A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

Microarray data are often extremely asymmetric in dimensionality,such as thousands or even tens of thousands of genes but only a few hundreds of samples or less.Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic.Therefore,it has been shown that selecting a small set of marker genes can lead to improved classification accuracy.In this paper,a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related ma...     

An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features (genes), they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter (q₀) in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.     

RALBA: a computation-aware load balancing scheduler for cloud computing

Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.     

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems

Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.     

DDOps: dual-direction operations for load balancing on non-dedicated heterogeneous distributed systems

Given the increasing prevalence of compute/data intensive applications, the explosive growth in data, and the emergence of cloud computing, there is an urgent need for effective approaches to support such applications in non-dedicated heterogeneous distributed environments. This paper proposes an efficient technique for handling parallel tasks, while dynamically maintaining load balancing. Such tasks include concurrently downloading files from replicated sources; simultaneously using multiple network interfaces for message transfers; and executing parallel computations on independent distributed processors. This technique, DDOps, (Dual Direction Operations) enables efficient utilization of available resources in a parallel/distributed environment without imposing any significant control overhead. The idea is based on the notion of producer pairs that perform tasks in parallel from opposite directions and the consumers that distribute and control the work and receive and combine the results. Most dynamic load balancing approaches require prior knowledge and/or constant monitoring at run time. In DDOps, load balancing does not require prior knowledge or run-time monitoring. Rather, load balancing is automatically inherent as the tasks are handled from the opposite directions, allowing the processing to continue until the producers meet indicating the completion of all tasks at the same time. Thus DDOps is most suitable for heterogeneous environments where resources vary in specifications, locations, and operating conditions. In addition, since DDOps does not require producers to communicate at all, the network effect is minimized.     

Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment

Cluster Computing - Applying the load balancing technique to allocate requests that dynamically enter the cloud environment is contributive in maintaining the system stability, reducing the...     

A heterogeneity-aware approach to load balancing of computational tasks: a theoretical and simulation study

One of the distinct characteristics of computing platforms shared by multiple users such as a cluster and a computational grid is heterogeneity on each computer and/or among computers. Temporal heterogeneity refers to variation, along the time dimension, of computing power available for a task on a computer, and spatial heterogeneity represents the variation among computers. In minimizing the average parallel execution time of a target task on a spatially heterogeneous computing system, it is not optimal to distribute the target task linearly proportional to the average computing powers available on computers. In this paper, effects of the temporal and spatial heterogeneity on performance of a target task have been analyzed in terms of the mean and standard deviation of parallel execution time. Based on the analysis results, an approach to load balancing for minimizing the average parallel execution time of a target task is described. The proposed approach whose validity has been verified through simulation considers temporal and spatial heterogeneities in addition to the average computing power on each computer.

The Relationship between Canopy Cover and Colony Size of the Wood Ant Formica lugubris - Implications for the Thermal Effects on a Keystone Ant Species

Climate change may affect ecosystems and biodiversity through the impacts of rising temperature on species’ body size. In terms of physiology and genetics, the colony is the unit of selection for ants so colony size can be considered the body size of a colony. For polydomous ant species, a colony is spread across several nests. This study aims to clarify how climate change may influence an ecologically significant ant species group by investigating thermal effects on wood ant colony size. The strong link between canopy cover and the local temperatures of wood ant’s nesting location provides a feasible approach for our study. Our results showed that nests were larger in shadier areas where the thermal environment was colder and more stable compared to open areas. Colonies (sum of nests in a polydomous colony) also tended to be larger in shadier areas than in open areas. In addition to temperature, our results supported that food resource availability may be an additional factor mediating the relationship between canopy cover and nest size. The effects of canopy cover on total colony size may act at the nest level because of the positive relationship between total colony size and mean nest size, rather than at the colony level due to lack of link between canopy cover and number of nests per colony. Causal relationships between the environment and the life-history characteristics may suggest possible future impacts of climate change on these species.     

From one linear genome to a graph-based pan-genome: a new era for genomics

Science China Life Sciences -     

Sting,Carry and Stock: How Corpse Availability Can Regulate De-Centralized Task Allocation in a Ponerine Ant Colony

We develop a model to produce plausible patterns of task partitioning in the ponerine ant Ectatomma ruidum based on the availability of living prey and prey corpses. The model is based on the organizational capabilities of a “common stomach” through which the colony utilizes the availability of a natural (food) substance as a major communication channel to regulate the income and expenditure of the very same substance. This communication channel has also a central role in regulating task partitioning of collective hunting behavior in a supply&demand-driven manner. Our model shows that task partitioning of the collective hunting behavior in E. ruidum can be explained by regulation due to a common stomach system. The saturation of the common stomach provides accessible information to individual ants so that they can adjust their hunting behavior accordingly by engaging in or by abandoning from stinging or transporting tasks. The common stomach is able to establish and to keep stabilized an effective mix of workforce to exploit the prey population and to transport food into the nest. This system is also able to react to external perturbations in a de-centralized homeostatic way, such as to changes in the prey density or to accumulation of food in the nest. In case of stable conditions the system develops towards an equilibrium concerning colony size and prey density. Our model shows that organization of work through a common stomach system can allow Ectatomma ruidum to collectively forage for food in a robust, reactive and reliable way. The model is compared to previously published models that followed a different modeling approach. Based on our model analysis we also suggest a series of experiments for which our model gives plausible predictions. These predictions are used to formulate a set of testable hypotheses that should be investigated empirically in future experimentation.     

Comparative study of the server-initiated lowest algorithm using a load balancing index based on the process behavior for heterogeneous environment

The availability of low cost microcomputers and the evolution of computer networks have increased the development of distributed systems. In order to get a better process allocation on distributed environments, several load balancing algorithms have been proposed. Generally, these algorithms consider as the information policy’s load index the length of the CPU’s process waiting queue. This paper modifies the Server-Initiated Lowest algorithm by using a load index based on the resource occupation. Using this load index the Server-Initiated Lowest algorithm is compared to the Stable symmetrically initiated, which nowadays is defined as the best choice. The comparisons are made by using simulations. The simulations showed that the modified Server-Initiated Lowest algorithm had better results than the Symmetrically Initiated one.     

Diversity within a Colony Morphotype: Implications for Ecological Research

Sets of bacterial isolates with the same colony morphologies were selected from spread plates of bacteria from deep subsurface rock samples; each set had a unique morphology. API-rapid-NFT analysis revealed that isolates within a set were the same. Fatty acid methyl ester analysis of one set of isolates clustered organisms within the same species, defining variation between isolates at the biotype (subspecies) and strain levels. Metal resistances consistently tracked with colony morphology, while antibiotic resistances were less reliable.     

SubSeqer: a graph-based approach for the detection and identification of repetitive elements in low-complexity sequences

Summary: Low-complexity, repetitive protein sequences with alimited amino acid palette are abundant in nature, and manyof them play an important role in the structure and functionof certain types of proteins. However, such repetitive sequencesoften do not have rigidly defined motifs. Consequently, theidentification of these low-complexity repetitive elements hasproven challenging for existing pattern-matching algorithms.Here we introduce a new web-tool SubSeqer (http://compsysbio.org/subseqer/)which uses graphical visualization methods borrowed from proteininteraction studies to identify and characterize repetitiveelements in low-complexity sequences. Given their abundance,we suggest that SubSeqer represents a valuable resource forthe study of typically neglected low-complexity sequences. Contact: jparkin{at}sickkids.ca Associate Editor: Limsoon Wong     

GenoLink: a graph-based querying and browsing system for investigating the function of genes and proteins

Background  

A large variety of biological data can be represented by graphs. These graphs can be constructed from heterogeneous data coming from genomic and post-genomic technologies, but there is still need for tools aiming at exploring and analysing such graphs. This paper describes GenoLink, a software platform for the graphical querying and exploration of graphs.     

Colony ergonomics for a desert-dwelling bumblebee species (Hymenoptera: Apidae)

Abstract.

• 1 Data on worker traffic, size and concentration of nectar loads, and size and composition of pollen loads were collected for a colony of Bombus pennsylvanicus sonorus Say in the Chihuahuan Desert in Arizona, U.S.A.
• 2 Foraging activity increased through the morning to a peak level in early afternoon and then declined steadily thereafter. Pollen collection occurred primarily in the morning, whereas nectar was harvested throughout the day. Nectar loads decreased in size but increased in sugar concentration during the day.
• 3 Following field observations, we excavated the nest and counted the numbers of immatures and adults present and measured the honey and pollen reserves. A total of 150 workers were present, and we estimate that the colony would have produced 174 queens and 192 males. Food reserves appeared small: pollen and honey stored in the nest represented only 18% and 35%, respectively, of the daily input.
• 4 These results are compared to ergonomic data collected for B.vosnesenskii in the only other similar study.

     

Ant seed removal in a non-myrmecochorous Neotropical shrub: Implications for seed dispersal

This study investigated ant seed removal of Piper sancti-felicis, an early successional Neotropical shrub. Neotropical Piper are a classic example of bat-dispersed plants, but we suggest that ants are underappreciated dispersal agents. We identified eleven ant species from the genera Aphaenogaster, Ectatomma, Paratrechina, Pheidole, Trachymyrmex, and Wasmannia recruiting to and harvesting P. sancti-felicis seeds in forest edge and secondary forest sites at La Selva, Costa Rica. We also tested for differences in ant recruitment to five states in which ants can commonly encounter seeds: unripe fruit, ripe fruit, overripe fruit, bat feces, and cleaned seeds. Overall, ants harvested more seeds from ripe and overripe fruits than other states, but this varied among species. To better understand the mechanisms behind ant preferences for ripe/overripe fruit, we also studied how alkenylphenols, secondary metabolites found in high concentrations in P. sancti-felicis fruits, affected foraging behavior in one genus of potential ant dispersers, Ectatomma. We found no effects of alkenylphenols on recruitment of Ectatomma to fruits, and thus, these compounds are unlikely to explain differences in ant recruitment among fruits of different maturity. Considering that P. sancti-felicis seeds have no apparent adaptations for ant dispersal, and few ants removed seeds that were cleaned of pulp, we hypothesize that most ants are harvesting its seeds for the nutritional rewards in the attached pulp. This study emphasizes the importance of ants as important additional dispersers of P. sancti-felicis and suggests that other non-myrmecochorous, vertebrate-dispersed plants may similarly benefit from the recruitment to fruit by ants.