Search Results (12)

Selective gene delivery to defined cell populations remains one of the key challenges in lentiviral vector-based gene therapy. The vesicular stomatitis virus glycoprotein (VSV-G) confers high infectivity but lacks cell-type specificity because of the ubiquitous expression of its receptor, LDLR. To enable modular, receptor-specific targeting while retaining the production efficiency of VSV-G-pseudotyped vectors, we designed a bispecific adapter, 929-B6, comprising a VSV-G-binding nanobody and an ERBB2-binding DARPin 9.29. Anti-VSV-G nanobodies were isolated from an alpaca immune library and screened in cell-based pseudoreceptor assays to identify the optimal binder (VSVG-B6). The resulting adapter was evaluated with receptor-ablated (VSV-G_mut) and wild-type VSV-G-pseudotyped LVs across ERBB2-positive and -negative cell lines and in a mouse xenograft model. 929-B6 enabled efficient, receptor-specific transduction of ERBB2-expressing cells without increasing infection of ERBB2-negative controls. Pre-incubation of VSV-G_mut-pseudotyped LVs with 1–2 µg/mL 929-B6 increased transduction up to eight-fold in ERBB2⁺ cells, with similar but smaller effects for VSV-G and VSV-G_mut + 929R pseudotypes. Across breast cancer lines, transduction enhancement correlated with ERBB2 surface density, and co-culture experiments confirmed selective entry into ERBB2⁺ populations. In vivo imaging of ERBB2⁺ tumors revealed a visible tumor-localized luminescent signal following administration of 929-B6-treated vectors. The 929-B6 adapter provides a rapid, scalable means to retarget standard LV stocks toward chosen receptors without re-engineering the envelope or co-packaging pseudoreceptor plasmids. Its modularity suggests a generalizable platform for both gene therapy and oncolytic applications requiring flexible, receptor-defined tropism. Full article

The evaluation and enhancement of image aesthetics play a pivotal role in the development of visual media, impacting fields including photography, design, and computer vision. Composition, a key factor shaping visual aesthetics, significantly influences an image’s vividness and expressiveness. However, existing image optimization methods face practical challenges: compression-induced distortion, imprecise object extraction, and cropping-caused unnatural proportions or content loss. To tackle these issues, this paper proposes an image aesthetic evaluation with composition and similarity (IACS) method that harmonizes composition aesthetics and image similarity through a unified function. When evaluating composition aesthetics, the method calculates the distance between the main semantic line (or salient object) and the nearest rule-of-thirds line or central line. For images featuring prominent semantic lines, a modified Hough transform is utilized to detect the main semantic line, while for images containing salient objects, a salient object detection method based on luminance channel salience features (LCSF) is applied to determine the salient object region. In evaluating similarity, edge similarity measured by the Canny operator is combined with the structural similarity index (SSIM). Furthermore, we introduce a Framework for Image Aesthetic Evaluation with Composition and Similarity-Based Optimization (FIACSO), which uses semantic segmentation and generative adversarial networks (GANs) to optimize composition while preserving the original content. Compared with prior approaches, the proposed method improves both the aesthetic appeal and fidelity of optimized images. Subjective evaluation involving 30 participants further confirms that FIACSO outperforms existing methods in overall aesthetics, compositional harmony, and content integrity. Beyond methodological contributions, this study also offers practical value: it supports photographers in refining image composition without losing context, assists designers in creating balanced layouts with minimal distortion, and provides computational tools to enhance the efficiency and quality of visual media production. Full article

Inverse lithography technology (ILT) based on the gradient descent (GD) algorithm, which is a classical local optimal method, can effectively improve the lithographic imaging fidelity. However, due to the low-pass filtering effect of the lithography imaging system, GD, although able to converge quickly, is prone to fall into the local optimum for the information in the corner region of complex patterns. Considering the high-frequency information of the corner region during the optimization process, this paper proposes a resolution layering method to improve the efficiency of GD-based ILT algorithms. A corner-rounding-inspired target retargeting strategy is used to compensate for the over-optimization defect of GD for inversely optimizing the complex pattern layout. Furthermore, for ensuring the manufacturability of masks, differentiable top-hat and bottom-hat operations are employed to improve the optimization efficiency of the proposed method. To confirm the superiority of the proposed method, multiple optimization methods of ILT were compared. Numerical experiments show that the proposed method has higher optimization efficiency and effectively avoids the over-optimization. Full article

Background: As spontaneous renal artery dissection (SRAD) is a rare cause of abdominal pain, bilateral dissection is an extremely rare event. Only approximately two hundred cases of SRAD have been reported in the literature. The diagnosis is often delayed due to the rarity of the disease and non-specific clinical presentations such as flank pain, hypertension, fever, nausea, vomiting, and hematuria, which can be often misdiagnosed as a genito-urinary infection or gastrointestinal or bowel disease. Before 1980, the diagnosis of SRAD was mostly confirmed via autopsy or, rarely, via angiography. At present, the diagnosis is made using advanced imaging approaches, including computed tomography angiography (CTA) and magnetic resonance angiography (MRA), with a higher number of incidentally diagnosed SRADs. Methods: we performed laboratory tests and radiological examinations (computed abdominal tomography and multiplanar reconstruction) that revealed multiple infarctions and ischemic areas with hypoperfusion in the upper middle third of the left kidney and in a large part of middle and lower areas of the right kidney; the left renal artery exhibited increased intimal thickening and arteritis. Results: The multiplanar reconstruction revealed bilateral renal artery dissection and multiple arterial infarctions disseminated throughout both kidneys. After a clinical follow-up and hypertension retargeting, the patient was discharged with dual antiplatelet therapy and ACE inhibitor drugs. No lipid-lowering therapy was needed. Conclusions: Spontaneous renal artery dissection (SRAD) is a rare clinical event that typically presents with acute low-back or flank pain, hypertension, fever, hematuria, and acute renal failure. The condition could be misdiagnosed or receive a delayed diagnosis due to its relative rarity and non-specific presentation. The gold standard is enhanced computed tomography (CT) scans, and if the diagnosis is positive, vascular multiplanar reconstruction is generally suggested, as it can display lesions more clearly. Over 300 cases have been reported since the first characterization of SRAD; however, to date, a consensus has not been reached on the most appropriate treatment. Conservative therapy, open surgery, and intravascular intervention have been reported as treatments for SRAD. Full article

Image retargeting is a common computer graphics task which involves manipulating the size or aspect ratio of an image. This task often presents a challenge to the artist or user, because manipulating the size of an image necessitates some degree of data loss as pixels need to be removed to accommodate a different image size. We present an image retargeting framework which implements a confidence map generated by a segmentation model for content-aware resizing, allowing users to specify which subjects in an image to preserve using natural language prompts much like the role of an art director conversing with their artist. Using computer vision models to detect object positions also provides additional control over the composition of the retargeted image at various points in the image-processing pipeline. This object-based approach to energy map augmentation is incredibly flexible, because only minor adjustments to the processing of the energy maps can provide a significant degree of control over where seams—paths of pixels through the image—are removed, and how seam removal is prioritized in different sections of the image. It also provides additional control with techniques for object and background separation and recomposition. This research explores how several different types of deep-learning models can be integrated into this pipeline in order to easily make these decisions, and provide different retargeting results on the same image based on user input and compositional considerations. Because this is a framework based on existing machine-learning models, this approach will benefit from advancements in the rapidly developing fields of computer vision and large language models and can be extended for further natural language directorial controls over images. Full article

Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly higher brightness and, therefore, contrast levels than today’s models will become increasingly accessible and affordable to consumers. While HDR technology has gained prominence over the past few years, low dynamic range (LDR) content is still consumed due to a substantial volume of historical multimedia content being recorded and preserved in LDR. Although the amount of HDR content will continue to increase as HDR becomes more prevalent, a large portion of multimedia content currently remains in LDR. In addition, it is worth noting that although the HDR standard supports multimedia content with luminance levels up to 10,000 cd/m² (a standard measure of brightness), most HDR content is typically limited to a maximum brightness of around 1000 cd/m². This limitation aligns with the current capabilities of consumer HDR TVs but is a factor approximately five times brighter than current LDR TVs. To accurately present LDR content on a HDR display, it is processed through a dynamic range expansion process known as inverse tone mapping (iTM). This LDR to HDR conversion faces many challenges, including the inducement of noise artifacts, false contours, loss of details, desaturated colors, and temporal inconsistencies. This paper introduces complete inverse tone mapping, artifact suppression, and a highlight enhancement pipeline for video sequences designed to address these challenges. Our LDR-to-HDR technique is capable of adapting to the peak brightness of different displays, creating HDR video sequences with a peak luminance of up to 6000 cd/m². Furthermore, this paper presents the results of comprehensive objective and subjective experiments to evaluate the effectiveness of the proposed pipeline, focusing on two primary aspects: real-time operation capability and the quality of the HDR video output. Our findings indicate that our pipeline enables real-time processing of Full HD (FHD) video (1920 × 1080 pixels), even on hardware that has not been optimized for this task. Furthermore, we found that when applied to existing HDR content, typically capped at a brightness of 1000 cd/m², our pipeline notably enhances its perceived quality when displayed on a screen that can reach higher peak luminances. Full article

A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover’s distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover’s distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community. Full article

In this study, we analyzed the imaging maneuver time, retargeting maneuver time, and attitude maneuvering characteristics in the imaging section (Phase 1) and retargeting maneuver section (Phase 2) when taking multiple-target images in squint spotlight mode in a single pass of a passive SAR satellite. In particular, the synthetic aperture time and attitude maneuvering characteristics in the staring and sliding spotlight modes that can image the wider swath width while maintaining high resolution were compared and analyzed. In the sliding spotlight mode, the rotation center was located below the ground surface when the satellite was maneuvering towards the target. Steering and sliding maneuvers were performed when targeting, and the synthetic aperture time of the sliding spotlight was longer than that of the staring spotlight because overlapping imaging was performed on the point target. The satellite maneuvering during imaging can be considered as a time-fixed problem, because it was performed within synthetic aperture time according to resolution, incidence angle, swath width, etc., by minimizing the Doppler centroid variation. In order to optimize the retargeting maneuver time, an optimal analysis of the attitude maneuvering was carried out and the validity of the optimal analysis algorithm was confirmed. Finally, the scenario was analyzed by assuming a problem of imaging four targets with 5 × 5 km swath width in a 20 km × 20 km densely populated area. It was confirmed that if a squint angle of ±12 degrees is provided in a single pass, four high resolution images of 5 km × 5 km can be imaged in the sliding spotlight mode. Full article

The capsid structures of most Adeno-associated virus (AAV) serotypes, already assigned to an antigenic clade, have been previously determined. This study reports the remaining capsid structures of AAV7, AAV11, AAV12, and AAV13 determined by cryo-electron microscopy and three-dimensional image reconstruction to 2.96, 2.86, 2.54, and 2.76 Å resolution, respectively. These structures complete the structural atlas of the AAV serotype capsids. AAV7 represents the first clade D capsid structure; AAV11 and AAV12 are of a currently unassigned clade that would include AAV4; and AAV13 represents the first AAV2-AAV3 hybrid clade C capsid structure. These newly determined capsid structures all exhibit the AAV capsid features including 5-fold channels, 3-fold protrusions, 2-fold depressions, and a nucleotide binding pocket with an ordered nucleotide in genome-containing capsids. However, these structures have viral proteins that display clade-specific loop conformations. This structural characterization completes our three-dimensional library of the current AAV serotypes to provide an atlas of surface loop configurations compatible with capsid assembly and amenable for future vector engineering efforts. Derived vectors could improve gene delivery success with respect to specific tissue targeting, transduction efficiency, antigenicity or receptor retargeting. Full article

Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial–temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications. Full article

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques. Full article

In targeting the low correlation between existing image scaling quality assessment methods and subjective awareness, a content-aware retargeted image quality assessment algorithm is proposed, which is based on the structural similarity index. In this paper, a similarity index, that is, a local structural similarity algorithm, which can measure different sizes of the same image is proposed. The Speed Up Robust Feature (SURF) algorithm is used to extract the local structural similarity and the image content loss degree. The significant area ratio is calculated by extracting the saliency region and the retargeted image quality assessment function is obtained by linear fusion. In the CUHK image database and the MIT RetargetMe database, compared with four representative assessment algorithms and other latest four kinds of retargeted image quality assessment algorithms, the experiment proves that the proposed algorithm has a higher correlation with Mean Opinion Score (MOS) values and corresponds with the result of human subjective assessment. Full article