Metasurface Absorbers with Film-Coupled Au Nanoclusters to Achieve Broadband and Polarization-Independent Absorption of UV-Vis Light.

Metasurface absorbers (MAs) have attracted widespread interest in the recent study of subwavelength artificial optical metasurfaces, although most reported MAs suffer from the actualities of costly and time-consuming fabrications, narrow working bandwidth, polarization-dependent responses, etc., somewhat limiting their practical applications. Herein, we introduce a facile and low-cost method to fabricate MAs with excellent absorption performances via the self-assembly of synthesized Au nanoclusters (NCs) on a Au film spaced by a nanoscale-thick dielectric SiO2. Interestingly, the proposed MAs with well-designed Au film-coupled Au NCs (i.e., an appropriate surface coverage of Au NCs and the compatible thickness of the SiO2 spacer) exhibited a measured average absorbance above 90% within a broad UV-vis wavelength band (200-800 nm). In addition, owing to the MAs' topological symmetry, their UV-vis absorption behaviors presented polarization insensitivity with the incident light angles ranging from 20 to 50°. It has been demonstrated that the excited different surface plasmon resonance modes between Au NCs and the adjacent Au film were vital; in addition, the light-trapping effects from "V"-shaped structures of Au NCs were favorable for the designed MAs with enhanced light absorption. We believe that such MAs and the potential self-assembly fabrication strategy may facilitate scalable optical applications such as photothermalvoltaics, ultraviolet protection, optical storage, and sensing.

High Performance Graphene-C60 -Bismuth Telluride-C60 -Graphene Nanometer Thin Film Phototransistor with Adjustable Positive and Negative Responses.

Graphene is a promising candidate for the next-generation infrared array image sensors at room temperature due to its high mobility, tunable energy band, wide band absorption, and compatibility with complementary metal oxide semiconductor process. However, it is difficult to simultaneously obtain ultrafast response time and ultrahigh responsivity, which limits the further improvement of graphene photoconductive devices. Here, a novel graphene/C60 /bismuth telluride/C60 /graphene vertical heterojunction phototransistor is proposed. The response spectral range covers 400-1800 nm; the responsivity peak is 106 A W-1 ; and the peak detection rate and peak response speed reach 1014 Jones and 250 µs, respectively. In addition, the regulation of positive and negative photocurrents at a gate voltage is characterized and the ionization process in impurities of the designed phototransistor at a low temperature is analyzed. Tunable bidirectional response provides a new degree of freedom for phototransistors' signal resolution. The analysis of the dynamic change process of impurity energy level is conducted to improve the device's performance. From the perspective of manufacturing process, the ultrathin phototransistor (20-30 nm) is compatible with functional metasurface to realize wavelength or polarization selection, making it possible to achieve large-scale production of integrated spectrometer or polarization imaging sensor by nanoimprinting process.

Quantile regression for nonignorable missing data with its application of analyzing electronic medical records.

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

Heritability estimation for a linear combination of phenotypes via ridge regression.

MOTIVATION: The joint analysis of multiple phenotypes is important in many biological studies, such as plant and animal breeding. The heritability estimation for a linear combination of phenotypes is designed to account for correlation information. Existing methods for estimating heritability mainly focus on single phenotypes under random-effect models. These methods also require some stringent conditions, which calls for a more flexible and interpretable method for estimating heritability. Fixed-effect models emerge as a useful alternative. RESULTS: In this article, we propose a novel heritability estimator based on multivariate ridge regression for linear combinations of phenotypes, yielding accurate estimates in both sparse and dense cases. Under mild conditions in the high-dimensional setting, the proposed estimator appears to be consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is promising under different scenarios. Compared with independently combined heritability estimates in the case of multiple phenotypes, the proposed method significantly improves the performance by considering correlations among those phenotypes. We further demonstrate its application in heritability estimation and correlation analysis for the Oryza sativa rice dataset. AVAILABILITY AND IMPLEMENTATION: An R package implementing the proposed method is available at https://github.com/xg-SUFE1/MultiRidgeVar, where covariance estimates are also given together with heritability estimates. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Robust analysis of cancer heterogeneity for high-dimensional data.

Cancer heterogeneity plays an important role in the understanding of tumor etiology, progression, and response to treatment. To accommodate heterogeneity, cancer subgroup analysis has been extensively conducted. However, most of the existing studies share the limitation that they cannot accommodate heavy-tailed or contaminated outcomes and also high dimensional covariates, both of which are not uncommon in biomedical research. In this study, we propose a robust subgroup identification approach based on M-estimators together with concave and pairwise fusion penalties, which advances from existing studies by effectively accommodating high-dimensional data containing some outliers. The penalties are applied on both latent heterogeneity factors and covariates, where the estimation is expected to achieve subgroup identification and variable selection simultaneously, with the number of subgroups being apriori unknown. We innovatively develop an algorithm based on parallel computing strategy, with a significant advantage of capable of processing large-scale data. The convergence property of the proposed algorithm, oracle property of the penalized M-estimators, and selection consistency of the proposed BIC criterion are carefully established. Simulation and analysis of TCGA breast cancer data demonstrate that the proposed approach is promising to efficiently identify underlying subgroups in high-dimensional data.

Broadband and high-efficiency photonic spin-Hall effect with all-metallic metasurfaces.

In this paper, all-metallic reflective metasurfaces comprising S-shape streamline structures are proposed to achieve the photonic spin-Hall effect with average cross-polarization conversion efficiency exceeding ∼84% in the range of 8-14 µm. By comparing with all-metallic nanobricks, it is demonstrated that the electric field coupling could be enhanced by constructing a similar split ring resonator between adjacent unit elements to further improve its efficiency and bandwidth. As a proof of concept, the photonic spin Hall effect and spin-to-orbit angular momentum conversion could be observed by two metadevices with the maximum diffraction efficiency of ∼95.7%. Such an all-metallic configuration may provide a platform for various high-efficiency electromagnetic components, catenary optics, and practical applications.

Hierarchical metamaterials for laser-infrared-microwave compatible camouflage.

In this paper, a hierarchical metamaterial (HMM) is reported to achieve compatible camouflage for laser, thermal infrared detectors, and radar. The HMM consists of an all-metallic metasurface array (AMMA) integrated with a microwave absorber. The top AMMA plays two roles. First, the gradient metasurface can reduce the specular reflection at the laser wavelength of 1.06 µm to less than 5% by tailoring the wavefronts and redirecting the reflected energy to non-specular angles. Second, the AMMA acts as an infrared shielding and microwave transparent layer, ultralow surface emissivity (∼5%) in the infrared atmosphere window of 3-5 µm and 8-14 µm can be realized, and incident microwave can perfectly pass through the top AMMA and then be absorbed by the bottom microwave absorber. The absorption efficiency is over 90% in the broadband of 7-12.7 GHz up to incident angles of 40° for both TE and TM polarizations. These excellent performances indicate that our proposed HMM has promising applications in multispectral camouflage fields.

Calibration using constrained smoothing with applications to mass spectrometry data.

Linear regressions are commonly used to calibrate the signal measurements in proteomic analysis by mass spectrometry. However, with or without a monotone (e.g., log) transformation, data from such functional proteomic experiments are not necessarily linear or even monotone functions of protein (or peptide) concentration except over a very restricted range. A computationally efficient spline procedure improves upon linear regression. However, mass spectrometry data are not necessarily homoscedastic; more often the variation of measured concentrations increases disproportionately near the boundaries of the instruments measurement capability (dynamic range), that is, the upper and lower limits of quantitation. These calibration difficulties exist with other applications of mass spectrometry as well as with other broad-scale calibrations. Therefore the method proposed here uses a functional data approach to define the calibration curve and also the limits of quantitation under the two assumptions: (i) that the variance is a bounded, convex function of concentration; and (ii) that the calibration curve itself is monotone at least between the limits of quantitation, but not necessarily outside these limits. Within this paradigm, the limit of detection, where the signal is definitely present but not measurable with any accuracy, is also defined. An iterative approach draws on existing smoothing methods to account simultaneously for both restrictions and is shown to achieve the global optimal convergence rate under weak conditions. This approach can also be implemented when convexity is replaced by other (bounded) restrictions. Examples from Addona et al. (2009, Nature Biotechnology 27, 663-641) both motivate and illustrate the effectiveness of this functional data methodology when compared with the simpler linear regressions and spline techniques.

Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS).

Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation <0.15, peak width coefficient of variation <0.15, standard deviation of RT <0.15 min (9 s), and the RT drift <0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.

Variance component analysis of a multi-site study for the reproducibility of multiple reaction monitoring measurements of peptides in human plasma.

BACKGROUND: In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models. The factors of peptides and transitions are shown to be major contributors to the variance of the measurements considering heavy (isotopic) peptides alone. For the light ((12)C) peptides alone, in addition to these factors, the factor of study*peptide also contributes significantly to the variance of the measurements. Heterogeneous peptide component models as well as influence analysis identify the outlier peptides in the study, which are then excluded from the analysis. Using a log-log scale transformation and subtracting the heavy/isotopic peptide [internal standard] measurement from the peptide measurements (i.e., taking the logarithm of the peak area ratio in the original scale establishes that), the MRM measurements are overall consistent across laboratories following the same standard operating procedures, and the variance components related to sites, transitions and higher-order interaction terms involving sites have greatly reduced impact. Thus the heavy peptides have been effective in reducing apparent inter-site variability. In addition, the estimates of intercepts and slopes of the calibration curves are calculated for the sub-studies. CONCLUSIONS/SIGNIFICANCE: The MRM measurements are overall consistent across laboratories following the same standard operating procedures, and heavy peptides can be used as an effective internal standard for reducing apparent inter-site variability. Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research.

Wild bootstrap for quantile regression.

The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on median regression is carried out to compare various bootstrap methods. With a simple finite-sample correction, the wild bootstrap is shown to account for general forms of heteroscedasticity in a regression model with fixed design points.

Inference on Low-Rank Data Matrices with Applications to Microarray Data.

Probe-level microarray data are usually stored in matrices, where the row and column correspond to array and probe, respectively. Scientists routinely summarize each array by a single index as the expression level of each probe-set (gene). We examine the adequacy of a uni-dimensional summary for characterizing the data matrix of each probe-set. To do so, we propose a low-rank matrix model for the probe-level intensities, and develop a useful framework for testing the adequacy of uni-dimensionality against targeted alternatives. This is an interesting statistical problem where inference has to be made based on one data matrix whose entries are not i.i.d. We analyze the asymptotic properties of the proposed test statistics, and use Monte Carlo simulations to assess their small sample performance. Applications of the proposed tests to GeneChip data show that evidence against a uni-dimensional model is often indicative of practically relevant features of a probe-set.

Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data.

To determine cancer pathway activities in nine types of primary tumors and NCI60 cell lines, we applied an in silica approach by examining gene signatures reflective of consequent pathway activation using gene expression data. Supervised learning approaches predicted that the Ras pathway is active in approximately 70% of lung adenocarcinomas but inactive in most squamous cell carcinomas, pulmonary carcinoids, and small cell lung carcinomas. In contrast, the TGF-beta, TNF-alpha, Src, Myc, E2F3, and beta-catenin pathways are inactive in lung adenocarcinomas. We predicted an active Ras, Myc, Src, and/or E2F3 pathway in significant percentages of breast cancer, colorectal carcinoma, and gliomas. Our results also suggest that Ras may be the most prevailing oncogenic pathway. Additionally, many NCI60 cell lines exhibited a gene signature indicative of an active Ras, Myc, and/or Src, but not E2F3, beta-catenin, TNF-alpha, or TGF-beta pathway. To our knowledge, this is the first comprehensive survey of cancer pathway activities in nine major tumor types and the most widely used NCI60 cell lines. The "gene expression pathway signatures" we have defined could facilitate the understanding of molecular mechanisms in cancer development and provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screening.

DGEM--a microarray gene expression database for primary human disease tissues.

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.