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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.

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.

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 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.

(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.

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.

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.

For current steganalysis of the image content-adaptive steganography, there are multiple problems to be improved, such as high difficulty and low accuracy, when detecting images with various contents and textures. For this problem, an improved steganalytic method is proposed in this paper based on the pre-classification and feature selection. First, using the features extracted based on the dependency analysis of image adjacent data, the images with various content and texture complexities are pre-classified as multiple clusters by the K-means algorithms. Then, the performance of existing various steganalytic features are analyzed for different clusters of images, and the optimal features for each cluster are selected for final classification. The experimental results show that the detection accuracy could be improved by the proposed method, and the rationality and availability are also verified. At the same time, the analysis and experimental results in this paper also show that the images with rich content and complex texture should be paid more attention both in steganography and in steganalysis.

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.

In digital steganography, due to difficulties estimating the JPEG cover image, it is still very hard to accurately locate the hidden message embedded in a JPEG image. Therefore, this study proposes a payload location method for a category of pseudo-random scrambled JPEG image steganography. In order to estimate the quantized discrete cosine transform coefficients in the cover JPEG image, a cover JPEG image estimation method is proposed based on co-frequency sub-image filtering. The proposed payload location method defines a general residual, uses the estimated cover JPEG image to compute the residuals, and then employs the mean residuals of multiple stego images embedded along the same path to distinguish the stego positions. The proposed cover JPEG image estimation method constructs 64 co-frequency sub-images, and then filters the sub-image to estimate the cover JPEG image. Finally, using these methods, payload location algorithms are designed for two common JPEG image steganography algorithms: JSteg and F5. Experimental results show that the proposed location algorithms can effectively locate the stego positions in both JSteg and F5 steganography when the investigator possesses multiple stego images embedded along the same path. In addition, the location results can also be used to recover the steganography key to extract the embedded secret messages.

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.

The development of steganography technology threatens the security of privacy information in smart campus. To prevent privacy disclosure, a linguistic steganalysis method based on word embedding is proposed to detect the privacy information hidden in synonyms in the texts. With the continuous Skipgram language model, each synonym and words in its context are represented as word embeddings, which aims to encode semantic meanings of words into low-dimensional dense vectors. The context fitness, which characterizes the suitability of a synonym by its semantic correlations with context words, is effectively estimated by their corresponding word embeddings and weighted by TF-IDF values of context words. By analyzing the differences of context fitness values of synonyms in the same synonym set and the differences of those in the cover and stego text, three features are extracted and fed into a support vector machine classifier for steganalysis task. The experimental results show that the proposed steganalysis improves the average F-value at least 4.8% over two baselines. In addition, the detection performance can be further improved by learning better word embeddings.

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.

The intelligent production of sheet metal is a comprehensive technology involving control science, computer science, and sheet metal production. Intelligent rolling is an important development in the production process of sheet metal. In this paper, a new symmetrical four-roller bending (SFRB) process is introduced, which consists of feeding, pre-bending, reverse roll bending, second bending, and forward roll bending and unloading. A control strategy is proposed for the process, including on-line monitoring of curvature, on-line identification of the springback law, on-line prediction of final reduction, and control strategy. A convenient and reliable on-line curvature monitoring method is proposed. The quantitative relationship between the reduction and the curvature, in the form of a quadratic function, was established by physical experiments and numerical simulation, and the online identification of the springback law was realized. An on-line prediction method of the final reduction is proposed, and the determination principle of the reduction of three pre-bending processes is given. Finally, the control strategy of the SFRB process was verified by physical experiments. The relative error of the curvature radius of the final formed parts can be controlled within 0.8%. This research provides new insights into intelligent rolling.

The structure diversity of type II polyketide synthases-derived bacterial aromatic polyketides is often enhanced by enzyme controlled or spontaneous cyclizations. Here we report the discovery of bacterial aromatic polyketides generated from 5 different cyclization modes and pathway crosstalk between the host and the heterologous fluostatin biosynthetic gene cluster derived from a marine bacterium. The discovery of new compound SEK43F (2) represents an unusual carbon skeleton resulting from a pathway crosstalk, in which a pyrrole-like moiety derived from the host Streptomyces albus J1074 is fused to an aromatic polyketide SEK43 generated from the heterologous fluostatin type II PKSs. The occurrence of a new congener, fluoquinone (3), highlights a bacterial aromatic polyketide that is exceptionally derived from a characteristic fungal F-mode first-ring cyclization. This study expands our knowledge on the power of bacterial type II PKSs in diversifying aromatic polyketides.

Diazobenzofluorene-containing atypical angucyclines exhibit promising biological activities. Here we report the inactivation of an amidotransferase-encoding gene flsN3 in Micromonospora rosaria SCSIO N160, a producer of fluostatins. Bioinformatics analysis indicated that FlsN3 was involved in the diazo formation. Chemical investigation of the flsN3-inactivation mutant resulted in the isolation of a variety of angucycline aromatic polyketides, including four racemic aminobenzo[b]fluorenes stealthins D–G (9–12) harboring a stealthin C-like core skeleton with an acetone or butanone-like side chain. Their structures were elucidated on the basis of nuclear magnetic resonance (NMR) spectroscopic data and X-ray diffraction analysis. A plausible mechanism for the formation of stealthins D–G (9–12) was proposed. These results suggested a functional role of FlsN3 in the formation/modification of N–N bond-containing fluostatins.

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image. Then, the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification. Finally, the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis. We experimented on publicly available combined datasets of Bossbase1.01, Bows2, and ALASKA#2 with a quality factor of 75. The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84% for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion (J-UNIWARD) at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient (bpnzAC). Furthermore, at the payload of 0.2 bpnzAC, the improvement effect is minimal but also reaches 1.39%. Compared with the previous steganalysis based on deep learning, this method considers the differences between the training contents. It selects the proper detector for the image to be detected. Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images. The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis. [ABSTRACT FROM AUTHOR]