Essential conditions for evolution of communication within a species

A major obstacle in analyzing the evolution of information exchange and processing is our insufficient understanding of the underlying signaling and decision-making biological mechanisms. For instance, it is unclear why are humans unique in developing such extensive communication abilities. To treat this problem, a method based on the mutual information approach is developed that evaluates the information content of communication between interacting individuals through correlations of their behavior patterns (rather than calculating the information load of exchanged discrete signals, e.g. Shannon entropy). It predicts that correlated interactions of the indirect reciprocity type together with affective behavior and selection rules changing with time are necessary conditions for the emergence of significant information exchange. Population size variations accelerate this development. These results are supported by evidence of demographic bottlenecks, distinguishing human from other species’ (e.g. apes) evolution line. They indicate as well new pathways for evolution of information based phenomena, such as intelligence and complexity.     

DNA approach to solve clustering problem based on a mutual order

Clustering is regarded as a consortium of concepts and algorithms that are aimed at revealing a structure in highly dimensional data and arriving at a collection of meaningful relationships in data and information granules. The objective of this paper is to propose a DNA computing to support the development of clustering techniques. This approach is of particular interest when dealing with huge data sets, unknown number of clusters and encountering a heterogeneous character of available data. We present a detailed algorithm and show how the essential components of the clustering technique are realized through the corresponding mechanisms of DNA computing. Numerical examples offer a detailed insight into the performance of the DNA-based clustering.     

COBioSIFTER – A CORBA-Based Distributed Multi-Agent Biological Information Management System

In this paper, we describe the COBioSIFTER project that aims at constructing a framework for managing information from the biological domain. It uses a distributed multi-agent approach based on the CORBA middleware and contains an architecture made up of functionally heterogeneous agents. A series of experiments has been carried out with a prototype of COBioSIFTER to illustrate its advantages such as high performance, scalability, heterogeneity, and fault tolerance. The results of these experiments demonstrate the effectiveness of such an agent-based biological information framework.     

The MOSIX Direct File System Access Method for Supporting Scalable Cluster File Systems

MOSIX is a cluster management system that supports preemptive process migration. This paper presents the MOSIX Direct File System Access (DFSA), a provision that can improve the performance of cluster file systems by allowing a migrated process to directly access files in its current location. This capability, when combined with an appropriate file system, could substantially increase the I/O performance and reduce the network congestion by migrating an I/O intensive process to a file server rather than the traditional way of bringing the file's data to the process. DFSA is suitable for clusters that manage a pool of shared disks among multiple machines. With DFSA, it is possible to migrate parallel processes from a client node to file servers for parallel access to different files. Any consistent file system can be adjusted to work with DFSA. To test its performance, we developed the MOSIX File-System (MFS) which allows consistent parallel operations on different files. The paper describes DFSA and presents the performance of MFS with and without DFSA.     

基于质粒DNA匹配问题的分子算法

给定无向图,图的最小极大匹配问题是寻找每条边都不相邻的最大集中的最小者,这个问题是著名的NP-完全问题.1994年Adleman博士首次提出用DNA计算解决NP-完全问题,以编码的DNA序列为运算对象,通过分子生物学的运算操作解决复杂的数学难题,使得NP-完全问题的求解可能得到解决.提出了基于质粒DNA的无向图的最大匹配问题的DNA分子生物算法,通过限制性内切酶的酶切和凝胶电泳完成解的产生和最终接的分离,依据分子生物学的实验手段,算法是有效并且可行的.     

Rubidium Chloride Fuses Split Circadian Activity Rhythms in Hamsters Housed in Bright Constant Light

Chronotypic effects of rubidium (Rb) were examined in hamsters whose circadian activity rhythms had split into two components while they were housed in bright constant light. Seven of 12 hamsters receiving RbCl in drinking water for 10 weeks showed fusing of the components into an intact rhythm compared with none of 7 control hamsters (p = 0.016). Rb may modify coupling between circadian oscillators via reduced photic input to the suprachiasmatic nuclei. Alternative mechanisms include changes in potassium metabolism or endocrine function or behavioral changes that in turn alter circadian function. This normalization of a circadian anomaly by a putative antidepressant suggests that Rb may be valuable in strengthening coupling between oscillators in cases of human chronopathology, including those implicated in the etiology of some affective disorders.     

Crowd sourcing difficult problems in protein science*

Dedicated computing resources are expensive to develop, maintain, and administrate. Frequently, research groups require bursts of computing power, during which progress is still limited by available computing resources. One way to alleviate this bottleneck would be to use additional computing resources. Today, many computing devices remain idle most of the time. Passive volunteer computing exploits this unemployed reserve of computing power by allowing device‐owners to donate computing time on their own devices. Another complementary way to alleviate bottlenecks in computing resources is to use more efficient algorithms. Engaging volunteer computing employs human intuition to help solve challenging problems for which efficient algorithms are difficult to develop or unavailable. Designing engaging volunteer computing projects is challenging but can result in high‐quality solutions. Here, we highlight four examples.     

Structure_threader: An improved method for automation and parallelization of programs structure,fastStructure and MavericK on multicore CPU systems

Structure_threader is a program to parallelize multiple runs of genetic clustering software that does not make use of multithreading technology (structure , fastStructure and MavericK) on multicore computers. Our approach was benchmarked across multiple systems and displayed great speed improvements relative to the single‐threaded implementation, scaling very close to linearly with the number of physical cores used. Structure_threader was compared to previous software written for the same task—ParallelStructure and StrAuto and was proven to be the faster (up to 25% faster) wrapper under all tested scenarios. Furthermore, Structure_threader can perform several automatic and convenient operations, assisting the user in assessing the most biologically likely value of ‘K’ via implementations such as the “Evanno,” or “Thermodynamic Integration” tests and automatically draw the “meanQ” plots (static or interactive) for each value of K (or even combined plots). Structure_threader is written in python 3 and licensed under the GPLv3. It can be downloaded free of charge at https://github.com/StuntsPT/Structure_threader .     

An autonomic provisioning framework for outsourcing data center based on virtual appliances

As outsourcing data centers emerge to host applications and services from many different organizations, it is critical for data center owners to isolate different applications while dynamically and optimally allocate sharable resources among them. To address this issue, we propose a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers. We present the architecture of the data center with enriched autonomic features. We define a non-linear constrained optimization model for dynamic resource provisioning and present a novel analytic solution. Key factors, including virtualization overhead and reconfiguration delay, are incorporated into the model. Experimental results based on a prototype demonstrate that the system-level performance has been greatly improved by taking advantage of fine-grained server consolidation, and the whole system exhibits flexible adaptation in failure scenarios. Experiments with the impact of switching delay also show the efficiency of the framework due to significantly reduced provisioning time.

Autonomic resource management in virtualized data centers using fuzzy logic-based approaches

Data centers, as resource providers, are expected to deliver on performance guarantees while optimizing resource utilization to reduce cost. Virtualization techniques provide the opportunity of consolidating multiple separately managed containers of virtual resources on underutilized physical servers. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared physical resources to virtual containers and the management of their capacities to meet service-quality targets at the least cost. This paper proposes a two-level resource management system to dynamically allocate resources to individual virtual containers. It uses local controllers at the virtual-container level and a global controller at the resource-pool level. An important advantage of this two-level control architecture is that it allows independent controller designs for separately optimizing the performance of applications and the use of resources. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the local controller designs is their use of fuzzy logic-based approaches to efficiently and robustly deal with the complexity and uncertainties of dynamically changing workloads and resource usage. The global controller determines the resource allocation based on a proposed profit model, with the goal of maximizing the total profit of the data center. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.