Recent Developments of World-Line Monte Carlo Methods

World-line quantum Monte Carlo methods are reviewed with an emphasis on breakthroughs made in recent years. In particular, three algorithms -- the loop algorithm, the worm algorithm, and the directed-loop algorithm -- for updating world-line configurations are presented in a unified perspective. Detailed descriptions of the algorithms in specific cases are also given.Comment: To appear in Journal of Physical Society of Japa

Impact of a complex food microbiota on energy metabolism in the model organism Caenorhabditis elegans

The nematode Caenorhabditis elegans is widely used as a model system for research on aging, development, and host-pathogen interactions. Little is currently known about the mechanisms underlying the effects exerted by foodborne microbes. We took advantage of C. elegans to evaluate the impact of foodborne microbiota on well characterized physiological features of the worms. Foodborne lactic acid bacteria (LAB) consortium was used to feed nematodes and its composition was evaluated by 16S rDNA analysis and strain typing before and after colonization of the nematode gut. Lactobacillus delbrueckii, L. fermentum, and Leuconostoc lactis were identified as the main species and shown to display different worm gut colonization capacities. LAB supplementation appeared to decrease nematode lifespan compared to the animals fed with the conventional Escherichia coli nutrient source or a probiotic bacterial strain. Reduced brood size was also observed in microbiota-fed nematodes. Moreover, massive accumulation of lipid droplets was revealed by BODIPY staining. Altered expression of nhr-49, pept-1, and tub-1 genes, associated with obesity phenotypes, was demonstrated by RT-qPCR. Since several pathways are evolutionarily conserved in C. elegans, our results highlight the nematode as a valuable model system to investigate the effects of a complex microbial consortium on host energy metabolism

Structural Properties of the Caenorhabditis elegans Neuronal Network

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation

Sustaining Economic Exploitation of Complex Ecosystems in Computational Models of Coupled Human-Natural Networks

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically mod-eled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maxi-mum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natu-ral and social science include applying artificial intelligence, cloud computing, and multiplayer online games

Evolution of a polymodal sensory response network

Background: Avoidance of noxious stimuli is essential for the survival of an animal in its natural habitat. Some avoidance responses require polymodal sensory neurons, which sense a range of diverse stimuli, whereas other stimuli require a unimodal sensory neuron, which senses a single stimulus. Polymodality might have evolved to help animals quickly detect and respond to diverse noxious stimuli. Nematodes inhabit diverse habitats and most nematode nervous systems are composed of a small number of neurons, despite a wide assortment in nematode sizes. Given this observation, we speculated that cellular contribution to stereotyped avoidance behaviors would also be conserved between nematode species. The ASH neuron mediates avoidance of three classes of noxious stimuli in Caenorhabditis elegans. Two species of parasitic nematodes also utilize the ASH neuron to avoid certain stimuli. We wanted to extend our knowledge of avoidance behaviors by comparing multiple stimuli in a set of free-living nematode species. Results: We used comparative behavioral analysis and laser microsurgery to examine three avoidance behaviors in six diverse species of free-living nematodes. We found that all species tested exhibit avoidance of chemo-, mechano- and osmosensory stimuli. In C. elegans, the bilaterally symmetric polymodal ASH neurons detect all three classes of repellant. We identified the putative ASH neurons in different nematode species by their anatomical positions and showed that in all six species ablation of the ASH neurons resulted in an inability to avoid noxious stimuli. However, in the nematode Pristionchus pacificus, the ADL neuron in addition to the ASH neuron contributed to osmosensation. In the species Caenorhabditis sp. 3, only the ASH neuron was required to mediate nose touch avoidance instead of three neurons in C. elegans. These data suggest that different species can increase or decrease the contribution of additional, non-ASH sensory neurons mediating osmosensation and mechanosensation. Conclusion: The overall conservation of ASH mediated polymodal nociception suggests that it is an ancestral evolutionarily stable feature of sensation. However, the finding that contribution from non-ASH sensory neurons mediates polymodal nociception in some nematode species suggests that even in conserved sensory behaviors, the cellular response network is dynamic over evolutionary time, perhaps shaped by adaptation of each species to its environment

Detection and Prevention of Unknown Vulnerabilities on Enterprise IP Networks

Computer networks have long become the backbone of Enterprise Information System. The substantial share of the security problems are still encountered in Enterprise Network. Cyber espionage can effect Ethical, Military, Political and Economic interest anywhere. To provide secure computer networks, it is necessary to measure the relative effectiveness of security solution in the network. A network security metric enable a direct measurement and comparison of the amounts of security provided by different security solutions .In this paper we propose a novel security metric Zero Day Vulnerability Prevention Framework consists of bunches of algorithms. The above framework detects and prevents unknown vulnerabilities in Enterprise IP networks. It also protects the behavior of the sessions performed by the user from the huge range of attacks. It helps in monitoring database requests and prevents the attacks. The proposed framework also implements worm and virus detection to evaluate malware from the data. The system also presents scoring to the vulnerabilities and finally it performs security analysis with the help of Topological Vulnerability Analysis (TVA) tool. DOI: 10.17762/ijritcc2321-8169.15028