泛函连接网络计算软件及其在生物多样性研究中的应用

针对农田生物多样性分析的需要,研制出泛函连接网络（FLANN）计算软件。该软件由7个Java类和1个HTML文件组成,是一种Internet在线计算工具,可运行于多种操作系统和Web浏览器上,并在各种类型的PC及工作站上使用,可读取多种类型的数据库文件。对水稻田昆虫生物多样性的两组取样调查数据Zmar18和Zapr15,用生物多样性工具软件LUMP和非监督分类－离差平方和聚类法进行统计归纳及分类,分别划分为21个和20个功能群,各包含60个样本。以FLANN计算软件对昆虫生物多样性进行了模式分类分析。结果表明,泛函连接网络的模式分类及预测与实际测查结果吻合良好。泛函连接网络Internet在线计算软件的应用可促进生物多样性数据采集和分析的规范化,有利于数据和信息共享,也为形成高度的生物多样性智能分析系统提供了一种工具。     

A Software Suite for High-Performance Communications on Clusters of SMPs

A cluster, by opposition to a parallel computer, is a set of separate workstations interconnected by a high-speed network. The performances one can get on a cluster heavily depend on the performances of the lowest communication layers. In this paper, we address the special case where the cluster contains multi-processor machines. These shared-memory multi-processors desktop machines (SMPs) with 2 or 4 processors are now becoming very popular and present a high performance/price ratio. We present a software suite for achieving high-performance communications on a Myrinet-based cluster: BIP, BIP-SMP and MPI-BIP. The software suite supports single-processor (Intel PC and Digital Alpha) and multi-processor machines, as well as any combination of the two architectures.     

Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing

Although biosensors based on whole microbial cells have many advantages in terms of convenience, cost and durability, a major limitation of these sensors is often their inability to distinguish between different substrates of interest. This paper demonstrates that it is possible to use sensors entirely based upon whole microbial cells to selectively measure ethanol and glucose in mixtures. Amperometric sensors were constructed using immobilized cells of either Gluconobacter oxydans or Pichia methanolica. The bacterial cells of G. oxydans were sensitive to both substrates, while the yeast cells of P. methanolica oxidized only ethanol. Using chemometric principles of polynomial approximation, data from both of these sensors were processed to provide accurate estimates of glucose and ethanol over a concentration range of 1.0–8.0 mM (coefficients of determination, R²=0.99 for ethanol and 0.98 for glucose). When data were processed using an artificial neural network, glucose and ethanol were accurately estimated over a range of 1.0–10.0 mM (R²=0.99 for both substrates). The described methodology extends the sphere of utility for microbial sensors.     

The applicability of hns and fimA primers for detecting Salmonella in bioaerosols associated with animal and municipal wastes

The ability to rapidly screen environmental samples for specific pathogens such as Salmonella spp., is of particular importance in molecular epidemiology. Although gene amplification reactions allow the rapid detection of microorganisms, the use of appropriate oligonucleotide primers targeted against specific microbial genes is critical for accurate detection specificity and sensitivity. Primers such as fimA and hns have been previously shown to be specific for pure cultures of Salmonella. However, the analysis of environmental samples requires post-amplification hybridizations to detect amplicons, since the presence of inhibitory environmental components reduces amplification efficiency of the target organism. These sensitive post-amplification approaches also enable the detection of spurious amplification from non-target sequences. Bioaerosols associated with animal facilities and municipal wastes contain a diverse array of pathogens including Clostridium spp. In our studies, hybridization sensor data revealed spurious amplification of clostridial species with Salmonella hns primers. Specificity checks using type cultures of Clostridium spp. revealed non-specific amplification by hns primers. These results suggest that fimA primers may be better suited to screen Salmonella-specific sequences in environmental samples, especially those obtained from animal and municipal waste facilities.     

序列同源性分析软件Blast的WEB界面构建及其应用

基于局域网(Intranet)内的PC/Linux服务器, 构建了序列同源性分析软件Blast的WEB界面. 局域网内的所有计算机均可通过WEB方式访问该服务器进行公共数据库和自建数据库的查询,具有保密、高效、免费的优点,能够满足实验室和研究院所的大规模、快速数据分析任务.     

RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

A new software package, RASPA, for simulating adsorption and diffusion of molecules in flexible nanoporous materials is presented. The code implements the latest state-of-the-art algorithms for molecular dynamics and Monte Carlo (MC) in various ensembles including symplectic/measure-preserving integrators, Ewald summation, configurational-bias MC, continuous fractional component MC, reactive MC and Baker's minimisation. We show example applications of RASPA in computing coexistence properties, adsorption isotherms for single and multiple components, self- and collective diffusivities, reaction systems and visualisation. The software is released under the GNU General Public License.     

A decision-tree model to detect post-calving diseases based on rumination,activity, milk yield,BW and voluntary visits to the milking robot

Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows’ routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian’s diagnosis served as a binary reference for the model (healthy–sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.     

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity^1-5. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions.Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures^1,6,7. We developed a new model¹ which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (K_d) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of powerful agents to combat superbug infections.