Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort.

AIM: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features. METHODS: Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants' Mini-Mental State Examination (MMSE) scores. RESULTS: Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869]. CONCLUSIONS: Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.

Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): A randomized controlled trial.

BACKGROUND: Adaptive cardiac resynchronization therapy (aCRT) is known to have clinical benefits over conventional CRT, but the mechanisms are unclear. OBJECTIVE: Compare effects of aCRT and conventional CRT on electrical dyssynchrony. METHODS: A prospective, double-blind, 1:1 parallel-group assignment randomized controlled trial in patients receiving CRT for routine clinical indications. Participants underwent cardiac computed tomography and 128-electrode body surface mapping. The primary outcome was change in electrical dyssynchrony measured on the epicardial surface using noninvasive electrocardiographic imaging before and 6 months post-CRT. Ventricular electrical uncoupling (VEU) was calculated as the difference between the mean left ventricular (LV) and right ventricular (RV) activation times. An electrical dyssynchrony index (EDI) was computed as the standard deviation of local epicardial activation times. RESULTS: We randomized 27 participants (aged 64 ± 12 years; 34% female; 53% ischemic cardiomyopathy; LV ejection fraction 28% ± 8%; QRS duration 155 ± 21 ms; typical left bundle branch block [LBBB] in 13%) to conventional CRT (n = 15) vs aCRT (n = 12). In atypical LBBB (n = 11; 41%) with S waves in V5-V6, conduction block occurred in the anterior RV, as opposed to the interventricular groove in strict LBBB. As compared to baseline, VEU reduced post-CRT in the aCRT (median reduction 18.9 [interquartile range 4.3-29.2 ms; P = .034]), but not in the conventional CRT (21.4 [-30.0 to 49.9 ms; P = .525]) group. There were no differences in the degree of change in VEU and EDI indices between treatment groups. CONCLUSION: The effect of aCRT and conventional CRT on electrical dyssynchrony is largely similar, but only aCRT harmoniously reduced interventricular dyssynchrony by reducing RV uncoupling.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original data set (n=1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean=2.9±2.4; max=16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n=171) and for all unsuppressed zip codes (n=5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression -an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data.

BACKGROUND: There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE: To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS: This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS: Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION: Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

Mechanisms of Arrhythmogenicity in Hypertrophic Cardiomyopathy: Insight From Non-invasive Electrocardiographic Imaging.

BACKGROUND: Mechanisms of arrhythmogenicity in hypertrophic cardiomyopathy (HCM) are not well understood. OBJECTIVE: To characterize an electrophysiological substrate of HCM in comparison to ischemic cardiomyopathy (ICM), or healthy individuals. METHODS: We conducted a prospective case-control study. The study enrolled HCM patients at high risk for ventricular tachyarrhythmia (VT) [n = 10; age 61 ± 9 years; left ventricular ejection fraction (LVEF) 60 ± 9%], and three comparison groups: healthy individuals (n = 10; age 28 ± 6 years; LVEF > 70%), ICM patients with LV hypertrophy (LVH) and known VT (n = 10; age 64 ± 9 years; LVEF 31 ± 15%), and ICM patients with LVH and no known VT (n = 10; age 70 ± 7 years; LVEF 46 ± 16%). All participants underwent 12-lead ECG, cardiac CT or MRI, and 128-electrode body surface mapping (BioSemi ActiveTwo, Netherlands). Non-invasive voltage and activation maps were reconstructed using the open-source SCIRun (University of Utah) inverse problem-solving environment. RESULTS: In the epicardial basal anterior segment, HCM patients had the greatest ventricular activation dispersion [16.4 ± 5.5 vs. 13.1 ± 2.7 (ICM with VT) vs. 13.8 ± 4.3 (ICM no VT) vs. 8.1 ± 2.4 ms (Healthy); P = 0.0007], the largest unipolar voltage [1094 ± 211 vs. 934 ± 189 (ICM with VT) vs. 898 ± 358 (ICM no VT) vs. 842 ± 90 µV (Healthy); P = 0.023], and the greatest voltage dispersion [median (interquartile range) 215 (161-281) vs. 189 (143-208) (ICM with VT) vs. 158 (109-236) (ICM no VT) vs. 110 (106-168) µV (Healthy); P = 0.041]. Differences were also observed in other endo-and epicardial basal and apical segments. CONCLUSION: HCM is characterized by a greater activation dispersion in basal segments, a larger voltage, and a larger voltage dispersion through LV. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov Unique identifier: NCT02806479.

Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study.

BACKGROUND: The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). METHODS: Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. RESULTS: Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD). CONCLUSION: Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Importance of the heart vector origin point definition for an ECG analysis: The Atherosclerosis Risk in Communities (ARIC) study.

AIM: Our goal was to investigate the effect of a global XYZ median beat construction and the heart vector origin point definition on predictive accuracy of ECG biomarkers of sudden cardiac death (SCD). METHODS: Atherosclerosis Risk In Community study participants with analyzable digital ECGs were included (n = 15,768; 55% female, 73% white, mean age 54.2 ±â€¯5.8 y). We developed an algorithm to automatically detect the heart vector origin point on a median beat. Three different approaches to construct a global XYZ beat and two methods to locate origin point were compared. Global electrical heterogeneity was measured by sum absolute QRST integral (SAI QRST), spatial QRS-T angle, and spatial ventricular gradient (SVG) magnitude, azimuth, and elevation. Adjudicated SCD served as the primary outcome. RESULTS: There was high intra-observer (kappa 0.972) and inter-observer (kappa 0.984) agreement in a heart vector origin definition between an automated algorithm and a human. QRS was wider in a median beat that was constructed using R-peak alignment than in time-coherent beat (88.1 ±â€¯16.7 vs. 83.7 ±â€¯15.9 ms; P < 0.0001), and on a median beat constructed using QRS-onset as a zeroed baseline, vs. isoelectric origin point (86.7 ±â€¯15.9 vs. 83.7 ±â€¯15.9 ms; P < 0.0001). ROC AUC was significantly larger for QRS, QT, peak QRS-T angle, SVG elevation, and SAI QRST if measured on a time-coherent median beat, and for SAI QRST and SVG magnitude if measured on a median beat using isoelectric origin point. CONCLUSION: Time-coherent global XYZ median beat with physiologically meaningful definition of the heart vector's origin point improved predictive accuracy of SCD biomarkers.

Vectorcardiogram in athletes: The Sun Valley Ski Study.

BACKGROUND: Global electrical heterogeneity (GEH) is associated with sudden cardiac death (SCD) in adults of 45 years and above. However, GEH has not been previously measured in young athletes. The goal of this study was to establish a reference for vectorcardiograpic (VCG) metrics in male and female athletes. METHODS: Skiers (n = 140; mean age 19.2 ± 3.5 years; 66% male, 94% white; 53% professional athletes) were enrolled in a prospective cohort. Resting 12-lead ECGs were interpreted per the International ECG criteria. Associations of age, sex, and athletic performance with GEH were studied. RESULTS: In age and training level-adjusted analyses, male sex was associated with a larger T vector [T peak magnitude +186 (95% CI 106-266) µV] and a wider spatial QRS-T angle [+28.2 (17.3-39.2)°] as compared to women. Spatial QRS-T angle in the ECG left ventricular hypertrophy (LVH) voltage group (n = 21; 15%) and normal ECG group did not differ (67.7 ± 25.0 vs. 66.8 ± 28.2; p = 0.914), suggesting that ECG LVH voltage in athletes reflects physiological remodeling. In contrast, skiers with right ventricular hypertrophy (RVH) voltage (n = 26, 18.6%) had wider QRS-T angle (92.7 ± 29.6 vs. 66.8 ± 28.2°; p = 0.001), larger SAI QRST (194.9 ± 30.2 vs. 157.8 ± 42.6 mV × ms; p < 0.0001), but similar peak SVG vector magnitude (1976 ± 548 vs. 1939 ± 395 µV; p = 0.775) as compared to the normal ECG group. Better athletic performance was associated with the narrower QRS-T angle. Each 10% worsening in an athlete's Federation Internationale de' Ski downhill ranking percentile was associated with an increase in spatial QRS-T angle by 2.1 (95% CI 0.3-3.9) degrees (p = 0.013). CONCLUSION: Vectorcardiograpic adds nuances to ECG phenomena in athletes.

The utility of routine clinical 12-lead ECG in assessing eligibility for subcutaneous implantable cardioverter defibrillator.

INTRODUCTION: The subcutaneous implantable cardioverter-defibrillator (S-ICD) is a life-saving device. Recording of a specialized 3-lead electrocardiogram (ECG) is required for S-ICD eligibility assessment. The goals of this study were: (1) evaluate the effect of ECG filtering on S-ICD eligibility, and (2) simplify S-ICD eligibility assessment by development of an S-ICD ineligibility prediction tool, which utilizes the widely available routine 12-lead ECG. METHODS AND RESULTS: Prospective cross-sectional study participants [n = 68; 54% male; 94% white, with wide ranges of age (18-81 y), body mass index (19-53), QRS duration (66-150 ms), and left ventricular ejection fraction (37-77%)] underwent 12-lead supine, 3-lead supine and standing ECG recording. All 3-lead ECG recordings were assessed using the standard S-ICD pre-implantation ECG morphology screening. Backward, stepwise, logistic regression was used to build a model for 12-lead prediction of S-ICD eligibility. Select electrocardiogram waves and complexes: QRS, R-, S, and T-amplitudes on all 12 leads, averaged QT interval, QRS duration, and R/T ratio in the lead with the largest T wave (R/Tmax) were included as predictors. The effect of ECG filtering on ECG morphology was evaluated. A total of 9 participants (13%) failed S-ICD screening prior to filtering. Filtering at 3-40 Hz, similar to the S-ICD default, reduced S-ICD ineligibility to 4%. A regression model that included RII, SII-aVL, TI, II, aVL, aVF, V3-V6, and R/Tmax perfectly predicted S-ICD eligibility, with an Area Under the Receiver Operating Characteristic Curve of 1.0. CONCLUSION: Routine clinical 12-lead ECG can be used to predict S-ICD eligibility. ECG filtering may improve S-ICD eligibility.

Resource and Response Type Classification for Consumer Health Question Answering.

Health question answering systems often depend on the initial step of question type classification. Practitioners face several modeling choices for this component alone. We evaluate the effectiveness of different modeling choices in both the embeddings and architectural hyper-parameters of the classifier. In the process, we achieve improved performance over previous methods, achieving a new best 5-fold accuracy of 85.3% on the GARD dataset. The contribution of this work is to evaluate the performance of sentence classification methods on the task of consumer health question type classification and to contribute a dataset of 2,882 medical questions annotated for question type.

Torso geometry reconstruction and body surface electrode localization using three-dimensional photography.

We conducted a prospective clinical study (n=14; 29% female) to assess the accuracy of a three-dimensional (3D) photography-based method of torso geometry reconstruction and body surface electrodes localization. The position of 74 body surface electrocardiographic (ECG) electrodes (diameter 5mm) was defined by two methods: 3D photography, and CT (marker diameter 2mm) or MRI (marker size 10×20mm) imaging. Bland-Altman analysis showed good agreement in X (bias -2.5 [95% limits of agreement (LoA) -19.5 to 14.3] mm), Y (bias -0.1 [95% LoA -14.1 to 13.9] mm), and Z coordinates (bias -0.8 [95% LoA -15.6 to 14.2] mm), as defined by the CT/MRI imaging, and 3D photography. The average Hausdorff distance between the two torso geometry reconstructions was 11.17±3.05mm. Thus, accurate torso geometry reconstruction using 3D photography is feasible. Body surface ECG electrodes coordinates as defined by the CT/MRI imaging, and 3D photography, are in good agreement.

Bridging silicon nanoparticles and thermoelectrics: phenylacetylene functionalization.

Silicon is a promising alternative to current thermoelectric materials (Bi(2)Te(3)). Silicon nanoparticle based materials show especially low thermal conductivities due to their high number of interfaces, which increases the observed phonon scattering. The major obstacle with these materials is maintaining high electrical conductivity. Surface functionalization with phenylacetylene shows an electrical conductivity of 18.1 S m(-1) and Seebeck coefficient of 3228.8 µV K(-1) as well as maintaining a thermal conductivity of 0.1 W K(-1) m(-1). This gives a ZT of 0.6 at 300 K which is significant for a bulk silicon based material and is similar to that of other thermoelectric materials such as Mg(2)Si, PbTe and SiGe alloys.

Significantly enhanced energy output from 3D ordered macroporous structured Fe2O3/Al nanothermite film.

A three-dimensionally ordered macroporous Fe(2)O(3)/Al nanothermite membrane has been prepared with a polystyrene spheres template. The nanothermite, with an enhanced interfacial contact between fuel and oxidizer, outputs 2.83 kJ g(-1) of energy. This is significantly more than has been reported before. This approach, fully compatible with MEMS technology, provides an efficient way to produce micrometer thick three-dimensionally ordered nanostructured thermite films with overall spatial uniformity. These exciting achievements will greatly facilitate potential for the future development of applications of nanothermites.

Isolation of quiescent and nonquiescent cells from yeast stationary-phase cultures.

Quiescence is the most common and, arguably, most poorly understood cell cycle state. This is in part because pure populations of quiescent cells are typically difficult to isolate. We report the isolation and characterization of quiescent and nonquiescent cells from stationary-phase (SP) yeast cultures by density-gradient centrifugation. Quiescent cells are dense, unbudded daughter cells formed after glucose exhaustion. They synchronously reenter the mitotic cell cycle, suggesting that they are in a G(0) state. Nonquiescent cells are less dense, heterogeneous, and composed of replicatively older, asynchronous cells that rapidly lose the ability to reproduce. Microscopic and flow cytometric analysis revealed that nonquiescent cells accumulate more reactive oxygen species than quiescent cells, and over 21 d, about half exhibit signs of apoptosis and necrosis. The ability to isolate both quiescent and nonquiescent yeast cells from SP cultures provides a novel, tractable experimental system for studies of quiescence, chronological and replicative aging, apoptosis, and the cell cycle.