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ABSTRACT Membrane proteins, particularly those that are α-helical, such as transporters and G-protein-coupled receptors (GPCRs), have significant biological relevance. However, their expression and purification pose difficulties because of their poor water solubilities, which impedes progress in this field. The QTY method, a code-based protein-engineering approach, was recently developed to produce soluble transmembrane proteins. Here, we describe a comprehensive Web server built for QTY design and its relevance for in silico analyses. Typically, the simple design model is expected to require only 2 to 4 min of computer time, and the library design model requires 2 to 5 h, depending on the target protein size and the number of transmembrane helices. Detailed protocols for using the server with both the simple design and library design modules are provided. Methods for experiments following the QTY design are also included to facilitate the implementation of this approach. The design pipeline was further evaluated using microbial transmembrane proteins and structural alignment between the designed proteins and their origins by employing AlphaFold2. The results reveal that mutants generated by the developed pipeline were highly identical to their origins in terms of three-dimensional (3D) structures. In summary, the utilization of our Web server and associated protocols will enable QTY-based protein engineering to be implemented in a convenient, fast, accurate, and rational manner. The Protein Solubilizing Server (PSS) is publicly available at http://pss.sjtu.edu.cn. IMPORTANCE Water-soluble expression and purification are of considerable importance for protein identification and characterization. However, there has been a lack of an effective method for water-soluble expression of membrane proteins, which has severely hampered their studies. Here, an enabling comprehensive Web server, PSS, was developed for designing water-soluble mutants of α-helical membrane proteins, based on QTY design, a code-based protein-engineering approach. With microbial transmembrane proteins and GPCRs as examples, we systematically evaluated the server and demonstrated its successful performance. PSS is readily available for worldwide users as a Web-based tool, rendering QTY-based protein engineering convenient, efficient, accurate, and rational.

Cucurbita pepo is one of the earliest cultivated crops. It is native to Central and South America and is now widely cultivated all over the world for its rich nutrition, short growth period, and high yield, which make it suitable for intercropping. Hull-less C. pepo L. (HLCP) is a rare variant in nature that is easier to consume. Its seed has a seed kernel but lacks a seed coat. The molecular mechanism underlying the lack of seed coat development in the HLCP variety is not clear yet. The BGISEQ-500 sequencing platform was used to sequence 18 cDNA libraries of seed coats from hulled C. pepo (CP) and HLCP at three developmental stages (8, 18, and 28 days) post-pollination. We found that lignin accumulation in the seed coat of the HLCP variety was much lower than that of the CP variety. A total of 2,099 DEGs were identified in the CP variety, which were enriched mainly in the phenylpropanoid biosynthesis pathway, amino sugar, and nucleotide sugar metabolism pathways. A total of 1,831 DEGs were identified in the HLCP variety and found to be enriched mainly in the phenylpropanoid biosynthesis and metabolism pathways of starch and sucrose. Among the DEGs, hub proteins (FusA), protein kinases (IRAK4), and several transcription factors related to seed coat development (MYB, bHLH, NAC, AP2/EREBP, WRKY) were upregulated in the CP variety. The relative expression levels of 12 randomly selected DEGs were determined using quantitative real-time PCR analysis and found to be consistent with those obtained using RNA-Seq, with a correlation coefficient of 0.9474. We found that IRAK4 protein kinases, AP2/EREBP, MYB, bHLH, and NAC transcription factors may play important roles in seed coat development, leading to the formation of HLCP.

Rationally controlling cellular metabolism is of great importance but challenging owing to its highly complex and chaotic nature. Natural existing sensory proteins like histidine kinases (HKs) are understood as “sensitive nodes” of biological networks that can trigger disruptive metabolic reprogramming (MRP) upon perceiving environmental fluctuation. Here, the “sensitive node” genes were adopted to devise a global MRP platform consisting of a CRISPR interference-mediated dual-gene combinational knockdown toolbox and survivorship-based metabolic interaction decoding algorithm. The platform allows users to decode the interfering effects of n × n gene pairs while only requiring the synthesis of n pairs of primers. A total of 35 HK genes and 24 glycine metabolic genes were selected as the targets to determine the effectiveness of our platform in a Vibrio sp. FA2. The platform was applied to decode the interfering impact of HKs on antibiotic resistance in strain FA2. A pattern of combined knockdown of HK genes (sasA_8 and 04288) was demonstrated to be capable of reducing antibiotic resistance of Vibrio by 108-fold. Patterns of combined knockdown of glycine pathway genes (e.g., gcvT and ltaE) and several HK genes (e.g., cpxA and btsS) were also revealed to increase glycine production. Our platform may enable an efficient and rational approach for global MRP based on the elucidation of high-order gene interactions. A web-based 1-stop service (https://smrp.sjtu.edu.cn) is also provided to simplify the implementation of this smart strategy in a broad range of cells.

Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties. The new computational tool, AlphaFold2, offers highly accurate predictions of three-dimensional protein structures. The glutamine, threonine and tyrosine (QTY) code provides a systematic method of rendering hydrophobic sequences into hydrophilic ones. Here, we present computational studies of native integral membrane GLUTs with 12 transmembrane helical segments determined by X-ray crystallography and CryoEM, comparing the AlphaFold2-predicted native structure to their water-soluble QTY variants predicted by AlphaFold2. In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, glutamine (Q), threonine (T) and tyrosine (Y) rendering them water-soluble. We present the superposed structures of native GLUTs and their water-soluble QTY variants. The superposed structures show remarkable similar residue mean square distance values between 0.47 and 3.6 Å (most about 1–2 Å) despite >44% transmembrane amino acid differences. We also show the differences of hydrophobicity patches between the native membrane transporters and their QTY variants. We explain the rationale why the membrane protein QTY variants become water-soluble. Our study provides insight into the differences between the hydrophobic helices and hydrophilic helices, and offers confirmation of the QTY method for studying multispan transmembrane proteins and other aggregated proteins through their water-soluble variants.

Abstract In this study, the physiochemical properties of corn oil and its oxidative stability in an O/W emulsion were studied following short‐term (120 min) deep‐frying of chicken wing. The results showed that the levels of polyunsaturated fatty acids in corn oil decreased after frying. Furthermore, total polar compound content in frying oil was significantly increased to 11.3%. Fourier transform infrared spectra (FTIR) indicated that hydrolysis and oxidation reactions involving triglycerides occurred after frying. Additionally, the increased a* and b* values demonstrated that deep‐frying greatly enhanced the intensity of the red and yellow colors of corn oil. Frying reduced the oxidative stability of corn oil in an O/W emulsion as determined by the peroxide value and acid value. These findings indicated that short‐term deep‐frying of chicken wing deteriorated the quality of corn oil and decreased its oxidative stability in an O/W emulsion. Consumers should consider the potential hazards of food containing short‐term deep‐frying oil.

Reliable methods are always greatly desired for the practice of food inspection. Currently, most food inspection techniques are mainly dependent on the identification of special components, which neglect the combination effects of different components and often lead to biased results. By using Chinese liquors as an example, we developed a new food identification method based on the combination of machine learning with GC × GC/TOF-MS. The sample preparation methods SPME and LLE were compared and optimized for producing repeatable and high-quality data. Then, two machine learning algorithms were tried, and the support vector machine (SVM) algorithm was finally chosen for its better performance. It is shown that the method performs well in identifying both the geographical origins and flavor types of Chinese liquors, with high accuracies of 91.86% and 97.67%, respectively. It is also reasonable to propose that combining machine learning with advanced chromatography could be used for other foods with complex components.

Surface urban heat islands are widely focused on due to their close relationship with a series of environmental issues. Obtaining a precise footprint is an important prerequisite for heat island research. However, the land surface temperature curves used for calculating footprint are affected by factors such as the complexity of land-use types, thereby affecting the accuracy of footprint. Therefore, the rotational-scan exponential decay model is developed in this paper, which first takes the gravity center of an urban area as the origin of polar coordinates, specifies due north as the starting direction, and rotationally scans the suburbs that are within 20 km outside urban areas in a clockwise direction at an angle of 1°. The eligible suburbs are screened out according to the built-up area rate, water body rate, and merge tolerance. Then, exponential decay fitting of the temperature curve is performed to obtain the extension distance of the heat island and the background temperature, which are used to determine the final footprint. Based on the method, the footprints of 15 cities were calculated and compared with those of the traditional method. The results show that: (1) this method could effectively eliminate the influence of a large number of contiguous built-up areas and water bodies in the suburbs on the footprint calculation, thus greatly improving the accuracy of the temperature curve and footprint. (2) Three of four cities had the largest footprint boundary in spring. All four cities had the strongest heat island intensity in summer and the smallest footprint boundary and intensity in winter. (3) Coupling effect would aggravate the negative impact of heat islands in the suburbs and threaten the suburban environment. As a state-of-the-art method, it can enhance the calculation accuracy and precisely reflect the spatial pattern of footprint, which is of great significance for the sustainable development of cities.

Acetyl‐TAG are unusually structured triacylglycerols (TAG) that possess an acetate group at their sn‐3 position. Acetyl‐TAG have different physical properties compared to regular TAG, including lower viscosity and improved cold temperature properties, making acetyl‐TAG useful for different applications, including as a diesel replacement. These unusual TAG molecules can be synthesized in the seeds of transgenic plants through the expression of diacylglycerol acetyltransferase (DAcT) enzymes isolated from different Euonymus species. In this study, the kinematic viscosity as well as the crystallization and melting behavior of blends of acetyl‐TAG and regular TAG were examined to define goals for acetyl‐TAG synthesis in transgenic plants. Even small amounts of regular TAG when blended with acetyl‐TAG had a disproportionate effect on the viscosity of mixture. This effect of regular TAG in increasing kinematic viscosity was more pronounced at lower temperatures. Under slow cooling conditions, the two different TAGs and their blends possessed two main crystallization events with different degree of separation of the thermal transition, and the lower crystallization temperature decreased with increasing amounts of acetyl‐TAG in the blend. At higher cooling rates, one broad and tailed crystallization peak was observed. Heating thermograms indicate similar polymorphic behavior of the blends and a general peak shift to lower transition range with increasing acetyl‐TAG compared to the two pure lipids. This information about the viscosity and thermal properties of blends of TAG and acetyl‐TAG will provide useful targets for engineering higher levels of acetyl‐TAG in transgenic seeds. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware. Keywords: Design method, Biologically inspired design, Context-awareness, Intelligent design

State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives. Keywords: Cyber–physical systems (CPS), Digital twin (DT), Smart manufacturing, Correlation and comparison

Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models.

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Membrane separation is important for a range of industrial and medical applications. Here, the authors report on the formation of self-assembled protein membranes for size selective separation and demonstrate application in the separation of dyes, nanoparticles and in hemodialysis.