LEUTX regulates porcine embryonic genome activation in somatic cell nuclear transfer embryos.

Emerging evidence highlights the regulatory role of paired-like (PRD-like) homeobox transcription factors (TFs) in embryonic genome activation (EGA). However, the majority of PRD-like genes are lost in rodents, thus prompting an investigation into PRD-like TFs in other mammals. Here, we showed that PRD-like TFs were transiently expressed during EGA in human, monkey, and porcine fertilized embryos, yet they exhibited inadequate expression in their cloned embryos. This study, using pig as the research model, identified LEUTX as a key PRD-like activator of porcine EGA through genomic profiling and found that LEUTX overexpression restored EGA failure and improved preimplantation development and cloning efficiency in porcine cloned embryos. Mechanistically, LEUTX opened EGA-related genomic regions and established histone acetylation via recruiting acetyltransferases p300 and KAT2A. These findings reveal the regulatory mechanism of LEUTX to govern EGA in pigs, which may provide valuable insights into the study of early embryo development for other non-rodent mammals.

KLF4 facilitates chromatin accessibility remodeling in porcine early embryos.

Chromatin accessibility remodeling driven by pioneer factors is critical for the development of early embryos. Current studies have illustrated several pioneer factors as being important for agricultural animals, but what are the pioneer factors and how the pioneer factors remodel the chromatin accessibility in porcine early embryos is not clear. By employing low-input DNase-seq (liDNase-seq), we profiled the landscapes of chromatin accessibility in porcine early embryos and uncovered a unique chromatin accessibility reprogramming pattern during porcine preimplantation development. Our data revealed that KLF4 played critical roles in remodeling chromatin accessibility in porcine early embryos. Knocking down of KLF4 led to the reduction of chromatin accessibility in early embryos, whereas KLF4 overexpression promoted the chromatin openness in porcine blastocysts. Furthermore, KLF4 deficiency resulted in mitochondrial dysfunction and developmental failure of porcine embryos. In addition, we found that overexpression of KLF4 in blastocysts promoted lipid droplet accumulation, whereas knockdown of KLF4 disrupted this process. Taken together, our study revealed the chromatin accessibility dynamics and identified KLF4 as a key regulator in chromatin accessibility and cellular metabolism during porcine preimplantation embryo development.

Lithium-ion battery multi-scale modeling coupled with simplified electrochemical model and kinetic Monte Carlo model.

The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling mechanism between multiple scales is still incomplete. In this paper, a simplified electrochemical model (SEM) and a kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth model are used to study the multi-scale characteristics of LIBs. The single-particle SEM (SP-SEM) is described for macro scale, and a simple and self-consistent multi-particle SEM (MP-SEM) is developed. Then, the KMC-based SEI model is established for micro-scale molecular evolution. And, the two models are coupled to construct the full-cycle multi-scale model. After modeling, validation is performed by using a commercial 18650-type LIB. Finally, the effect of parameters on the SEI model is studied, including qualitative trend analysis and quantitative sensitivity analysis. The growth of SEI film with different particle sizes is studied by MP-SEM coupling simulation.

V-doped porous CoP nanoarrays grown on carbon cloth with optimized electronic structure for the hydrogen evolution reaction.

As an efficient, renewable and clean energy, hydrogen is expected to replace traditional fossil fuel energy in the future. Currently, platinum-based materials (Pt) are excellent electrocatalysts for hydrogen evolution reaction (HER), but their high cost and low natural abundance limit their widespread application. Therefore, it is urgent to develop low-cost, highly efficient and earth-abundant electrocatalysts to replace the precious platinum-based materials. In this study, a Co-based organic framework (ZIF-67) was grown on a flexible substrate carbon cloth (CC), and a V-doped CoP nanoarray (V-CoP/CC) was prepared using a simple in situ ion exchange/phosphating method. Due to its unique porous structure, effective doping of V atoms and the in situ electrode construction, the V-CoP/CC exhibited high electrolytic hydrogen evolution reaction (HER) performance, with an overpotential of 98 mV at a current density of 10 mA cm-2. This work has important theoretical and practical significance for in situ construction of heteroatom-doped CoP electrodes.

On the value of label and semantic information in domain generalization.

In this work, we tackle the domain generalization (DG) problem aiming to learn a universal predictor on several source domains and deploy it on an unseen target domain. Many existing DG approaches were mainly motivated by domain adaptation techniques to align the marginal feature distribution but ignored conditional relations and labeling information in the source domains, which are critical to ensure successful knowledge transfer. Although some recent advances started to take advantage of conditional semantic distributions, theoretical justifications were still missing. To this end, we investigate the theoretical guarantee for a successful generalization process by focusing on how to control the target domain error. Our results reveal that to control the target risk, one should jointly control the source errors that are weighted according to label information and align the semantic conditional distributions between different source domains. The theoretical analysis then leads to an efficient algorithm to control the label distributions as well as match the semantic conditional distributions. To verify the effectiveness of our method, we evaluate it against recent baseline algorithms on several benchmarks. We also conducted experiments to verify the performance under label distribution shift to demonstrate the necessity of leveraging the labeling and semantic information. Empirical results show that the proposed method outperforms most of the baseline methods and shows state-of-the-art performances.

Episodic task agnostic contrastive training for multi-task learning.

Learning knowledge from different tasks to improve the general learning performance is crucial for designing an efficient algorithm. In this work, we tackle the Multi-task Learning (MTL) problem, where the learner extracts the knowledge from different tasks simultaneously with limited data. Previous works have been designing the MTL models by taking advantage of the transfer learning techniques, requiring the knowledge of the task index, which is not realistic in many practical scenarios. In contrast, we consider the scenario that the task index is not explicitly known, under which the features extracted by the neural networks are task agnostic. To learn the task agnostic invariant features, we implement model agnostic meta-learning by leveraging the episodic training scheme to capture the common features across tasks. Apart from the episodic training scheme, we further implemented a contrastive learning objective to improve the feature compactness for a better prediction boundary in the embedding space. We conduct extensive experiments on several benchmarks compared with several recent strong baselines to demonstrate the effectiveness of the proposed method. The results showed that our method provides a practical solution for real-world scenarios, where the task index is agnostic to the learner and can outperform several strong baselines, achieving state-of-the-art performances.

Towards interactional management for power batteries of electric vehicles.

With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications.

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model.

The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health.

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Exogenous 5-aminolevulinic acid alleviates low-temperature damage by modulating the xanthophyll cycle and nutrient uptake in tomato seedlings.

5-Aminolevulinic acid (ALA), an antioxidant existing in plants, has been widely reported to participate in the process of coping with cold stress of plants. In this study, exogenous ALA promoted the growth of tomato plants and alleviated the appearance of purple tomato leaves under low-temperature stress. At the same time, exogenous ALA improved antioxidant enzyme activities, SlSOD gene expression, Fv/Fm, and proline contents and reduced H2O2 contents, SlRBOH gene expression, relative electrical conductivity, and malondialdehyde contents to alleviate the damage caused by low temperature to tomato seedlings. Compared with low-temperature stress, spraying exogenous ALA before low-temperature stress could restore the indicators of photochemical quenching, actual photochemical efficiency, electron transport rate, and nonphotochemical quenching to normal. Exogenous ALA could increase the total contents of the xanthophyll cycle pool, the positive de-epoxidation rate of the xanthophyll cycle and improved the expression levels of key genes in the xanthophyll cycle under low-temperature stress. In addition, we found that exogenous ALA significantly enhanced the absorption of mineral nutrients, promoted the transfer and distribution of mineral nutrients to the leaves, and improved the expression levels of mineral nutrient absorption-related genes, which were all conducive to the improved adaptation of tomato seedlings under low-temperature stress. In summary, the application of exogenous ALA can increase tomato seedlings' tolerance to low-temperature stress by improving the xanthophyll cycle and the ability of the absorption of mineral nutrients in tomato seedlings.

Adaptive Neural Network-Based Event-Triggered SOC Observer With Application to a Stochastic Battery Model.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

Strategies and characterization methods for achieving high performance PEO-based solid-state lithium-ion batteries.

Polyethylene oxide (PEO) based polymer electrolytes have been widely used in solid-state lithium batteries (SSBs) owing to the high solubility of lithium salt, favourable ionic conductivity, flexibility for improved interfacial contact and scalable processing. In this work, we summarize the main limitations remaining to be solved before the large-scale commercialization of PEO-based SSBs, including (1) improving ionic conductivity toward high-rate performance and lower operating temperature, (2) enhancing mechanical strength for improved lithium dendrite resistance and large-scale processing, (3) strengthening electrochemical stability to match high energy density electrodes with high voltage, and (4) achieving high thermal stability toward safe operation. Meanwhile, the characterization methods to investigate the ion transportation mechanism, lithium dendrite growth and decomposition reaction are also discussed.

An Authentication and Secure Communication Scheme for In-Vehicle Networks Based on SOME/IP.

The rapid development of intelligent networked vehicles (ICVs) has brought many positive effects. Unfortunately, connecting to the outside exposes ICVs to security threats. Using secure protocols is an important approach to protect ICVs from hacker attacks and has become a hot research area for vehicle security. However, most of the previous studies were carried out on V2X networks, while those on in-vehicle networks (IVNs) did not involve Ethernet. To this end, oriented to the new IVNs based on Ethernet, we designed an efficient secure scheme, including an authentication scheme using the Scalable Service-Oriented Middleware over IP (SOME/IP) protocol and a secure communication scheme modifying the payload field of the original SOME/IP data frame. The security analysis shows that the designed authentication scheme can provide mutual identity authentication for communicating parties and ensure the confidentiality of the issued temporary session key; the designed authentication and secure communication scheme can resist the common malicious attacks conjointly. The performance experiments based on embedded devices show that the additional overhead introduced by the secure scheme is very limited. The secure scheme proposed in this article can promote the popularization of the SOME/IP protocol in IVNs and contribute to the secure communication of IVNs.

Design of a CANFD to SOME/IP Gateway Considering Security for In-Vehicle Networks.

In recent years, Ethernet has been introduced into vehicular networks to cope with the increasing demand for bandwidth and complexity in communication networks. To exchange data between controller area network (CAN) and Ethernet, a gateway system is required to provide a communication interface. Additionally, the existence of networked devices exposes automobiles to cyber security threats. Against this background, a gateway for CAN/CAN with flexible data-rate (CANFD) to scalable service-oriented middleware over IP (SOME/IP) protocol conversion is designed, and security schemes are implemented in the routing process to provide integrity and confidentiality protections. Based on NXP-S32G, the designed gateway is implemented and evaluated. Under most operating conditions, the CPU and the RAM usage are less than 5% and 20 MB, respectively. Devices running a Linux operating system can easily bear such a system resource overhead. The latency caused by the security scheme accounts for about 25% of the entire protocol conversion latency. Considering the security protection provided by the security scheme, this overhead is worthwhile. The results show that the designed gateway can ensure a CAN/CANFD to SOME/IP protocol conversion with a low system resource overhead and a low latency while effectively resisting hacker attacks such as frame forgery, tampering, and sniffing.

RBOH1-dependent H2O2 mediates spermine-induced antioxidant enzyme system to enhance tomato seedling tolerance to salinity-alkalinity stress.

Salinity-alkalinity stress is a limiting factor in tomato production in the world. Plants perceive salinity-alkalinity stress by activating signaling pathways to increase plant tolerance (Xu et al., 2020). Here, we investigated whether spermine (Spm) induces respiratory burst oxidase homolog 1 (RBOH1) and hydrogen peroxide (H2O2) signaling in response to salinity-alkalinity stress in tomato. The results showed that exogenous Spm induced the expression of RBOH1 and the accumulation of H2O2 under normal condition. Accordingly, we tested the function of H2O2 signal in tomato seedlings and found that exogenous H2O2 increased the expression levels of Cu/Zn-superoxide dismutase (Cu/Zn-SOD), catalase 1 (CAT1), cytosolic ascorbate peroxidase (cAPX), and glutathione reductase 1 (GR1) and the activities of SOD (EC 1.15.1.1), CAT (EC 1.11.1.6), ascorbate peroxidase (APX; EC 1.11.1.11), and GR (EC 1.6.4.2) in tomato seedlings under salinity-alkalinity stress. DMTU increased the malondialdehyde (MDA) content and relative electrical conductivity, and the relative water content (RWC), and accelerated leaf yellowing in tomato seedlings under salinity-alkalinity stress, even though we sprayed Spm on tomato leaves. We also found that RBOH1 silencing decreased the expression levels of Cu/Zn-SOD, CAT1, cAPX, and GR1 and the activities of SOD, CAT, APX, and GR when tomato seedlings were under salinity-alkalinity stress. Exogenous Spm did not increase RWC and decrease MDA content in RBOH1 silencing tomato seedlings under salinity-alkalinity stress.

A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter.

An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.

Square-root unscented Kalman filter for ammonia coverage ratio and input ammonia estimations in diesel-engine urea-SCR system.

The selective catalytic reduction (SCR) is a resultful technical approach to diminish the NOx emission in the diesel-engine exhaust. And closed-loop control is of vital importance for SCR system to realize high NOx reduction and low ammonia tailpipe leakage. The input ammonia and the ammonia coverage ratio are two crucial parameters, but the cost of a sensor measuring the former is high, and the latter cannot be obtained by the sensor directly. Thus an observer based on square-root unscented Kalman filter is designed to estimate the two parameters. Meanwhile, a three-state model is established to mathematically describe the SCR system. The simulation results show that the observer can make an outstanding estimation on the state variables, and it shows strong robustness to the external interference.

A particle filter for ammonia coverage ratio and input simultaneous estimations in Diesel-engine SCR system.

As NOx emissions legislation for Diesel-engines is becoming more stringent than ever before, an aftertreatment system has been widely used in many countries. Specifically, to reduce the NOx emissions, a selective catalytic reduction(SCR) system has become one of the most promising techniques for Diesel-engine vehicle applications. In the SCR system, input ammonia concentration and ammonia coverage ratio are regarded as essential states in the control-oriental model. Currently, an ammonia sensor placed before the SCR Can is a good strategy for the input ammonia concentration value. However, physical sensor would increase the SCR system cost and the ammonia coverage ratio information cannot be directly measured by physical sensor. Aiming to tackle this problem, an observer based on particle filter(PF) is investigated to estimate the input ammonia concentration and ammonia coverage ratio. Simulation results through the experimentally-validated full vehicle simulator cX-Emission show that the performance of observer based on PF is outstanding, and the estimation error is very small.

Preparation of anticoagulant polyvinylidene fluoride hollow fiber hemodialysis membranes.

In this study, polyvinylidene fluoride (PVDF) hollow fiber membranes (HFMs) were modified by coating with polyvinyl alcohol (PVA) and chitosan. The influences of PVA and chitosan amount on PVDF membrane mechanical and separation performance were investigated. The results showed that the modified PVDF membranes had better mechanical and separation performance when the amount of PVA and chitosan was 20 mg/m2. At the same time, the biocompatibility of PVDF membranes was also investigated. Compared with virgin PVDF membranes, the modified PVDF membranes showed better anticoagulation, hydrophilicity, less bovine serum albumin (BSA) adsorption, and lower hemolytic ratio. The anticoagulation behavior of modified PVDF membranes coating with PVA had been obviously improved. Prothrombin time (PT) and activated partial thromboplastin time (APTT) of the modified PVDF membrane are 44.8 s and 72.5 s while the PT and APTT of virgin PVDF membrane are 15.6 s and 37.3 s. The advancing water contact angle (WCA) and BSA adsorption of the modified PVDF membrane coating with PVA are 24° and 23 mg/m2 while virgin PVDF membrane is 52° and 49 mg/m2. However, further biocompatibility evaluation is needed to obtain a more comprehensive conclusion.