Gain control in a proprioceptive feedback loop as a prerequisite for working close to instability

In the artificially closed femur-tibia control system of stick insects oscillations were induced in 3 different ways: Increasing the phase-shift by introducing an electronic delay, afference sign reversal and coupling the tibia to an inert mass. In all 3 cases the oscillations stopped after some time. The gain of the open-loop system was significantly smaller after the oscillations. Afference sign reversal by surgically crossing of the receptor apodeme of the femoral chordotonal organ for 25–85 days does not lead to altered characteristics of the control loop. When sinusoidal passive movements are forced upon the intact femur-tibia joint the forces resisting these movements do not decrease with time. In contrast to direct stimulation of the femoral chordotonal organ, these passive movements also influence the contralateral leg. The experiments show that the gain-control system of the femur-tibia control loop of stick insects consists of at least two components: A sensitization system (with inputs from many kinds of stimuli indicating some kind of disturbance) increases the gain of all reflex loops. A specific habituation-like system decreases the gain with repetitive stimulation only of one control system.Abbreviations fCO femoral chordotonal organ - SETi slow extensor tibiae motor neuron     

National Alliance on Art and Industry Shows New Design Trend

Since the Bush administration enacted the No Child Left Behind Act (NCLB) in 2002, lawmakers and school administrators have questioned what changes, if any, the Obama administration will make. This article discusses the effects of NCLB on nontested subjects, specifically music and arts in the general curriculum. Major effects on scheduling and funding policies have forced educators to reconsider how advocacy for the arts should be approached.     

Growth performance,haematological assessment and chemical composition of Nile tilapia,Oreochromis niloticus (Linnaeus, 1758) fed different levels of Aloe vera extract as feed additives in a closed aquaculture system

A 105-day experimental trial was conducted to assess different levels of dietary Aleo vera extract supplementation on water quality parameters, proximate composition, growth performance and haematological parameters of fry Oreochromis niloticus. Four different percentages of dietary leaf extract powder of Aleo vera (ALE) with a basal feed, designated as, i.e., T0 (Control group; without ALE), T1 (1% ALE), T2 (2% ALE), and T3 (3% ALE). Fish fry was reared in concrete tanks (7.0 m, 1.6 m, 1.0: L, W, H; water volume 11.2 m3/tank), with an average initial weight 4.04 ± 0.03 g/ fry, and each treatment was triplicated. Fry was randomly distributed at a stocking rate of 450 individuals/ tanks. The water quality parameters revealed that temperature, pH, salinity, dissolved oxygen (DO) and nitrates were found in a promising range as given by FAO/WHO limits. However, the record values obtained for Electric Conductivity (EC), Total dissolved solids (TDS), and alkalinities were not found in all tanks' suitable range according to FAO/WHO limits. The results revealed a significant impact of different percentages of dietary ALE supplementation on fry's body composition and haematological parameters. Moreover, the final body weight, final body length, average daily weight gain (g), net weight gain (g) and specific growth rate (%) were significantly higher (p < 0.05) in T1 and T2 compared with T0 and T3 treatments. The poorest feed conversion ratio was recorded in the T2 group compared with other treatments. Thus, the current study provides information about the nutritional quality of Nile tilapia culturing in Pakistan.     

Anti-salmonella properties of kefir yeast isolates: An in vitro screening for potential infection control

The rise of antibiotic resistance has increased the need for alternative ways of preventing and treating enteropathogenic bacterial infection. Various probiotic bacteria have been used in animal and human. However, Saccharomyces boulardii is the only yeast currently used in humans as probiotic. There is scarce research conducted on yeast species commonly found in kefir despite its claimed potential preventative and curative effects. This work focused on adhesion properties, and antibacterial metabolites produced by Kluyveromyces lactis and Saccharomyces unisporus isolated from traditional kefir grains compared to Saccharomyces boulardii strains. Adhesion and sedimentation assay, slide agglutination, microscopy and turbidimetry assay were used to analyze adhesion of Salmonella Arizonae and Salmonella Typhimurium onto yeast cells. Salmonella growth inhibition due to the antimicrobial metabolites produced by yeasts in killer toxin medium was analyzed by slab on the lawn, turbidimetry, tube dilution and solid agar plating assays. Alcohol and antimicrobial proteins production by yeasts in killer toxin medium were analyzed using gas chromatography and shotgun proteomics, respectively. Salmonella adhered onto viable and non-viable yeast isolates cell wall. Adhesion was visualized using scanning electron microscope. Yeasts-fermented killer toxin medium showed Salmonella growth inhibition. The highest alcohol concentration detected was 1.55%, and proteins with known antimicrobial properties including cathelicidin, xanthine dehydrogenase, mucin-1, lactadherin, lactoperoxidase, serum amyloid A protein and lactotransferrin were detected in yeasts fermented killer medium. These proteins are suggested to be responsible for the observed growth inhibition effect of yeasts-fermented killer toxin medium. Kluyveromyces lactis and Saccharomyces unisporus have anti-salmonella effect comparable to Saccharomyces boulardii strains, and therefore have potential to control Salmonella infection.     

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.     

Cancer immunotherapy, mathematical modeling and optimal control

Clinical immunologists, among other problems, routinely face a question: what is the best time and dose for a certain therapeutic agent to be administered to the patient in order to decrease/eradicate the pathological condition? In cancer immunotherapies the therapeutic agent is something able to elicit an immune response against cancer. The immune response has its own dynamics that depends on the immunogenicity of the therapeutic agent and on the duration of the immune response. The question then is "how can we decide when and how much of the drug to inject so to have a prolonged and effective immune response to the cancer?". This question can be addressed in mathematical terms in two stages: first one construct a mathematical model describing the cancer-immune interaction and secondly one applies the theory of optimal control to determine when and to which extent to stimulate the immune system by means of an immunotherapeutic agent administered in discrete variable doses within the therapeutic period. The solution of this mathematical problem is described and discussed in this article. We show that the method employed can be applied to find the optimal protocol in a variety of clinical problems where the kinetics of the drug or treatment and its influence on the physiologic/pathologic functions have been described by a system of ordinary differential equations.     

Impact of Admission and Cache Replacement Policies on Response Times of Jobs on Data Grids

Caching techniques have been used widely to improve the performance gaps of storage hierarchies in computing systems. Little is known about the impact of policies on the response times of jobs that access and process very large files in data grids, particularly when data and computations on the data have to be co-located on the same host. In data intensive applications that access large data files over wide area network environment, such as data-grids, the combination of policies for job servicing (or scheduling), caching and cache replacement can significantly impact the performance of grid jobs. We present preliminary results of a simulation study that combines an admission policy with a cache replacement policy when servicing jobs submitted to a storage resource manager.The results show that, in comparison to a first come first serve policy, the response times of jobs are significantly improved, for practical limits of disk cache sizes, when the jobs that are back-logged to access the same files are taken into consideration in scheduling the next file to be retrieved into the disk cache. Not only are the response times of jobs improved, but also the metric measures for caching policies, such as the hit ratio and the average cost per retrieval, are improved irrespective of the cache replacement policy used. Ekow Otoo is research staff scientist with the scientific data management group at Lawrence Berkeley National Laboratory, University of California, Berkeley. He received his B.Sc. degree in Electrical Engineering from the University of Science and Technology, Kumasi, Ghana and a post graduate diploma in Computer Science from the University of Ghana, Legon. In 1977, he received his M.Sc. degree in Computer Science from the University of Newcastle Upon Tyne in Britain and his Ph.D. degree in Computer Science from McGill University, Montreal, Canada in 1983. He joined the faculty of the School of Computer Science, Carleton University, in 1983 and from 1987 to 1999, he was a tenured faculty member of the School of Computer Science, Carleton University, Ottawa, Canada. He has served as research consultant to Bell Northern Research, Ottawa, Canada, and as a research project consultant to the GIS Division, Geomatics Canada, Natural Resources Canada, from 1990 to 1998. Ekow Otoo is a member of the ACM and IEEE. His research interests include database management systems, data structures and algorithms, parallel I/O for high performance computing, parallel and distributed computing. Doron Rotem is currently a senior staff scientist and a member of the Data Management group at the Lawrence Berkeley National Lab. His research interests include Grid Computing, Workflow, Scientific Data Management and Paralled and Distributed Computing and Algorithms. He has published over 80 papers in international journals and conferences in these areas. Prior to that, Dr Rotem co-founded and served as a CTO of a startup company, called CommerceRoute, that made software products in the area of workflow and data integration and before that, he was an Associate Professor in the Department of Computer Science, University of Waterloo, Canada. Dr. Rotem holds a B.Sc degree in Mathematics and Statistics from the Hebrew University, Jerusalem, Israel and a Ph.D. in Computer Science from the University of the Witwatersrand, Johannesburg, South Africa. Arie Shoshani is a senior staff scientist at Lawrence Berkeley National Laboratory. He joined LBNL in 1976. He heads the Scientific Data Management Group. He received his Ph.D. from Princeton University in 1969. From 1969 to 1976, he was a researcher at System Development Corporation, where he worked on the Network Control Program for the ARPAnet, distributed databases, database conversion, and natural language interfaces to data management systems. His current areas of work include data models, query languages, temporal data, statistical and scientific database management, storage management on tertiary storage, and grid storage middleware. Arie is also the director of a Scientific Data Management (SDM) Integrated Software Infrastructure Center (ISIC), one of seven centers selected by the SciDAC program at DOE in 2001. In this capacity, he is coordinating the work of collaborators from 4 DOE laboratories and 4 universities (see: http://sdmcenter.lbl.gov). Dr. Shoshani has published over 65 technical papers in refereed journals and conferences, chaired several workshops, conferences, and panels in database management; and served on numerous program committees for various database conferences. He also served as an associate editor for the ACM Transactions on Database Systems. He was elected a member of the VLDB Endowment Board, served as the Publication Board Chairperson for the VLDB Journal, and as the Vice-President of the VLDB Endowment. His home page is http://www.lbl.gov/arie.     

The subjective value of delayed and probabilistic outcomes: Outcome size matters for gains but not for losses

The subjective value of a reward (gain) is related to factors such as its size, the delay to its receipt and the probability of its receipt. We examined whether the subjective value of losses was similarly affected by these factors in 128 adults. Participants chose between immediate/certain gains or losses and larger delayed/probabilistic gains or losses. Rewards of $100 were devalued as a function of their delay (“discounted”) relatively less than $10 gains while probabilistic $100 rewards were discounted relatively more than $10 rewards. However, there was no effect of outcome size on discounting of delayed or probabilistic losses. For delayed outcomes of each size, the degree to which gains were discounted was positively correlated with the degree to which losses were discounted, whereas for probabilistic outcomes, no such correlation was observed. These results suggest that the processes underlying the subjective valuation of losses are different from those underlying the subjective valuation of gains.     

A taxonomy of application scheduling tools for high performance cluster computing

Application scheduling plays an important role in high-performance cluster computing. Application scheduling can be classified as job scheduling and task scheduling. This paper presents a survey on the software tools for the graph-based scheduling on cluster systems with the focus on task scheduling. The tasks of a parallel or distributed application can be properly scheduled onto multi-processors in order to optimize the performance of the program (e.g., execution time or resource utilization). In general, scheduling algorithms are designed based on the notion of task graph that represents the relationship of parallel tasks. The scheduling algorithms map the nodes of a graph to the processors in order to minimize overall execution time. Although many scheduling algorithms have been proposed in the literature, surprisingly not many practical tools can be found in practical use. After discussing the fundamental scheduling techniques, we propose a framework and taxonomy for the scheduling tools on clusters. Using this framework, the features of existing scheduling tools are analyzed and compared. We also discuss the important issues in improving the usability of the scheduling tools. This work is supported by the Hong Kong Polytechnic University under grant H-ZJ80 and by NASA Ames Research Center by a cooperative grant agreement with the University of Texas at Arlington. Jiannong Cao received the BSc degree in computer science from Nanjing University, Nanjing, China in 1982, and the MSc and the Ph.D degrees in computer science from Washington State University, Pullman, WA, USA, in 1986 and 1990 respectively. He is currently an associate professor in Department of Computing at the Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department. He was on the faculty of computer science at James Cook University and University of Adelaide in Australia, and City University of Hong Kong. His research interests include parallel and distributed computing, networking, mobile computing, fault tolerance, and distributed software architecture and tools. He has published over 120 technical papers in the above areas. He has served as a member of editorial boards of several international journals, a reviewer for international journals/conference proceedings, and also as an organizing/programme committee member for many international conferences. Dr. Cao is a member of the IEEE Computer Society, the IEEE Communication Society, IEEE, and ACM. He is also a member of the IEEE Technical Committee on Distributed Processing, IEEE Technical Committee on Parallel Processing, IEEE Technical Committee on Fault Tolerant Computing, and Computer Architecture Professional Committee of the China Computer Federation. Alvin Chan is currently an assistant professor at the Hong Kong Polytechnic University. He graduated from the University of New South Wales with a Ph.D. degree in 1995 and was subsequently employed as a Research Scientist by the CSIRO, Australia. From 1997 to 1998, he was employed by the Centre for Wireless Communications, National University of Singapore as a Program Manager. Dr. Chan is one of the founding members and director of a university spin-off company, Information Access Technology Limited. He is an active consultant and has been providing consultancy services to both local and overseas companies. His research interests include mobile computing, context-aware computing and smart card applications. Yudong Sun received the B.S. and M.S. degrees from Shanghai Jiao Tong University, China. He received Ph.D. degree from the University of Hong Kong in 2002, all in computer science. From 1988 to 1996, he was among the teaching staff in Department of Computer Science and Engineering at Shanghai Jiao Tong University. From 2002 to 2003, he held a research position at the Hong Kong Polytechnic University. At present, he is a Research Associate in School of Computing Science at University of Newcastle upon Tyne, UK. His research interests include parallel and distributed computing, Web services, Grid computing, and bioinformatics. Sajal K. Das is currently a Professor of Computer Science and Engineering and the Founding Director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington. His current research interests include resource and mobility management in wireless networks, mobile and pervasive computing, sensor networks, mobile internet, parallel processing, and grid computing. He has published over 250 research papers, and holds four US patents in wireless mobile networks. He received the Best Paper Awards in ACM MobiCom’99, ICOIN-16, ACM, MSWiM’00 and ACM/IEEE PADS’97. Dr. Das serves on the Editorial Boards of IEEE Transactions on Mobile Computing, ACM/Kluwer Wireless Networks, Parallel Processing Letters, Journal of Parallel Algorithms and Applications. He served as General Chair of IEEE PerCom’04, IWDC’04, MASCOTS’02 ACM WoWMoM’00-02; General Vice Chair of IEEE PerCom’03, ACM MobiCom’00 and IEEE HiPC’00-01; Program Chair of IWDC’02, WoWMoM’98-99; TPC Vice Chair of ICPADS’02; and as TPC member of numerous IEEE and ACM conferences. Minyi Guo received his Ph.D. degree in information science from University of Tsukuba, Japan in 1998. From 1998 to 2000, Dr. Guo had been a research scientist of NEC Soft, Ltd. Japan. He is currently a professor at the Department of Computer Software, The University of Aizu, Japan. From 2001 to 2003, he was a visiting professor of Georgia State University, USA, Hong Kong Polytechnic University, Hong Kong. Dr. Guo has served as general chair, program committee or organizing committee chair for many international conferences, and delivered more than 20 invited talks in USA, Australia, China, and Japan. He is the editor-in-chief of the Journal of Embedded Systems. He is also in editorial board of International Journal of High Performance Computing and Networking, Journal of Embedded Computing, Journal of Parallel and Distributed Scientific and Engineering Computing, and International Journal of Computer and Applications. Dr. Guo’s research interests include parallel and distributed processing, parallelizing compilers, data parallel languages, data mining, molecular computing and software engineering. He is a member of the ACM, IEEE, IEEE Computer Society, and IEICE. He is listed in Marquis Who’s Who in Science and Engineering.     

Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.