Mechanistic Insights into the Folding Mechanism of Region V in Ice-Binding Protein Secreted by Marinomonas primoryensis Revealed by Single-Molecule Force Spectroscopy.

The Gram-negative bacteria Marinomonas primoryensis secrete an ice-binding protein (MpIBP), which is a vital bacterial adhesin facilitating the adaptation and survival of the bacteria in the harsh Antarctic environment. The C-terminal region of MpIBP, known as region V (RV), is the first domain to be exported into the Ca2+-rich extracellular environment and acts as a folding nucleus for the entire adhesin. However, the mechanisms underlying the secretion and folding of RV remain poorly understood. Here, we used optical tweezers (OT) to investigate the secretion and folding mechanisms of RV at the single-molecule level. In the absence of Ca2+, apo-RV remains unstructured, while Ca2+-bound RV folds into a mechanically stable structure. The folding of RV could occur via the formation of an intermediate state. Even though this folding intermediate is "hidden" during the folding process of wild type RV in vitro, it likely forms in vivo and plays an important role in facilitating protein secretion. Additionally, our results revealed that the N-terminal part of the RV can significantly stabilize its C-terminal structure. Our study paves the way for further investigations into the structure and functions of MpIBP that help bacteria survive in challenging environments.

Deep learning for precise axial localization of trapped microspheres in reflective optical systems.

High-precision axial localization measurement is an important part of micro-nanometer optical measurement, but there have been issues such as low calibration efficiency, poor accuracy, and cumbersome measurement, especially in reflected light illumination systems, where the lack of clarity of imaging details leads to the low accuracy of commonly used methods. Herein, we develop a trained residual neural network coupled with a convenient data acquisition strategy to address this challenge. Our method improves the axial localization precision of microspheres in both reflective illumination systems and transmission illumination systems. Using this new localization method, the reference position of the trapped microsphere can be extracted from the identification results, namely the "positioning point" among the experimental groups. This point relies on the unique signal characteristics of each sample measurement, eliminates systematic repeatability errors when performing identification across samples, and improves the localization precision of different samples. This method has been verified on both transmission and reflected illumination optical tweezers platforms. We will bring greater convenience to measurements in solution environments and will provide higher-order guarantees for force spectroscopy measurements in scenarios such as microsphere-based super-resolution microscopy and the surface mechanical properties of adherent flexible materials and cells.

Addressing the imaging limitations of a microsphere-assisted nanoscope.

In the past decade, microsphere-assisted nanoscopy has been developed rapidly to overcome the diffraction limit. However, due to the limited size and high surface curvature of microspheres, the magnified imaging still suffers from problems like limited view scope, imaging distortion, and low contrast. In this paper, we specialize in the imaging mechanism of microspheres and find irradiance as the key factor for microsphere imaging quality. Utilizing a modified optical tweezer system, we achieve precise manipulation of microspheres and further propose a high-quality large-field magnified imaging scheme. The results show that the imaging area of 5 µm microspheres can reach 16×12 µm2 with the minimum identifiable feature of 137 nm. This scheme provides a new solution for extending the measuring scope of microsphere-assisted nanoscope, and will certainly promote the application of this technology in practice.

Simulation and Experiment of the Trapping Trajectory for Janus Particles in Linearly Polarized Optical Traps.

The highly focused laser beam is capable of confining micro-sized particle in its focus. This is widely known as optical trapping. The Janus particle is composed of two hemispheres with different refractive indexes. In a linearly polarized optical trap, the Janus particle tends to align itself to an orientation where the interface of the two hemispheres is parallel to the laser propagation as well as the polarization direction. This enables a controllable approach that rotates the trapped particle with fine accuracy and could be used in partial measurement. However, due to the complexity of the interaction of the optical field and refractive index distribution, the trapping trajectory of the Janus particle in the linearly polarized optical trap is still uncovered. In this paper, we focus on the dynamic trapping process and the steady position and orientation of the Janus particle in the optical trap from both simulation and experimental aspects. The trapping process recorded by a high speed camera coincides with the simulation result calculated using the T-matrix model, which not only reveals the trapping trajectory, but also provides a practical simulation solution for more complicated structures and trapping motions.

Learning-based event locating for single-molecule force spectroscopy.

Acquiring events massively from single-molecule force spectroscopy (SMFS) experiments, which is crucial for revealing important biophysical information, is usually not straightforward. A significant amount of human labor is usually required to identify events in the measured force spectrum during measuring or before performing further data analysis. This prevents the experiment from being done in a fully-automated manner or scaling with the throughput of the measuring setup. In this work, we attempt to tackle this problem with a deep learning approach. A deep neural network model is developed to infer the occurrence of the events using the data stream from the measuring setup. We demonstrated that the proposed method could achieve high accuracy with force spectrums of a variety of samples from both optical tweezers and AFMs by learning from user-given samples instead of complicated manual algorithm designing or parameter tuning. Furthermore, we found that the trained model can be used to perform event detection on datasets measured from a different optical tweezer setup, showing the potential of being leveraged in more complex deep learning schemes.

A self-sustainable wearable multi-modular E-textile bioenergy microgrid system.

Despite the fast development of various energy harvesting and storage devices, their judicious integration into efficient, autonomous, and sustainable wearable systems has not been widely explored. Here, we introduce the concept and design principles of e-textile microgrids by demonstrating a multi-module bioenergy microgrid system. Unlike earlier hybrid wearable systems, the presented e-textile microgrid relies solely on human activity to work synergistically, harvesting biochemical and biomechanical energy using sweat-based biofuel cells and triboelectric generators, and regulating the harvested energy via supercapacitors for high-power output. Through energy budgeting, the e-textile system can efficiently power liquid crystal displays continuously or a sweat sensor-electrochromic display system in pulsed sessions, with half the booting time and triple the runtime in a 10-min exercise session. Implementing "compatible form factors, commensurate performance, and complementary functionality" design principles, the flexible, textile-based bioenergy microgrid offers attractive prospects for the design and operation of efficient, sustainable, and autonomous wearable systems.

Fractal superconducting nanowire single-photon detectors with reduced polarization sensitivity.

We demonstrate superconducting nanowire single-photon detectors (SNSPDs) based on a fractal design of the nanowires to reduce the polarization sensitivity of detection efficiency. We patterned niobium titanium nitride thin films into Peano curves with a linewidth of 100 nm and integrated the nanowires with optical microcavities to enhance their optical absorption. At a base temperature of 2.6 K, the fractal SNSPD exhibited a polarization-maximum device efficiency of 67% and a polarization-minimum device efficiency of 61% at a wavelength of 1550 nm. Therefore, the polarization sensitivity, defined as their ratio, was 1.1, lower than the polarization sensitivity of the SNSPDs in the meander design. The reduced polarization sensitivity of the detector could be maintained for higher-order spatial modes in multimode optical fibers and could tolerate misalignment between the optical mode and the detector. This fractal design is applicable to both amorphous and polycrystalline materials that are commonly used for making SNSPDs.

Cellphone-Enabled Microwell-Based Microbead Aggregation Assay for Portable Biomarker Detection.

Quantitative biomarker detection methods featured with rapidity, high accuracy, and label-free are demonstrated for the development of point-of-care (POC) technologies or "beside" diagnostics. Microbead aggregation via protein-specific linkage provides an effective approach for selective capture of biomarkers from the samples, and can directly readout the presence and amount of the targets. However, sensors or microfluidic analyzers that can accurately quantify the microbead aggregation are scared. In this work, we demonstrate a microwell-based microbeads analyzing system, by which online manipulations of microbeads including trapping, arraying, and rotations can be realized, providing a series of microfluidic approaches to layout the aggregated microbeads for further convenient characterizations. Prostate specific antigen is detected using the proposed system, demonstrating the limit of detection as low as 0.125 ng/mL (3.67 pM). A two-step reaction kinetics model is proposed for the first time to explain the dynamic process of microbeads aggregation. The developed microbeads aggregation analysis system has the advantages of label-free detection, high throughput, and low cost, showing great potential for portable biomarker detection.