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Slaughter performance, sensory, and conventional physicochemical indexes of 300 days old Yanjin blackbone chicken and Piao chicken were measured and compared, to investigate the differences in slaughter performance and meat physicochemical properties between Yanjin blackbone chicken and Piao chicken. The results showed that both Yanjin blackbone chicken and Piao chicken had good meat performance, and the breast muscle rate of female Yanjin blackbone chicken was significantly higher than that of male Yanjin blackbone chicken and Piao chicken (P

To address the problems of limited number of substitutable words and low watermark extraction efficiency in the existing natural language digital watermarking methods, a creative method based on context word prediction and window compression coding was proposed.Firstly, the contextual semantic features of each word in the original text were automatically learned through a neural network language model, and then the candidate word set for each word was predicted, thus the number of substitutable words that could be utilized for carrying watermark information was expanded.Meanwhile, considering the difference of the semantic impact caused by the substitutions of candidate words at different positions, the watermark information was embedded into each window containing several words, and the selection of candidate words for watermark embedding was optimized by the similarity between sentences before and after performing word substitutions.Finally, a semantic-independent window compression coding method was proposed, which encoded each window as appointed watermark information in terms of the character information of words contained in the window.So that during watermark extraction, the dependence on the original context at the position of word substitution was eliminated.The experimental results show that the proposed method greatly improves the watermark extraction efficiency with high embedding capacity and text quality.

Abstract Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler–Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.

Adopting ecological agricultural technologies (EATs) is an effective way to guarantee food security and protect the environment. With the advancement of the digital village strategy, the continuous integration of the digital economy with the rural economy has gradually become an emerging driving force for sustainable agriculture. However, few studies have examined the impact of farmers’ participation in the digital economy in different links of the agricultural industry chain on EATs adoption from a micro perspective. Using data from 2,825 grain farmers from the 2020 China Rural Revitalization Survey, this paper applies the treatment effect model and the mediating effect model to empirically analyze the impact and mechanism of participation in the digital economy on farmers’ EATs adoption from an ecological effect perspective. This paper characterizes farmers’ participation in the digital economy in terms of digital production, digital sales, and digital finance. The results show that participation in the digital economy significantly promotes farmers’ EATs adoption. The sub-dimensional test found that participation in digital production, digital sales, and digital finance significantly promotes farmers’ EATs adoption. The above findings remain robust after a series of robustness tests, such as changing the estimation method and replacing the dependent variable. Mechanism analysis shows that participation in the digital economy, especially digital sales and digital finance, could promote farmers’ EATs adoption by increasing the demand for agricultural machinery services, improving information availability, and enhancing food safety cognition; participation in the digital production could promote farmers’ EATs adoption by increasing the demand for agricultural machinery services. Results of heterogeneity analysis show that the promotion effect of participation in the digital economy on EATs adoption is more substantial among farmers with higher digital literacy and larger cultivated land scales. The findings of this paper provide insights for China and other countries to effectively use digital technology to promote sustainable agriculture under the pressure of achieving sustainable development goals.

To address the problem that task-irrelevant objects such as cars, pedestrians and sky, will interfere with the extracted feature descriptors in cross-view image geo-localization, this paper proposes a novel method for cross-view image geo-localization, named as AENet. The method includes two main parts: an attention efficient network fusing channel and spatial attention mechanisms and a triplet loss function based on a multiple hard samples weighting strategy. In the first part, the EfficientNetV2 network is used to extract features from the images and preliminarily filter irrelevant features from the channel dimension, then the Triplet Attention layer is applied to further filter irrelevant features from the spatial dimension. In the second part, a multiple hard samples weighting strategy is proposed to enhance the learning of hard samples. Experimental results show that our proposed method significantly outperforms the state-of-the-art method on two existing benchmark datasets.

● The choice of a modern agriculture project was found to be rational for professional farmers to reduce interference from non-market factors. The success of this project is due to its capacity to successfully fill the structural hole in the market transaction network. ● Professional farmers were shown to start their businesses by occupying the 'self-benefit' or 'mutual-benefit' structural hole of the market transaction network in economically developed areas. ● Professional farmers were found to occupy the organizational-governance structural hole of the rural social relationship network and the 'mutual-benefit' structural hole of the market network to start their businesses in traditional agricultural areas. A comparative multi-case analysis of professional farmer entrepreneurship cases in China was performed by applying the structural hole theory. The results confirmed four views. (1) Choosing the modern agriculture project entrepreneurship is rational for professional farmers, who return from urban, to reduce the interference from non-market factors. The success of this project stems from its ability to successfully occupy the structural hole of the market trading network. (2) In economically developed areas, professional farmers start their businesses and reduce transaction costs with factors by occupying 'self-benefit' or 'mutual-benefit' structural holes in market networks. (3) In traditional agricultural areas, for reducing factor transaction costs, professional farmers occupy the organizational-governance structural hole of rural social relationship networks and the mutual-benefit structural hole of market networks to start their businesses. (4) The embedding order of these two structural holes will change depending on the differences in the local resource endowment. This article proposes some suggestions to encourage professional farmers to develop featured agricultural projects, build a close benefit-linked mechanism with smallholders through the provision of socialized services, and participate in the governance of rural communities.

Stem canker and black scurf of potatoes, caused by Rhizoctonia solani, are economically important diseases. Although the field application of sodium silicate has been shown to improve potato’s resistance against R. solani, the underlying mechanism remains unclear. In this study, we examined this resistance using transcriptomic analysis. Potato stems inoculated with R. solani were treated with sodium silicate, while a control group received no sodium silicate treatment. The plants were grown under natural environmental conditions at the farm of Inner Mongolia Agricultural University. Potato stems were sampled 4, 8, and 12 days after treatment. Total RNA was extracted using the TRIzol reagent and transformed into cDNA. The cDNA was sequenced, the reads were aligned, and the expression levels of genes were quantified and compared between the treated and control groups. A total of 1491 genes were identified as differentially expressed genes (DEGs). Furthermore, these DEGs were found to be involved in hydrolase activity, plant–pathogen interactions, hormone signal transduction, and the phenylpropanoid biosynthesis pathway. To confirm the up- and down-regulation of DEGs, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on randomly selected genes. The results showed that the application of sodium silicate induces a complex defense network in potato plants involving physical barriers, innate immunity, phytohormone signaling, and various phenylpropanoid compounds to combat R. solani infection. This study provides valuable insights into the molecular mechanisms underlying sodium-silicate-induced resistance and its potential for reducing stem canker and black scurf in potato crops.

The biosynthesis of xanthones has not been well documented. Here, the authors report that monooxygenase FlsO1 catalyzes three successive oxidations – hydroxylation, epoxidation and Baeyer–Villiger oxidation—to form the xanthone scaffold in actinomycetes.

Epoxide ring opening reactions are important in both biological processes and synthetic applications. Here, the authors show that flavin cofactors can catalyze reductive and oxidative epoxide ring opening reactions and propose the underlying mechanisms.

Privacy protection is the key to maintaining the Internet of Things (IoT) communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key to checking steganography security, and its ultimate goal is to extract embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.

Abstract The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.

Abstract When embedding secret message into image by steganography with matrix encoding, there are still no effective methods to recover the stego key because it is difficult to statistically distinguish the stego coefficient sequences selected by true and false keys. Therefore, this paper proposes a method for recovering the stego key of a typical JPEG (Joint Photographic Experts Group) image steganography—F5 which composes of the check matrix and shuffling key. Firstly, the check matrix is recovered based on the embedding ratio estimated by quantitative steganalysis. The shuffling key is then recovered based on the distribution difference between the bit sequences extracted by the true and false shuffling keys. Additionally, the cardinality of the shuffling key space is significantly reduced by examining the extracted encoding parameter and message length. Experimental results show that the proposed method can recover the stego key accurately and efficiently, even when the existing Xu’s method fails for the high or very low embedding ratio.

(S)-equol (EQ) is an isoflavone with high estrogen-like activity in the human body, and is only produced by some gut bacteria in vivo. It plays an important role in maintaining individual health, however, the dearth of resources associated with (S)-EQ-producing bacteria has seriously restricted the production and application of (S)-EQ. We report here a new functional gene KEC48-07020 (K-07020) that was identified from a chick (S)-EQ-producing bacterium (Clostridium sp. ZJ6, ZJ6). We found that recombinant protein of K-07020 possessed similar function to daidzein reductase (DZNR), which can convert daidzein (DZN) into R/S-dihydrodaidzein (R/S-DHD). Interestingly, K-07020 can reversely convert (R/S)-DHD (DHD oxidase) into DZN even without cofactors under aerobic conditions. Additionally, high concentrations of (S)-EQ can directly promote DHD oxidase but inhibit DZNR activity. Molecular docking and site-directed mutagenesis revealed that the amino acid > Arg75 was the active site of DHD oxidase. Subsequently, an engineered E. coli strain based on K-07020 was constructed and showed higher yield of (S)-EQ than the engineered bacteria from our previous work. Metagenomics analysis and PCR detection surprisingly revealed that K-07020 and related bacteria may be prevalent in the gut of humans and animals. Overall, a new DZNR from ZJ6 was found and identified in this study, and its bidirectional enzyme activities and wide distribution in the gut of humans and animals provide alternative strategies for revealing the individual regulatory mechanisms of (S)-EQ-producing bacteria.

Steganography poses a serious challenge to forensics because investigators cannot identify even traces of secret messages embedded using a steganographer. Contrarily, the objective of locating steganalysis is to locate the embedded message, which should help extract the secret message. In this paper, a methodology of locating steganalysis using quantitative steganalysis is presented for multiple stego images with embedded messages along the same embedding path. Three typical quantitative steganalysis methods are applied to the methodology to locate the messages embedded using LSB re-placement. Experimental results show that the presented methods can reliably estimate the embedding positions, which verifies the validity of the presented methodology. The presented methodology points out a new use of quantitative steganalysis, and further demonstrates that it is necessary to design more precise quantitative steganalysis methods.

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

Benzofluorene-containing angucyclines, bacterial natural compounds with potential use as therapeutics/antibiotics, occur as dimers. Here, the authors elucidated the dimerization mechanism which turned out to work spontaneously, without enzymatic catalysis.

It is one of the potential threats to the Internet of Things to reveal confidential messages by color image steganography. The existing color image steganalysis algorithm based on channel geometric transformation measures owns higher accuracy than the others, but it fails to utilize the correlation between the gradient amplitudes of different color channels. Therefore, this article points out that the color image steganography weakens the correlation between the gradient amplitudes of different color channels and proposes a color image steganalysis algorithm based on channel gradient correlation. The proposed algorithm extracts the co-occurrence matrix feature from the gradient amplitude residuals among different color channels and then combines it with the existing color image steganalysis features to train the ensemble classifier for color image steganalysis. The experimental results show that, for WOW and S-UNIWARD steganography, compared with the existing algorithms, the proposed algorithm outperforms the existing algorithms.