Tumour extracellular vesicles and particles induce liver metabolic dysfunction.

Cancer alters the function of multiple organs beyond those targeted by metastasis1,2. Here we show that inflammation, fatty liver and dysregulated metabolism are hallmarks of systemically affected livers in mouse models and in patients with extrahepatic metastasis. We identified tumour-derived extracellular vesicles and particles (EVPs) as crucial mediators of cancer-induced hepatic reprogramming, which could be reversed by reducing tumour EVP secretion via depletion of Rab27a. All EVP subpopulations, exosomes and principally exomeres, could dysregulate hepatic function. The fatty acid cargo of tumour EVPs-particularly palmitic acid-induced secretion of tumour necrosis factor (TNF) by Kupffer cells, generating a pro-inflammatory microenvironment, suppressing fatty acid metabolism and oxidative phosphorylation, and promoting fatty liver formation. Notably, Kupffer cell ablation or TNF blockade markedly decreased tumour-induced fatty liver generation. Tumour implantation or pre-treatment with tumour EVPs diminished cytochrome P450 gene expression and attenuated drug metabolism in a TNF-dependent manner. We also observed fatty liver and decreased cytochrome P450 expression at diagnosis in tumour-free livers of patients with pancreatic cancer who later developed extrahepatic metastasis, highlighting the clinical relevance of our findings. Notably, tumour EVP education enhanced side effects of chemotherapy, including bone marrow suppression and cardiotoxicity, suggesting that metabolic reprogramming of the liver by tumour-derived EVPs may limit chemotherapy tolerance in patients with cancer. Our results reveal how tumour-derived EVPs dysregulate hepatic function and their targetable potential, alongside TNF inhibition, for preventing fatty liver formation and enhancing the efficacy of chemotherapy.

Tea green leafhopper infestations affect tea plant growth by altering the synthesis of brassinolide.

Tea green leafhoppers are insects widely distributed in major tea-growing areas. At present, less attention has been paid to the study on effect of tea green leafhopper infestation on tea growth phenotype. In this study, tea green leafhoppers were used to treat tea branches in laboratory and co-treated with brassinolide (BL), the highest bioactivity of brassinosteroids (BRs), in tea garden. The results showed that the expression of genes related to BRs synthesis was inhibited and BL content was reduced in tea shoots after infestation by tea green leafhoppers. In addition, area of each leaf position, length and diameter of internodes, and the biomass of the tender shoots of tea plant were decreased after infestation by tea green leafhoppers. The number of trichomes, leaf thickness, palisade tissue thickness and cuticle thickness of tea shoots were increased after tea green leafhoppers infestation. BL spraying could partially recover the phenotypic changes of tea branches caused by tea green leafhoppers infestation. Further studies showed that tea green leafhoppers infestation may regulate the expression of CsDWF4 (a key gene for BL synthesis) through transcription factors CsFP1 and CsTCP1a, which finally affect the BL content. Moreover, BL was applied to inhibit the tea green leafhoppers infestation on tea shoots. In conclusion, our study revealed the effect of plant hormone BL-mediated tea green leafhoppers infestation on the growth phenotype of tea plants.

Microbial characterization of Sichuan Baoning vinegar: lactic acid bacteria, acetic acid bacteria and yeasts.

Sichuan Baoning vinegar, a typical representative of Sichuan bran vinegar, is a famous traditional fermented food made from cereals in China. At present, there are few studies on microbial characterization of culturable microorganisms in solid-state fermentation of Sichuan bran vinegar. To comprehensively understand the diversity of lactic acid bacteria, acetic acid bacteria and yeasts, which play an important role in the fermentation of Sichuan bran vinegar, traditional culture-dependent methods combined with morphological, biochemical, and molecular identification techniques were employed to screen and identify these isolates. A total of 34 lactic acid bacteria isolates, 39 acetic acid bacteria isolates, and 48 yeast isolates were obtained. Lactic acid bacteria were dominated by Enterococcus durans, Leuconostoc citreum, Lactococcus lactis, and Lactiplantibacillus plantarum, respectively. Latilactobacillus sakei was the first discovery in cereal vinegar. Acetic acid bacteria were mainly Acetobacter pomorum and A. pasteurianus. The dominant yeast isolates were Saccharomyces cerevisiae, in addition to four non-Saccharomyces yeasts. DNA fingerprinting revealed that isolates belonging to the same species exhibited intraspecific diversity, and there were differences between phenotypic and genotypic classification results. This study further enriches studies on cereal vinegar and lays a foundation for the development of vinegar starters.

Intestinal microbiota and gene expression alterations in leopard coral grouper (Plectropomus leopardus) under enteritis.

Enteritis poses a significant threat to fish farming, characterized by symptoms of intestinal and hepatic inflammation, physiological dysfunction, and dysbiosis. Focused on the leopard coral grouper (Plectropomus leopardus) with an enteritis outbreak on a South China Sea farm, our prior scrutiny did not find any abnormalities in feeding or conventional water quality factors, nor were any specific pathogen infections related to enteritis identified. This study further elucidates their intestinal flora alterations, host responses, and their interactions to uncover the underlying pathogenetic mechanisms and facilitate effective prevention and management strategies. Enteritis-affected fish exhibited substantial differences in intestinal flora compared to control fish (P = 0.001). Notably, norank_f_Alcaligenaceae, which has a negative impact on fish health, predominated in enteritis-affected fish (91.76 %), while the probiotic genus Lactococcus dominated in controls (93.90 %). Additionally, certain genera with pathogenesis potentials like Achromobacter, Sphingomonas, and Streptococcus were more abundant in diseased fish, whereas Enterococcus and Clostridium_sensu_stricto with probiotic potentials were enriched in control fish. At the transcriptomic level, strong inflammatory responses, accompanied by impaired metabolic functions, tissue damage, and iron death signaling activation were observed in the intestines and liver during enteritis. Furthermore, correlation analysis highlighted that potential pathogen groups were positively associated with inflammation and tissue damage genes while presenting negatively correlated with metabolic function-related genes. In conclusion, dysbiosis in the intestinal microbiome, particularly an aberrantly high abundance of Alcaligenaceae with pathogenic potential may be the main trigger for this enteritis outbreak. Alcaligenaceae alongside Achromobacter, Sphingomonas, and Streptococcus emerged as biomarkers for enteritis, whereas some species of Lactococcus, Clostridium_sensu_stricto, and Enterococcus showed promise as probiotics to alleviate enteritis symptoms. These findings enhance our understanding of enteritis pathogenesis, highlight intestinal microbiota shifts in leopard coral grouper, and propose biomarkers for monitoring, probiotic selection, and enteritis management.

Zero-shot reconstruction of ocean sound speed field tensors: A deep plug-and-play approach.

Reconstructing a three-dimensional ocean sound speed field (SSF) from limited and noisy measurements presents an ill-posed and challenging inverse problem. Existing methods used a number of pre-specified priors (e.g., low-rank tensor and tensor neural network structures) to address this issue. However, the SSFs are often too complex to be accurately described by these pre-defined priors. While utilizing neural network-based priors trained on historical SSF data may be a viable workaround, acquiring SSF data remains a nontrivial task. This work starts with a key observation: Although natural images and SSFs admit fairly different characteristics, their denoising processes appear to share similar traits-as both remove random components from more structured signals. This observation allows us to incorporate deep denoisers trained using extensive natural images to realize zero-shot SSF reconstruction, without any extra training or network modifications. To implement this idea, an alternating direction method of multipliers (ADMM) algorithm using such a deep denoiser is proposed, which is reminiscent of the plug-and-play scheme from medical imaging. Our plug-and-play framework is tailored for SSF recovery such that the learned denoiser can be simultaneously used with other handcrafted SSF priors. Extensive numerical studies show that the new framework largely outperforms state-of-the-art baselines, especially under widely recognized challenging scenarios, e.g., when the SSF samples are taken as tensor fibers. The code is available at https://github.com/OceanSTARLab/DeepPnP.

Learning data distribution of three-dimensional ocean sound speed fields via diffusion models.

The probability distribution of three-dimensional sound speed fields (3D SSFs) in an ocean region encapsulates vital information about their variations, serving as valuable data-driven priors for SSF inversion tasks. However, learning such a distribution is challenging due to the high dimensionality and complexity of 3D SSFs. To tackle this challenge, we propose employing the diffusion model, a cutting-edge deep generative model that has showcased remarkable performance in diverse domains, including image and audio processing. Nonetheless, applying this approach to 3D ocean SSFs encounters two primary hurdles. First, the lack of publicly available well-crafted 3D SSF datasets impedes training and evaluation. Second, 3D SSF data consist of multiple 2D layers with varying variances, which can lead to uneven denoising during the reverse process. To surmount these obstacles, we introduce a novel 3D SSF dataset called 3DSSF, specifically designed for training and evaluating deep generative models. In addition, we devise a high-capacity neural architecture for the diffusion model to effectively handle variations in 3D sound speeds. Furthermore, we employ state-of-the-art continuous-time-based optimization method and predictor-corrector scheme for high-performance training and sampling. Notably, this paper presents the first evaluation of the diffusion model's effectiveness in generating 3D SSF data. Numerical experiments validate the proposed method's strong ability to learn the underlying data distribution of 3D SSFs, and highlight its effectiveness in assisting SSF inversion tasks and subsequently characterizing the transmission loss of underwater acoustics.

Insights into antibiotic and heavy metal resistance interactions in Escherichia coli isolated from livestock manure and fertilized soil.

Heavy metal and antibiotic-resistant bacteria from livestock feces are ecological and public health problems. However, the distribution and relationships of antibiotic resistance genes (ARGs), heavy metal resistance genes (HMRGs), and virulence factors (VFs) and their transmission mechanisms remain unclear. Therefore, we investigated the resistance of Escherichia coli, the prevalence of its ARGs, HMRGs, and VFs, and their transmission mechanisms in livestock fresh feces (FF), composted feces (CF), and fertilized soil (FS). In total, 99.54% (n = 221) and 91.44% (n = 203) of E. coli were resistant to at least one antibiotic and one heavy metal, respectively. Additionally, 72.52% (n = 161) were multi-drug resistant (MDR), of which Cu-resistant E. coli accounted for 72.67% (117/161). More than 99.34% (88/89) of E. coli carried multidrug ARGs, VFs, and the Cu resistance genes cueO and cusABCRFS. The Cu resistance genes cueO and cusABCRFS were mainly located on chromosomes, and cueO and cusF were positively associated with HMRGs, ARGs, and VFs. The Cu resistance genes pcoABCDRS were located on the plasmid pLKYL-P02 flanked by ARGs in PF18C from FF group and on chromosomes flanked by HMRGs in SAXZ1-1 from FS group. These results improved our understanding of bacterial multidrug and heavy metal resistance in the environment.

T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.

BACKGROUND: Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging. RESULTS: This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments. First, a lightweight module, C2fG2, and an efficient feature extraction module, DBS, are introduced into the backbone and neck of the YOLOv5 baseline model. Second, the head network of the model is pruned to achieve further lightweighting. Finally, the dynamic detection head is integrated to mitigate the feature loss caused by lightweighting. The experimental data show that T-YOLO achieves a mean average precision (mAP) of 84.1%, the total number of parameters for model training (Params) is 11.26 million (M), and the number of floating-point operations (FLOPs) is 17.2 Giga (G). Compared with the baseline YOLOv5 model, T-YOLO reduces Params by 47% and lowers FLOPs by 65%. T-YOLO also outperforms the existing optimal detection YOLOv8 model by 7.5% in terms of mAP. CONCLUSION: The T-YOLO model proposed in this study performs well in detecting small tea nutrient buds. It provides a decision-making basis for tea farmers to manage smart tea gardens. The T-YOLO model outperforms mainstream detection models on the public dataset, Global Wheat Head Detection (GWHD), which offers a reference for the construction of lightweight and efficient detection models for other small target crops. © 2024 Society of Chemical Industry.

Experimental Demonstration of Laser Guiding and Wakefield Acceleration in a Curved Plasma Channel.

Curved plasma channels have been proposed to guide intense lasers for various applications, such as x-ray laser emission, compact synchrotron radiation, and multistage laser wakefield acceleration [e.g. J. Luo et al., Phys. Rev. Lett. 120, 154801 (2018)PRLTAO0031-900710.1103/PhysRevLett.120.154801]. Here, a carefully designed experiment shows evidences of intense laser guidance and wakefield acceleration in a centimeter-scale curved plasma channel. Both experiments and simulations indicate that when the channel curvature radius is gradually increased and the laser incidence offset is optimized, the transverse oscillation of the laser beam can be mitigated, and the stably guided laser pulse excites wakefields and accelerates electrons along the curved plasma channel to a maximum energy of 0.7 GeV. Our results also show that such a channel exhibits good potential for seamless multistage laser wakefield acceleration.

TroTNFSF6, a tumor necrosis factor ligand superfamily member, promotes antibacterial immune response of golden pompano, Trachinotus ovatus.

Tumor necrosis factor ligand superfamily member 6 (TNFSF6), also known as FasL/CD95L, is essential for maintaining the body's immune homeostasis. However, the current reports on TNFSF6 in fish are relatively scarce. In the present study, we conducted functional analyses of a TNFSF6 (TroTNFSF6) from the teleost fish golden pompano (Trachinotus ovatus). TroTNFSF6 is composed of 228 amino acids and has a low similarity with other species (9.65%-58.79%). TroTNFSF6 was expressed in the 11 tissues tested and was significantly up-regulated after Edwardsiella tarda infection. In vivo, overexpression of TroTNFSF6 effectively stimulated the AKP and ACP activities, and reduced bacterial infection in fish tissues. Correspondingly, knockdown of TroTNFSF6 expression resulted in increasing bacterial dissemination and colonization in fish tissues. In vitro, recombinant TroTNFSF6 protein promoted the proliferation of T. ovatus head kidney lymphocytes (HKLs), and promoted the apoptosis of murine liver cancer cells (Hepa1-6). The results indicated that TroTNFSF6 plays an important role in the T. ovatus antibacterial immunity. These observations will facilitate the future in-depth study of teleost TNFSF6.

CpG ODN 1668 as TLR9 agonist mediates humpback grouper (Cromileptes altivelis) antibacterial immune responses.

Cromileptes altivelis (humpback grouper) is the main farmed species in the southern coastal area of China owing to its important economic value. Toll-like receptor 9 (TLR9) belongs to the toll-like receptor (TLR) family and functions as a pattern recognition receptor, recognising unmethylated oligodeoxynucleotides containing the CpG motif (CpG ODNs) in bacterial and viral genomes, thereby activating host immune response. In this study, the C. altivelis TLR9 (CaTLR9) ligand CpG ODN 1668 was screened and found to significantly enhance the antibacterial immunity of humpback grouper in vivo and head kidney lymphocytes (HKLs) in vitro. In addition, CpG ODN 1668 also promoted the cell proliferation and immune gene expression of HKLs and strengthened the phagocytosis activity of head kidney macrophages. However, when the CaTLR9 expression was knocked down in the humpback group, the expression levels of TLR9, myeloid differentiation factor 88 (Myd88), tumour necrosis factor-α (TNF-α), interferon γ (IFN-γ), interleukin-1ß (IL-1ß), IL-6, and IL-8 were significantly reduced, and the antibacterial immune effects induced by CpG ODN 1668 were mostly abolished. Therefore, CpG ODN 1668 induced antibacterial immune responses in a CaTLR9-dependent pathway. These results enhance the knowledge of the antibacterial immunity of fish TLR signalling pathways and have important implications for exploring natural antibacterial molecules in fish.

Surface Enhancement Effects of Tiny SnO2 Nanoparticle Modification on α-Fe2O3 for Room-Temperature NH3 Sensing.

The development of a gas sensor capable of detecting ammonia with high selectivity and rapid response at room temperature has consistently posed a formidable challenge. To address this issue, the present study utilized a one-step solvothermal method to co-assemble α-Fe2O3 and SnO2 by evenly covering SnO2 nanoparticles on the surface of α-Fe2O3. By controlling the morphology and Fe/Sn mole ratio of the composite, the as-prepared sample exhibits high-performance detection of NH3. At room temperature conditions, a gas sensor composed of α-Fe2O3@3%SnO2 demonstrates a rapid response time of 14 s and a notable sensitivity of 83.9% when detecting 100 ppm ammonia. Experiments and density functional theory (DFT) calculations suggest that the adsorption capacity of α-Fe2O3 to ammonia is enhanced by the surface effect provided by SnO2. Meanwhile, the existence of SnO2 tailors the pore structure and effective surface area of α-Fe2O3, creating multiple channels for the diffusion and adsorption of ammonia molecules. Additionally, an N-N heterostructure is formed between α-Fe2O3 and SnO2, which enhances the potential energy barrier and improves the ammonia sensing performance. Demonstration experiments have proved that the sensor shows significant advantages over commercial sensors in the process of ammonia detection in agricultural facilities. This work provides new insights into the perspectives on ammonia detection at room temperature.

Cypermethrin adsorption by Lactiplantibacillus plantarum and its behavior in a simulated fecal fermentation model.

The presence of cypermethrin in the environment and food poses a significant threat to human health. Lactic acid bacteria have shown promise as effective absorbents for xenobiotics and well behaved in wide range of applications. This study aimed to characterize the biosorption behavior of cypermethrin by Lactiplantibacillus plantarum RS60, focusing on cellular components, functional groups, kinetics, and isotherms. Results indicated that RS60 exopolysaccharides played a crucial role removing cypermethrin, with the cell wall and protoplast contributing 71.50% and 30.29% to the overall removal, respectively. Notably, peptidoglycans exhibited a high affinity for cypermethrin binding. The presence of various cellular surface groups including -OH, -NH, -CH3, -CH2, -CH, -P = O, and -CO was responsible for the efficient removal of pollutants. Additionally, the biosorption process demonstrated a good fit with pseudo-second-order and Langmuir-Freundlich isotherm. The biosorption of cypermethrin by L. plantarum RS60 involved complex chemical and physical interactions, as well as intraparticle diffusion and film diffusion. RS60 also effectively reduced cypermethrin residues in a fecal fermentation model, highlighting its potential in mitigating cypermethrin exposure in humans and animals. These findings provided valuable insights into the mechanisms underlying cypermethrin biosorption by lactic acid bacteria and supported the advancement of their application in environmental and health-related contexts. KEY POINTS: • Cypermethrin adsorption by L. plantarum was clarified. • Cell wall and protoplast showed cypermethrin binding ability. • L. plantarum can reduce cypermethrin in a fecal fermentation model.

Interpretable deep dictionary learning for sound speed profiles with uncertainties.

Uncertainties abound in sound speed profiles (SSPs) measured/estimated by modern ocean observing systems, which impede the knowledge acquisition and downstream underwater applications. To reduce the SSP uncertainties and draw insights into specific ocean processes, an interpretable deep dictionary learning model is proposed to cater for uncertain SSP processing. In particular, two kinds of SSP uncertainties are considered: measurement errors, which generally exist in the form of Gaussian noises; and the disturbances/anomalies caused by potential ocean dynamics, which occur at some specific depths and durations. To learn the generative patterns of these uncertainties while maintaining the interpretability of the resulting deep model, the adopted scheme first unrolls the classical K-singular value decomposition algorithm into a neural network, and trains this neural network in a supervised learning manner. The training data and model initializations are judiciously designed to incorporate the environmental properties of ocean SSPs. Experimental results demonstrate the superior performance of the proposed method over the classical baseline in mitigating noise corruptions, detecting, and localizing SSP disturbances/anomalies.

Approach to active control of scattered sound field from underwater targets.

Active control of scattered sound fields is of great significance for the acoustic stealth of underwater targets. In this paper, we propose an approach to control the target scattered field based on the measurement of a single holographic surface. Compared to existing methods, our approach significantly reduces the required number of hydrophones and only relies on the incident direction as prior information. First, we introduce a sound field separation method that uses the measurement of a single holographic surface to extract scattered field near the scatterer. Then two control strategies are presented to reduce redundant sound power outside the selected direction radiated by secondary sources in different situations. Finally, the proposed method is verified by the simulation based on finite element method and the experiment conducted in an anechoic tank. Experimental results in the tank show that the scattered sound pressure level in far-field is reduced by at least 10 dB at 2 kHz after activating the real-time control system.

Graph-guided Bayesian matrix completion for ocean sound speed field reconstruction.

Reconstructing ocean sound speed field (SSF) from limited and noisy measurements/estimates is crucial for many ocean acoustic applications, including underwater tomography, target localization/tracking, and communications. Classical reconstruction methods include deterministic approaches (e.g., spline interpolation) and geostatistical methods (e.g., kriging). They exhibit a strong link to linear regression and Gaussian process regression in machine learning (ML) literature, by uniformly viewing them as supervised regression models that learn the mapping from the geographical locations to the sound speed outputs. From a unified ML perspective, theoretical analysis indicates that classical reconstruction methods have several drawbacks, such as the sensitivity to noises and high computational cost. To overcome these drawbacks, inspired by the recent thriving development of graph machine learning, we introduce graph-guided Bayesian low-rank matrix completions (LRMCs) for fine-scale and accurate ocean SSF reconstruction. In particular, a more general graph-guided LRMC model is proposed that encompasses the state-of-the-art one as a special case. The proposed model and the associated inference algorithm simultaneously exploit the global (low-rankness) and local (graph structure) information of ocean sound speed data, thus striking an outstanding balance of reconstruction accuracy and computational complexity. Numerical results using real-life ocean SSF data have demonstrated the encouraging performances of the proposed approaches.

Striking the right balance: Three-dimensional ocean sound speed field reconstruction using tensor neural networks.

Accurately reconstructing a three-dimensional (3D) ocean sound speed field (SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task. To tackle this challenge, a large body of reconstruction methods has been developed, including spline interpolation, matrix/tensor-based completion, and deep neural networks (DNNs)-based reconstruction. However, a principled analysis of their effectiveness in 3D SSF reconstruction is still lacking. This paper performs a thorough analysis of the reconstruction error and highlights the need for a balanced representation model that integrates expressiveness and conciseness. To meet this requirement, a 3D SSF-tailored tensor DNN is proposed, which uses tensor computations and DNN architectures to achieve remarkable 3D SSF reconstruction. The proposed model not only includes the previous tensor-based SSF representation model as a special case but also has a natural ability to reject noise. The numerical results using the South China Sea 3D SSF data demonstrate that the proposed method outperforms state-of-the-art methods. The code is available at https://github.com/OceanSTARLab/Tensor-Neural-Network.

Enhancing the robustness of ocean sound speed profile representation via interpretable deep matrix decomposition.

Developing an effective and robust representation model for ocean sound speed profiles (SSPs) is crucial for numerous ocean acoustic applications. However, the performance of existing sound speed profile (SSP) representation methods, such as empirical orthogonal function and K-singular value decomposition, heavily relies on the number of selected basis functions. This could lead to overfitting of noise, as these methods are unable to distinguish between signals and noise during the basis function learning process. To overcome these limitations and effectively learn a large number of basis functions with strong representation power from potentially noisy SSP data, we propose a novel algorithm called deep matrix decomposition (deep MD). This algorithm utilizes untrained deep neural networks as priors to reject noise within the interpretable matrix decomposition framework. To achieve optimal performance with deep MD, we propose a stopping strategy based on the rank estimate to determine the termination epoch. Experimental results using real-life datasets demonstrate that deep MD is robust against various types of noise and outperforms traditional SSP representation methods in terms of SSP reconstruction and characterizing the transmission loss in underwater acoustics.

Transcriptomic analysis reveals peripheral pathway in 3-phenoxybenzoic acid degradation by Aspergillus oryzae M-4.

As a major intermediate metabolite of synthetic pyrethroids, the occurrence of 3-phenoxybenzoic acid hinders the decomposition of the parent pesticide and poses uncertain risks to environmental ecology and living organisms. Strain Aspergillus oryzae M-4 was previously reported to degrade 3-PBA and several substances were identified as downstream transformation products (TPs). But the mechanism underlying the cleavage of ether bond remains largely unclear. Here, we attempted to address such concern through identifying the peripheral TPs and analyzing transcriptomics, coupled with serial batch degradation experiments. Analysis results of chromatographic/mass spectrometry suggested that 3-PBA underwent twice hydroxylation, to yield mono- and dihydroxylated 3-PBA successively. In parallel, a mutual transformation between 3-PBA and 3-phenoxybenzyl alcohol (3-PBOH) also existed. The proposal of peripheral pathway represents an important advance towards fully understanding the whole 3-PBA metabolism in M-4. A specific altered metabolization was found for the first time, that is, resting cells of M-4 skipped the reduction step and initiate hydroxylation directly, by comparison with growing cells. Transcriptome analysis indicated that 3-PBA induced the up-regulation of genes related to energy investment, oxidative stress response, membrane transport and DNA repair. In-depth functional interpretation of differential expression genes suggested that the generation 3-PBOH and hydroxylated 3-PBA may be due to the participation of flavin-dependent monooxygenases (FMOs) and cytochrome P450 (CYP450), respectively. This study provides new insight to reveal the biodegradation mechanism of 3-PBA by A. oryzae M-4.

Effect of Koji on Flavor Compounds and Sensory Characteristics of Rice Shochu.

Koji is an important starter for rice shochu brewing and influences the rice shochu quality. Consequently, we studied the impacts of koji on the flavor compounds and sensory characteristics of rice shochu using molds Aspergillus kawachii SICC 3.917 (A-K), Aspergillus oryzae SICC 3.79(A-O), Aspergillus Niger CICC 2372 (A-N), Rhizopus oryzae CICC 40260 (R-O), and the traditional starter Qu (control). The effects of koji on the aroma components, free amino acids (FAAs), and overall sensory aspects of rice shochu were studied. These findings indicated that koji significantly affected the rice shochu's quality. The content of total FAAs in rice shochu A-K (30.586 ± 0.944 mg/L) and A-O (29.919 ± 0.278 mg/L) was higher than others. The content of flavor compounds revealed that the aroma of rice shochu with various koji varied greatly from the smells of alcohols and esters. Shochu A-O had a higher concentration of aroma compounds and it exhibited a strong aroma and harmonious taste compared with the others. This research using taste compounds, FAAs, flavor intensity, and partial least squares regression (PLSR) showed that shochu A-O appeared to possess the best sensory qualities, with elevated concentrations of alcohols and sweet FAAs and lesser concentrations of sour FAAs. Therefore, the A-O mold is promising for the manufacture of rice shochu with excellent flavor and sensory characteristics.