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Lycopene represents one of the central compounds in the carotenoid pathway and it exhibits a potent antioxidant ability with wide potential applications in medicine, food, and cosmetics. The microbial production of lycopene has received increasing concern in recent years. Corynebacterium glutamicum (C. glutamicum) is considered to be a safe and beneficial industrial production platform, naturally endowed with the ability to produce lycopene. However, the scarcity of efficient genetic tools and the challenge of identifying crucial metabolic genes impede further research on C. glutamicum for achieving high-yield lycopene production. To address these challenges, a novel genetic editing toolkit, CRISPR/MAD7 system, was established and developed. By optimizing the promoter, ORI and PAM sequences, the CRISPR/MAD7 system facilitated highly efficient gene deletion and exhibited a broad spectrum of PAM sites. Notably, 25 kb of DNA from the genome was successfully deleted. In addition, the CRISPR/MAD7 system was effectively utilized in the metabolic engineering of C. glutamicum, allowing for the simultaneous knockout of crtEb and crtR genes in one step to enhance the accumulation of lycopene by blocking the branching pathway. Through screening crucial genes such as crtE, crtB, crtI, idsA, idi, and cg0722, an optimal carotenogenic gene combination was obtained. Particularly, cg0722, a membrane protein gene, was found to play a vital role in lycopene production. Therefore, the CBIEbR strain was obtained by overexpressing cg0722, crtB, and crtI while strategically blocking the by-products of the lycopene pathway. As a result, the final engineered strain produced lycopene at 405.02 mg/L (9.52 mg/g dry cell weight, DCW) in fed-batch fermentation, representing the highest reported lycopene yield in C. glutamicum to date. In this study, a powerful and precise genetic tool was used to engineer C. glutamicum for lycopene production. Through the modifications between the host cell and

Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor executing a DSA within a finite timeframe due to their limited computational resources. These observations serve as the impetus for this paper to investigate a time-restricted DSA (TR-DSA) model on Proof-of-Work based blockchains. In this TR-DSA model, an attacker only mines its branch within a finite timeframe, and the TR-DSA is considered unsuccessful if the attacker's branch fails to surpass the honest miners' branch when the honest miners' branch has grown by a specific number of blocks. First, we developed a general closed-form expression for the success probability of a TR-DSA. This developed probability not only can assist in evaluating the risk of a DSA on blockchain applications with timely tasks, but also can enable practical attackers with limited computational resources to assess the feasibility and expected reward of launching a TR-DSA. In addition, we provide rigorous proof that the success probability of a TR-DSA is no greater than that of a time-unrestricted DSA where the attacker indefinitely mines its branch. This result implies that blockchain applications with timely tasks are less vulnerable to DSAs than blockchain applications that provide attackers with an unlimited timeframe for their attacks. Furthermore, we show that the success probability of a TR-DSA is always smaller than one even though the attacker controls more than half of the hash rate in the network. This result alerts attackers that there is still a risk of failure in launching a TR-DSA even if they amass a majority of the hash rate in the network., Comment: 13 pages, 8 figures. arXiv admin note: text overlap with arXiv:2304.09965

Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood. To this end, we introduce a unified framework for Adam-type algorithms (called UAdam). This is equipped with a general form of the second-order moment, which makes it possible to include Adam and its variants as special cases, such as NAdam, AMSGrad, AdaBound, AdaFom, and Adan. This is supported by a rigorous convergence analysis of UAdam in the non-convex stochastic setting, showing that UAdam converges to the neighborhood of stationary points with the rate of $\mathcal{O}(1/T)$. Furthermore, the size of neighborhood decreases as $\beta$ increases. Importantly, our analysis only requires the first-order momentum factor to be close enough to 1, without any restrictions on the second-order momentum factor. Theoretical results also show that vanilla Adam can converge by selecting appropriate hyperparameters, which provides a theoretical guarantee for the analysis, applications, and further developments of the whole class of Adam-type algorithms.

Distributed detection over a blockchain-aided Internet of Things (BIoT) network in the presence of attacks is considered, where the integrated blockchain is employed to secure data exchanges over the BIoT as well as data storage at the agents of the BIoT. We consider a general adversary model where attackers jointly exploit the vulnerability of IoT devices and that of the blockchain employed in the BIoT. The optimal attacking strategy which minimizes the Kullback-Leibler divergence is pursued. It can be shown that this optimization problem is nonconvex, and hence it is generally intractable to find the globally optimal solution to such a problem. To overcome this issue, we first propose a relaxation method that can convert the original nonconvex optimization problem into a convex optimization problem, and then the analytic expression for the optimal solution to the relaxed convex optimization problem is derived. The optimal value of the relaxed convex optimization problem provides a detection performance guarantee for the BIoT in the presence of attacks. In addition, we develop a coordinate descent algorithm which is based on a capped water-filling method to solve the relaxed convex optimization problem, and moreover, we show that the convergence of the proposed coordinate descent algorithm can be guaranteed., Comment: 16 pages, 4 figures. This work has been submitted to the IEEE TIFS

Recently, blockchain has been applied in various fields to secure data exchanges and storage in decentralized systems. In a blockchain application where the task of the application which makes use of the data stored in a blockchain has to be accomplished by a time instant, the employed blockchain is essentially finitely-long. In this paper, we consider a general finitely-long blockchain model which is generalized from most existing works on finitely-long blockchain applications, and take the first step towards characterizing the vulnerability of finitely-long blockchains in securing data against double-spending attacks. For the first time, we develop a general closed-form expression for the probability of success in launching a double-spending attack on a finitely-long blockchain. This probability essentially characterizes the vulnerability of finitely-long blockchains. Then, we prove that the probability of success in launching a double-spending attack on a finitely-long blockchain is no greater than that on an infinitely-long blockchain, which implies that finitely-long blockchains are less vulnerable to double-spending attacks than infinitely-long blockchains. Moreover, we show that unlike infinitely-long blockchains which can be surely paralyzed by a 51% attack, finitely-long blockchains are more resistant to 51% attacks., Comment: 13 pages. 8 figures. This work has been submitted to the IEEE

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches., Comment: Accepted by the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)

Potentiodynamic polarization and potentiostatic passive measurements were performed to study the effects of alternating voltage (AV) interference on duplex stainless steel (DSS) 2205 corrosion. The results reveal that AV signals cause positive shift in open circuit potential (OCP) and strong oscillations on polarization curves of DSS 2205. For potentiostatic passive condition, the passive current increases with increasing the amplitude of AV signals with a frequency of 50 Hz. The vibrations on polarization and passivation are weakened after increase in frequency (>50 Hz). AV signals with frequency smaller than 50 Hz also induce evident oscillation on OCP, polarization, and passivation on samples. The high-amplitude AV interferences with 50 and 100 Hz induce big pits on DSS 2205 samples, the pitting corrosion however is inhibited with further increasing the frequency. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.

Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.

Mycoplasma contamination is a common problem in cell culture and can alter cellular functions. Since cell metabolism is either directly or indirectly involved in every aspect of cell function, it is important to detect changes to the cellular metabolome after mycoplasma infection. In this study, liquid chromatography mass spectrometry (LC/MS)-based metabolomics was used to investigate the effect of mycoplasma contamination on the cellular metabolism of human pancreatic carcinoma cells (PANC-1). Multivariate analysis demonstrated that mycoplasma contamination induced significant metabolic changes in PANC-1 cells. Twenty-three metabolites were identified and found to be involved in arginine and purine metabolism and energy supply. This study demonstrates that mycoplasma contamination significantly alters cellular metabolite levels, confirming the compelling need for routine checking of cell cultures for mycoplasma contamination, particularly when used for metabolomics studies. Graphical abstract Metabolomics reveals mycoplasma contamination changes the metabolome of PANC-1 cells.

The performance of deep submicron designs can be affected by various parametric variations, manufacturing defects, noise or modeling errors that are all statistical in nature. In this paper, we propose a methodology to capture the effects of these statistical variations on circuit performance. It incorporates statistical information into timing analysis to compute the performance sensitivity of internal signals subject to a given type of defect, noise or variation sources. Next, we propose a novel path and segment selection methodology for delay testing based on the results of statistical performance sensitivity analysis. The objective of path/segment selection is to identify a small set of paths and segments such that the delay tests for the selected paths/segments guarantee the detection of performance failure. We apply the proposed path selection technique for selection of a set of paths for dynamic timing analysis considering power supply noise effects. Our experimental results demonstrate the difference in estimated circuit performance for the case when power supply noise effects are considered versus when these effects are ignored. Thus, they indicate the need for considering power supply noise effects on delays during path selection and dynamic timing analysis.

The performance of deep submicron designs can be affected by various parametric variations, manufacturing defects, noise or modeling errors that are all statistical in nature. In this paper, we propose a methodology to capture the effects of these statistical variations on circuit performance. It incorporates statistical information into timing analysis to compute the performance sensitivity of internal signals subject to a given type of defect, noise or variation sources. Next, we propose a novel path and segment selection methodology for delay testing based on the results of statistical performance sensitivity analysis. The objective of path/segment selection is to identify a small set of paths and segments such that the delay tests for the selected paths/segments guarantee the detection of performance failure. We apply the proposed path selection technique for selection of a set of paths for dynamic timing analysis considering power supply noise effects. Our experimental results demonstrate the difference in estimated circuit performance for the case when power supply noise effects are considered versus when these effects are ignored. Thus, they indicate the need for considering power supply noise effects on delays during path selection and dynamic timing analysis.

The performance of deep submicron designs can be affected by various parametric variations, manufacturing defects, noise or modeling errors that are all statistical in nature. In this paper, we propose a methodology to capture the effects of these statistical variations on circuit performance. It incorporates statistical information into timing analysis to compute the performance sensitivity of internal signals subject to a given type of defect, noise or variation sources. Next, we propose a novel path and segment selection methodology for delay testing based on the results of statistical performance sensitivity analysis. The objective of path/segment selection is to identify a small set of paths and segments such that the delay tests for the selected paths/segments guarantee the detection of performance failure. We apply the proposed path selection technique for selection of a set of paths for dynamic timing analysis considering power supply noise effects. Our experimental results demonstrate the difference in estimated circuit performance for the case when power supply noise effects are considered versus when these effects are ignored. Thus, they indicate the need for considering power supply noise effects on delays during path selection and dynamic timing analysis.

Dai, N., Wan, Y., Liu, Y., Sun, Y., Zhang, L.-C., Jiang, Y., & Li, J. (2018). Studies on pitting corrosion in austenitic stainless steel interfered by square‐wave alternating voltage with different parameters using multi‐potential steps method. Materials and Corrosion, 69(12), 1741-1757. Available here

Dai, N., Wu, J., Zhang, L. -., Yin, L., Yang, Y., Jiang, Y., & Li, J. (2019). Pitting and etching behaviors occurring in duplex stainless steel 2205 in the presence of alternating voltage interference. Construction and Building Materials, 202, 877-890. Available here

Dai, N., Wu, J., Zhang, L. -., Sun, Y., Liu, Y., Yang, Y., . . . Li, J. (2019). Alternating voltage induced oscillation on electrochemical behavior and pitting corrosion in duplex stainless steel 2205. Materials and Corrosion, 70(3), 419-433. Available here.