Glycated proteome: From reaction to intervention

Glycation, a nonenzymatic reaction between reducing sugars and proteins, is a proteome wide phenomenon, predominantly observed in diabetes due to hyperglycemia. Glycated proteome of plasma, kidney, lens, and brain are implicated in the pathogenesis of various diseases, including diabetic complications, neurodegenerative diseases, cancer, and aging. This review discusses the strategies to characterize protein glycation, its functional implications in different diseases, and intervention strategies to protect the deleterious effects of protein glycation.     

Gestural systems for the voice: performance approaches and repertoire

ABSTRACT

Singing, like dance, emerges directly from the body. The voice, in combination with whole body movement, constitutes a potent form of self-expression. Gestural systems offer a specialized context in which to explore the intersection between voice and movement. The practice-based investigation presented in this article charts the development of an original musical work, Intangible Spaces, which gives form to the invisible aspects of voice and movement through gestural control, physical modelling synthesis and visual feedback. I draw on embodied and performative autoethnographic methods to capture the felt sensations and sound-movement associations that arise during the composition process. I also explore the performance approaches of key practitioners in the area to gain a broader understanding of the ways in which musicians leverage existing performance skills to uncover novel connections between movement and voice in gestural performance.     

The space complexity of unbounded timestamps

The timestamp problem captures a fundamental aspect of asynchronous distributed computing. It allows processes to label events throughout the system with timestamps that provide information about the real-time ordering of those events. We consider the space complexity of wait-free implementations of timestamps from shared read-write registers in a system of n processes. We prove an lower bound on the number of registers required. If the timestamps are elements of a nowhere dense set, for example the integers, we prove a stronger, and tight, lower bound of n. However, if timestamps are not from a nowhere dense set, this bound can be beaten: we give an implementation that uses n − 1 (single-writer) registers. We also consider the special case of anonymous implementations, where processes are programmed identically and do not have unique identifiers. In contrast to the general case, we prove anonymous timestamp implementations require n registers. We also give an implementation to prove that this lower bound is tight. This is the first anonymous timestamp implementation that uses a finite number of registers.     

Aberrant protein expression in plasma and kidney tissue during experimental obstructive nephropathy

Kidney failure is a major health problem worldwide. Patients with end-stage renal disease require intensive medical support by dialysis or kidney transplantation. Current methods for diagnosis of kidney disease are either invasive or insensitive, and renal function may decline by as much as 50% before it can be detected using current techniques. The goal of this study was, therefore, to identify biomarkers of kidney disease (associated with renal fibrosis) that can be used for the development of a non-invasive clinical test for early disease detection. We utilized two protein-profiling technologies (SELDI-TOF MS and 2-D) to screen the plasma and kidney proteome for aberrantly expressed proteins in an experimental mouse model of unilateral uretric obstruction, which mimics the pathology of human renal disease. Several differentially regulated proteins were detected at the plasma level of day-3-obstructed animals, which included serum amyloid A1, fibrinogen α, haptoglobin precursor protein, haptoglobin and major urinary proteins 11 and 8. Differentially expressed proteins detected at the tissue level included ras-like activator protein 2, haptoglobin precursor protein, malate dehydrogenase, α enolase and murine urinary protein (all p<0.05 versus controls). Immunohistochemistry was used to confirm the up-regulation of fibrinogen. Interestingly, these proteins are largely separated into four major classes: (i) acute-phase reactants (ii) cell-signaling molecules (iii) molecules involved in cell growth and metabolism and (iv) urinary proteins. These results provide new insights into the pathology of obstructive nephropathy and may facilitate the development of specific assay(s) to detect and monitor renal fibrosis.     

Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.     

Linking air quality and watershed models for environmental assessments: Analysis of the effects of model-specific precipitation estimates on calculated water flux

Directly linking air quality and watershed models could provide an effective method for estimating spatially-explicit inputs of atmospheric contaminants to watershed biogeochemical models. However, to adequately link air and watershed models for wet deposition estimates, each model’s temporal and spatial representation of precipitation needs to be consistent. We explore how precipitation implemented within the Community Multi-Scale Air Quality Model (CMAQ) model algorithms, and multiple spatially-explicit precipitation datasets that could be used to improve the CMAQ model deposition estimates, links with the standard precipitation sources used to calibrate watershed models (i.e., rain gage data) via modeled water fluxes. Simulations are run using a grid-based watershed mercury model (GBMM) in two watersheds. Modeled monthly runoff suggests that multiple resolution Parameter-elevations Regressions on Independent Slopes Model (PRISM) and National Multi-sensor Precipitation Analysis Stage IV (NPA) data generate similar monthly runoff estimates, with comparable or greater accuracy when evaluated against stream gage data than that produced by the base rain gage data. However, across longer time periods, simulated water balances using 36 km Pennsylvania State University/National Center for Atmospheric Research mesoscale model (MM5) data are similar to that of base data. The investigation also examines the implications our results, providing suggestions for linking air quality and watershed fate and transport models.     

Comparing measures of model selection for penalized splines in Cox models

This article presents an application and a simulation study of model fit criteria for selecting the optimal degree of smoothness for penalized splines in Cox models. The criteria considered were the Akaike information criterion, the corrected AIC, two formulations of the Bayesian information criterion, and a generalized cross-validation method. The estimated curves selected by the five methods were compared to each other in a study of rectal cancer mortality in autoworkers. In the stimulation study, we estimated the fit of the penalized spline models in six exposure-response scenarios, using the five model fit criteria. The methods were compared on the basis of a mean squared error score and the power and size of hypothesis tests for any effect and for detecting nonlinearity. All comparisons were made across a range in the total sample size and number of cases.     

Adapting application execution in CMPs using helper threads

In parallel to the changes in both the architecture domain–the move toward chip multiprocessors (CMPs)–and the application domain–the move toward increasingly data-intensive workloads–issues such as performance, energy efficiency and CPU availability are becoming increasingly critical. The CPU availability can change dynamically due to several reasons such as thermal overload, increase in transient errors, or operating system scheduling. An important question in this context is how to adapt, in a CMP, the execution of a given application to CPU availability change at runtime. Our paper studies this problem, targeting the energy-delay product (EDP) as the main metric to optimize. We first discuss that, in adapting the application execution to the varying CPU availability, one needs to consider the number of CPUs to use, the number of application threads to accommodate and the voltage/frequency levels to employ (if the CMP has this capability). We then propose to use helper threads to adapt the application execution to CPU availability change in general with the goal of minimizing the EDP. The helper thread runs parallel to the application execution threads and tries to determine the ideal number of CPUs, threads and voltage/frequency levels to employ at any given point in execution. We illustrate this idea using four applications (Fast Fourier Transform, MultiGrid, LU decomposition and Conjugate Gradient) under different execution scenarios. The results collected through our experiments are very promising and indicate that significant EDP reductions are possible using helper threads. For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively. We also discuss how our approach can be extended to address multi-programmed workloads.