Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

In large-scale high-performance computing systems, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs are becoming an increasingly important concern to system designers and administrators. When a compute node fails to function properly, health-related data are valuable for troubleshooting. However, it is challenging to effectively identify anomalies from the voluminous amount of noisy, high-dimensional data. Manual detection is time-consuming and error-prone. It does not scale well. In this paper, we present an autonomic mechanism for anomaly detection in coalition clusters. It is composed of a set of techniques that facilitates automatic analysis of system health data. We apply data transformation to format health data in a uniform manner. Then principal variables are chosen by feature selection, which reduces the data size. Clustering and outlier detection are explored to identify nodes with anomalous behavior. We evaluate our prototype implementation on a production institution-wide computational grid. The results show that our mechanism can effectively detect faulty nodes with high accuracy and low computation overhead.

A cost-effective approach to increasing the processing throughput of many digital signal processing (DSP) systems is described.. This movement is achieved by migrating some of the basic computational elements to memory. The classic Von Neumann bottleneck is circumvented by localizing the computations to memory, and a high throughput is achieved by exploiting the memory architecture. For certain DSP applications that require a simple type of operation to be applied to a large amount of data, e.g. pattern recognition or matrix-matrix multiplications, the increase in throughput is very high. It is also shown that a system built with these memories is easier to use than systolic arrays or multiprocessor systems. As examples, compared to conventional solutions, a 10* improvement is shown for a continuous speech recognizer and a 16* improvement for a 128*128 matrix multiplication. Higher-orders-of-magnitude improvements are possible for larger problems and more memory chips.>

A description is given of the scatter code, a new technique for mapping sensor data into a form suitable for processing by associative neural networks. By overthrowing the assumption that the sensor mapping must preserve order in addition to closeness, a mapping can be generated that has much more capacity and flexibility than previous mappings. The first advantage is the practically limitless number of points in the new mapping, which eliminates the need to compress sensor information and lose resolution. The second advantage is the ability to control the radius of association in the mapped sensor data by varying the rate at which points in the Hamming space become orthogonal. The radius of association can be varied within the mapping to take advantage of special cases when information about the sensor or about the expected data point is known

A unified design representation can work. This has always been considered impractical due to major problems of run time, space, tool interdependencies, and multiple representations. We have addressed these problems and found them tractable. We present our solutions to these problems, gained through the experience of developing the Droid design system.

The authors report a quantitative comparison of area evaluation metrics for CMOS transistor placement, along with a novel analysis technique to determine their absolute merit. The transistor placer TOPOLOGIZER was used to generate test case placements. The resolution of transistor placement algorithms based on metrics (e.g. simulated annealing, branch and bound) are limited to the resolution of the metric. The best metric studied was only able to correctly identify, with 90% accuracy, the better of two placements whose layout areas differed by more than 30%.>

Abstract The problem of describing graphs representing a communication network which are optimally reliable has been studied both for the case of vertex failures and for the case of edge failures. We give a combinatorial definition in each case and show that under certain conditions the first definition implies the second. We use this result to investigate the existence of graphs which in some sense are optimally reliable with respect to both vertex and edge failures.

Abstract The number of vertex cut sets of size k in a graph of connectivity k has been used as a measure of network reliability. Let G be a regular graph with N vertices, valency k, connectivity k, and with the minimum number of vertex cut sets with k vertices. The problem of constructing such a graph G for each pair (N, k) is known to be difficult. We show how to construct infinite families of such graphs in various cases. These cases are spread through the range 3/8 ≤ k/N <1. We also deal with cases in which k is small and cases with N ‐ k small.