Cholecystokinin-B Receptor-Targeted Nanoparticle for Imaging and Detection of Precancerous Lesions in the Pancreas

Survival from pancreatic cancer remains extremely poor, in part because this malignancy is not diagnosed in the early stages, and precancerous pancreatic intraepithelial neoplasia (PanIN) lesions are not seen on routine radiographic imaging. Since the cholecystokinin-B receptor (CCK-BR) becomes over-expressed in PanIN lesions, it may serve as a target for early detection. We developed a biodegradable fluorescent polyplex nanoparticle (NP) that selectively targets the CCK-BR. The NP was complexed to a fluorescent oligonucleotide with Alexa Fluor 647 for far-red imaging and to an oligonucleotide conjugated to Alexa Fluor 488 for localization by immunohistochemistry. Fluorescence was detected over the pancreas of five- to ten-month-old LSL-KrasG12D/+; P48-Cre (KC) mice only after the injection of the receptor target-specific NP and not after injection of untargeted NP. Ex vivo tissue imaging and selective immunohistochemistry confirmed particle localization only to PanIN lesions in the pancreas and not in other organs, supporting the tissue specificity. A human pancreas tissue microarray demonstrated immunoreactivity for the CCK-BR only in the PanIN lesions and not in normal pancreas tissue. The long-term goal would be to develop this imaging tool for screening human subjects at high risk for pancreatic cancer to enable early cancer detection.

Particle localization using local gradients and its application to nanometer stabilization of a microscope

Accurate localization of single particles plays an increasingly important role in a range of biological techniques, including single molecule tracking and localization-based superresolution microscopy. Such techniques require fast and accurate particle localization algorithms as well as nanometer-scale stability of the microscope. Here, we present a universal method for three-dimensional localization of single labeled and unlabeled particles based on local gradient calculation of microscopy images. The method outperforms current techniques in high noise conditions, and it is capable of nanometer accuracy localization of nano- and micro-particles with sub-ms calculation time. By localizing a fixed particle as fiducial mark and running a feedback loop, we demonstrate its applicability for active drift correction in sensitive nanomechanical measurements such as optical trapping and superresolution imaging. A multiplatform open software package comprising a set of tools for local gradient calculation in brightfield and fluorescence microscopy is shared to the scientific community.

Acoustofluidic localization of sparse particles on a piezoelectric resonant sensor for nanogram-scale mass measurements

The ability to weigh microsubstances present in low concentrations is an important tool for environmental monitoring and chemical analysis. For instance, developing a rapid analysis platform that identifies the material type of microplastics in seawater would help evaluate the potential toxicity to marine organisms. In this study, we demonstrate the integration of two different techniques that bring together the functions of sparse particle localization and miniaturized mass sensing on a microelectromechanical system (MEMS) chip for enhanced detection and minimization of negative measurements. The droplet sample for analysis is loaded onto the MEMS chip containing a resonant mass sensor. Through the coupling of a surface acoustic wave (SAW) from a SAW transducer into the chip, the initially dispersed microparticles in the droplet are localized over the detection area of the MEMS sensor, which is only 200 µm wide. The accreted mass of the particles is then calibrated against the resulting shift in resonant frequency of the sensor. The SAW device and MEMS chip are detachable after use, allowing the reuse of the SAW device part of the setup instead of the disposal of both parts. Our platform maintains the strengths of noncontact and label-free dual-chip acoustofluidic devices, demonstrating for the first time an integrated microparticle manipulation and real-time mass measurement platform useful for the analysis of sparse microsubstances.

Using differential phase for 3D localization of tracer particles in digital inline holographic microscopy PIV/PTV (DIHM-PIV/PTV)

Digital inline holographic microscopy PIV/PTV (DIHM-PIV/PTV) has the ability to provide 4-dimensional (4D), i.e. time-resolved, 3-component 3-dimensional (3C-3D) flow measurement with high spatial and temporal resolution, compact optical setup and minimal calibration Sun et al. (2020) compared to most other volumetric techniques such as tomo-PIV, defocusing PIV, etc. Despite all these advantages DIHMPIV/PTV has not yet developed into a standard laboratory tool due to some major limitations such as the extended depth-of-focus (DOF) problem and the virtual image effect which cause artefacts in the standard reconstruction volume limiting the seeding concentration and thus the achievable velocity spatial resolution. In order to mitigate the above-mentioned limitations we present a novel particle localization and extraction methodology which allows the minimization of these artefacts from the standard reconstruction and perform PIV/PTV analysis on the particle volume fields only. The proposed algorithm is based on the differential phase, which is the axial phase shift of the object wave compared to the reference plane wave propagation.

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface

Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-level areas represent a challenge for mobile robot navigation due to the sudden change in reference sensors as visual, inertial, or laser scan instruments. Using multiple cooperative robots is advantageous for mapping and localization since they permit rapid exploration of the environment and provide higher redundancy than using a single robot. This study proposes a multi-robot localization using two robots (leader and follower) to perform a fast and robust environment exploration on multi-level areas. The leader robot is equipped with a 3D LIDAR for 2.5D mapping and a Kinect camera for RGB image acquisition. Using 3D LIDAR, the leader robot obtains information for particle localization, with particles sampled from the walls and obstacle tangents. We employ a convolutional neural network on the RGB images for multi-level area detection. Once the leader robot detects a multi-level area, it generates a path and sends a notification to the follower robot to go into the detected location. The follower robot utilizes a 2D LIDAR to explore the boundaries of the even areas and generate a 2D map using an extension of the iterative closest point. The 2D map is utilized as a re-localization resource in case of failure of the leader robot.

Hierarchy of many-body invariants and quantized magnetization in anomalous Floquet insulators

We uncover a new family of few-body topological phases in periodically driven fermionic systems in two dimensions. These phases, which we term correlation-induced anomalous Floquet insulators (CIAFIs), are characterized by quantized contributions to the bulk magnetization from multi-particle correlations, and are classified by a family of integer-valued topological invariants. The CIAFI phases do not require many-body localization, but arise in the generic situation of k-particle localization, where the system is localized (due to disorder) for any finite number of particles up to a maximum number, k. We moreover show that, when fully many-body localized, periodically driven systems of interacting fermions in two dimensions are characterized by a quantized magnetization in the bulk, thus confirming the quantization of magnetization of the anomalous Floquet insulator. We demonstrate our results with numerical simulations.

Information-Efficient, Off-Center Sampling Results in Improved Precision in 3D Single-Particle Tracking Microscopy

In this work, we present a 3D single-particle tracking system that can apply tailored sampling patterns to selectively extract photons that yield the most information for particle localization. We demonstrate that off-center sampling at locations predicted by Fisher information utilizes photons most efficiently. When performing localization in a single dimension, optimized off-center sampling patterns gave doubled precision compared to uniform sampling. A ~20% increase in precision compared to uniform sampling can be achieved when a similar off-center pattern is used in 3D localization. Here, we systematically investigated the photon efficiency of different emission patterns in a diffraction-limited system and achieved higher precision than uniform sampling. The ability to maximize information from the limited number of photons demonstrated here is critical for particle tracking applications in biological samples, where photons may be limited.

Information-Efficient, Off-Center Sampling Results in Improved Precision in 3D Single Particle Tracking Microscopy

In this work, we present a 3D single-particle tracking system that can apply tailored sampling patterns to selectively extract photons that yield the most information for particle localization. We demonstrate that off-center sampling at locations predicted by Fisher information utilizes photons most efficiently. When performing localization in a single dimension, optimized off-center sampling patterns gave doubled precision compared to uniform sampling. A ~20% increase in precision compared to uniform sampling can be achieved when a similar off-center pattern is used in 3D localization. This work is the first to thoroughly investigate the photon efficiency of different emission patterns in a diffraction-limited system and achieve higher precision than uniform sampling. The ability to maximize information from the limited number of photons demonstrated here is critical for particle tracking applications in biological samples, where photons may be limited.