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Walnut protein has a high gluten content and compact structure, which limits its water solubility and affects its applications. Therefore, improving the sustainability of walnut proteins is an urgent issue that must be addressed. Physical modification can directly alter the structure of walnut proteins, leading to enhanced functional properties. Chemical modifications typically involve the introduction of exogenous substances that react with walnut proteins to obtain novel products with improved processing attributes. As a highly specific modification technique, biomodification uses enzymes or microorganisms to break down walnut proteins into small peptide molecules or cross-link them to form soluble polymers, thereby enhancing their functional properties and bioactivity. This review presents various methods for modifying walnut proteins and their effects on the structure and functional properties of walnut proteins. The challenges associated with the application and development of these unique technologies are also discussed.

Abstract Background Milk production traits are complex traits with vital economic importance in the camel industry. However, the genetic mechanisms regulating milk production traits in camels remain poorly understood. Therefore, we aimed to identify candidate genes and metabolic pathways that affect milk production traits in Bactrian camels. Methods We classified camels (fourth parity) as low- or high-yield, examined pregnant camels using B-mode ultrasonography, observed the microscopic changes in the mammary gland using hematoxylin and eosin (HE) staining, and used RNA sequencing to identify differentially expressed genes (DEGs) and pathways. Results The average standard milk yield over the 300 days during parity was recorded as 470.18 ± 9.75 and 978.34 ± 3.80 kg in low- and high-performance camels, respectively. Nine female Junggar Bactrian camels were subjected to transcriptome sequencing, and 609 and 393 DEGs were identified in the low-yield vs. high-yield (WDL vs. WGH) and pregnancy versus colostrum period (RSQ vs. CRQ) comparison groups, respectively. The DEGs were compared with genes associated with milk production traits in the Animal Quantitative Trait Loci database and in Alashan Bactrian camels, and 65 and 46 overlapping candidate genes were obtained, respectively. Functional enrichment and protein–protein interaction network analyses of the DEGs and candidate genes were conducted. After comparing our results with those of other livestock studies, we identified 16 signaling pathways and 27 core candidate genes associated with maternal parturition, estrogen regulation, initiation of lactation, and milk production traits. The pathways suggest that emerged milk production involves the regulation of multiple complex metabolic and cellular developmental processes in camels. Finally, the RNA sequencing results were validated using quantitative real-time PCR; the 15 selected genes exhibited consistent expression changes. Conclusions This study identified DEGs and metabolic pathways affecting maternal parturition and milk production traits. The results provides a theoretical foundation for further research on the molecular mechanism of genes related to milk production traits in camels. Furthermore, these findings will help improve breeding strategies to achieve the desired milk yield in camels. Graphical Abstract

Abstract Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning‐based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high‐image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non‐local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi‐scale feature information from the echo and image data. PixelShuffle is utilised for up‐sampling to generate high‐quality echo data. The experimental results on MSTAR and Sentinel‐1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.

Currently, CO2 conversion and utilization have become a key to mitigate the global warming. In this study, a novel separate-type autothermal dry reforming of methane (S-ATDRM) system is proposed and simulated, in which the methane dry reforming combined with methane partial oxidation is performed in a circulating fluidized bed with exergy recuperation to eliminate the negative effect of the products of CH4 partial oxidation on the DRM reaction and further improve the CO2 conversion efficiency. The results demonstrate that this S-ATDRM system can achieve an exergy efficiency of 84.7 %, and about 1055.7 kW of exergy can be recuperated from the process for crude syngas cooling and reapplied for pre-heating of feedstocks of CO2, O2 and CH4. It is found that the largest exergy destruction in this system occurs in the partial oxidation reactor, which occupies ca. 45.6 % of the whole exergy loss. Comparing with the conventional ATDRM system, although the exergy of S-ATDRM system is decreased by approximately 0.3 %, the CO2 conversion is substantially increased by about 11.3 %.

IntroductionThe demand for camel milk, which has unique therapeutic properties, is increasing. The mammary gland is the organ in mammals responsible for the production and quality of milk. However, few studies have investigated the genes or pathways related to mammary gland growth and development in Bactrian camels. This study aimed to compare the morphological changes in mammary gland tissue and transcriptome expression profiles between young and adult female Bactrian camels and to explore the potential candidate genes and signaling pathways related to mammary gland development.MethodsThree 2 years-old female camels and three 5 years-old adult female camels were maintained in the same environment. The parenchyma of the mammary gland tissue was sampled from the camels using percutaneous needle biopsy. Morphological changes were observed using hematoxylin-eosin staining. High-throughput RNA sequencing was performed using the Illumina HiSeq platform to analyze changes in the transcriptome between young and adult camels. Functional enrichment, pathway enrichment, and protein–protein interaction networks were also analyzed. Gene expression was verified using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsHistomorphological analysis showed that the mammary ducts and mammary epithelial cells in adult female camels were greatly developed and differentiated from those in young camels. Transcriptome analysis showed that 2,851 differentially expressed genes were obtained in the adult camel group compared to the young camel group, of which 1,420 were upregulated, 1,431 were downregulated, and 2,419 encoded proteins. Functional enrichment analysis revealed that the upregulated genes were significantly enriched for 24 pathways, including the Hedgehog signaling pathway which is closely related to mammary gland development. The downregulated genes were significantly enriched for seven pathways, among these the Wnt signaling pathway was significantly related to mammary gland development. The protein–protein interaction network sorted the nodes according to the degree of gene interaction and identified nine candidate genes: PRKAB2, PRKAG3, PLCB4, BTRC, GLI1, WIF1, DKK2, FZD3, and WNT4. The expression of fifteen genes randomly detected by qRT-PCR showed results consistent with those of the transcriptome analysis.DiscussionPreliminary findings indicate that the Hedgehog, Wnt, oxytocin, insulin, and steroid biosynthesis signaling pathways have important effects on mammary gland development in dairy camels. Given the importance of these pathways and the interconnections of the involved genes, the genes in these pathways should be considered potential candidate genes. This study provides a theoretical basis for elucidating the molecular mechanisms associated with mammary gland development and milk production in Bactrian camels.

IntroductionThe quality of pressed walnut oil can be improved by moderate roasting treatment.MethodsThis study compared physicochemical characteristics and antioxidant ability of walnut oils pressed from differently roasted pretreated walnuts, analyzed the correlation among these indicators by using Pearson correlation coefficient and correlation coefficient heatmap, and evaluated the volatile organic compounds (VOCs) of walnut oil under optimal pretreatment roasting conditions using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS).ResultsHierarchical cluster analysis (HCA) and principal component analysis (PCA) were able to remarkably distinguish walnut oil produced by different roasting processes. In addition, correlation analysis showed that there was a significant impact among indicators. There were 73 VOCs were identified in the optimum roasted treated walnut oil, consisting of 30 aldehydes, 13 alcohols, 11 ketones, 10 esters, 5 acids, 2 oxygen-containing heterocycles, 1 nitrogen-containing heterocycle and 1 other compound. GC-IMS results showed that aldehydes contributed significantly to the volatile flavor profile of walnut oil, especially (E)-2-heptenal, (E)-2-pentenal and hexenal.DiscussionThe properties of walnut oil based on varying roasting pretreatment of walnut kernels were significantly differentiated. Roasting at 120°C for 20 min is a suitable pretreatment roasting condition for pressing walnut oil. Roasting at 120°C for 20 min is a suitable pretreatment roasting condition for pressing walnut oil.

ABSTRACT Populus euphratica Oliv. has a high tolerance for drought, salinity, and alkalinity. The main purpose of this study is to explore the effects of environments of different salinity intensities on endophytic community structure and the possible roles of endophytes in the tolerance of host plants. The characterization of endogenous bacteria in diversity has been investigated by using the Illumina high-throughput sequencing technique. The research showed that endophytic bacteria of P. euphratica in an extremely saline environment had low species diversity, especially in sap tissue. The dominant phyla in all groups were Proteobacteria, Actinobacteria, and Bacteroidetes. Notably, Firmicutes (relative abundance >5%) was a different dominant phylum in the samples from the high-saline environment compared with the relatively low-saline-environment group. The linear discriminant analysis effect size (LEfSe) analysis found that there were significant differences in different saline environments of Cytophagaceae (family), Rhodobacteraceae (family), and Rhodobacterales (order). These results indicated that the composition of the endogenous bacterial community was related to the growth environment of host plants. The predictive analysis of KEGG pathways and enzymes showed that the abundance of some enzymes and metabolic pathways of endophytes of P. euphratica increased with the increase of soil salinity, and most of the enzymes were related to energy metabolism and carbohydrate metabolism. These findings suggested that the endogenous bacteria of the host plant had different expression mechanisms under different degrees of stress, and this mechanism was very obvious in the distribution of endophytes, while the function of the endogenous bacteria needs to be further explored. IMPORTANCE Euphrates poplar (Populus euphratica Oliv.), as the only tree species that grows in the desert, has tenacious vitality with the characteristics of cold tolerance, drought tolerance, salt-alkali tolerance, and wind-sand resistance. P. euphratica has a long growth cycle and a high growth rate, which can break wind, fix sand, green the environment, and protect farmland, making it an important afforestation tree species in arid and semiarid areas. The area of P. euphratica in Xinjiang accounts for 91.1% of its area in China. Studying the endophytic bacteria of P. euphratica can give people a systematic understanding of it and the adaptability of the endogenous flora to the host and special environments. In this study, by analyzing the endophytic bacteria of P. euphratica in different saline-alkali regions of Xinjiang, it was found that the bacteria in different tissues of P. euphratica changed with the change of soil salinity. Especially in the sap tissue of P. euphratica under extremely high salinity, the diversity of endogenous bacteria was significantly lower than that in other tissues. These differential bacteria under different salinities were mostly related to the stress resistance of themselves and the host. Not only that, we also selected a strain of Bacillus with high stress resistance from the tissues of P. euphratica, which can survive under the extreme conditions of 10% NaCl and pH 11. We obtained a genome completion map of this strain, named it Bacillus haynesii P19 (GenBank accession no. PRJNA648288), and tried to use it for fermentation but in a different work, so as to develop it into a promising industrial fermentation chassis bacterium. Therefore, this study was of great significance for the understanding of endophytic bacteria in P. euphratica and the acquisition of extremophilic microbial resources.

A transport bed flash calcination (TBFC) process applied to magnesite is systematically investigated through process simulation to optimize the energy-saving strategy. The high-temperature calciner flue gas is used to preheat the fed magnesite, while the sensible heat with the caustic calcined magnesia (CCM) product is cooled by air sent to the calciner. Pre-decomposition of magnesite during preheating is considered on basis of the kinetics measured using a micro fluidized bed reaction analyzer that allows the minimized effect of external diffusion on reaction. With staged fuel gas supply the TBFC process allows the equivalence ratios around 1.2 for combustion. The preferred arrangement of stages for magnesite preheating and CCM cooling are respectively 4 and 2, leading to the energy consumption of 4100 kJ/kg-CCM and the energy efficiency of 66.8%, which is almost doubly higher than the 33.9% of the conventional reverberatory furnaces (RF). The pre-decomposition occurs mainly in the 1st-stage preheater, and the maximal conversion is about 13%. Varying the stages of preheating appears more influential on the energy saving than varying the cooling stages, while residence time above 1 s in the preheaters has limited effect.

The main application of the filter bank in radar signal processing is the wideband digital channelized receiver. In order to save hardware resources of the field programmable gate array (FPGA), the efficient channelized receiver structure using polyphase filter has been designed. However, when the number of channels is greatly increased or the transition bandwidth of the filter bank (FB) becomes very narrow, it still consumes large amounts of hardware resources. Therefore, a novel efficient channelized receiver structure based on frequency response masking (FRM) and modulation filter bank is proposed in this paper. The proposed novel structure has less computational complexity in implementing FB with narrow transition bandwidth (NTB). Compared with other efficient FRM-based structures, the proposed novel structure is more suitable for wideband digital channelization receiver. The proposed novel structure is verified correctly by Matlab simulation. The FPGA implementation of the polyphase channelized receiver structure and the proposed novel efficient FRM-based channelized receiver structure are completed by Xilinx System Generator. The result shows that the proposed novel efficient FRM-based channelized receiver structure can save 48.09% of the DSP48E1 hardware resources compared with the polyphase channelized receiver structure and has low group delay characteristic.

With the popularity and development of network technology and the Internet, intrusion detection systems (IDSs), which can identify attacks, have been developed. Traditional intrusion detection algorithms typically employ mining association rules to identify intrusion behaviors. However, they fail to fully extract the characteristic information of user behaviors and encounter various problems, such as high false alarm rate (FAR), poor generalization capability, and poor timeliness. In this paper, we propose a network intrusion detection model based on a convolutional neural network-IDS (CNN-IDS). Redundant and irrelevant features in the network traffic data are first removed using different dimensionality reduction methods. Features of the dimensionality reduction data are automatically extracted using the CNN, and more effective information for identifying intrusion is extracted by supervised learning. To reduce the computational cost, we convert the original traffic vector format into an image format and use a standard KDD-CUP99 dataset to evaluate the performance of the proposed CNN model. The experimental results indicate that the AC, FAR, and timeliness of the CNN-IDS model are higher than those of traditional algorithms. Therefore, the model we propose has not only research significance but also practical value.

There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.