10 Gb/s adaptive receive-side merged near-end and far-end crosstalk cancellation circuitry in 65 nm CMOS

Crosstalk between neighboring channels can have significant impact on system bit-error rate (BER) as serial I/O data rates scale above 10 Gb/s. This paper presents receive-side circuitry which merges the cancellation of both near-end and far-end crosstalk (NEXT/FEXT) and can automatically adapt to different channel environments and variations in process, voltage, and temperature. NEXT cancellation is realized with a novel 3-tap FIR filter which combines two traditional FIR filter taps and a continuous-time band-pass filter IIR tap for efficient crosstalk cancellation, with all filter tap coefficients automatically determined via an on-die sign–sign least-mean-square (SS-LMS) adaptation engine. FEXT cancellation is realized by coupling the aggressor signal through a differentiator circuit whose gain is automatically adjusted with a power-detection-based adaptation loop. A prototype fabricated in a general purpose 65-nm CMOS process includes the adaptive NEXT and FEXT circuitry, along with a continuous-time linear equalizer (CTLE) to compensate for frequency-dependent channel loss. Enabling the crosstalk cancellation circuitry while operating at 10 Gb/s over coupled 4-in FR4 transmission line channels with NEXT and FEXT aggressors opens a previously closed eye and allows for a 0.2 UI timing margin at a BER = 10^?9. Total power including the NEXT/FEXT crosstalk cancellation circuitry, CTLE, and high-speed output buffer is 34.6 mW, and the core circuit area occupies 0.3 mm².     

Software piracy among computing students: a Bruneian scenario

Software piracy committed by university students in the western world has been published widely in various computer literature. Little is reported however about why and what proportion of students, particularly computing students, in a South East Asian Country like Brunei Darussalam, use pirated software. A survey was thus conducted among the computing students of all the higher educational institutions in Brunei Darussalam. A normative model was also developed that brought together eight factors related to student demography, nature of educational institutions and copyright laws. This model was empirically tested using 91 responses received from the survey. Several interesting findings emerged. Firstly, nearly two-thirds of the students admitted to having used pirated software. Secondly, entertainment was the prominent reason for using pirated software. Thirdly, gender, computer experience and ownership of a personal computer, were found to affect students' use of pirated software. Fourthly, even those students who supported the introduction of copyright laws committed software piracy. The implications of these findings are discussed, and are compared with some related studies. Finally, limitations of the study are mentioned, and areas of further research are identified.     

Positionally defined, binary semiconductor nanoparticles synthesized by scanning probe block copolymer lithography

We report the first method for synthesizing binary semiconductor materials by scanning probe block copolymer lithography (SPBCL) in desired locations on a surface. In this work, we utilize SPBCL to create polymer features containing a desired amount of Cd(2+), which is defined by the feature volume. When they are subsequently reacted in H(2)S in the vapor phase, a single CdS nanoparticle is formed in each block copolymer (BCP) feature. The CdS nanoparticles were shown to be both crystalline and luminescent. Importantly, the CdS nanoparticle sizes can be tuned since their diameters depend on the volume of the originally deposited BCP feature.     

Correction to Psychotherapist Mindfulness and the Psychotherapy Process, Bruce et al (2010).

Reports an error in Psychotherapist mindfulness and the psychotherapy process by Noah G. Bruce, Rachel Manber, Shauna L. Shapiro and Michael J. Constantino (Psychotherapy: Theory, Research, Practice, Training, 2010[Mar], Vol 47[1], 83-97). The order of authorship and the affiliations of the authors was incorrectly printed. The correct order and affiliations are as follows: Noah Bruce, Shauna L. Shapiro, Michael J. Constantino, and Rachel Manber; Kaiser Permanente, Santa Clara University, University of Massachusetts, Stanford University. (The following abstract of the original article appeared in record 2010-05168-010.) A psychotherapist’s ability to relate to his or her patients is essential for decreasing patient suffering and promoting patient growth. However, the psychotherapy field has identified few effective means for training psychotherapists in this ability. In this conceptual article, we propose that mindfulness practice may be a means for training psychotherapists to better relate to their patients. We posit that mindfulness is a means of self-attunement that increases one’s ability to attune to others (in this case, patients) and that this interpersonal attunement ultimately helps patients achieve greater self-attunement that, in turn, fosters decreased symptom severity, greater well-being, and better interpersonal relationships. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dynameomics: design of a computational lab workflow and scientific data repository for protein simulations

Dynameomics is a project to investigate and catalog the native-state dynamics and thermal unfolding pathways of representatives of all protein folds using solvated molecular dynamics simulations, as described in the preceding paper. Here we introduce the design of the molecular dynamics data warehouse, a scalable, reliable repository that houses simulation data that vastly simplifies management and access. In the succeeding paper, we describe the development of a complementary multidimensional database. A single protein unfolding or native-state simulation can take weeks to months to complete, and produces gigabytes of coordinate and analysis data. Mining information from over 3000 completed simulations is complicated and time-consuming. Even the simplest queries involve writing intricate programs that must be built from low-level file system access primitives and include significant logic to correctly locate and parse data of interest. As a result, programs to answer questions that require data from hundreds of simulations are very difficult to write. Thus, organization and access to simulation data have been major obstacles to the discovery of new knowledge in the Dynameomics project. This repository is used internally and is the foundation of the Dynameomics portal site http://www.dynameomics.org. By organizing simulation data into a scalable, manageable and accessible form, we can begin to address substantial questions that move us closer to solving biomedical and bioengineering problems.     

Pacific Tracker 2 – Expert System (PacTrac2-ES) behavioural assessment and intervention tool for the Pacific Kids DASH for Health (PacDASH) study

The Pacific Tracker (PacTrac) is a computer program designed to analyse food intakes of individuals from the Pacific Region. PacTrac’s original output included servings of daily intake of food groups according to the United States Food Guide Pyramid, nutrient intake recommendations, and a comparison to other national nutrition recommendations. PacTrac was made available for public use through the Hawaii Foods website (hawaiifoods.hawaii.edu). PacTrac2 is an updated and expanded version of PacTrac that uses the United States MyPyramid/MyPlate food groups in household units of daily intake, rather than servings. In addition, the PacTrac2 includes a physical activity analysis tool which quantifies minutes of physical activities and their intensities based on energy estimates from the compendium of physical activity and research on children. An Expert System (ES) – a computerised decision tree to guide behaviour change – was developed using information on self-efficacy and stage of readiness to change, and the fruit and vegetable intake and physical activity information from PacTrac2. The ES produces reports for the child, the parent/guardian, and the child’s physician with child-specific strategies, targeted behavioural information, and feedback tailored to the child. PacTrac2-ES was designed for the Pacific Kids DASH for Health (PacDASH) intervention study, conducted in the Kaiser Permanente health care system in Hawaii. The intervention is based on the child’s self-efficacy and stage of readiness to change intake of fruits and vegetables and physical activity, with a goal of maintaining body weight to prevent obesity. The intervention is complemented with stage-based mailers addressing the environment for physical activity and fruit and vegetable intake and newsletters that address related behaviours (sedentary activity and a DASH eating approach). This project is the first to expand the PacTrac to contain children’s foods and physical activities from the Pacific Region and to use current US MyPyramid/MyPlate food and physical activity analysis and guidance systems, and to develop and implement an Expert System for fruits, vegetables and physical activity of 5–8-year-old children. The PacTrac2-ES was used in the PacDASH study and will be used for other programs to promote healthy eating and physical activity of children in the Pacific Region.     

Measurements of Smoke Characteristics in HVAC Ducts

The characteristics of smoke traveling in an HVAC duct have been observed along with the response of selected duct smoke detectors. The simulated HVAC system consists of a 9 m long duct, 0.45 m in diameter. An exhaust fan is placed at one end of the duct and is capable of inducing airflow rates that range from 0 to 1.5 m ³/s. The flow is controlled by means of a manual damper. On the upstream end of the duct there is a square exhaust hood approximately 2.2 m at the bottom and 0.3 m at the top. The bottom of the hood is approximately 2.5 m above the floor a shroud extends down to approximately 1.5 m above the floor. The test section, placed immediately downstream of the hood, is 3.5 m long duct with a square cross section of 0.4 m on a side. The instrumentation includes oxygen, carbon monoxide and carbon dioxide gas analyzers and a load cell to determine the energy release rate of the fires tested. The smoke within the duct is characterized by means of a laser light sheet and charge coupled device (CCD) camera, two white light source and photocell ensembles, a Pitot tube and an array of eight thermocouples placed on the vertical plane of symmetry. A smoke detector was placed at the downstream end of the test section. Two types of detectors were tested, ionization and photoelectric, with a single sampling probe geometry. The fires tested cover a wide range of fuels (propane, heptane, toluene, toluene/heptane mixture, shredded paper, polyurethane foam, wood cribs) with the peak energy release rates up to 800 kW. The smoke detector performance, temperature, flow field, smoke particle size and particle distributions are dependent on the fire characteristics and airflow through the duct. The different measurements could be scaled by means of the fire size and airflow rate but left a strong dependency on the fuel and burning characteristics (i.e., smoldering, flaming). The optical density and mass optical density are analyzed as metrics for characterizing smoke and smoke detector response. Detailed comparisons between the different metrics used are presented throughout this work. Clear evidence of stratification and aging of the smoke along the duct are also presented. The limitations of the present configuration and the need for a larger scale study are also discussed.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.