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Influence map (IM) is often used as a decision supporting technology in game artificial intelligence (AI). However, the traditional influence map does not describe dynamic information. Some improved IM models can describe dynamic information, but not accurately enough. When an object moves, it would produce large influence in its moving direction than other directions. Therefore, the influence produce by the object to a location depends on the relation between the location and the object’s moving direction. This paper proposed a dynamic influence map model based on distance adjustment, DADIM. This model produces different influence values in different direction by adjusting the “distance” between two locations. This method can encode dynamic information into the influence map easily. Experiments show this model avoids the weakness of dynamic influence map with location prediction. Compared with other influence maps, this model could improve the performance of the game AI with time complexity being unchanged.

In this paper, a new deep learning and machine learning combined model is proposed for malware behavior analysis. One part of it analyzes the dependency relation in API (Application Programming Interface) call sequence at the functional level, and extracts features for random forest to learn and classify. The other part employs a bidirectional residual neural network to study the API sequence and discover malware with redundant information preprocessing. In the API call sequence, future information is much more important for conjecturing the semantic of the current API call. We conducted experiments on a malware dataset. The experiment results show that both methods can effectively detect malwares. However, the combined framework has better classification performance. The classification accuracy of the combined malware detection architecture is 0.967.

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood.MethodsWe developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology.ResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC.ConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

Mobile crowdsensing is a people-centric sensing system based on users’ contributions and incentive mechanisms aim at stimulating them. In our work, we have rethought the design of incentive mechanisms through a game-theoretic methodology. Thus, we have introduced a multi-layer social sensing framework, where humans as social sensors interact on multiple social layers and various services. We have proposed to weigh these dynamic interactions by including the concept of homophily, that is a human-related factor related to the similarity and frequency of interactions on the multiplex network. We have modelled the evolutionary dynamics of sensing behaviours by defining a mathematical framework based on multiplex EGT, quantifying the impact of homophily, network heterogeneity and various social dilemmas. We have detected the configurations of social dilemmas and network structures that lead to the emergence and sustainability of human cooperation. Moreover, we have defined and evaluated local and global Nash equilibrium points by including the concepts of homophily and heterogeneity. Therefore, we have analytically defined and measured novel statistical measures of social honesty, QoI and users’ behavioural reputation scores based on the evolutionary dynamics. Through the proposed methodology we have defined the Decision Support System (DSS) and a novel incentive mechanism by operating on the policies in terms of users’ reputation scores, that also incorporate users’ behaviours other than quality and quantity of contributions. To evaluate our methodology experimentally, we consider a real dataset on vehicular traffic monitoring crowdsensing application, Waze, and we have derived the disbursement of incentives by also comparing our method with baselines. Experimental results demonstrate that our methodology, based on both quality and quantity of reports and the local or microscopic spatio-temporal distribution of behaviours, is able to better discriminate users’ behaviours. This multi-scale characterisation of users (both global and local) represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems.

Background Tuberculosis (TB) is one of the main causes of mortality on the globe. Besides the full implementation of Revised National Tuberculosis Control Programme (RNTCP), TB continues to be a major public health problem in India. Methods In the present study, parameters of a TB model are estimated using Ensemble Kalman filter (EnKf) approach. Infection rate and fraction of smear positive cases of TB are estimated in context of India. Results and Conclusions Results reveal that the infection rate is highest in Manipur and the ratio of smear positive cases is highest in Pondicherry. The infection rate of TB in Manipur is found to be 2.57 per quarter for the period 2006–2011.

As natural systems continuously evolve, the human cooperation dilemma represents an increasingly more challenging question. Humans cooperate in natural and social systems, but how it happens and what are the mechanisms which rule the emergence of cooperation, represent an open and fascinating issue. In this work, we investigate the evolution of cooperation through the analysis of the evolutionary dynamics of behaviours within the social network, where nodes can choose to cooperate or defect following the classical social dilemmas represented by Prisoner’s Dilemma and Snowdrift games. To this aim, we introduce a sociological concept and statistical estimator, “Critical Mass”, to detect the minimum initial seed of cooperators able to trigger the diffusion process, and the centrality measure to select within the social network. Selecting different spatial configurations of the Critical Mass nodes, we highlight how the emergence of cooperation can be influenced by this spatial choice of the initial core in the network. Moreover, we target to shed light how the concept of homophily, a social shaping factor for which “birds of a feather flock together”, can affect the evolutionary process. Our findings show that homophily allows speeding up the diffusion process and make quicker the convergence towards human cooperation, while centrality measure and thus the Critical Mass selection, play a key role in the evolution showing how the spatial configurations can create some hidden patterns, partially counterbalancing the impact of homophily.

Highlights • Machine learning was used to develop models to predict COVID-19 positive patient. • Features were extracted from patient data using string matching algorithms. • Constructed a novel dataset from unstructured hospitalized patient information. • Used descriptive statistical analysis for frequency calculation of patient symptoms. • Identified significant symptoms of COVID-19 patients using five different ML models., The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.

Personal keystroke modes are difficult to imitate and can therefore be used for identity authentication. The keystroke habits of a person can be learned according to the keystroke data generated when the person inputs free text. Detecting a user's keystroke habits as the user enters text can continuously verify the user's identity without affecting user input. The method proposed in this paper authenticates users via their keystrokes when they type free text. The user keystroke data is divided into a fixed-length keystroke sequence, which is then converted into a keystroke vector sequence according to the time feature of the keystroke. A model that combines a convolutional neural network and a recursive neural network is used to learn a sequence of individual keystroke vectors to obtain individual keystroke features for identity authentication. The model is tested using two open datasets, and the best false rejection rate (FRR) is found to be (2.07%,6.61%), the best false acceptance rate (FAR) is found to be (3.26%, 5.31%), and the best equal error rate (EER) is found to be (2.67%, 5.97%).

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.

Biologically inspired computation has been recently used with mathematical models towards the design of new synthetic organisms. In this work, we use Pareto optimality to optimize these organisms in a multi-objective fashion. We infer the best knockout strategies to perform specific tasks in bacteria, which involve concurrent maximization/minimization of multiple functions (codomain) and optimization of several decision variables (domain). Furthermore, we propose and exploit a mapping between the metabolism and a register machine. We show that optimized bacteria have computational capability and act as molecular Turing machines programmed using a Pareto optimal solution. Finally, we investigate communication between bacteria as a means to evaluate their computational capability. We report that the density and gradient of the Pareto curve are useful tools to compare models and understand their structure, while modelling organisms as computers proves useful to carry out computation using biological machines with specific input–output conditions, as well as to estimate the bacterial computational effort for specific tasks.

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organisation and ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi –omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

Bacteria has been proposed in recent years as one approach to achieve molecular communication. Bacterial cells can harbour DNA encoded information and can deliver this information from one nanomachine to another by swimming (motility). One aspect of bacterial communication that could further enhance the performance of information delivery in bacterial nanonetworks is conjugation . Conjugation involves forming a physical connection between the bacteria in order to transfer DNA molecules (i.e., plasmids or chromosomes). However, the fragile physical connection between the bacteria is prone to breakage, in particular under mechanical stress. In this paper, a simple Forward and Reverse coding process is proposed to enhance the performance of information delivery in bacterial nanonetworks. The coding process involves segmenting messages into blocks and integrating this into the bacterial chromosome. Simulation work have been conducted to validate the efficiency of the coding process, where the results have shown positive performance compared to approaches that do not utilize coding or pure conjugation.

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks., Comment: Submitted to "Communication in Nonlinear Science and Numerical Simulation"

As the field of molecular communication continues to grow, numerous solutions have been proposed to enable communication between nanomachines. Amongst these solutions, bacteria communication nanonetworks has been proposed as a promising approach for molecular communication. This is driven by a number of attractive properties found in bacteria, which includes biased motility toward the destination through chemotaxis process, as well as the ability of bacteria to transfer genetic information between each other using conjugation. Bacterial conjugation is a major mechanism for Lateral Gene Transfer (LGT) that enables information transfer among bacteria. In this paper, we propose an opportunistic routing process in bacteria communication network using these two properties. The paper presents the simulation work to analyze the performance of message delivery for three different topology shapes, which includes grid, hexagon, and T-shape topologies. The aim of simulating on different shape topologies is to determine the impact that conjugation will have to improve message delivery. In all topologies, the use of conjugation helped improve the reliability of message delivery to the destination point. The paper will analyze various commonly used metrics used in communication networks, such as the average delay, the number of messages, as well as the distribution of messages and their originating node. The conjugation process is most beneficial in complexed shaped topologies, where the directionality from the source to the destination is a number of hops apart, as represented in the T-shape topology.

In this paper numerical transient simulations were used to investigate the air flow patterns, distribution and velocity, and the particulate dispersion inside an existing typical hospitalization room equipped with an advanced Heating Ventilation Air Conditioning (HVAC), with Variable Air Volume (VAV) primary air system designed for immune-suppressed patients never modelled before. The three-dimensional models of the room consider different, most typical, positions of the patients. Results indicate the best conditions for the high induction air inlet diffuser and the scheme of pressures imposed in the room to provide the effective means of controlling flows containing virus droplets. We believe that our work exemplifies the usefulness of numerical investigations of HVAC performances in real situations and provides important recommendations towards disease control and careful design and optimization of ventilation in hospital settings.

We report a preliminary investigation on interactions between networked social communities using the Ising model to analyze the spread of rumours. The inner opinion of a given community is forced to change through the introduction of a unique external source and we analyze how the other communities react to this change. We model two conceptual external sources: namely, “Strong-belief”, and “Propaganda”, by an infinitely strong inhomogeneous external field and a finite uniform external field, respectively. In the former case, the community changes independently from other communities while in the latter case according also to interactions with the other communities. We apply our model to synthetic networks as well as various real world data ranging from human physical contact networks to online social networks. The experimental results using real world data clearly demonstrate two distinct scenarios of phase transitions.

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.

Vehicular sensor networks (VSNs) enable brand new and promising sensing applications, such as traffic reporting, relief to environmental monitoring, and distributed surveillance. In our past work, we have designed and implemented {em MobEyes}, a middleware solution to support VSN-based urban monitoring, where agent vehicles (e.g., police cars) move around and harvest meta-data about sensed information from regular VSN-enabled vehicles. In typical urban sensing operations, multiple agents should collaborate in harvesting and searching for key meta-data in parallel. Thus, it is critical to effectively coordinate the harvesting operations of multiple agents in a decentralized and lightweight way. The paper presents a novel meta-data harvesting algorithm, called {em datataxis}, whose primary idea is to effectively cover a large search area by properly alternating foraging behaviors inspired by E. coli chemotaxis and L'{e}vy flights to favor agent movements towards ``information patches'' where the concentration of not-harvested meta-data is high. The proposal avoids harvesting work duplication via stigmergy-based prevention of useless concentration of agents in the same region at the same time. We have validated datataxis via extensive simulations that demonstrate how the proposed bio-inspired behavior of harvesting agents effectively balances their movements, by outperforming other decentralized strategies. Moreover, our solution has shown to be robust and to work well under a wide range of operation parameters, thus making it easily and rapidly deployable for different urban sensing operations.

The human genome contains repetitive DNA at different level of sequence length, number and dispersion. Highly repetitive DNA is particularly rich in homo- and di-nucleotide repeats, while middle repetitive DNA is rich of families of interspersed, mobile elements hundreds of base pairs (bp) long, among which belong the Alu families. A link between homo- and di-polymeric tracts and mobile elements has been recently highlighted. In particular, the mobility of Alu repeats, which form 10% of the human genome, has been correlated with the length of poly(A) tracts located at one end of the Alu. These tracts have a rigid and non-bendable structure and have an inhibitory effect on nucleosomes, which normally compact the DNA. We performed a statistical analysis of the genome-wide distribution of lengths and inter-tract separations of poly(X) and poly(XY) tracts in the human genome. Our study shows that in humans the length distributions of these sequences reflect the dynamics of their expansion and DNA replication. By means of general tools from linguistics, we show that the latter play the role of highly-significant content-bearing terms in the DNA text. Furthermore, we find that such tracts are positioned in a non-random fashion, with an apparent periodicity of 150 bases. This allows us to extend the link between repetitive, highly mobile elements such as Alus and low-complexity words in human DNA. More precisely, we show that Alus are sources of poly(X) tracts, which in turn affect in a subtle way the combination and diversification of gene expression and the fixation of multigene families.

Tracing the history of molecular changes in coronaviruses using phylogenetic methods can provide powerful insights into the patterns of modification to sequences that underlie alteration to selective pressure and molecular function in the SARS-CoV (severe acute respiratory syndrome coronavirus) genome. The topology and branch lengths of the phylogenetic relationships among the family Coronaviridae, including SARS-CoV, have been estimated using the replicase polyprotein. The spike protein fragments S1 (involved in receptor-binding) and S2 (involved in membrane fusion) have been found to have different mutation rates. Fragment S1 can be further divided into two regions (S1A, which comprises approximately the first 400 nucleotides, and S1B, comprising the next 280) that also show different rates of mutation. The phylogeny presented on the basis of S1B shows that SARS-CoV is closely related to MHV (murine hepatitis virus), which is known to bind the murine receptor CEACAM1. The predicted structure, accessibility and mutation rate of the S1B region is also presented. Because anti-SARS drugs based on S2 heptads have short half-lives and are difficult to manufacture, our findings suggest that the S1B region might be of interest for anti-SARS drug discovery.

Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes, including inflammation, allergy and neoplasia. Here we use maximum likelihood methods and bootstrap methods to investigate both the phylogenetic relationships in a large set of human chemokine receptor sequences and the relationships between chemokine receptors and their nearest neighbors. We found that CCR and CXCR families are not homogeneous. We also provide evidences that angiotensin receptors are the closest neighbors. Other close neighbors include opioid, somatostatin and melanin-concentrating hormone receptors. The phylogenetic analysis suggests ancient paralogous relationships and establishes a link between immune, metabolic and neural systems modulation. We complement our findings with a structural analysis based on wavelet methods of the major branches of chemokine receptors phylogeny. We hypothesize that receptors very close in the tree can form heterodimers. Our analyses reveal different characteristics of amino acid hydrophobicity and volume propensity in the different subfamilies. We also found that the second extra-cytoplasmic loop has higher rates of evolution than the internal loops and transmembrane segments, suggesting that selection, shifting, reassignments and broadening of receptor binding specificities involve mainly this loop.

The number of achaete-scute genes increased during insect evolution, particularly in the Diptera lineage. Sequence comparison indicates that the four achaete-scute genes of Drosophila result from three independent duplication events. After duplication, the new genes acquired individual expression patterns but, in Drosophila, their products can compensate for one another, which raises the question: why retain all four genes? The complexity of the spatial expression of these genes on the notum increased in the lineage leading to the higher Diptera, allowing the development of stereotyped bristle patterns. This probably coincided in time with gene duplication events, raising the possibility that an increase in gene copy number might have provided the flexibility necessary for more complex transcriptional regulation.

“All models are wrong, but some are useful.” George Box, statistician Nowdays since modeling is growing of importance, this sentence would reflect many things: the continuous improvement of developing new models in all scientific fields, the different level of abstractions that a model could express and our difficulties in modelling multiscale dynamical systems. If the scientific progress relies on asking the right questions, we observe that different modelling methodologies often suggest slightly different questions and provide similar or different answers. In the figure on the right, a point in parameter space, given by a set of parameter values, defines a dynamics on the state space. If the system is prepared in an initial condition, then the dynamics typically lead to an attractor, pictured here as a star. We believe that a better understanding of the compositional framework of different modelling will bring easiness in making sense of large amount of heterogenesous data gathered on varison scales and also in robust parameter estimation and reverse engineering properties of various types of networks and parameters relationship. We found that when the system has a multiscale structure, formal methods help in guiding the exploration across dimensionality. In particular we report that Shape Calculus can effectively interface with a wide range of methodologies for example: Hybrid Automata, Stochastic Simulations (implemented as Gillespie algorithm or software Agent) and Model Checking, Bayesian inference and Model Checking, ODE (Ordinary Differential Equation) and PDE. We can imagine Shape Calculus as “high order” set of levers that pull the low order procedures implemented. Here we investigate the emerging behavior of connecting the Shape high levers (the tissue, the social network) with the low lever types of gear-bevel, spur-lever, helical gear constituents, etc (the cells, the individuals). Multi-Method Multi-Level approach

Background: The β 2 -adrenoceptor polymorphisms occurring at amino acid positions 16 (arginine to glycine) and 27 (glutamine to glutamate) are known to be functionally relevant and also disease-modifying in subjects with asthma. However, the contribution of these polymorphisms to the development of the asthmatic phenotype or other markers for allergic disease remains to be established. Objective: This large family study examines the contributions of these polymorphisms in determining the heritable component of markers for allergic disease in asthmatic families. Methods: Three hundred twenty-four individuals from 60 families multiplex for asthma selected by means of an asthmatic proband were characterized for the following markers of allergic disease: asthma, atopy, and serum IgE. The polymerase chain reaction was used to generate a 234 base pair fragment spanning the region of interest, and the β 2 -adrenoceptor polymorphism was then defined by allele-specific oligonucleotide hybridization. Segregation analysis was then performed. Results: We found a significant association ( p = 0.009) between the glutamine 27 β 2 -adrenoceptor polymorphism and elevated levels of IgE, which was supported by the observation of linkage between IgE and β 2 -adrenoceptor polymorphisms at locus 27 ( p = 0.037). However, there was no association between either the arginine-glycine 16 or the glutamine-glutamate 27 β 2 -adrenoceptor polymorphism and an increased risk of asthma or atopy per se. Conclusion: The glutamine 27 β 2 -adrenoceptor polymorphism appears to contribute to IgE variability in families with asthma. However, it seems that although both amino acid 16 and 27 β 2 -adrenoceptor polymorphisms are disease-modifying in subjects with asthma, they do not contribute markedly to the development of the asthmatic phenotype. (J Allergy Clin Immunol 1997;100:261-5.)

We have analysed some aspects of the primary structure of the chromosome of the prokaryote Haemophilus influenzae and of the eukaryote Saccharomyces cerevisiae that share the same G+C content. In particular, we have investigated genomic patchiness over the gene size level (10 Kb) and that patchiness due to long homogeneous tracts. Long polypurine and polypyimidine tracts that are largely over-represented in S. cerevisiae chromosomes and under-represented in H. influenzae , are responsible for a large fraction of long correlation signals. Generating mechanisms of long homogeneous tracts are DNA replication slippage and duplication events that appear to be linked processes driving chromosome primary structure evolution.

Bone marrow hematopoietic stem cells (HSCs) are crucial to maintain lifelong production of all blood cells. Although HSCs divide infrequently, it is thought that the entire HSC pool turns over every few weeks, suggesting that HSCs regularly enter and exit cell cycle. Here, we combine flow cytometry with label-retaining assays (BrdU and histone H2B-GFP) to identify a population of dormant mouse HSCs (d-HSCs) within the lin(-)Sca1(+)cKit(+)CD150(+)CD48(-)CD34(-) population. Computational modeling suggests that d-HSCs divide about every 145 days, or five times per lifetime. d-HSCs harbor the vast majority of multilineage long-term self-renewal activity. While they form a silent reservoir of the most potent HSCs during homeostasis, they are efficiently activated to self-renew in response to bone marrow injury or G-CSF stimulation. After re-establishment of homeostasis, activated HSCs return to dormancy, suggesting that HSCs are not stochastically entering the cell cycle but reversibly switch from dormancy to self-renewal under conditions of hematopoietic stress.