Single Target Research Articles

Metasurface Enabled Multi‐Target and Multi‐Wavelength Diffraction Neural Networks

AbstractBenefiting from low power consumption and high processing speed, there is a growing interest in diffraction neural networks (DNNs), which are typically showcased with 3D printing devices, leading to large volumes, high costs, and low levels of integration. Metasurfaces can desirably manipulate wavefronts of electromagnetic waves, providing a compact platform for mimicking DNNs with novel functions. Although multi‐wavelength and multi‐target recognition provides a richer and more detailed understanding of complex environments, existing architectures are primarily trained to classify a single target at a specific wavelength. A metasurface approach is proposed to design multiplexed DNNs that can classify multiple targets and spatial sequences across various wavelengths in multiple channels. To realize multi‐task processing, the dielectric metasurface is designed based on phase and wavelength multiplexing, which can integrate multi‐target DNNs with different tasks such as operating at distinct wavelengths and classifying diverse targets. The efficacy of this method is exemplified through the numerical simulation and experimental demonstration of recognizing a single target with two wavelengths, two targets at a single wavelength, and two targets at dual wavelengths. This compact metasurface approach enables the design of multi‐target and multi‐wavelength DNNs, opening a new window to develop massively parallel processing and versatile artificial intelligence systems.

Single Target Tracking With Unknown Detection Status: Filter Design and Stability Analysis

Single Target Tracking With Unknown Detection Status: Filter Design and Stability Analysis

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

3252: Control limits for single isocenter multiple brain targets SRS PSQA with a high resolution detector

3252: Control limits for single isocenter multiple brain targets SRS PSQA with a high resolution detector

A tetrahedral framework nucleic acids-based gene therapeutic nanococktail alleviates cartilage damage and protects against osteoarthritis progression

Gene therapy based on therapeutic microRNAs (miRNAs) shows great potential in Osteoarthritis (OA) treatment. However, individual miRNAs application is limited by the single target, the instability, and the low efficiency of cell internalization. Here, we identified two potential candidate miRNAs, miR-140-5p and miR-455-3p, exhibited combined enhancing effects against OA progression. Furthermore, a nanococktail therapeutic platform, T-(140 + 455) was constructed by employing tetrahedral framework nucleic acids (tFNAs) to codeliver miR-140-5p/miR-455-3p and achieve enhanced gene therapy for OA. Compared with the delivery of individual miRNAs, T-(140 + 455) can effectively reverse the cell phenotype and metabolism of OA chondrocytes. Moreover, the nanococktail T-(140 + 455) reprogrammed macrophage polarization by modulating the crosstalk between chondrocytes and macrophages, combatting the damage caused by inflammation. The OA rat model treated with nanococktail T-(140 + 455) exhibited favorable performance in halting cartilage damage and regulating extracellular matrix (ECM) metabolic homeostasis. Thus, tFNA-based nanococktails enhance the delivery and efficacy of miRNAs, offering a promising strategy for OA gene therapy.

Vision-based autonomous robots calibration for large-size workspace using ArUco map and single camera systems

The low positioning accuracy of industrial robots limits their application in industry. Vision-based kinematic calibration, known for its rapid processing and economic efficiency, is an effective solution to enhance this accuracy. However, most of these methods are constrained by the camera's field of view, limiting their effectiveness in large workspaces. This paper proposes a novel calibration framework composed of monocular vision and computer vision techniques using ArUco markers. Firstly, a robot positioning error model was established by considering the kinematic error based on the Modified Denavit-Hartenberg model. Subsequently, a calibrated camera was used to create an ArUco map as an alternative to traditional single calibration targets. The map was constructed by stitching images of ArUco markers with unique identifiers, and its accuracy was enhanced through closed-loop detection and global optimization that minimizes reprojection errors. Then, initial hand-eye parameters were determined, followed by acquiring the robot's end-effector pose through the ArUco map. The Levenberg-Marquardt algorithm was employed for calibration, involving iterative refinement of hand-eye and kinematic parameters. Finally, experimental validation was conducted on the KUKA kr500 industrial robot, with laser tracker measurements as the reference standard. Compared to the traditional checkerboard method, this new approach not only expands the calibration space but also significantly reduces the robot's absolute positioning error, from 1.359 mm to 0.472 mm.

A Volume-limited Radio Search for Magnetic Activity in 140 Exoplanets with the Very Large Array

We present results from a search for radio emission in 77 stellar systems hosting 140 exoplanets, predominantly within 17.5 pc using the Very Large Array (VLA) at 4–8 GHz. This is the largest and most sensitive search to date for radio emission in exoplanetary systems in the GHz frequency range. We obtained new observations of 58 systems and analyzed archival observations of an additional 19 systems. Our choice of frequency and volume limit is motivated by radio detections of ultracool dwarfs (UCDs), including T dwarfs with masses at the exoplanet threshold of ∼13 M J. Our surveyed exoplanets span a mass range of ≈10−3–10 M J and semimajor axes of ≈10−2–10 au. We detect a single target—GJ 3323 (M4) hosting two exoplanets with minimum masses of 2 and 2.3 M ⊕—with a circular polarization fraction of ≈40%; the radio luminosity agrees with its known X-ray luminosity and the Güdel–Benz relation for stellar activity suggesting a likely stellar origin, but the high circular polarization fraction may also be indicative of star–planet interaction. For the remaining sources our 3σ upper limits are generally L ν ≲ 1012.5 erg s−1 Hz−1, comparable to the lowest radio luminosities in UCDs. Our results are consistent with previous targeted searches of individual systems at GHz frequencies while greatly expanding the sample size. Our sensitivity is comparable to predicted fluxes for some systems considered candidates for detectable star–planet interaction. Observations with future instruments such as the Square Kilometre Array and Next-Generation VLA will be necessary to further constrain emission mechanisms from exoplanet systems at GHz frequencies.

Robotic MR-guided high dose rate brachytherapy needle implantation in the prostate (ROBiNSon)—a proof-of-concept study

Objective. A robotic needle implant device for MR-guided high-dose-rate (HDR) prostate brachytherapy was developed. This study aimed to assess the feasibility and spatial accuracy of HDR brachytherapy using the robotic device, for a single intraprostatic target point. Approach. Five patients were treated from November 2019–June 2022 with the robot. The robot fits a 1.5 T MR scanner and the needle can be shifted and angulated. An intraprocedural MR scan was fused with the diagnostic MR and one preplanned needle position was selected for robotic insertion. The needle entry point and angles were set for a needle tip target point within the intraprostatic target volume. The needle was tapped stepwise towards the target point pneumatically. Final needle position was verified with MR, followed by plan optimization and dose delivery. Any remaining planned needles were inserted manually. Needle tip to geometrical target error (NTG-error) was defined as the deviation of the actual tip position relative to the predefined geometric target point, using MR-coordinates. Needle tip to treatment target error (NTT-error) was defined as the deviation of the actual tip position relative to the treatment target point, using fused MR-images pre- and post-needle implantation taking into account prostate deformation. Difference between NTT-error and NTG-error and fiducial marker shifts indicated prostate movement. For determining prostate deformation, the Jaccard index and prostate volumes were assessed. Main results. The robotic device was able to tap the needle to the planned depth for all patients. Mean robotic procedure duration was 142 min. NTG-error was 3.2 (range 1.1–6.7) mm and NTT-error 4.5 (range 2.6–9.6) mm. Marker displacements were smaller than 3 mm. No treatment-related acute toxicity was reported. Feasibility of needle placement within the prostate was considered adequate. Significance. MR-guided robotic needle insertion is feasible with a mean geometric accuracy of 3.2 mm and <3 mm prostate movement.

Reinforcement learning-based multi-impulse rendezvous approach for satellite constellation reconfiguration

This paper presents a satellite constellation orbital reconfiguration method based on the intelligent multi-impulse rendezvous considering the impulse-magnitude limit. This problem is solved by two steps: the single-target rendezvous trajectory optimization and the multi-target rendezvous mission planning. For single target, the multi-impulse rendezvous problem is transformed into a Markov decision process and solved by a reinforcement learning algorithm. The rewards of the critic networks are used as estimation of fuel consumption, while the actions of the actor networks are used as initial values for further optimization. For multiple targets, an efficient Monte-Carlo tree search method is used to realize the global optimal solution of the satellite constellation reconfiguration problem. Finally, the numerical examples show that the proposed method can rapidly optimize the multi-impulse constellation reconfiguration problem.

Non-Medical Applications of Inorganic Medicines. A Switch Based on Mechanistic Knowledge.

Metals have been used in medicine for centuries. However, it was not until much later that the effects of inorganic drugs could be rationalized from a mechanistic point of view. Today, thanks to the technologies available, this approach has been functionally developed and implemented. It has been found that there is probably no single biological target for the pharmacological effects of most inorganic drugs. Herein, we present an overview of some integrated and multi-technique approaches to elucidate the molecular interactions underlying the biological effects of metallodrugs. On this premise, selected examples are used to illustrate how the information obtained on metal-based drugs and their respective mechanisms can become relevant for applications in fields other than medicine. For example, some well-known metallodrugs, which have been shown to bind specific amino acid residues of proteins, can be used to solve problems related to protein structure elucidation in crystallographic studies. Diruthenium tetraacetate can be used to catalyze the conversion of hydroxylamines to nitrones with a high selectivity when bound to lysozyme. Finally, a case study is presented in which an unprecedented palladium/arsenic-mediated catalytic cycle for nitrile hydration was discovered thanks to previous studies on the solution chemistry of the anticancer compound arsenoplatin-1 (AP-1).

Using Functional Genomic Data in Monocytes/Macrophages and Genotyping to Nominate Disease-Driving Single Nucleotide Polymorphisms and Target Genes in Juvenile Idiopathic Arthritis.

GWAS have identified multiple regions that confer risk for juvenile idiopathic arthritis (JIA). However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk is impeded by the SNPs' that identify risk loci being in linkage disequilibrium (LD) with hundreds of other SNPs. Since the causal SNPs remain unknown, it is difficult to identify target genes and use genetic information to inform patient care. We used genotyping and functional data in primary human monocytes/macrophages to nominate disease-driving SNPs on JIA risk haplotypes and identify their likely target genes. We identified JIA risk haplotypes using Immunochip data from Hinks et al (Nature Gen 2013) and the meta-analysis from McIntosh et al (Arthritis Rheum 2017). We used genotyping data from 3,939 children with JIA and 14,412 healthy controls to identify SNPs that: (1) were situated within open chromatin in multiple immune cell types and (2) were more common in children with JIA than the controls (p< 0.05). We intersected the chosen SNPs (n=846) with regions of bi-directional transcription initiation characteristic of non-coding regulatory regions detected using dREG to analyze GRO-seq data. Finally, we used MicroC data to identify gene promoters interacting with the regulatory regions harboring the candidate causal SNPs. We identified 190 SNPs that overlap with dREG peaks in monocytes and126 SNPs that overlap with dREG peaks in macrophages. Of these SNPs, 101 were situated within dREG peaks in both monocytes and macrophages, suggesting that these SNPs exert their effects independent of the cellular activation state. MicroC data in monocytes identified 20 genes/transcripts whose promoters interact with the enhancers harboring the SNPs of interest. SNPs in JIA risk regions that are candidate causal variants can be further screened using functional data such as GRO-seq. This process identifies a finite number of candidate causal SNPs, the majority of which are likely to exert their biological effects independent of cellular activation state in monocytes. Three-dimensional chromatin data generated with MicroC identifies genes likely to be influenced by these SNPs. These studies demonstrate the importance of investigations into the role of innate immunity in JIA.

Design and application of UAV single target tracking system based on YOLOv5

In aerial reconnaissance, pursuit, and post-disaster search and rescue, rotorcraft UAVs are valued for their agility. A key challenge is target tracking in complex environments with no prior information, dense obstacles, GNSS signal loss, and occluded targets. This paper introduces a YOLOv5-based UAV tracking system to address detection and path planning. The quadrotor UAV model ensures stable flight through mathematical modeling and simulation. YOLOv5 enhances detection performance with real-time tracking. Feedback control enables autonomous obstacle avoidance and target tracking. Results show adaptability and robustness, supporting reconnaissance and rescue missions effectively.

Discovering new mode-of-action pesticide leads inhibiting protein-protein interactions: example targeting plant O-acetylserine sulfhydrylase.

The widespread evolution of pesticide resistance poses a significant challenge to current agriculture, necessitating the discovery of molecules with new modes of action. Despite extensive efforts, no major molecules with new modes of action have been commercialized for decades. Most pesticides function by binding to specific pockets on target enzymes, enabling a single target site mutation to confer resistance. An alternative approach is the disruption of protein-protein interactions (PPI), which require complementary mutations on both interacting partners for resistance to occur. Thus, our aim is the discovery and design of small-molecule inhibitors that target the interface of the PPI complex of O-acetylserine sulfhydrylase (OASS) and serine acetyltransferase (SAT), key obligatory interacting plant enzymes involved in the biosynthesis of the amino acid cysteine. By employing in silico filtering techniques on a virtual library of 30 million small molecules, we identified initial hits capable of binding OASS and interfering with its interaction with a peptide derived from SAT with a half-maximal inhibitory concentration (IC50) of 34 μm. Subsequently, we conducted molecular chemical optimizations, generating an early lead molecule (PJ4) with an IC50 value of 4 μm. PJ4 successfully inhibited the germination of Arabidopsis thaliana seedlings and inhibited clover growth in a pre-emergence application at an effective concentration of 4.6 kg ha-1. These new compounds described herein can serve as promising leads for further optimization as herbicides with a new mode-of-action. This technology can be used for discovering new modes of action chemicals inhibiting all pest groups. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

DCE-CT parameters as new functional imaging biomarkers at baseline and during immune checkpoint inhibitor therapy in patients with lung cancer – a feasibility study

BackgroundWith the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors.MethodsThirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BVdecon), blood flow deconvolution (BFdecon), blood flow maximum slope (BFMax slope) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses.ResultsHigh baseline Blood Volume (BVdecon) (> 12.97 ml × 100 g−1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BFdecon (> 24.50% for OS and > 12.04% for PFS) was associated with an unfavorable OS (8.7 months vs 23.1 months; p < 0.025) and PFS (2.5 vs 13.7 months; p < 0.018). The relative change in BFdecon (categorical) on day seven was a predictor of OS (HR 0.26, CI95: 0.06 to 0.93 p = 0.039) and PFS (HR 0.27, CI95: 0.09 to 0.85 p = 0.026).ConclusionDCE-CT-identified parameters may serve as potential prognostic biomarkers at baseline and during early treatment in patients with NSCLC treated with immune checkpoint inhibitor therapy.

Quantitative Hydrogen-Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries.

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients (KD) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict KD from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Osteoporosis treatment: current drugs and future developments.

Osteoporosis is a common systemic metabolic disease characterized by a decrease in bone density and bone mass, destruction of bone tissue microstructure, and increased bone fragility leading to fracture susceptibility. Pharmacological treatment of osteoporosis is the focus of current research, and anti-osteoporosis drugs usually play a role in inhibiting bone resorption, promoting bone formation, and having a dual role. However, most of the drugs have the disadvantages of single target and high toxic and side effects. There are many types of traditional Chinese medicines (TCM), from a wide range of sources and mostly plants. Herbal plants have unique advantages in regulating the relationship between osteoporosis and the immune system, acupuncture therapy has significant therapeutic effects in combination with medicine for osteoporosis. The target cells and specific molecular mechanisms of TCM in preventing and treating osteoporosis have not been fully elucidated. At present, there is a lack of comprehensive understanding of the pathological mechanism of the disease. Therefore, a better understanding of the pathological signaling pathways and key molecules involved in the pathogenesis of osteoporosis is crucial for the design of therapeutic targets and drug development. In this paper, we review the development and current status of anti-osteoporosis drugs currently in clinical application and under development to provide relevant basis and reference for drug prevention and treatment of osteoporosis, with the aim of promoting pharmacological research and new drug development.

No longer stuck in the past: new advances in artificial intelligence and molecular assays for parasitology screening and diagnosis.

Emerging technologies are revolutionizing parasitology diagnostics and challenging traditional methods reliant on microscopic analysis or serological confirmation, which are known for their limitations in sensitivity and specificity. This article sheds light on the transformative potential of artificial intelligence and molecular assays in the field, promising more accurate and efficient detection methods. Artificial intelligence has emerged as a promising tool for blood and stool parasite review, when paired with comprehensive databases and expert oversight result in heightened specificity and sensitivity of diagnoses while also increasing efficiency. Significant strides have been made in nucleic acid testing for multiplex panels for enteric pathogen. Both multiplex and single target panels for Plasmodium , Babesia , filaria, and kinetoplastids have been developed and garnered regulatory approval, notably for blood donor screening in the United States. Additional technologies such as MALDI-TOF, metagenomics, flow cytometry, and CRISPR-Cas are under investigation for their diagnostic utility and are currently in the preliminary stages of research and feasibility assessment. Recent implementation of artificial intelligence and digital microscopy has enabled swift smear screening and diagnosis, although widespread implementation remains limited. Simultaneously, molecular assays - both targeted and multiplex panels are promising and have demonstrated promise in numerous studies with some assays securing regulatory approval recently. Additional technologies are under investigation for their diagnostic utility and are compelling avenues for future proof-of-concept diagnostics.

Vpit: real-time embedded single object 3D tracking using voxel pseudo images

In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo images for 3D SOT. The input point cloud is structured by pillar-based voxelization, and the resulting pseudo image is used as an input to a 2D-like Siamese SOT method. The pseudo image is created in the Bird’s-eye View (BEV) coordinates; and therefore, the objects in it have constant size. Thus, only the object rotation can change in the new coordinate system and not the object scale. For this reason, we replace multi-scale search with a multi-rotation search, where differently rotated search regions are compared against a single target representation to predict both position and rotation of the object. Experiments on KITTI [1] Tracking dataset show that VPIT is the fastest 3D SOT method and maintains competitive Success and Precision values. Application of a SOT method in a real-world scenario meets with limitations such as lower computational capabilities of embedded devices and a latency-unforgiving environment, where the method is forced to skip certain data frames if the inference speed is not high enough. We implement a real-time evaluation protocol and show that other methods lose most of their performance on embedded devices; while, VPIT maintains its ability to track the object.

A Three–Year Comparison of Fluctuations in the Occurrence of the Giant Jellyfish (Nemopilema nomurai)

In this study, acoustic, sighting, trawl, and marine environmental surveys were used to determine the vertical distribution and density of giant jellyfish that have been observed in Korean waters over the past 3 years. From 2020 to 2022, annual surveys were conducted in May and July in the East China Sea and waters adjacent to South Korea. The acoustic data were processed by identifying and eliminating all signals considered as noise while excluding those suspected to be jellyfish signals. Subsequently, a single target detection method was employed. Giant jellyfish are distributed mostly in the middle and low layers. In May 2021, the average population density of giant jellyfish was recorded as 11.6 (ind./ha), which was the highest density. In July 2022, this value decreased to 1.7 (ind./ha), marking the lowest density. The sighting survey, which allows for the identification of jellyfish distributed in the surface layer, exhibited a difference of approximately 0.13 times compared to the acoustic survey conducted in the middle and low layers in 2020. In 2021 and 2022, this difference was approximately 0.11 times and 0.24 times, respectively. The average of this difference was 0.16 times or greater.

Effect of Zr/Ti molar ratio on film growth, hardness and friction, and wear properties of TiAlZrN hard films

The variation of the ratio between metal elements in nitride hard films is one of the important research directions for regulating the comprehensive mechanical properties of hard films. In this study, TiAlZrN hard films with fixed Al content (35-37 at%) and different Zr/Ti molar ratios were prepared by designing combinations of single Zr targets, TiZr and TiAl alloy targets, and by adjusting the target current. The crystal growth, phase structure, and hardness of TiAlZrN hard films with different Zr/Ti molar ratios and friction and wear properties were investigated. The experimental results show that the droplets on the surface of TiAlZrN hard films prepared using Zr-containing targets increased significantly. The hard film exhibits TiAlZrN (111) crystal face-oriented growth on a selective basis. The TiAlZrN hard film with a Zr/Ti molar ratio of 0.49 has the highest hardness value of 45.1 ± 1.5 GPa. TiAlZrN hard films with a Zr/Ti molar ratio of 0.80 have the best wear resistance with a friction coefficient of 0.4–0.6 and a hardness value of 39.2 ± 1.5 GPa. Higher hardness values lead to more severe abrasive wear and influence the wear resistance of hard films. This study provides a reliable basis for the design of hard film compositions with excellent overall mechanical properties.