Transition metal dichalcogenides (TMDCs) are at the forefront of nanophotonics because of their exceptional optical characteristics. The 2D architecture of TMDCs facilitates efficient light absorption and emission, holding tantalizing potential for next-generation nanophotonic and quantum devices. Yet, the atomic thinness limits their interaction volume with light, affecting light-matter interaction and quantum efficiency. The light coupling in the 2D layered TMDCs can be enhanced by integration with photonic structure, and the metasurfaces supporting bound states in the continuum (BICs) offer strong confinement of optical fields, ideal for coupling with 2D TMDCs. Here, we demonstrate enhanced light-matter coupling by integrating TMDC monolayers, including WSe2 and MoS2, with a finite-area membrane metasurface, leading to amplified and high-quality-factor (Q-factor) spontaneous emission from quasi-BIC-coupled TMDC monolayers. The high-Q-factor emission extends over an area with a scale of a few micrometers while maintaining the high-Q factor across the emission area. Notably, the suspended finite-area membrane metasurface, which is freestanding in air rather than positioned atop a substrate, minimizes radiation loss while enhancing light-matter interaction in the TMDC monolayer. Furthermore, the predominantly in-plane dipole orientation of excitons within TMDC monolayers results in distinctive enhancement behaviors for emission, contingent on the excitation power, when coupled with quasi-BIC modes exhibiting TE and TM resonances. This work introduces a nanophotonic platform for robust coupling of membrane metasurfaces with 2D materials, offering possibilities for developing 2D material-based nanophotonic and quantum devices.

Dynamic control for a strong circular dichroism (CD) response is essential in engineering applications such as polarization manipulation, sensing, and imaging. Here, we propose and experimentally demonstrate a broadband tunable dual CD response via bound states in the continuum (BICs) in two-dimensional topologically protected metasurfaces composed of all-dielectric Si chiral grating structures that generate a pair of mixed and degenerated BIC mode and circular dichroic mode (CDM) as an additional degree of freedom in CD manipulation. It is found that a singular CD peak of nearly 100% at 1.6 μm can be achieved by CDM when BIC is hidden under normal incidence, while the CD peak can be split into two in which peak wavelengths can be precisely and linearly tuned over a bandwidth of 180 nm by the incident angle when the BIC mode is excited under oblique incidence. Additionally, dynamic modulation of output polarization states from linear to circular can be arbitrarily achieved at the split CD peaks by controlling the incident angle when asymmetry perturbations on chiral gratings are introduced due to the decoupling of various polarization states at Γ point by BIC to different positions in K space. The proposed chiral grating metasurface exhibits unique angle-sensitive tunable CD spectral characteristics, making it ideal for hyperspectral and spin-selective wavefront shaping, and holds significant promise in various applications such as optical security, angle sensors, chiral lasers, nonlinear filters, and other active chiral optical devices.

UNASSIGNED: Atomic layer deposition (ALD) is a method that can deposit zirconia uniformly on an atomic basis. The effect of deposited zirconia on titanium implants using ALD was evaluated in vivo.
UNASSIGNED: Machined titanium implants (MTIs) were used as the Control. MTIs treated by sandblasting with large grit and acid etching (SA) and MTIs deposited with zirconia using ALD are referred to as Groups S and Z, respectively. Twelve implants were prepared for each group. Six rabbits were used as experimental animals. To evaluate the osteogenesis and osteocyte aspects around the implants, radiological and histological analyses were performed. The bone-to-implant contact (BIC) ratio was measured and statistically analyzed to evaluate the osseointegration capabilities.
UNASSIGNED: In the micro-CT analysis, more radiopaque bone tissues were observed around the implants in Groups S and Z. Histological observation found that Groups S and Z had more and denser mature bone tissues around the implants in the cortical bone area. Many new and mature bone tissues were also observed in the medullary cavity area. For the BIC ratio, Groups S and Z were significantly higher than the Control in the cortical bone area (P < 0.017), but there was no significant difference between Groups S and Z.
UNASSIGNED: MTIs deposited with zirconia using ALD (Group Z) radiologically and histologically showed more mature bone formation and activated osteocytes compared with MTIs (Control). Group Z also had a significantly higher BIC ratio than the Control. Within the limitations of this study, depositing zirconia on the surface of MTIs using ALD can improve osseointegration in vivo.

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10-4 and 6.94×10-5. Based on our selected model, the significance of TNSC\'s effect in predicting the risk of IM is justified with a p-value less than 10-6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

Prohibited additives in infant formula severely affect the health of infants. Terahertz (THz) spectroscopy has enormous application potential in analyte detection due to its rich fingerprint information content. However, there is limited research on the mixtures of multiple analytes. In this study, we propose a split ring metasurface that supports magnetic dipole bound states in the continuum (BIC). By breaking the symmetry, quasi-BIC with a high quality (Q) factor can be generated. Utilizing an angle-scanning strategy, the frequency of the resonance dip can be shifted, resulting in the plotting of an envelope curve which can reflect the molecular fingerprint of the analytes. Two prohibited additives found in infant formula, melamine and vanillin, can be identified in different proportions. Furthermore, a metric similar to the resolution in chromatographic analysis is introduced and calculated to be 0.61, indicating that these two additives can be detected simultaneously. Our research provides a new solution for detecting additives in infant formula.

OBJECTIVE: To reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines.
METHODS: R software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200-3000) and signal-to-noise ratios (SNRs, 0.5-4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated.
RESULTS: In setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data.
CONCLUSIONS: Categorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings.

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.

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.

Plasmon resonances can greatly enhance light-matter interactions of two-dimensional van der Waals materials. However, the quality factor of plasmonic resonances is limited. Here, we demonstrate a plasmonic quasi-bound state in the continuum (quasi-BIC), which is composed of gold nanorod pairs. Through controlling the rotation angle of the nanorods, the quality factor of the plasmonic BIC mode can be tuned. Simulation results show that the plasmonic BIC combines the advantages of high-quality factor from the BIC effect and small mode volume from plasmonic resonance. Experiment results show that the designed plasmonic BIC mode exhibits a quality factor higher than 15 at the wavelength of around 1250 nm. Through integrating the plasmonic bound state structure with monolayer molybdenum ditelluride (MoTe2), the exciton emission of MoTe2 in the PL spectrum split into two exciton-polariton modes, which is attributed to the high Q factor and strong interaction between the BIC mode and excitons of MoTe2.

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.
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.
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.
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.