Behavior Of Colonies Research Articles

Using within-day hive weight changes to measure environmental effects on honey bee colonies.

Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony’s daily activity cycle, hive weight change at night, hive weight loss due to departing foragers and weight gain due to returning foragers. Assumptions about the meaning of the timing and size of the morning weight changes were tested in a third study by delaying the forager departure times from one to three hours using screen entrance gates. A regression of planned vs. observed departure delays showed that the initial hive weight loss around dawn was largely due to foragers. In a similar experiment in Australia, hive weight loss due to departing foragers in the morning was correlated with net bee traffic (difference between the number of departing bees and the number of arriving bees) and from those data the payload of the arriving bees was estimated to be 0.02 g. The piecewise regression approach was then used to analyze a fifth study involving hives with and without access to natural forage. The analysis showed that, during a commercial pollination event, hives with previous access to forage had a significantly higher rate of weight gain as the foragers returned in the afternoon, and, in the weeks after the pollination event, a significantly higher rate of weight loss in the morning, as foragers departed. This combination of continuous weight data and piecewise regression proved effective in detecting treatment differences in foraging activity that other methods failed to detect.

Effects of genetic relatedness, spatial distance, and context on intraspecifi c aggression in the red wood ant Formica pratensis (Hymenoptera: Formicidae)

In the present study, we tested the level of aggression of monodomous and polydomous colonies of the wood ant Formica pratensis (Hymenoptera: Formicidae) with a behavioral assay in nature and laboratory conditions to see if the ants from neighboring colonies are more tolerant or more aggressive to each other than those from greater distances. We also tested how context (nature and laboratory conditions) affected aggression. Our results showed that the monodomous colonies were highly aggressive to all neighboring or nonneighboring conspecifics in nature irrespective of the spatial distance. The polydomous colony showed no aggression towards neighboring conspecifics but the level of aggression increased with increasing spatial distance between the colonies. The level of aggression of tested colonies in laboratory conditions was significantly low, irrespective of whether they were monodomous or polydomous, indicating that aggression is context dependent. A DNA barcoding technique based on mitochondrial COI gene sequencing was applied to determine the genetic relatedness between the colonies. The results of the genetic analysis, in combination with results of behavioral assays, revealed that aggression behavior of the polydomous colony was affected by both the genetic relatedness and the spatial distance between the colonies while there was no clear separation of effects of genetic relatedness and spatial distance on aggression in the monodomous colonies.

267 - Elucidation of human ips cell behaviors in colonies based on kinetic model

Understanding the origin of heterogeneous cell population which makes the quality control of final products difficult is core issue in human iPS cells (hiPSCs) bioprocessing. Focusing on hiPSC colonies cultured on feeder cells, we found that cells at the periphery of colony had higher average movement rate than cells at the center of colony. This difference in cell movement rate might lead to spatial heterogeneity of cell fate decision in colony. Therefore, we developed a kinetic model to understand the origin of this phenomenon which was difficult to clarify by in vitro study. Our kinetic model was developed based on the cellular automaton approach and described several fundamental cell behaviors including cell division, contact inhibition, cell migration, cell—cell and cell—substrate interactions. This kinetic model helped us to realize the heterogeneity of cell population which is impossible when using continuous model. All parameters used in our kinetic model were estimated directly or indirectly from experimental data. With all estimated parameter values, the properness of our kinetic model was indicated by good agreement between simulated and experimental data (P > 0.05). Using the kinetic model to understand the spatial heterogeneity of cell movement rate in colony, we explored that cell division was main reason for this phenomenon. When we stopped cell division in silico, average cell movement rate at the periphery of colony decreased to same level as at the center of colony (Figure 1). Specifically, when a mother cell wants to divide without any vacant space around it, it will push neighbor cells outward to have vacant space for placing of daughter cell. Therefore, hiPSCs at the periphery of colony were pushed drastically by inner cells and thus had higher movement rate. Meanwhile, hiPSCs at the center of colony underwent contact inhibition and thus were not affected by cell division. Our result also showed directly proportional relationship between frequency of cell pushing and cell movement rate in colony that proved the role of cell division. Our kinetic model is expected to be useful tool for study about behaviors of hiPSCs and proposing good strategies to improve hiPSCs bioprocessing.

ALGORITHM BASED ON ARTIFICIAL BEE COLONY FOR UNFOLDING OF NEUTRON SPECTRA OBTAINED WITH BONNER SPHERES.

Occupational neutron fields usually have energies from the thermal range to some MeV and the characterization of the spectra is essential for estimation of the radioprotection quantities. Thus, the spectrum must be unfolded based on a limited number of measurements. This study implemented an algorithm based on the bee colonies behavior, named Artificial Bee Colony (ABC), where the intelligent behavior of the bees in search of food is reproduced to perform the unfolding of neutron spectra. The experimental measurements used Bonner spheres and 6LiI (Eu) detector, with irradiations using a thermal neutron flux and three reference fields: 241Am-Be, 252Cf and 252Cf (D2O). The ABC obtained good estimation of the expected spectrum even without previous information and its results were closer to expected spectra than those obtained by the SPUNIT algorithm.

Spatial fidelity of workers predicts collective response to disturbance in a social insect

Individuals in social insect colonies cooperate to perform collective work. While colonies often respond to changing environmental conditions by flexibly reallocating workers to different tasks, the factors determining which workers switch and why are not well understood. Here, we use an automated tracking system to continuously monitor nest behavior and foraging activity of uniquely identified workers from entire bumble bee (Bombus impatiens) colonies foraging in a natural outdoor environment. We show that most foraging is performed by a small number of workers and that the intensity and distribution of foraging is actively regulated at the colony level in response to forager removal. By analyzing worker nest behavior before and after forager removal, we show that spatial fidelity of workers within the nest generates uneven interaction with relevant localized information sources, and predicts which workers initiate foraging after disturbance. Our results highlight the importance of spatial fidelity for structuring information flow and regulating collective behavior in social insect colonies.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Controlling the Balance of Exploration and Exploitation in ACO Algorithm

Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper proposes to modify the main probabilistic method to overcome the drawbacks of the exploration problem and produces global optimal results in high dimensional space. Experiments on six variant of ant colony optimization indicate that the proposed work produces high-quality results in terms of shortest route.

RETRACTED ARTICLE: Ant System with Negative for the Hospital Ward Color Planning

Inspired by the intelligent foraging behavior of ant colony, this paper proposes an improved ant colony foraging algorithm (AS-N) for complex engineering optimization problems. The main idea of AS-N is to add negative feedback. The algorithm proposed in this paper increases the negative feedback information to adjust the path selection strategy and pheromone updating mechanism. The new algorithm is tested on the hospital ward color planning problem. Statistical analysis highlights the significant performance improvement due to the life-cycle foraging strategy and shows that the proposed algorithm outperforms the reference algorithms.

EVALUATION OF ETHOLOGICAL AND PRODUCTION TRAITS IN VARIOUS GENOTYPES OF MACEDONIAN HONEY BEE SUBSPECIES

The honey bee colonony is a complex society which has a wide range of behaviors. The mostimportant are swarming, colony defense and hygienic behavior. These ethological traits have animpact on the honey yields and are of significant interests of the honey bee breeders. Therefore theethological characteristics are recognized in selection and breeding programs. According to thebreeding programs selection is directed to reduction of swarming and colony defense behavior andincreased expression of hygienic behavior. The aim of this research was to evaluate thesecharacteristics in 100 honey bee colonies from four genotypes (A, B, C and D) of the autochthonoushoney bee population (Apis mellifera macedonica) in one of the registered honey bee queenbreeding stations, located in Ohrid region, during 2016.The research included: scoring of thedefensive and the swarming behavior of honey bee colonies according to four point system, testinghygienic behavior using Pin-test method and scoring according five point system as well asdetermining honey yield by weighing of extracted honey in kg per honey bee colony and scoring thehoney yield according to the four point system. The results did not show statistically significantdifferences concerning swarming behavior and honey yield between colonies from the fourgenotypes. The results have shown statistical significant differences in the average cleaning success(hygienic behavior) after 24 hours, between A and B (p=0.0096) genotypes, and in the defensivebehavior, between A and D (p=0.0166) and between C and D (p=0.0333) genotypes.

New scheme for breast cancer detection and staging using ant colony algorithm

The ant colony optimisation (ACO) is an optimisation technique which first proposed ant-based metaheuristic developed by Marco Dorigo. The stimulating source of ant colony optimisation is a searching behaviour of real ant colonies. The proposed hybrid method consists of an enhanced ACO algorithm Discrete Wavelet Transform (DWT) Principle component analysis (PCA) and TNM (The size of the breast tumour (T), nearby lymph nodes (N), Metastasised (M)) system for detecting the edges of the tumour, feature extraction, feature reduction and tumour staging. For the early detection of breast cancer and staging the proposed analysis has been done with two different image modalities which is mammogram images and PET images.

Modeling cooperating micro-organisms in antibiotic environment

Recent experiments with the bacteria Paenibacillus vortex reveal a remarkable strategy enabling it to cope with antibiotics by cooperating with a different bacterium—Escherichia coli. While P. vortex is a highly effective swarmer, it is sensitive to the antibiotic ampicillin. On the other hand, E. coli can degrade ampicillin but is non-motile when grown on high agar percentages. The two bacterial species form a shared colony in which E. coli is transported by P. vortex and E. coli detoxifies the ampicillin. The paper presents a simplified model, consisting of coupled reaction-diffusion equations, describing the development of ring patterns in the shared colony. Our results demonstrate some of the possible cooperative movement strategies bacteria utilize in order to survive harsh conditions. In addition, we explore the behavior of mixed colonies under new conditions such as antibiotic gradients, synchronization between colonies and possible dynamics of a 3-species system including P. vortex, E. coli and a carbon producing algae that provides nutrients under illuminated, nutrient poor conditions. The derived model was able to simulate an asymmetric relationship between two or three micro-organisms where cooperation is required for survival. Computationally, in order to avoid numerical artifacts due to symmetries within the discretizing grid, the model was solved using a second order Vectorizable Random Lattices method, which is developed as a finite volume scheme on a random grid.

DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM

Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulations results reveal about the better performance of the proposed Refined ABC algorithm (RABC) algorithm in reducing the real power loss and the voltage profiles within the limits.

Abstract ID: 134 Ant colony algorithm for driving variance reduction techniques in Monte Carlo simulations

The use of variance reduction techniques (VRTs) in Monte Carlo calculations is mandatory in different situations in which low scoring statistics occurs, for example, when the dose deposited by radiation in small detectors in complex geometries is calculated. The efficiency of this kind of simulations could be extremely low because a huge amount of CPU time is spent on tracking particles that do not contribute to deposited dose. VRTs (e.g. Russian roulette and splitting) can improve the efficiency in this type of calculations, but unfortunately, these techniques are extremely problem-dependent, and general recipes to minimize the variance cannot be given. To solve this problem, we propose to use a method based on an ant colony algorithm that allows driving the application of VRTs with minimal user intervention and is able to increase the efficiency in low statistics Monte Carlo simulations independently of the geometry. These kind of algorithms are inspired by the behavior of actual ant colonies and permit to create optimization tools to solve complex problems such as those of finding global minima. The ant colony optimization algorithm (ACOA) here developed “learns” where to apply VRTs to favor the increase of efficiency, obtaining information from the simulation itself and generating the so-called importance maps. ACOA has been successfully employed in calculations regarding the study of dose deposition in different problems: electron beams from linear accelerators, dose deposition calculations on MOSFET detectors [1] , radiosurgery photon beams [2] , improvement of dose distribution on monoisocentric beam split technique [3] and calculations of specific absorbed fractions in non-sealed radioisotope treatments [4] . The efficiency improvements range from 10 to 100 times or even more in case of high energy photon beams.

Characterizing vertical migration of Microcystis aeruginosa and conditions for algal bloom development based on a light‐driven migration model

AbstractLight‐driven vertical migration is critical in dense aggregation of Microcystis aeruginosa during algal blooms under reduced turbulence conditions in natural water bodies. This study examined the vertical migration characteristics of Microcystis aeruginosa in calm water based on a colony migration model with consideration of cell density change, and demonstrated the effects of mucilage fraction, colony size, irradiance intensity, and water turbidity. The results suggest that colonies with larger radii and under higher irradiance usually had a larger daily averaged retention time at the water surface (DRT). In addition to colony size, mucilage was found to be important in changing the vertical migration behavior of Microcystis colonies, in which increasing mucilage volume fraction can increase the migrating velocity of the colony as well as the length of time it remains at the water surface. Increase of light extinction also favors the aggregation of colonies at the surface. An approximate critical value of 2400 µmol photons m2 s−1 for maximum irradiance was found for persistent algal bloom development under the given simulation conditions. Extremely small colonies exposed to irradiance below the critical value were not likely to migrate to the water surface to form algal blooms. According to the DRT values, three regions with different ranges of irradiance and colony size were proposed as the critical conditions for algal bloom development. Rough comparisons to field observations suggested that these results were reasonable and meaningful, and have the potential to be applied in real cases following further validation by more detailed investigations.

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

Bacterial mats of the oxide ores in the world ocean

Bacterial mats, formed by successively accumulating biofilms, are the main constructive component of the oxide ferromanganese ores on the oceanic floor. The coordinated behavior of the bacterial colonies in the biofilms controlled the growth of the stromatolites and onkolites structures. Biofilms of the stromatolite bacterial mats represent the microbial community, with the thread bacteria forming a poiygonal network that determines a piliar structure of the crusts. Bacterial mats in nodules are festoon-shaped. Biofilms in festoons intensely interact with the environment, assimilating petrogenic components and consuming the sedimentary material.

Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects.

Characterizing the complex spatial and temporal interactions among cells in a biological system (i.e. bacterial colony, microbiome, tissue, etc.) remains a challenge. Metabolic cooperativity in these systems can arise due to the subtle interplay between microenvironmental conditions and the cells’ regulatory machinery, often involving cascades of intra- and extracellular signalling molecules. In the simplest of cases, as demonstrated in a recent study of the model organism Escherichia coli, metabolic cross-feeding can arise in monoclonal colonies of bacteria driven merely by spatial heterogeneity in the availability of growth substrates; namely, acetate, glucose and oxygen. Another recent study demonstrated that even closely related E. coli strains evolved different glucose utilization and acetate production capabilities, hinting at the possibility of subtle differences in metabolic cooperativity and the resulting growth behavior of these organisms. Taking a first step towards understanding the complex spatio-temporal interactions within microbial populations, we performed a parametric study of E. coli growth on an agar substrate and probed the dependence of colony behavior on: 1) strain-specific metabolic characteristics, and 2) the geometry of the underlying substrate. To do so, we employed a recently developed multiscale technique named 3D dynamic flux balance analysis which couples reaction-diffusion simulations with iterative steady-state metabolic modeling. Key measures examined include colony growth rate and shape (height vs. width), metabolite production/consumption and concentration profiles, and the emergence of metabolic cooperativity and the fractions of cell phenotypes. Five closely related strains of E. coli, which exhibit large variation in glucose consumption and organic acid production potential, were studied. The onset of metabolic cooperativity was found to vary substantially between these five strains by up to 10 hours and the relative fraction of acetate utilizing cells within the colonies varied by a factor of two. Additionally, growth with six different geometries designed to mimic those that might be found in a laboratory, a microfluidic device, and inside a living organism were considered. Geometries were found to have complex, often nonlinear effects on colony growth and cross-feeding with “hard” features resulting in larger effect than “soft” features. These results demonstrate that strain-specific features and spatial constraints imposed by the growth substrate can have significant effects even for microbial populations as simple as isogenic E. coli colonies.

Pathway profiles based on gene-set enrichment analysis in the honey bee Apis mellifera under brood rearing-suppressed conditions

Perturbation of normal behaviors in honey bee colonies by any external factor can immediately reduce the colony's capacity for brood rearing, which can eventually lead to colony collapse. To investigate the effects of brood-rearing suppression on the biology of honey bee workers, gene-set enrichment analysis of the transcriptomes of worker bees with or without suppressed brood rearing was performed. When brood rearing was suppressed, pathways associated with both protein degradation and synthesis were simultaneously over-represented in both nurses and foragers, and their overall pathway representation profiles resembled those of normal foragers and nurses, respectively. Thus, obstruction of normal labor induced over-representation in pathways related with reshaping of worker bee physiology, suggesting that transition of labor is physiologically reversible. In addition, some genes associated with the regulation of neuronal excitability, cellular and nutritional stress and aggressiveness were over-expressed under brood rearing suppression perhaps to manage in-hive stress under unfavorable conditions.

Synthetic quorum sensing in model microcapsule colonies

Biological quorum sensing refers to the ability of cells to gauge their population density and collectively initiate a new behavior once a critical density is reached. Designing synthetic materials systems that exhibit quorum sensing-like behavior could enable the fabrication of devices with both self-recognition and self-regulating functionality. Herein, we develop models for a colony of synthetic microcapsules that communicate by producing and releasing signaling molecules. Production of the chemicals is regulated by a biomimetic negative feedback loop, the "repressilator" network. Through theory and simulation, we show that the chemical behavior of such capsules is sensitive to both the density and number of capsules in the colony. For example, decreasing the spacing between a fixed number of capsules can trigger a transition in chemical activity from the steady, repressed state to large-amplitude oscillations in chemical production. Alternatively, for a fixed density, an increase in the number of capsules in the colony can also promote a transition into the oscillatory state. This configuration-dependent behavior of the capsule colony exemplifies quorum-sensing behavior. Using our theoretical model, we predict the transitions from the steady state to oscillatory behavior as a function of the colony size and capsule density.

An improved double-population artificial bee colony algorithm based on heterogeneous comprehensive learning

The artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms that is inspired by the forging behavior of honeybee colonies. To improve the convergence precision of the ABC algorithm, accelerate the search speed of finding the best solution and control the balance between exploration and exploitation, we propose an improved double-population ABC algorithm based on heterogeneous comprehensive learning (HCLIABC). In this algorithm, the swarm is divided into exploration-subpopulation named group 1 and exploitation-subpopulation named group 2. Illuminated by particle swarm optimization (PSO), the food source will be updated on all dimensions rather than on a randomly selected dimension. Meanwhile HCL strategy is used to generate the exemplars for two subpopulations. In addition, opposition-based learning is used to improve the quality of initial swarm, and multiplicative weight update method is used to update the selection probability of the double-population in employed bees phase. To evaluate the remarkable performance of the improved algorithm, we conduct comparative experiments of 18 unimodal, multimodal, and rotated benchmark functions on dimensions 30 and 100. Computational results demonstrate that HCLIABC can effectively prevent premature convergence and produce competitive optimization precision and convergence speed compared with several popular and classic DE, PSO and ABC variants.