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Embryonic stem cells (ESC) have the ability to epigenetically silence endogenous and exogenous retroviral sequences. Trim28 plays an important role in establishing this silencing, but less is known about the role other Trim proteins play. The Tif1 family is a sub-group of the Trim family, which possess histone binding ability in addition to the distinctive RING domain. Here, we have examined the interaction between three Tif1 family members, namely Trim24, Trim28 and Trim33, and their function in retroviral silencing. We identify a complex formed in ESC, comprised of these three proteins. We further show that when Trim33 is depleted, the complex collapses and silencing efficiency of both endogenous and exogenous sequences is reduced. Similar transcriptional activation takes place when Trim24 is depleted. Analysis of the H3K9me3 chromatin modification showed a decrease in this repressive mark, following both Trim24 and Trim33 depletion. As Trim28 is an identified binding partner of the H3K9 methyltransferase ESET, this further supports the involvement of Trim28 in the complex. The results presented here suggest that a complex of Tif1 family members, each of which possesses different specificity and efficiency, contributes to the silencing of retroviral sequences in ESC.

Background Moloney murine leukemia virus (MLV) replication is suppressed in mouse embryonic stem cells (ESCs) by the Trim28-SETDB1 complex. The chromatin remodeler Smarcad1 interacts with Trim28 and was suggested to allow the deposition of the histone variant H3.3. However, the role of Trim28, H3.3, and Smarcad1 in MLV repression in ESCs still needs to be fully understood. Results In this study, we used MLV to explore the role of Smarcad1 in retroviral silencing in ESCs. We show that Smarcad1 is immediately recruited to the MLV provirus. Based on the repression dynamics of a GFP-reporter MLV, our findings suggest that Smarcad1 plays a critical role in the establishment and maintenance of MLV repression, as well as other Trim28-targeted genomic loci. Furthermore, Smarcad1 is important for stabilizing and strengthening Trim28 binding to the provirus over time, and its presence around the provirus is needed for proper deposition of H3.3 on the provirus. Surprisingly, the combined depletion of Smarcad1 and Trim28 results in enhanced MLV derepression, suggesting that these two proteins may also function independently to maintain repressive chromatin states. Conclusions Overall, the results of this study provide evidence for the crucial role of Smarcad1 in the silencing of retroviral elements in embryonic stem cells. Further research is needed to fully understand how Smarcad1 and Trim28 cooperate and their implications for gene expression and genomic stability.

Background Integration of retroviruses into the host genome can impair the genomic and epigenomic integrity of the cell. As a defense mechanism, epigenetic modifications on the proviral DNA repress retroviral sequences in mouse embryonic stem cells (ESC). Here, we focus on the histone 3 variant H3.3, which is abundant in active transcription zones, as well as centromeres and heterochromatinized repeat elements, e.g., endogenous retroviruses (ERV). Results To understand the involvement of H3.3 in the epigenetic silencing of retroviral sequences in ESC, we depleted the H3.3 genes in ESC and transduced the cells with GFP-labeled MLV pseudovirus. This led to altered retroviral repression and reduced Trim28 recruitment, which consequently led to a loss of heterochromatinization in proviral sequences. Interestingly, we show that H3.3 depletion has a differential effect depending on which of the two genes coding for H3.3, H3f3a or H3f3b , are knocked out. Depletion of H3f3a resulted in a transient upregulation of incoming retroviral expression and ERVs, while the depletion of H3f3b did not have the same effect and repression was maintained. However, the depletion of both genes resulted in a stable activation of the retroviral promoter. These findings suggest that H3.3 is important for regulating retroviral gene expression in mouse ESC and provide evidence for a distinct function of the two H3.3 genes in this regulation. Furthermore, we show that Trim28 is needed for depositing H3.3 in retroviral sequences, suggesting a functional interaction between Trim28 recruitment and H3.3 loading. Conclusions Identifying the molecular mechanisms by which H3.3 and Trim28 interact and regulate retroviral gene expression could provide a deeper understanding of the fundamental processes involved in retroviral silencing and the general regulation of gene expression, thus providing new answers to a central question of stem cell biology.

Detecting distinctive regions in point clouds is a fundamental task in shape analysis, critical for applications such as fine-grained classification, shape retrieval, and shape matching. Recent unsupervised deep learning approaches have shown promise, moving beyond hand-crafted features and labeled data. However, their results as well as their specific distinctive point selection mechanisms leave room for improvement. This work aims to enhance these approaches and extend them to more general learning scenarios. We propose two key algorithmic improvements: first, an attention-based mechanism for selecting distinctive points, and second, a novel semantic consistency loss that enhances the framework’s ability to identify meaningful distinctive regions consistently within a given shape. Additionally, we extend the framework to a few-shot learning setup, useful in cases where distinctive regions are ambiguous or poorly defined. To support our research, we have constructed what we believe to be the first benchmark with ground-truth distinctive region labels. Our experimental results, conducted across multiple real and synthetic datasets, demonstrate that our approach, dubbed distinctive region attention-guided detection in point clouds (DRAG), provides significant improvements over state-of-the-art methods.

Generating medical reports for X-ray images presents a significant challenge, particularly in unpaired scenarios where access to paired image-report data for training is unavailable. Previous works have typically learned a joint embedding space for images and reports, necessitating a specific labeling schema for both. We introduce an innovative approach that eliminates the need for consistent labeling schemas, thereby enhancing data accessibility and enabling the use of incompatible datasets. This approach is based on cycle-consistent mapping functions that transform image embeddings into report embeddings, coupled with report auto-encoding for medical report generation. Our model and objectives consider intricate local details and the overarching semantic context within images and reports. This approach facilitates the learning of effective mapping functions, resulting in the generation of coherent reports. It outperforms state-of-the-art results in unpaired chest X-ray report generation, demonstrating improvements in both language and clinical metrics.

Every picture tells a story. In photography, the story is portrayed by a composition of objects, commonly referred to as the subjects of the piece. Were we to remove these objects, the story would be lost. When manipulating images, either for artistic rendering or cropping, it is crucial that the story of the piece remains intact. As a result, the knowledge of the location of these prominent objects is essential. We propose an approach for saliency detection that combines previously suggested patch distinctness with an object probability map. The object probability map infers the most probable locations of the subjects of the photograph according to highly distinct salient cues. The benefits of the proposed approach are demonstrated through state-of-the-art results on common data sets. We further show the benefit of our method in various manipulations of real-world photographs while preserving their meaning.

Point clouds are gaining prominence as a method for representing 3D shapes, but their irregular structure poses a challenge for deep learning methods. In this paper we propose CloudWalker, a novel method for learning 3D shapes using random walks. Previous works attempt to adapt Convolutional Neural Networks (CNNs) or impose a grid or mesh structure to 3D point clouds. This work presents a different approach for representing and learning the shape from a given point set. The key idea is to impose structure on the point set by multiple random walks through the cloud for exploring different regions of the 3D object. Then we learn a per-point and per-walk representation and aggregate multiple walk predictions at inference. Our approach achieves state-of-the-art results for two 3D shape analysis tasks: classification and retrieval.

Archaeological artifacts are an essential element of archaeological research. They provide evidence of the past and enable archaeologists to obtain qualified conclusion. Nowadays, many artifacts are scanned by 3D scanners. While convenient in many aspects, the 3D representation is often unsuitable for further analysis, due to flaws in the scanning process or defects in the original artifacts. We propose a new approach for automatic processing of scanned artifacts. It is based on the definition of a new direction field on surfaces (a normalized vector field), termed the prominent field . The prominent field is oriented with respect to the prominent feature curves of the surface. We demonstrate the applicability of the prominent field in two applications. The first is surface enhancement of archaeological artifacts, which helps enhance eroded features and remove scanning noise. The second is artificial coloring that can replace manual artifact illustration in archaeological reports.

Mesh segmentation has become a necessary ingredient in many applications in computer graphics. This paper proposes a novel hierarchical mesh segmentation algorithm, which is based on new methods for prominent feature point and core extraction. The algorithm has several benefits. First, it is invariant both to the pose of the model and to different proportions between the model’s components. Second, it produces correct hierarchical segmentations of meshes, both in the coarse levels of the hierarchy and in the fine levels, where tiny segments are extracted. Finally, the boundaries between the segments go along the natural seams of the models.

A polygonal mesh is the most-commonly used representation of surfaces in computer graphics. Therefore, it is not surprising that a number of mesh classification networks have recently been proposed. However, while adversarial attacks are wildly researched in 2D, the field of adversarial meshes is under explored. This paper proposes a novel, unified, and general adversarial attack, which leads to misclassification of several state-of-the-art mesh classification neural networks. Our attack approach is black-box, i.e. it has access only to the network's predictions, but not to the network's full architecture or gradients. The key idea is to train a network to imitate a given classification network. This is done by utilizing random walks along the mesh surface, which gather geometric information. These walks provide insight onto the regions of the mesh that are important for the correct prediction of the given classification network. These mesh regions are then modified more than other regions in order to attack the network in a manner that is barely visible to the naked eye.