Reesejespersen9201

UV-B light induced viral reactivation from latency in 100% of mice as measured by HSV-1 antigen deposition in the cornea. Further, unlike conventional HSK models, viral reactivation resulted in focal retraction of sensory nerves and corneal opacity. Dependent on CD4+ T cells, inflammation foci were innervated by sympathetic nerves.

Collectively, our data reveal that sectoral corneal sensory nerve retraction and replacement of sympathetic nerves were involved in the progressive pathology that is dependent on CD4+ T cells after viral reactivation from HSV-1 latency in the UV-B induced recurrent HSK mouse model.

Collectively, our data reveal that sectoral corneal sensory nerve retraction and replacement of sympathetic nerves were involved in the progressive pathology that is dependent on CD4+ T cells after viral reactivation from HSV-1 latency in the UV-B induced recurrent HSK mouse model.

Based on our preview evidence that reduced nuclear content of the transcription factor Myc-associated protein X (MAX) is an early event associated with degeneration of retinal ganglion cells (RGCs), in the present study, our purpose was to test whether the overexpression of human MAX had a neuroprotective effect against RGC injury.

Overexpression of either MAX or green fluorescent protein (GFP) in the retina was achieved by intravitreal injections of recombinant adenovirus-associated viruses (rAAVs). Lister Hooded rats were used in three models of RGC degeneration (1) cultures of retinal explants for 30 hours ex vivo from the eyes of 14-day-old rats that had received intravitreal injections of rAAV2-MAX or the control vector rAAV2-GFP at birth; (2) an optic nerve crush model, in which 1-month-old rats received intravitreal injection of either rAAV2-MAX or rAAV2-GFP and, 4 weeks later, were operated on; and (3) an ocular hypertension (OHT) glaucoma model, in which 1-month-old rats received intravitreal injlaucomatous neurodegeneration based on overexpression of MAX.Plinia phitrantha and P. cauliflora are Myrtaceae species with recognized horticultural and pharmacological potential. Nevertheless, studies on molecular genetics and the evolution of these species are absent in the literature. In this study, we report the complete plastid genome sequence of these species and an analysis of structural and evolutive features of the plastid genome within the tribe Myrteae. The two plastid genomes present the conserved quadripartite structure and are similar to already reported plastid genomes of Myrteae species concerning the size, number, and order of the genes. A total of 69-70 SSR loci, 353 single nucleotide polymorphisms, and 574 indels were identified in P. phitrantha and P. caulifora. Observed evolutive features of the plastid genomes support the development of programs for the conservation and breeding of Plinia. The phylogenomic analysis based on the complete plastid genome sequence of 15 Myrteae species presented a robust phylogenetic signal and evolutive traits of the tribe. Ten hotspots of nucleotide diversity were identified, evidence of purifying selection was observed in 27 genes, and relative conservation of the plastid genomes was confirmed for Myrteae. Altogether, the outcomes of the present study provide support for planning conservation, breeding, and biotechnological programs for Plinia species.In this work, we investigate the possibility of inducing valence transitions, i.e. transitions between different defect configurations, by transforming a nematic shell into a nematic droplet. Our shells are liquid crystal droplets containing a smaller aqueous droplet inside, which are suspended in an aqueous phase. When osmotically de-swelling the inner droplet, the shell progressively increases its thickness until it eventually becomes a single droplet. During the process, the shell energy landscape evolves, triggering a response in the system. We observe two different scenarios. Either the inner droplet progressively shrinks and disappears, inducing a defect reorganization, or it is expelled from the shell at a critical radius of the inner droplet, abruptly changing the geometry of the system. We use numerical simulations and modeling to investigate the origin of these behaviors. We find that the selected route depends on the defect structure and the energetics of the system as it evolves. The critical inner radius and time for expulsion depend on the osmotic pressure of the outer phase, suggesting that the flow through the shell plays a role in the process.The need for a wound dressing material that can accelerate wound healing is increasing and will last for a long time. In this study, cerium oxide nanoparticle (CeNP) incorporated poly-L-lactic acid (PLLA)-gelatin composite fiber membranes were fabricated using established electrospinning techniques for use as a low-cost sustainable wound dressing material. The obtained membranes were characterized for their morphology, and physical, mechanical and biological properties. The results showed that the membranes maintained an integrated morphology, and demonstrated water absorption and improved mechanical properties. An in vitro cell proliferation test confirmed that the cells presented better activities over the composite fiber membranes. In the rat scalding model, rapid wound recombination was observed. All these data suggested that electrospun CeNP incorporated PLLA-gelatin composite fiber membranes can be an ideal dressing substitute that can be used for wound healing applications. DT-061 Furthermore, the use of biodegradable polymers and environmentally sustainable production technologies presented better sustainability for the commercial production of these composite membranes promoting tissue regeneration and scar remodeling.Novel two-dimensional kagome metal-organic frameworks with mononuclear Zr4+/Hf4+ nodes chelated by benzene-1,4-dihydroxamate linkers were synthesized. The MOFs, namely SUM-1, are chemically robust and kinetically favorable, as confirmed by theoretical and experimental studies. SUM-1(Zr) can be readily made into large (∼100 μm) single crystals and nanoplates (∼50 nm), constituting a versatile MOF platform.Natural high-performance materials have inspired the exploration of novel materials from protein building blocks. The ability of proteins to self-organize into amyloid-like nanofibrils has opened an avenue to new materials by hierarchical assembly processes. As the mechanisms by which proteins form nanofibrils are becoming clear, the challenge now is to understand how the nanofibrils can be designed to form larger structures with defined order. We here report the spontaneous and reproducible formation of ordered microstructure in solution cast films from whey protein nanofibrils. The structural features are directly connected to the nanostructure of the protein fibrils, which is itself determined by the molecular structure of the building blocks. Hence, a hierarchical assembly process ranging over more than six orders of magnitude in size is described. The fibril length distribution is found to be the main determinant of the microstructure and the assembly process originates in restricted capillary flow induced by the solvent evaporation. We demonstrate that the structural features can be switched on and off by controlling the length distribution or the evaporation rate without losing the functional properties of the protein nanofibrils.The electronic properties of layered two-dimensional (2D) transition-metal dichalcogenide (TMD) van der Waals (vdW) heterostructures are strongly dependent on their layer number (N). However, extremely large computational resources are required to investigate the layer-dependent TMD vdW heterostructures for every possible combination if N varies in a large range. Fortunately, the machine learning (ML) technique provides a feasible way to probe this problem. In this work, based on the density functional theory (DFT) calculations combined with the ML technique, we effectively predict the layer-dependent electronic properties of TMD vdW heterostructures composed of MoS2, WS2, MoSe2, WSe2, MoTe2, or WTe2, in which the layer number varies from 2-10. The cross-validation scores of our trained ML models in predicting the bandgaps as well as the band edge positions exceed 90%, suggesting excellent performance. The predicted results show that in the case of a few-layer system, the number of layers has a significant effect on the electronic properties. The bandgap and band alignment could be dramatically changed from bilayer to triple-layer heterostructures. However, with the increase of the number of layers, the electronic properties change, and some general trends can be summarized. When the layer number is larger than 8, the properties of the TMD heterostructures tend to be stable, and the influence of the layer number decreases. Based on these results, our work not only sheds light on the understanding of the layer-dependent electronic properties of multi-layer TMD vdW heterostructures, but also provides an efficient way to accelerate the discovery of functional materials.Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones. Synthetic data is generated using Blender to supplement experimental training data for the machine learning model. This synthetic data captures key characteristics of confocal images, including slicing in the z-direction and Gaussian noise. We find that the best performance is achieved with a model trained on a mixture of synthetic data and experimental data. This model achieves a mean Average Precision (mAP) of ∼85%, and accurately measures the degree of assembly and distribution of self-assembled stack sizes for different cone diameters. Minor discrepancies between machine learning and hand labeled data is discussed in terms of the quality of synthetic data, and differences in cones of different sizes.Computational methods to understand interactions in bio-complex systems are however limited to time-scales typically much shorter than in Nature. For example, on the nanoscale level, interactions between nanoparticles (NPs)/molecules/peptides and membranes are central in complex biomolecular processes such as membrane-coated NPs or cellular uptake. This can be remedied by the application of e.g. Jarzynski's equality where thermodynamic properties are extracted from non-equilibrium simulations. Although, the out of equilibrium work leads to non-conservative forces. We here propose a correction Pair Forces method, that removes these forces. Our proposed method is based on the calculation of pulling forces in backward and forward directions for the Jarzynski free-energy estimator using steered molecular dynamics simulation. Our results show that this leads to much improvement for NP-membrane translocation free energies. Although here we have demonstrated the application of the method in molecular dynamics simulation, it could be applied for experimental approaches.