Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.     

Accessory artificial circulation in complex treatment of experimental acute toxic pulmonary edema

In experiments on 50 dogs with toxic acute edema of the lung, induced with intravenous injection of 0.1% silver nitrate, the authors have studied the efficacy of accessory artificial circulation and "conservative" therapy. During the perfusion a discharge of the right portions, adequate extracorporeal gas metabolism, normalization of blood gas and acid-base balance were noted; an intensity of pulmonary edema is descreased. An intensive therapy for pulmonary edema was found to be more effective in association of "conservative" treatment with venoarterial perfusion and blood oxygenation.     

An Efficient Multibody Dynamics Model for Internal Combustion Engine Systems

The equations of motion for the major components in an internalcombustion engine are developed herein using a recursive formulation.These components include the (rigid) engine block, pistons, connectingrods, (flexible) crankshaft, balance shafts, main bearings, and enginemounts. Relative coordinates are employed that automatically satisfy allconstraints and therefore lead to the minimum set of ordinarydifferential equations of motion. The derivation of the equations ofmotion is automated through the use of computer algebra as the precursorto automatically generating the computational (C or Fortran) subroutinesfor numerical integration. The entire automated procedure forms thebasis for an engine modeling template that may be used to supportthe up-front design of engines for noise and vibration targets.This procedure is demonstrated on an example engine under free(idealized) and firing conditions and the predicted engine responses arecompared with results from an ADAMS model. Results obtained by usingdifferent bearing models, including linear, nonlinear, and hydrodynamicbearing models, are discussed in detail.     

Two realizations of a general feature extraction framework

A general feature extraction framework is proposed as an extension of conventional linear discriminant analysis. Two nonlinear feature extraction algorithms based on this framework are investigated. The first is a kernel function feature extraction (KFFE) algorithm. A disturbance term is introduced to regularize the algorithm. Moreover, it is revealed that some existing nonlinear feature extraction algorithms are the special cases of this KFFE algorithm. The second feature extraction algorithm, mean-STD¹-norm feature extraction algorithm, is also derived from the framework. Experiments based on both synthetic and real data are presented to demonstrate the performance of both feature extraction algorithms.     

Computerized detection and segmentation of mitochondria on electron microscope images

Mitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high-throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live-wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42 are true and 10 are false detections. After false ellipses are eliminated manually, 14 out of 15 fully seen mitochondria and 4 out of 7 partially seen mitochondria are successfully detected. When compared with the segmentation of a trained reader, 91% Dice similarity coefficient was achieved with an average 4.9 nm boundary error.     

The Role of Organizational Vision in Guiding Idea Generation within SME Contexts

New ideas are required if our organizations are going to survive and grow. Without the ability to think differently, firms can quickly stagnate and decline. A key contributor to the success (or otherwise) of the ideation process is the extent to which organizations provide effective control mechanisms, with existing literature drawing tentative links between this and the subject of organizational vision. The aim of this paper is to explore the control of idea generation in SME contexts, examining the interaction between organizational vision and the guidance of ideation in these settings. Through in‐depth qualitative research with a range of SMEs, this study shows that structure in the form of an outlining framework assists in opening a liminal space for ideation. More importantly, this paper provides empirical evidence, which demonstrates that organizational visions provide this framework by acting as a target, assisting the development of relevant ideas. Ideation, within the SME, is argued to be fundamentally subject to, and contingent upon, the direction of the organization.     

Overview of underground coal gasification operations at Chinchilla,Australia

Underground coal gasification (UCG) is a process which converts deep, un-mineable or difficult to mine coal resources into syngas which can then be converted into valuable end products such as electric power, liquid fuels, synthetic natural gas and chemicals. This paper provides a summary of the UCG operations conducted at the Chinchilla Demonstration Facility in Australia, focusing on gasifiers constructed using directional drilling. A number of the experiences and key lessons learned from operating multiple underground gasifiers over several years at the facility are described. Implications for the implementation in commercial projects using UCG are also discussed. Finally, the potential of UCG as a method for producing syngas from deep coal is discussed and some of the challenges and opportunities are summarized.     

磷酸硼对樟子松的阻燃影响

采用磷酸硼对木材进行改性处理,测定了处理后试材的阻燃性、抗流失性、吸湿性、力学性能.研究结果表明,磷酸硼处理后试材,吸药量达到20.47 kg/m3以上时,垂直燃烧实验可达到到F-0级水平；通过灼烧实验,磷酸硼处理的木粉在500℃灼烧时残留率可达到97.997％;此改性剂具有较好阻燃性能和一定的抗流失性,常压下磷酸硼处理的樟子松试件LRV为50.28％；木材顺纹抗压强度平均下降了 8.67％,无明显变化;木材抗弯曲压强度平均下降了 27.95％.但磷酸硼阻燃剂在高湿条件下有一定的吸湿性,使用性能有待改进,并应注意采取防护措施.     

Field D* path-finding on weighted triangulated and tetrahedral meshes

Classic shortest path algorithms operate on graphs, which are suitable for problems that can be represented by weighted nodes or edges. Finding a shortest path through a set of weighted regions is more difficult and only approximate solutions tend to scale well. The Field D* algorithm efficiently calculates an approximate, interpolated shortest path through a set of weighted regions and was designed for navigating robots through terrains with varying characteristics. Field D* operates on unit grid or quad-tree data structures, which require high resolutions to accurately model the boundaries of irregular world structures. In this paper, we extend the Field D* cost functions to 2D triangulations and 3D tetrahedral meshes: structures which model polygonal world structures more accurately. Since robots typically have limited resources available for computation and storage, we pay particular attention to computation and storage overheads when detailing our extensions. We begin by providing analytic solutions to the minimum of each cost function for 2D triangles and 3D tetrahedra. Our triangle implementation provides a 50 % improvement in performance over an existing triangle implementation. While our 3D extension to tetrahedra is the first full analytic extension of Field D* to 3D, previous work only provided an approximate minimization for a single cost function on a 3D cube with unit lengths. Each cost function is expressed in terms of a general function whose characteristics can be exploited to reduce the calculations required to find a minimum. These characteristics can also be exploited to cache the majority of cost functions, producing a speedup of up to 28 % in the 3D tetrahedral case. We demonstrate that, in environments composed of non-grid aligned data, Multi-resolution quad-tree Field D* requires an order of magnitude more faces and between 15 and 20 times more node expansions, to produce a path of similar cost to one produced by a triangle implementation of Field D* on a lower resolution triangulation. We provide examples of 3D pathing through models of complex topology, including pathing through anatomical structures extracted from a medical data set. To summarise, this paper details a robust and efficient extension of Field D* pathing to data sets represented by weighted triangles and tetrahedra, and also provides empirical data which demonstrates the reduction in storage and computation costs that accrue when one chooses such a representation over the more commonly used quad-tree and grid-based alternatives.