Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data

We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.     

QTL-seq for rapid identification of candidate genes for flowering time in broccoli × cabbage

Key message

A major QTL controlling early flowering in broccoli × cabbage was identified by marker analysis and next-generation sequencing, corresponding to GRF6 gene conditioning flowering time in Arabidopsis.

Abstract

Flowering is an important agronomic trait for hybrid production in broccoli and cabbage, but the genetic mechanism underlying this process is unknown. In this study, segregation analysis with BC₁P1, BC₁P2, F₂, and F_2:3 populations derived from a cross between two inbred lines “195” (late-flowering) and “93219” (early flowering) suggested that flowering time is a quantitative trait. Next, employing a next-generation sequencing-based whole-genome QTL-seq strategy, we identified a major genomic region harboring a robust flowering time QTL using an F₂ mapping population, designated Ef2.1 on cabbage chromosome 2 for early flowering. Ef2.1 was further validated by indel (insertion or deletion) marker-based classical QTL mapping, explaining 51.5% (LOD = 37.67) and 54.0% (LOD = 40.5) of the phenotypic variation in F₂ and F_2:3 populations, respectively. Combined QTL-seq and classical QTL analysis narrowed down Ef1.1 to a 228-kb genomic region containing 29 genes. A cabbage gene, Bol024659, was identified in this region, which is a homolog of GRF6, a major gene regulating flowering in Arabidopsis, and was designated BolGRF6. qRT-PCR study of the expression level of BolGRF6 revealed significantly higher expression in the early flowering genotypes. Taken together, our results provide support for BolGRF6 as a possible candidate gene for early flowering in the broccoli line 93219. The identified candidate genomic regions and genes may be useful for molecular breeding to improve broccoli and cabbage flowering times.

     

Conditions for Global Dynamic Stability of a Class of Resource-Bounded Model Ecosystems

This paper studies a class of dynamical systems that model multi-species ecosystems. These systems are ‘resource bounded’ in the sense that species compete to utilize an underlying limiting resource or substrate. This boundedness means that the relevant state space can be reduced to a simplex, with coordinates representing the proportions of substrate utilized by the various species. If the vector field is inward pointing on the boundary of the simplex, the state space is forward invariant under the system flow, a requirement that can be interpreted as the presence of non-zero exogenous recruitment. We consider conditions under which these model systems have a unique interior equilibrium that is globally asymptotically stable. The systems we consider generalize classical multi-species Lotka–Volterra systems, the behaviour of which is characterized by properties of the community (or interaction) matrix. However, the more general systems considered here are not characterized by a single matrix, but rather a family of matrices. We develop a set of ‘explicit conditions’ on the basis of a notion of ‘uniform diagonal dominance’ for such a family of matrices, that allows us to extract a set of sufficient conditions for global asymptotic stability based on properties of a single, derived matrix. Examples of these explicit conditions are discussed.     

Energy efficient scheduling for parallel applications on mobile clusters

During the past decade, cluster computing and mobile communication technologies have been extensively deployed and widely applied because of their giant commercial value. The rapid technological advancement makes it feasible to integrate these two technologies and a revolutionary application called mobile cluster computing is arising on the horizon. Mobile cluster computing technology can further enhance the power of our laptops and mobile devices by running parallel applications. However, scheduling parallel applications on mobile clusters is technically challenging due to the significant communication latency and limited battery life of mobile devices. Therefore, shortening schedule length and conserving energy consumption have become two major concerns in designing efficient and energy-aware scheduling algorithms for mobile clusters. In this paper, we propose two novel scheduling strategies aimed at leveraging performance and power consumption for parallel applications running on mobile clusters. Our research focuses on scheduling precedence constrained parallel tasks and thus duplication heuristics are applied to schedule parallel tasks to minimize communication overheads. However, existing duplication algorithms are developed with consideration of schedule lengths, completely ignoring energy consumption of clusters. In this regard, we design two energy-aware duplication scheduling algorithms, called EADUS and TEBUS, to schedule precedence constrained parallel tasks with a complexity of O(n ²), where n is the number of tasks in a parallel task set. Unlike the existing duplication-based scheduling algorithms that replicate all the possible predecessors of each task, the proposed algorithms judiciously replicate predecessors of a task if the duplication can help in conserving energy. Our energy-aware scheduling strategies are conducive to balancing scheduling lengths and energy savings of a set of precedence constrained parallel tasks. We conducted extensive experiments using both synthetic benchmarks and real-world applications to compare our algorithms with two existing approaches. Experimental results based on simulated mobile clusters demonstrate the effectiveness and practicality of the proposed duplication-based scheduling strategies. For example, EADUS and TABUS can reduce energy consumption for the Gaussian Elimination application by averages of 16.08% and 8.1% with merely 5.7% and 2.2% increase in schedule length respectively.

Coherent Infomax as a Computational Goal for Neural Systems

Signal processing in the cerebral cortex is thought to involve a common multi-purpose algorithm embodied in a canonical cortical micro-circuit that is replicated many times over both within and across cortical regions. Operation of this algorithm produces widely distributed but coherent and relevant patterns of activity. The theory of Coherent Infomax provides a formal specification of the objectives of such an algorithm. It also formally derives specifications for both the short-term processing dynamics and for the learning rules whereby the connection strengths between units in the network can be adapted to the environment in which the system finds itself. A central assumption of the theory is that the local processors can combine reliable signal coding with flexible use of those codes because they have two classes of synaptic connection: driving connections which specify the information content of the neural signals, and contextual connections which modulate that signal processing. Here, we make the biological relevance of this theory more explicit by putting more emphasis upon the contextual guidance of ongoing processing, by showing that Coherent Infomax is consistent with a particular Bayesian interpretation for the contextual guidance of learning and processing, by explicitly specifying rules for on-line learning, and by suggesting approximations by which the learning rules can be made computationally feasible within systems composed of very many local processors.     

A Mechanism for Ultra-Slow Oscillations in the Cortical Default Network

When the brain is in its noncognitive “idling” state, functional MRI measurements reveal the activation of default cortical networks whose activity is suppressed during cognitive processing. This default or background mode is characterized by ultra-slow BOLD oscillations (∼0.05 Hz), signaling extremely slow cycling in cortical metabolic demand across distinct cortical regions. Here we describe a model of the cortex which predicts that slow cycling of cortical activity can arise naturally as a result of nonlinear interactions between temporal (Hopf) and spatial (Turing) instabilities. The Hopf instability is triggered by delays in the inhibitory postsynaptic response, while the Turing instability is precipitated by increases in the strength of the gap-junction coupling between interneurons. We comment on possible implications for slow dendritic computation and information processing.     

A Periodic Droop Model for Two Species Competition in A Chemostat

Both uniform persistence and the existence of periodic coexistence state are established for a periodically forced Droop model on two phytoplankton species competition in a chemostat under some appropriate conditions. Numerical simulations using biological data are presented as well to illustrate the main result. Research supported in part by the NSERC of Canada and the MITACS of Canada.     

Tracking transaction footprints for non-intrusive end-to-end monitoring

Existing transaction monitoring solutions are either platform-specific or rely on instrumentation techniques, which limit their applicability. Consequently, transaction monitoring in enterprise environments often involves the manual collation of information spread across a variety of infrastructure elements and applications, and is a time-consuming and labor-intensive task. To address this problem, we have developed an online, non-intrusive and platform-agnostic solution for transaction monitoring. The solution includes a transaction model discovery component that leverages historical system log files, containing transaction footprints and generates a model of the transaction in terms of valid sequence of steps that a transaction instance may execute and the expected footprint patterns at each step. The online monitoring system, in turn, takes in only (a) online system log files and (b) the transaction model, as inputs and generates a dynamic execution profile of ongoing transaction instances that allows their status to be tracked at individual and aggregate levels, even when transaction footprints do not necessarily carry correlating identifiers as those injected through instrumentation. In this paper, we describe the transaction model discovery and monitoring system including the architecture and algorithms, followed by results from an empirical study, ongoing work on run-time model validation and directions for future research.

Studies on process optimization methods for rapamycin production using Streptomyces hygroscopicus ATCC 29253

Rapamycin is a high-value product finding immense use as a drug, in organ transplantation, and as a potential immunosuppressant. Optimization of fermentation parameters of rapamycin production by Streptomyces hygroscopicus NRRL 5491 has been carried out. The low titer value of rapamycin in the original producer strain limits its applicability at industrial level. This study aims at improving the production of rapamycin by optimizing the nutrient requirements. Addition of l-lysine increased the production of rapamycin up to a significant level which supports the fact that it acts as precursor for rapamycin production, as found in previous studies. Effect of optimized medium on the Streptomyces growth rate as well as rapamycin production has been studied. The optimization study incorporates one at a time parameter optimization studies followed by tool-based hybrid methodology. This methodology includes the Plackett–Burman design (PBD) method, artificial neural networks (ANN), and genetic algorithms (GA). PBD screened mannose, soyabean meal, and l-lysine concentrations as significant factors for rapamycin production. ANN was used to construct rapamycin production model. This strategy has led to a significant increase of rapamycin production up to 320.89 mg/L at GA optimized concentrations of 25.47, 15.39, and 17.48 g/L for mannose, soyabean meal, and l-lysine, respectively. The present study must find its application in scale-up study for industrial level production of rapamycin.     

Searching for Spatial Patterns in a Pollinator–Plant–Herbivore Mathematical Model

This paper deals with the spatio-temporal dynamics of a pollinator–plant–herbivore mathematical model. The full model consists of three nonlinear reaction–diffusion–advection equations defined on a rectangular region. In view of analyzing the full model, we firstly consider the temporal dynamics of three homogeneous cases. The first one is a model for a mutualistic interaction (pollinator–plant), later on a sort of predator–prey (plant–herbivore) interaction model is studied. In both cases, the interaction term is described by a Holling response of type II. Finally, by considering that the plant population is the unique feeding source for the herbivores, a mathematical model for the three interacting populations is considered. By incorporating a constant diffusion term into the equations for the pollinators and herbivores, we numerically study the spatiotemporal dynamics of the first two mentioned models. For the full model, a constant diffusion and advection terms are included in the equation for the pollinators. For the resulting model, we sketch the proof of the existence, positiveness, and boundedness of solution for an initial and boundary values problem. In order to see the separated effect of the diffusion and advection terms on the final population distributions, a set of numerical simulations are included. We used homogeneous Dirichlet and Neumann boundary conditions.     

Importance of co-cultivation medium pH for successful Agrobacterium-mediated transformation of Lilium × formolongi

An efficient system for Agrobacterium-mediated transformation of Lilium × formolongi was established by preventing the drastic drop of pH in the co-cultivation medium with MES. Meristematic nodular calli were inoculated with an overnight culture of A. tumefaciens strain EHA101 containing the plasmid pIG121-Hm which harbored intron-containing β-glucuronidase (GUS), hygromycin phosphotransferase (HPT), and neomycin phosphotransfease II (NPTII) genes. After three days of co-cultivation on 2 g/l gellan gum-solidified MS medium containing 100 μM acetosyringone, 30 g/l sucrose, 1 mg/l picloram and different concentrations of MES, they were cultured on the same medium containing 12.5 mg/l meropenem to eliminate Agrobacterium for 2 weeks and then transferred onto medium containing the same concentration of meropenem and 25 mg/l hygromycin for selecting putative transgenic calli. Transient GUS expression was only observed by adding MES to co-cultivation medium. Hygromycin-resistant transgenic calli were obtained only when MES was added to the co-cultivation medium especially at 10 mM. The hygromycin-resistant calli were successfully regenerated into plantlets after transferring onto picloram-free medium. Transformation of plants was confirmed by histochemical GUS assay, PCR analysis and Southern blot analysis.     

A middleware approach for pipelining communications in clusters

The Pipelining Communications Middleware (PCM) approach provides a flexible, simple, high-performance mechanism to connect parallel programs running on high performance computers or clusters. This approach enables parallel programs to communicate and coordinate with each other to address larger problems than a single program can solve. The motivation behind the PCM approach grew out of using files as an intermediate transfer stage between processing by different programs. Our approach supersedes this practice by using streaming data set transfers as an “online” communication channel between simultaneously active parallel programs. Thus, the PCM approach addresses the issue of sending data from a parallel program to another parallel program without exposing details such as number of nodes allocated to the program, specific node identifiers, etc. This paper outlines and analyzes our proposed computation and communication model to provide efficient and convenient communications between parallel programs running on high performance computing systems or clusters. We also discuss the PCM challenges as well as current PCM implementations. Our approach achieves scalability, transparency, coordination, synchronization and flow control, and efficient programming. We experimented with data parallel applications to evaluate the performance of the PCM approach. Our experiment results show that the PCM approach achieves nearly ideal throughput that scales linearly with the underlying network medium speed. PCM performs well with small and large data transfers. Furthermore, our experiments show that network infrastructure plays the most significant role in the PCM performance.

Use of tetrapeptide signals for protein secondary-structure prediction

This paper develops a novel sequence-based method, tetra-peptide-based increment of diversity with quadratic discriminant analysis (TPIDQD for short), for protein secondary-structure prediction. The proposed TPIDQD method is based on tetra-peptide signals and is used to predict the structure of the central residue of a sequence fragment. The three-state overall per-residue accuracy (Q ₃) is about 80% in the threefold cross-validated test for 21-residue fragments in the CB513 dataset. The accuracy can be further improved by  taking long-range sequence information (fragments of more than 21 residues) into account in prediction. The results show the tetra-peptide signals can indeed reflect some relationship between an amino acid’s sequence and its secondary structure, indicating the importance of  tetra-peptide signals as the protein folding code in the protein structure prediction.     

Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions

This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima. This work is partially supported by a project grant from the Ministry of Human Resources Development, India and by DRDO-IISc Mathematical Engineering Program.     

Age and Sex Structured Model for Assessing the Demographic Impact of Mother-to-Child Transmission of HIV/AIDS

Age and sex structured HIV/AIDS model with explicit incubation period is proposed as a system of delay differential equations. The model consists of two age groups that are children (0–14 years) and adults (15–49 years). Thus, the model considers both mother-to-child transmission (MTCT) and heterosexual transmission of HIV in a community. MTCT can occur prenatally, at labour and delivery or postnatally through breastfeeding. In the model, we consider the children age group as a one-sex formulation and divide the adult age group into a two-sex structure consisting of females and males. The important mathematical features of the model are analysed. The disease-free and endemic equilibria are found and their stabilities investigated. We use the Lyapunov functional approach to show the local stability of the endemic equilibrium. Qualitative analysis of the model including positivity and boundedness of solutions, and persistence are also presented. The basic reproductive number (ℛ₀) for the model shows that the adult population is responsible for the spread HIV/AIDS epidemic, thus up-to-date developed HIV/AIDS models to assess intervention strategies have focused much on heterosexual transmission by the adult population and the children population has received little attention. We numerically analyse the HIV/AIDS model to assess the community benefits of using antiretroviral drugs in reducing MTCT and the effects of breastfeeding in settings with high HIV/AIDS prevalence ratio using demographic and epidemiological parameters for Zimbabwe.     

The Dynamics of Turing Patterns for Morphogen-Regulated Growing Domains with Cellular Response Delays

Since its conception in 1952, the Turing paradigm for pattern formation has been the subject of numerous theoretical investigations. Experimentally, this mechanism was first demonstrated in chemical reactions over 20 years ago and, more recently, several examples of biological self-organisation have also been implicated as Turing systems. One criticism of the Turing model is its lack of robustness, not only with respect to fluctuations in the initial conditions, but also with respect to the inclusion of delays in critical feedback processes such as gene expression. In this work we investigate the possibilities for Turing patterns on growing domains where the morphogens additionally regulate domain growth, incorporating delays in the feedback between signalling and domain growth, as well as gene expression. We present results for the proto-typical Schnakenberg and Gierer–Meinhardt systems: exploring the dynamics of these systems suggests a reconsideration of the basic Turing mechanism for pattern formation on morphogen-regulated growing domains as well as highlighting when feedback delays on domain growth are important for pattern formation.     

Medical Product Development, Innovation, and Life-Cycle Regulation: The Challenges for Biostatistics

The article provides a perspective on the challenges for biostatistics as well as on contributions that biostatisticians are making and can make to medical product development and regulation and what the future might be in these areas. The current environment in the United States for pharmaceutical development and regulation is discussed along with the expectations that the public has for how medical products should contribute to public heath. The globalization of research and the use of study designs that incorporate multi-regional populations present new challenges for design and inference. The emerging interest in and development of the science of safety assessment and quantitative approaches to risk evaluation is considered. Guidance development, especially in the area of clinical trials design, continues to be one of the needs that FDA is asked to meet. Guidance development is proceeding for non-inferiority study designs, adaptive designs, multiple endpoints in clinical trials, and missing outcome data in clinical trials. Biostatisticians will be asked and challenged to take on leadership roles in new areas such as personalized medicine, biomarker and genomics, development of new tools for visual display of clinical data, quality assurance and monitoring in clinical trials.