Aberrant oligodendroglial-vascular interactions disrupt the blood-brain barrier, triggering CNS inflammation.

Disruption of the blood-brain barrier (BBB) is critical to initiation and perpetuation of disease in multiple sclerosis (MS). We report an interaction between oligodendroglia and vasculature in MS that distinguishes human white matter injury from normal rodent demyelinating injury. We find perivascular clustering of oligodendrocyte precursor cells (OPCs) in certain active MS lesions, representing an inability to properly detach from vessels following perivascular migration. Perivascular OPCs can themselves disrupt the BBB, interfering with astrocyte endfeet and endothelial tight junction integrity, resulting in altered vascular permeability and an associated CNS inflammation. Aberrant Wnt tone in OPCs mediates their dysfunctional vascular detachment and also leads to OPC secretion of Wif1, which interferes with Wnt ligand function on endothelial tight junction integrity. Evidence for this defective oligodendroglial-vascular interaction in MS suggests that aberrant OPC perivascular migration not only impairs their lesion recruitment but can also act as a disease perpetuator via disruption of the BBB

High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

Generating photorealistic images of human faces at scale remains a prohibitively difficult task using computer graphics approaches. This is because these require the simulation of light to be photorealistic, which in turn requires physically accurate modelling of geometry, materials, and light sources, for both the head and the surrounding scene. Non-photorealistic renders however are increasingly easy to produce. In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data. The process of learning usually corresponds to a loss of control over the shape and appearance of the generated images. For instance, even simple disentangling tasks such as modifying the hair independently of the face, which is trivial to accomplish in a computer graphics approach, remains an open research question. In this work, we propose an algorithm that matches a non-photorealistic, synthetically generated image to a latent vector of a pretrained StyleGAN2 model which, in turn, maps the vector to a photorealistic image of a person of the same pose, expression, hair, and lighting. In contrast to most previous work, we require no synthetic training data. To the best of our knowledge, this is the first algorithm of its kind to work at a resolution of 1K and represents a significant leap forward in visual realism

Dynamic shear fracture toughness and failure characteristics of Ti–6Al–4V alloy under high loading rates

A novel 2-bar/double-shear impact (2B/2SI) loading technique is used to study the dynamic mode II (shear) fracture characteristics of Ti–6Al–4V. The new specimen design, to be used in combination with a standard split Hopkinson pressure bar, circumvent classical limitations associated with conventional one-point impact methods. This paper presents a combined experimental-numerical approach to determining the mode II fracture toughness of Ti–6Al–4V for a broad range of loading rates between 1.10 × 10^{−2}- 4.98 × 10^{7} (MPa, m^{1/2}s^{−1}). Results showed only a slight initial increase in toughness, which increases abruptly with loading rates beyond 10^{6} (MPa, m^{1/2}s^{−1}). Fractographic examination showed distinctively different mechanisms in operation at the microscale, depending on the rate of loading. Failure is through a brittle-ductile, mixed-mode fracture under quasi-static conditions; by contrast, the fracture surface exhibited fractographic features of adiabatic shear bands (ASB) and material melting/re-solidification under dynamic conditions. High-speed photography showed that both dynamic shear fracture (DSF) and ASB occurred during the same loading process. Interactions between DSF and ASB were observed to influence the dominant failure mechanism of the material at high loading rates

Is the sigma-1 receptor a potential pharmacological target for cardiac pathologies? A systematic review.

Sigma-1 receptors are ligand-regulated chaperone proteins, involved in several cellular mechanisms. The aim of this systematic review was to examine the effects that the sigma-1 receptor has on the cardiovascular system. The interaction targets and proposed mechanisms of action of sigma-1 receptors were explored, with the aim of determining if the sigma-1 receptor is a potential pharmacological target for cardiac pathologies. This systematic review was conducted according to the PRISMA guidelines and these were used to critically appraise eligible studies. Pubmed and Scopus were systematically searched for articles investigating sigma-1 receptors in the cardiovascular system. Papers identified by the search terms were then subject to analysis against pre-determined inclusion criteria. 23 manuscripts met the inclusion criteria and were included in this review. The experimental platforms, experimental techniques utilised and the results of the studies were summarised. The sigma-1 receptor is found to be implicated in cardioprotection, via various mechanisms including stimulating the Akt-eNOS pathway, and reduction of Ca2 + leakage into the cytosol via modulating certain calcium channels. Sigma-1 receptors are also found to modulate other cardiac ion channels including different subtypes of potassium and sodium channels and have been shown to modulate intracardiac neuron excitability. The sigma-1 receptor is a potential therapeutic target for treatment of cardiac pathologies, particularly cardiac hypertrophy. We therefore suggest investigating the cardioprotective mechanisms of sigma-1 receptor function, alongside proposed potential ligands that can stimulate these functions

Axin2 as regulatory and therapeutic target in newborn brain injury and remyelination.

Permanent damage to white matter tracts, comprising axons and myelinating oligodendrocytes, is an important component of brain injuries of the newborn that cause cerebral palsy and cognitive disabilities, as well as multiple sclerosis in adults. However, regulatory factors relevant in human developmental myelin disorders and in myelin regeneration are unclear. We found that AXIN2 was expressed in immature oligodendrocyte progenitor cells (OLPs) in white matter lesions of human newborns with neonatal hypoxic-ischemic and gliotic brain damage, as well as in active multiple sclerosis lesions in adults. Axin2 is a target of Wnt transcriptional activation that negatively feeds back on the pathway, promoting β-catenin degradation. We found that Axin2 function was essential for normal kinetics of remyelination. The small molecule inhibitor XAV939, which targets the enzymatic activity of tankyrase, acted to stabilize Axin2 levels in OLPs from brain and spinal cord and accelerated their differentiation and myelination after hypoxic and demyelinating injury. Together, these findings indicate that Axin2 is an essential regulator of remyelination and that it might serve as a pharmacological checkpoint in this process

Partitioned Least Squares

In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result. The output allows practitioners to assess the importance of each group and of each variable in the group. We formally show that the new formulation is not convex and provide two alternative methods to deal with the problem: one non-exact method based on an alternating least squares approach; and one exact method based on a reformulation of the problem using an exponential number of sub-problems whose minimum is guaranteed to be the optimal solution. We formally show the correctness of the exact method and also compare the two solutions showing that the exact solution provides better results in a fraction of the time required by the alternating least squares solution (assuming that the number of partitions is small). For the sake of completeness, we also provide an alternative branch and bound algorithm that can be used in place of the exact method when the number of partitions is too large, and a proof of NP-completeness of the optimization problem introduced in this paper

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods