but the environment selects” first proposed by Baas-Becking resolves the apparent contradiction. The first part of Baas-Becking doctrine states that microbes are not dispersal-limited and therefore are neutral prone, and the second part reiterates that the freely dispersed microbes must endure selection by the environment. Therefore, in most cases, it is the host environment that ultimately shapes the community assembly and tip the human microbiome to niche regime.

Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems

Biological structures and organizations in nature, from gene, molecular, immune systems, and biological populations, to ecological communities, are built to stand against perturbations and biological robustness is therefore ubiquitous. Furthermore, it is intuitively obvious that the counterpart of bio-robustness in engineered systems is fault tolerance. With the objective to stimulate inspiration for building reliable and survivable computer networks, this paper reviews the state-of-the-art research on bio-robustness at different biological scales (level) including gene, molecular networks, immune systems, population, and community. Besides identifying the biological/ecological principles and mechanisms relevant to biological robustness, we also review major theories related to the origins of bio-robustness, such as evolutionary game theory, self-organization and emergent behaviors. Evolutionary game theory, which we present in a relative comprehensive introduction, provides an ideal framework to model the reliability and survivability of computer networks, especially the wireless sensor networks. We also present our perspectives on the reliability and survivability of computer networks, particularly wireless sensor and ad hoc networks, based on the principles and mechanisms of bio-robustness reviewed in the paper. Finally, we propose four open questions including three in engineering and one in DNA code robustness to demonstrate the bidirectional nature of the interactions between bio-robustness and engineering fault tolerance.

Dynamic populations in genetic algorithms

Biological populations are dynamic in both space and time, that is, the population size of a species fluctuates across their habitats over time. There are rarely any static or fixed size populations in nature. In evolutionary computation (EC), population size is one of the most important parameters and it received attention from EC pioneers from the very beginning. Despite many attempts to optimize the population sizing, the prevailing scheme in EC is still possibly the simplest --- the fixed size population. This is in strong contrast with population entities in nature. In this paper, we explore the effects of dynamic (fluctuating) populations on the performance of genetic algorithms (GA). In particular, we test five dynamic population-sizing patterns: random fluctuating population, increasing population, decreasing population, bell-shaped population, and inverse bell-shaped population and compare them against the fixed size population. Our experiment shows very promising results that the dynamic populations perform more efficiently than the traditional fixed size populations, in terms of the number of fitness function evaluations and memory space requirements. We also analyze why the dynamic populations should perform superior to the fixed size populations from the biological perspective.

Towards a unified definition for reliability, survivability and resilience (I): the conceptual framework inspired by the handicap principle and ecological stability

Reliability function [R(t) = P(T ≫ t)] (where T is the lifetime or failure time, and P is the probability) serves as the definition of reliability very successfully in the sense that (i) it acts as an excellent pedagogical model; (ii) its fundamental concept, although often extended extensively, still holds in complex real-world reliability analysis. In contrast with reliability analysis, there is not a commonly accepted survivability function that assumes a similar role as the reliability function does in reliability analysis. In this article, I first analyze the challenges of defining such a survivability function that is similar to reliability function, and then argue that by synthesizing the existing definitions developed by leading scholars of survivability research [notably, Ellison et al. (1997), T1A1 group (ANSI T1A1.2, 2001), Knight (2003), Liu & Trivedi (2004), “ResiliNets Project” (ReSterbenz & Hutchinson et al. 2009)], a unified definition for reliability and survivability in the form of a 4-tuple: Survivability = [Resistance, Resilience, Persistence, Failure Counter], with the notion that resistance is equivalent to traditional reliability, is meaningful. Although the 4-tuple definition is much more complex than the reliability function, I argue that it possesses promising potential to become a survivability definition with the two similar properties demonstrated by the traditional reliability function. My arguments are based on two lines of developments: (i) The first three elements of the 4-tuple capture critical aspects of survivability and each of them possesses rigorously defined mathematical models. These models, developed in a series of our previous studies (Ma & Krings 2008a-d, Ma 2008, 2009a-c), when integrated together, form a modeling architecture for performing many real-world reliability and survivability analyses. This contribution from the new 4-tuple definition corresponds to the second property of the traditional reliability function. (ii) The 4-tuple definition is also inspired by two biological theories: the handicap principle that governs the honesty (reliability) of animal communication, and the stability theory of ecological systems. Both theories are examples of natures versions of ‘reliability’ and ‘survivability’. How nature evolves both reliable and survivable (super reliable) features should be inspirational to the study of engineering reliability and survivability.