Angiogenesis in tissue engineering : Breathing life into constructed tissue substitutes

Long-term function of three-dimensional (3D) tissue constructs depends on adequate vascularization after implantation. Accordingly, research in tissue engineering has focused on the analysis of angiogenesis. For this purpose, 2 sophisticated in vivo models (the chorioallantoic membrane and the dorsal skinfold chamber) have recently been introduced in tissue engineering research, allowing a more detailed analysis of angiogenic dysfunction and engraftment failure. To achieve vascularization of tissue constructs, several approaches are currently under investigation. These include the modification of biomaterial properties of scaffolds and the stimulation of blood vessel development and maturation by different growth factors using slow-release devices through pre-encapsulated microspheres. Moreover, new microvascular networks in tissue substitutes can be engineered by using endothelial cells and stem cells or by creating arteriovenous shunt loops. Nonetheless, the currently used techniques are not sufficient to induce the rapid vascularization necessary for an adequate cellular oxygen supply. Thus, future directions of research should focus on the creation of microvascular networks within 3D tissue constructs in vitro before implantation or by co-stimulation of angiogenesis and parenchymal cell proliferation to engineer the vascularized tissue substitute in situ

Label-free 3D visualization of cellular and tissue structures in intact muscle with second and third harmonic generation microscopy.

Second and Third Harmonic Generation (SHG and THG) microscopy is based on optical effects which are induced by specific inherent physical properties of a specimen. As a multi-photon laser scanning approach which is not based on fluorescence it combines the advantages of a label-free technique with restriction of signal generation to the focal plane, thus allowing high resolution 3D reconstruction of image volumes without out-of-focus background several hundred micrometers deep into the tissue. While in mammalian soft tissues SHG is mostly restricted to collagen fibers and striated muscle myosin, THG is induced at a large variety of structures, since it is generated at interfaces such as refraction index changes within the focal volume of the excitation laser. Besides, colorants such as hemoglobin can cause resonance enhancement, leading to intense THG signals. We applied SHG and THG microscopy to murine (Mus musculus) muscles, an established model system for physiological research, to investigate their potential for label-free tissue imaging. In addition to collagen fibers and muscle fiber substructure, THG allowed us to visualize blood vessel walls and erythrocytes as well as white blood cells adhering to vessel walls, residing in or moving through the extravascular tissue. Moreover peripheral nerve fibers could be clearly identified. Structure down to the nuclear chromatin distribution was visualized in 3D and with more detail than obtainable by bright field microscopy. To our knowledge, most of these objects have not been visualized previously by THG or any label-free 3D approach. THG allows label-free microscopy with inherent optical sectioning and therefore may offer similar improvements compared to bright field microscopy as does confocal laser scanning microscopy compared to conventional fluorescence microscopy

One-shot Detail Retouching with Patch Space Neural Field based Transformation Blending

Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an input image based on just a single pair of before and after example images. Our approach provides accurate and generalizable detail edit transfer to new images. We achieve these by proposing a new representation for image to image maps. Specifically, we propose neural field based transformation blending in the patch space for defining patch to patch transformations for each frequency band. This parametrization of the map with anchor transformations and associated weights, and spatio-spectral localized patches, allows us to capture details well while staying generalizable. We evaluate our technique both on known ground truth filtes and artist retouching edits. Our method accurately transfers complex detail retouching edits

Visual distortion of body size modulates pain perception

Pain is a complex subjective experience, that can be shaped by several cognitive, psychological and even contextual variables. For example, simply viewing the body reduces the reported intensity of acute physical pain. We investigated whether this visually induced analgesia can be modulated by the visually depicted size of the stimulated body part. We measured contact heat-pain thresholds, while participants viewed either their own hand or a neutral object, at real size, enlarged, or reduced. Vision of the body was analgesic, increasing heat-pain thresholds by ~ 4°C. Importantly, enlargement of the viewed hand enhanced this analgesia, while looking at a reduced hand decreased it. These results demonstrate that visual distortions of body size modulate sensory components of pain, and reveal a clear functional relation between the perception of pain and the representation of the body

Deep Image Harmonization

Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods