Optical Reconstruction Research Articles

Three-dimensional computer holography with phase space tailoring

Computer holography is a prominent technique for reconstructing customized three-dimensional (3D) diffraction fields. However, the quality of optical reconstruction remains a fundamental challenge in 3D computer holography, especially for the 3D diffraction fields with physically continuous and extensive depth range. Here, we propose a 3D computer-generated hologram (CGH) optimization framework with phase space tailoring. Based on phase space analysis of the space and frequency properties in both lateral and axial directions, the intensity of the 3D diffraction field is adequately sampled across a large depth range. This sampling ensures the reconstructed intensity distribution to be comprehensively constrained with physical consistency. A physics-informed loss function is constructed based on the phase space tailoring to optimize the CGH with suppression of vortex stagnation. Numerical and optical experiments demonstrate the proposed method significantly enhances the 3D optical reconstructions with suppressed speckle noise across a continuous and extensive depth range.

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.

Unlocking Intracellular Protein Delivery by Harnessing Polymersomes Synthesized at Microliter Volumes using Photo-PISA.

Efficient delivery of therapeutic proteins and vaccine antigens to intracellular targets is challenging due to generally poor cell membrane permeation and endolysosomal entrapment causing degradation. Herein, these challenges are addressed by developing an oxygen-tolerant photoinitiated polymerization-induced self-assembly (Photo-PISA) process, allowing for the microliter-scale (10µL) synthesis of protein-loaded polymersomes directly in 1536-well plates. High-resolution techniques capable of analysis at a single particle level are employed to analyze protein encapsulation and release mechanisms. Using confocal microscopy and super-resolution stochastic optical reconstruction microscopy (STORM) imaging, their ability to deliver proteins into the cytosol following endosomal escape is subsequently visualized. Lastly, the adaptability of these polymersomes is exploited to encapsulate and deliver a prototype vaccine antigen, demonstrating its ability to activate antigen-presenting cells and support antigen cross-presentation for applications in subunit vaccines and cancer immunotherapy. This combination of ultralow volume synthesis and efficient intracellular delivery holds significant promise for unlocking the high throughput screening of a broad range of otherwise cost-prohibitive or early-stage therapeutic protein and vaccine antigen candidates that can be difficult to obtain in large quantities. The versatility of this platform for rapid screening of intracellular protein delivery can result in significant advancements across the fields of nanomedicine and biomedical engineering.

Super-resolution imaging reveals the role of DDR1 cluster in NSCLC proliferation

Discoidin domain receptor 1 (DDR1), a transmembrane protein, is crucial in tumor development. Prior studies have demonstrated a significant correlation between protein cluster distribution on the cell membrane and tumor evolution. However, the precise spatial distribution characteristics of DDR1 on cell membranes and their impact on tumor development remain unclear. In this study, we conducted gene expression analysis to investigate DDR1 expression in non-small cell lung cancer (NSCLC) and its association with patient prognosis. We also employed direct stochastic optical reconstruction microscopy (dSTORM) imaging to examine DDR1's spatial distribution in NSCLC cells and tissues. Our findings indicate that DDR1 forms larger, tighter, and more abundant clusters in cancer cells and tissues compared to their normal counterparts. Notably, we observed that the enhanced aggregation of DDR1 clusters increased the likelihood of interaction with SRC, thereby activating the SRC-STAT3 signaling pathway in NSCLC cells and promoting cell proliferation. This study provides novel insights into the role of DDR1 aggregation in tumor proliferation, confirms DDR1 as a potential tumor marker, and serves as a valuable resource for future drug development.

OGRN: optical-guided residual dense network for SAR image super-resolution reconstruction network

ABSTRACT Although Convolutional Neural Networks have significantly improved the development of SAR image super-resolution (SR) technology in recent years, it is a very challenging problem to reconstruct SAR image with large-scale factors, such as ×4 and ×8 due to limited available information from low-resolution image. The co-registered high-resolution optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics. Compared with single-frame SAR image SR reconstruction technology, optical image-guided SAR image SR reconstruction technology has better performance. This paper proposes an optical-guided residual dense network (OGRN) for SAR image super-resolution reconstruction network. The network is an end-to-end network. Its most important characteristic is that the SR model of SAR images can be obtained through optical image assistance during training, and high-quality SAR images can be generated without optical image assistance during testing. Firstly, a new dense residual backbone network that extracts high-frequency information is constructed. It can extract and propagate high-frequency features of SAR images efficiently to improve the feature representation ability of the network for high-frequency features. Then, the extracted high-frequency features of the SAR image are used to generate an optical image through the designed SAR-to-Optical image translation network. Finally, a reconstruction module is constructed based on the proposed fusion module for optical and SAR images. The fusion module fuses the high-frequency feature information of the extracted optical images with the SAR image features to provide features for SAR image SR reconstruction. Extensive experiments conducted on Sen1-2 and QXS datasets demonstrated that under the guidance of optical image, our OGRN can achieve excellent performance in both quantitative assessment metrics and visual quality.

Nanoscale organization of cardiac calcium channels is dependent on thyroid hormone status.

Thyroid hormone dysfunction is frequently observed in patients with chronic illnesses including heart failure which increases risk of adverse events. This study examined effects of thyroid hormones (TH) on cardiac T-tubule (TT) integrity, Ca2+ sparks, and nanoscale organization of ion channels in excitation-contraction (EC)-coupling, including L-type calcium channel (Cav1.2), ryanodine receptor-type 2 (RyR2), and junctophilin-2 (Jph2). TH deficiency was established in adult female rats by propyl-thiouracil (PTU) ingestion for 8 weeks; followed by randomization to continued PTU without or with oral triiodo-L-thyronine (T3; 10 ug/kg/d) for two additional weeks (PTU+T3). Confocal microscopy of isolated cardiomyocytes (CM) showed significant misalignment of TTs, and increased Ca2+ sparks in thyroid-deficient CMs. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis of STochastic Optical Reconstruction Microscopy (STORM) images showed decreased (p<0.0001) RyR2 cluster number per cell area in PTU CMs compared to euthyroid (EU) control myocytes, and this was normalized by T3-treatment. Cav1.2 channels and Jph2 localized within 210 nm radius of the RyR2 clusters were significantly reduced in PTU myocytes, and these values were increased with T3 treatment. A significant percentage of the RyR2 clusters in the PTU myocytes had neither Cav1.2 or Jph2, suggesting fewer functional clusters in EC-coupling. Nearest neighbor distances between RyR2 clusters were greater (p<0.001) in PTU cells compared to EU and T3-treated CMs that corresponds to disarray of TTs at the sarcomere z-discs. These results support a regulatory role of T3 in the nanoscale organization of RyR2 clusters and co-localization of Cav1.2 and Jph2 in optimizing EC-coupling.

DEEP Q-NETWORK (DQN) PARTIALOCCLUSION SEGMENTATION AND BACKTRACKING SEARCH OPTIMIZATION ALGORITHM (BSOA) WITH OPTICAL FLOW RECONSTRUCTION FOR FACIAL EXPRESSION EMOTION RECOGNITION

Video facial expression recognition (FER) has garnered a lot of attention recently and is helpful for several applications. Although many algorithms demonstrate impressive performance in a controlled environment without occlusion, identification in the presence of partial facial occlusion remains a challenging issue. Solutions based on reconstructing the obscured area of the face have been suggested as a way to deal with occlusions. These options mostly rely on the face’s shape or texture. Nonetheless, the resemblance in facial expressions among individuals appears to be a valuable advantage for the reconstruction. For semantic segmentation based on occlusions, Reinforcement Learning (RL) is introduced as the initial stage. From a pool of unlabeled data, an agent learns a policy to choose a subset of tiny informative image patches to be tagged instead of full images. In the second stage, a trained Backtracking Search Algorithm (BSA) is used to rebuild optical flows that have been distorted by the occlusion. On obtaining optical flows estimated from occluded facial frames, AEs restore optical flows of occluded regions. These recovered optical flows become inputs to anticipate classes f expressions. Optical flux reconstructions then classify stages. This study evaluates classification model’s performances for face expression identification based on Very Deep Convolution Networks (VGGNet). Furthermore, it produces more accurate confusion matrices and proposes approaches for the KMU-FED and CK+ databases, respectively. The results are evaluated using metrics including recall, f-measure, accuracy, and precision.

Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution.

The characterization of single extracellular vesicle (EV) has been an emerging tool for the early detection of various diseases despite there being challenges regarding how to interpret data with different protocols or instruments. In this work, standard EV particles were characterized for single CD9+, single CD81+ or double CD9+/CD81+ tetraspanin molecule positivity with two single EV analytic technologies in order to optimize their EV sample preparation after antibody labelling and analysis methods: NanoImager for direct stochastic optical reconstruction microscopy (dSTORM)-based EV imaging and characterization, and Flow NanoAnalyzer for flow-based EV quantification and characterization. False positives from antibody aggregates were found during dSTORM-based NanoImager imaging. Analysis of particle radius with lognormal fittings of probability density histogram enabled the removal of antibody aggregates and corrected EV quantification. Furthermore, different machine learning models were trained to differentiate antibody aggregates from EV particles and correct EV quantification with increased double CD9+/CD81+ population. With Flow NanoAnalyzer, EV samples were prepared with different dilution or fractionation methods, which increased the detection rate of CD9+/CD81+ EV population. Comparing the EV phenotype percentages measured by two instruments, differences in double positive and single positive particles existed after percentage correction, which might be due to the different detection limit of each instrument. Our study reveals that the characterization of individual EVs for tetraspanin positivity varies between two platforms-the NanoImager and the Flow NanoAnalyzer-depending on the EV sample preparation methods used after antibody labelling. Additionally, we applied machine learning models to correct for false positive particles identified in imaging-based results by fitting size distribution data.

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-Supervised Deep Learning Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multi-omics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different sub-regions of frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular co-localization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.Significance Statement Powerful computational approaches such as machine learning (ML) can interrogate large-scale multi-omics datasets to predict disease-associated genes unbiasedly via systemic study. This study presents a new disease gene identification (digID) computational framework using semi-supervised deep learning classifier. Empowered by the super-resolution imaging and the spatial biology paradigm, we further revealed that the ML model predicted AD-related G-protein signaling is subject to spatial expression dysregulation. Therefore, computational discoveries require independent biological validation to yield medical insights and our data highlight three novel G-protein genes and their signaling networks to be potential new AD therapeutic targets.

A novel m-xylylene-diamine/glucose based-supramolecular eutectogels with tissue clearing for three dimensional histological imaging

Hydrogel-based tissue clearing technologies have shown significant promise for deep-tissue imaging and subcellular-level optical 3D reconstruction of whole organs. This study proposes a novel approach utilizing a deep eutectic solvent (DES) formulated with glucose and m-xylylene-diamine (MXDA) to create a highly efficient tissue-clearing hydrogel system named the passive hydrogel clearing system (PHCS). PHCS achieved efficient tissue clearing through a single-step tissue gelation process. The resulting hydrogel-tissue complex exhibited thermoreversible properties, transitioning into a sol state upon heating and vice versa upon cooling. Notably, PHCS enabled media embedding, facilitating immunofluorescence histopathology. Additionally, the system demonstrated compatibility with various fluorescent probes, particularly lipophilic dyes. Our study successfully employed PHCS for the reconstruction of vascular structures within the intestine, enabling the generation of a 3D pathology model. These findings suggest that PHCS is a promising novel method for fabricating hydrogels for tissue clearing and holds great potential for application as a mounting medium for morphological imaging.

DivIVA controls the dynamics of septum splitting and cell elongation in Streptococcus pneumoniae.

Bacterial shape and division rely on the dynamics of cell wall assembly, which involves regulated synthesis and cleavage of the peptidoglycan. In ovococci, these processes are coordinated within an annular mid-cell region with nanometric dimensions. More precisely, the cross-wall synthesized by the divisome is split to generate a lateral wall, whose expansion is insured by the insertion of the so-called peripheral peptidoglycan by the elongasome. Septum cleavage and peripheral peptidoglycan synthesis are, thus, crucial remodeling events for ovococcal cell division and elongation. The structural DivIVA protein has long been known as a major regulator of these processes, but its mode of action remains unknown. Here, we integrate click chemistry-based peptidoglycan labeling, direct stochastic optical reconstruction microscopy, and in silico modeling, as well as epifluorescence and stimulated emission depletion microscopy to investigate the role of DivIVA in Streptococcus pneumoniae cell morphogenesis. Our work reveals two distinct phases of peptidoglycan remodeling during the cell cycle that are differentially controlled by DivIVA. In particular, we show that DivIVA ensures homogeneous septum cleavage and peripheral peptidoglycan synthesis around the division site and their maintenance throughout the cell cycle. Our data additionally suggest that DivIVA impacts the contribution of the elongasome and class A penicillin-binding proteins to cell elongation. We also report the position of DivIVA on either side of the septum, consistent with its known affinity for negatively curved membranes. Finally, we take the opportunity provided by these new observations to propose hypotheses for the mechanism of action of this key morphogenetic protein.IMPORTANCEThis study sheds light on fundamental processes governing bacterial growth and division, using integrated click chemistry, advanced microscopy, and computational modeling approaches. It addresses cell wall synthesis mechanisms in the opportunistic human pathogen Streptococcus pneumoniae, responsible for a range of illnesses (otitis, pneumonia, meningitis, septicemia) and for one million deaths every year worldwide. This bacterium belongs to the morphological group of ovococci, which includes many streptococcal and enterococcal pathogens. In this study, we have dissected the function of DivIVA, which is a structural protein involved in cell division, morphogenesis, and chromosome partitioning in Gram-positive bacteria. This work unveils the role of DivIVA in the orchestration of cell division and elongation along the pneumococcal cell cycle. It not only enhances our understanding of how ovoid bacteria proliferate but also offers the opportunity to consider how DivIVA might serve as a scaffold and sensor for particular membrane regions, thereby participating in various cell cycle processes.

Super-resolved fluorescence imaging utilising accessible stochastic optical reconstruction microscopy (easySTORM) implemented on a low-cost, modular open-source (openFrame) microscope

Abstract Optical super-resolution microscope is a powerful tool for the life sciences, including cell biology and pathology yielding information on, e.g., biological structure and protein distribution in the nano-scale range. In recent years, there has been an exponential rise in the interest to apply super-resolution microscopes in diverse areas, but its wider utility is hindered by the cost of purchasing and maintaining super-resolved microscopes. In this paper we present the implementation of easySTORM, an accessible implementation of stochastic optical reconstruction microscopy (STORM) at Indian Institute of Technology Guwahati (IITG), India, in a flexible, user friendly and cost-effective manner using a state-of-the-art, modular, open-source, optical microscope platform called: "openFrame". Providing comparable imaging performance to commercial optical super-resolution microscopes, the openFrame-based implementation of easySTORM uses in-expensive multimode diode lasers and industry grade CMOS cameras, and the open-source and modular nature of the instrument makes it easy to maintain and to upgrade. To demonstrate its successful implementation at IITG, we image quantum dots and actin-tubulin structure in both normal and cancer cells, resolved features separated by a few tens of nanometers. This work demonstrates that openFrame-enabled easySTORM instrumentation can be widely accessible to provide affordable, research grade super-resolution microscopy capability for academic and medical research.&amp;#xD;

Simulation of Inhomogeneous Refractive Index Fields Induced by Hot Tailored Forming Components

For effective quality control in hot forming applications, it is imperative that optical geometry reconstruction is conducted while the workpiece remains in its hot state to ensure the early detection of material distortion effects. This requirement is especially vital in the fabrication of hybrid bulk metal components with locally adapted properties, which are essential to address current challenges and demands on components, as well as to conserve resources. The utilization of hybrid materials results in differing thermal expansion coefficients, which significantly impact the shrinkage behavior of the component. Furthermore, the formation of these components involves exposure to high‐temperature gradients up to 1100 °C, causing the surrounding air to heat up and result in density variations. These variations generate an inhomogeneous refractive index field (IRIF), which substantially affects the surface reconstruction capabilities of optical measurement systems. Understanding the formation and dynamics of these refractive index fields is crucial to minimize uncertainties in optical measurements. This study aims to provide a comprehensive simulation model for the IRIF generated by hot‐forged tailored forming components. Utilizing this model, a priori information on the propagation characteristics of the IRIF can be obtained, thereby facilitating optimized positioning of the measurement system and minimizing reconstruction errors.

A novel super-resolution microscopy platform for cutaneous alpha-synuclein detection in Parkinson's disease.

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

Reduced bioenergetics and mitochondrial fragmentation in human primary cytotrophoblasts induced by an EGFR-targeting chemical mixture

Exposures to complex environmental chemical mixtures during pregnancy reach and target the feto-placental unit. This study investigates the influence of environmental chemical mixtures on placental bioenergetics. Recognizing the essential role of the epidermal growth factor receptor (EGFR) in placental development and its role in stimulating glycolysis and mitochondrial respiration in trophoblast cells, we explored the effects of chemicals known to disrupt EGFR signaling on cellular energy production. Human primary cytotrophoblasts (hCTBs) and a first-trimester extravillous trophoblast cell line (HTR-8/SVneo) were exposed to a mixture of EGFR-interfering chemicals, including atrazine, bisphenol S, niclosamide, PCB-126, PCB-153, and trans-nonachlor. An RNA sequencing approach revealed that the mixture altered the transcriptional signature of genes involved in cellular energetics. Next, the impact of the mixture on cellular bioenergetics was evaluated using a combination of mitochondrial and glycolytic stress tests, ATP production, glucose consumption, lactate synthesis, and super-resolution imaging. The chemical mixture did not alter basal oxygen consumption but diminished the maximum respiratory capacity in a dose-dependent manner, indicating a disruption of mitochondrial function. The respiratory capacity and ATP production were increased by EGF, while the Chem-Mix reduced both EGF- and non-EGF-mediated oxygen consumption rate in hCTBs. A similar pattern was observed in the glycolytic medium acidification, with EGF increasing the acidification, and the Chem-Mix blocking EGF-induced glycolytic acidification. Furthermore, direct stochastic optical reconstruction microscopy (dSTORM) imaging demonstrated that the Chem-Mix led to a reduction of the mitochondrial network architecture, with findings supported by a decrease in the abundance of OPA1, a mitochondrial membrane GTPase involved in mitochondrial fusion. In conclusion, we demonstrated that a mixture of EGFR-disrupting chemicals alters mitochondrial remodeling, resulting in disturbed cellular bioenergetics, reducing the capacity of human cytotrophoblast cells to generate energy. Future studies should investigate the mechanism by which mitochondrial dynamics are disrupted and the pathological significance of these findings.

Enhanced Performance of the Optimized Dye CF583R in Direct Stochastic Optical Reconstruction Microscopy of Active Zones in Drosophila Melanogaster.

Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as "gold standard" fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R's performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts.

An unsupervised video anomaly detection method via Optical Flow decomposition and Spatio-Temporal feature learning

The purpose of this paper is to present an unsupervised video anomaly detection method using Optical Flow decomposition and Spatio-Temporal feature learning (OFST). This method employs a combination of optical flow reconstruction and video frame prediction to achieve satisfactory results. The proposed OFST framework is composed of two modules: the Multi-Granularity Memory-augmented Autoencoder with Optical Flow Decomposition (MG-MemAE-OFD) and a Two-Stream Network based on Spatio-Temporal feature learning (TSN-ST). The MG-MemAE-OFD module is composed of three functional blocks: optical flow decomposition, autoencoder, and multi-granularity memory networks. The optical flow decomposition block is used to extract the main motion information of objects in optical flow, and the granularity memory network is utilized to memorize normal patterns and improve the quality of the reconstructions. To predict video frames, we introduce a two-stream network based on spatiotemporal feature learning (TSN-ST), which adopts parallel standard Transformer blocks and a temporal block to learn spatiotemporal features from video frames and optical flows. The OFST combines these two modules so that the prediction error of abnormal samples is further increased due to the larger reconstruction error. In contrast, the normal samples obtain a lower reconstruction error and prediction error. Therefore, the anomaly detection capability of the method is greatly enhanced. Our proposed model was evaluated on public datasets. Specifically, in terms of the area under the curve (AUC), our model achieved an accuracy of 85.74% on the Ped1 dataset, 99.62% on the Ped2 dataset, 93.89% on the Avenue dataset, and 76.0% on the ShanghaiTech Dataset. Our experimental results show an average improvement of 1.2% compared to the current state-of-the-art.

Optical fiber shape reconstruction algorithm based on 3D Euler spiral model

Abstract This paper presents a reconstruction algorithm that uses a 3D Euler spiral model to construct the microsegment of a 3D space curve to improve the reconstruction efficiency. Euler spiral is a curve whose shape information changes linearly with arc length, which improves the current assumption of the reconstruction algorithms that the shape information of the micro-segment is constant, and effectively reduces the number of interpolations required, and thus improves the efficiency of the shape algorithm. To verify the effectiveness of this algorithm, a method for constructing random space curves is proposed. Three random space curves of different lengths are reconstructed and the results are compared with the reconstruction algorithms based on the homogeneous transformation matrix and Bishop frame. The results show that this model can significantly improve the efficiency of the algorithm. Under the random space curves of 1 m, 10 m, and 100 m, the efficiency is enhanced by about 15, 27, and 30 times respectively. Prepare a shape sensor with a length of 1465mm to verify the highest reconstruction accuracy of 3D Euler spiral model, which is consistent with the simulation results. The results provide a solid foundation for further research in the field of shape sensing, and show potential for promoting the development of applications that rely on real-time shape measurements.&amp;#xD;

Multiplexed Nanoscopy via Buffer Exchange.

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision.

An Extremely Sparse Tomography Reconstruction of a Multispectral Temperature Field without Any a Priori Knowledge.

When undertaking optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. In order to reconstruct the radiation field of unknown materials or in situations where a priori knowledge cannot be obtained, this paper proposes an extremely sparse tomography multispectral temperature field reconstruction algorithm that analyzes the similarity (the similarity here compares and calculates the Euclidean distance of the spectral emissivity values at various wavelengths between different spectral curves) of radiation characteristics of materials under the same pressure and concentration but different temperature, describes the similarity between the radiation information of the tested field using the dynamic time warping (DTW) algorithm, and uses the similarity sum of the radiation information among the subregions of the temperature field as the optimization objective. This is combined with the equation-constrained optimization algorithm and multispectral thermometry to establish the statistical law between the missing information and finally realize the reconstruction of the temperature field. Simulation experiments show that, without any a priori knowledge, the method in this paper can realize reconstruction of the temperature field with an accuracy of 1.53-12.05% under two projection angles and has fewer projection angles and stronger robustness than other methods.