Circuital characterisation of space-charge motion with a time-varying applied bias.
Ontology highlight
ABSTRACT:
SUBMITTER: Kim C
PROVIDER: S-EPMC4488748 | biostudies-other | 2015
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Escherichia coli chemotactic motion in spatiotemporally varying environments is studied by using a computational model based on a coarse-grained description of the intracellular signaling pathway dynamics. We find that the cell's chemotaxis drift velocity v(d) is a constant in an exponential attractant concentration gradient [L] proportional, variantexp(Gx). v(d) depends linearly on the exponential gradient G before it saturates when G is larger than a critical value G(C). We find that G(C) is determined by the intracellular adaptation rate k(R) with a simple scaling law: G(C) infinity k(1/2)(R). The linear dependence of v(d) on G = d(ln[L])/dx directly demonstrates E. coli's ability in sensing the derivative of the logarithmic attractant concentration. The existence of the limiting gradient G(C) and its scaling with k(R) are explained by the underlying intracellular adaptation dynamics and the flagellar motor response characteristics. For individual cells, we find that the overall average run length in an exponential gradient is longer than that in a homogeneous environment, which is caused by the constant kinase activity shift (decrease). The forward runs (up the gradient) are longer than the backward runs, as expected; and depending on the exact gradient, the (shorter) backward runs can be comparable to runs in a spatially homogeneous environment, consistent with previous experiments. In (spatial) ligand gradients that also vary in time, the chemotaxis motion is damped as the frequency omega of the time-varying spatial gradient becomes faster than a critical value omega(c), which is controlled by the cell's chemotaxis adaptation rate k(R). Finally, our model, with no adjustable parameters, agrees quantitatively with the classical capillary assay experiments where the attractant concentration changes both in space and time. Our model can thus be used to study E. coli chemotaxis behavior in arbitrary spatiotemporally varying environments. Further experiments are suggested to test some of the model predictions.
| S-EPMC2851563 | biostudies-literature
Project description:PurposeIn studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change.MethodsTo illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks.ResultsWith monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching.ConclusionOur simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.
| S-EPMC9292390 | biostudies-literature
Project description:Transient absorption spectroscopy on subpicosecond to second time scales is used to investigate photogenerated charge carrier recombination in Si-doped nanostructured hematite (?-Fe2O3) photoanodes as a function of applied bias. For unbiased hematite, this recombination exhibits a 50% decay time of ~6 ps, ~10(3) times faster than that of TiO2 under comparable conditions. Anodic bias significantly retards hematite recombination dynamics, and causes the appearance of electron trapping on ps-?s time scales. These ultrafast recombination dynamics, their retardation by applied bias, and the associated electron trapping are discussed in terms of their implications for efficient water oxidation.
| S-EPMC4210134 | biostudies-literature
Project description:Piezocrystals, especially the relaxor-based ferroelectric crystals, have been subject to intense investigation and development within the past three decades, motivated by the performance advantages offered by their ultrahigh piezoelectric coefficients and higher electromechanical coupling coefficients than piezoceramics. Structural anisotropy of piezocrystals also provides opportunities for devices to operate in novel vibration modes, such as the d36 face shear mode, with domain engineering and special crystal cuts. These piezocrystal characteristics contribute to their potential usage in a wide range of low- and high-power ultrasound applications. In such applications, conventional piezoelectric materials are presently subject to varying mechanical stress/pressure, temperature and electric field conditions. However, as observed previously, piezocrystal properties are significantly affected by a single such condition or a combination of conditions. Laboratory characterisation of the piezocrystal properties under these conditions is therefore essential to fully understand these materials and to allow electroacoustic transducer design in realistic scenarios. This will help to establish the extent to which these high performance piezocrystals can replace conventional piezoceramics in demanding applications. However, such characterisation requires specific experimental arrangements, examples of which are reported here, along with relevant results. The measurements include high frequency-resolution impedance spectroscopy with the piezocrystal material under mechanical stress 0-60 MPa, temperature 20-200 °C, high electric AC drive and DC bias. A laser Doppler vibrometer and infrared thermal camera are also integrated into the measurement system for vibration mode shape scanning and thermal conditioning with high AC drive. Three generations of piezocrystal have been tested: (I) binary, PMN-PT; (II) ternary, PIN-PMN-PT; and (III) doped ternary, Mn:PIN-PMN-PT. Utilising resonant mode analysis, variations in elastic, dielectric and piezoelectric constants and coupling coefficients have been analysed, and tests with thermal conditioning have been carried out to assess the stability of the piezocrystals under high power conditions.
| S-EPMC5458827 | biostudies-other
Project description:BackgroundEthnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK.MethodUsing a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake and spatio-temporal correlation structure.FindingsComparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 297% to 335%. Similarly, prevalence increases by 10% from 037% to 041%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: in the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis.InterpretationAt the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.FundingsEPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR.
| S-EPMC8597886 | biostudies-literature
Project description:After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their confinement measures in the face of critical damage to socioeconomic structures. At this stage, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease. Though it is difficult to trace back individual transmission of infections whose incubation periods are long and highly variable, estimating the average spreading rate is possible if a proper mathematical model can be devised to analyze daily event-occurrences. To render an accurate assessment, we have devised a state-space method for fitting a discrete-time variant of the Hawkes process to a given dataset of daily confirmed cases. The proposed method detects changes occurring in each country and assesses the impact of social events in terms of the temporally varying reproduction number, which corresponds to the average number of cases directly caused by a single infected case. Moreover, the proposed method can be used to predict the possible consequences of alternative political measures. This information can serve as a reference for behavioral guidelines that should be adopted according to the varying risk of infection.
| S-EPMC7875393 | biostudies-literature
Project description:Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
| S-EPMC8378847 | biostudies-literature
Project description:The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.
| S-EPMC9111166 | biostudies-literature
Project description:Charge transport and separation in mechanically-driven, droplet-based ion sources are investigated using computational analysis and supporting experiments. A first-principles model of electrohydrodynamics (EHD) and charge migration is formulated and implemented using FLUENT CFD software for jet/droplet formation. For validation, classical experiments of electrospraying from a thin capillary are simulated, specifically, the transient EHD cone-jet formation of a fluid with finite electrical conductivity, and the Taylor cone formation in a perfectly electrically-conducting fluid. The model is also used to investigate the microscopic physics of droplet charging in mechanically-driven droplet-based ion sources, such as array of micromachined ultrasonic electrospray (AMUSE). Here, AMUSE is subject to DC and AC electric fields of varying amplitude and phase, with respect to a time-varying mechanical force driving the droplet formation. For the DC-charging case, a linear relationship is demonstrated between the charge carried by each droplet and an applied electric field magnitude, in agreement with previously reported experiments. For the AC-charging case, a judiciously-chosen phase-shift in the time-varying mechanical (driving ejection) and electrical (driving charge transport) signals allows for a significantly increased amount of charge, of desired polarity, to be pumped into a droplet upon ejection. Complementary experimental measurements of electrospray electrical current and charge-per-droplet, produced by the AMUSE ion source, are performed and support theoretical predictions for both DC- and AC-charging cases. The theoretical model and simulation tools provide a versatile and general analytical framework for fundamental investigations of coupled electrohydrodynamics and charge transport. The model also allows for the exploration of different configurations and operating modes to optimize charge separation in atmospheric pressure electrohydrodynamic ion sources under static and dynamic electrical and mechanical fields.
| S-EPMC2847640 | biostudies-literature
Project description:Electrical breakdown is one of the most important physical phenomena in electrical and electronic engineering. Since the early 20(th) century, many theories and models of electrical breakdown have been proposed, but the origin of one key issue, that the explanation for dc breakdown strength being twice or higher than ac breakdown strength in insulating materials, remains unclear. Here, by employing a bipolar charge transport model, we investigate the space charge dynamics in both dc and ac breakdown processes. We demonstrate the differences in charge accumulations under both dc and ac stresses and estimate the breakdown strength, which is modulated by the electric field distortion induced by space charge. It is concluded that dc breakdown initializes in the bulk whereas ac breakdown initializes in the vicinity of the sample-electrode interface. Compared with dc breakdown, the lower breakdown strength under ac stress and the decreasing breakdown strength with an increase in applied frequency, are both attributed to the electric field distortion induced by space charges located in the vicinity of the electrodes.
| S-EPMC5013471 | biostudies-literature