Notes

Broadband internet, plastic photonic, visual add-drop filtration together with 3  dB bandwidths up to 11  THz: publisher's take note.
hip perivascular niche, in a serum-free flowable culture, could provide the next level of complexity of an in vitro system to study the influence of glioma stem cells on brain endothelium.We demonstrate a novel approach for controlling the line defect formation in microscopic wrinkling structures by patterned plasma treatment of elastomeric surfaces. Wrinkles were formed on polydimethylsiloxane (PDMS) surfaces exposed to low-pressure plasma under uniaxial stretching and subsequent relaxation. The wrinkling wavelength λ can be regulated via the treatment time and choice of plasma process gases (H2, N2). Sequential masking allows for changing these parameters on micron-scale dimensions. Thus, abrupt changes of the wrinkling wavelength become feasible and result in line defects located at the boundary zone between areas of different wavelengths. Wavelengths, morphology, and mechanical properties of the respective areas are investigated by Atomic Force Microscopy and agree quantitatively with predictions of analytical models for wrinkle formation. Notably, the approach allows for the first time the realization of a dramatic wavelength change up to a factor of 7 to control the location of the branching zone. This allows structures with a fixed but also with a strictly alternating branching behavior. The morphology inside the branching zone is compared with finite element methods and shows semi-quantitative agreement. Thus our finding opens new perspectives for "programming" hierarchical wrinkling patterns with potential applications in optics, tribology, and biomimetic structuring of surfaces.Microorganisms and plants represent major sources of natural compounds with a plethora of bioactive properties. Among these, plant natural products (PNPs) remain indispensable to human health. With few exceptions, PNP-based pharmaceuticals come from plant specialized metabolisms and display a structure far too complex for a profitable production by total chemical synthesis. Accordingly, their industrial processes of supply are still mostly based on the extraction of final products or precursors directly from plant materials. This implies that particular contexts (e.g. pandemics, climate changes) and natural resource overexploitation are main drivers for the high production cost and recurrent supply shortages. Recently, biotechnological manufacturing alternatives gave rise to a multitude of benchmark studies implementing the production of important PNPs in various heterologous hosts. Here, we spotlight unprecedented advancements in the field of metabolic engineering dedicated to the heterologous production of a prominent series of PNPs that were achieved during the year 2020. We also discuss how the knowledge accumulated in recent years could pave the way for a broader manufacturing palette of natural products from a wide range of natural resources.The use of a solution process to grow perovskite thin films allows to extend the material processability. It is known that the physicochemical properties of the perovskite material can be tuned by altering the solution precursors as well as by controlling the crystal growth of the film. This advancement necessarily implies the need for an understanding of the kinetic phenomena for the thin-film formation. Therefore, in this work we review the state of the art of perovskite hybrid crystal growth, starting from a comprehensive theoretical description towards broad experimental investigations. One part of the study focuses on rapid thermal annealing as a tool to control nucleation and crystal growth. We deduce that controlling crystal growth with high-precision photonic sintering simplifies the experimental framework required to understand perovskite crystallization. These types of synthesis methods open a new empirical parameter space. All this knowledge serves to improve the perovskite synthesis and the thin films' quality, which will result in higher device performances.Aeration in foods has been widely utilised in the food industry to develop novel foods with enhanced sensorial characteristics. Specifically, aeration at the micron-sized scale has a significant impact on the microstructure where micro-bubbles interact with the other microstructural features in chocolates. This study aims to determine the effect of micro-aeration on the mechanical properties of chocolate products, which are directly correlated with textural attributes such as hardness and crumbliness. Uniaxial compression tests were performed to determine the mechanical properties such as Poisson's ratio, Young's modulus and macroscopic yield strength together with fracture tests to estimate the fracture toughness. In vivo mastication tests were also conducted to investigate the link between the fracture properties and fragmentation during the first two chewing cycles. The uniaxial stress-strain data were used to calibrate a viscoplastic constitutive law. The results showed that micro-aeration significantly affects mechanical properties such as Young's modulus, yield and fracture stresses, as well as fracture toughness. Semagacestat in vivo In addition, it enhances the brittle nature of the chocolate, as evidenced by lower fracture stress but also lower fracture toughness leading to higher fragmentation, in agreement with observations in the in vivo mastication tests. As evidenced by the XRT images and the stress-strain measurements micro-aeration hinders the re-arrangement of the microscopic features inside the chocolate during the material's deformation. The work provides a new insight of the role of bubbles on the bulk behaviour of complex multiphase materials, such as chocolates, and defines the mechanical properties which are important input parameters for the development of oral processing simulations.The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose Multimodal Routing, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality features. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, mean-while keeping competitive performance compared to state-of-the-art methods.
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