Bright Nonblinking Photoluminescence with Blinking Lifetime from a Nanocavity-Coupled Quantum Dot.

Colloidal quantum dots (QDs) are excellent luminescent nanomaterials for many optoelectronic applications. However, photoluminescence blinking has limited their practical use. Coupling QDs to plasmonic nanostructures shows potential in suppressing blinking. However, the underlying mechanism remains unclear and debated, hampering the development of bright nonblinking dots. Here, by deterministically coupling a QD to a plasmonic nanocavity, we clarify the mechanism and demonstrate unprecedented single-QD brightness. In particular, we report for the first time that a blinking QD could obtain nonblinking photoluminescence with a blinking lifetime through coupling to the nanocavity. We show that the plasmon-enhanced radiative decay outcompetes the nonradiative Auger process, enabling similar quantum yields for charged and neutral excitons in the same dot. Meanwhile, we demonstrate a record photon detection rate of 17 MHz from a colloidal QD, indicating an experimental photon generation rate of more than 500 MHz. These findings pave the way for ultrabright nonblinking QDs, benefiting diverse QD-based applications.

Defocus-integration interferometric scattering microscopy for speckle suppression and enhancing nanoparticle detection on a substrate.

Direct optical detection and imaging of single nanoparticles on a substrate in wide field underpin vast applications across different research fields. However, speckles originating from the unavoidable random surface undulations of the substrate ultimately limit the size of the decipherable nanoparticles by the current optical techniques, including the ultrasensitive interferometric scattering microscopy (iSCAT). Here, we report a defocus-integration iSCAT to suppress the speckle noise and to enhance the detection and imaging of single nanoparticles on an ultra-flat glass substrate and a silicon wafer. In particular, we discover distinct symmetry properties of the scattering phase between the nanoparticle and the surface undulations that cause the speckles. Consequently, we develop the defocus-integration technique to suppress the speckles. We experimentally achieve an enhancement of the signal-to-noise ratio by 6.9 dB for the nanoparticle detection. We demonstrate that the technique is generally applicable for nanoparticles of various materials and for both low and high refractive index substrates.

Tunable resonance Raman scattering of quantum dots in a nonlinear excitation regime.

Controllable tuning of electron-phonon coupling strength and excited state dynamics is important for the understanding of resonance Raman scattering in low-dimensional semiconductors. Here, we report a significant and reversible field-induced modulation in absolute resonance Raman intensity of quantum dots using ionic liquid gating. Meanwhile, a potential-dependent nonlinear relationship is present between Raman intensity and excitation power density. By exploring the parameter space within a time domain model, we find that the Raman intensity variation is mainly determined by the homogeneous linewidth. We further propose that the Fermi level positions and exciton species play key roles in the excited state decay rates.

Direct Electro Plasmonic and Optic Modulation via a Nanoscopic Electron Reservoir.

Direct electrical tuning of localized plasmons at optical frequencies boasts the fascinating prospects of being ultrafast and energy efficient and having an ultrasmall footprint. However, the prospects are obscured by the grand challenge of effectively modulating the very large number of conduction electrons in three-dimensional metallic structures. Here we propose the concept of nanoscopic electron reservoir (NER) for direct electro plasmonic and electro-optic modulation. A NER is a few-to-ten-nanometer size metal feature on a metal host and supports a localized plasmon mode. We provide a general guideline to construct highly electrically susceptible NERs and theoretically demonstrate pronounced direct electrical tuning of the plasmon mode by exploiting the nonclassical effects of conduction electrons. Moreover, we show the electro-plasmonic tuning can be efficiently translated into modulation of optical scattering by utilizing the antenna effect of the metal host for the NER. Our work extends the landscape of electro plasmonic modulation and opens appealing new opportunities for quantum plasmonics.

Optical Fingerprint of Flat Substrate Surface and Marker-Free Lateral Displacement Detection with Angstrom-Level Precision.

We report that flat substrates such as glass coverslips with surface roughness well below 0.5 nm feature notable speckle patterns when observed with high-sensitivity interference microscopy. We uncover that these speckle patterns unambiguously originate from the subnanometer surface undulations, and develop an intuitive model to illustrate how subnanometer nonresonant dielectric features could generate pronounced interference contrast in the far field. We introduce the concept of optical fingerprint for the deterministic speckle pattern associated with a particular substrate surface area and intentionally enhance the speckle amplitudes for potential applications. We demonstrate such optical fingerprints can be leveraged for reproducible position identification and marker-free lateral displacement detection with an experimental precision of 0.22 nm. The reproducible position identification allows us to detect new nanoscopic features developed during laborious processes performed outside of the microscope. The demonstrated capability for ultrasensitive displacement detection may find applications in the semiconductor industry and superresolution optical microscopy.

General Framework of Canonical Quasinormal Mode Analysis for Extreme Nano-optics.

Optical phenomena associated with an extremely localized field should be understood with considerations of nonlocal and quantum effects, which pose a hurdle to conceptualize the physics with a picture of eigenmodes. Here we first propose a generalized Lorentz model to describe general nonlocal media under linear mean-field approximation and formulate source-free Maxwell's equations as a linear eigenvalue problem to define the quasinormal modes. Then we introduce an orthonormalization scheme for the modes and establish a canonical quasinormal mode framework for general nonlocal media. Explicit formalisms for metals described by a quantum hydrodynamic model and polar dielectrics with nonlocal response are exemplified. The framework enables for the first time a direct modal analysis of mode transition in the quantum tunneling regime and provides physical insights beyond usual far-field spectroscopic analysis. Applied to nonlocal polar dielectrics, the framework also unveils the important roles of longitudinal phonon polaritons in optical response.

Bright Optical Eigenmode of 1 nm

We report on the discovery and rationale to devise bright single optical eigenmodes that feature quantum-optical mode volumes of about 1 nm^{3}. Our findings rely on the development and application of a quasinormal mode theory that self-consistently treats fields and electron nonlocality, spill-out, and Landau damping around atomistic protrusions on a metallic nanoantenna. By outpacing Landau damping with radiation via properly designed antenna modes, the extremely localized modes become bright with radiation efficiencies reaching 30% and could provide up to 4×10^{7} times intensity enhancement.

Deciphering Charging Status, Absolute Quantum Efficiency, and Absorption Cross Section of Multicarrier States in Single Colloidal Quantum Dots.

Upon photo- or electrical-excitation, colloidal quantum dots (QDs) are often found in multicarrier states due to multiphoton absorption, photocharging, or imbalanced carrier injection of the QDs. While many of these multicarrier states are observed in single-dot spectroscopy, their properties are not well studied due to random charging/discharging, emission intensity intermittency, and uncontrolled surface defects of single QDs. Here we report in situ deciphering of the charging status, precisely assessing the absorption cross section, and determining the absolute emission quantum yield of monoexciton and biexciton states for neutral, positively charged, and negatively charged single core/shell CdSe/CdS QDs. We uncover very different photon statistics of the three charge states in single QDs and unambiguously identify their charge signs together with the information on their photoluminescence decay dynamics. We then show their distinct photoluminescence saturation behaviors and evaluate the absolute values of absorption cross sections and quantum efficiencies of monoexcitons and biexcitons. We demonstrate that the addition of an extra hole or electron in a QD not only changes its emission properties but also varies its absorption cross section.

Perfectly matched layers for nonlocal media with hydrodynamic-Drude description: a transformation optics approach.

We develop a transformation optics theory for the nonlocal media in the hydrodynamic Drude model by generalizing the free-electron current density equation to a transformation invariant form. Applying the transformation optics theory, perfectly matched layers (PMLs) for the nonlocal media are theoretically formulated and implemented in frequency domain with finite element method. The nonlocal PMLs are shown to absorb outgoing surface and volume plasmons without inducing unphysical reflections. The effectiveness of the nonlocal PMLs is quantitatively demonstrated by the behaviors that the numerical errors continuously approach zero with increasing linear mesh density.

Guided surface-volume plasmon modes in an ultrathin film at Drude damping limit.

We theoretically demonstrate the existence of surface-volume plasmon modes in an ultrathin film at Drude damping limit and the direct optical excitation of the modes. Unlike volume plasmons of longitudinal nature and confinement in the film, the surface-volume plasmon modes have considerable transverse components localized along the surface of the thin film. The transverse component provides an interface with electromagnetic radiation, which allows excitation by localized optical sources. The surface-volume plasmon modes have extreme field confinement and can propagate around sharp corners with little distortion and loss.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions.

Fabrication and characterization of plasmonic nanocone antennas for strong spontaneous emission enhancement.

Plasmonic antennas are attractive nanostructures for a large variety of studies ranging from fundamental aspects of light-matter interactions at the nanoscale to industry-relevant applications such as ultrasensitive sensing, enhanced absorption in solar cells or solar fuel generation. A particularly interesting feature of these antennas is that they can enhance the fluorescence properties of emitters. Theoretical calculations have shown that nanocone antennas provide ideal results, but a high degree of manufacturing precision and control is needed to reach optimal performance. In this study, we report on the fabrication of nanocones with base diameters and heights in the range of 100 nm with variable aspect ratios using focused ion beam milling of sputtered nano-crystalline gold layers. The controlled fabrication process allows us to obtain cones with tailored plasmon resonances. The measured plasmon spectra show very good agreement with finite-difference time-domain calculations. Theoretical investigations predict that these nanocones can enhance the spontaneous emission rate of a quantum emitter by several hundred times while keeping its quantum efficiency above 60%.

Integrative analysis of independent transcriptome data for rare diseases.

High-throughput technologies used to interrogate transcriptomes have been generating a great amount of publicly available gene expression data. For rare diseases that lack of clinical samples and research funding, there is a practical benefit to jointly analyze existing data sets commonly related to a specific rare disease. In this study, we collected a number of independently generated transcriptome data sets from four species: human, fly, mouse and worm. All data sets included samples with both normal and abnormal mitochondrial function. We reprocessed each data set to standardize format, scale and gene annotation and used HomoloGene database to map genes between species. Standardized procedure was also applied to compare gene expression profiles of normal and abnormal mitochondrial function to identify differentially expressed genes. We further used meta-analysis and other integrative analyses to recognize patterns across data sets and species. Novel insights related to mitochondrial dysfunction was revealed via these analyses, such as a group of genes consistently dysregulated by impaired mitochondrial function in multiple species. This study created a template for the study of rare diseases using genomic technologies and advanced statistical methods. All data and results generated by this study are freely available and stored at http://goo.gl/nOGWC2, to support further data mining.

Design, synthesis, and antibacterial activity against rice bacterial leaf blight and leaf streak of 2,5-substituted-1,3,4-oxadiazole/thiadiazole sulfone derivative.

A series of 2,5-substituted-1,3,4-oxadiazole/thiadiazole sulfone derivatives were synthesized and evaluated for their antibacterial activities against rice bacterial leaf blight and leaf streak caused by Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicolaby via the turbidimeter test in vitro. Antibacterial bioassay results indicated that most compounds demonstrated good inhibitory effect antibacterial bioactivities against rice bacterial leaf blight and leaf streak. Among the title compounds, compound 6c demonstrated the best inhibitory effect against rice bacterial leaf blight and leaf streak with half-maximal effective concentration (EC50) values of 1.07 and 7.14 µg/mL, respectively, which were even better than those of commercial agents such as Bismerthiazol and Thiediazole Copper. In vivo antibacterial activities tests at greenhouse conditions demonstrated that the controlling effect of compounds 6c (43.5%) and 6g (42.4%) against rice bacterial leaf blight were better than those of Bismerthiazol (25.5%) and Thiediazole Copper (37.5%).

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

BACKGROUND: Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. METHODS: We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. RESULTS: A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified. CONCLUSIONS: The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods: Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings: The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation: The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding: This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.

Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction.

Most proteins execute their functions through interacting with other proteins. Thus, understanding protein-protein interactions (PPIs) is essential to decipher biological functions in a living cell. To predict large-scale PPIs, effective and efficient computational approaches are desirable to integrate heterogeneous data sources provided by advanced technologies. In this paper, we extend our previous work on a Bayesian classifier for human PPI predictions from model organisms, by introducing a tree-augmented naïve Bayes (TAN) classifier. TAN maintains the simplicity and robustness of a naïve Bayes classifier while allows for the dependence among variables. Our empirical results show that by integrating features extracted from microarray expression measurements, Gene Ontology values, and orthologous scores, TAN achieves higher classification accuracy than the manually constructed Bayesian network classifier and naïve Bayes. For human PPI prediction, TAN obtains 88% sensitivity while keeping a reasonable 70% specificity on testing samples.

Coherent interaction of light with a metallic structure coupled to a single quantum emitter: from superabsorption to cloaking.

We provide a general theoretical platform based on quantized radiation in absorptive and inhomogeneous media for investigating the coherent interaction of light with material structures in the immediate vicinity of quantum emitters. In the case of a very small metallic cluster, we demonstrate extreme regimes where a single emitter can either counteract or enhance particle absorption by 3 orders of magnitude. For larger structures, we show that an emitter can eliminate both scattering and absorption and cloak a plasmonic antenna. We provide physical interpretations of our results and discuss their applications in active metamaterials and quantum plasmonics.

KUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions.

KUPS (The University of Kansas Proteomics Service) provides high-quality protein-protein interaction (PPI) data for researchers developing and evaluating computational models for predicting PPIs by allowing users to construct ready-to-use data sets of interacting protein pairs (IPPs), non-interacting protein pairs (NIPs) and associated features. Multiple filters and options allow the user to control the make-up of the IPPs and NIPs as well as the quality of the resultant data sets. Each data set is built from the overall database, which includes 185 446 IPPs and ∼1.5 billion NIPs from five primary databases: IntAct, HPRD, MINT, UniProt and the Gene Ontology. The IPP set can be set to specific model organisms, interaction types and experimental evidence. The NIP set can be generated using four different strategies, which can alleviate biased estimation problems. Lastly, multiple features can be provided for all of the IPP and NIP pairs. Additionally, KUPS provides two benchmark data sets to help researchers compare their algorithms to existing approaches. KUPS is freely available at http://www.ittc.ku.edu/chenlab.

Effects of long-term no-tillage on the functional potential of microorganisms involved in the nitrogen, phosphorus and sulfur cycles of black soil.

Understanding the effects of different tillage practices on functional microbial abundance and composition in nitrogen (N), phosphorus (P) and sulfur (S) cycles are essential for the sustainable utilization of black soils. Based on an 8-year field experiment located in Changchun, Jilin Province, we analyzed the abundance and composition of N, P and S cycling microorganisms and their driving factors in different depths of black soil under no til-lage (NT) and conventional tillage (CT). Results showed that compared with CT, NT significantly increased soil water content (WC) and microbial biomass carbon (MBC) at soil depth of 0-20 cm. Compared with CT, NT significantly increased the abundances of functional and encoding genes related to N, P and S cycling, including the nosZ gene encoding N2O reductase, the ureC gene performing organic nitrogen ammoniation, the nifH gene encoding nitrogenase ferritin, the functional genes phnK and phoD driving organic phosphorus mineralization, the encoding pyrroloquinoline quinone synthase ppqC gene and the encoding exopolyphosphate esterase ppX gene, and the soxY and yedZ genes driving sulfur oxidation. The results of variation partitioning analysis and redundancy analysis showed that soil basic properties were the main factors affecting the microbial composition of N, P and S cycle functions (the total interpretation rate was 28.1%), and that MBC and WC were the most important drivers of the functional potential of soil microorganisms in N, P and S cycling. Overall, long-term no tillage could increase the abundance of functional genes of soil microorganisms by affecting soil environment. From the perspective of molecular biology, our results elucidated that no tillage could be used as an effective soil management measure to improve soil health and maintain green agricultural development.