Bovidae-based gelatin: Extractions method,physicochemical and functional properties,applications, and future trends

Gelatin is one of the most important multifunctional biopolymers and is widely used as an essential ingredient in food, pharmaceutical, and cosmetics. Porcine gelatin is regarded as the leading source of gelatin globally then followed by bovine gelatin. Porcine sources are favored over other sources since they are less expensive. However, porcine gelatin is religiously prohibited to be consumed by Muslims and the Jewish community. It is predicted that the global demand for gelatin will increase significantly in the future. Therefore, a sustainable source of gelatin with efficient production and free of disease transmission must be developed. The highest quality of Bovidae-based gelatin (BG) was acquired through alkaline pretreatment, which displayed excellent physicochemical and rheological properties. The utilization of mammalian- and plant-based enzyme significantly increased the gelatin yield. The emulsifying and foaming properties of BG also showed good stability when incorporated into food and pharmaceutical products. Manipulation of extraction conditions has enabled the development of custom-made gelatin with desired properties. This review highlighted the various modifications of extraction and processing methods to improve the physicochemical and functional properties of Bovidae-based gelatin. An in-depth analysis of the crucial stage of collagen breakdown is also discussed, which involved acid, alkaline, and enzyme pretreatment, respectively. In addition, the unique characteristics and primary qualities of BG including protein content, amphoteric property, gel strength, emulsifying and viscosity properties, and foaming ability were presented. Finally, the applications and prospects of BG as the preferred gelatin source globally were outlined.     

Essential (Mg,Fe, Zn and Cu) and Non-Essential (Cd and Pb) Elements in Predatory Insects (Vespa crabro and Vespa velutina): A Molecular Perspective

The recent introduction of the Asian yellow-legged hornet, Vespa velutina, into Europe has raised concern regarding the threat to honeybees and the competition with the European hornet, Vespa crabro. The aim of this study was to investigated essential (Mg, Fe, Zn, Cu) and non-essential (Cd and Pb) elements in these two species. Element concentrations were determined in the whole body and separately in the head, thorax and abdomen using atomic absorption spectrometry (AAS). The changes in essential element concentration and speciation during metamorphosis were also studied using size exclusion chromatography followed by AAS and proteomic analysis. In both species, the essential elements were more concentrated in the abdomen due to the presence of fat bodies. Magnesium, Fe and Zn concentrations were significantly higher in V. crabro than in V. velutina and could have been related to the higher aerobic energy demand of the former species required to sustain foraging flight. Low concentrations of Cd and Pb were indicative of low environmental exposure. The concentration and speciation of essential elements, particularly Fe, varied among the developmental stages, indicating a modification of ligand preferences during metamorphosis. Overall, the results in the present study provide a better understanding of the hornet metal metabolism and a foundation for additional studies.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Analysis of surface and interior degradation of gas diffusion layer with accelerated stress tests for polymer electrolyte membrane fuel cell

The effects of surface and interior degradation of the gas diffusion layer (GDL) on the performance and durability of polymer electrolyte membrane fuel cells (PEMFCs) have been investigated using three freeze-thaw accelerated stress tests (ASTs). Three ASTs (ex-situ, in-situ, and new methods) are designed from freezing ?30 °C to thawing 80 °C by immersing, supplying, and bubbling, respectively. The ex-situ method is designed for surface degradation of the GDL. Change of surface morphology from hydrophobic to hydrophilic by surface degradation of GDL causes low capillary pressure which decreased PEMFC performance. The in-situ method is designed for the interior degradation of the GDL. A decrease in the ratio of the porosity to tortuosity by interior degradation of the GDL deteriorates PEMFC performance. Moreover, the new method showed combined effects for both surface and interior degradation of the GDL. It was identified that the main factor that deteriorated the fuel cell performance was the increase in mass transport resistance by interior degradation of GDL. In conclusion, this study aims to investigate the causes of degraded GDL on the PEMFC performance into the surface and interior degradation and provide the design guideline of high-durability GDL for the PEMFC.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Plant Copper Amine Oxidases: Key Players in Hormone Signaling Leading to Stress-Induced Phenotypic Plasticity

Polyamines are ubiquitous, low-molecular-weight aliphatic compounds, present in living organisms and essential for cell growth and differentiation. Copper amine oxidases (CuAOs) oxidize polyamines to aminoaldehydes releasing ammonium and hydrogen peroxide, which participates in the complex network of reactive oxygen species acting as signaling molecules involved in responses to biotic and abiotic stresses. CuAOs have been identified and characterized in different plant species, but the most extensive study on a CuAO gene family has been carried out in Arabidopsis thaliana. Growing attention has been devoted in the last years to the investigation of the CuAO expression pattern during development and in response to an array of stress and stress-related hormones, events in which recent studies have highlighted CuAOs to play a key role by modulation of a multilevel phenotypic plasticity expression. In this review, the attention will be focused on the involvement of different AtCuAOs in the IAA/JA/ABA signal transduction pathways which mediate stress-induced phenotypic plasticity events.     

Understanding δ-Bi2O3 fluorite degradation mechanism and related solutions for promising applications in distributed multigeneration

Oxygen selective membrane on the base of cermet δ-Bi₂O₃/Ag with an interpenetrating structure has the maximum potential efficiency of air separation. However, the degradation processes, including the phase degradation of fluorite δ-Bi₂O₃, do not make it possible to create a membrane with the required perfection and durability. In this work, the ordering of oxygen vacancies with the transformation of fluorite into the rhombohedral phase (S.G. R-3) was studied by powder HT XRD in situ at 600 °C on dense Bi_0.78Er_0.2Hf_0.02O_1.51 ceramics. Fast regeneration of disordered fluorite occurs at T = 640–700 °C. The phase degradation of fluorite due to the segregation of dopants at the second stage leads into stable phases - sillenite, tetragonal or rhombohedral phase (S.G. R-3m), depending on the composition of δ-Bi₂O₃. Fast regeneration of fluorite occurs when heated to 820 °C, which is unacceptable for membranes. Analysis of all available data allows us to propose approaches to optimize the composition of δ-Bi₂O₃ and technical solutions for creating durable oxygen selective membranes with promising use in distributed multigeneration. As a result of the analysis, a new solid electrolyte with better parameters was obtained.