Multiple Independent Processes Research Articles

Agelessness is possible under the disposable soma theory but system complexity makes it unlikely

Although demographic studies have failed to find evidence of aging in certain animal species, classic evolutionary theories of aging struggle to explain how evolution could favor agelessness in such cases. Here, we develop mathematical models of the disposable soma theory to identify conditions in which agelessness would be evolutionarily favored. For any given type of damage that could accumulate and cause age-accelerating mortality risk, we find that evolution could select for its complete removal if the mortality risk it poses is severe enough and its repair does not pose too large of a penalty to reproduction. Environmental factors such as extrinsic mortality and the form of population density-dependent regulation also play a large role in determining the optimal rate of aging and whether agelessness should be evolutionarily favored. However, in a system with multiple sources of damage and multiple independent repair processes, avoiding aging is rarely evolutionarily favorable. Pleiotropic repair processes, such as those that could be present in asexual fissioning organisms, make agelessness more likely but do not guarantee it. Our results indicate that agelessness could be favored by evolution in narrow contexts but that multiple types of damage and repair make agelessness unlikely to arise in sufficiently complex organisms.

Dominant role of grazing and snow cover variability on vegetation shifts in the drylands of Kazakhstan

Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km2), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km2). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ.

Improving Pareto Local Search Using Cooperative Parallelism Strategies for Multiobjective Combinatorial Optimization.

Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

Unraveling the evolutionary origin of the P5CS gene: a story of gene fusion and horizontal transfer.

The accumulation of proline in response to the most diverse types of stress is a widespread defense mechanism. In prokaryotes, fungi, and certain unicellular eukaryotes (green algae), the first two reactions of proline biosynthesis occur through two distinct enzymes, γ-glutamyl kinase (GK E.C. 2.7.2.11) and γ-glutamyl phosphate reductase (GPR E.C. 1.2.1.41), encoded by two different genes, ProB and ProA, respectively. Plants, animals, and a few unicellular eukaryotes carry out these reactions through a single bifunctional enzyme, the Δ1-pyrroline-5-carboxylate synthase (P5CS), which has the GK and GPR domains fused. To better understand the origin and diversification of the P5CS gene, we use a robust phylogenetic approach with a broad sampling of the P5CS, ProB and ProA genes, including species from all three domains of life. Our results suggest that the collected P5CS genes have arisen from a single fusion event between the ProA and ProB gene paralogs. A peculiar fusion event occurred in an ancestral eukaryotic lineage and was spread to other lineages through horizontal gene transfer. As for the diversification of this gene family, the phylogeny of the P5CS gene in plants shows that there have been multiple independent processes of duplication and loss of this gene, with the duplications being related to old polyploidy events.

Group-Based Random Access and Data Transmission Scheme for Massive MTC Networks

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

Parallel Neural Multiprocessing with Gamma Frequency Latencies.

The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

Blood-based protein mediators of senility with replications across biofluids and cohorts.

Dementia severity can be quantitatively described by the latent dementia phenotype ‘δ’ and its various composite ‘homologues’. We have explored δ’s blood-based protein biomarkers in the Texas Alzheimer’s Research and Care Consortium. However, it would be convenient to replicate them in the Alzheimer’s Disease Neuroimaging Initiative. To that end, we have engineered a δ homologue from the observed cognitive performance measures common to both projects [i.e. ‘d:Texas Alzheimer’s Research and Care Consortium to Alzheimer’s Disease Neuroimaging Initiative’ (dT2A)]. In this analysis, we confirm 13/22 serum proteins as partial mediators of age’s effect on dementia severity as measured by dT2A in the Texas Alzheimer’s Research and Care Consortium and then replicate 4/13 in the Alzheimer’s Disease Neuroimaging Initiative’s plasma data. The replicated mediators of age-specific effects on dementia severity are adiponectin, follicle-stimulating hormone, pancreatic polypeptide and resistin. In their aggregate, the 13 confirmed age-specific mediators suggest that ‘cognitive frailty’ pays a role in dementia severity as measured by δ. We provide both discriminant and concordant support for that hypothesis. Weight, calculated low-density lipoprotein and body mass index are partial mediators of age’s effect in the Texas Alzheimer’s Research and Care Consortium. Biomarkers related to other disease processes (e.g. cerebrospinal fluid Alzheimer’s disease-specific biomarkers in the Alzheimer’s Disease Neuroimaging Initiative) are not. It now appears that dementia severity is the sum of multiple independent processes impacting δ. Each may have a unique set of mediating biomarkers. Age’s unique effect appears to be at least partially mediated through proteins related to frailty. Age-specific mediation effects can be replicated across cohorts and biofluids. These proteins may offer targets for the remediation of age-specific cognitive decline (aka ‘senility’), help distinguish it from other determinants of dementia severity and/or provide clues to the biology of Aging Proper.

Distributed Plasticity Drives Visual Habituation Learning in Larval Zebrafish

Habituation is a simple form of learning where animals learn to reduce their responses to repeated innocuous stimuli [1]. Habituation is thought to occur via at least two temporally and molecularly distinct mechanisms, which lead to short-term memories that last for seconds to minutes and long-term memories that last for hours or longer [1, 2]. Here, we focus on long-term habituation, which, due to the extended time course, necessitates stable alterations to circuit properties [2-4]. In its simplest form, long-term habituation could result from a plasticity event at a single point in a circuit, and many studies have focused on identifying the site and underlying mechanism of plasticity [5-10]. However, it is possible that these individual sites are only one of many points in the circuit where plasticity is occurring. Indeed, studies of short-term habituation in C.elegans indicate that in this paradigm, multiple genetically separable mechanisms operate to adapt specific aspects of behavior [11-13]. Here, we use a visual assay in which larval zebrafish habituate their response to sudden reductions in illumination (dark flashes) [14, 15]. Through behavioral analyses, we find that multiple components of the dark-flash response habituate independently of one another using different molecular mechanisms. This is consistent with a modular model in which habituation originates from multiple independent processes, each adapting specific components of behavior. This may allow animals to more specifically or flexibly habituate based on stimulus context or internal states.

Temporal similarity metrics for latent network reconstruction: The role of time-lag decay

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag function strongly affects the metrics’ reconstruction accuracy, depending on the network’s clustering coefficient, and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks.

MMS Observations of Electrostatic Waves in an Oblique Shock Crossing

AbstractHigh‐resolution particle and wave measurements taken during an oblique bow shock crossing by the Magnetospheric Multiscale (MMS) mission are analyzed. Two regions of differing magnetic behavior are identified within the shock, one with active magnetic fluctuations and one with laminar interplanetary magnetic field topology. A prominent reflected ion population is observed in both regions. The active magnetic region is characterized by large‐amplitude (>100 mV/m) electrostatic solitary waves, electron Bernstein waves, and ion acoustic waves, along with intermittent current activity and localized electron heating. In the region of laminar magnetic field, ion acoustic waves are prominently observed. Solar wind ion deceleration is observed in both regions of active and laminar magnetic field. All observations suggest that solar wind deceleration can occur as a result of multiple independent processes, in this case current and ion‐ion instabilities.

Numerical framework for the computation of urban flux footprints employing large-eddy simulation and Lagrangian stochastic modeling

Abstract. Conventional footprint models cannot account for the heterogeneity of the urban landscape imposing a pronounced uncertainty on the spatial interpretation of eddy-covariance (EC) flux measurements in urban studies. This work introduces a computational methodology that enables the generation of detailed footprints in arbitrarily complex urban flux measurements sites. The methodology is based on conducting high-resolution large-eddy simulation (LES) and Lagrangian stochastic (LS) particle analysis on a model that features a detailed topographic description of a real urban environment. The approach utilizes an arbitrarily sized target volume set around the sensor in the LES domain, to collect a dataset of LS particles which are seeded from the potential source area of the measurement and captured at the sensor site. The urban footprint is generated from this dataset through a piecewise postprocessing procedure, which divides the footprint evaluation into multiple independent processes that each yield an intermediate result. These results are ultimately selectively combined to produce the final footprint. The strategy reduces the computational cost of the LES–LS simulation and incorporates techniques to account for the complications that arise when the EC sensor is mounted on a building instead of a conventional flux tower. The presented computational framework also introduces a result assessment strategy which utilizes the obtained urban footprint together with a detailed land cover type dataset to estimate the potential error that may arise if analytically derived footprint models were employed instead. The methodology is demonstrated with a case study that concentrates on generating the footprint for a building-mounted EC measurement station in downtown Helsinki, Finland, under the neutrally stratified atmospheric boundary layer.

Joint arrival process of multiple independent batch Markovian arrival processes

The joint arrival process of multiple independent batch Markovian arrival processes (BMAP) is analyzed. First, the parameter matrices of the joint arrival process are constructed, and their relations with the parameter matrices of the component BMAPs are also obtained. Second, it is proved that the parameter matrices of the joint arrival process have the same properties as the BMAP; and based on these properties, in analyzing the queueing models, some relations for the joint arrival process either can inherit from the existing literature on the BMAP and the multiple-BMAP, or can be derived with the same complexity as the case for the BMAP.

SMARTS

Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We have developed the Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS), a distributed microscopic traffic simulator that can utilize multiple independent processes in parallel. SMARTS can perform fast large-scale simulations. For example, when simulating 1 million vehicles in an area the size of Melbourne, the system runs 1.14 times faster than real time with 30 computing nodes and 0.2s simulation timestep. SMARTS supports various driver models and traffic rules, such as the car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate, and monitor simulations. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1% accuracy in predicting traffic on a 10km freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.

Quantifying Noisy Attractors: From Heteroclinic to Excitable Networks

Attractors of dynamical systems may be networks in phase space that can be heteroclinic (where there are dynamical connections between simple invariant sets) or excitable (where a perturbation threshold needs to be crossed to a dynamical connection between “nodes''). Such network attractors can display a high degree of sensitivity to noise both in terms of the regions of phase space visited and in terms of the sequence of transitions around the network. The two types of network are intimately related---one can directly bifurcate to the other. In this paper we attempt to quantify the effect of additive noise on such network attractors. Noise increases the average rate at which the networks are explored and can result in “macroscopic” random motion around the network. We perform an asymptotic analysis of local behavior of an escape model near heteroclinic/excitable nodes in the limit of noise $\eta\rightarrow 0^+$ as a model for the mean residence time $T$ near equilibria. The heteroclinic network case has $T$ proportional to $-\ln\eta$ while the excitable network has $T$ given by a Kramers' law, proportional to $\exp (B/\eta^2)$. There is singular scaling behavior (where $T$ is proportional to $1/\eta$) at the bifurcation between the two types of network. We also explore transition probabilities between nodes of the network in the presence of anisotropic noise. For low levels of noise, numerical results suggest that a (heteroclinic or excitable) network can approximately realize any set of transition probabilities and any sufficiently large mean residence times at the given nodes. We show that this can be well modeled in our example network by multiple independent escape processes, where the direction of first escape determines the transition. This suggests that it is feasible to design noisy network attractors with arbitrary Markov transition probabilities and residence times.

Working Memory Models: Insights From Neuroimaging.

FIGURE 1. The Atkinson and Shiffrin Model of memory is diagramed in the gray panel. Sensory information is perceived and suppressed via selective attention processes, then briefly held in the limited-capacity modality-specific sensory register. Information enhanced by selective attention enters into short term memory (STM). The process of rehearsal (maintenance) can sustain information in STM. Information in STM is automatically encoded (stored, consolidated) into long term memory (LTM). Although flawed, this model continues to provide a simple and easy approach to understanding memory. Assumptions that proved to be inconsistent with research included that any information in STM will transfer to LTM and that STM is required

Single-Copy Nuclear Genes Place Haustorial Hydnoraceae within Piperales and Reveal a Cretaceous Origin of Multiple Parasitic Angiosperm Lineages

Extreme haustorial parasites have long captured the interest of naturalists and scientists with their greatly reduced and highly specialized morphology. Along with the reduction or loss of photosynthesis, the plastid genome often decays as photosynthetic genes are released from selective constraint. This makes it challenging to use traditional plastid genes for parasitic plant phylogenetics, and has driven the search for alternative phylogenetic and molecular evolutionary markers. Thus, evolutionary studies, such as molecular clock-based age estimates, are not yet available for all parasitic lineages. In the present study, we extracted 14 nuclear single copy genes (nSCG) from Illumina transcriptome data from one of the “strangest plants in the world”, Hydnora visseri (Hydnoraceae). A ∼15,000 character molecular dataset, based on all three genomic compartments, shows the utility of nSCG for reconstructing phylogenetic relationships in parasitic lineages. A relaxed molecular clock approach with the same multi-locus dataset, revealed an ancient age of ∼91 MYA for Hydnoraceae. We then estimated the stem ages of all independently originated parasitic angiosperm lineages using a published dataset, which also revealed a Cretaceous origin for Balanophoraceae, Cynomoriaceae and Apodanthaceae. With the exception of Santalales, older parasite lineages tend to be more specialized with respect to trophic level and have lower species diversity. We thus propose the “temporal specialization hypothesis” (TSH) implementing multiple independent specialization processes over time during parasitic angiosperm evolution.

Robust l 2 – l ∞ filtering for discrete‐time Markovian jump linear systems with multiple sensor faults, uncertain transition probabilities and time‐varying delays

In this study, the authors present the research results on the robust investigate the robust l2 –l∞ filtering for Markovian jump linear systems with multiple sensor faults, uncertain probability transition matrix and time-varying delays. The multiple sensor faults are modelled as multiple independent Bernoulli processes with constant probabilities. The uncertain probability transition matrix is modelled via the polytopic uncertainties for each row in the transition matrix. By using the augmentation method, the filtering error system with stochastic variables is derived. Since of the stochastic variables, the traditional stability condition is not qualified for the analysis of the filtering error systems. Thus, the exponentially mean-square stability and the robust l2 –l∞ performance are adopted for the filtering error system. By choosing the Lyapunov-based method, sufficient conditions which can guarantee the exponentially mean-square stability and the robust l2 –l∞ performance are obtained in the forms of matrix inequalities. Based on these conditions, the filter design method is proposed and the estimator parameters can be obtained by solving a set of linear matrix inequalities. Finally, a numerical example with two modes is used to show the design procedure and the effectiveness of the proposed design approach.

The semantics and verification of timed service choreography

As a service composition and coordination language, the service choreography gives the global and neutral view on the collaboration among a collection of highly distributed services involving multiple different organizations or heterogeneous independent processes. In this paper, we extend the service choreography by introducing the explicit time activity, which can be used to specify and reason about the timed behaviour of Web service choreography. Then we explore an execution model for the proposed timed service choreography which possesses several novel features, such as timed activity, choreography composition, exception handling and finalization. Furthermore, a set of mapping rules is elaborately designed to translate the timed choreography into communicating sequential programs processes, thus the corresponding simulation and verification of Web services choreographies with timing restrictions can be carried out in the model checker process analysis toolkit. The case study shows that our approach is both effective and practical.

Asymptotically Efficient Multichannel Estimation for Opportunistic Spectrum Access

The problem of estimating the parameters of multiple independent continuous-time Markov on-off processes is considered. The objective is to minimize the total mean square error (MSE) under a constraint on the total sensing time. The Fisher information matrix for the primary traffic model and the maximum likelihood estimator are obtained. A sequential estimation strategy is proposed which operates under an epoch structure with growing epoch length. Specifically, the total sensing time in the current epoch is allocated among the on-off processes based on the current estimates of the parameters using observations obtained in previous epochs. It is shown that this sequential estimation strategy is asymptotically efficient as the total sensing time increases. This result finds application in opportunistic spectrum access where secondary users need to estimate the channel occupancy model of the primary system for efficient exploitation of spectrum opportunities.

A single series representation of multiple independent ARMA processes

This article shows that multiple independent time series from the same ARMA process can be represented by a single univariate ARMA time series through an interleaving of the original series. Using this result, existing univariate modelling software can be used to fit a single ARMA time series model simultaneously to multiple independent realizations of the same ARMA process. The interleaving approach and its properties will be presented and compared with alternative estimation options. It will be applied to the modelling of 66 years of daily maximum temperatures for Perth, Western Australia and to other time series models.