Simulation of carbon nanotube based p-n junction diodes

Taking advantage of the unique characteristics of an ambipolar carbon nanotube field effect transistor (CNTFET), a ‘p-n junction’ is simulated along the single-walled carbon nanotube channel using two separate gates close to the source and drain of the CNTFET, respectively. The current-voltage characteristics of the double-gated CNTFET are calculated using a semiclassical method based on the Schottky barrier field effect transistor mechanism. The calculation results show a good rectification performance of the p-n junction.     

Residual encoder-decoder up-sampling for structural preservation in noise removal

Multimedia Tools and Applications - While denoising 3D MRI, structural preservation is a very critical process in the medical region. However, Rician noise in MRI has affected the image quality...     

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.     

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%.     

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.     

Proteomic examination of Leishmania chagasi plasma membrane proteins: Contrast between avirulent and virulent (metacyclic) parasite forms

Purpose: About two million new cases of leishmaniasis with 50 000 associated deaths occur worldwide each year. Promastigotes of the causative Leishmania spp. develop from the procyclic stage to the highly virulent metacyclic stage within the sand fly vector. We hypothesized that proteins important for promastigote virulence might be uniquely represented in the plasma membrane of metacyclic, but not procyclic, promastigotes. Experimental design: Procyclic (logarithmic) promastigotes and purified metacyclic promastigotes from stationary phase cultures of Leishmania chagasi were used to prepare membrane preparations either by surface biotinylation‐streptavidin affinity separation or by octyl glucoside detergent extraction. Results: These membrane fractions were enriched over 130‐ and 250‐fold, respectively, as estimated by Western blotting for the plasma membrane's major surface protease. Hundreds or dozens of proteins were identified by LC‐MS/MS in the surface biotinylation or detergent extraction, respectively. Confocal microscopy suggested the difference between the lists was due to the fact that proteins localized both on the surface membrane and within the flagellar pocket were accessible to surface biotinylation, whereas only proteins on the membrane were obtained by detergent extraction. Using detergent extraction, we found different proteins were present in membranes of the procyclic stage compared to metacyclic stage promastigotes. Several dozen were stage specific. Conclusions and clinical relevance: These data provide a foundation for identifying virulence factors in the plasma membranes of Leishmania spp. promastigotes during metacyclogenesis.     

Leader’s dilemma game: An experimental design for cyber insider threat research

One of the problems with insider threat research is the lack of a complete 360° view of an insider threat dataset due to inadequate experimental design. This has prevented us from modeling a computational system to protect against insider threat situations. This paper provides a contemporary methodological approach for using online games to simulate insider betrayal for predictive behavioral research. The Leader’s Dilemma Game simulates an insider betrayal scenario for analyzing organizational trust relationships, providing an opportunity to examine the trustworthiness of focal individuals, as measured by humans as sensors engaging in computer-mediated communication. This experimental design provides a window into trustworthiness attribution that can generate a rigorous and relevant behavioral dataset, and contributes to building a cyber laboratory that advances future insider threat study.