BIC1 acts as a transcriptional coactivator to promote brassinosteroid signaling and plant growth.

The BRASSINAZOLE-RESISTANT 1 (BZR1) transcription factor family plays an essential role in plant brassinosteroid (BR) signaling, but the signaling mechanism through which BZR1 and its homologs cooperate with certain coactivators to facilitate transcription of target genes remains incompletely understood. In this study, we used an efficient protein interaction screening system to identify blue-light inhibitor of cryptochromes 1 (BIC1) as a new BZR1-interacting protein in Arabidopsis thaliana. We show that BIC1 positively regulates BR signaling and acts as a transcriptional coactivator for BZR1-dependent activation of BR-responsive genes. Simultaneously, BIC1 interacts with the transcription factor PIF4 to synergistically and interdependently activate expression of downstream genes including PIF4 itself, and to promote plant growth. Chromatin immunoprecipitation assays demonstrate that BIC1 and BZR1/PIF4 interdependently associate with the promoters of common target genes. In addition, we show that the interaction between BIC1 and BZR1 is evolutionally conserved in the model monocot plant Triticum aestivum (bread wheat). Together, our results reveal mechanistic details of BR signaling mediated by a transcriptional activation module BIC1/BZR1/PIF4 and thus provide new insights into the molecular mechanisms underlying the integration of BR and light signaling in plants.

A Quasi-Bound States in the Continuum Dielectric Metasurface-Based Antenna-Reactor Photocatalyst.

Metasurfaces are a class of two-dimensional artificial resonators, creating new opportunities for strong light-matter interactions. One type of nonradiative optical metasurface that enables substantial light concentration is based on quasi-Bound States in the Continuum (quasi-BIC). Here we report the design and fabrication of a quasi-BIC dielectric metasurface that serves as an optical frequency antenna for photocatalysis. By depositing Ni nanoparticle reactors onto the metasurface, we create an antenna-reactor photocatalyst, where the virtually lossless metasurface funnels light to drive a chemical reaction. This quasi-BIC-Ni antenna-reactor drives H2 dissociation under resonant illumination, showing strong polarization, wavelength, and optical power dependencies. Both E-field-induced electronic and photothermal heating effects drive the reaction, supported by load-dependent reactivity studies and our theoretical model. This study unlocks new opportunities for photocatalysis that employ dielectric metasurfaces for light harvesting in an antenna-reactor format.

Simultaneous variable selection and estimation for survival data via the Gaussian seamless- L 0 $$ {L}_0 $$ penalty.

We propose a new simultaneous variable selection and estimation procedure with the Gaussian seamless- L 0 $$ {L}_0 $$ (GSELO) penalty for Cox proportional hazard model and additive hazards model. The GSELO procedure shows good potential to improve the existing variable selection methods by taking strength from both best subset selection (BSS) and regularization. In addition, we develop an iterative algorithm to implement the proposed procedure in a computationally efficient way. Theoretically, we establish the convergence properties of the algorithm and asymptotic theoretical properties of the proposed procedure. Since parameter tuning is crucial to the performance of the GSELO procedure, we also propose an extended Bayesian information criteria (EBIC) parameter selector for the GSELO procedure. Simulated and real data studies have demonstrated the prediction performance and effectiveness of the proposed method over several state-of-the-art methods.

Bictegravir/Emtricitabine/Tenofovir Alafenamide in a Multicentre Cohort: Real-Life Experience From Spain.

BACKGROUND: The evaluation of bictegravir, emtricitabine, and tenofovir alafenamide (BIC/FTC/TAF) in clinical trials has shown high rates of virological suppression but information about its use in real-life settings is scarce. OBJECTIVE: To evaluate the effectiveness, safety, durability, and predictive variables of therapeutic failure of BIC/FTC/TAF in a real-life cohort. METHODS: This observational, retrospective, multicentered cohort study included treatment-naive (TN) and treatment-experienced (TE) adult patients living with HIV (PLWH) who started treatment with BIC/FTC/TAF from January 1, 2019, to January 31, 2022. Treatment effectiveness (based on intention-to-treat [ITT], modified ITT [mITT], and on-treatment [OT]), tolerability, and safety were evaluated in all patients who started BIC/FTC/TAF antiretroviral therapy. RESULTS: We included a total of 505 PLWH of whom 79 (16.6%) were TN and 426 (83.4%) were TE. Patients were followed up for a median (interquartile range [IQR]) of 19.6 (9.6-27.3) months, and 76% and 56% of PLWH reached month 6 and month 12 of treatment, respectively. Rates of TN PLWH with HIV-RNA <50 copies/mL in the OT, mITT, and ITT groups were 94%, 80%, and 62%, respectively, after 12 months of BIC/FTC/TAF treatment. Rates of TE PLWH with HIV-RNA <50 copies/mL were 91%, 88%, and 75% at month 12. The multivariate analysis revealed that neither age, sex, CD4 cell count <200 cells/µL, or viral load >100 000 copies/mL were associated with therapeutic failure. CONCLUSION AND RELEVANCE: Our real-life data showed that BIC/FTC/TAF is effective and safe for use in the treatment of both TN and TE patients in clinical practice.

Estimation of l0 Norm Penalized Models: A Statistical Treatment.

Fitting penalized models for the purpose of merging the estimation and model selection problem has become commonplace in statistical practice. Of the various regularization strategies that can be leveraged to this end, the use of the l0 norm to penalize parameter estimation poses the most daunting model fitting task. In fact, this particular strategy requires an end user to solve a non-convex NP-hard optimization problem irregardless of the underlying data model. For this reason, the use of the l0 norm as a regularization strategy has been woefully under utilized. To obviate this difficulty, a strategy to solve such problems that is generally accessible by the statistical community is developed. The approach can be adopted to solve l0 norm penalized problems across a very broad class of models, can be implemented using existing software, and is computationally efficient. The performance of the method is demonstrated through in-depth numerical experiments and through using it to analyze several prototypical data sets.

Permittivity-Asymmetric Quasi-Bound States in the Continuum.

Breaking the in-plane geometric symmetry of dielectric metasurfaces allows us to access a set of electromagnetic states termed symmetry-protected quasi-bound states in the continuum (qBICs). Here we demonstrate that qBICs can also be accessed by a symmetry breaking in the permittivity of the comprising materials. While the physical size of atoms imposes a limit on the lowest achievable geometrical asymmetry, weak permittivity modulations due to carrier doping, and electro-optical Pockels and Kerr effects, usually considered insignificant, open the possibility of infinitesimal permittivity asymmetries for on-demand, dynamically tunable resonances of extremely high quality factors. As a proof-of-principle, we probe the excitation of permittivity-asymmetric qBICs (ε-qBICs) using a prototype Si/TiO2 metasurface, in which the asymmetry in the unit cell is provided by the permittivity contrast of the materials. ε-qBICs are also numerically demonstrated in 1D gratings, where quality-factor enhancement and tailored interference phenomena of qBICs are shown via the interplay of geometrical and permittivity asymmetries.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data.

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Modeling and maximum likelihood based inference of interval-censored data with unknown upper limits and time-dependent covariates.

Due to the nature of study design or other reasons, the upper limits of the interval-censored data with multiple visits are unknown. A naïve approach is to treat the last observed time as the exact event time, which may induce biased estimators of the model parameters. In this paper, we first develop a Cox model with time-dependent covariates for the event time and a proportional hazards model with frailty for the gap time. We then construct the upper limits using the latent gap times to resolve the issue of interval-censored event time data with unknown upper limits. A data-augmentation technique and a Monte Carlo EM (MCEM) algorithm are developed to facilitate computation. Theoretical properties of the computational algorithm are also investigated. Additionally, new model comparison criteria are developed to assess the fit of the gap time data as well as the fit of the event time data conditional on the gap time data. Our proposed method compares favorably with competing methods in both simulation study and real data analysis.

Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization.

INTRODUCTION: The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. METHODS: In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. RESULTS: The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. CONCLUSION: The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.

Robust photogalvanic effect, full spin polarization and pure spin current in the BiC photodetector by vacancy- and substitution-doping.

The photogalvanic effects (PGEs) in low-dimensional devices have attracted great interests recently. Herein, based on non-equilibrium Green's function combined with density functional theory, we investigated spin-dependent PGE phenomena in the BiC photodetector for the case of linearly polarized light and zero bias. Due to the presence of strong spin-orbital interaction (SOI) and C3v symmetry for the BiC monolayer, the armchair and zigzag BiC photodetectors produce robust spin-dependent PGEs which possess the cos(2θ) and sin(2θ) relations on the photon energies. Especially, the armchair and Bi-vacancy armchair BiC photodetector can produce fully spin polarization, and pure spin current was found in the armchair and zigzag BiC photodetector. Furthermore, after introducing the Bi-vacancy, C-vacancy, Bi-doping and C-doping respectively, corresponding armchair and zigzag BiC photodetector can produce higher spin-dependent PGEs for their Cs symmetry. Moreover, the behaviors of spin-dependent photoresponse are highly anisotropic and can be tuned by the photon energy. This work suggested great potential applications of the BiC monolayer on PGE-driven photodetectors in low energy-consumption optoelectronics and spintronic devices. .

Treatment persistence of bictegravir/emtricitabine/tenofovir alafenamide and efavirenz + lamivudine + tenofovir disoproxil among HIV-1 patients newly starting treatment in Hunan Province in China.

BACKGROUND: Though bictegravir/emtricitabine/tenofovir (BIC/FTC/TAF) have been regulatory approved and included in the National Reimbursement Drug List in China, due to the affordability concern, generic version of efavirenz + lamivudine + tenofovir (EFV + 3TC + TDF) is still recommended as the first-line therapy in the clinical guideline and widely used in clinical practice. The aim of the study is to assess the persistence with first-line BIC/TAF/TAF and EFV + 3TC + TDF in newly treated HIV-1 patients in the real-world setting in Hunan Province in China. METHODS: A retrospective analysis of the medical records of HIV patients initiating first-line antiretroviral therapy in the First Hospital of Changsha in January 1st, 2021-July 31st, 2022 was conducted. Persistence was assessed as the number of days on the therapy from the index until treatment discontinuation or end of data availability. Kaplan-Meier Curves and Cox Proportional Hazard models were used to evaluate the discontinuation rates. Subgroup analysis was performed excluding BIC/FTC/TAF patients with treatment discontinuation due to economic reason, and EFV + 3TC + TDF patients with a viral load > 500,000 copies/mL. RESULTS: A total of 310 eligible patients were included in the study, with 244 and 66 patients in the BIC/FTC/TAF group and EFV + 3TC + TDF group, respectively. Compared with EFV + 3TC + TDF patients, BIC/FTC/TAF patients were older, more living in the capital city currently, and had significantly higher total cholesterol and low-density level (all p < 0.05). No significant difference was shown in the time to discontinuation between BIC/FTC/TAF patients and EFV + 3TC + TDF patients. After excluding BIC/FTC/TAF patients with treatment discontinuation due to economic reason, EFV + 3TC + TDF group were shown to have a significantly higher risk of discontinuation than BIC/FTC/TAF group (hazard ratio [HR] = 11.1, 95% confidence interval [CI] = 1.3-93.2). After further removing the EFV + 3TC + TDF patients with a viral load > 500,000 copies/mL, the analysis showed similar results (HR = 10.1, 95% CI = 1.2-84.1). 79.4% of the EFV + 3TC + TDF patients discontinued treatment due to clinical reasons, while 83.3% of the BIC/FTC/TAF patients discontinued treatment due to economic reasons. CONCLUSIONS: Compared with BIC/FTC/TAF, EFV + TDF + 3TC patients were significantly more likely to discontinue the first-line treatment in Hunan Province in China.

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.

BACKGROUND: This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification of risk factors for timely diagnosis and improved patient outcomes. The researchers conducted a literature review to address previous studies' limitations in using machine learning to assess CVD risk factors. METHODS: This study analyzed data from 1024 patients to identify the significant CVD risk factors based on sex. The data comprising 13 features, such as demographic, lifestyle, and clinical factors, were obtained from the UCI repository and preprocessed to eliminate missing information. The analysis was performed using principal component analysis (PCA) and latent class analysis (LCA) to determine the major CVD risk factors and to identify any homogeneous subgroups between male and female patients. Data analysis was performed using XLSTAT Software. This software provides a comprehensive suite of tools for Data Analysis, Machine Learning, and Statistical Solutions for MS Excel. RESULTS: This study showed significant sex differences in CVD risk factors. 8 out of 13 risk factors affecting male and female patients found that males and females share 4 of the eight risk factors. Identified latent profiles of CVD patients, suggesting the presence of subgroups among CVD patients. These findings provide valuable insights into the impact of sex differences on CVD risk factors. Moreover, they have important implications for healthcare professionals, who can use this information to develop individualized prevention and treatment plans. The results highlight the need for further research to elucidate these disparities better and develop more effective CVD prevention measures. CONCLUSIONS: The study explored the sex differences in the CVD risk factors and the presence of subgroups among CVD patients using ML techniques. The results revealed sex-specific differences in risk factors and the existence of subgroups among CVD patients, thus providing essential insights for personalized prevention and treatment plans. Hence, further research is necessary to understand these disparities better and improve CVD prevention.

Quantile forward regression for high-dimensional survival data.

Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates' effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD) and derives the final model based on the extended Bayesian Information Criterion (EBIC). We demonstrate that the proposed method enjoys a sure screening property and selection consistency. We apply it to the national health survey dataset to show the advantages of a quantile-specific prediction model. Finally, we discuss potential extensions of our approach, including the nonlinear model and the globally concerned quantile regression coefficients model.

Effect of systemic antiresorptive medication on the histopathological parameters of implant osseointegration in an in vivo rodent study.

BACKGROUND: The purpose of this study was to evaluate the osseointegration of zirconia and titanium implants in the rat maxilla in specimens under systemic antiresorptive therapy. MATERIALS AND METHODS: After 4 weeks of systematic medication administration (either zoledronic acid or alendronic acid), 54 rats received one zirconia and one titanium implants that were immediately inserted in the rat maxilla after tooth extraction. Twelve weeks after implant placement, histopathological samples were evaluated for implant osteointegration parameters. RESULTS: The bone-implant-contact (BIC) ratio revealed no significant inter-group or inter-material differences. The distance between the implant shoulder to the bone level was significantly greater around the titanium implants of the zoledronic acid group compared to the zirconia implants of the control group (p = 0.0005). On average, signs of new bone formation could be detected in all groups, although often without statistical differences. Signs of bone necrosis were only detected around the zirconia implants of the control group (p < 0.05). CONCLUSIONS: At the 3-month follow-up, no implant material was demonstrably better than the others in terms of osseointegration metrics under systemic antiresorptive therapy. Further studies are necessary to determine whether there are differences in the osseointegration behavior of the different materials.

Arabidopsis cryptochrome 1 undergoes COP1 and LRBs-dependent degradation in response to high blue light.

Arabidopsis cryptochrome 1 (CRY1) is an important blue light photoreceptor that promotes photomorphogenesis under blue light. The blue light photoreceptors CRY2 and phototropin 1, and the red/far-red light photoreceptors phytochromes B and A undergo degradation in response to blue and red light, respectively. This study investigated whether and how CRY1 might undergo degradation in response to high-intensity blue light (HBL). We demonstrated that CRY1 is ubiquitinated and degraded through the 26S proteasome pathway in response to HBL. We found that the E3 ubiquitin ligase constitutive photomorphogenic 1 (COP1) is involved in mediating HBL-induced ubiquitination and degradation of CRY1. We also found that the E3 ubiquitin ligases LRBs physically interact with CRY1 and are also involved in mediating CRY1 ubiquitination and degradation in response to HBL. We further demonstrated that blue-light inhibitor of cryptochromes 1 interacts with CRY1 in a blue-light-dependent manner to inhibit CRY1 dimerization/oligomerization, leading to the repression of HBL-induced degradation of CRY1. Our findings indicate that the regulation of CRY1 stability in HBL is coordinated by COP1 and LRBs, which provides a mechanism by which CRY1 attenuates its own signaling and optimizes photomorphogenesis under HBL.

Improved nonparametric penalized maximum likelihood estimation for arbitrarily censored survival data.

Nonparametric maximum likelihood estimation encompasses a group of classic methods to estimate distribution-associated functions from potentially censored and truncated data, with extensive applications in survival analysis. These methods, including the Kaplan-Meier estimator and Turnbull's method, often result in overfitting, especially when the sample size is small. We propose an improvement to these methods by applying kernel smoothing to their raw estimates, based on a BIC-type loss function that balances the trade-off between optimizing model fit and controlling model complexity. In the context of a longitudinal study with repeated observations, we detail our proposed smoothing procedure and optimization algorithm. With extensive simulation studies over multiple realistic scenarios, we demonstrate that our smoothing-based procedure provides better overall accuracy in both survival function estimation and individual-level time-to-event prediction (imputation) by reducing overfitting. Our smoothing procedure decreases the bias (discrepancy between the estimated and true simulated survival function) using interval-censored data by up to 48% compared to the raw un-smoothed estimate, with similar improvements of up to 34% and 23% in within-sample and out-of-sample prediction, respectively. Our smoothing algorithm also demonstrates significant overall improvement across all three metrics when compared to a popular semiparametric B-splines estimation method. Finally, we apply our method to real data on censored breast cancer diagnosis, which similarly shows improvement when compared to empirical survival estimates from uncensored data. We provide an R package, SISE, for implementing our penalized likelihood method.

The impact of travelling on the COVID-19 infection cases in Germany.

BACKGROUND: COVID-19 continues to disrupt social lives and the economy of many countries and challenges their healthcare capacities. Looking back at the situation in Germany in 2020, the number of cases increased exponentially in early March. Social restrictions were imposed by closing e.g. schools, shops, cafés and restaurants, as well as borders for travellers. This reaped success as the infection rate descended significantly in early April. In mid July, however, the numbers started to rise again. Of particular reasons was that from mid June onwards, the travel ban has widely been cancelled or at least loosened. We aim to measure the impact of travellers on the overall infection dynamics for the case of (relatively) few infectives and no vaccinations available. We also want to analyse under which conditions political travelling measures are relevant, in particular in comparison to local measures. By travel restrictions in our model we mean all possible measures that equally reduce the possibility of infected returnees to further spread the disease in Germany, e.g. travel bans, lockdown, post-arrival tests and quarantines. METHODS: To analyse the impact of travellers, we present three variants of an susceptible-exposed-infected-recovered-deceased model to describe disease dynamics in Germany. Epidemiological parameters such as transmission rate, lethality, and detection rate of infected individuals are incorporated. We compare a model without inclusion of travellers and two models with a rate measuring the impact of travellers incorporating incidence data from the Johns Hopkins University. Parameter estimation was performed with the aid of the Monte-Carlo-based Metropolis algorithm. All models are compared in terms of validity and simplicity. Further, we perform sensitivity analyses of the model to observe on which of the model parameters show the largest influence the results. In particular, we compare local and international travelling measures and identify regions in which one of these shows larger relevance than the other. RESULTS: In the comparison of the three models, both models with the traveller impact rate yield significantly better results than the model without this rate. The model including a piecewise constant travel impact rate yields the best results in the sense of maximal likelihood and minimal Bayesian Information Criterion. We synthesize from model simulations and analyses that travellers had a strong impact on the overall infection cases in the considered time interval. By a comparison of the reproductive ratios of the models under traveller/no-traveller scenarios, we found that higher traveller numbers likely induce higher transmission rates and infection cases even in the further course, which is one possible explanation to the start of the second wave in Germany as of autumn 2020. The sensitivity analyses show that the travelling parameter, among others, shows a larger impact on the results. We also found that the relevance of travel measures depends on the value of the transmission parameter: In domains with a lower transmission parameter, caused either by the current variant or local measures, it is found that handling the travel parameters is more relevant than those with lower value of the transmission. CONCLUSIONS: We conclude that travellers is an important factor in controlling infection cases during pandemics. Depending on the current situation, travel restrictions can be part of a policy to reduce infection numbers, especially when case numbers and transmission rate are low. The results of the sensitivity analyses also show that travel measures are more effective when the local transmission is already reduced, so a combination of those two appears to be optimal. In any case, supervision of the influence of travellers should always be undertaken, as another pandemic or wave can happen in the upcoming years and vaccinations and basic hygiene rules alone might not be able to prevent further infection waves.

Multilevel log linear model to estimate the risk factors associated with infant mortality in Ethiopia: further analysis of 2016 EDHS.

BACKGROUND: Infant mortality is defined as the death of a child at any time after birth and before the child's first birthday. Sub-Saharan Africa has the highest infant and child mortality rate in the world. Infant and child mortality rates are higher in Ethiopia. A study was carried out to estimate the risk factors that affect infant mortality in Ethiopia. METHOD: The EDHS- 2016 data set was used for this study. A total of 10,547 mothers from 11 regions were included in the study's findings. To estimate the risk factors associated with infant mortality in Ethiopia, several count models (Poisson, Negative Binomial, Zero-Infated Poisson, Zero-Infated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial) were considered. RESULT: The average number of infant deaths was 0.526, with a variance of 0.994, indicating over-dispersion. The highest mean number of infant death occurred in Somali (0.69) and the lowest in Addis Ababa (0.089). Among the multilevel log linear models, the ZINB regression model with deviance (17,868.74), AIC (17,938.74), and BIC (1892.97) are chosen as the best model for estimating the risk factors affecting infant mortality in Ethiopia. However, the results of a multilevel ZINB model with a random intercept and slope model revealed that residence, mother's age, household size, mother's age at first birth, breast feeding, child weight, contraceptive use, birth order, wealth index, father education level, and birth interval are associated with infant mortality in Ethiopia. CONCLUSION: Infant deaths remains high and infant deaths per mother differ across regions. An optimal fit was found to the data based on a multilevel ZINB model. We suggest fitting the ZINB model to count data with excess zeros originating from unknown sources such as infant mortality.

New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.

BACKGROUND: Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. METHODS: We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. RESULTS: In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. CONCLUSION: Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite.

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.