Research on the destroy characteristics of PTFE/Cu composite liner to explosive reactive armor.

The jet generated through PTFE based inert material liner has the characteristics of low energy, low density, and large aspect ratio, which can effectively achieve the "penetration without explosion" of explosive reactive armor. PTFE/Cu composite material liner with various densities is prepared, to research the roles of preparation procedure and density in the destroy effect of jet on reactive armor. Through numerical simulation research, it was found that there was no reaction at all in the explosive layer penetrated by the jet generated by the sinter liner molded, while the explosive layer penetrated by the jet generated through the hot-pressing sintering and extrusion molding liner experienced local reactions on the jet impact channel, and the overall explosive layer did not undergo any reaction. Through experimental verification, it has been proven that all three types of jets have achieved "penetration without explosion" on explosive reactive armor.

Learning-enabled data transmission with up to 32 multiplexed orbital angular momentum channels through a commercial multi-mode fiber.

Multiplexing orbital angular momentum (OAM) modes enable high-capacity optical communication. However, the highly similar speckle patterns of adjacent OAM modes produced by strong mode coupling in common fibers prevent the utility of OAM channel demultiplexing. In this paper, we propose a machine learning-supported fractional OAM-multiplexed data transmission system to sort highly scattered data from up to 32 multiplexed OAM channels propagating through a commercial multi-mode fiber parallelly with an accuracy of >99.92%, which is the largest bit number of OAM superstates reported to date (to the best of our knowledge). Here, by learning limited samples, unseen OAM superstates during the training process can be predicted precisely, which reduces the explosive quantity of the dataset. To verify its application, both gray and colored images, encoded by the given system, have been successfully transmitted with error rates of <0.26%. Our work might provide a promising avenue for high-capacity OAM optical communication in scattering environments.

Research on the factors influencing the process of prefabricated fragments penetrating finite thickness concrete.

The damage to the back of the target plate is a phenomenon that occurs when concrete is subjected to high-speed impact. In order to study the motion parameters of prefabricated spherical fragments penetrating finite thickness concrete targets at high speeds and the occurrence rules of concrete damage, as well as the impact of target back damage on the motion of fragments, experiments were conducted on 100 mm finite thickness concrete targets with prefabricated spherical fragments. The concrete model parameters in LS-DYNA were modified based on the residual velocity of fragments, and numerical simulations were conducted on the penetration of prefabricated fragments with different impact velocities and concrete target plates with different thicknesses. By analyzing the location of concrete target plate damage, the relationship between concrete thickness and concrete damage was obtained; Combining the motion parameters of fragment penetration process, the phenomenon of concrete collapse was linked to fragment motion, and the influence of concrete thickness on fragment motion parameters was analyzed. The results indicate that the thickness of the finite thickness concrete target plate and the penetration speed of fragments have a significant impact on the damage state of the target back, and further affect the motion change response stage during the penetration process of prefabricated fragments.

Fundamental probing limit on the high-order orbital angular momentum of light.

The orbital angular momentum (OAM) of light, possessing an infinite-dimensional degree of freedom, holds significant potential to enhance the capacity of optical communication and information processing in both classical and quantum regimes. Despite various methods developed to accurately measure OAM modes, the probing limit of the highest-order OAM remains an open question. Here, we report an accurate recognition of superhigh-order OAM using a convolutional neural network approach with an improved ResNeXt architecture, based on conjugated interference patterns. A type of hybrid beam carrying double OAM modes is utilized to provide more controllable degrees of freedom for greater recognition of the OAM modes. Our contribution advances the OAM recognition limit from manual counting to machine learning. Results demonstrate that, within our optical system, the maximum recognizable OAM modes exceed l = ±690 with an accuracy surpassing 99.93%, the highest achieved by spatial light modulator to date. Enlarging the active area of the CCD sensor extends the number of recognizable OAM modes to 1300, constrained only by the CCD resolution limit. Additionally, we explore the identification of fractional high-order OAM modes with a resolution of 0.1 from l = ±600.0 to l = ±600.9, achieving a high accuracy of 97.86%.

High resolution spectroscopy of B0u+←X0g+ transitions in 130Te2: Reference lines spanning the 443.2-451.4 nm region.

Molecular tellurium (130Te2) has long been regarded as a promising candidate for a variety of spectroscopic applications, especially as a frequency reference. Absorption lines of 130Te2 were published extensively in the past few decades, however, the spectral resolution was not sufficiently high to be considered a precision spectroscopic reference. Here, a high resolution spectral atlas of 130Te2 Doppler-free saturated absorption transitions is reported, and used to assign multiple ro-vibrational bands in the B(Σu-3)0u+←X(Σu-3)0g+ electronic transition via the Dunham model, giving updated Dunham parameters and diatomic constants for the B(Σu-3)0u+ state at an unprecedented level of precision. Our findings reveal spectroscopic properties of 130Te2 that might eventually contribute to frequency reference in precision measurement such as the quest for non-zero electron's Electric Dipole Moment (eEDM).

Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Isoform-selective TGF-ß3 inhibition for systemic sclerosis.

BACKGROUND: Transforming growth factor ß (TGF-ß) is implicated as a key mediator of pathological fibrosis, but its pleiotropic activity in a range of homeostatic functions presents challenges to its safe and effective therapeutic targeting. There are three isoforms of TGF-ß, TGF-ß1, TGF-ß2, and TGF-ß3, which bind to a common receptor complex composed of TGF-ßR1 and TGF-ßR2 to induce similar intracellular signals in vitro. We have recently shown that the cellular expression patterns and activation thresholds of TGF-ß2 and TGF-ß3 are distinct from those of TGF-ß1 and that selective short-term TGF-ß2 and TGF-ß3 inhibition can attenuate fibrosis in vivo without promoting excessive inflammation. Isoform-selective inhibition of TGF-ß may therefore provide a therapeutic opportunity for patients with chronic fibrotic disorders. METHODS: Transcriptomic profiling of skin biopsies from patients with systemic sclerosis (SSc) from multiple clinical trials was performed to evaluate the role of TGF-ß3 in this disease. Antibody humanization, biochemical characterization, crystallization, and pre-clinical experiments were performed to further characterize an anti-TGF-ß3 antibody. FINDINGS: In the skin of patients with SSc, TGF-ß3 expression is uniquely correlated with biomarkers of TGF-ß signaling and disease severity. Crystallographic studies establish a structural basis for selective TGF-ß3 inhibition with a potent and selective monoclonal antibody that attenuates fibrosis effectively in vivo at clinically translatable exposures. Toxicology studies suggest that, as opposed to pan-TGF-ß inhibitors, this anti-TGF-ß3 antibody has a favorable safety profile for chronic administration. CONCLUSION: We establish a rationale for targeting TGF-ß3 in SSc with a favorable therapeutic index. FUNDING: This study was funded by Genentech, Inc.

Alteriqipengyuania flavescens sp. nov., isolated from Pearl River Estuary sediment.

Two Gram-stain-negative, aerobic, rod-shaped, non-motile and golden yellow pigmented bacteria, designated as SCSIO 75105T and SCSIO 75732, were isolated from sediment in the Pearl River Estuary, Guangdong Province, PR China. Cells were positive for catalase and oxidase. Growth occurred at 10-37 °C (optimum, 28 °C), pH 6.0-10.0 (optimum, pH 7.0) and 0-5.0 % (w/v) NaCl (optimum, 2.0-3.0 %). The 16S rRNA gene analysis indicated that these two isolates shared a similarity of 100 % each other. The 16S rRNA gene sequence analysis indicated that these two isolates showed highest similarity to Altererythrobacter ishigakiensis CGMCC 1.14979T (97.3 %). However, a phylogenetic tree based on 288 orthologous clusters indicated that these two isolates were closely related to Alteriqipengyuania halimionae CPA5T. The average nucleotide identity, average amino acid identity, digital DNA-DNA hybridization and evolutionary distance values between the two isolates and Alteriqipengyuania halimionae CPA5T were 73.7-74.0 %, 65.2 %, 19.5 % and 0.24, respectively. The genomic DNA G+C content of both isolates was 65.2 mol%. The major cellular fatty acids were C18 : 1 ω7c, C17 : 1 ω6c and summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c), and Q-10 was the respiratory quinone. The polar lipid profile contained diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine, sphingoglycolipid and one unidentified glycolipid. On the basis of the results of phenotypic, physiological, chemotaxonomic and genotypic characterization, strains SCSIO 75105T and SCSIO 75732 are considered to represent a novel species in the genus Alteriqipengyuania, for which the name Alteriqipengyuania flavescens sp. nov. is proposed. The type strain is SCSIO 75105T (=KCTC 92502T=MCCC 1K07993T).

Study on the Effect of PTFE/Cu Composite Material Preparation Process on Penetration Performance.

The jet formed by the traditional metal liner has a slender shape. The diameter of the jet head is consistent with that of the tail, and the ductility is good. When it is used to penetrate the target, it has a good damage effect. The low-density jet formed by the PTFE/Cu liner, according to the different preparation processes and densities, has different degrees of radial expansion. This phenomenon may lead to the expansion of the jet head during the penetration process, resulting in a damage effect, which is different from the previous jet on the target. In this paper, the numerical simulation of PTFE/Cu liners with different preparation processes penetrating steel targets is carried out, and the effects of different preparation processes and liner density on the penetration characteristics of jets penetrating steel targets are compared and analyzed. The PTFE/Cu shaped charge liner was processed according to different preparation processes, and the jet penetration steel target experiment was carried out, so as to verify and analyze the numerical simulation results.

Study on the Equation of State and Jet Forming of 3D-Printed PLA and PLA-Cu Materials.

In order to improve the research and development efficiency and quality of low-density liners in production and scientific research development, PLA and PLA-Cu composite liners were prepared based on 3D-printing technology. In this paper, the relationship between the shock wave velocity D and the particle velocity u of PLA and PLA-Cu materials was tested by a one-stage light gas gun experiment device, and then the Grüneisen equation of state parameters of the two materials was obtained by fitting. The forming process of the two jets was numerically simulated by using the equation of state. When combined with the pulsed X-ray shooting results of the jets, it was found that the jets of the two materials showed obvious characteristics of "expansion particle flow", and the head of the PLA jet had a gasification phenomenon. The length of the PLA jet at 20 µs in the numerical simulation was 127.2 mm, and the average length of the PLA jet at 20 µs in the pulsed X-ray shooting experiment was 100.45 mm. The length of the PLA jet gasification part accounted for about 21% of the total length of the jet. The average velocity of the head of the PLA jet is 7798.35 m/s, and the average velocity of the head of the PLA-Cu jet is 8104.25 m/s. In this paper, 3D-printing technology is used to prepare the liner for the first time, aiming to open up a new preparation technology and provide a new material selection for low-density material liners.

Study on the forming characteristics of polytetrafluoroethylene/copper jet with different preparation processes.

In this paper, PTFE/Cu composite material for liner is taken as the research object, and the preparation process and jet forming characteristics of PTFE/Cu composite liner are studied. The liners were prepared by extrusion molding, molded sintering and hot-pressing sintering. Due to different preparation processes, different microstructures of the liner can occur, including defects such as pores and microcracks, resulting in different strength and density of the liner, leading to differences in the forming characteristics of the jet. Therefore, the forming process of the jet was simulated by the finite element numerical simulation software. It was found that there was obvious radial expansion effect in the head of the jet, but with the increase of density, the radial expansion effect was weakened, and the jet velocity decreased gradually. The strength and densification of the shaped charge liner prepared by different processes were different. The densification of the molded sintering liner was generally better than that of the other two kinds of shaped charge liners. As a result, the velocity of the jet formed by the molded sintering liner is always the highest, with a numerical simulation velocity of 6642 m/s and an experimental velocity of 6534.7 m/s. The second is the jet of the hot-pressing sintering liner and the lowest velocity is the jet of the extrusion molding cover, with a numerical simulation velocity of 6482 m/s, while the experimental velocity is only 6397.9 m/s. The jet velocity measured by the pulse X-ray experiment was compared with the velocity of the numerical simulation, and the error was within 2.96%, which verifies the accuracy of the numerical simulation.

Regularized Simple Multiple Kernel k -Means With Kernel Average Alignment.

Multiple kernel clustering (MKC) aims to learn an optimal kernel to better serve for clustering from several precomputed basic kernels. Most MKC algorithms adhere to a common assumption that an optimal kernel is linearly combined by basic kernels. Based on a min-max framework, a newly proposed MKC method termed simple multiple kernel k -means (SimpleMKKM) can acquire a high-quality unified kernel. Although SimpleMKKM has achieved promising clustering performance, we observe that it cannot benefit from any prior knowledge. This would cause the learned partition matrix may seriously deviate from the expected one, especially in clustering tasks where the ground truth is absent during the learning course. To tackle this issue, we propose a novel algorithm termed regularized simple multiple kernel k -means with kernel average alignment (R-SMKKM-KAA). According to the experimental results of existing MKC algorithms, the average partition is a strong baseline to reflect true clustering. To gain knowledge from the average partition, we add the average alignment as a regularization term to prevent the learned unified partition from being far from the average partition. After that, we have designed an efficient solving algorithm to optimize the new resulting problem. In this way, both the incorporated prior knowledge and the combination of basic kernels are helpful to learn better unified partition. Consequently, the clustering performance can be significantly improved. Extensive experiments on nine common datasets have sufficiently demonstrated the effectiveness of incorporation of prior knowledge into SimpleMKKM.

Theoretical Investigation of Spectroscopic Properties of the Alkaline-Earth-Metal Monohydrides toward Laser Cooling and Magneto-Optical Trapping.

Alkaline-earth-metal monohydrides MH (M = Be, Mg, Ca, Sr, Ba) have long been regarded as promising candidates toward laser cooling and trapping; however, their rich internal level structures that are amenable to magneto-optical trapping have not been completely explored. Here, we first systematically evaluated Franck-Condon factors of these alkaline-earth-metal monohydrides in the A2Π1/2 ← X2Σ+ transition, exploiting three respective methods (the Morse potential, the closed-form approximation, and the Rydberg-Klein-Rees method). The effective Hamiltonian matrix was introduced for MgH, CaH, SrH, and BaH individually in order to figure out their molecular hyperfine structures of X2Σ+, the transition wavelengths in the vacuum, and hyperfine branching ratios of A2Π1/2(J' = 1/2,+) ← X2Σ+(N = 1,-), followed by possible sideband modulation proposals to address all hyperfine manifolds. Lastly, the Zeeman energy level structures and associated magnetic g factors of the ground state X2Σ+(N = 1,-) were also presented. Our theoretical results here not only shed more light on the molecular spectroscopy of alkaline-earth-metal monohydrides toward laser cooling and magneto-optical trapping but also can contribute to research in molecular collisions involving few-atom molecular systems, spectral analysis in astrophysics and astrochemistry, and even precision measurement of fundamental constants such as the quest for nonzero detection of electron's electric dipole moment.

Overdamped Phonon Diffusion and Nontrivial Electronic Structure Leading to a High Thermoelectric Figure of Merit in KCu5Se3.

Thermoelectric copper selenides are highly attractive owing to not only their constituent nontoxic, abundant elements but also their ultralow liquid-like lattice thermal conductivity (κlat). For the first time, the promising thermoelectric properties of the new KCu5Se3 are reported herein, showing a high power factor (PF = 9.0 µWcm-1 K-2) and an intrinsically ultralow κlat = 0.48 Wm-1 K-1. The doped K1-xBaxCu5Se3 (x = 0.03) realizes a figure-of-merit ZT = 1.3 at 950 K. The crystallographic structure of KCu5Se3 allows complex lattice dynamics that obey a rare dual-phonon transport model well describing a high scattering rate and an extremely short phonon lifetime that are attributed to interband phonon tunneling, confinement of the transverse acoustic branches, and temperature-dependent anharmonic renormalization, all of which generate an unprecedently high contribution of the diffusive phonons (70% at 300 K). The overall weak chemical bonding feature of KCu5Se3 gives K+ cations a quiescence behavior that further blocks the heat flux transfer. In addition, the valence band edge energy dispersion of KCu5Se3 is quasilinear that allows a large Seebeck coefficient even at high hole concentrations. These in-depth understandings of the ultralow lattice thermal conductivity provide new insights into the property-oriented design and synthesis of advanced complex chalcogenide materials.

Comparative genomic insights into habitat adaptation of coral-associated Prosthecochloris.

Green sulfur bacteria (GSB) are a distinct group of anoxygenic phototrophic bacteria that are found in many ecological niches. Prosthecochloris, a marine representative genus of GSB, was found to be dominant in some coral skeletons. However, how coral-associated Prosthecochloris (CAP) adapts to diurnal changing microenvironments in coral skeletons is still poorly understood. In this study, three Prosthecochloris genomes were obtained through enrichment culture from the skeleton of the stony coral Galaxea fascicularis. These divergent three genomes belonged to Prosthecochloris marina and two genomes were circular. Comparative genomic analysis showed that between the CAP and non-CAP clades, CAP genomes possess specialized metabolic capacities (CO oxidation, CO2 hydration and sulfur oxidation), gas vesicles (vertical migration in coral skeletons), and cbb 3-type cytochrome c oxidases (oxygen tolerance and gene regulation) to adapt to the microenvironments of coral skeletons. Within the CAP clade, variable polysaccharide synthesis gene clusters and phage defense systems may endow bacteria with differential cell surface structures and phage susceptibility, driving strain-level evolution. Furthermore, mobile genetic elements (MGEs) or evidence of horizontal gene transfer (HGT) were found in most of the genomic loci containing the above genes, suggesting that MGEs play an important role in the evolutionary diversification between CAP and non-CAP strains and within CAP clade strains. Our results provide insight into the adaptive strategy and population evolution of endolithic Prosthecochloris strains in coral skeletons.

Evaluation of the Different Nutritional and Environmental Parameters on Microbial Pyrene Degradation by Mangrove Culturable Bacteria.

Mangrove ecosystems play curial roles in providing many ecological services and alleviating global climate change. However, they are in decline globally, mainly threatened by human activities and global warming, and organic pollutants, especially PAHs, are among the crucial reasons. Microbial remediation is a cost-effective and environmentally friendly way of alleviating PAH contamination. Therefore, understanding the effects of environmental and nutritional parameters on the biodegradation of polycyclic aromatic hydrocarbons (PAHs) is significant for the bioremediation of PAH contamination. In the present study, five bacterial strains, designated as Bp1 (Genus Rhodococcus), Sp8 (Genus Nitratireductor), Sp13 (Genus Marinobacter), Sp23 (Genus Pseudonocardia), and Sp24 (Genus Mycolicibacterium), have been isolated from mangrove sediment and their ring hydroxylating dioxygenase (RHD) genes have been successfully amplified. Afterward, their degradation abilities were comprehensively evaluated under normal cultural (monoculture and co-culture) and different nutritional (tryptone, yeast extract, peptone, glucose, sucrose, and NPK fertilizer) and environmental (cetyl trimethyl ammonium bromide (CTAB), sodium dodecyl sulfate (SDS)) parameters, as well with different co-contaminants (phenanthrene and naphthalene) and heavy metals (Cd2+, Cu2+, Fe3+, Ni2+, Mg2+, Mn2+, and Co2+). The results showed that strain Sp24 had the highest pyrene degradation rate (85%) in the monoculture experiment after being cultured for 15 days. Adding nitrogen- and carbon-rich sources, including tryptone, peptone, and yeast extract, generally endorsed pyrene degradation. In contrast, the effects of carbon sources (glucose and sucrose) on pyrene degradation were distinct for different bacterial strains. Furthermore, the addition of NPK fertilizer, SDS, Tween-80, phenanthrene, and naphthalene enhanced the bacterial abilities of pyrene removal significantly (p < 0.05). Heavy metals significantly reduced all bacterial isolates' degradation potentials (p < 0.05). The bacterial consortia containing high bio-surfactant-producing strains showed substantially higher pyrene degradation. Moreover, the consortia of three and five bacterial strains showed more degradation efficiency than those of two bacterial strains. These results provide helpful microbial resources for mangrove ecological remediation and insight into optimized culture strategies for the microbial degradation of PAHs.

Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model.

The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used for early diagnosis since they are associated with a brain disorder. This work aims to find a better way to represent electroencephalography (EEG) signals and enhance the classification accuracy of individuals with Parkinson's disease using EEG signals. In this paper, we present two hybrid deep neural networks (DNN) that combine convolutional neural networks with long short-term memory to diagnose Parkinson's disease using EEG signals, that is, through the establishment of parallel and series combined models. The deep CNN network is utilized to acquire the structural features of ECG signals and extract meaningful information from them, after which the signals are sent via a long short-term memory network to extract the features' context dependency. The proposed architecture was able to achieve 97.6% specificity, 97.1% sensitivity, and 98.6% accuracy for a parallel model and 99.1% specificity, 98.5% sensitivity, and 99.7% accuracy for a series model, both in 3-class classification (PD patients with medication, PD patients without medication and healthy).

Multiple Kernel Clustering With Compressed Subspace Alignment.

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n2) memory consumption and O(n3) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O(n) and O(n2) , respectively. The proposed algorithm adopts a novel sampling strategy to enhance the performance and accelerate the speed of MKC. Specifically, we first mathematically model the sampling process and then learn it simultaneously during the procedure of information fusion. By this way, the generated anchor point set can better serve data reconstruction across different views, leading to improved discriminative capability of the reconstruction matrix and boosted clustering performance. Although the integrated sampling process makes the proposed algorithm less efficient than the linear complexity algorithms, the elaborate formulation makes our algorithm straightforward for parallelization. Through the acceleration of GPU and multicore techniques, our algorithm achieves superior performance against the compared state-of-the-art methods on six datasets with comparable time cost to the linear complexity algorithms.

E 3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection.

Existing unsupervised outlier detection (OD) solutions face a grave challenge with surging visual data like images. Although deep neural networks (DNNs) prove successful for visual data, deep OD remains difficult due to OD's unsupervised nature. This paper proposes a novel framework named E 3Outlier that can perform effective and end-to-end deep outlier removal. Its core idea is to introduce self-supervision into deep OD. Specifically, our major solution is to adopt a discriminative learning paradigm that creates multiple pseudo classes from given unlabeled data by various data operations, which enables us to apply prevalent discriminative DNNs (e.g., ResNet) to the unsupervised OD problem. Then, with theoretical and empirical demonstration, we argue that inlier priority, a property that encourages DNN to prioritize inliers during self-supervised learning, makes it possible to perform end-to-end OD. Meanwhile, unlike frequently-used outlierness measures (e.g., density, proximity) in previous OD methods, we explore network uncertainty and validate it as a highly effective outlierness measure, while two practical score refinement strategies are also designed to improve OD performance. Finally, in addition to the discriminative learning paradigm above, we also explore the solutions that exploit other learning paradigms (i.e., generative learning and contrastive learning) to introduce self-supervision for E 3Outlier. Such extendibility not only brings further performance gain on relatively difficult datasets, but also enables E 3Outlier to be applied to other OD applications like video abnormal event detection. Extensive experiments demonstrate that E 3Outlier can considerably outperform state-of-the-art counterparts by 10%-30% AUROC. Demo codes are available at https://github.com/demonzyj56/E3Outlier.

Development of an 18F-labeled anti-human CD8 VHH for same-day immunoPET imaging.

PURPOSE: Cancer immunotherapies (CITs) have revolutionized the treatment of certain cancers, but many patients fail to respond or relapse from current therapies, prompting the need for new CIT agents. CD8+ T cells play a central role in the activity of many CITs, and thus, the rapid imaging of CD8+ cells could provide a critical biomarker for new CIT agents. However, existing 89Zr-labeled CD8 PET imaging reagents exhibit a long circulatory half-life and high radiation burden that limit potential applications such as same-day and longitudinal imaging. METHODS: To this end, we discovered and developed a 13-kDa single-domain antibody (VHH5v2) against human CD8 to enable high-quality, same-day imaging with a reduced radiation burden. To enable sensitive and rapid imaging, we employed a site-specific conjugation strategy to introduce an 18F radiolabel to the VHH. RESULTS: The anti-CD8 VHH, VHH5v2, demonstrated binding to a membrane distal epitope of human CD8 with a binding affinity (KD) of 500 pM. Subsequent imaging experiments in several xenografts that express varying levels of CD8 demonstrated rapid tumor uptake and fast clearance from the blood. High-quality images were obtained within 1 h post-injection and could quantitatively differentiate the tumor models based on CD8 expression level. CONCLUSION: Our work reveals the potential of this anti-human CD8 VHH [18F]F-VHH5v2 to enable rapid and specific imaging of CD8+ cells in the clinic.