A new degree of freedom for memory allocation in clusters

Improvements in parallel computing hardware usually involve increments in the number of available resources for a given application such as the number of computing cores and the amount of memory. In the case of shared-memory computers, the increase in computing resources and available memory is usually constrained by the coherency protocol, whose overhead rises with system size, limiting the scalability of the final system. In this paper we propose an efficient and cost-effective way to increase the memory available for a given application by leveraging free memory in other computers in the cluster. Our proposal is based on the observation that many applications benefit from having more memory resources but do not require more computing cores, thus reducing the requirements for cache coherency and allowing a simpler implementation and better scalability. Simulation results show that, when additional mechanisms intended to hide remote memory latency are used, execution time of applications that use our proposal is similar to the time required to execute them in a computer populated with enough local memory, thus validating the feasibility of our proposal. We are currently building a prototype that implements our ideas. The first results from real executions in this prototype demonstrate not only that our proposal works but also that it can efficiently execute applications that make use of remote memory resources.     

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.     

A spike-based model of binaural sound localization

This paper describes a spike-based model of binaural sound localization using interaural time differences (ITDs). To handle the problem of temporal coding and to facilitate a hardware implementation all neurons are simulated by a spike response model, which includes postsynaptic potentials (PSPs) and a refractory period. A winner-take-all (WTA) network selects the dominant source from the representation of the sound's angles of incidences, and can be biased by a multisensory support. We use simulations on real audio data to investigate the function and the practical application of the system.     

An optimized human resource management model for cloud-edge computing in the internet of things

The use of cloud-edge technology creates significant potential for cost reduction, efficiency and resource management. These features have encouraged users and organizations to use intelligence federated cloud-edge paradigm in Internet of Things (IoT). Human Resource Management (HRM) is one of the important challenges in federated cloud-edge computing. Since hardware and software resources in the edge environment are allocated for responding human requests, selecting optimal resources based on Quality of Service (QoS) factors is a critical and important issue in the IoT environments. The HRM can be considered as an NP-problem in a way that with proper selection, allocation and monitoring resource, system efficiency increases and response time decreases. In this study, an optimization model is presented for the HRM problem using Whale Optimization Algorithm (WOA) in cloud-edge computing. Experimental results show that the proposed model was able to improve minimum response time, cost of allocation and increasing number of allocated human resources in two different scenarios compared to the other meta-heuristic algorithms.

     

Foraging in nature: Contrasting responses to resource heterogeneity at small‐ and large‐spatial scales

A key problem faced by foragers is how to forage when resources are distributed heterogeneously in space. This heterogeneity and associated trade‐offs may change with spatial scale. Furthermore, foragers may also have to optimize acquiring multiple resources. Such complexity of decision‐making while foraging is poorly understood. We studied the butterfly Ypthima huebneri to examine how foraging decisions of adults are influenced by spatial scale and multiple resources. We predicted that, at a small‐spatial scale, the time spent foraging in a patch should be proportional to resources in the patch, but at large‐spatial scales, due to limitations arising from large travel costs, this relationship should turn negative. We also predicted that both adult and larval resources should jointly affect foraging butterflies. To test these predictions, we laid eleven plots and sub‐divided them into patches. We mapped nectar and larval resources and measured butterfly behavior in these patches and plots. We found that adult foraging behavior showed contrasting relationships with adult resource density at small versus large‐spatial scales. At the smaller‐spatial scale, butterflies spent more time feeding in resource‐rich patches, whereas at the large‐scale, butterflies spent more time feeding in resource‐poor plots. Furthermore, both adult and larval resources appeared to affect foraging decisions, suggesting that individuals may optimize search costs for different resources. Overall, our findings suggest that the variation in foraging behavior seen in foragers might result from animals responding to complex ecological conditions, such as resource heterogeneity at multiple spatial scales and the challenges of tracking multiple resources.     

The ideal free distribution as an evolutionarily stable state in density‐dependent population games

In classical games that have been applied to ecology, individual fitness is either density independent or population density is fixed. This article focuses on the habitat selection game where fitness depends on the population density that evolves over time. This model assumes that changes in animal distribution operate on a fast time scale when compared to demographic processes. Of particular interest is whether it is true, as one might expect, that resident phenotypes who use density‐dependent optimal foraging strategies are evolutionarily stable with respect to invasions by mutant strategies. In fact, we show that evolutionary stability does not require that residents use the evolutionarily stable strategy (ESS) at every population density; rather it is the combined resident–mutant system that must be at an evolutionary stable state. That is, the separation of time scales assumption between behavioral and ecological processes does not imply that these processes are independent. When only consumer population dynamics in several habitats are considered (i. e. when resources do not undergo population dynamics), we show that the existence of optimal foragers forces the resident‐mutant system to approach carrying capacity in each habitat even though the mutants do not die out. Thus, the ideal free distribution (IFD) for the single‐species habitat selection game becomes an evolutionarily stable state that describes a mixture of resident and mutant phenotypes rather than a strategy adopted by all individuals in the system. Also discussed is how these results are affected when animal distribution and demographic processes act on the same time scale.     

FPNA: interaction between FPGA and neural computation

Neural networks are usually considered as naturally parallel computing models. But the number of operators and the complex connection graph of standard neural models can not be directly handled by digital hardware devices. More particularly, several works show that programmable digital hardware is a real opportunity for flexible hardware implementations of neural networks. And yet many area and topology problems arise when standard neural models are implemented onto programmable circuits such as FPGAs, so that the fast FPGA technology improvements can not be fully exploited. Therefore neural network hardware implementations need to reconcile simple hardware topologies with complex neural architectures. The theoretical and practical framework developed, allows this combination thanks to some principles of configurable hardware that are applied to neural computation: Field Programmable Neural Arrays (FPNA) lead to powerful neural architectures that are easy to map onto FPGAs, thanks to a simplified topology and an original data exchange scheme. This paper shows how FPGAs have led to the definition of the FPNA computation paradigm. Then it shows how FPNAs contribute to current and future FPGA-based neural implementations by solving the general problems that are raised by the implementation of complex neural networks onto FPGAs.     

Biodiversity conservation and agricultural sustainability: towards a new paradigm of 'ecoagriculture' landscapes

The dominant late twentieth century model of land use segregated agricultural production from areas managed for biodiversity conservation. This module is no longer adequate in much of the world. The Millennium Ecosystem Assessment confirmed that agriculture has dramatically increased its ecological footprint. Rural communities depend on key components of biodiversity and ecosystem services that are found in non-domestic habitats. Fortunately, agricultural landscapes can be designed and managed to host wild biodiversity of many types, with neutral or even positive effects on agricultural production and livelihoods. Innovative practitioners, scientists and indigenous land managers are adapting, designing and managing diverse types of 'ecoagriculture' landscapes to generate positive co-benefits for production, biodiversity and local people. We assess the potentials and limitations for successful conservation of biodiversity in productive agricultural landscapes, the feasibility of making such approaches financially viable, and the organizational, governance and policy frameworks needed to enable ecoagriculture planning and implementation at a globally significant scale. We conclude that effectively conserving wild biodiversity in agricultural landscapes will require increased research, policy coordination and strategic support to agricultural communities and conservationists.     

Landscape scale, heterogeneity, and the viability of Serengeti grazers

Species persistence can be threatened by substantial temporal variation in food resources over time. On the other hand, spatial heterogeneity in resources at the landscape scale might allow mobile consumers to compensate for temporal variability in resource availability at the local scale. We evaluated this hypothesis, using an extensive data set on foraging, grass growth, and movement by Thomson's gazelles living on the Serengeti Plains. Here we show that modelled populations of Thomson's gazelles can only persist under Serengeti conditions in the face of observed levels of rainfall stochasticity by making adaptive movements to take advantage of ephemeral spatial distributions of food resources. More importantly, our models suggest that Thomson's gazelles in Serengeti require unrestricted access to relatively large areas of grassland (> 1600 km²) to guarantee long‐term persistence, particularly when there is positive spatial autocorrelation in resource abundance, as is the case in Serengeti. If this proves to be true for other species and/or other systems, then understanding of complex behavioural responses to spatially and temporally heterogeneous food supplies may be essential to successful conservation of grazing herbivores.     

A Genetic-Based Vision System for Cross-Functional Integration in Flexible Manufacturing: A Tutorial and Application

Machine vision has the potential to significantly impact both quality and productivity in automated manufacturing, due to its versatility, flexibility, and relative speed. Unfortunately, algorithmic development has not kept pace with advances in vision hardware technology, particularly in the areas of analysis and decision making. In this article, a tutorial is presented that explains how a genetic algorithm can be applied to vision systems for shape analysis and quality assessment. The control parameters for the algorithm are optimized by conducting experiments of Taguchi's approach to parameter design. The main objective behind this algorithm is to explain an application of the vision system that uses upstream design data of machined parts of different types for downstream metrology and quality decision making in the environment of flexible manufacturing. The part types used for demonstration are restricted to planar polygonal profiles generated by projecting 3D objects onto a 2D inspection plane. The input to the system is a set of boundary features of the part being analyzed, and the outputs from the system include the estimators of size, orientation, position, and out-of-profile error of the part. The system can analyze machined parts of different types without modifying software programs and parameter settings, which makes it generic and flexible, and is inherently suitable for on-line implementation in FMS environments.     

High-efficiency gene inactivation and replacement system for gram-positive bacteria.

A system for high-efficiency single- and double-crossover homologous integration in gram-positive bacteria has been developed, with Lactococcus lactis as a model system. The system is based on a thermosensitive broad-host-range rolling-circle plasmid, pG+host5, which contains a pBR322 replicon for propagation in Escherichia coli at 37 degrees C. A nested set of L. lactis chromosomal fragments cloned onto pG+host5 were used to show that the single-crossover integration frequency was logarithmically proportional to the length of homology for DNA fragments between 0.35 and 2.5 kb. Using random chromosomal 1-kb fragments, we showed that homologous integration can occur along the entire chromosome. We made use of the reported stimulatory effect of rolling-circle replication on intramolecular recombination to develop a protocol for gene replacement. Cultures were first maintained at 37 degrees C to select for a bacterial population enriched for plasmid integrants; activation of the integrated rolling-circle plasmid by a temperature shift to 28 degrees C resulted in efficient plasmid excision by homologous recombination and replacement of a chromosomal gene by the plasmid-carried modified copy. More than 50% of cells underwent replacement recombination when selection was applied for the replacing gene. Between 1 and 40% of cells underwent replacement recombination when no selection was applied. Chromosomal insertions and deletions were obtained in this way. These results show that gene replacement can be obtained at an extremely high efficiency by making use of the thermosensitive rolling-circle nature of the delivery vector. This procedure is applicable to numerous gram-positive bacteria.     

Modeling and analysis of biooxidation of gold bearing pyrite-arsenopyrite concentrates by Thiobacillus ferrooxidans

The results of modeling the biooxidation of a mixed sulfidic concentrate by Thiobacillus ferrooxidans is reported here. A kinetic model, which accounts for the dissolution of sulfide matrix due to both bacterial attachment onto the mineral surface and indirect leaching, has been proposed. A comprehensive system approach is employed for modeling the complex biooxidation process by (a) the decomposition of the complete system into several subsystems, (b) modeling individual systems, and (c) integrating the subsystems model in a final system model. The model for subsystems was developed by writing mass balance equations for the different species involved. The bacterial balance accounts for its growth, both on solid substrate and in solution, and for the attachment to and detachment from the surface. The kinetic parameters of the model were determined by designing the experiments in such a manner that only one subsystem was operational. This model was tested in both laboratory scale batch and continuous biooxidation processes. The model predictions agreed with the experimental data reasonably well. A further analysis of the model was carried out to predict the conditions for efficient biooxidation. Studies on the effect of residence time and pulp density on steady-state behavior showed that there is a critical residence time and pulp density below which washout conditions occur. Operation at pulp densities lower than 5% and residence times lower than 72 h was found unfavorable for efficient leaching.     

产业生态系统资源代谢分析方法

产业生态系统是由企业群、资源及环境组成的社会-经济-环境复合生态系统。资源代谢是其功能运行的重要保障。资源代谢在时间和空间尺度上的耗竭及阻滞是造成严重生态环境问题的主要原因。根据生态学原理,运用物质流分析手段解析了产业生态系统的物质流、能流及资金流结构,构建了产业生态系统资源代谢分析模型,提出了资源输入-使用-输出-循环共生四方面的资源代谢分析指标体系和基于模糊综合分析的资源代谢问题树分析方法。在此基础上提出了基于循环共生网络结构模型的生态管理模式。以期为产业资源的生态管理提供方法支撑。     

Scalable and highly parallel implementation of Smith-Waterman on graphics processing unit using CUDA

Program development environments have enabled graphics processing units (GPUs) to become an attractive high performance computing platform for the scientific community. A commonly posed problem in computational biology is protein database searching for functional similarities. The most accurate algorithm for sequence alignments is Smith-Waterman (SW). However, due to its computational complexity and rapidly increasing database sizes, the process becomes more and more time consuming making cluster based systems more desirable. Therefore, scalable and highly parallel methods are necessary to make SW a viable solution for life science researchers. In this paper we evaluate how SW fits onto the target GPU architecture by exploring ways to map the program architecture on the processor architecture. We develop new techniques to reduce the memory footprint of the application while exploiting the memory hierarchy of the GPU. With this implementation, GSW, we overcome the on chip memory size constraint, achieving 23× speedup compared to a serial implementation. Results show that as the query length increases our speedup almost stays stable indicating the solid scalability of our approach. Additionally this is a first of a kind implementation which purely runs on the GPU instead of a CPU-GPU integrated environment, making our design suitable for porting onto a cluster of GPUs.     

Fully integrated downstream process to enable next-generation manufacturing

Next-generation manufacturing (NGM) has evolved over the past decade to a point where large biopharmaceutical organizations are making large investments in the technology and considering implementation in clinical and commercial processes. There are many well-considered reasons to implement NGM. For the most part, organizations will not fund NGM unless the implementation benefits the funding organization by providing reduced costs, reduced time, or additional needed capabilities. Productivity improvements gained from continuous purification are shown in this work, which used a new system that fully integrates and automates several downstream unit operations of a biopharmaceutical process to provide flexibility and easy implementation of NGM. The equipment and automation needed to support NGM can be complicated and expensive. Biopharmaceutical Process Development considered two options as follows: (1) design its own NGM system or (2) buy a prebuilt system. PAK BioSolutions offers a turn-key automated and integrated system that can operate up to four continuous purification stages simultaneously, while maintaining a small footprint in the manufacturing plant. The system provides significant cost benefits (~10× lower) compared with the alternative—integration of many different pieces of equipment through a Distributed Control System that would require significant engineering time for design, automation, and integration. Integrated and Continuous Biomanufacturing can lead to significant reductions in facility size, reduced manufacturing costs, and enhanced product quality when compared with the traditional batch mode of operation. The system uses new automation strategies that robustly link unit operations. We present the optimized process fit, sterility and bioburden control strategy, and automation features (such as pH feedback control and in-line detergent addition), which enabled continuous operation of a 14-day end-to-end monoclonal antibody purification process at the clinical manufacturing scale.     

3DMolNavi: A web-based retrieval and navigation tool for flexible molecular shape comparison

Background

As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed.

Results

Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs.

Conclusions

The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary.     

Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.     

Influence of the Gas-Water Interface on Transport of Microorganisms through Unsaturated Porous Media

In this article, a new mechanism influencing the transport of microorganisms through unsaturated porous media is examined, and a new method for directly visualizing bacterial behavior within a porous medium under controlled chemical and flow conditions is introduced. Resting cells of hydrophilic and relatively hydrophobic bacterial strains isolated from groundwater were used as model microorganisms. The degree of hydrophobicity was determined by contact-angle measurements. Glass micromodels allowed the direct observation of bacterial behavior on a pore scale, and three types of sand columns with different gas saturations provided quantitative measurements of the observed phenomena on a porous medium scale. The reproducibility of each break-through curve was established in three to five repeated experiments. The data collected from the column experiments can be explained by phenomena directly observed in the micromodel experiments. The retention rate of bacteria is proportional to the gas saturation in porous media because of the preferential sorption of bacteria onto the gas-water interface over the solid-water interface. The degree of sorption is controlled mainly by cell surface hydrophobicity under the simulated groundwater conditions because of hydrophobic forces between the organisms and the interfaces. The sorption onto the gas-water interface is essentially irreversible because of capillary forces. This preferential and irreversible sorption at the gas-water interface strongly influences the movement and spatial distribution of microorganisms.     

Assessing priority areas by imperilled species: insights from the European butterflies

Biodiversity hotspots have been variously defined in terms of species richness, endemic species or imperilled species. The use of imperilled species to locate priority areas is particularly problematic, because an area that hosts a large number of imperilled species is likely to be under severe threats, making less effective conservation efforts. A possibly way to answer this problem is to assess species threats at two spatial scales. Then, areas which host concentrations of species that are imperilled at the larger scale, but not at the smaller scale, can be considered as priority areas where conservation efforts are expected to be more effective. An application of this procedure to the European butterfly fauna with the Biodiversity Conservation Concern index calculated with two IUCN red listings (European and national) allowed the construction of a four-celled model that reflects different types of conservation priority. This combined use of international and regional red lists may be a tool to make practical decisions (e.g. allocation of funds or legislative actions) to preserve imperilled species.