Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued GOMEA (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible

Adult tissue-derived neural crest‐like stem cells: Sources, regulatory networks, and translational potential: Concise review

Neural crest (NC) cells are a multipotent stem cell population that gives rise to a diverse array of cell types in the body, including peripheral neurons, Schwann cells (SC), craniofacial cartilage and bone, smooth muscle cells, and melanocytes. NC formation and differentiation into specific lineages takes place in response to a set of highly regulated signaling and transcriptional events within the neural plate border. Pre‐migratory NC cells initially are contained within the dorsal neural tube from which they subsequently emigrate, migrating to often distant sites in the periphery. Following their migration and differentiation, some NC‐like cells persist in adult tissues in a nascent multipotent state, making them potential candidates for autologous cell therapy. This review discusses the gene regulatory network responsible for NC development and maintenance of multipotency. We summarize the genes and signaling pathways that have been implicated in the differentiation of a post‐migratory NC into mature myelinating SC. We elaborate on the signals and transcription factors involved in the acquisition of immature SC fate, axonal sorting of unmyelinated neuronal axons, and finally the path toward mature myelinating SC, which envelope axons within myelin sheaths, facilitating electrical signal propagation. The gene regulatory events guiding development of SC in‐vivo provides insights into means for differentiating NC‐like cells from adult human tissues into functional SC, which have the potential to provide autologous cell sources for the treatment of demyelinating and neurodegenerative disorders

Multi-objective dual simplex-mesh based deformable image registration for 3D medical images – proof of concept

Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration

Morea: A GPU-accelerated evolutionary algorithm for multi-objective deformable registration of 3d medical images

Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a proper image registration approach to achieve this could unleash a number of applications requiring information to be transferred between images. Clinical adoption is currently hampered by many existing methods requiring extensive configuration effort before each use, or not being able to (realistically) capture large deformations. A recent multi-objective approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation model has shown promise, exposing the trade-offs inherent to image registration problems and modeling large deformations in 2D. This work builds on this promise and introduces MOREA: the first evolutionary algorithm-based multi-objective approach to deformable registration of 3D images capable of tackling large deformations. MOREA includes a 3D biomechanical mesh model for physical plausibility and is fully GPU-accelerated. We compare MOREA to two state-of-the-art approaches on abdominal CT scans of 4 cervical cancer patients, with the latter two approaches configured for the best results per patient. Without requiring per-patient configuration, MOREA significantly outperforms these approaches on 3 of the 4 patients that represent the most difficult cases

Neural crest stem cells from human epidermis of aged donors maintain their multipotency in vitro and in vivo

Neural crest (NC) cells are multipotent stem cells that arise from the embryonic ectoderm, delaminate from the neural tube in early vertebrate development and migrate throughout the developing embryo, where they differentiate into various cell lineages. Here we show that multipotent and functional NC cells can be derived by induction with a growth factor cocktail containing FGF2 and IGF1 from cultures of human inter-follicular keratinocytes (KC) isolated from elderly donors. Adult NC cells exhibited longer doubling times as compared to neonatal NC cells, but showed limited signs of cellular senescence despite the advanced age of the donors and exhibited significantly younger epigenetic age as compared to KC. They also maintained their multipotency, as evidenced by their ability to differentiate into all NC-specific lineages including neurons, Schwann cells, melanocytes, and smooth muscle cells (SMC). Notably, upon implantation into chick embryos, adult NC cells behaved similar to their embryonic counterparts, migrated along stereotypical pathways and contributed to multiple NC derivatives in ovo. These results suggest that KC-derived NC cells may provide an easily accessible, autologous source of stem cells that can be used for treatment of neurodegenerative diseases or as a model system for studying disease pathophysiology and drug development

Reprogramming Postnatal Human Epidermal Keratinocytes toward Functional Neural Crest Fates

During development, neural crest cells are induced by signaling events at the neural plate border of all vertebrate embryos. Initially arising within the central nervous system, neural crest cells subsequently undergo an epithelial to mesenchymal transition to migrate into the periphery, where they differentiate into diverse cell types. Here we provide evidence that postnatal human epidermal keratinocytes, in response to FGF2 and IGF1 signals, can be reprogrammed toward a neural crest fate. Genome-wide transcriptome analyses show that keratinocyte-derived neural crest cells are similar to those derived from human embryonic stem cells. Moreover, they give rise in vitro and in vivo to neural crest derivatives such as peripheral neurons, melanocytes, Schwann cells and mesenchymal cells (osteocytes, chondrocytes, adipocytes and smooth muscle). By demonstrating that human KRT14+ keratinocytes can form neural crest cells, even from clones of single cells, our results have important implications in stem cell biology and regenerative medicine