Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury Objective: Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury. Methods: Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances. Results: 99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers. Conclusion: We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI. The diagnosis and treatment of traumatic brain injury (TBI) has received considerable attention. The military community may provide the biggest contribution to this interest because the signature injury of the wars in Iraq and Afghanistan is mild TBI (mTBI) [1] . mTBI is sometimes referred to as concussion, although the latter term is becoming less common in clinical and research contexts. The athletic community is also concerned with this condition, especially football and fighting sports, but also rugby, hockey, and soccer [2] [3] [4] [5] [6] [7] [8] . Although less newsworthy, falls cause the majority of head injuries in the US, with nearly 1.7 million TBI cases annually [9] . Worldwide, the annual incidence of mild TBI is estimated to be above 600 per 100,000 and, in addition to falls, motorcycle and bicycle accidents are also major causes [10] . As important as improvements in care are for veterans and athletes, such improvements can have a much broader impact on the health of communities around the world.. Although there is a need to improve the treatment of brain injury, perhaps the most significant hurdle is diagnosing mTBI. Current diagnostic standards are adequate for moderate and severe TBI because their signs and symptoms are more easily identifiable, but about 70-90% of TBI is mild, also known as concussion, and still difficult to recognize [10] . Additionally, the World Health Organization estimates that many mild injuries are not even seen by a health care practitioner because this lack of obvious and urgent symptoms fails to motivate patients to seek care [10] . Unfortunately, this does not mean that there are no long-term sequelae resulting from mTBI. According to current clinical research, mTBI sequelae include cognitive dysfunction, post-traumatic stress disorder, depression, anxiety, and dementia [2, 11] .. However, there are no currently accepted markers for clinical diagnosis of mTBI. Different organizations have created schematic tools for diagnosis, but these are subjective and the organizations do not completely agree on what constitutes a concussion [12] . For the greatest impact for military applications and throughout the world, as well as to minimize costs, a blood-based test would be ideal. Thus far such a test has not been found. There have been several candidate substances (S100B, neuron-specific enolase, glial acidic fibrillary protein, etc.), but none have succeeded for effective diagnosis of mild injury [13] . Because the search for a single biomarker has not succeeded, a composite panel may be an effective alternative. We present a method to help facilitate the identification of substances that have potential as biomarkers, which can then be validated experimentally.. As demonstrated with systems biology [14] , the molecular interactions that occur after neurological injury are complex. There may be no serum value for any of the individual components of this complicated interplay that are specific to neural injury. However, some specific combination of these values included in a panel has much greater potential for diagnostic accuracy. The first step in investigating which substances belong in such a panel is to identify the potential candidates for inclusion. In this paper, we describe a methodology to provide a list of substances that is intended to establish a base of current knowledge and provide insight into the development of a biomarker panel for mTBI diagnosis. We apply natural language processing to MEDLINE citations to extract semantic predications, which we represent as a network of potentially relevant substances interacting with their physiological environment. These semantic predications are subjectrelation-object triples, where the subjects and objects are UMLS concepts and the relation is derived from the UMLS Semantic Network as appropriate for a given concept pair. We then use network analysis techniques to identify a list that is focused on highly significant substances. We have explored the creation of a molecular interaction network that represents neural injury and is composed of semantic predications automatically extracted from the literature. We achieved our goal of providing substances with potential as biomarkers to support the diagnosis of mTBI. The methodology is based on a network of semantic predications representing the interaction of substances observed subsequent to neural insult. Combining semantic predications of TBI substance interactions into a network in this way correlates well with systems biology (and by extension, systems medicine), which is concerned with the complex network interplay of a biological unit and represents injury and illness as a perturbation to the network.. Predications were extracted by SemRep and the component subject or object concepts were mapped to nodes and their relationships (predicates) mapped to edges, creating a network of relations. This network represents a summary of the physiological and pharmacogenomic space of neurological injury, as presented in the literature included in MEDLINE. To identify clinically significant candidates for mTBI biomarkers, the network was then filtered by degree centrality and frequency, greatly reducing the density of concepts and relationships. The resulting network produced 17 compounds to be considered as mTBI biomarkers, both previously investigated and novel as TBI biomarker candidates. The interaction of several of these is discussed as the basis for a panel of biomarkers to more effectively diagnose mTBI than is currently possible. Most substances identified in this study as worthy of consideration as mTBI biomarkers fall into four general categories: previously studied biomarkers (amyloid beta-protein precursor, brain-derived neurotrophic factor, fibroblast growth factor 2, glial fibrillary acidic protein, neuron-specific enolase, S100b); neurotransmitters (glutamate, dopamine, norepinephrine); inflammation and cell injury markers (interleukin-6, calpain breakdown products, malondialdehyde, superoxide dismutase); and ubiquitous substances (glucose, lactate, calcium). Although all of the resulting substances were reviewed in depth during the methodology, the following illustrate the information contained in the resulting mTBI biomarker network and the information retrieved during the validation process. These examples suggest possible implications for clinical practice retrieved directly from the research literature. Several manual reviews have been undertaken to survey potential biomarkers for TBI [39, 40] and more specifically mTBI [41] [42] [43] . These authors search for citations specifically detailing clinical research of mTBI biomarkers and therefore contain only potential biomarkers that have already been investigated. Another limitation of the studies is the small number of citations reviewed (ranging from 26 [42] to 107 [43] ) due to the limitations of human review. Although no automated detection of potential TBI biomarkers exists in the literature, there are automatic systems to help diagnose other disorders, for example diabetes and obesity [44] . Although not related to mTBI, there is research related to the literaturebased discovery of other types of interaction networks Figure 1 Network of glutamate predications. Subject and object concepts are represented as nodes and predicates are represented as edges. Glutamate is common to all three predications and is, therefore, the most highly connected node in the network.. (though not specifically for biomarkers). One automatically generates an interaction network detailing gene involvement in vaccine-related fever using 170,000 citations from a PubMed search and a vaccine-specific ontology [45] . Another used citations containing the PubMed MeSH term human and containing sentences related to interferon-gamma, from which relationships were extracted and ranked using graph metrics [46] . Jordan et al. [47] present a keyword search method for identifying putative biomarkers for breast and lung cancer by searching for genes and proteins associated with a biological fluid keyword and either cancer. However, none of this work has made use of semantic predications, as we have, in the formation of an interaction network. There is a large body of work on literature-based discovery approaches many of which use SemRep semantic predications [26, [48] [49] [50] [51] [52] [53] [54] . These approaches may generate systems for discovery [55] [56] [57] [58] or are specific applications to predict various phenomena such as interactions between genes and proteins [46, 59] , cancer treatments [60, 61] , adverse drug reactions [49] , drug-drug interactions [50] , drug repurposing [51, 62] , asthma gene associations [63] , treatments for neovascularization in diabetic retinopathy [52] , relations between psychiatric and somatic diseases [64] , genes related to reactive oxygen species and diabetes [65] , and mechanisms for sleep disturbance [25] and the obesity paradox [53] . As shown in Figure 2 , citations related to nervous system trauma were retrieved from PubMed. From these, predications were extracted that contain a substance as the subject or object. These predications were organized into a single network which is then filtered to select for the most highly connected and frequent components. The substances included in this summary network serve as the list of potential mTBI biomarkers.

O O O B I O O O O O B I I O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A framework for building hypercubes using MapReduce The European Space Agency's Gaia mission will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), by providing unprecedented position, parallax, proper motion, and radial velocity measurements for about one billion stars. The resulting ∧ catalog will be made available to the scientific community and will be analyzed in many different ways, including the production of a variety of statistics. The latter will often entail the generation of multidimensional histograms and hypercubes as part of the precomputed statistics for each data release, or for scientific analysis involving either the final data products or the raw data coming from the satellite instruments.In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigm but without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission.In addition, we put forward the advantages and shortcomings of the deployment of the framework on a public cloud provider, benchmark against other popular solutions available (that are not always the best for such ad-hoc applications), and describe some user experiences with the framework, which was employed for a number of dedicated astronomical data analysis techniques workshops.

O B O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O


Approximations of the packet error rate under quasi-static fading in direct and relayed links The packet error rate (PER) is a metric of choice to compute the practical performance of communication systems experiencing block fading, e.g. fading processes whose coherence time is relatively slow when compared to the symbol transmission rate. For these types of channels, we derive a closed-form asymptotic expression which approximates the value of the PER for high signal-to-noise ratio (SNR). We also provide another approximation based on a unit-step formulation of the symbol error rate (SER). We show that the two approximations are related and may be derived from one another, thereby allowing us to obtain closed-form approximations of the block-fading PER in both coded and uncoded systems. We then show how these approximations may be used in practice, through the derivation of a packet error outage (PEO) metric covering the case where the links experience shadowing on top of block fading, as well as asymptotically optimal power allocations in relay channels under a block-fading hypothesis. In this paper, we studied the PER of communication systems subject to block fading effects. We derived a closed form upper bound on the coding gain, leading to asymptotic approximations similar to those of [6] for fast fading channels. We then studied unit-step approximations of the PER and showed that the approximation can be quite close on the whole SNR range if the threshold of the unit-step is chosen wisely. For fading models behaving polynomially near 0, we showed that the optimal threshold of the unit-step approximation, w.r.t. both absolute and relative error criterions, and the coding gain of the asymptotic approximation are directly related and may be deduced from one another. This allows a simple treatment of both coded and uncoded transmission schemes. We then applied these results to two practical use cases. By defining a packet error outage metric, we showed how to use the asymptotic approximation to derive a performance evaluation of systems subject to both fading and shadowing effects simultaneously. Finally, in the context of cooperative communications, we derived asymptotically optimal power allocations for relay channels which were showed to provide gains on the whole SNR range.

O O O B I I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Algorithm for haplotype resolution and block partitioning for partial XOR-genotype data Problems of haplotyping and block partitioning have been extensively studied with regard to the regular genotype data, but more cost-efficient data called XOR-genotypes remain under-investigated. Previous studies developed methods for haplotyping of short-sequence partial XOR-genotypes. In this paper we propose a new algorithm that performs haplotyping of long-range partial XOR-genotype data with possibility of missing entries, and in addition simultaneously finds the block structure for the given data. Our method is implemented as a fast and practical algorithm. We also investigate the effect of the percentage of fully genotyped individuals in a sample on the accuracy of results with and without the missing data. The algorithm is validated by testing on the HapMap data. Obtained results show good prediction rates both for samples with and without missing data. The accuracy of prediction of XOR sites is not significantly affected by the presence of 10% or less missing data. Single nucleotide polymorphisms (SNPs) are the most common type of mutations expressed by changes in a single position within DNA sequence, which are observed in at least 1% of the population. Most SNPs are bi-allelic meaning that they are defined by only two possible nucleotides (alleles) at their specific positions. Variation in the human genome underlies the differentiating features present in the population. Humans are diploid organisms, i.e., each chromosome is made of two distinct copies which are separately called haplotypes. A pair of haplotypes (inherited from the mother and the father) is a result of genotype decomposition and is considered to be a genotype with a known phase, i.e., genotypes with alleles assigned to one of the two chromosomes.. Knowledge of haplotypes decomposition of any genotype is extremely valuable in the large-scale analysis of complex human diseases, which are affected by combinations of multiple linked mutations and a set of environmental factors. For this reason, haplotype-based analysis has proven to be much more powerful in mapping complex human diseases than single-locus (SNP) based studies. Available molecular technologies do not allow cheap and efficient haplotype sequencing (also called haplotyping or haplotype resolution). Thus, the problem of haplotyping heavily relies on computational methods. Recent studies have demonstrated that the human genome has discrete block structures with limited haplotype diversity within each block. Using haplotypes partitioned into blocks allows yet another significant improvement in the process of mapping complex human diseases since it narrows the study of the genotype (or a pair of haplotypes) to the analysis of a specific region.. There has been significant effort done in developing haplotyping and block partitioning methods [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] , which resulted in fast and efficient algorithms. The demand for a new type of genotype data characterized by some degree of incompleteness has appeared more recently. This type of data can be produced in an extremely cost-efficient way. In particular, there is a number of less expensive molecular technologies (such as Denaturing High-Performance Liquid Chromatography (DHPLC) [16] ) that produce genotypes with partial information with regard to SNPs: they can only distinguish if a particular SNP position is heterozygous or homozygous for a given individual, but provide no information regarding the type of allele (out of the two possible) it carries at each position. This DHPLC method permits significant reduction of the cost of genotyping compared to conventionally used genotyping methods. Genotypes produced as a result of such partial genotyping technologies are called XOR-genotypes. Efficient methods of haplotyping and block partitioning for XOR-genotypes are still under investigation. Thus, it is essential to develop effective computational methods that can use XOR-genotypes for haplotyping and block partitioning.. Up to date there exists only one strategy [17] [18] [19] that was developed to solve the problem of XOR-haplotyping. The core of this strategy is the construction of the perfect phylogeny tree out of collection of XOR-genotypes (using GREAL -an implementation of the algorithm for Graph Realization). The constructed phylogenetic tree is then used to select few individuals which need to be fully genotyped. The phylogenetic tree and selected fully genotyped individuals are then applied to the rest of the sample to make complete haplotype inference. This algorithm also requires availability of full genotypes for completely homogeneous individuals. Although this strategy requires at most three heterogeneous individuals to be fully genotyped, there is still room for further improvement. First, the Perfect Phylogeny assumption used in GREAL is only valid within short sequences of SNPs or blocks, which usually are limited by 30 SNPs [18] and almost never exceed size of 100 SNPs. Thus, GREAL is not well suited for the long-range genotype data. Second, a special selection of individuals to be fully genotyped is performed as one of the steps. Thus, no arbitrarily mixed collections of XOR and full genotypes are assumed, which may be required to be practical in real world applications. Third, the strategy was implemented as several steps that need to be conducted separately: application of GREAL, introduction of the fully genotyped sequences, resolution of the rest of the sample. A direct XOR-genotypes-to-haplotypes algorithm would be preferred for practical applications. Forth, the utility of the XOR-haplotyping could be further improved by taking into consideration missing data that is often an unavoidable component of the real data samples.. This paper describes a new method for performing XOR-haplotyping. Our algorithm XOR-HAPLOGEN is designed to introduce a number of substantial improvements over existing methods: the proposed algorithm is able to process very long sequences of SNPs (the size of the input genotype matrix is currently limited by approximately 350,000 entries); the algorithm can process arbitrarily mixed XOR-genotype data (part of the data can be represented by XOR and part by the full genotype sequences); XOR-HAPLOGEN produces the haplotype matrix for the entire set of input individuals together with a block structure and its profile (lists of haplotype patterns, their frequencies for each block and block boundary scores); finally, XOR-HAPLOGEN also incorporates missing data into the model and, thus, is able to infer missing data entries. XOR-HAPL-OGEN is an improved version of our earlier developed algorithm HAPLOGEN [20] for haplotyping and block partitioning that was originally designed for use on a fully genotyped set of individuals and showed accuracy and speed comparable to that of existing methods for haplotype resolution [11, [13] [14] [15] . Procedurally XOR-HAPLOGEN is an extended version of HAPLOGEN to enable it to process XOR-genotypes in addition to regular genotypes. XOR-HAPLO-GEN is implemented as an R-package and has polynomial time complexity O(n 3 m. 2 ), where n is the number of individuals and m is the length of genotypes in a sample. The proposed algorithm is designed to process any number of XOR-genotypes with the only requirement that there would be a non-empty set of fully genotyped individuals in the sample. XOR-HAPLOGEN achieves good accuracy in haplotype decomposition with and without presence of missing data and is not significantly affected by the percentage of fully genotyped individuals in a sample. Operation of XOR-HAPLOGEN is loosely based on the principles of genetic algorithms: the population of potential solutions is encoded as binary strings; evolution of the population is performed by application of genetic operations such as mutation and selection based on the fitness value of each individual.. The rest of the paper is organized as follows: Section 2 describes the main assumptions and concepts used in the proposed algorithm. Section 3 explains the proposed algorithm in details and Section 4 provides results from validating the algorithm on publicly available data sets. Conclusions and directions for future work are given in Section 5. The proposed algorithm for XOR-haplotyping possesses several new valuable features that increase its practicality. XOR-HAPLO-GEN can process large data sets (up to 350,000 entries of the genotype matrix) and, in particular, is suitable for processing long SNP sequences taking into consideration haplotype block structure. XOR-HAPLOGEN can also be applied to the data with missing values. The algorithm is designed to infer haplotype resolution and construct block structure for XOR-genotype data samples with some non-empty subset of individuals represented by full genotypes. In testing the algorithm on the CEU data, with samples including different percentages of full genotypes and also with and without presence of the 10% missing data, the algorithm was able to produce haplotype resolution with low prediction errors for XOR sites (3.25-11.19% for samples without missing data, 5.47-11.47% for samples with missing data), heterogeneous (2.1-16.96% and 3.99-19.09%, respectively) and missing data sites (7.72-17.96% for 10% missing data). The accuracy of prediction was proportional to the percentage of fully genotyped individuals in a sample. While missing data contribute in some degree to the prediction error, the prediction errors for XOR sites are not affected. The prediction of XOR sites is accurate for samples with more than 10% of fully genotyped individuals. XOR-HAPLOGEN may be the first algorithm that allows processing of long-range SNP sequences with flexible amount of XOR-genotypes and possibility of missing data. Due to this fact the comparison of the results to other algorithms was not possible.. The proposed algorithm XOR-HAPLOGEN also produces the block structure which shows consistency for different percentages of included full genotypes. The block structure is stable for samples with 50% and higher percentages of full genotypes (with and without missing data).. As a final note, XOR-HAPLOGEN showed very competitive computational speed: it was as fast as the best algorithms for haplotyping and block partitioning [11, [13] [14] [15] designed for full genotypes when tested on the same data.. The fact that some individuals may contribute more information than the others and thus facilitate a significant decrease of the error rates leads the prospects for future research. The future work should include investigation of the ways to perform controlled selection of the fully genotyped individuals in order to increase the overall effectiveness of the proposed method. Although it was shown that for 10% of the missing data the prediction error for XOR sites is not significantly affected, the extent of the effect of the missing data on the accuracy of prediction of XOR sites may also be need to be studied. Algorithm's results consist of predicted XOR and heterogeneous sites and predicted block structure. Although attractiveness of the results depends on one's threshold for acceptable amount of error and the availability of fully genotyped data, the overall error levels are encouraging. For example, the prediction error for XOR sites in samples without missing data (column 2 of Table 1 ) dropped significantly from 11.19% (at 10% full genotyping) to less than 6.55% for the samples with only 20% or more of full genotypes. As can be expected, in the samples with missing data the overall error rates are higher due to the loss of information (Table 1 and 2) . Fig. 3 illustrates the average prediction errors for XOR sites (inset (a)) and the average switch rates for samples with and without missing data (inset (b)) in a comparative fashion. These graphs reveal one important detail: prediction errors for XOR sites are practically not affected by the presence of missing data for samples with 70% or less of full genotypes unlike the switch rates, which exhibit consistently lower rates for the samples without missing data. The overall difference between the switch rates across all percentages of full genotyping for samples with and without missing data is within 1.5-3% range.. Next, the stability of the block structure for samples with different percentages of included full genotypes was evaluated. Figs. 4 and 5 show the average block boundary scores calculated from 10 generated samples for each percentage of included full genotypes for groups with no missing and missing data, respectively. These figures show a significant amount of noise from the loss of information in homogeneous sites for the data sets with very low percentages of fully genotyped individuals. The noise gradually diminishes to reveal a very distinct block structure as the number of fully genotyped individuals in sample grows. This fact can be demonstrated by plotting a fraction of loci with low scores (for example, with scores less than 0.1 as shown in Fig. 6(a) ) and the median scores (Fig. 6(b) ) against the percentage of full genotyping. Thus, noise reduction is shown by increasing fraction of loci with low scores and decreasing score median. As can be expected, the fraction of loci with low scores is higher and the score median is lower for samples with missing data due to the additional noise created by the lost alleles.. Despite the noise the highest block boundary scores seem to be in a very good consistency even for the lowest percentages of fully genotyped individuals in a sample (when compared to the 100% baseline). For example, from Figs. 4 and 5 it is clear that, among others, the peaks of block boundary scores occurring around 30th, 47-50th, 120th, 150th and 170th positions stand out for most percentages above 0.25 threshold of full genotyping for both missing and no missing data. In addition, as can be seen from Fig. 5 missing data contributes to less distinct patterns in block boundary scores when they are compared for the same percentages of full genotypes in Fig. 4 (no missing data) .. The consistency of the patterns of possible block boundaries with each other and with the 100% baseline can be shown by computing cross-correlation function for every pair of signals (block boundary scores) corresponding to two different levels of full genotyping. Cross-correlation serves as the measure of similarity Fig. 3 . Prediction errors for XOR sites for samples with and without the presence of 10% missing data (a), switch rates for samples with and without the presence of 10% missing data (b). Therefore, it can be concluded that the block structure produced by the algorithm shows satisfactory pattern consistency even if percentage of fully genotyped individual included in a sample is low. Overall block boundary scores tend to clear up from most of the noise when the percentage of full genotypes is greater or equal than 50%.. XOR-HAPLOGEN took about 3.5 minutes to process each data set on the 2.3 GHz processor (either with or without the missing data) which was within the range of the running time of the best algorithms for regular (full-genotype) haplotyping and block partitioning [11, 13, 15] . The acceptable level of the prediction accuracy (which itself depends on the percentage of the fully genotyped individuals in the sample) in general should depend on the allowed cost-error trade-off. For this particular set of samples the prediction errors for XOR sites err XOR 's decrease significantly for sample with greater than 10% of full genotypes (with or without missing data). Thus it is preferable to use more than 10% of the fully genotyped individuals in a sample. Error rates err all and swr drop to low values and the block structure becomes stable for samples with 50% and higher percentages of full genotypes. Thus samples with 50% or more of full genotypes are the preferred choice for practical applications. We used the HapMap data described in [23, 24] and publicly available at http://www.hapmap.org to test XOR-HAPLOGEN. In particular XOR-HAPLOGEN was applied to the CEU data which were collected in 1980 from US residents with northern and western European ancestry by the Centre d'Etude du Polymorphisme Humain (CEPH) and consist of the genotypes of 30 family trios. The HapMap web site provides the phased data which were obtained using the PHASE software. The program PHASE implements methods for estimating haplotypes from population genotype data [8] . The phasing process used the trio information where available so that the published haplotypes are of a very high accuracy [24] . The HapMap Consortium reported extremely low estimated switch rate (error occur every 8 Mb in CEU as given in [24] ) which for all practical purposes can be regarded as negligible. We randomly selected a chromosome (chromosome 2) and took first 500 positions having the same location (first 500 positions) along the selected chromosome. These data contained 2.2% of loci that were homogeneous for all 30 genotypes. None of the loci was heterogeneous for all genotypes in the sample. On average, any given genotype had 21% of heterogeneous loci.

O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Spectral Decomposition for the Search and Analysis of RNA Secondary Structure Scales in RNA, based on geometrical considerations, can be exploited for the analysis and prediction of RNA structures. By using spectral decomposition, geometric information that relates to a given RNA fold can be reduced to a single positive scalar number, the second eigenvalue of the Laplacian matrix corresponding to the tree-graph representation of the RNA secondary structure. Along with the free energy of the structure, being the most important scalar number in the prediction of RNA folding by energy minimization methods, the second eigenvalue of the Laplacian matrix can be used as an effective signature for locating a target folded structure given a set of RNA folds. Furthermore, the second eigenvector of the Laplacian matrix can be used to partition large RNA structures into smaller fragments. An illustrative example is given for the use of the second eigenvalue to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs.

O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A genetic approach for long term virtual organization distribution An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. A Virtual Organization (VO) is a complex entity where dynamic collections of individuals and institutions agree to share resources (software services, computational resources, etc.). 1, 2, 3, 4 Some works have already stated that Multi-agent systems (MAS) and agent organizations are one of the possible technologies for the im-plementation of VOs.. 1,2,4 MAS are distributed systems where software agents are executed. These agents are defined as social, reactive and proactive according to its user's goals. 5 Computational performance is one of the key factors in the VO success.. 3 For instance, a poor optimized VO may offer services with response times that are unacceptable for the agents/users that request them. Since service consumers are likely to change service providers in a competitive environment, offering high quality services is crucial. 6, 7, 8, 9 A proper distribution of agents across the shared resources may pursue such goal since avoiding host overload may reduce service time response. Ideally, all of the resources should be employed with similar usage rates and none of them should be overloaded. The problem of distributing workload across shared distributed computational resources is known in the literature as load balancing. 10, 11, 12 In this article, we are interested in the problem of load balancing in long term VOs. Long term/Permanent VOs usually offer complex and stable services that persist over time (e.g., travel services, e-commerce services, etc.). 13, 14 Although this type of VO may need to adapt itself to accommodate new agents/resources, the initial agent distribution may remain as a basis for a long time.. In MAS, it is natural for agents to have different levels of confidence or untrust in their partners. Some of the partners may have not interacted enough to be considered as "trusted", or they may have experienced some problems that have hindered their relationship (e.g., payment difficulties). 15, 16, 17, 18 As a result of these experiences, some resource owners may be reluctant to share computational resources with certain partners. Additionally, open MAS make also reasonable to assume that different agents may have different software requirements (operating system, software libraries, etc.). 19 Consequently, these requirements are to be taken into account when distributing agents across shared resources.. Thus, the problem of load balancing in Virtual Organization consists of assigning resources (hosts) to agents. Due to the fact that the system may become large-scaled, the problem may become a large combinatorial problem where the load balancing must be optimized. Classic approaches, like A and A* algorithms, 20, 21, 22 are not computationally efficient enough to deal with such problems. However, Genetic algorithms (GAs) are algorithms based on the process of evolution in nature. 23, 24, 25, 26 They have proven to be especially interesting in solving optimization problems in large search spaces, and in situations where very little information about the domain is available. 27, 28, 29, 30 GAs are based in a population of solutions which iteratively converges towards high quality solutions. Several operations (crossover, mutation and selection) are performed over the population in order to produce the next generation (iteration) of solutions.. In this article, a solution for agent distribution across hosts in an agent-based VO is proposed. This approach is based on a Genetic Algorithm (GA) that is meant to be applied just after the VO formation phase in order to provide a basis distribution for a long term VO. The proposed GA takes into account trust issues, software requirements of the agents, and load balancing. Our goal is to give a proper distribution of agents in a long term VO according to the load balance and trust issues. More specifically, host overload is avoided while providing agents with the software requirements needed by agents (e.g., libraries). Two different aspects are optimized with our proposed solution: load balance and untrust level in the VO. This proposal is based on our previous work. 31 The main difference between this present work and our previous work is our proposed abstract MAS architecture, the methodology employed for the experiments and parameter tuning, and new experiments which show the performance of our proposed architecture in different scenarios (e.g., fitness evolution through time, different importance for load and untrust levels, etc.).. The remainder of this work is organized as follows. First, in Sec. 2, we describe some related work in the area of grid load balancing and multi-agent management. In Sec. 3, a formal definition of the problem is given. After that, an abstract architecture which supports VO load balancing is described in Sec. 4. The design of the GA is thoroughly described in Sec. 5. In Sec. 6, experiments that were carried out to validate the proposed GA are detailed. It includes how a good set of parameters were found, a comparison with different heuristics which solve the same problem, and an experiment where different importance is given to the untrust level and the load level. Finally, some lines of future work and conclusions are pointed out. In this work, an elitist Genetic Algorithm for the distribution of agents across the shared hosts in a long term agent-based Virtual Organization has been presented. The goal is to provide a proper distribution of agents so that poor performance due to computational problems is avoided. The main differences between the proposed method and other approaches, which mainly come from grid computing, are that the proposed method takes into account agent heterogeneity and partners' trust in each other. The problem is solved by means of a genetic mediated solution which is performed on top of an abstract multi-agent systems platform. The designed abstract architecture is currently being developed using the THOMAS framework, 2, 44 which runs on top of JADE and Magentix agent platform.. 34,36 THOMAS provides support for virtual organizations in open multi-agent systems. More specifically, it provides services for managing agent registration, managing agent organizations, and publishing agent services.. The proposed genetic algorithm employs mutation operator, ranking selection, and a special crossover operation which we have named as elitist crossover. When this operator is performed, one of the children inherits the most promising segments from the parents with higher probability. A subfitness function which employs domain specific information is used in order to assess the quality of the segments. Experimental results have shown that better results are achieved when our proposed GA is configured to work as uniform crossover using a highly elitist crossover operation.. Moreover, some experiments have been designed and carried out in order to compare the proposed approach with several well-known heuristics. More specifically, it has been compared with a grasp implementation, uniform crossover and several multipoint crossover genetic algorithms. Results imply that the proposed genetic algorithm is able to get statistically better results than the other methods. Moreover, it is robust to larger problems and more difficult problem instances.. Despite the fact that its goal is to provide solutions for long term VOs, the proposed genetic algorithm has been able to outperform faster heuristics (e.g., grasp) in relatively small time.. One of the issues that has not been thoroughly covered in this article is how to adapt the VO when new resources/partners dynamically appear. On the one hand, it seems reasonable to employ the designed genetic algorithm for a basis distribution for a long term VO. On the other hand, other heuristics are able to get less fit solutions very quickly (e.g., grasp). Therefore, it may be adequate to employ faster heuristics to adapt the current distribution to new changes. Once the distribution quality has decreased to a certain threshold, it may be advisable to recalculate a new solution by means of the proposed genetic algorithm. Nevertheless, this issue needs further research and it is appointed as future work. To the best of our knowledge, the problem of VO distribution in Multi-agent Systems has not been addressed before. However, there are three related areas: Multiagent systems management, agent-based virtual organization formation, and grid computing load balancing by means of genetic algorithms.. Multi-agent system management consists of the visualization and control of the physical and virtual resources located in a multi-agent system. 32 Several MAS platforms include management tools that allow agents to be distributed across platforms hosts. 19, 32, 33, 34, 35 Sanchez-Anguix, et al., 32 propose a multi-agent management architecture based on the Magentix platform.. 36 It provides a graphical tool which allows several management operations such as MAS visualization, agent creation, agent removal, and MAS' history visualization. However, the type of management is not automatic since it is carried out by a human operator. On top of that, trust issues are very restrictive since remote operations require SSH username and password. JADE and JADEX provide graphical tools which allow management actions to be carried out by a human operator. 34, 35 Nevertheless, its main aim is to provide developer tools instead of management tools, since remote actions can be performed without any kind of security. Aglets offers mobility services to agents, but as far as we know, trust and load balancing are not taken into account. 33 In Giampapa, et al., a graphical interface is presented which helps human administrators to launch agents based on their software requirements and the requirements offered by the different platform hosts. 19 In that sense, the idea is similar to the way we propose to handle agent requirements. However, these operations are launched by a human operator and they do not seek to automatically balance workload on hosts.. One of the most important issues regarding VO's is how they are formed. Partners should be selected according to several criteria such as the services they offer, service quality, which computational resources they provide, trust, and so forth. Norman, et al., propose methods for the rapid formation of agent-based VO according to the required service requested by an agent. 4 The solution is based on a constraint satisfaction problem solver which is used by a service provider agent in order to determine whether it can provide the full service, its current VO can provide the service, or it needs to form a VO to provide the service. Hoogendoorn, et al., propose a negotiation framework for VO formation. 37 Customer agents request for a set of tasks that need to be completed in a specific time interval and specific order. The system gathers bids from service provider agents regarding their preferences to perform the requested tasks and allocates them according to this information. Our proposed approach comes naturally after the process of VO formation. However, our approach focuses on VOs where computing resources may be pooled for different agents to be used, which is not considered in the previous works.. Grid load balancing is a close research area to our proposed work. 38, 39, 40 However, there is a main difference between both which resides in the nature of both types of systems. In grid systems, load balancing is usually achieved by distributing tasks among the different hosts which are part of the grid system. Tasks may be one-shot processes, whereas agents perform several tasks during their lifetime in the system, which may last until the end of the system's execution (e.g., a sell service in a e-commerce application). Thus, in grid systems, time response for tasks is usually the object of optimization. Given agents' characteristics, it may be more reasonable to provide balanced resource utilization in a MAS setting. Therefore, system optimization actions such as load balancing have slightly different goals in grid and MAS systems. Regarding grid load balancing works, Di Martino studied the use of a GA whose aim is task distribution in a Grid environment. 38 In this work, it is assumed that different tasks have different constraints which affect where they can be executed. The GA takes these constraints into account and looks for a correct task allocation. However, trust issues are not taken into account by the GA. Later, Cao, et al., used a GA to schedule different tasks in a local grid environment.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Feasibility of pupillary light reflex analysis to identify vitamin A deficiency in Japanese black cattle An image segmentation method to calculate pupil sizes of cattle was developed.Two cattle were identified as severe vitamin A deficiency by our method.Cattle had smaller resting pupil in the dark during vitamin A deficient stage. To produce beef with a higher marbling standard, Japanese black cattle farmers usually actively attempt to lower the serum vitamin A level in the fattening period to a minimum desired level (about 30IU/dL). However, early identification of suboptimal vitamin A deficiency in the cattle is important to prevent them from becoming susceptible to contracting serious diseases. In this study, we investigated the feasibility of using Pupillary Light Reflex (PLR) analysis to identify vitamin A deficient cattle during this fattening period. PLRs of 43 cattle were recorded monthly from June 2012 to February 2013 using a 2CCD camera based handheld machine vision system. A new image processing algorithm to segment the pupil from the background was developed. Compared with manually selected results, the root mean square error associated with the constriction amplitude (CA) acquired by the image processing algorithm was only 2.3%, indicating the effectiveness of this algorithm. No significant differences were found between results of CA from fattening cattle in the high (>60IU/dL) and low (<30IU/dL) vitamin A period. However, two fattening cattle were identified with severe vitamin A deficiency, because of their weak PLR (CA<10%). Initial Pupil Roundness (IPR) results showed cattle in a dark environment possessed less-dilated pupils during the vitamin A deficient period (p<0.05). These results highlight the potential, as well as the limitations of this method. Due to the natural variation of PLR in healthy cattle, monthly measurements are not sufficient to make accurate identification. To realize early identification of severe vitamin A deficiency in fattening cattle, a more frequent PLR measurements regime needs to be explored.

O O B I I O O O B I I O B I I O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O


Accelerating Boolean Matching Using Bloom Filter Boolean matching is a fundamental problem in FPGA synthesis, but existing Boolean matchers are not scalable to complex PLBs (programmable logic blocks) and large circuits. This paper proposes a filter-based Boolean matching method, F-BM, which accelerates Boolean matching using lookup tables implemented by Bloom filters storing precalculated matching results. To show the effectiveness of the proposed F-BM, a post-mapping re-synthesis minimizing area which employs Boolean matching as the kernel has been implemented. Tested on a broad selection of benchmarks, the re-synthesizer using F-BM is 80X faster with 0.5% more area, compared with the one using a SAT-based Boolean matcher. Boolean matching is a widely used technique in field programmable gate array (FPGA) technology mapping [7] , post-mapping re-synthesis [1] and architecture evaluation [2] . Ideally, an FPGA Boolean matcher should be scalable to large Boolean functions and complex PLB structures in terms of both runtime and memory, and be flexible to accommodate different PLB structures. Most existing Boolean matching algorithms are based on function decomposition [4] , [8] or function canonical forms [5] , [6] .. mentable functions of that PLB should be considered as matching candidates. Due to the computation complexity of canonical forms, this technique can only handle functions with limited input size.. Recently, due to significant improvements of modern SAT solver [11] , SAT-based Boolean matching (SAT-BM) [1] was proposed, which offers great flexibility in handling various PLB structures. However, even with numerous improvements [3] , [7] , [12] , the expensive computational complexity of SAT-BM still limits its application to complex PLBs.. In this paper, we use Bloom filter [9] based lookup tables to accelerate Boolean matching, where partial sets of pre-calculated implementable and non-implementable functions of target PLBs are stored. These lookup tables help to quickly filter out non-implementable functions in multiple calls of a Boolean matcher, and the time-consuming SAT-BM is only called for remaining functions. Different from a lookup table-based Boolean matching [15] , our filter-based Boolean matching (F-BM) is capable of handling more complex PLBs.. To verify the effectiveness of the proposed F-BM, we integrate it into a post-mapping re-synthesis algorithm which minimizes area (i.e., LUT number) with the same logic depth constraint [1] . Tested on three different benchmark sets (MCNC, IWLS and Industrial designs) using 3 GB memory, the re-synthesizer geared with F-BM is 80X faster with 0.5% more area than the one with the state-ofart SAT-BM [12] . With a 1500-second timeout, which is a common practice to avoid excessive runtime, F-BM based re-synthesizer reduces 2X more LUTs than re-synthesizer based on SAT-BM [12] .. The remaining of the paper is organized as follows. Section 2 introduces preliminaries. Section 3 describes the proposed F-BM algorithm. Section 4 presents the postmapping re-synthesis using the proposed F-BM and experimental results. Section 5 concludes the paper. In this paper, we have presented F-BM, which accelerates Boolean matching using Bloom filter-based lookup tables to quickly prune non-implementable functions with affordable memory. Using post-mapping re-synthesis which minimizes area without increasing logic depth as an application, experiments on MCNC, IWLS 2005 and Industrial design benchmark sets show that re-synthesizer geared with F-BM using 3 GB memory space is 80X faster than the one with a stateof-art SAT-BM [12] , with only 0.5% more area. To achieve the same area, F-BM with learn strategy is still more than 5X faster with the same memory cost.. In the future, we will target functions with more inputs and explore different structures of the Bloom filter. For example, we observe that top-10% most common 9-input Boolean functions in MCNC circuits cover 50% of 9-input cuts, and thus we can design a multi-level Bloom filter with different false positive rates at different levels, e.g., a lower false positive rate for 10% of most frequent functions and a higher false positive rate for the rest, to trade accuracy to memory. In addition, we plan to apply our F-BM to other CAD tasks, such as technology mapping, physical synthesis, etc. Finally, we'll seek methods to store matched configurations as well as satisfiability information to further reduce the number of SAT calls.

O B I O B I B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O


Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity Background: A substrate cycle is a set of metabolic reactions, arranged in a loop, which does not result in net consumption or production of the metabolites. The cycle operates by transforming a cofactor, e.g. oxidizing a reducing equivalent. Substrate cycles have been found experimentally in many parts of metabolism; however, their physiological roles remain unclear. As genome-scale metabolic models become increasingly available, there is now the opportunity to comprehensively catalogue substrate cycles, and gain additional insight into this potentially important motif of metabolic networks. In this study, we present a novel algorithm for the discovery of substrate cycles in large scale metabolic networks by identifying cyclical EFMs in hierarchical modules designed to preserve cyclical interactions using our ShReD metric. Since ShReD-based modules naturally group reactions together based on the shared consumption and production of metabolic cofactors, they serve as a suitable search space to identify substrate cycles that have a net consumption or production of specific cofactors. We show that several representative substrate cycles are identified within their functional context based on known metabolic pathways whose reactions are present in each module. More importantly, we show that many of those substrate cycles are not identified if alternative partition metrics are used instead of ShReD to determine the hierarchical modularity, demonstrating the value of our ShReD-based algorithm for discovering substrate cycles.. The methodology used to identify the substrate cycle motif can be used to exhaustively enumerate cyclical EFMs in hierarchically arranged modules, allowing the analysis to circumvent the scalability limitation of existing EFM analysis tools. Compared to connectivity-based metrics, the ShReD metric used here is more likely to preserve cyclical interactions among network components, and thus is well suited to generating hierarchically partitioned modules for cyclical EFM analysis. To illustrate the advantage of ShReD-based partitioning, we show that several substrate cycles identified in the ShReD modules and reported in the literature are not identified in modules generated using a representative connectivitybased partition metric. In this study, we utilized a network partition approach to identify substrate cycles in the context of metabolic modules. Substrate cycles have been found experimentally in many parts of metabolism, particularly across opposing reactions of glycolysis and gluconeogenesis. However, due to the complexity of metabolic networks, comprehensive characterization has been difficult. In conjunction with large-scale metabolic models, computational approaches offer the benefit that they can systematically consider most (if not all) reactions in a cell type or organism.. Performing motif searches on large-scale networks is challenging, and often intractable, unless the motif is very small. One way to address this problem is to place a upper limit on the number of components in the motif, but this can be arbitrary [16] . On the other hand, finding substrate cycles by enumerating all directed cycles of any size is computationally intractable, because the number of cycles may grow non-linearly with the number of reactions in a cycle. Recently, Gebauer and coworkers addressed this challenge by narrowing their search to cyclical EFMs that metabolize a specific cofactor, ATP. While the same algorithm could be used to target other cofactors, there is the potential drawback that relationships between cyclical EFMs involving different cofactors could be missed. In our work, we instead apply a constraint on the search space. To identify appropriate search spaces while retaining contextual information, we hierarchically partition the network into modules such that the modules are enriched in the desired motif, i.e. metabolic cycles. The trade-off here is that we compromise on the possibility of finding longer cyclical EFMs, as shown by the relatively short cyclical EFM lengths (Figure 3) . Even for the module with the largest number of cyclical EFMs, the lengths range between 2 and 13 reactions (Additional file 1: Figure S1 ), compared to lengths of up to 100 reactions that can be identified from a global analysis [8] . The benefit of a modular approach is that one can be exhaustive in searching for all cyclical EFMs in a given module at a particular hierarchical resolution, which can then facilitate the Table 2 Number of cyclical EFMs associated with each cofactor Figure 3 For each module, the median cyclical EFM length is plotted against the number of reactions in the module. The median cyclical EFM lengths for the Hepatonet1 modules span between 2 and 8 reaction steps.. association of a cyclical EFM with a recognizable metabolic function. Defining an appropriate neighborhood in the network to search for a motif can be challenging, and has often relied on a heuristic such as setting a fixed path distance around a reaction of interest. ShReD-based partition provides a systematically derived grouping of reactions that are enriched with metabolic cycles, and thus defines a natural search space for substrate cycles.. In this paper, we have been thus far careful not to use the terms 'cyclical EFM' and 'substrate cycle' interchangeably. The cyclical EFMs were identified solely based on the stoichiometry and reaction directionality as specified in the published model. While these EFMs could represent substrate cycles, it cannot be concluded that the substrate cycle is active in vivo. For example, many of the cyclical EFMs involve two reversible reactions where one consumes NADP + and produces NADPH in the forward direction, and the other consumes NADH and produces NAD + in the reverse direction (Table 1 , module # 144,976, Figure 2A ). However, whether or not these reactions can indeed operate in opposite directions at the same time with a net conversion of NADP to NADPH at the expense of NADH (or vice versa) depends on the intracellular conditions (e.g. reactant and product concentrations) influencing the thermodynamic driving force. One example of this type of substrate cycle that has been observed in vivo occurs between isocitrate and ketoglutarate, where NAD-dependent isocitrate dehydrogenase (IDH) catalyzes the conversion of isocitrate to ketoglutarate and NADP-dependent IDH catalyzes the conversion of ketoglutarate back to isocitrate [17] .. The thermodynamic feasibility of these substrate cycles could in principle be determined based on Gibbs free energy estimates and experimental flux data [18] . Several earlier studies have considered thermodynamic constraints specifically in the context of cycles. Beard and co-workers formulated the "loop law", which states that the net flux around a loop internal to a reaction network must be zero [19] . It has been shown that these internal loops could be identified using extreme pathway analysis [20] or by enumerating linear combinations of vectors that span the null space of the underlying reaction network. While mathematically rigorous, enumerating null space vector combinations can become intractable for large-scale problems, similar to EFM analysis. More recently, additional methods have been described that utilize the Gibbs free energy criterion of the Second Law [21] to eliminate thermodynamically infeasible reaction cycles. A computationally attractive alternative that does not rely on Gibbs energy parameters was described by Schellenberger and coworkers, who showed that the loop law can be integrated into constraint-based flux analysis by reformulating the optimization problem as a modified mixed integer program (MIP) [22] . The main difference between the present work and these previous studies lies in the type of loop reactions that are targeted for identification. The aforementioned studies sought to eliminate infeasible loops, i.e. futile cycles, when computing flux distributions, whereas the present work seeks to identify feasible loops, i.e. substrate cycles.. In interpreting the results, one also has to consider the "goodness" of the modular partitions. Our algorithm places reactions into groups so as to enrich the modules in cycles. However, it is possible that the algorithm finds a locally optimal result due to the nonlinearity of the problem. If this happens early in the partition process, then it is possible that our algorithm does not identify some substrate cycles. For example, Peterson and coworkers have reported on a metabolic cycle in the rat liver involving pyruvate carboxylase, malate dehydrogenase, and malic enzyme with net oxidation of NADH and NADPH [23] . Our algorithm does not identify this substrate cycle, because cytosolic malic enzyme groups with mitochondrial pyruvate carboxylase and malate dehydrogenase only near the parent module (# 141,802), which is too large for EFM enumeration. One way to address this limitation is to include activity data. Recently, we showed that weighting the edges with metabolic flux data results in different modularity, reflecting the metabolic state of the system [13] . We found that weighting the edges to reflect reaction engagements better ensured that highly active cycles are prioritized in partitioning the network. The practical challenge in adopting this scheme to analyze large-scale networks is obtaining flux data from isotope labeling experiments. On the other hand, it has been shown that methods such as flux balance analysis (FBA) and flux variability analysis (FVA) could reasonably circumscribe the feasible space of flux distributions for a number of genome-scale models.. Prospectively, the discovery of new substrate cycles could yield novel drug targets for metabolic diseases such as obesity, diabetes and cancer [24] . For example, a proposed mechanism for the insulin sensitizing action of thiazolidinedione (TZD) drugs is to induce glycerol kinase gene expression and thereby promote a substrate cycle between lipolysis and fatty acid esterification [25] . Induction of glycerol kinase and activation of the "futile" cycle could reduce the efflux of free fatty acids, whose levels in serum correlate with obesity-related insulin insensitivity. While it remains to be established that this futile cycle is a dominant mechanism in vivo, other studies have corroborated that the glycerol kinase-dependent substrate cycle is a viable mechanism for limiting free fatty acid efflux from human adipose tissue [26] . In addition, a study on cancer cachexia found that the activity of triglyceride-fatty acid substrate cycle was elevated in tumors of murine white adipose tissue due to differential expression of the cycle enzymes [27] , which sets up an intriguing possibility of modulating tumor growth by manipulating these enzymes.. The Cori cycle is another substrate cycle with increased activity in cachectic patients, where lactic acid produced in the tumor is converted to glucose in the liver at the expense of ATP [28] .. The methodology presented in this paper could be extended to identify other substrate cycles that span multiple organ systems, similar to the Cory Cycle. This will obviously require metabolic models that describe more than one cell type or tissue. In this regard, genome-scale metabolic models representing the metabolic networks of multiple interacting cell types [29] or organisms [30] offer exciting possibilities to identify novel, inter-organ or inter-species substrate cycles in the context of recognizable biochemical functions as defined by the corresponding module. Cellular metabolism is exceedingly complex, involving the coordinated actions of many enzymes and regulatory molecules. A useful way to model cellular metabolism is to represent it as a network of biochemical reactions, where one enzyme-catalyzed reaction connects to another through shared reactants, products, and/or cofactors. A common theme in the study of these systems has been to relate a network's function(s) to its layout or "topology". For example, evolved networks typically harbor hubs to which less connected nodes attach as they join the network. This type of "small-world" property has been demonstrated for metabolic networks, with implications for evolution of metabolic functions. Another important property is modularity; i.e., these networks appear to contain smaller subsystems, analogous to integrated circuit modules comprising a larger digital circuit, which has practical implications for engineering biological cells to acquire new synthetic functions [1] .. Several studies have shown that the modules in a metabolic network have hierarchy [2] . These studies utilized graphs, which facilitated the use of established algorithms to compute topological properties. Recently, we introduced a graph-based metric, termed Shortest Retroactive Distance (ShReD), to assess the degree of mutual influence, or retroactive interactions, between reactions in a metabolic network. This metric was used to hierarchically partition a metabolic network into modules that are enriched in allosteric feedback loops and metabolic cycles [3] . Partitioning a network to achieve enrichment of a particular feature, or motif, offers the benefit that the motif can be studied in its modular context.. One motif of particular interest is the substrate cycle, which refers to a set of reactions that forms a loop and does not lead to a net production or consumption of the participating metabolites. Thus defined, a substrate cycle would be thermodynamically infeasible without coupling the cycle's operation to a thermodynamically favorable process such as ATP hydrolysis. Examples of experimentally confirmed substrate cycles involve opposing reactions in glycolysis and gluconeogenesis: inter-conversion of glucose and glucose 6-phosphate; fructose 6-phosphate and fructose 1,6-bisphosphate; and phosphoenolpyruvate and pyruvate. Often also labeled as futile cycles for the seemingly wasteful energy expenditure, substrate cycles have more recently been ascribed physiological functions, for example, thermogenesis [4] . Futile or substrate cycles are not to be confused with infeasible loops, which refer to thermodynamically infeasible cycles.. In the context of cellular homeostasis, substrate cycles could enable the cell to maintain an independent steadystate cycle flux, where the cycle flux can in theory fluctuate without directly altering other fluxes in the metabolic network, provided the cycle does not drastically disturb the cofactor pools. This feature could promote local robustness, which is a key property of modularity [5] . Conversely, if a substrate cycle significantly impacts the consumption or generation of a particular cofactor, then the enzymes of the cycle could be manipulated to selectively adjust the cofactor level while minimally perturbing other parts of metabolism. This type of direct and selective targeting of cofactors could be quite useful in modulating cellular energy expenditure. A substrate cycle in adipose tissue, esterification and hydrolysis of triglycerides, has been investigated as a target to treat obesity through manipulation of cellular bioenergetics [6] . Differential expression of substrate cycle enzymes has also been explored as a possible approach to modulate cancer cell metabolism [7] .. In this light, comprehensively characterizing substrate cycles embedded throughout metabolism could not only shed light on the physiological role of this motif, but also discover potentially novel targets to manipulate metabolic function. Recently, Gebauer and coworkers showed that cyclical elementary flux modes (EFMs) are potential substrate cycles [8] . Using EFMEvolver [9] , the authors found more than 200,000 cyclical EFMs, with a median length (defined as the number of reactions in an EFM) of 35. However, even this large number is likely an underestimate, as the authors focused on substrate cycles involving one specific cofactor, ATP. Given the very large number of potential substrate cycles, the analysis would be greatly facilitated by placing the cycles into context. We present in this paper an approach for identifying substrate cycles in the context of hierarchical modularity. We employ the ShReD metric to partition a large-scale reconstruction of human liver metabolism (HepatoNet1) [10] into modules enriched in metabolic cycles, and conduct an EFM analysis on each module at varying levels of partition hierarchy. We find that it is possible to complete an exhaustive EFM enumeration for all but a small number of modules at the top of the hierarchy. Interestingly, many of the cyclical EFMs identified in this study span several metabolic modules, including transport, lipid synthesis, folate metabolism, sugar metabolism, and amino acid metabolism. The operation of these cyclical EFMs are coupled to many different cofactors as well as transport of inorganic hub compounds such as sulfate, phosphate, and hydronium ions.

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Performance analysis of broadcast protocols in ad hoc networks based on self-pruning Abstract-Self-pruning is an effective method to reduce broadcast redundancy in ad hoc wireless networks. Unlike flooding, in a self-pruning broadcast protocol, a node may not forward a broadcast packet if a certain self-pruning condition is satisfied based on the neighborhood information. For each broadcasting, only a subset of nodes forward the broadcast packet and still guarantee the complete network delivery under the ideal network situation that no packet is lost due to packet collision and node mobility. We evaluate the performance of the family of self-pruning protocols under various network situations with ns2. The objective is to observe the efficiency and reliability of these protocols as a function of network density, congestion, and mobility, and provide a guideline of implementation in the "real world". Our performance analysis reveals that the protocol reliability is barely affected by packet collision. However, most self-pruning protocols suffer from low delivery ratio in highly mobile networks. We further explore various techniques that improve the delivery ratio and show that both high efficiency and reliability can be achieved in highly mobile networks. Ad hoc wireless networks (or simply ad hoc networks) are dynamic in nature, where global information/infrastructure such as minimal spanning tree is no longer suitable to support broadcasting. Flooding is a simple approach to broadcasting without global information/infrastructure; in flooding, a broadcast packet is forwarded exactly once by every node. However, due to the broadcast nature of wireless communication media, redundant transmissions in blind flooding may cause the broadcast storm problem [1] , in which redundant packets cause contention and collision.. Self-pruning is an effective method in reducing broadcast redundancy. In self-pruning-based broadcast protocols [2] , [3] , [4] , [5] , [6] , [7] , [8] , each node collects neighborhood topology information via exchanging "Hello" messages and extracts broadcast history information from incoming broadcast packets. Each node decides its role in a specific broadcasting: it either becomes a forward node and forwards the broadcast packet, or becomes a non-forward node and does nothing. Collectively, forward nodes, including the source node, form a connected dominating set (CDS) and ensure the coverage. A set of nodes is a dominating set if every node in the network is either in the set or a neighbor of a node in the set. Recently, we proposed a general self-pruning scheme [9] that combines the strength of several existing broadcast protocols. According. This work was supported in part by NSF grants CCR 9900646, CCR 0329741, ANI 0073736, and EIA 0130806. Email:{fdai,jie}@cse.fau.edu.. to this generic scheme, several existing broadcast algorithms can be viewed as special cases of a coverage condition with various configuration parameters. The term "coverage" comes from the fact that if a node's neighbor set is "covered" by several forward nodes, it becomes a non-forward node. A simulation study was conducted to compare the performance of several special cases of the coverage condition, and examine the effects of several implementation options. Although the coverage condition is a localized algorithm that is based on only local information and converges in constant rounds, it exhibits similar average efficiency (i.e., percentage of forward nodes) to several non-localized broadcast algorithms that provide constant approximation ratios. However, those protocols were simulated in networks without collision or mobility.. In this paper, we first evaluate the family of self-pruning protocols in "real" networks with collision, contention, and mobility. Simulation results show that collision is not a serious problem when a small forwarding jitter delay (≤ 1ms) is used, and contention is relieved with efficient self-pruning protocols. However, many efficient self-pruning protocols exhibit relatively low delivery ratio in mobile networks. One drawback of the self-pruning method is that it demands accurate neighborhood information and cannot ensure the coverage with outdated topology information. Then we discuss five optimization techniques, and show that, using appropriate techniques, high reliability (i.e., high delivery ratio) can be achieved with high efficiency in highly mobile networks.. Williams and Camp [10] have also simulated a self-pruning protocol the Scalable Broadcast Algorithm (SBA) [7] . SBA is a special case of our general self-pruning scheme. Compared with other self-pruning protocols, SBA has lower pruning efficiency (i.e., higher broadcast redundancy), causes higher endto-end delay, and is more resilient to mobility. A variation of SBA called the neighbor coverage scheme, which is proposed and simulated by Tseng et al [11] , shows similar properties in the simulation. A simulation study of the entire protocol family is still necessary in order to achieve both efficiency and reliability via the general self-pruning scheme.

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