Highly Efficient Biobased Synthesis of Acrylic Acid

Petrochemical based polymers, paints and coatings are cornerstones of modern industry but our future sustainable society demands greener processes and renewable feedstock materials. A challenge is to access platform monomers from biomass resources while integrating the principles of green chemistry in their chemical synthesis. We present a synthesis route starting from biomass-derived furfural towards the commonly used monomers maleic anhydride and acrylic acid, implementing environmentally benign photooxygenation, aerobic oxidation and ethenolysis reactions. Maleic anhydride and acrylic acid, transformed into sodium acrylate, were isolated in yields of 85 % (2 steps) and 81 % (4 steps), respectively. With minimal waste and high atom efficiency, this biobased route provides a viable alternative to access key monomers

Tunable microstructured membranes in organ‐on‐chip to monitor trans‐endothelial hydraulic resistance

Tissue engineering is an interdisciplinary field, wherein scientists from different backgrounds collaborate to address the challenge of replacing damaged tissues and organs through the in vitro fabrication of functional and transplantable biological structures. Because the development and optimization of tissue engineering strategies rely on the complex interaction of cells, materials, and the physical–chemical tissue microenvironment, there is a need for experimental models that allow controlled studies of these aspects. Organs-on-chips (OOCs) have recently emerged as in vitro models that capture the complexity of human tissues in a controlled manner, while including functional readouts related to human organ physiology. OOCs consist of multiple microfluidic cell culture compartments, which are interfaced by porous membranes or hydrogels in which human cells can be cultured, thereby providing a controlled culture environment that resembles the microenvironment of a certain organ, including mechanical, biochemical, and geometrical aspects. Because OOCs provide both a well-controlled microenvironment and functional readouts, they provide a unique opportunity to incorporate, evaluate, and optimize materials for tissue engineering. In this study, we introduce a polymeric blend membrane with a three-dimensional double-porous morphology prepared from a poly(ɛ-caprolactone)–chitosan blends (PCL–CHT) by a modified liquid-induced phase inversion technique. The membranes have different physicochemical, microstructural, and morphological properties depending on different PCL–CHT ratios. Big surface pores (macrovoids) provide a suitable microenvironment for the incorporation of cells or growth factors, whereas an interconnected small porous (macroporous) network allows transfer of essential nutrients, diffusion of oxygen, and removal of waste. Human umbilical vein endothelial cells were seeded on the blend membranes embedded inside an OOC device. The cellular hydraulic resistance was evaluated by perfusing culture medium at a realistic transendothelial pressure of 20 cmH2O or 2 kPa at 37°C after 1 and 3 days postseeding. By introducing and increasing CHT weight percentage, the resistance of the cellular barrier after 3 days was significantly improved. The high tuneability over the membrane physicochemical and architectural characteristics might potentially allow studies of cell–matrix interaction, cell transportation, and barrier function for optimization of vascular scaffolds using OOCs

Eigenvalue asymptotics for weighted Laplace equations on rough Riemannian manifolds with boundary

Our topological setting is a smooth compact manifold of dimension two or higher with smooth boundary. Although this underlying topological structure is smooth, the Riemannian metric tensor is only assumed to be bounded and measurable. This is known as a rough Riemannian manifold. For a large class of boundary conditions we demonstrate a Weyl law for the asymptotics of the eigenvalues of the Laplacian associated to a rough metric. Moreover, we obtain eigenvalue asymptotics for weighted Laplace equations associated to a rough metric. Of particular novelty is that the weight function is not assumed to be of fixed sign, and thus the eigenvalues may be both positive and negative. Key ingredients in the proofs were demonstrated by Birman and Solomjak nearly fifty years ago in their seminal work on eigenvalue asymptotics. In addition to determining the eigenvalue asymptotics in the rough Riemannian manifold setting for weighted Laplace equations, we also wish to promote their achievements which may have further applications to modern problems

Ellipro scores of donor epitope specific HLA antibodies are not associated with kidney graft survival

In kidney transplantation, donor HLA antibodies are a risk factor for graft loss. Accessibility of donor eplets for HLA antibodies is predicted by the ElliPro score. The clinical usefulness of those scores in relation to transplant outcome is unknown. In a large Dutch kidney transplant cohort, Ellipro scores of pretransplant donor antibodies that can be assigned to known eplets (donor epitope specific HLA antibodies [DESAs]) were compared between early graft failure and long surviving deceased donor transplants. We did not observe a significant Ellipro score difference between the two cohorts, nor significant differences in graft survival between transplants with DESAs having high versus low total Ellipro scores. We conclude that Ellipro scores cannot be used to identify DESAs associated with early versus late kidney graft loss in deceased donor transplants.</p

Determination of the clinical relevance of donor epitope-specific HLA-antibodies in kidney transplantation

In kidney transplantation, survival rates are still partly impaired due to the deleterious effects of donor specific HLA antibodies (DSA). However, not all luminex-defined DSA appear to be clinically relevant. Further analysis of DSA recognizing polymorphic amino acid configurations, called eplets or functional epitopes, might improve the discrimination between clinically relevant vs. irrelevant HLA antibodies. To evaluate which donor epitope-specific HLA antibodies (DESAs) are clinically important in kidney graft survival, relevant and irrelevant DESAs were discerned in a Dutch cohort of 4690 patients using Kaplan–Meier analysis and tested in a cox proportional hazard (CPH) model including nonimmunological variables. Pre-transplant DESAs were detected in 439 patients (9.4%). The presence of certain clinically relevant DESAs was significantly associated with increased risk on graft loss in deceased donor transplantations (p &lt; 0.0001). The antibodies recognized six epitopes of HLA Class I, 3 of HLA-DR, and 1 of HLA-DQ, and most antibodies were directed to HLA-B (47%). Fifty-three patients (69.7%) had DESA against one donor epitope (range 1–5). Long-term graft survival rate in patients with clinically relevant DESA was 32%, rendering DESA a superior parameter to classical DSA (60%). In the CPH model, the hazard ratio (95% CI) of clinically relevant DESAs was 2.45 (1.84–3.25) in deceased donation, and 2.22 (1.25–3.95) in living donation. In conclusion, the developed model shows the deleterious effect of clinically relevant DESAs on graft outcome which outperformed traditional DSA-based risk analysis on antigen level.</p

Ellipro scores of donor epitope specific HLA antibodies are not associated with kidney graft survival

In kidney transplantation, donor HLA antibodies are a risk factor for graft loss. Accessibility of donor eplets for HLA antibodies is predicted by the ElliPro score. The clinical usefulness of those scores in relation to transplant outcome is unknown. In a large Dutch kidney transplant cohort, Ellipro scores of pretransplant donor antibodies that can be assigned to known eplets (donor epitope specific HLA antibodies [DESAs]) were compared between early graft failure and long surviving deceased donor transplants. We did not observe a significant Ellipro score difference between the two cohorts, nor significant differences in graft survival between transplants with DESAs having high versus low total Ellipro scores. We conclude that Ellipro scores cannot be used to identify DESAs associated with early versus late kidney graft loss in deceased donor transplants

Bare bones differential evolution

The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria