Association Analysis Techniques for Discovering Functional Modules from Microarray Data

An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets

Enhancing the functional content of protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, they face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we explore the use of the concept of common neighborhood similarity (CNS), which is a form of local structure in networks, to address these issues. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of S. cerevisiae interactions, and a set of 136 GO terms, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the $HC.cont$ measure proposed here performs particularly well for this task. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures, especially $HC.cont$, to prune out noisy edges and introduce new links between functionally related proteins

Aspects of Nanoelectronics in Materials Development

Nanotechnology is an enabling technology that potentially impacts all aspects of the chip-making practice from materials to devices, to circuits, and to system-level architecture. Nanoelectronics is an interdisciplinary division which refers to the use of nanotechnology in electronic components. The materials and devices used in nanoelectronics are so small that the interatomic interactions and quantum mechanical properties of such materials need to be studied extensively. Various electronic devices manufactured at nanoscale have been established: devices having negative differential resistance, switches which can be electrically configured, tunneling junctions, carbon nanotube (CNT) transistor, and unimolecular transistor. Some devices have also been linked together to form circuits proficient of performing functions such as logic functions and basic memory. Some of the widely used materials in nanoelectronics include zero-dimensional materials like quantum dots; one-dimensional materials like nanotubes and nanowires; nanoclusters and nanocomposites; carbon-based materials like carbon nanotubes (CNTs), fullerenes and graphene; etc. Plastic C nanoelectronics is also a prominent research area with collaboration between the materials science, chemistry, physics, nanotechnology, and engineering communities. As one of the most promising contenders, C nanostructures, either 2D graphene or quasi-1D CNTs, have unlocked entirely new standpoints concerning the C-based electronics. This chapter focuses on the approaches of nanotechnology toward nanoelectronics, materials used in nanoelectronics and the applications of nanoelectronics related to carbon-based materials in the field of thin-film transistors, printed electronics (PE), artificial skin and muscle, wearable electronics, flexible gas sensors, multifunctional and responsive elastomers, and plastic solar panels

Optimization of Work Zone Segments on Urban Roads Using Cellular Automata Model in Mixed Traffic

Increased delays and reduced speeds in work zones leads to congestion. This can be improved by optimizing the work zone length. The focus of this study is to model work zones using cellular automata model and to find the effects of work zones on traffic flow. The methodology adopted in the study involved creating work-zone on the road by blocking some of the cells and then determining traffic characteristics such as delay and queue lengths for model validation. For this the lateral movement rules of the existing Cellular Automata model were modified in order to replicate the traffic movement near work zones. This model is calibrated and validated using data from work zone observed near a metro rail station in Delhi. From the analysis it was evident that the queue length increased with increase in the length of work zone. Several relationships were tried between delay and work zone length. Among them the rational form was found suitable

An Efficient method of image compression by merging IWPT transform coding with index vector Quantization through FNN

Abstract--By the use of Neural Network, it was found that the reconstructed image has least image complexity and the image size has been reduced considerably by reducing the number of samples. This causes a remarkable increase in quality of the reconstructed image. A new quantization method is proposed in this paper. This method is useful for enhancement of compression quality when each kind of neural network is used to compress the image. Quantization, involved in image processing is achieved by compressing a range of values to a single quantum value. When the number of discrete symbols in a given stream is reduced, the stream becomes more compressible. For example, reducing the number of colors required to represent a digital image makes it possible to reduce its file size. This causes a remarkable enhancement in quality of the reconstructed image. For testing the proposed method we use IWPT transform coding and by merging it with the proposed quantization method a new compression algorithm is obtained. Then results of compression by the merged method are compared with some other transform methods. Compression time and complexity in the merged method is also better than JPEG and make it suitable for the systems with low processor and hardware implementation. Obtained results show that the proposed compression algorithm increases the compression quality of the images remarkably

Convexity meningiomas posing difficult challenges for neurosurgeons: Two case reports and literature review

Meningiomas have been intriguing since the time of Harvey Cushing. Although, most of them have been conquered by “today’s neurosurgeons”, still they can pose difficult challenges sometimes. Convexity meningiomas are relatively easier to tackle especially if they are not too large and do not displace critical neurovascular structures. However, they can complicate matters at times and hence, extreme precautions need to be practised. We report two such cases of convexity meningiomas with unusual set of events where one had an unusual post operative complication and the other had an unusual mode of presentation. Extradural hematomas (EDH) are a common complication after intracranial surgeries. They are usually picked up in the early post-operative scans and are managed according to their size and mass effect. The first patient is a 60 year old female where delayed EDH was detected after a sudden bout of hypertension after the initial scan after 48 hours of surgery was normal. Intraoperatively, middle meningeal artery (MMA) had re-bled due to this sudden rise in the blood pressure. Second case is a 33 year old female who presented with an intracerebral bleed due to hemorrhage within a convexity meningioma.&nbsp