Biosynthetic Characterization, Heterologous Production, and Genomics-Guided Discovery of GABA-Containing Fungal Heptapeptides.

The biosynthetic gene cluster of γ-aminobutyric acid (GABA)-containing fungal cyclic heptapeptides unguisins A (1) and B (2) was identified in the fungus Aspergillus violaceofuscus CBS 115571. In vitro enzymatic reactions and gene deletion experiments revealed that the unguisin pathway involves the alanine racemase UngC to provide d-alanine, which is then accepted by the first adenylation domain of the nonribosomal peptide synthetase (NRPS) UngA. Intriguingly, the hydrolase UngD was found to transform unguisins into previously undescribed linear peptides. Subsequently, heterologous production of these peptides in Aspergillus oryzae was achieved, in which we established a methodology to readily introduce a large NRPS gene into the fungal host. Finally, genome mining revealed new unguisin congeners, each containing a (2R,3R)-ß-methylphenylalanine residue.

Drug Discovery Based on Fluorine-Containing Glycomimetics.

Glycomimetics, which are synthetic molecules designed to mimic the structures and functions of natural carbohydrates, have been developed to overcome the limitations associated with natural carbohydrates. The fluorination of carbohydrates has emerged as a promising solution to dramatically enhance the metabolic stability, bioavailability, and protein-binding affinity of natural carbohydrates. In this review, the fluorination methods used to prepare the fluorinated carbohydrates, the effects of fluorination on the physical, chemical, and biological characteristics of natural sugars, and the biological activities of fluorinated sugars are presented.

[Dumai moxibustion combined with low-dose tadalafil for erectile dysfunction: Evaluation of therapeutic effect].

OBJECTIVE: To investigate the clinical effect of dumai (governor meridian) moxibustion combined with low-dose tadalafil in the treatment of ED with decline of vital gate fire. METHODS: We enrolled in this study 130 ED patients with decline of vital gate fire who met the inclusion criteria and equally randomized them into a control and an experimental group, the former treated with low-dose tadalafil tablets at 5 mg once a day while the latter by dumai moxibustion once a week in addition, all for 4 weeks. Of the total number of subjects, 62 in the control group and 63 in the experimental group completed the experiment. We recorded the scores on IIEF-5, Erection Quality Scale (EQS), Erection Hardness Scale (EHS), TCM symptoms and Treatment Satisfaction Scale (TSS) as well as the penile hemodynamic parameters peak systolic velocity (PSV), end diastolic velocity (EDV) and resistance index (RI) before and after treatment and compared them between the two groups. RESULTS: The total response rate was significantly higher in the experimental group than in the control (87.30% vs 66.13%, P < 0.05). IIEF-5, EQS, EHS and TSS scores, PSV and RI were markedly increased while TCM symptoms and EDV remarkably decreased in both groups after treatment (P < 0.05), even more significantly in the experimental than in the control group (P < 0.05). CONCLUSION: Dumai moxibustion combined with low-dose tadalafil can improve erectile function, increase penile blood flow velocity and alleviate clinical symptoms in ED patients with decline of vital gate fire, with definite clinical effect and safety.

Depside Bond Formation by the Starter-Unit Acyltransferase Domain of a Fungal Polyketide Synthase.

Depsides are polyphenolic molecules comprising two or more phenolic acid derivatives linked by an ester bond, which is called a depside bond in these molecules. Despite more than a century of intensive research on depsides, the biosynthetic mechanism of depside bond formation remains unclear. In this study, we discovered a polyketide synthase, DrcA, from the fungus Aspergillus duricaulis CBS 481.65 and found that DrcA synthesizes CJ-20,557 (1), a heterodimeric depside composed of 3-methylorsellinic acid and 3,5-dimethylorsellinic acid. Moreover, we determined that depside bond formation is catalyzed by the starter-unit acyltransferase (SAT) domain of DrcA. Remarkably, this is a previously undescribed form of SAT domain chemistry. Further investigation revealed that 1 is transformed into duricamidepside (2), a depside-amino acid conjugate, by the single-module nonribosomal peptide synthetase DrcB.

Molecular and Computational Bases for Spirofuranone Formation in Setosusin Biosynthesis.

The 3(2H)-furanone unit is observed in many biologically active natural products, as represented by the antifungal medication griseofulvin. Setosusin (1) is a fungal meroditerpenoid featuring a unique spiro-fused 3(2H)-furanone moiety; however, the biosynthetic basis for spirofuranone formation has not been investigated since its isolation. Therefore, in this study we identified the biosynthetic gene cluster of 1 in the fungus Aspergillus duricaulis CBS 481.65 and elucidated its biosynthetic pathway by heterologous reconstitution of related enzyme activities in Aspergillus oryzae. To understand the reaction mechanism to afford spirofuranone, we subsequently performed a series of in vivo and in vitro isotope-incorporation experiments and theoretical calculations. The results indicated that SetF, the cytochrome P450 enzyme that is critical for spirofuranone synthesis, not only performs the epoxidation of the polyketide portion of the substrate but also facilitates the protonation-initiated structural rearrangement to yield 1. Finally, a mutagenesis experiment using SetF identified Lys303 as one of the potential catalytic residues that are important for spirofuranone synthesis.

Heterologous Biosynthesis of Tetrahydroxanthone Dimers: Determination of Key Factors for Selective or Divergent Synthesis.

Tetrahydroxanthone dimers are fungal products, among which secalonic acid D (1) is one of the most studied compounds because of its potent biological activity. Because the biosynthetic gene cluster of 1 has been previously identified, we sought to heterologously produce 1 in Aspergillus oryzae by expressing the relevant biosynthetic genes. However, our initial attempt of the total biosynthesis of 1 failed; instead, it produced four isomers of 1 due to the activity of an endogenous enzyme of A. oryzae. Subsequent overexpression of the Baeyer-Villiger monooxygenase, AacuH, which competes with the endogenous enzyme, altered the product profile and successfully generated 1. Characterization of the key biosynthetic enzymes revealed the surprising substrate promiscuity of the dimerizing enzyme, AacuE, and indicated that efficient synthesis of 1 requires highly selective preparation of the tetrahydroxanthone monomer, which is apparently controlled by AacuH. This study facilitates engineered biosynthesis of tetrahydroxanthone dimers both in a selective and divergent manner.

Structural Basis for Isomerization Reactions in Fungal Tetrahydroxanthone Biosynthesis and Diversification.

The novel isomerase NsrQ, from Aspergillus novofumigatus, is a key enzyme in the biosynthesis of fungal tetrahydroxanthones and is responsible for dearomatizing cyclization to provide a tetrahydroxanthone scaffold. NsrQ catalyzes a two-step isomerization reaction, involving the isomerization of allylic alcohol and subsequent inversion of configuration at the methyl group. We report on the biochemical and structural characterizations of NsrQ, and its homologue Dcr3, from Diaporthe longicolla. The crystal structures of NsrQ and Dcr3 revealed their similar overall structures, with a cone-shaped α+ß barrel fold, to those of the nuclear transport factor 2-like superfamily enzymes. Furthermore, the structures of Dcr3 and NsrQ variants complexed with substrate analogues and the site-directed mutagenesis studies identified the catalytic residues and the important hydrophobic residues in shaping the active site pocket for substrate binding. These enzymes thus utilize Glu and His residues as acid-base catalysts. Based on these observations, we proposed a detailed reaction mechanism for NsrQ-catalyzed isomerization reactions.

One Polyketide Synthase, Two Distinct Products: Trans-Acting Enzyme-Controlled Product Divergence in Calbistrin Biosynthesis.

Calbistrins are fungal polyketides consisting of the characteristic decalin and polyene moieties. Although the biosynthetic gene cluster of calbistrin A was recently identified, the pathway of calbistrin A biosynthesis has largely remained uninvestigated. Herein, we investigated the mechanism by which the backbone structures of calbistrins are formed, by heterologous and in vitro reconstitution of the biosynthesis and a structural biological study. Intriguingly, our analyses revealed that the decalin and polyene portions of calbistrins are synthesized by the single polyketide synthase (PKS) CalA, with the aid of the trans-acting enoylreductase CalK and the trans-acting C-methyltransferase CalH, respectively. We also determined that the esterification of the two polyketide parts is catalyzed by the acyltransferase CalD. Our study has uncovered a novel dual-functional PKS and thus broadened our understanding of how fungi synthesize diverse polyketide natural products.

Revisiting the Trade-Off Between Accuracy and Robustness Via Weight Distribution of Filters.

Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enhancing the robustness, the accuracy for clean examples will decline to a certain extent, implying a trade-off existed between the accuracy and adversarial robustness. In this paper, to meet the trade-off problem, we theoretically explore the underlying reason for the difference of the filters' weight distribution between standard-trained and robust-trained models and then argue that this is an intrinsic property for static neural networks, thus they are difficult to fundamentally improve the accuracy and adversarial robustness at the same time. Based on this analysis, we propose a sample- wise dynamic network architecture named Adversarial Weight-Varied Network (AW-Net), which focuses on dealing with clean and adversarial examples with a "divide and rule" weight strategy. The AW-Net adaptively adjusts the network's weights based on regulation signals generated by an adversarial router, which is directly influenced by the input sample. Benefiting from the dynamic network architecture, clean and adversarial examples can be processed with different network weights, which provides the potential to enhance both accuracy and adversarial robustness. A series of experiments demonstrate that our AW-Net is architecture-friendly to handle both clean and adversarial examples and can achieve better trade-off performance than state-of-the-art robust models.

Mitigating Accuracy-Robustness Trade-Off Via Balanced Multi-Teacher Adversarial Distillation.

Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance towards clean examples is negatively affected after Adversarial Training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in Adversarial Training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Balanced Multi-Teacher Adversarial Robustness Distillation (B-MTARD) to guide the model's Adversarial Training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on three public datasets demonstrate that B-MTARD outperforms the state-of-the-art methods against various adversarial attacks.

Unified Adversarial Patch for Visible-Infrared Cross-Modal Attacks in the Physical World.

Physical adversarial attacks have put a severe threat to DNN-based object detectors. To enhance security, a combination of visible and infrared sensors is deployed in various scenarios, which has proven effective in disabling existing single-modal physical attacks. To further demonstrate the potential risks in such cases, we design a unified adversarial patch that can perform cross-modal physical attacks, achieving evasion in both modalities simultaneously with a single patch. Given the different imaging mechanisms of visible and infrared sensors, our work manipulates patches' shape features, which can be captured in different modalities when they undergo changes. To deal with challenges, we propose a novel boundary-limited shape optimization approach that aims to achieve compact and smooth shapes for the adversarial patch, making it easy to implement in the physical world. And a score-aware iterative evaluation method is also introduced to balance the fooling degree between visible and infrared detectors during optimization, which guides the adversarial patch to iteratively reduce the predicted scores of the multi-modal sensors. Furthermore, we propose an Affine-Transformation-based enhancement strategy that makes the learnable shape robust to various angles, thus mitigating the issue of shape deformation caused by different shooting angles in the real world. Our method is evaluated against several state-of-the-art object detectors, achieving an Attack Success Rate (ASR) of over 80%. We also demonstrate the effectiveness of our approach in physical-world scenarios under various settings, including different angles, distances, postures, and scenes for both visible and infrared sensors.

Improving Fast Adversarial Training With Prior-Guided Knowledge.

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box attack scenarios. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high training time. In this paper, we investigate the relationship between adversarial example quality and catastrophic overfitting by comparing the training processes of standard adversarial training and FAT. We find that catastrophic overfitting occurs when the attack success rate of adversarial examples becomes worse. Based on this observation, we propose a positive prior-guided adversarial initialization to prevent overfitting by improving adversarial example quality without extra training time. This initialization is generated by using high-quality adversarial perturbations from the historical training process. We provide theoretical analysis for the proposed initialization and propose a prior-guided regularization method that boosts the smoothness of the loss function. Additionally, we design a prior-guided ensemble FAT method that averages the different model weights of historical models using different decay rates. Our proposed method, called FGSM-PGK, assembles the prior-guided knowledge, i.e., the prior-guided initialization and model weights, acquired during the historical training process. The proposed method can effectively improve the model's adversarial robustness in white-box attack scenarios. Evaluations of four datasets demonstrate the superiority of the proposed method.

Adversarial Sticker: A Stealthy Attack Method in the Physical World.

To assess the vulnerability of deep learning in the physical world, recent works introduce adversarial patches and apply them on different tasks. In this paper, we propose another kind of adversarial patch: the Meaningful Adversarial Sticker, a physically feasible and stealthy attack method by using real stickers existing in our life. Unlike the previous adversarial patches by designing perturbations, our method manipulates the sticker's pasting position and rotation angle on the objects to perform physical attacks. Because the position and rotation angle are less affected by the printing loss and color distortion, adversarial stickers can keep good attacking performance in the physical world. Besides, to make adversarial stickers more practical in real scenes, we conduct attacks in the black-box setting with the limited information rather than the white-box setting with all the details of threat models. To effectively solve for the sticker's parameters, we design the Region based Heuristic Differential Evolution Algorithm, which utilizes the new-found regional aggregation of effective solutions and the adaptive adjustment strategy of the evaluation criteria. Our method is comprehensively verified in the face recognition and then extended to the image retrieval and traffic sign recognition. Extensive experiments show the proposed method is effective and efficient in complex physical conditions and has a good generalization for different tasks.

Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos.

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same time.

Simultaneously Optimizing Perturbations and Positions for Black-Box Adversarial Patch Attacks.

Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch's content. This reveals that the positions and perturbations are both important to the adversarial attack. For that, in this article, we propose a novel method to simultaneously optimize the position and perturbation for an adversarial patch, and thus obtain a high attack success rate in the black-box setting. Technically, we regard the patch's position, the pre-designed hyper-parameters to determine the patch's perturbations as the variables, and utilize the reinforcement learning framework to simultaneously solve for the optimal solution based on the rewards obtained from the target model with a small number of queries. Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency. Besides, experiments on the commercial FR service and physical environments confirm its practical application value. We also extend our method to the traffic sign recognition task to verify its generalization ability.

Branching and converging pathways in fungal natural product biosynthesis.

In nature, organic molecules with great structural diversity and complexity are synthesized by utilizing a relatively small number of starting materials. A synthetic strategy adopted by nature is pathway branching, in which a common biosynthetic intermediate is transformed into different end products. A natural product can also be synthesized by the fusion of two or more precursors generated from separate metabolic pathways. This review article summarizes several representative branching and converging pathways in fungal natural product biosynthesis to illuminate how fungi are capable of synthesizing a diverse array of natural products.

Unraveling the Fungal Strategy for Tetrahydroxanthone Biosynthesis and Diversification.

In vivo and in vitro reconstitution of the biosynthesis of fungal tetrahydroxanthones (THXs), blennolides A and C, led to the identification and characterization of the key enzymes for THX biosynthesis and diversification. The unusual isomerase NsrQ plays a crucial role in the THX skeleton synthesis probably by catalyzing the 1,2-hydride shift and methyl group epimerization, thus allowing dearomatizing cyclization to provide the THX architecture.

Discovery of Pyranoviolin A and Its Biosynthetic Gene Cluster in Aspergillus violaceofuscus.

A new polyketide-non-ribosomal peptide hybrid molecule, pyranoviolin A (1), was discovered from the genome-sequenced fungus Aspergillus violaceofuscus CBS 115571 and was characterized to be the first pyranonigrin analog harboring the C-3 methoxy group. Examination of the genome sequence of the fungus identified a putative biosynthetic gene cluster of 1, which was designated as the pyv cluster. The gene deletion experiment of the polyketide synthase (PKS)-non-ribosomal peptide synthetase (NRPS) hybrid gene in the cluster confirmed the involvement of the pyv cluster in the pyranoviolin A biosynthesis. Finally, a plausible biosynthetic route leading to 1 has been proposed based on the bioinformatic analysis. Our study indicates that metabolite analysis of genome-sequenced microorganisms whose metabolites have been largely unexplored facilitates the discovery of new secondary metabolites along with their biosynthetic gene clusters.

High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation.

Reversible data hiding in encrypted images has attracted considerable attention from the communities of privacy security and protection. The success of the previous methods in this area has shown that a superior performance can be achieved by exploiting the redundancy within the image. Specifically, because the pixels in the local structures (like patches or regions) have a strong similarity, they can be heavily compressed, thus resulting in a large hiding room. In this paper, to better explore the correlation between neighbor pixels, we propose to consider the patch-level sparse representation when hiding the secret data. The widely used sparse coding technique has demonstrated that a patch can be linearly represented by some atoms in an over-complete dictionary. As the sparse coding is an approximation solution, the leading residual errors are encoded and self-embedded within the cover image. Furthermore, the learned dictionary is also embedded into the encrypted image. Thanks to the powerful representation of sparse coding, a large vacated room can be achieved, and thus the data hider can embed more secret messages in the encrypted image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of the embedding rate and the image quality.

Robust Face Clustering Via Tensor Decomposition.

Face clustering is a key component either in image managements or video analysis. Wild human faces vary with the poses, expressions, and illumination changes. All kinds of noises, like block occlusions, random pixel corruptions, and various disguises may also destroy the consistency of faces referring to the same person. This motivates us to develop a robust face clustering algorithm that is less sensitive to these noises. To retain the underlying structured information within facial images, we use tensors to represent faces, and then accomplish the clustering task based on the tensor data. The proposed algorithm is called robust tensor clustering (RTC), which firstly finds a lower-rank approximation of the original tensor data using a L1 norm optimization function. Because L1 norm does not exaggerate the effect of noises compared with L2 norm, the minimization of the L1 norm approximation function makes RTC robust. Then, we compute high-order singular value decomposition of this approximate tensor to obtain the final clustering results. Different from traditional algorithms solving the approximation function with a greedy strategy, we utilize a nongreedy strategy to obtain a better solution. Experiments conducted on the benchmark facial datasets and gait sequences demonstrate that RTC has better performance than the state-of-the-art clustering algorithms and is more robust to noises.