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Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses., Comment: In CVPR Workshops, 2022

Motion and interaction of social insects (such as ants) have been studied by many researchers to understand the clustering mechanism. Most studies in the field of ant behavior have only focused on indoor environments, while outdoor environments are still underexplored. In this paper, we collect 10 videos of ant colonies from different indoor and outdoor scenes. And we develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of ants in the video. In all 5354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114112 annotations. Moreover, we provide visual analysis tools to assess and validate the technical quality and reproducibility of our data. It is hoped that this dataset will contribute to a deeper exploration on the behavior of the ant colony.

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However, they can only resist a small number of malicious clients in practice. It is still an open challenge how to defend against model poisoning attacks with a large number of malicious clients. Our FLDetector addresses this challenge via detecting malicious clients. FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients. Our key observation is that, in model poisoning attacks, the model updates from a client in multiple iterations are inconsistent. Therefore, FLDetector detects malicious clients via checking their model-updates consistency. Roughly speaking, the server predicts a client's model update in each iteration based on its historical model updates using the Cauchy mean value theorem and L-BFGS, and flags a client as malicious if the received model update from the client and the predicted model update are inconsistent in multiple iterations. Our extensive experiments on three benchmark datasets show that FLDetector can accurately detect malicious clients in multiple state-of-the-art model poisoning attacks. After removing the detected malicious clients, existing Byzantine-robust FL methods can learn accurate global models.Our code is available at https://github.com/zaixizhang/FLDetector., Comment: Accepted by KDD 2022 (Research Track)

Lei Yu,1,* Xiaofang Xie,1,* Xiaoyu Cao,1 Junren Chen,1 Guanru Chen,1 Yan Chen,1 Gangmin Li,1 Junyuan Qin,1 Fu Peng,2 Cheng Peng1 1State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 2Department of Pharmacology, Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fu PengDepartment of Pharmacology, Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, People’s Republic of ChinaTel/Fax +86 28-61800018Email fujing126@yeah.netCheng PengState Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People’s Republic of ChinaTel/Fax +86 28-61800231Email cdtcmpengcheng@126.comAbstract: Cancer is still an insurmountable problem for humans and critically attacking human health. In recent years, natural products have gained increasing attention in the field of anti-tumor due to their extensive sources and minimal side effects. Maslinic acid (MA), a pentacyclic triterpene acid mainly derived from the olive tree (Olea europaea L.) has been confirmed to possess great anti-cancer effects. This paper reviewed the inhibitory effect of MA and its derivatives on lung cancer, colon cancer, ovarian cancer, gastric cancer, lymphatic, leukemia, breast cancer, pancreatic cancer, melanoma, prostate cancer, renal cell carcinoma, gallbladder cancer, and bladder cancer, among others. MA inhibited the proliferation of various tumor cells and showed lower IC50 values in melanoma 518A2 cells and gastric cancer MKN28 cells compared with other cell lines. A series of semi-synthetic derivatives obtained by modifying MA chemical structure have been shown to have high cytotoxicity to human tumor cell lines, but low cytotoxicity to non-malignant cells, which is conducive to developing its potential as a chemotherapeutic agent. These studies suggest that MA derivatives have broad prospects in the development of antitumor therapeutics in the future and warrant further study.Keywords: maslinic acid, tumor, mechanism, derivatives

Deepfakes pose growing challenges to the trust of information on the Internet. Thus, detecting deepfakes has attracted increasing attentions from both academia and industry. State-of-the-art deepfake detection methods consist of two key components, i.e., face extractor and face classifier, which extract the face region in an image and classify it to be real/fake, respectively. Existing studies mainly focused on improving the detection performance in non-adversarial settings, leaving security of deepfake detection in adversarial settings largely unexplored. In this work, we aim to bridge the gap. In particular, we perform a systematic measurement study to understand the security of the state-of-the-art deepfake detection methods in adversarial settings. We use two large-scale public deepfakes data sources including FaceForensics++ and Facebook Deepfake Detection Challenge, where the deepfakes are fake face images; and we train state-of-the-art deepfake detection methods. These detection methods can achieve 0.94--0.99 accuracies in non-adversarial settings on these datasets. However, our measurement results uncover multiple security limitations of the deepfake detection methods in adversarial settings. First, we find that an attacker can evade a face extractor, i.e., the face extractor fails to extract the correct face regions, via adding small Gaussian noise to its deepfake images. Second, we find that a face classifier trained using deepfakes generated by one method cannot detect deepfakes generated by another method, i.e., an attacker can evade detection via generating deepfakes using a new method. Third, we find that an attacker can leverage backdoor attacks developed by the adversarial machine learning community to evade a face classifier. Our results highlight that deepfake detection should consider the adversarial nature of the problem., To appear in ICDF2C 2021

Chiral organic cages can assist enantio-selective supramolecular polymerization through a catalyzed assembly (catassembly) strategy, like chaperones assist the assembly of biomolecules., Cage catalysis has emerged as an important approach for mimicking enzymatic reactions by increasing the reaction rate and/or product selectivity of various types of covalent reactions. Here, we extend the catalytic application of cage compounds to the field of non-covalent molecular assembly. Acid-stable chiral imine cages are found to catalyze the supramolecular polymerization of porphyrins with an accelerated assembling rate and increased product enantioselectivity. Because the imine cages have a stronger interaction with porphyrin monomers and a weaker interaction with porphyrin assemblies, they can fully automatically detach from the assembled products without being consumed during the catalytic process. We reveal the kinetics of the auto-detachment of cages and the chirality growth of the assemblies using spectroscopic characterization studies. We find that the passivation groups attached to the cages are important for maintaining the structural stability of the cages during catalyzed assembly, and that the steric geometries of the cages can profoundly affect the efficiency of chiral regulation. This strategy demonstrates a new type of catalytic application of cage compounds in the field of molecular assembly, and paves the way to controlling supramolecular polymerization through a catalytic pathway.

Hirudin, an acidic polypeptide secreted by the salivary glands of Hirudo medicinalis (also known as “Shuizhi” in traditional Chinese medicine), is the strongest natural specific inhibitor of thrombin found so far. Hirudin has been demonstrated to possess potent anti-thrombotic effect in previous studies. Recently, increasing researches have focused on the anti-thrombotic activity of the derivatives of hirudin, mainly because these derivatives have stronger antithrombotic activity and lower bleeding risk. Additionally, various bioactivities of hirudin have been reported as well, including wound repair effect, anti-fibrosis effect, effect on diabetic complications, anti-tumor effect, anti-hyperuricemia effect, effect on cerebral hemorrhage, and others. Therefore, by collecting and summarizing publications from the recent two decades, the pharmacological activities, pharmacokinetics, novel preparations and derivatives, as well as toxicity of hirudin were systematically reviewed in this paper. In addition, the clinical application, the underlying mechanisms of pharmacological effects, the dose-effect relationship, and the development potential in new drug research of hirudin were discussed on the purpose of providing new ideas for application of hirudin in treating related diseases.

Federated learning enables clients to collaboratively learn a shared global model without sharing their local training data with a cloud server. However, malicious clients can corrupt the global model to predict incorrect labels for testing examples. Existing defenses against malicious clients leverage Byzantine-robust federated learning methods. However, these methods cannot provably guarantee that the predicted label for a testing example is not affected by malicious clients. We bridge this gap via ensemble federated learning. In particular, given any base federated learning algorithm, we use the algorithm to learn multiple global models, each of which is learnt using a randomly selected subset of clients. When predicting the label of a testing example, we take majority vote among the global models. We show that our ensemble federated learning with any base federated learning algorithm is provably secure against malicious clients. Specifically, the label predicted by our ensemble global model for a testing example is provably not affected by a bounded number of malicious clients. Moreover, we show that our derived bound is tight. We evaluate our method on MNIST and Human Activity Recognition datasets. For instance, our method can achieve a certified accuracy of 88% on MNIST when 20 out of 1,000 clients are malicious., Accepted by AAAI-21. For slides, see https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf . For the talk, see https://www.youtube.com/watch?v=LP4uqW18yA0

Existing deepfake-detection methods focus on passive detection, i.e., they detect fake face images via exploiting the artifacts produced during deepfake manipulation. A key limitation of passive detection is that it cannot detect fake faces that are generated by new deepfake generation methods. In this work, we propose FaceGuard, a proactive deepfake-detection framework. FaceGuard embeds a watermark into a real face image before it is published on social media. Given a face image that claims to be an individual (e.g., Nicolas Cage), FaceGuard extracts a watermark from it and predicts the face image to be fake if the extracted watermark does not match well with the individual's ground truth one. A key component of FaceGuard is a new deep-learning-based watermarking method, which is 1) robust to normal image post-processing such as JPEG compression, Gaussian blurring, cropping, and resizing, but 2) fragile to deepfake manipulation. Our evaluation on multiple datasets shows that FaceGuard can detect deepfakes accurately and outperforms existing methods.

Additional file2: Table S2. Quartiles of △SOFA, Lac, FGF21, PCT, TNF-α, IL-6, IL-10, and CRP used in the Cox regression analysis.

Additional file1: Table S1. Baseline clinical characteristics of patients in the Sepsis + ARDS group and the Sepsis-only group.

Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern autonomous greenhouses, equipped with sensors and actuators, provide a promising solution to the problem by empowering precise control for high-efficient food production. However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task. With the advances of artificial intelligence (AI), the internet of things (IoT), and cloud computing technologies, we are hopeful to provide a solution to automate and smarten greenhouse control to address the above challenges. In this paper, we propose a smart agriculture solution named iGrow, for autonomous greenhouse control (AGC): (1) for the first time, we formulate the AGC problem as a Markov decision process (MDP) optimization problem; (2) we design a neural network-based simulator incorporated with the incremental mechanism to simulate the complete planting process of an autonomous greenhouse, which provides a testbed for the optimization of control strategies; (3) we propose a closed-loop bi-level optimization algorithm, which can dynamically re-optimize the greenhouse control strategy with newly observed data during real-world production. We not only conduct simulation experiments but also deploy iGrow in real scenarios, and experimental results demonstrate the effectiveness and superiority of iGrow in autonomous greenhouse simulation and optimal control. Particularly, compelling results from the tomato pilot project in real autonomous greenhouses show that our solution significantly increases crop yield (+10.15\%) and net profit (+92.70\%) with statistical significance compared to planting experts., Comment: 9 pages, 5 figures, 2 tables, accepted by AAAI 2022

Local Differential Privacy (LDP) protocols enable an untrusted server to perform privacy-preserving, federated data analytics. Various LDP protocols have been developed for different types of data such as categorical data, numerical data, and key-value data. Due to their distributed settings, LDP protocols are fundamentally vulnerable to poisoning attacks, in which fake users manipulate the server's analytics results via sending carefully crafted data to the server. However, existing poisoning attacks focused on LDP protocols for simple data types such as categorical and numerical data, leaving the security of LDP protocols for more advanced data types such as key-value data unexplored. In this work, we aim to bridge the gap by introducing novel poisoning attacks to LDP protocols for key-value data. In such a LDP protocol, a server aims to simultaneously estimate the frequency and mean value of each key among some users, each of whom possesses a set of key-value pairs. Our poisoning attacks aim to simultaneously maximize the frequencies and mean values of some attacker-chosen target keys via sending carefully crafted data from some fake users to the sever. Specifically, since our attacks have two objectives, we formulate them as a two-objective optimization problem. Moreover, we propose a method to approximately solve the two-objective optimization problem, from which we obtain the optimal crafted data the fake users should send to the server. We demonstrate the effectiveness of our attacks to three LDP protocols for key-value data both theoretically and empirically. We also explore two defenses against our attacks, which are effective in some scenarios but have limited effectiveness in other scenarios. Our results highlight the needs for new defenses against our poisoning attacks., Comment: To appear in USENIX Security Symposium, 2022

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via modifying, adding, and/or removing some carefully selected training examples, such that the corrupted classifier makes incorrect predictions as the attacker desires. The key idea of state-of-the-art certified defenses against data poisoning attacks and backdoor attacks is to create a majority vote mechanism to predict the label of a testing example. Moreover, each voter is a base classifier trained on a subset of the training dataset. Classical simple learning algorithms such as k nearest neighbors (kNN) and radius nearest neighbors (rNN) have intrinsic majority vote mechanisms. In this work, we show that the intrinsic majority vote mechanisms in kNN and rNN already provide certified robustness guarantees against data poisoning attacks and backdoor attacks. Moreover, our evaluation results on MNIST and CIFAR10 show that the intrinsic certified robustness guarantees of kNN and rNN outperform those provided by state-of-the-art certified defenses. Our results serve as standard baselines for future certified defenses against data poisoning attacks and backdoor attacks., To appear in AAAI Conference on Artificial Intelligence, 2022

Backdoor attack is a severe security threat to deep neural networks (DNNs). We envision that, like adversarial examples, there will be a cat-and-mouse game for backdoor attacks, i.e., new empirical defenses are developed to defend against backdoor attacks but they are soon broken by strong adaptive backdoor attacks. To prevent such cat-and-mouse game, we take the first step towards certified defenses against backdoor attacks. Specifically, in this work, we study the feasibility and effectiveness of certifying robustness against backdoor attacks using a recent technique called randomized smoothing. Randomized smoothing was originally developed to certify robustness against adversarial examples. We generalize randomized smoothing to defend against backdoor attacks. Our results show the theoretical feasibility of using randomized smoothing to certify robustness against backdoor attacks. However, we also find that existing randomized smoothing methods have limited effectiveness at defending against backdoor attacks, which highlight the needs of new theory and methods to certify robustness against backdoor attacks., Comment: CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision, 2020. DeepMind Best Extended Abstract

Top-k predictions are used in many real-world applications such as machine learning as a service, recommender systems, and web searches. $\ell_0$-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed input. $\ell_0$-norm adversarial perturbation is easy to interpret and can be implemented in the physical world. Therefore, certifying robustness of top-$k$ predictions against $\ell_0$-norm adversarial perturbation is important. However, existing studies either focused on certifying $\ell_0$-norm robustness of top-$1$ predictions or $\ell_2$-norm robustness of top-$k$ predictions. In this work, we aim to bridge the gap. Our approach is based on randomized smoothing, which builds a provably robust classifier from an arbitrary classifier via randomizing an input. Our major theoretical contribution is an almost tight $\ell_0$-norm certified robustness guarantee for top-$k$ predictions. We empirically evaluate our method on CIFAR10 and ImageNet. For instance, our method can build a classifier that achieves a certified top-3 accuracy of 69.2\% on ImageNet when an attacker can arbitrarily perturb 5 pixels of a testing image., Comment: Published as a conference paper at ICLR 2022

Liver fibrosis resulting from continuous long-term hepatic damage represents a heavy burden worldwide. Liver fibrosis is recognized as a complicated pathogenic mechanism with extracellular matrix (ECM) accumulation and hepatic stellate cell (HSC) activation. A series of drugs demonstrate significant antifibrotic activity in vitro and in vivo. No specific agents with ideally clinical efficacy for liver fibrosis treatment have been developed. In this review, we summarized the antifibrotic effects and molecular mechanisms of 29 kinds of common natural products. The mechanism of these compounds is correlated with anti-inflammatory, antiapoptotic, and antifibrotic activities. Moreover, parenchymal hepatic cell survival, HSC deactivation, and ECM degradation by interfering with multiple targets and signaling pathways are also involved in the antifibrotic effects of these compounds. However, there remain two bottlenecks for clinical breakthroughs. The low bioavailability of natural products should be improved, and the combined application of two or more compounds should be investigated for more prominent pharmacological effects. In summary, exploration on natural products against liver fibrosis is becoming increasingly extensive. Therefore, natural products are potential resources for the development of agents to treat liver fibrosis.

Complex arrangements of tetraphenylethylene motifs resulted in a variety of chiralities of face-rotating sandwich structures (FRSs)., Chiral tetraphenylethylene (TPE) derivatives have great potential in chiral recognition and circularly polarized luminescence. However, they were mainly constructed through introducing chiral substituents at the periphery of the TPE moiety, which required additional chemical modifications and limited the variety of chiralities of products. Herein, we constructed a series of chiral face-rotating sandwich structures (FRSs) through restricting the phenyl flipping of TPE without introducing any chiral substituents. In FRSs, the complex arrangements of TPE motifs resulted in a variety of chiralities. We also found that non-covalent repulsive interactions in vertices caused the facial hetero-directionality of FRSs, and the hydrogen bonds between imine bonds and hydroxy groups induced excited-state intramolecular proton transfer (ESIPT) emission of FRSs. In addition, the fluorescence intensity of FRSs decreases with the addition of trifluoroacetic acid. This study provides new insights into the rational design of chiral assemblies from aggregation-induced emission (AIE) active building blocks through restriction of intramolecular rotation (RIR).

This study presents an improved probabilistic production simulation method to facilitate the cost–benefit analysis of pumped hydro storage. To capture the coherent feature of power system operation, the traditional form of probabilistic production simulation is strengthened under a three-fold computational framework. First, the chronological sequences of renewable energy generation and imbalanced power are produced according to a set of feature metrics. Then, the imbalanced power series are modified through the long-term scheduling of pumped hydro storage. At last, the regular steps of probabilistic production simulation, including construction of equivalent energy function and convolution operation, are implemented to derive the operational cost and reliability metrics of power system. Case studies on IEEE-RTS79 system demonstrate the effectiveness of the proposed simulation method, which enables the quantitative assessment for cost-benefits of pumped hydro storage towards a high-penetration renewable energy integrated power system.

Patchouli alcohol (PA), a tricyclic sesquiterpene, is one of the critical bioactive ingredients and is mainly isolated from aerial part ofPogostemon cablin(known as guanghuoxiang in China) belonging to Labiatae. So far, PA has been widely applied in perfume industries. This review was written with the use of reliable information published between 1974 and 2016 from libraries and electronic researches including NCKI, PubMed, Reaxys, ACS, ScienceDirect, Springer, and Wiley-Blackwell, aiming at presenting comprehensive outline of security, pharmacokinetics, and bioactivities of PA and at further providing a potential guide in exploring the PA and its use in various medical fields. We found that PA maybe was a low toxic drug that was acquired numerously through vegetable oil isolation and chemical synthesis and its stability and low water dissolution were improved in pharmaceutics. It also possessed specific pharmacokinetic characteristics, such as two-compartment open model, first-order kinetic elimination, and certain biometabolism and biotransformation process, and was shown to have multiple biological activities, that is, immunomodulatory, anti-inflammatory, antioxidative, antitumor, antimicrobial, insecticidal, antiatherogenic, antiemetic, whitening, and sedative activity. However, the systematic evaluations of preparation, pharmaceutics, toxicology, pharmacokinetics, and bioactivities underlying molecular mechanisms of action also required further investigation prior to practices of PA in clinic.

The aggregation of flexible resources in demand side demonstrates a great potential to address the challenges brought by wind power integration. In this paper, we presented a probabilistic method to assess the synthetic benefits of demand response (DR) programs for power system operation under wind power uncertainty. First, the price-based DR programs are described through the elaborated modeling of demand-price elasticity and consumers satisfaction. Then, a short-term stochastic simulation method is proposed, where the randomness of wind generation is captured by Latin Hypercube sampling, and a two-stage stochastic program framework is developed to simulate the consumption re-scheduling in response to dynamic electricity tariffs. Moreover, the linearization technique is introduced to convexity the consumers satisfaction constraints, so that the assessment model converts to a computationally friendly mixed integer linear program (MILP). The simulation outcomes yield the DR performance metrics in several aspects, e.g., economics, renewable energy utilization, and reserve capacity adequacy. Numerical results validate the effectiveness of the proposed method, which demonstrates the benefits of DR by significantly improving the wind power utilization with a reduced operational cost and guaranteed consumers satisfaction.

It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-$k$ predictions are more relevant. In this work, we aim to derive certified robustness for top-$k$ predictions. In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example. We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier. We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions when using randomized smoothing with Gaussian noise. We find that generalizing the certified robustness from top-1 to top-$k$ predictions faces significant technical challenges. We also empirically evaluate our method on CIFAR10 and ImageNet. For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255). Our code is publicly available at: \url{https://github.com/jjy1994/Certify_Topk}., Comment: ICLR 2020, code is available at this: https://github.com/jjy1994/Certify_Topk

This paper proposes a set of closed-form conditions to ensure the strong duality of second-order cone program (SOCP) formulation for AC power flow in radial power networks. In addition, numerical evaluations on IEEE 33-bus test networks and a real-world distribution network are performed to demonstrate the validity of the proposed conditions., 3 pages

Patchouli alcohol (PA), a tricyclic sesquiterpene, is one of the critical bioactive ingredients and is mainly isolated from aerial part of Pogostemon cablin (known as guanghuoxiang in China) belonging to Labiatae. So far, PA has been widely applied in perfume industries. This review was written with the use of reliable information published between 1974 and 2016 from libraries and electronic researches including NCKI, PubMed, Reaxys, ACS, ScienceDirect, Springer, and Wiley-Blackwell, aiming at presenting comprehensive outline of security, pharmacokinetics, and bioactivities of PA and at further providing a potential guide in exploring the PA and its use in various medical fields. We found that PA maybe was a low toxic drug that was acquired numerously through vegetable oil isolation and chemical synthesis and its stability and low water dissolution were improved in pharmaceutics. It also possessed specific pharmacokinetic characteristics, such as two-compartment open model, first-order kinetic elimination, and certain biometabolism and biotransformation process, and was shown to have multiple biological activities, that is, immunomodulatory, anti-inflammatory, antioxidative, antitumor, antimicrobial, insecticidal, antiatherogenic, antiemetic, whitening, and sedative activity. However, the systematic evaluations of preparation, pharmaceutics, toxicology, pharmacokinetics, and bioactivities underlying molecular mechanisms of action also required further investigation prior to practices of PA in clinic.

Threonylcarbamoyladenosine (t[superscript 6]A) is a modified nucleoside universally conserved in tRNAs in all three kingdoms of life. The recently discovered genes for t6A synthesis, including tsaC and tsaD, are essential in model prokaryotes but not essential in yeast. These genes had been identified as antibacterial targets even before their functions were known. However, the molecular basis for this prokaryotic-specific essentiality has remained a mystery. Here, we show that t[superscript 6]A is a strong positive determinant for aminoacylation of tRNA by bacterial-type but not by eukaryotic-type isoleucyl-tRNA synthetases and might also be a determinant for the essential enzyme tRNA[superscript Ile]-lysidine synthetase. We confirm that t6A is essential in Escherichia coli and a survey of genomewide essentiality studies shows that genes for t[superscrip 6]A synthesis are essential in most prokaryotes. This essentiality phenotype is not universal in Bacteria as t[superscript 6]A is dispensable in Deinococcus radiodurans, Thermus thermophilus, Synechocystis PCC6803 and Streptococcus mutans. Proteomic analysis of t6A- D. radiodurans strains revealed an induction of the proteotoxic stress response and identified genes whose translation is most affected by the absence of t[superscript 6]A in tRNAs. Thus, although t[superscript 6]A is universally conserved in tRNAs, its role in translation might vary greatly between organisms., National Institutes of Health (U.S.) (Grant R01 GM17151)

A gas diffusion electrode (air electrode) with a high current efficiency of electro-synthesizing H2O2 using O2 in air was prepared. The several systems with air electrode as cathode of electro-synthesizing H2O2 on the reaction spot for degrading aniline in aqueous—electro-Fenton system, photo-excitation electro-H2O2 system and photo-electro-Fenton system, were developed. The rates of decomposition of H2O2 and mineralization of aniline were experimentally measured respectively under different conditions, and the results indicated there has an excellent parallel relation between decomposition rate of H2O2 and mineralization rate of aniline. Especially, photo-electro-Fenton system, where H2O2 is decomposed the fastest, is the best system of oxidizing and degrading organic toxicants. Compared photo-electro-Fenton system with photo-Fenton system, important role is revealed in the interface of air electrode. In this paper, the mineralization mechanism of aniline in the photo-electro-Fenton system was also discussed.