Giant exciton-enhanced shift currents and direct current conduction with subbandgap photo excitations produced by many-electron interactions

Shift current is a direct current generated from nonlinear light–matter interaction in a noncentrosymmetric crystal and is considered a promising candidate for next-generation photovoltaic devices. The mechanism for shift currents in real materials is, however, still not well understood, especially if electron–hole interactions are included. Here, we employ a first-principles interacting Green’s-function approach on the Keldysh contour with real-time propagation to study photocurrents generated by nonlinear optical processes under continuous wave illumination in real materials. We demonstrate a strong direct current shift current at subbandgap excitation frequencies in monolayer GeS due to strongly bound excitons, as well as a giant excitonic enhancement in the shift current coefficients at above bandgap photon frequencies. Our results suggest that atomically thin two-dimensional materials may be promising building blocks for next-generation shift current devices.

When continuous wave light is shone on a noncentrosymmetric crystal, a direct current (DC) can arise due to a second-order optical response of the crystal. The origin of this current is interpreted to be related to the “shift” (1–4) of the intracell coordinates of the excited electron. This so-called shift current is proposed as an alternative to the photocurrent generated by traditional semiconductor p–n junctions (i.e., a junction between hole-doped [p-type] and electron-doped [n-type] semiconductors) for photovoltaic applications (5, 6). Unlike conventional photovoltaic devices, shift current is a bulk phenomenon, which does not require a p–n junction to separate the optically generated electron–hole pair for a DC. Moreover, recent studies reveal that the photocarriers in shift current can have long travel distances, which is distinct from the usual drift transport mechanism in traditional solar cells (7, 8) and makes shift current a promising candidate for efficient energy conversion.Despite many investigations over the past decade, a basic understanding of shift currents is far from complete. Most theoretical studies to date rely on the assumption of having noninteracting particles (3–6, 9–11). Given that it is well known that light-induced electron–hole pairs can form bound or resonant excitons (correlated electron–hole states), which dominate and qualitatively change the absorption features of semiconductors, electron–hole interactions or excitons are expected to play a large role in shift currents, especially for reduced dimensional systems. However, it is not straight forward to generalize existing ab initio methods [such as the ab initio GW plus Bethe-Salpeter equation (GW-BSE) approach (12)], used to understand and compute excitonic effects in linear optical absorption, to study nonlinear optical responses. Different model approaches to investigate the effects of many-electron interactions on nonlinear optical responses of materials have been proposed. For instance, a Floquet-based model Hamiltonian formalism showed that excitonic effects enhance nonlinear response (13). In the specific case of second harmonic generation, first-principles approaches have been developed and applied to real materials, for instance, by making an approximation to the full many-body perturbation theory treatment (14, 15) or to the time-dependent density function theory, in which electron interaction effects are taken into account via simplified kernels (16). A real-time formulation based on propagating the time-dependent Schrodinger equation has also been developed (17) and applied to second harmonic generation (18). For shift currents in real materials, only one recent study considered the effects of excitons on the linear optical coefficient that might influence shift currents, but these authors included only the effects of excitons on the electromagnetic field profile in a bulk sample, and the crucial process of shift current generation itself is still treated within an independent-particle picture (11). Thus, there is still no first-principles calculation and understanding of the role of many-electron interactions, particularly those due to excitons, on shift currents.Here, we show from first principles that 1) bound exciton states in the band gap can generate substantial shift currents, and 2) excitonic effects in the electron–hole continuum part of the spectrum can also greatly enhance shift currents due to the enhancement of the optical matrix elements from the coherence of the electron–hole pairs and to interexciton couplings that arose in the nonlinear responses.     

Time-dependent ROC analysis for censored biomarker data due to limit of detection

Receiver operating characteristic (ROC) curve is a well-established analysis method to evaluate biomarker’s discrimination accuracy for binary outcomes. When the endpoint of interest is time to event outcome such as time to cancer recurrence, a biomarker’s time-varying discriminatory performance is often assessed by time-dependent ROC analysis. In practice, biomarkers are often imprecisely measured due to the limitation of assay sensitivity. The values below the limit of detection are not detectable. Ignorance of such data characteristic may lead to inaccurate estimation of marker’s potential discriminatory power. The objective of this article is to extend time-dependent ROC method to censored biomarker data by using parameter estimates from the Cox regression model that accommodates censored biomarker measurements. In the simulation study, the proposed methods are shown to outperform the simple substitution method that has been conventionally adopted for handling censored data. Application data are also given to illustrate our methods.     

Outcome after extended follow-up in a prospective study of operable breast cancer: key factors and a prognostic index

In 1990, 215 patients with operable breast cancer were entered into a prospective study of the prognostic significance of five biochemical markers and 15 other factors (pathological/chronological/patient). After a median follow-up of 6.6 years, there were 77 recurrences and 77 deaths (59 breast cancer-related). By univariate analysis, patient outcome related significantly to 13 factors. By multivariate analysis, the most important of nine independent factors were: number of nodes involved, steroid receptors (for oestrogen or progestogen), age, clinical or pathological tumour size and grade. Receptors and grade exerted their influence only in the first 3 years. Progestogen receptors (immunohistochemical) and oestrogen receptors (biochemical) were of similar prognostic significance. The two receptors were correlated (r=+0.50, P=0.001) and displaced each other from the analytical model but some evidence for the additivity of their prognostic values was seen when their levels were discordant.     

Hierarchical Generalised Linear Models with Time-Dependent Clustering: Assessing the Effect of Health Sector Reform on Patient Outcomes in New Zealand

New Zealand has one of the most reformed health systems in the world. This paper is primarily concerned with modelling the impact on hospital outcomes of the reforms of the early 1990s, when as part of a major, health sector wide reform process, the administration of public hospitals passed from elected Area Health Boards (AHBs) to Crown Health Enterprises (CHEs) operating under a competitive model of health care provision dominated by the funder/purchaser/provider split. The impact of reform processes on public hospitals is of particular interest since they consume 40%–50% of public expenditure on health, and have been repeatedly restructured in an attempt to contain the ever-expanding cost of health care. There is concern among both health professionals and the general public that these restructurings are reducing the quality of hospital services, and therefore negatively effecting patient outcomes. Using data from a study of 34 New Zealand public hospitals, we discuss the application of Bayesian hierarchical generalised linear models to the analysis of trends in patient outcomes over the period 1988–2001. The time-varying nature of the grouping of hospitals within larger health authorities complicates the application of HGLMs because the cluster structure of the data changes over the study period. An approach to dealing with such time-dependent clustering by introducing period-specific authority level effects is developed. The analysis does not support the proposition that higher level authorities had an effect on outcome trends, or that the administrative changeover from AHBs to CHEs impacted on 60-day post-admission mortality.     

Time-Dependent Oral Absorption Models

The plasma concentration–time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, k_a(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted C_max values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, k_a(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted C_max values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient k_a(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.     

Bootstrapping complex time-to-event data without individual patient data,with a view toward time-dependent exposures

We consider nonparametric and semiparametric resampling of multistate event histories by simulating multistate trajectories from an empirical multivariate hazard measure. One advantage of our approach is that it does not necessarily require individual patient data, but may be based on published information. This is also attractive for both study planning and simulating realistic real-world event history data in general. The concept extends to left-truncation and right-censoring mechanisms, nondegenerate initial distributions, and nonproportional as well as non-Markov settings. A special focus is on its connection to simulating survival data with time-dependent covariates. For the case of qualitative time-dependent exposures, we demonstrate that our proposal gives a more natural interpretation of how such data evolve over the course of time than many of the competing approaches. The multistate perspective avoids any latent failure time structure and sampling spaces impossible in real life, whereas its parsimony follows the principle of Occam's razor. We also suggest empirical simulation as a novel bootstrap procedure to assess estimation uncertainty in the absence of individual patient data. This is not possible for established procedures such as Efron's bootstrap. A simulation study investigating the effect of liver functionality on survival in patients with liver cirrhosis serves as a proof of concept. Example code is provided.     

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding

In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators. We illustrate the method by an application to data from the LEADER cardiovascular outcomes trial.     

Circadian and time-dependent variability in tacrolimus pharmacokinetics

Tacrolimus (TAC) is considered a critical dose drug. The purpose of our study was to investigate circadian and time-dependent changes in TAC pharmacokinetics over the first year after kidney transplantation. Pharmacokinetic (PK) studies were performed in 26 recipients of first living donor kidney transplants at day 7 after morning (a.m.) and evening (p.m.) doses of TAC. Additional serial PK studies were carried out in nine patients at month 6 (M6) and month 12 (M12). Blood samples were collected before 1, 1.5, 2, 2.5, 3, 4, 6, 8 and 12 h after TAC administration. Demographics, TAC and adjunctive immunosuppressive doses, hematology, and biochemistry were recorded in each PK study. Mean age was 37 years, body mass index 23 kg/m(2), 58% males, and 85% Caucasian. Higher AUC (231.4 vs. 220 ng.h/mL, P = 0.06) and C(max) (34.1 +/- 12.6 vs. 24.4 +/- 9.8 ng/mL, P < 0.001), and lower T(max) (1.6 +/- 0.8 vs. 2.7 +/- 2.0 h, P = 0.05) values were observed comparing a.m. and p.m. administrations. Comparing D7, M6 and M12, there was a significant increase in dose-normalized AUC (31.4 +/- 22.2 vs. 50.1 +/- 33 vs. 39.2 +/- 24.4 ng.h/mL/mg, P = 0.005), C(max) (4.4 +/- 2.4 vs. 7.8 +/- 3.5 vs. 6.0 +/- 3.3 ng/mL/mg, P < 0.001) and T(max) (1.6 +/- 1.1 vs. 1.7 +/- 0.4 vs. 1.8 +/- 0.8 h, P = 0.006), respectively. Over the first year the intraindividual variability of dose-normalized AUC, C(max) and C(0) were 82%, 72%, and 90%, respectively. No significant changes were observed comparing inter-individual variability of dose-normalized AUC (21%, 24%, 33%), C(max) (46%, 45%, 55%), C(0) (49%, 83%, 81%) at D7, M6 and M12, respectively. We observed a good correlation between a.m. and p.m. TAC AUC (r(2) = 0.90) and C(0) (r(2) = 0.88). Tacrolimus pharmacokinetics display circadian variation suggesting a slower and delayed absorption phase at nighttime. Tacrolimus also showed time-dependent PK changes, suggesting an improvement in absorption during the first 6 months. Despite circadian variation we observed good correlations between a.m. and p.m. TAC AUC (r(2) = 0.90) and C(0) (r(2) = 0.88) and between C(0) and total daily TAC exposure (a.m. + p.m. AUC) suggesting that trough-guided therapeutic monitoring is still a reliable and simple strategy to optimize the clinical use of TAC.     

用STATA软件拟合含时间相依协变量的Cox模型

目的:介绍含有时间相依协变量的Cox比例危险模型的简明理论极其STATA软件实现。方法:使用一个艾滋病治疗的例子,说明如何以STATA软件实现含有时间相依协变量的Cox比例危险模型,并解释其含义。结果:所设置的时间相依协变量(tvc(age))的检验P=0.693,而不包含时间作用的自变量age的检验P=0.008,提示该变量与时间不存在相依性,可以使用Cox模型的简单形式。结论:考虑是否含有时间相依协变量是正确拟合Cox比例危险模型的重要一环,在工作实践中必须给予充分的重视;而STATA软件能够方便灵活的拟合这种含有时间相依协变量的Cox模型。     

The pharmacodynamic properties of azithromycin in a kinetics-of-kill model and implications for bacterial conjunctivitis treatment

Introduction

Antibiotics have traditionally been classified as bactericidal or bacteriostatic. Azithromycin belongs to the parent class of macrolides that are characteristically bacteriostatic. Some evidence suggests that this mol-ecule demonstrates bactericidal kill and has concentration-dependent effects. This study tests the hypothesis that azithromycin demonstrates a bactericidal, concentration-dependent antibiotic effect at concentrations corresponding to and exceeding published tear and conjunctival levels.

Methods

The antibacterial activity of different concentrations of azithromycin 1% in DuraSite® (AzaSite®; Inspire Pharmaceuticals Inc, Durham, NC, USA) was evaluated using a kinetics-of-kill model. Recent conjunctivitis isolates of Staphylococcus aureus, Streptococcus pneumoniae or Haemophilus influenzae were exposed to four concentrations of azithromycin (100, 250, 500 and 750 μg/ml). Starting concentrations were similar to the maximum concentrations (Cmax) that have been demonstrated in conjunctiva (83 μg/g) and tears (288 μg/ml) following topical ocular administration. The percentage of surviving bacteria at 30 and 60 minutes following exposure to each concentration were determined.

Results

Azithromycin failed to demonstrate bactericidal activity (i.e. a 3-log reduction in surviving bacteria) against S. aureus, S. pneumoniae or H. influenzae. Furthermore, the rate and extent of antibacterial activity with azithromycin did not change with higher concentrations, even at the highest tested concentration of 750 μg/ml.

Conclusion

Similar to the parent macrolide class, azithromycin demonstrates bacteriostatic activity against common conjunctival pathogens up to the maximum tested concentration of 750 μg/ml (i.e. 2.6-times and 9-times published Cmax tear and conjunctival concentration, respectively). Azithromycin’s bacteriostatic effects and prolonged elimination half-life will likely lead to a corresponding increase in the emergence of macrolide-resistant isolates.