2,497 publications from this institution
An architecture for a single-chip functional tester which reduces the cost of testing application-specific integrated circuits (ASICs) is presented. The data generator/receiver (DGR) contains a large RAM to store the test vectors, an address sequencer for implementing simple testing loops, and a flexible set of drivers/receivers for the device-under-test (DUT) pins. A prototype design has been fabricated in 3-/spl mu/m CMOS double-level-metal technology, and contains 65 K transistors in a 9.2/spl times/7.9-mm/SUP 2/ die. A minimum operating cycle time of 90 ns (11 MHz), and a power dissipation of 300 mW was obtained for 5-V operation. A 2-/spl mu/m version of this design, just a shrink of the original chip, has been fabricated and operates over 16 megavector/s.
Real number modeling of analog circuits in hardware description languages (HDLs) has become more common as a part of mixed-signal SoC validation. We propose two methods that both improve the fidelity and simulation speed, and make the event-driven, piecewise linear (PWL) analog functional models easier to write. First we use the accuracy set by users to dynamically determine when a new output segment should be emitted, which is computed without any iteration. This capability allows designers to trade accuracy for simulation speed of analog models without any time-consuming model calibration/error estimation, and creates models which generate events only when needed to maintain output accuracy. We next extend this method to eliminate limit-cycle oscillations that occur when simulating circuits with continuous-time feedback in a discrete-time event simulator. Handling this feedback efficiently allows the user to create the system model from simpler component models. The performance of this modeling approach is demonstrated on various analog filter models, an operational amplifier, and a high-speed, wireline transceiver system, and is 3.1 $\times$ faster than an optimally-chosen, fixed-time step simulation for the transceiver.
Introduction Antimicrobial resistance represents a growing global concern, with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci representing major contributors. Linezolid (LNZ) is one of the few available treatments for such infections, yet resistance has already been documented. It is therefore essential to accurately detect resistant strains to provide adequate treatment to patients and prevent nosocomial dissemination. This study evaluates the performance of four commercial methods employed by laboratories for determining Gram-positive cocci susceptibility to LNZ. Methods A panel of 99 stains from our routine and National Reference Centre (NRC) collections were studied, including 26 S. aureus (15 LNZ-resistant), 26 S. epidermidis (14 LNZ-resistant), 27 E. faecium (23 LNZ-resistant) and 20 E. faecalis (6 LNZ-resistant). Susceptibility to LNZ was determined by home-made broth microdilution method (BMD), considered as gold standard, and by four commercial methods namely disk diffusion (LNZ10, Bio-Rad, USA), gradient strip diffusion (E-TEST®, bioMérieux, France), broth microdilution assay (UMIC, Biocentric, France) and automated antimicrobial susceptibilty testing (AST; VITEK 2, bioMérieux). For the diffusion methods and UMIC, two incubation times were evaluated (18h and 48h). EUCAST guidelines and breakpoints were used. Categorical agreement (CA), major errors (ME's) and very major errors (VME's) were defined as recommended by the FDA: commercially available antibiotics methods should have a CA ≥ 90%, and a percentage of ME's ≤ 3% and of VME's ≤ 1.5%. Results All commercial methods exhibited similar performance, with accurate classification of LNZ- susceptible strains. However, all failed in detecting LNZ-resistance, except for S. epidermidis. The overall percentage of VME among S. aureus was 66.7%. Among E. faecium, it was 78.3% and 82.6% when using Vitek2 or another method, respectively. Among E. faecalis, it was 21.4% and 14.3% when using UMIC or another method, respectively. The percentage of VME's decreased with additional incubation times. The use of E-test exhibited the lowest percentage of VME's. However, none of the methods achieved the expected 90% CA. Discussion Even though discrepancies in LNZ AST methods have been reported, no practical guidelines are available for routine laboratories. According to this study, the following algorithm is proposed: if the strain is classified as LNZ-resistant, it should be considered as such and a PCR could be performed to reveal LNZ resistance genes, if the strain is classified as susceptible and if the patient must be treated with LNZ, an E-test read at 48h is recommended. Given the potential for false-susceptible results, particularly with S. aureus and E. faecium, it is crucial to monitor patients undergoing LNZ treatment. Should further analysis be required, the strains can be submitted to the NRC. Conclusion There is currently no optimal commercial method for the detection of LNZ-resistance. We therefore propose an algorithm that should assist laboratories in a routine practice context.
Abstract Meta‐analysis has major strengths, but sometimes it can often lead to wrong and misleading answers. In this SRSM presidential address, I discuss some case studies that exemplify these problems, including examples from meta‐analyses of both clinical trials and observational associations. I also discuss issues of effect size estimation, bias (in particular significance‐chasing biases), and credibility in meta‐research. I examine the factors that affect the credibility of meta‐analyses, including magnitude of effects, multiplicity of analyses, scale of data, flexibility of analyses, reporting, and conflicts of interest. Under the current circumstances, a survey of expert meta‐analysts attending the SRSM meeting showed that most of them believe that the true effect is practically equally likely to lie within the 95% confidence interval of a meta‐analysis or outside of it. Finally, I address the placement of meta‐analysis in the wider current research agenda and make a plea for adoption of more prospective meta‐designs. In many/most/all fields, all primary original research may be designed, executed, and interpreted as a prospective meta‐analysis. Copyright © 2011 John Wiley & Sons, Ltd.
Cancer is a complex phenotype resulting from the interaction of inherited and environ-mental factors. Many studies have sought to unravel this complexity by investigating onegene at a time or by considering pairwise gene-gene and gene-environment interactions.These studies have not proven as successful as hoped and led to a growing appreciation thata more comprehensive strategy is required (1). This broader strategy centers on the conceptof the biochemical pathway, which has been defined as "the sequence of reactionsundergone by a compound or class of compounds in a living organism" (2). A reaction is "achemical change, where the transformation of one or more components into new substancesoccurs, accompanied by energy changes." Some biochemical pathways are linear,proceeding in a step-by-step fashion from one molecule to another. Other pathways arebranching, generating two or more products. Pathways can also have feedback loops, forexample, when the product of a pathway controls the rate of its own synthesis throughinhibition of an early step. Each pathway is organized by the links in its chemical reactions,with the product of one reaction providing a substrate for an enzyme that catalyzes asubsequent reaction (3,4). It is evident that a pathwaywide perspective provides a broaderconceptual and analytical strategy for the detection of potential disease associations. Onecan expect the analysis of genetic variation across entire biological pathways to be morelikely to reveal the association of candidate genes with cancer risk than studies limited tosingle genes.
Abstract Background: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. Methods: Searches were conducted for meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from 01-2016 to 10-2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size (N≤100, N>100, and N>370 the upper 75 th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance between summary ES was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N≤100 studies on the estimated summary ES. Results: A total of 1,602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 avg. meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N≤100 studies comprised 22% (0-58%) and 21% (0-83%) of studies. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES were comprised of ≥40% of N≤100 studies was 0.29. The ABS ES was 0.46 when summary ES comprised of >80% of both pilot/feasibility and N≤100 studies. Where ≤40% of the studies comprising a summary ES had N>370, the ABS ES ranged from 0.20-0.30. Concordance was low when removing both pilot/feasibility and N≤100 studies (kappa=0.53) and restricting analyses only to the largest studies (N>370, kappa=0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. Conclusions: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N≤100 studies, summary ES can be affected markedly and should be interpreted with caution.
We have developed a new tool to look at how students interact with circuits during the troubleshooting process. The online tool was originally designed to analyze individual troubleshooting strategy for large classes, but it also works well in the COVID-era to facilitate remote learning. While there are a number of tools that allow students to virtually interact with circuits, none supported both breadboard graphics and recording all student interactions, which were necessary to create an authentic troubleshooting situation that could be analyzed by the researchers afterwards. Therefore, we created our own circuit and data analysis tool using HTML5, CSS, and JavaScript, which utilizes breadboard imagery from Fritzing and runs on most modern browsers. Unlike a traditional paper-and-pencil test, the interactive, online tool allows us to see how students react to new information and measure domain knowledge beyond theory—including interpreting physical circuits and making measurements. Instead of relying on students to tell us everything on their mind, we can use their actions as a proxy for their thought processes. This paper describes how we developed the tool and some preliminary data on how students debug.
Mega-trials for blockbusters are readily feasible. There would be no cost for the government or health care system. Drug production cost to industry (as opposed to sales cost) is negligible. The proposed mandate could easily stipulate that blockbuster manufacturers donate drugs and placebos for such trials. Total cost to the industry would be approximately 1% of their cumulative sales for the product.
