Target Image Research Articles

Synthesis and Evaluation of Benzylic 18F-Labeled N-Biphenylalkynyl Nipecotic Acid Derivatives for PET Imaging of GABA Transporter 1.

As the main inhibitory neurotransmission system, the GABAergic system poses an interesting yet underutilized target for molecular brain imaging. While PET imaging of postsynaptic GABAergic neurons has been accomplished using radiolabeled benzodiazepines targeting the GABAA receptor, the development of presynaptic radioligands targeting GABA transporter 1 (GAT1) has been unsuccessful thus far. Therefore, we developed a novel GAT1-addressing radioligand and investigated its applicability as a PET tracer in rodents. We selected a lipophilic nipecotic acid scaffold that is known to bind selectively to GAT1 as the basis for our radioligand. To obtain the desired candidate radiotracer [18F]4, ester-protected radioligands [18F]11a-b were synthesized through aliphatic nucleophilic radiofluorination of the respective bromo-precursors, after which chemical deprotection was attempted using various conditions. Because these deprotections were unsuccessful, it was evaluated whether the ethyl ester [18F]11a could function as a prodrug and afford the active radioligand [18F]4 after in vivo ester hydrolysis by esterases. Unfortunately, PET imaging studies in a rat model using [18F]11a showed no brain uptake of the radiotracer. Instead, significant uptake of radioactivity was observed in the liver and bones, the latter being caused by radiodefluorination of the PET tracer. Since the PET tracer developed in this study was found to be unstable, further efforts should investigate the development of a more stable GAT1-addressing PET tracer without the potential labile benzyl fluoride moiety. Moreover, as the still intact fraction of the radiotracer did not cross the BBB, options other than the prodrug approach should be considered to increase the BBB permeability of future GAT1 radioligands.

A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets

A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC–CAF crosstalk.

Characteristics of TSPO expression in marmoset EAE

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) and is a leading non-traumatic cause of disability in young adults. The 18 kDa Translocator Protein (TSPO) is a mitochondrial protein and positron emission tomography (PET)-imaging target that is highly expressed in MS brain lesions. It is used as an inflammatory biomarker and has been proposed as a therapeutic target. However, its specific pathological significance in humans is not well understood. Experimental autoimmune encephalomyelitis (EAE) in the common marmoset is a well-established primate model of MS. Studying TSPO expression in this model will enhance our understanding of its expression in MS. This study therefore characterizes patterns of TSPO expression in fixed CNS tissues from one non-EAE control marmoset and 8 EAE marmosets using multiplex immunofluorescence. In control CNS tissue, we find that TSPO is expressed in the leptomeninges, ependyma, and over two-thirds of Iba1 + microglia, but not astrocytes or neurons. In Iba1 + cells in both control and acute EAE tissue, we find that TSPO is co-expressed with markers of antigen presentation (CD74), early activation (MRP14), phagocytosis (CD163) and anti-inflammatory phenotype (Arg1); a high level of TSPO expression is not restricted to a particular microglial phenotype. While TSPO is expressed in over 88% of activated Iba1 + cells in acute lesions in marmoset EAE, it also is sometimes observed in subsets of astrocytes and neurons. Additionally, we find the percentage of Iba1 + cells expressing TSPO declines significantly in lesions > 5 months old and may be as low as 13% in chronic lesions. However, we also find increased astrocytic TSPO expression in chronic-appearing lesions with astrogliosis. Finally, we find expression of TSPO in a subset of neurons, most frequently GLS2 + glutamatergic neurons. The shift in TSPO expression from Iba + microglia/macrophages to astrocytes over time is similar to patterns suggested by earlier neuropathology studies in MS. Thus, marmoset EAE appears to be a clinically relevant model for the study of TSPO in immune dysregulation in human disease.Graphical

A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery

This paper implements an unsupervised single-image sharpening method for multispectral images, focusing on Sentinel-2 and Landsat 8 imagery. Our method combines traditional model-based methods with neural network optimization techniques. Our method solves the same optimization problem as traditional model-based methods while leveraging neural network optimization techniques through a customized U-Net architecture and specialized loss function. The key innovation lies in simultaneously optimizing a low-rank approximation of the target image and a linear transformation from the subspace to the sharpened image within an unsupervised training framework. Our method offers several distinct advantages: it requires no external training data beyond the image being processed, it provides fast training speeds through a compact, interpretable network model, and most importantly, it adapts to different input images without requiring extensive parameter tuning—a common limitation of traditional methods. The method was developed with a focus on sharpening Sentinel-2 imagery. The Copernicus Sentinel-2 satellite constellation captures images at three different spatial resolutions, 10, 20, and 60 m, and many applications benefit from a unified 10 m resolution. Still, the method’s effectiveness extends to other remote sensing tasks, achieving competitive results in both sharpening and multisensor fusion scenarios. It is evaluated using both real and simulated data, and its versatility is shown through successful applications to Sentinel-2 sharpening and Sentinel-2/Landsat 8 fusion. In comparison with leading methods, it is shown to give excellent results.

A Global-Scale Overlapping Pixels Calculation Method for Whisk-Broom Payloads with Multi-Module-Staggered Longlinear-Array Detectors

A multi-module staggered (MMS) long-linear-array (LLA) detector is presently recognized as an effective and widely adopted means of improving the field of view (FOV) of in-orbit optical line-array cameras. In particular, in terms of low-orbit whisk-broom payloads, the MMS LLA detector combined with the one-dimensional scanning mirror is capable of achieving both large-swath and high-resolution imaging. However, because of the complexity of the instantaneous relative motion model (IRMM) of the whisk-broom imaging mechanism, it is really difficult to determine and verify the actual numbers of overlapping pixels of adjacent detector sub-module images and consecutive images in the same and opposite scanning directions, which are exceedingly crucial to the instrument design pre-launch as well as the in-orbit geometric quantitative processing and application post-launch. Therefore, in this paper, aiming at addressing the problems above, we propose a global-scale overlapping pixels calculation method based on the IRMM and rigorous geometric positioning model (RGPM) of the whisk-broom payloads with an MMS LLA detector. First, in accordance with the imaging theory and the specific optical–mechanical structure, the RGPM of the whisk-broom payload is constructed and introduced elaborately. Then, we qualitatively analyze the variation tendency of the overlapping pixels of adjacent detector sub-module images with the IRMM of the imaging targets, and establish the associated overlapping pixels calculation model based on the RGPM. And subsequently, the global-scale overlapping pixels calculation models for consecutive images of the same and opposite scanning directions of the whisk-broom payload are also built. Finally, the corresponding verification method is presented in detail. The proposed method is validated using both simulation data and in-orbit payload data from the Thermal Infrared Spectrometer (TIS) of the Sustainable Development Goals Satellite-1 (SDGSAT-1), launched on 5 November 2021, demonstrating its effectiveness and accuracy with overlapping pixel errors of less than 0.3 pixels between sub-modules and less than 0.5 pixels between consecutive scanning images. Generally, this method is suitable and versatile for the other scanning cameras with a MMS LLA detector because of the similarity of the imaging mechanism.

Effect of Seabed Type on Image Segmentation of an Underwater Object Obtained from a Side Scan Sonar Using a Deep Learning Approach

This study examines the impact of seabed conditions on image segmentation for seabed target images acquired via side-scan sonar during sea experiments. The dataset comprised cylindrical target images overlying on two seabed types, mud and sand, categorized accordingly. The deep learning algorithm (U-NET) was utilized for image segmentation. The analysis focused on two key factors influencing segmentation performance: the weighting method of the cross-entropy loss function and the combination of datasets categorized by seabed type for training, validation, and testing. The results revealed three key findings. First, applying equal weights to the loss function yielded better segmentation performance compared to pixel-frequency-based weighting. This improvement is indicated by Intersection over Union (IoU) for the highlight class in dataset 2 (0.41 compared to 0.37). Second, images from the mud area were easier to segment than those from the sand area. This was due to the clearer intensity contrast between the target highlight and background. This difference is indicated by the IoU for the highlight class (0.63 compared to 0.41). Finally, a network trained on a combined dataset from both seabed types improved segmentation performance. This improvement was observed in challenging conditions, such as sand areas. In comparison, a network trained on a single-seabed dataset showed lower performance. The IoU values for the highlight class in sand area images are as follows: 0.34 for training on mud, 0.41 for training on sand, and 0.45 for training on both.

Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar

Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods.

Developing Orthogonal Fluorescent RNAs for Photoactive Dual‐Color Imaging of RNAs in Live Cells

AbstractFluorogenic RNA aptamers have revolutionized the visualization of RNAs within complex cellular processes. A representative category of them employs the derivatives of green fluorescent protein chromophore, 4‐hydroxybenzlidene imidazolinone (HBI), as chromophores. However, the structural homogeneity of their chromophoric backbones causes severe cross‐reactivity with other homologous chromophores. This limitation impairs their multiplexing capabilities, which are essential for the simultaneous visualization of multiple RNA species in live cells. Herein, we rationally designed a series of red‐shifted chromophores and employed SELEX‐independent engineering to develop a novel fluorogenic RNA aptamer, mSquash. mSquash displays specific and intense fluorescence upon binding with our red‐shifted chromophore DFHBFPD (Ex/Em=501/624 nm). The mSquash/DFHBFPD allows orthogonal imaging of selected RNA targets alongside the established Broccoli/DFHBI‐1T (Ex/Em=472/501 nm), facilitating multiplexed live cell imaging of various targets. Moreover, we expanded the application of fluorescent RNA to photoactive imaging by constructing two genetically encoded photoactivatable fluorescent RNAs for the first time. This innovative approach allows photoactivatable control of fluorescent RNAs via specific light wavelengths (365 nm and 450 nm), enabling spatiotemporal dual‐color imaging of RNAs in live cells. Our findings represent a significant advancement in fluorescent RNA‐based orthogonal imaging and spatiotemporal analysis of RNAs.

Preclinical Evaluation of 68Ga/177Lu-Labeled FAP-Targeted Peptide for Tumor Radiopharmaceutical Imaging and Therapy.

Fibroblast activation protein (FAP) has been considered a promising target for tumor imaging and therapy. This study designed a novel peptide, FAP-HXN, specifically targeting FAP and exhibiting significant potential as a radionuclide-labeled theranostic agent. Preclinical studies were conducted to evaluate the potency, selectivity, and efficacy of FAP-HXN. Methods: FAP-HXN was synthesized and characterized for selectivity and specificity toward FAP. Cellular uptake of the radiolabeled FAP-HXN in human embryonic kidney (HEK)-293-FAP cells with high expressions of FAP was evaluated. The diagnostic and therapeutic potential of 68Ga- and 177Lu-labeled radioligands was evaluated in HEK-293-FAP tumor-bearing mice compared with the FAP-targeting peptide FAP-2286. Results: FAP-HXN demonstrated high binding ability to human and mouse sources of FAP. Moreover, the invivo studies confirmed the high affinity and specificity of radiolabeled FAP-HXN. Small-animal PET imaging demonstrated that [68Ga]Ga-FAP-HXN had continuous tumor uptake in FAP-positive tumors after administration compared with [68Ga]Ga-FAP-2286. In the therapeutic experiments, [177Lu]Lu-FAP-HXN showed significant antitumor activity in HEK-293-FAP xenografts at well-tolerated doses, which also exhibited longer tumor retention and better tumor growth inhibition compared with [177Lu]Lu-FAP-2286. Conclusion: The preclinical studies revealed that radiolabeled FAP-HXN had high tumor uptake, prolonged retention, and significant anticancer efficacy in HEK-293-FAP xenografts. FAP-HXN shows promising potential as a novel theranostic radioligand for FAP-positive tumors.

Polarization-independent full-color holographic movie with a single metasurface free from crosstalk.

Metasurface holograms offer advantages, such as a wide viewing angle, compact size, and high resolution. However, projecting a full-color movie using a single hologram without polarization dependence has remained challenging. Here, we report a full-color dielectric metasurface holographic movie with a resolution of 512 × 512. Eight frames were multiplexed across blue (445 nm), green (532 nm), and red (633 nm) color channels, achieving a maximum reconstruction rate of 5.6 frames per second. The superposition of the three wavelengths was achieved by adjusting the resolution and position of each target image while maintaining a constant pitch of the meta-atoms. Additionally, we identified the positions of crosstalk images generated that occur due to fabrication errors and proposed and demonstrated conditions and corrections to ensure they do not overlap with the intended images. The superimposition of phase distributions for each wavelength was achieved using the least squares error method, based on a library of over 20,000 types of meta-atoms. These results are anticipated to advance the future development of three-dimensional metasurface holographic movies.

Synchronization‐Free Single‐Photon 3D Imaging

Abstract3D imaging using single‐photon time‐of‐flight (ToF) detection has emerged as a potential solution for sensing in challenging scenarios. However, most schemes require strict synchronization between the illuminating light transmitter and the detector, which greatly limits their practicality and flexibility. Here, a synchronization‐free single‐photon 3D imaging scheme that exploits only binary‐encoded illumination light and a photon time‐stamping high‐speed detection system are proposed. The correlation between the arrival times of photons detected by individual pixels is utilized to achieve clock synchronization, and further calculate the ToF differences of photons detected by different pixels to extract the depth information of the imaging target. This scheme can adapt to conditions with signals as weak as photons per pulse per pixel. This work provides a simple, flexible method for realizing 3D imaging in long‐range and high‐loss scenes, paving the way for future applications in large‐scale, high‐complexity real‐world scenarios.

LLM-Enhanced Composed Image Retrieval: An Intent Uncertainty-Aware Linguistic-Visual Dual Channel Matching Model

Composed image retrieval (CoIR) involves a multi-modal query of the reference image and modification text describing the desired changes, allowing users to express image retrieval intents flexibly and effectively. The key of CoIR lies in how to properly reason the search intent from the multi-modal query. Existing work either aligns the composite embedding of the multi-modal query and the target image embedding in the visual domain through late-fusion or converts all images into text descriptions and leverage large language models (LLM) for text semantic reasoning. However, this single-modality reasoning approach fails to comprehensively and interpretably capture the users’ ambiguous and uncertain intents in the multi-modal queries, incurring the inconsistency between retrieved results and ground truth. Besides, the expensive manually annotated datasets limit the further performance improvement of CoIR. To this end, this article proposes an LLM-enhanced Intent Uncertainty-Aware Linguistic-Visual Dual Channel Matching Model (IUDC), which combines the strengths of multi-modal late-fusion and LLMs for CoIR. We first construct an LLM-based triplet augmentation strategy to generate more synthetic training triplets. Based on this, the core of IUDC consists of two matching channels: the semantic matching channel is responsible for intent reasoning on the aspect-level attributes extracted by an LLM, and the visual matching channel accounts for the fine-grained visual matching between multi-modal fusion embedding and target images. Considering the intent uncertainty presented in the multi-modal queries, we introduce Probability Distribution Encoder (PDE) to project the intents as probabilistic distributions in the two matching channels. Consequently, a mutually enhanced module is designed to share knowledge between the visual and semantic representations for better representation learning. Finally, the matching scores of two channels are added to retrieve the target image. Extensive experiments conducted on two real datasets demonstrate the effectiveness and superiority of our model. Notably, with the help of the proposed LLM-based triplet augmentation strategy, our model achieves a new record of state-of-the-art performance among all datasets.

PET imaging of 52Mn labeled DOTATATE and DOTAJR11

Manganese-52 is gaining interest as an isotope for PET imaging due to its desirable decay and chemical properties for radiopharmaceutical development. Somatostatin receptor 2 (SSTR2) is significantly overexpressed by neuroendocrine tumors (NETs) and is an important target for nuclear imaging and therapy. As an agonist, [68Ga]Ga-DOTATATE has demonstrated significant internalization upon interaction with receptor ligands, whereas [68Ga]Ga-DOTA-JR11(as an antagonist) exhibits limited internalization but better pharmacokinetics and increased tumor uptake. The goal of this study was to label both DOTATATE and DOTA-JR11 peptides with 52Mn in high radiochemical yields (RCY) and sufficient specific activity. A comparison of these two compounds was performed in in vitro and in vivo studies in animals with somatostatin receptor-positive xenografts to characterize differences in cell, tumor, and tissue uptake. Radiolabeling of DOTATATE and DOTA-JR11 was carried out by combining varying concentrations of the peptides with [52Mn]MnCl2. In vitro stability of the radiotracers was determined in mouse serum. In vitro cell uptake and internalization assays were performed in SSTR2 + AR42J cells and negative controls. In vivo biodistribution and longitudinal PET imaging was evaluated in mice bearing AR42J tumors. Both [52Mn]Mn-DOTATATE and [52Mn]Mn-DOTA-JR11 showed affinity for SSTR2 in AR42J cells. However, the uptake of [52Mn]Mn-DOTATATE was higher (11.95 ± 0.71%/ mg) compared to [52Mn]Mn-DOTA-JR11 (7.31 ± 0.38%/ mg) after 2 h incubation. After 4 h incubation, 53.13 ± 1.83% of the total accumulated activity of [52Mn]Mn-DOTATATE was internalized, whereas only 20.85 ± 0.59% of the total accumulated activity of [52Mn]Mn-DOTA-JR11 was internalized. The PET images revealed similar biodistribution results, with [52Mn]Mn-DOTATATE showing a significant tumor uptake of 11.16 ± 2.97% ID/g, while [52Mn]Mn-DOTA-JR11 exhibited a lower tumor uptake of 2.11 ± 0.30% ID/g 4 h post-injection. The synthesis of both radiotracers was accomplished with high RCY and purity. The cell uptake and internalization of [52Mn]Mn-DOTATATE showed higher levels compared to [52Mn]Mn-DOTA-JR11. PET images of the radiotracers in AR42J tumor bearing mice demonstrated similar biodistribution in all organs except the tumor, with [52Mn]Mn-DOTATATE showing higher tumor uptake compared to [52Mn]Mn-DOTA-JR11. The variations in the properties of these tracers could be used to guide further imaging and treatment studies.

The [18F] F-PSMA Probe: Chemical Perspectives.

This study discusses the chemical perspectives of the [18F]F-PSMA probe, a pivotal tool in prostate cancer imaging. [18F]Fluorine, a positron emitter with a half-life of 109.8 minutes, is produced in a cyclotron by bombarding [18O]-enriched targets with protons. The chemistry of this isotope parallels that of stable fluorine, facilitating its use in positron emission tomography (PET). The synthesis of [18F]F-PSMA involves a nucleophilic substitution (SN1) reaction, where [18F]fluoride ion replaces a leaving group in the precursor molecule. Prostate-specific membrane antigen (PSMA) is highly expressed in prostate cancer cells, making it a crucial target for imaging. PSMA-targeted radioligands, such as [68Ga]Ga-PSMA-11, [18F]F-DCFPyL, and [99mTc]Tc-PSMA-I&S, bind to the extracellular domain of PSMA, enabling precise imaging. The design of PSMA radiotracers incorporates specific targeting moieties, functional groups for radiolabeling, and linkers to maintain binding affinity and pharmacokinetics. Common linkers include aliphatic, aromatic, peptide-based, and polyethylene glycol structures, while functional groups like tosylate and PyTFP are used for efficient [18F]fluorination. This review aims to elucidate the main linker and reactions in order to optimize these components to improve imaging sensitivity and specificity in detecting prostate cancer.

Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery.

Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or unknown objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones.

An Imaging Method for Marine Targets in Corner Reflector Jamming Scenario Based on Time–Frequency Analysis and Modified Clean Technique

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm.

Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar target reconstruction technology is being carried out. This paper reports the sequential multimodal underwater single-photon lidar adaptive target reconstruction algorithm based on spatiotemporal sequence fusion, which has strong information extraction and noise filtering ability and can reconstruct the target depth and reflective intensity information from complex echo photon time counts and spatial pixel relationships. The method consists of three steps: data preprocessing, sequence-optimized extreme value inference filtering, and collaborative variation strategy for image optimization to achieve high-quality target reconstruction in complex underwater environments. Simulation and test results show that the target reconstruction method outperforms the current imaging algorithms, and the built single-photon lidar system achieves underwater lateral and distance resolution of 5 mm and 2.5cm@6AL, respectively. This indicates that the method has a great advantage in sparse photon counting imaging and possesses the capability of underwater target imaging under the background of strong light noise. It also provides a good solution for underwater target imaging of small slow-moving targets with long-distance and high-resolution.

Effect of hearing experience on preschool-aged children's eye gaze to a talker during spoken language processing.

Speechreading-gathering speech information from talkers' faces-supports speech perception when speech acoustics are degraded. Benefitting from speechreading, however, requires listeners to visually fixate talkers during face-to-face interactions. The purpose of this study is to test the hypothesis that preschool-aged children allocate their eye gaze to a talker when speech acoustics are degraded. We implemented a looking-while-listening paradigm to quantify children's eye gaze to an unfamiliar female talker and two images of familiar objects presented on a screen while the children listened to speech. We tested 31 children (12 girls), ages 26-48months, who had normal hearing (NH group, n = 19) or bilateral sensorineural hearing loss and used hearing devices (D/HH group, n = 12). Children's eye gaze was video-recorded as the talker verbally labeled one of the images, either in quiet or in the presence of an unfamiliar two-talker male speech masker. Children's eye gaze to the target image, distractor image, and female talker was coded every 33ms off-line by trained observers. Bootstrapped differences of time series (BDOTS) analyses and ternary plots were used to determine differences in visual fixations of the talker between listening conditions in the NH and D/HH groups. Results suggest that the NH group visually fixated the talker more in the masker condition than in quiet. We did not observe statistically discernable differences in visual fixations of the talker between the listening conditions for the D/HH group. Gaze patterns of the NH group in the masker condition looked like gaze patterns of the D/HH group.

Developing Orthogonal Fluorescent RNAs for Photoactive Dual-Color Imaging of RNAs in Live Cells.

Fluorogenic RNA aptamers have revolutionized the visualization of RNAs within complex cellular processes. A representative category of them employs the derivatives of green fluorescent protein chromophore, 4-hydroxybenzlidene imidazolinone (HBI), as chromophores. However, the structural homogeneity of their chromophoric backbones causes severe cross-reactivity with other homologous chromophores. This limitation impairs their multiplexing capabilities, which are essential for the simultaneous visualization of multiple RNA species in live cells. Herein, we rationally designed a series of red-shifted chromophores and employed SELEX-independent engineering to develop a novel fluorogenic RNA aptamer, mSquash. mSquash displays specific and intense fluorescence upon binding with our red-shifted chromophore DFHBFPD (Ex/Em=501/624 nm). The mSquash/DFHBFPD allows orthogonal imaging of selected RNA targets alongside the established Broccoli/DFHBI-1T (Ex/Em=472/501 nm), facilitating multiplexed live cell imaging of various targets. Moreover, we expanded the application of fluorescent RNA to photoactive imaging by constructing two genetically encoded photoactivatable fluorescent RNAs for the first time. This innovative approach allows photoactivatable control of fluorescent RNAs via specific light wavelengths (365 nm and 450 nm), enabling spatiotemporal dual-color imaging of RNAs in live cells. Our findings represent a significant advancement in fluorescent RNA-based orthogonal imaging and spatiotemporal analysis of RNAs.