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.

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.

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.

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.

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.

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.

Photonic-circuited resonance fluorescence of single molecules with an ultrastable lifetime-limited transition.

Resonance fluorescence as the emission of a resonantly-excited two-level quantum system promises indistinguishable single photons and coherent high-fidelity quantum-state manipulation of the matter qubit, which underpin many quantum information processing protocols. Real applications of the protocols demand high degrees of scalability and stability of the experimental platform, and thus favor quantum systems integrated on one chip. However, the on-chip solution confronts several formidable challenges compromising the scalability prospect, such as the randomness, spectral wandering and scattering background of the integrated quantum systems near heterogeneous and nanofabricated material interfaces. Here we report an organic-inorganic hybrid integrated quantum photonic platform that circuits background-free resonance fluorescence of single molecules with an ultrastable lifetime-limited transition. Our platform allows a collective alignment of the dipole orientations of many isolated molecules with the photonic waveguide. We demonstrate on-chip generation, beam splitting and routing of resonance-fluorescence single photons with a signal-to-background ratio over 3000 in the waveguide at the weak excitation limit. Crucially, we show the photonic-circuited single molecules possess a lifetime-limited-linewidth transition and exhibit inhomogeneous spectral broadenings of only about 5% over hours' measurements. These findings and the versatility of our platform pave the way for scalable quantum photonic networks.

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.

Tunable Performance of Quantum Dot-MoS2 Hybrid Photodetectors via Interface Engineering.

Heterostructures of quantum dots (QDs) and two-dimensional (2D) materials show promising potential for photodetection applications owing to their combination of high optical absorption and good in-plane carrier mobility. In this work, the performance of QD-2D photodetectors is tuned by band engineering. Devices are fabricated by coating MoS2 nanosheets with InP QDs, type-I core-shell InP/ZnS QDs, and type-II core-shell InP/CdS QDs. Comparative spectroscopic and photoelectric studies of different hybrids show that the energy band alignment and shell thickness can influence the efficiency of charge transfer (CT), energy transfer (ET), and defect-related processes between QDs and MoS2. Benefiting from efficient CT between the QDs and MoS2, a significant enhancement of responsivity and detectivity is observed in thick-shell InP/CdS QD-MoS2 devices. Our results demonstrate the feasibility of using core-shell QDs for regulating the ET and CT efficiency in heterostructures and highlight the importance of interface band design in QD-2D and other low-dimensional photodetectors.

High Glucose Activated Cardiac Fibroblasts by a Disruption of Mitochondria-Associated Membranes.

Cardiac fibrosis is evident even in the situation without a significant cardiomyocyte loss in diabetic cardiomyopathy and a high glucose (HG) level independently activates the cardiac fibroblasts (CFs) and promotes cell proliferation. Mitochondrial respiration and glycolysis, which are key for cell proliferation and the mitochondria-associated membranes (MAMs), are critically involved in this process. However, the roles and the underlying mechanism of MAMs in the proliferation of HG-induced CFs are largely unknown. The proliferation and apoptosis of CFs responding to HG treatment were evaluated. The MAMs were quantified, and the mitochondrial respiration and cellular glycolytic levels were determined using the Seahorse XF analyzer. The changes of signal transducer and activator of transcription 3 (STAT3) and mitofusin-2 (MFN2) in responding to HG were also determined, the effects of which on cell proliferation, MAMs, and mitochondrial respiration were assessed. The effects of STAT3 on MFN2 transcription was determined by the dual-luciferase reporter assay (DLRA) and chromatin immunoprecipitation (CHIP). HG-induced CFs proliferation increased the glycolytic levels and adenosine triphosphate (ATP) production, while mitochondrial respiration was inhibited. The MAMs and MFN2 expressions were significantly reduced on the HG treatment, and the restoration of MFN2 expression counteracted the effects of HG on cell proliferation, mitochondrial respiration of the MAMs, glycolytic levels, and ATP production. The mitochondrial STAT3 contents were not changed by HG, but the levels of phosphorylated STAT3 and nuclear STAT3 were increased. The inhibition of STAT3 reversed the reduction of MFN2 levels induced by HG. The DLRA and CHIP directly demonstrated the negative regulation of MFN2 by STAT3 at the transcription levels via interacting with the sequences in the MFN2 promoter region locating at about -400 bp counting from the start site of transcription. The present study demonstrated that the HG independently induced CFs proliferation via promoting STAT3 translocation to the nucleus, which switched the mitochondrial respiration to glycolysis to produce ATP by inhibiting MAMs in an MFN2-depression manner.

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.

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.

On the emission pattern of nanoscopic emitters in planar anisotropic matrix and nanoantenna structures.

Single nanoscopic emitters embedded in the crystalline matrix have become a valuable resource for emerging nanophotonics and quantum technologies. The generally anisotropic nature of the matrix strongly affects the emission properties of the quantum emitters, in particular, when the matrix is assembled in nanophotonic structures. We report on rigorous analysis and engineering of spontaneous emission from single emitters coupled to nanoantenna and planar anisotropic antenna structures. By developing a convenient theoretical method with efficient numerical implementation, we show that accurate modeling of the anisotropy is essential in predicting the emission pattern for many important systems, such as single molecules in the solid-state matrix, isolated defects in 2D materials and so on. In particular, we illustrate the amplified effects of material anisotropy and geometrical anisotropy for emitters coupled to planar antenna and nanoantenna structures. We show that with an appropriate design of the anisotropy, a strong enhancement of the emission rate and a nearly collimated beam from single emitters can be simultaneously achieved.

Learnable despeckling framework for optical coherence tomography images.

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

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.

Orientation Preferences and Motion Sickness Induced in a Virtual Reality Environment.

BACKGROUND: Astronauts' orientation preferences tend to correlate with their susceptibility to space motion sickness (SMS). Orientation preferences appear universally, since variable sensory cue priorities are used between individuals. However, SMS susceptibility changes after proper training, while orientation preferences seem to be intrinsic proclivities. The present study was conducted to investigate whether orientation preferences change if susceptibility is reduced after repeated exposure to a virtual reality (VR) stimulus environment that induces SMS. METHODS: A horizontal supine posture was chosen to create a sensory context similar to weightlessness, and two VR devices were used to produce a highly immersive virtual scene. Subjects were randomly allocated to an experimental group (trained through exposure to a provocative rotating virtual scene) and a control group (untrained). All subjects' orientation preferences were measured twice with the same interval, but the experimental group was trained three times during the interval, while the control group was not. RESULTS: Trained subjects were less susceptible to SMS, with symptom scores reduced by 40%. Compared with untrained subjects, trained subjects' orientation preferences were significantly different between pre- and posttraining assessments. Trained subjects depended less on visual cues, whereas few subjects demonstrated the opposite tendency. CONCLUSION: Results suggest that visual information may be inefficient and unreliable for body orientation and stabilization in a rotating visual scene, while reprioritizing preferences for different sensory cues was dynamic and asymmetric between individuals. The present findings should facilitate customization of efficient and proper training for astronauts with different sensory prioritization preferences and dynamic characteristics.Chen W, Chao J-G, Zhang Y, Wang J-K, Chen X-W, Tan C. Orientation preferences and motion sickness induced in a virtual reality environment. Aerosp Med Hum Perform. 2017; 88(10):903-910.

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.