True Target Research Articles

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression

Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (e.g., penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.

Genome-Wide Analysis of p53 Targets Reveals SCN2A as a Novel Player in p53-Induced Cell Arrest in HPV-Positive Cells

The host transcription factor p53 is a critical tumor suppressor in HPV-induced carcinogenesis, regulating target genes involved in cell cycle arrest and apoptosis. However, the p53 targets have not been thoroughly analyzed in HPV-infected cells. In this study, p53 signaling in HPV16 and HPV18 cells was activated by depleting the viral oncoprotein E6. Subsequently, p53-regulated genes were identified by comparing them with genes altered in p53-silenced cells. True p53 targets were defined as genes with at least one overlapping p53 binding site and ChIP peak near their locus. Our analysis revealed that while some p53 targets were common to both the HPV16 and HPV18 cells, the majority of the targets differed between these two types, potentially contributing to the varying prevalence of HPV16 and HPV18 in cervical cancer. Additionally, we identified SCN2A as a novel p53 target involved in p53-induced cell cycle arrest in HPV-related carcinogenesis. This study provides new insights into the mechanisms by which p53 inhibits HPV-induced carcinogenesis.

Self-learning based joint multi image super-resolution and sub-pixel registration

Multi Image Super-resolution (MISR) refers to the task of enhancing the spatial resolution of a stack of low-resolution (LR) images representing the same scene. Although many deep learning-based single image super-resolution (SISR) technologies have recently been developed, deep learning has not been widely exploited for MISR, even though it can achieve higher reconstruction accuracy because more information can be extracted from the stack of LR images. One of the primary obstacles encountered by deep networks when addressing the MISR problem is the variability in the number of LR images that act as input to the network. This impedes the feasibility of adopting an end-to-end learning approach, because the varying number of input images makes it difficult to construct a training dataset for the network. Another challenge arises from the requirement to align the LR input images to generate high-resolution (HR) image of high quality, which requires complex and sophisticated methods.In this paper, we propose a self-learning based method that can simultaneously perform super-resolution and sub-pixel registration of multiple LR images. The proposed method trains a neural network with only the LR images as input and without any true target HR images; i.e., the proposed method requires no extra training dataset. Therefore, it is easy to use the proposed method to deal with different numbers of input images. To our knowledge this is the first time that a neural network is trained using only LR images to perform a joint MISR and sub-pixel registration. Experimental results confirmed that the HR images generated by the proposed method achieved better results in both quantitative and qualitative evaluations than those generated by other deep learning-based methods.

Don't jump the gun quite yet: aiming for the true target in plant neurobiology research.

In their recent paper, Kingsland and Taiz argue that proponents of plant intelligence and plant neurobiology misuse historical sources to support their claims, suggesting a pattern of bias. They critique the reliance on subjective judgments and the systematic misuse of past literature by notable scientists. This response addresses their criticisms while adhering to Rapoport's rules to foster constructive academic dialogue. We emphasize the importance of evidence-based research and highlight areas of agreement, including the fallacy of appealing to authority and the necessity for more robust empirical evidence. However, we also challenge their selective citation practices and argue that their narrative itself is subject to the same criticisms they levy. By examining recent works and pointing out overlooked rebuttals, we aim to clarify misconceptions and advocate for a more nuanced understanding of plant intelligence research. This dialogue underscores the need for rigorous, respectful scientific discourse to advance the field.

UAV Routing for Enhancing the Performance of a Classifier-in-the-loop

Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface to aid them in classification. The performance of a human as a (binary) classifier-in-the-loop is characterized by probabilities of correctly classifying objects (or points of interest) as a true target or a false target. These two probabilities depend on the time spent collecting information at a point of interest (POI), known as dwell time. The information gain associated with collecting information at a POI is then a function of dwell time and discounted by the revisit time, i.e., the duration between consecutive revisits to the same POI, to ensure that the vehicle covers all POIs in a timely manner. The objective of the routing problem for classification is to route the vehicles optimally, which is a discrete problem, and determine the optimal dwell time at each POI, which is a continuous optimization problem, to maximize the total discounted information gain while visiting every POI at least once. Due to the coupled discrete and continuous problem, which makes the problem hard to solve, we make a simplifying assumption that the information gain is discounted exponentially by the revisit time; this assumption enables one to decouple the problem of routing with the problem of determining optimal dwell time at each POI for a single vehicle problem. For the multi-vehicle problem, since the problem involves task partitioning between vehicles in addition to routing and dwell time computation, we provide a fast heuristic to obtain high-quality feasible solutions.

Night work, circadian rhythm, and cognitive performance: A field study with airport security screeners

The effects of circadian rhythms and night work on performance have been extensively studied using standardized, non-work-related tasks in laboratory settings. However, field research on work performance is scarce in this domain. This study addresses this gap by analyzing four million behavioral responses from 1437 security officers at an international airport. We compared threat detection performance during the routine security screening of passengers' baggage X-ray images across night shifts (shift starting between 0:00 and 2:59), early morning shifts (starting between 3:00 and 5:59), and standard morning shifts (starting between 6:00 and 7:59). Processing times followed the circadian rhythm of attention found in laboratory studies, indicating that the rhythm affects real-life work performance. False alarm rates (i.e. false target present responses) were slightly higher during night and early morning shifts than during standard morning shifts, with no significant difference in the security-relevant hit rates (i.e. true target present responses). Furthermore, we found no performance differences between night work and early morning shifts, suggesting that both can disrupt employees’ natural sleep patterns with implications on performance.

Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios.

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets.

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

Advanced Method for Improving Marine Target Tracking Based on Multiple-Plot Processing of Radar Images

Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide.

Cognitive dual coprime frequency diverse array MIMO radar network for target discrimination and main‐lobe interference mitigation

AbstractThe authors propose a novel dual coprime frequency diverse array (FDA) multiple input multiple output (DCFDA‐MIMO) radar network design, empowered by cognitive capabilities, aimed at target discrimination and mitigation of interference present in the standalone radar systems. That is, the proposed DCFDA‐MIMO design capitalises on the complementary advantages of FDAs for target discrimination and coprime arrays for enhanced resolution, resulting in superior performance. Additionally, the proposed DCFDA‐MIMO network employs a 2D multiple signal classification algorithm to achieve high‐resolution target localisation. By incorporating cognitive techniques based on the action‐perception cycle, the proposed approach demonstrates notable improvements in multiple target detection and tracking accuracy with fewer number of antenna elements as compared to existing techniques. Furthermore, it enhances individual radar beamforming performance for interference suppression and true target detection without prior information.

Adaptive target detection for an FDA-MIMO radar in a mainlobe deceptive jamming and a partially homogeneous noise

In this paper, we investigate the adaptive target detection problem for a collocated frequency diverse array multiple-input multiple-output (FDA-MIMO) radar in the presence of mainlobe deceptive jamming and partially homogeneous noise with an unknown covariance matrix and scaling factor. Precisely, we establish a received signal model that includes the true target and false counterparts echoes in the presence of Gaussian noise (it includes thermal noise, interference suppression, and clutter after range compensation.). Furthermore, we consider that the range information of the true target and mainlobe deceptive targets is not identical. Therefore, we project the corresponding transmit steering vectors onto different subspaces with known subspace matrices but unknown coordinates. On the other hand, the true and false targets' receive steering vectors are considered to be from the same subspace. Finally, following the generalized likelihood ratio test (GLRT), the Rao and the Wald criteria, we design three adaptive detectors with two-step criteria, i.e., TGLRT, TRao, and TWald. We note that the proposed detectors maintain a constant false alarm rate (CFAR) against the scaling factor and the noise covariance matrix. The extensive numerical simulation results have validated the effectiveness of the proposed detectors.

Abstract PO3-24-08: Establishment of a selective culture method for breast cancer stem cells derived from patient tissue and a subgroup analysis of cancer stem cells

Abstract Purpose Cancer stem cells are considered to cause recurrence and metastasis due to their resistance to drug and radiation therapy. Breast cancer stem cells are therefore the true target for the treatment of breast cancer. In this study, we established a method to selectively culture breast cancer stem cells from patient-derived breast cancer tissue, and we analyzed their characteristics using immunostaining to identify gene expression. Method Breast cancer tissues from ER+, HER2- and drug-naive patients were fragmented and cultured using low-adherent plates (spheroid culture). We investigated the stemness of the breast cancer cells obtained by the spheroid culture by immunostaining the breast cancer stem cell markers and examining their proliferation and differentiation in a mouse transplantation model. The diversity of the cells was further examined by means of the expression of the EMT markers and genetic analysis. Results In 48 cases, the proportion of CD44+/CD24- breast cancer cells increased from 13.8% to 61.6% in the spheroid culture. In addition, even small numbers of cells obtained by the spheroid culture were transplanted into mice, and the transplanted cells subsequently differentiated in the mice, demonstrating their stemness. ER expression was negatively changed in 52.1% of cases, and the expression of EMT markers, Twist, Snail, and Vimentin, was increased from 43.8% to 75.0%, 12.9% to 58.1%, and 7.71% to 37.7%, respectively. A DNA microarray with 14 cases showed that breast cancer stem cells obtained from ER+ and HER2- breast cancer tissue were classified into two groups. Conclusion We demonstrated that breast cancer stem cells were selectively cultured from patient-derived breast cancer tissue in a spheroid culture. Furthermore, breast cancer stem cells have different characteristics from breast cancer cells in primary tumors and are diverse at an EMT level, and in genetic analyses. Table. Expression of primary tumors and spheroids Citation Format: Satoshi Sueoka, Takayuki Kadoya, Kai Azusa, Shinsuke Sasada, Akiko Emi, Morihito Okada, Yukino Kobayashi, Koh Nakayama. Establishment of a selective culture method for breast cancer stem cells derived from patient tissue and a subgroup analysis of cancer stem cells [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-08.

Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood. Using busulfan as an example application and area under the concentration–time curve (AUC) as a target exposure metric, these estimation methods were compared using retrospective patient data (N = 246) and simulated precision dosing treatment courses. NCA was performed with or without peak extension, and MAP Bayesian estimation was performed using either the one-compartment Shukla model or the two-compartment McCune model. All methods showed good agreement on real-world data (correlation coefficients of 0.945–0.998) as assessed by Bland–Altman plots, although agreement between NCA and MAP methods was higher during the first dosing interval (0.982–0.994) compared to subsequent dosing intervals (0.918–0.938). In dose adjustment simulations, both NCA and MAP estimated high target attainment (> 98%) although true simulated target attainment was lower for NCA (63–66%) versus MAP (91–93%). The largest differences in AUC estimation were due to different assumptions for the shape of the concentration curve during the infusion phase, followed by how the methods considered time-dependent clearance and concentration–time points collected in earlier intervals. In conclusion, although AUC estimates between the two methods showed good correlation, in a simulated study, MAP lead to higher target attainment. When changing from one method to another, or changing infusion duration and other factors, optimum estimated exposure targets may require adjusting to maintain a consistent exposure.

Advertisement Synthesis Network for Automatic Advertisement Image Synthesis

Image advertising is widely used by companies to advertise their products and increase awareness of their brands. With the constant development of image generation techniques, automatic compositing of advertisement images has also been widely studied. However, the existing algorithms cannot synthesise consistent-looking advertisement images for a given product. The key challenge is to stitch a given product into a scene that matches the style of the product while maintaining a consistent-looking. To solve this problem, this paper proposes a new two-stage automatic advertisement image generation model, called Advertisement Synthesis Network (ASNet), which explores a two-stage generation framework to synthesise consistent-looking product advertisement images. Specifically, ASNet first generates a preliminary target product scene using Pre-Synthesis and then extracts scene features using Pseudo-Target Object Encoder (PTOE) and true target features using Real Target Object Encoder (RTOE), respectively. Finally, we inject the acquired features into the pretrained diffusion model and reconstruct them in the preliminary generated target goods scene. Extensive experiments have shown that the method achieves better results in all three performance metrics related to the quality of the synthesised image compared to other methods. In addition, we have done a simple and preliminary study on the effect of synthetic advertisement images on real consumers’ purchase intention and brand perception. The results of the study show that the advertisement images synthesised by the model proposed in this paper have a positive impact on consumer purchase intention and brand perception.

Mainlobe deceptive jammer suppression with DEPC-MIMO radar with joint transmit–receive design

This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression.

An evolutionarily conserved phosphoserine-arginine salt bridge in the interface between ribosomal proteins uS4 and uS5 regulates translational accuracy in Saccharomyces cerevisiae.

Protein-protein and protein-rRNA interactions at the interface between ribosomal proteins uS4 and uS5 are thought to maintain the accuracy of protein synthesis by increasing selection of cognate aminoacyl-tRNAs. Selection involves a major conformational change-domain closure-that stabilizes aminoacyl-tRNA in the ribosomal acceptor (A) site. This has been thought a constitutive function of the ribosome ensuring consistent accuracy. Recently, the Saccharomyces cerevisiae Ctk1 cyclin-dependent kinase was demonstrated to ensure translational accuracy and Ser238 of uS5 proposed as its target. Surprisingly, Ser238 is outside the uS4-uS5 interface and no obvious mechanism has been proposed to explain its role. We show that the true target of Ctk1 regulation is another uS5 residue, Ser176, which lies in the interface opposite to Arg57 of uS4. Based on site specific mutagenesis, we propose that phospho-Ser176 forms a salt bridge with Arg57, which should increase selectivity by strengthening the interface. Genetic data show that Ctk1 regulates accuracy indirectly;the data suggest that the kinase Ypk2 directly phosphorylates Ser176. A second kinase pathway involving TORC1 and Pkc1 can inhibit this effect. The level of accuracy appears to depend on competitive action of these two pathways to regulate the level of Ser176 phosphorylation.

SAR Multi-Angle Observation Method for Multipath Suppression in Enclosed Spaces

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images.

Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion Priors

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis. In synthetic data experiments and on real-world datasets, our model successfully captures all true targets while simultaneously proposing fewer computationally costly false positives. With this approach, we take an important step towards fully automating the detection of high-risk subregions, but we wish to emphasize the importance of prudent skepticism until it has been tested and proven on more diverse data.

One-pot synthesis of tetrahydropyrimidinecarboxamides enabling in vitro anticancer activity: a combinative study with clinically relevant brain-penetrant drugs.

In this study, we describe a one-pot three-component synthesis of bioactive tetrahydopyrimidinecarboxamide derivatives employing lanthanum triflate as a catalyst. Out of the synthesized compounds, 4f had the most potent anti-cancer activity and impeded cell cycle progression effectively. Anti-cancer bioactivity was observed in 4f against liver, breast, and lung cancers as well as primary patient-derived glioblastoma cell lines. Compound 4f effectively inhibited the 3D neurosphere formation in primary patient-derived glioma stem cells. Specifically, 4f exhibited synergistic cytotoxicity with the EGFR inhibitor that is the clinical epidermal growth factor receptor inhibitor osimertinib. 4f does not exhibit anti-kinase activity and is cytostatic in nature, and further work is needed to understand the true molecular target of 4f and its derivatives. Through our current work, we establish a promising tetrahydopyrimidinecarboxamide-based lead compound with anti-cancer activity, which may exhibit potent anti-cancer activity in combination with specific clinically relevant small molecule kinase inhibitors.

Rare but diverse off-target and somatic mutations found in field and greenhouse grown trees expressing CRISPR/Cas9.

Introduction: CRISPR gene editing, while highly efficient in creating desired mutations, also has the potential to cause off-target mutations. This risk is especially high in clonally propagated plants, where editing reagents may remain in the genome for long periods of time or in perpetuity. We studied a diverse population of Populus and Eucalyptus trees that had CRISPR/Cas9-containing transgenes that targeted one or two types of floral development genes, homologs of LEAFY and AGAMOUS. Methods: Using a targeted sequence approach, we studied approximately 20,000 genomic sites with degenerate sequence homology of up to five base pairs relative to guide RNA (gRNA) target sites. We analyzed those sites in 96 individual tree samples that represented 37 independent insertion events containing one or multiples of six unique gRNAs. Results: We found low rates of off-target mutations, with rates of 1.2 × 10-9 in poplar and 3.1 × 10-10 in eucalypts, respectively, comparable to that expected due to sexual reproduction. The rates of mutation were highly idiosyncratic among sites and not predicted by sequence similarity to the target sites; a subset of two gRNAs showed off-target editing of four unique genomic sites with up to five mismatches relative to the true target sites, reaching fixation in some gene insertion events and clonal ramets. The location of off-target mutations relative to the PAM site were essentially identical to that seen with on-target CRISPR mutations. Discussion: The low rates observed support many other studies in plants that suggest that the rates of off-target mutagenesis from CRISPR/Cas9 transgenes are negligible; our study extends this conclusion to trees and other long-lived plants where CRISPR/Cas9 transgenes were present in the genome for approximately four years.