Study of the Impact of Replacement Granularity and Associated Strategies on Video Caching

Partial caching of large media objects such as video files has been proposed recently as the caching of entire objects can easily exhaust the storage resources of a proxy server. In this paper the idea of segmenting video files into chunks and applying replacement decisions at the chunk level rather than on entire videos is examined. It is shown that a higher byte hit ratio (BHR) can be achieved by appropriately adjusting the replacement granularity. The price paid for the improved BHR performance is that the replacement algorithm takes a longer time to converge to the steady state BHR. For the segmentation of video into chunks two methods are presented. The Fixed Chunk Size segmentation scheme that is rather simple and reveals the basic trade-off between byte hit ratio (BHR) and responsiveness to changes of popularity; the Variable Chunk Size segmentation scheme that uses the request frequencies to dynamically adjust the size of the chunk and is shown to be capable of combining a small response time with high BHR. Moreover, a variation of the fixed chunk size segmentation scheme is presented, which is shown to improve its performance by switching between different chunk sizes. Video segmentation is also considered as a mechanism to provide for caching differentiation based on access costs. By employing access cost dependent chunk sizes an overall access cost reduction is demonstrated.     

Enhancing Confidence in Drinking Water Quality for Improved Risk Decisions

Reliance is often placed on “indirect” indicators to detect changes in water quality in real time. However, there are challenges in using these indirect indicators, as they cannot replace standardized laboratory tests. As an alternative, reliance on three successive test cases is examined, based on “joint and conditional probabilities” of hazard (contaminant level exceeding acceptable level) that could enhance confidence in monitoring results: (i) a simple and quick screening analysis sensor (primary analyzer, having type-1 error, i.e., α = 0.10 and type-2 error, i.e., β = 0.05), (ii) a repeat of the analysis with the same rapid analyzer is feasible, and (iii) further analysis of the sample with a highly reliable capability is feasible (secondary analyzer with α = 0.05 and β = 0.02). This three-level monitoring protocol for obtaining the posterior probability of hazard is examined and a method is proposed for taking improved risk management decisions by accounting for sensitivity and specificity of monitoring instruments. The revised probability of hazard will ensure that a positive harmful detect is proven false or real with much greater certainty and series of action (or no action) is taken at the earliest.     

Automatic analysis of composite physical signals using non-negative factorization and information criterion

In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negative signal components, which are expressed by model functions. For analyzing the physical characteristics of a measured signal sequence, the parameters of the model functions are estimated. Furthermore, in order to quantitatively analyze real measurement data and to reduce the risk of improper decisions, it is necessary to obtain the statistical characteristics from several sequences rather than just a single sequence. In the present paper, we propose an automatic method by which to analyze composite signals using non-negative factorization and an information criterion. The proposed method decomposes the composite signal sequences using non-negative factorization subjected to parametric base functions. The number of components (i.e., rank) is also estimated using Akaike's information criterion. Experiments using simulated and real data reveal that the proposed method automatically estimates the acceptable ranks and parameters.     

Inferring gene regulatory networks with time delays using a genetic algorithm

Recently a state-space model with time delays for inferring gene regulatory networks was proposed. It was assumed that each regulation between two internal state variables had multiple time delays. This assumption caused underestimation of the model with many current gene expression datasets. In biological reality, one regulatory relationship may have just a single time delay, and not multiple time delays. This study employs Boolean variables to capture the existence of the time-delayed regulatory relationships in gene regulatory networks in terms of the state-space model. As the solution space of time delayed relationships is too large for an exhaustive search, a genetic algorithm (GA) is proposed to determine the optimal Boolean variables (the optimal time-delayed regulatory relationships). Coupled with the proposed GA, Bayesian information criterion (BIC) and probabilistic principle component analysis (PPCA) are employed to infer gene regulatory networks with time delays. Computational experiments are performed on two real gene expression datasets. The results show that the GA is effective at finding time-delayed regulatory relationships. Moreover, the inferred gene regulatory networks with time delays from the datasets improve the prediction accuracy and possess more of the expected properties of a real network, compared to a gene regulatory network without time delays.     

A modular neural network architecture for pattern classification based on different feature sets

We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g., combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.     

Network properties, species abundance and evolution in a model of evolutionary ecology

We study the evolution of the network properties of a populated network embedded in a genotype space characterized by either a low or a high number of potential links, with particular emphasis on the connectivity and clustering. Evolution produces two distinct types of network. When a specific genotype is only able to influence a few other genotypes, the ecosystem consists of separate non-interacting clusters (i.e. isolated compartments) in genotype space. When different types may influence a large number of other sites, the network becomes one large interconnected cluster. The distribution of interaction strengths--but not the number of connections--changes significantly with time. We find that the species abundance is only realistic for a high level of species connectivity. This suggests that real ecosystems form one interconnected whole in which selection leads to stronger interactions between the different types. Analogies with niche and neutral theory and assembly models are also considered.     

Group behavior time series anomaly detection in specific network space based on separation degree

Specific network space, including virtual space and practical space, is a space for executing group behavior on specified regions via network. Due to the variability and unpredictability of time series in group behavior in special network space, the detection of normal and abnormal borders faces significant challenges. The parameters in traditional time series mode need to be predefined such as clustering method and anomaly detection methods science the results influentially depend on the selection of parameters. According to the characteristics of data, this paper proposes an efficient method called separation degree algorithm that can construct the self-adaptive interval based on the separation degree model to filter out anomaly data in virtual and practical spaces. The advantage allows us to automatically find the self-adaptive interval to improve the accuracy and applicability of anomaly detection based on the characteristics of the data instead of set parameters of traditional methods in network space. The extensive experimental result shows that the proposed method can effectively detect anomaly data from different spaces.     

Stress-free state of the red blood cell membrane and the deformation of its skeleton

The response of a red blood cell (RBC) to deformation depends on its membrane, a composite of a lipid bilayer and a skeleton, which is a closed, twodimensional network of spectrin tetramers as its bonds. The deformation of the skeleton and its lateral redistribution are studied in terms of the RBC resting state for a fixed geometry of the RBC, partially aspirated into a micropipette. The geometry of the RBC skeleton in its initial state is taken to be either two concentric circles, a references biconcave shape or a sphere. It is assumed that in its initial state the skeleton is distributed laterally in a homogeneous manner with its bonds either unstressed, presenting its stress-free state, or prestressed. The lateral distribution was calculated using a variational calculation. It was assumed that the spectrin tetramer bonds exhibit a linear elasticity. The results showed a significant effect of the initial skeleton geometry on its lateral distribution in the deformed state. The proposed model is used to analyze the measurements of skeleton extension ratios by the method of applying two modes of RBC micropipette aspiration.     

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

LPS-induced bronchial hyperreactivity: interference by mIL-10 differs according to site of delivery

When administered to mice systemically or via the airways, LPS induces bronchoconstriction (BC) and/or bronchopulmonary hyperreactivity (BHR), associated with inflammation. Accordingly, a relationship between inflammation and allergic and nonallergic BHR can be hypothesized. We therefore studied the interference of the anti-inflammatory cytokine murine IL-10 (mIL-10) with LPS-induced lung inflammation, BC, and BHR. mIL-10 was administered directly into the airways by intranasal instillation or generated in vivo after muscle electrotransfer of mIL-10-encoding plasmid. Electrotransfer led to high mIL-10 circulating levels for a longer time than after the injection of recombinant mIL-10 (rmIL-10). rmIL-10 administered intranasally reduced lung inflammation and BHR after LPS administration into airways. It also reduced the ex vivo production of TNF-alpha by LPS-stimulated lung tissue explants. Two days after electrotransfer, mIL-10 blood levels were elevated, but lung inflammation, BC, and BHR persisted unaffected. Blood mIL-10 reaches the airways poorly, which probably accounts for the ineffectiveness of mIL-10-encoding plasmid electrotransfer. When LPS was aerosolized 15 days after electrotransfer, lung inflammation persisted but BHR was significantly reduced, an effect that may be related to the longer exposure of the relevant cells to mIL-10. The dissociation between inflammation and BHR indicates that both are not directly correlated. In conclusion, this study shows that mIL-10 is efficient against BHR when present in the airway compartment. Despite this, the muscle electrotransfer with mIL-10-encoding plasmid showed a protective effect against BHR after a delay of 2 wk that should be further investigated.     

Monitoring and Identification of Sepsis Development through a Composite Measure of Heart Rate Variability

Tracking the physiological conditions of a patient developing infection is of utmost importance to provide optimal care at an early stage. This work presents a procedure to integrate multiple measures of heart rate variability into a unique measure for the tracking of sepsis development. An early warning system is used to illustrate its potential clinical value. The study involved 17 adults (age median 51 (interquartile range 46–62)) who experienced a period of neutropenia following chemoradiotherapy and bone marrow transplant; 14 developed sepsis, and 3 did not. A comprehensive panel (N = 92) of variability measures was calculated for 5 min-windows throughout the period of monitoring (12±4 days). Variability measures underwent filtering and two steps of data reduction with the objective of enhancing the information related to the greatest degree of change. The proposed composite measure was capable of tracking the development of sepsis in 12 out of 14 patients. Simulating a real-time monitoring setting, the sum of the energy over the very low frequency range of the composite measure was used to classify the probability of developing sepsis. The composite revealed information about the onset of sepsis about 60 hours (median value) before of sepsis diagnosis. In a real monitoring setting this quicker detection time would be associated to increased efficacy in the treatment of sepsis, therefore highlighting the potential clinical utility of a composite measure of variability.     

DF++ : an adaptive buffer-aware probabilistic delegation forwarding protocol for Delay Tolerant Network

The delegated forwarding (DF) curb transmissions by forwarding the message to a node that holds high quality value seen by the message. However, DF assumes infinite buffer space that is not possible in real time applications. In addition, quality value computation considers the encountering history and does not account for additional network parameters such as aging and transitive connectivity. In this paper, we have proposed a routing protocol called as DF++ that compute quality value based on probabilistic model used in PRoPHET protocol and forwards the message to current node by adaptive computation of available buffer space. We have compared performance of DF++ with DF, Epidemic and PRoPHET routing protocols. The proposed DF++ has higher delivery probability and fewer message drop and transmissions.     

A space-time delay neural network for motion recognition and its application to lipreading

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.     

Risk‐adjusted monitoring of time to event in the presence of long‐term survivors

The use of control charts for monitoring schemes in medical context should consider adjustments to incorporate the specific risk for each individual. Some authors propose the use of a risk‐adjusted survival time cumulative sum (RAST CUSUM) control chart to monitor a time‐to‐event outcome, possibly right censored, using conventional survival models, which do not contemplate the possibility of cure of a patient. We propose to extend this approach considering a risk‐adjusted CUSUM chart, based on a cure rate model. We consider a regression model in which the covariates affect the cure fraction. The CUSUM scores are obtained for Weibull and log‐logistic promotion time model to monitor a scale parameter for nonimmune individuals. A simulation study was conducted to evaluate and compare the performance of the proposed chart (RACUF CUSUM) with RAST CUSUM, based on optimal control limits and average run length in different situations. As a result, we note that the RAST CUSUM chart is inappropriate when applied to data with a cure rate, while the proposed RACUF CUSUM chart seems to have similar performance if applied to data without a cure rate. The proposed chart is illustrated with simulated data and with a real data set of patients with heart failure treated at the Heart Institute (InCor), at the University of São Paulo, Brazil.     

Interaction characteristics and development pattern of sustainability system in BHR (Bohai Rim) and YRD (Yangtze River) regions,China

The well-run structure, function and dynamics of complex regional sustainability system are critical for maintaining regional sustainability stability, so exploring the interaction characteristics and development pattern of the system should be considerably significant. Based on the new comprehensive index system of sustainability system, the indices of social, economic and eco-environmental dimensions and nine thematic indices are firstly presented for BHR (Bohai Rim) and YRD (Yangtze River) regions during the period 2001–2010, and then the regional sustainability changes are further tracked by regional sustainability index (RSI) and regional coordination index (RCI). Furthermore, we mainly focus on the dynamic interaction characteristics and propose green development index (GDI) to reflect the development pattern of BHR and YRD from the perspective of taking all indicators into account. The study results show that: the average annual growth rates of the indices of social, economic and eco-environmental dimensions of the BHR region are 10.00%, 19.11% and 0.37% during the study period, while the ones of YRD region are 7.32%, 20.25% and 0.46%, respectively, demonstrating that economic progress is the fastest-improving pillar for RSI of the two regions; the standard deviations of the indices of three dimensions that can construct a triangle diagram and the amount of that failed to construct are both decreasing from 2001 to 2012, showing that the RCI of two regions have made some progresses; BHR have stayed in some positions where eco-environmental level gradually decreases (from 0.50 to 0.45) with the progression of socioeconomic situations (from 0.20 to 0.50), while BHR in a position where eco-environmental level gradually increases (from 0.53 to 0.57) with the progression of socioeconomic situations (from 0.25 to 0.55), revealing dynamic interaction characteristics, and then in their evolution trajectories, YRD has been principally at the intermediate sustainable development stage while BHR always at the potentially unsustainable one; the green social or economic development degree, associated with the uneven resource utilization and eco-environment occupancy among provinces, should contribute most to regional development pattern. The study guides a direction where the policy makers need to improve in regional sustainability management.     

Estimating Z‐ring radius and contraction in dividing Escherichia coli

We present a fluorescence recovery after photobleaching‐based method for monitoring the progression of septal Z‐ring contraction in dividing Escherichia coli cells. In a large number of cells undergoing division, we irreversibly bleached cytosolically expressed Enhanced Green Fluorescent Protein on one side of the septal invagination and followed the fluorescence relaxation on both sides of the septum. Since the relaxation time depends on the cross‐sectional area of the septum, it can be used to determine the septal radius r. Assuming that the fraction of the observed cells with r‐values in a given interval reflects the duration of that interval in the division process we could derive an approximate time‐course for the contraction event, as a population average. By applying the method repeatedly on individual cells, the contraction process was also followed in real time. On a population average level, our data are best described by a linear contraction process in time. However, on the single cell level the contraction processes display a complex behaviour, with varying levels of activity. The proposed approach provides a simple yet versatile method for studying Z‐ring contraction in vivo, and will help to elucidate its underlying mechanisms.     

A Full Bayesian Approach for Boolean Genetic Network Inference

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.     

Mixed‐Effects State‐Space Models for Analysis of Longitudinal Dynamic Systems

Summary The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject‐specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed‐effects state‐space models based on the longitudinal feature of dynamic systems. State‐space models with mixed‐effects components are very flexible in modeling the serial correlation of within‐subject observations and between‐subject variations. The Bayesian approach and the maximum likelihood method for standard mixed‐effects models and state‐space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E‐step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed‐effects state‐space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling.     

Classification of β-hairpin repeat proteins

In recent years, a number of new protein structures that possess tandem repeats have emerged. Many of these proteins are comprised of tandem arrays of β-hairpins. Today, the amount and variety of the data on these β-hairpin repeat (BHR) structures have reached a level that requires detailed analysis and further classification. In this paper, we classified the BHR proteins, compared structures, sequences of repeat motifs, functions and distribution across the major taxonomic kingdoms of life and within organisms. As a result, we identified six different BHR folds in tandem repeat proteins of Class III (elongated structures) and one BHR fold (up-and-down β-barrel) in Class IV (“closed” structures). Our survey reveals the high incidence of the BHR proteins among bacteria and viruses and their possible relationship to the structures of amyloid fibrils. It indicates that BHR folds will be an attractive target for future structural studies, especially in the context of age-related amyloidosis and emerging infectious diseases. This work allowed us to update the RepeatsDB database, which contains annotated tandem repeat protein structures and to construct sequence profiles based on BHR structural alignments.