H2O2-dependent oxidation of the transcription factor GmNTL1 promotes salt tolerance in soybean.

Reactive oxygen species (ROS) play an essential role in plant growth and responses to environmental stresses. Plant cells sense and transduce ROS signaling directly via hydrogen peroxide (H2O2)-mediated posttranslational modifications (PTMs) on protein cysteine residues. Here, we show that the H2O2-mediated cysteine oxidation of NAC WITH TRANS-MEMBRANE MOTIF1-LIKE 1 (GmNTL1) in soybean (Glycine max) during salt stress promotes its release from the endoplasmic reticulum (ER) membrane and translocation to the nucleus. We further show that an oxidative posttranslational modification on GmNTL1 residue Cys-247 steers downstream amplification of ROS production by binding to and activating the promoters of RESPIRATORY BURST OXIDASE HOMOLOG B (GmRbohB) genes, thereby creating a feed-forward loop to fine-tune GmNTL1 activity. In addition, oxidation of GmNTL1 Cys-247 directly promotes the expression of CATION H+ EXCHANGER 1 (GmCHX1)/SALT TOLERANCE-ASSOCIATED GENE ON CHROMOSOME 3 (GmSALT3) and Na+/H+ Antiporter 1 (GmNHX1). Accordingly, transgenic overexpression of GmNTL1 in soybean increases the H2O2 levels and K+/Na+ ratio in the cell, promotes salt tolerance, and increases yield under salt stress, while an RNA interference-mediated knockdown of GmNTL1 elicits the opposite effects. Our results reveal that the salt-induced oxidation of GmNTL1 promotes its relocation and transcriptional activity through an H2O2-mediated posttranslational modification on cysteine that improves resilience of soybean against salt stress.

A computational and experimental study of cis-trans isomeric pesticides based on collision-induced dissociation of high-resolution mass spectrometry.

RATIONALE: Pesticide isomers are widely available in agricultural production and may vary widely in biological activity, potency, and toxicity. Chromatographic and mass spectrometric analysis of pesticide isomers is challenging due to structural similarities. METHODS: Based on liquid chromatography time-of-flight mass spectrometry, identification of cis-trans isomeric pesticides was achieved through retention time, characteristic fragment ions, and relative abundance ratio. Furthermore, theoretical and basic research has been conducted on the differences in characteristic fragment ions and their relative abundance ratios of cis-trans isomers. On the one hand, the cleavage pathways of six cis-trans isomers were elucidated through collision-induced dissociation to explain different fragment ions of the isomers. On the other hand, for those with the same fragment ions but different abundance ratios, energy-resolved mass spectrometry combined with computational chemical density functional theory in terms of kinetics, thermodynamics, and bond lengths was employed to explain the reasons for the differences in characteristic fragment ions and their abundance ratios. RESULTS: A high-resolution mass spectrometry method was developed for the separation and analysis of cis-trans isomers of pesticides in traditional Chinese medicine Radix Codonopsis, and six pesticide isomers were distinguished by retention time, product ions, and relative abundance ratios. The limits of quantification of the six pesticides were up to 10 µg/kg, and the linear ranges of them were 10-200 µg/kg, with coefficients of determination (R2) > 0.99, which demonstrated the good linearity of the six pesticides. The recoveries of the pesticides at spiked concentrations of 10, 20, and 100 µg/kg reached 70-120% with relative standard deviations ≤20%. CONCLUSIONS: It was demonstrated that the application of the method was well suited for accurate qualitative and quantitative analysis for isomers with different structures, which could avoid false-negative results caused by ignoring other isomers effectively.

A magnetically reusable Ce-MOF/GO/Fe3O4 composite for effective photocatalytic degradation of chlortetracycline.

Herein, we report a novel 1/GO/Fe3O4 photocatalyst, comprising Ce(BTB)(H2O) (MOF-1, H3BTB = 1,3,5-benzenetrisbenzoic acid), graphene oxide (GO), and iron oxide (Fe3O4) for photocatalytic degradation of chlortetracycline (CTC). This design enables the effective transfer of electrons from the MOF to GO, thereby reducing the photoelectron-hole recombination rate. Therefore, the optimized 1/GO/Fe3O4 photocatalyst with H2O2 shows the highest photocatalytic activity toward CTC. The kinetic constant is 5.4 times that in the system of MOF-1 and hydrogen peroxide, which usually acted as efficient electron acceptors to improve the photocatalytic performance of MOFs. More importantly, light absorption is extended from the ultraviolet to the visible region. Furthermore, 1/GO/Fe3O4 can be quickly recycled under an applied magnetic field and displays outstanding stability and reusability. According to the radical trapping experiments and electron paramagnetic resonance results, hydroxyl radicals, superoxide radicals, and holes all contribute to excellent photocatalytic activity. The possible catalytic mechanism of 1/GO/Fe3O4 is tentatively proposed. This work aims to explore the synergistic effect between metal-organic frameworks (MOFs) and GO, and provide a theoretical basis for MOF-based composites to remove antibiotic contaminants in the environment.

Nucleotide de novo synthesis increases breast cancer stemness and metastasis via cGMP-PKG-MAPK signaling pathway.

Metabolic reprogramming to fulfill the biosynthetic and bioenergetic demands of cancer cells has aroused great interest in recent years. However, metabolic reprogramming for cancer metastasis has not been well elucidated. Here, we screened a subpopulation of breast cancer cells with highly metastatic capacity to the lung in mice and investigated the metabolic alternations by analyzing the metabolome and the transcriptome, which were confirmed in breast cancer cells, mouse models, and patients' tissues. The effects and the mechanisms of nucleotide de novo synthesis in cancer metastasis were further evaluated in vitro and in vivo. In our study, we report an increased nucleotide de novo synthesis as a key metabolic hallmark in metastatic breast cancer cells and revealed that enforced nucleotide de novo synthesis was enough to drive the metastasis of breast cancer cells. An increased key metabolite of de novo synthesis, guanosine-5'-triphosphate (GTP), is able to generate more cyclic guanosine monophosphate (cGMP) to activate cGMP-dependent protein kinases PKG and downstream MAPK pathway, resulting in the increased tumor cell stemness and metastasis. Blocking de novo synthesis by silencing phosphoribosylpyrophosphate synthetase 2 (PRPS2) can effectively decrease the stemness of breast cancer cells and reduce the lung metastasis. More interestingly, in breast cancer patients, the level of plasma uric acid (UA), a downstream metabolite of purine, is tightly correlated with patient's survival. Our study uncovered that increased de novo synthesis is a metabolic hallmark of metastatic breast cancer cells and its metabolites can regulate the signaling pathway to promote the stemness and metastasis of breast cancer.

Withdrawn: MicroRNA-214-3p protects against myocardial ischemia-reperfusion injury by targeting demethylase lysine demethylase 3A.

The above article from Cell Biology International, published online on 5 December 2022, on Wiley Online Library (https://doi.org/10.1002/cbin.11920), has been withdrawn by agreement between the journal Editor in Chief, Sergio Schenkman, and John Wiley and Sons Ltd. The withdrawal has been agreed due to a technical error at the publisher that caused the article to be mistakenly published online.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.

Analysis of Hypoxia Inducible Factor-1α Expression and Its Effects on Glycolysis of Esophageal Carcinoma.

We investigated the regulatory effects of hypoxia-inducible factor-1a (HIF-1α) on glycolysis metabolism in esophageal carcinoma (ESCA) cells. A series of bioinformatics databases and tools were used to investigate the expression and role of HIF-1α in ESCA. The expression of HIF-1a in ESCA tissues and adjacent tissues was validated by real-time PCR. Small interfering RNA (siRNA) was used to inhibit HIF-1α-related genes in human ESCA cells (Eca109 and KYSE150). Cell proliferation was detected by the CCK-8 assay. The expression of HIF-1α and glycolytic enzymes were investigated by real-time PCR and Western blot. HIF-1α is highly expressed in ESCA and is involved in many biological processes such as cell hypoxia reaction, glucose metabolic process. Further in vitro experiments showed that expression of HIF-1α in Eca109 and KYSE150 significantly increased under hypoxia compared with normoxia conditions. Also, the glucose uptake and lactate production under hypoxia were higher. The expression levels of hexokinase 2 (HK2) and pyruvate dehydrogenase kinase 1 (PDK1), glycolysis-related genes, were significantly increased under hypoxia. After siRNA knockdown of HIF-1a in Eca109 and KYSE150, the glucose uptake and lactate production, as well as cell proliferation were significantly decreased under hypoxia, and HK2 and PDK1 were significantly downregulated. HIF-1α promotes glycolysis of ESCA cells by upregulating the expression of HK2 and PDK1 under hypoxia.

Changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China.

To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24-2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM2.5/PM10 and NO2/SO2 were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO2/SO2 presented a responding decline, especially in YRD (-5.01), YH (-3.87), and MYR (-3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3-P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1-P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM2.5/PM10 increased in P1-P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO2 presented a stronger association with PM2.5/PM10 variation than CO; and PM2.5 with NO2/SO2 variation than PM10. In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.

The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.

Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.

Pt and Mo Co-Decorated MnO2 Nanorods with Superior Resistance to H2O, Sintering, and HCl for Catalytic Oxidation of Chlorobenzene.

MnO2 nanorods with exposed (110), (100), or (310) facets were prepared and investigated for catalytic oxidation of chlorobenzene, then the (110)-exposed MnO2 nanorod was screened as the candidate parent and further modified by Pt and/or Mo with different contents. The loading of Pt enhanced activity and versatility of the pristine MnO2, but the polychlorinated byproducts and Cl2 were promoted, conversely, as the decoration of Mo inhibited the polychlorinated byproducts and improved durability. Determination of structure and properties suggested that Pt facilitated the formation of more oxygen vacancies/Mn3+ and surface adsorbed oxygen weakened the bonds of surface lattice oxygen, while Mo stabilized surface lattice oxygen and increased acid sites, especially Brønsted acid sites. Expectedly, Pt and Mo bifunctionally modified MnO2 presented a preferable activity, selectivity, and durability along with the super resistance to H2O, high-temperature, and HCl, and no prominent deactivation was observed within 30 h at 300 °C under dry and humid conditions, even at high-temperature aging at 600 °C and HCl-pretreatment (7 h). In this work, the optimized Mo and Pt codecorated MnO2 was considered a promising catalyst toward practical applications for catalytic oxidation of actual Cl-VOCs emissions.

A NeuroD1 AAV-Based Gene Therapy for Functional Brain Repair after Ischemic Injury through In Vivo Astrocyte-to-Neuron Conversion.

Adult mammalian brains have largely lost neuroregeneration capability except for a few niches. Previous studies have converted glial cells into neurons, but the total number of neurons generated is limited and the therapeutic potential is unclear. Here, we demonstrate that NeuroD1-mediated in situ astrocyte-to-neuron conversion can regenerate a large number of functional new neurons after ischemic injury. Specifically, using NeuroD1 adeno-associated virus (AAV)-based gene therapy, we were able to regenerate one third of the total lost neurons caused by ischemic injury and simultaneously protect another one third of injured neurons, leading to a significant neuronal recovery. RNA sequencing and immunostaining confirmed neuronal recovery after cell conversion at both the mRNA level and protein level. Brain slice recordings found that the astrocyte-converted neurons showed robust action potentials and synaptic responses at 2 months after NeuroD1 expression. Anterograde and retrograde tracing revealed long-range axonal projections from astrocyte-converted neurons to their target regions in a time-dependent manner. Behavioral analyses showed a significant improvement of both motor and cognitive functions after cell conversion. Together, these results demonstrate that in vivo cell conversion technology through NeuroD1-based gene therapy can regenerate a large number of functional new neurons to restore lost neuronal functions after injury.

Hypoxia protects H9c2 cells against Ferroptosis through SENP1-mediated protein DeSUMOylation.

Hypoxia affects proliferation, differentiation, as well as death of cardiomyocyte, and plays an important role in the development of myocardial ischemia. However, the detailed mechanisms through which hypoxia regulates cardiomyocyte ferroptosis have not been explored. In this study, we revealed that hypoxia suppresses the proliferation, migration, and erastin-induced ferroptosis of H9c2 cells. First, we confirmed the upregulation of SENP1 in H9c2 cells cultured under hypoxic conditions. Through adenovirus-mediated SENP1 gene transfection, we demonstrated that SENP1 overexpression could enhance H9c2 cell proliferation and migration while also protecting H9c2 cells from erastin-induced ferroptosis. Furthermore, through immunoprecipitation and western blotting, we confirmed that SENP1 mediated deSUMOylation of HIF-1α and ACSL4 in H9c2 cells. In conclusion, this study describes the underlying mechanism through which hypoxia upregulates SENP1 expression, in turn protecting against ferroptosis via the regulation of HIF-1α and ACSL4 deSUMOylation. Our findings provide a theoretical foundation for the development of novel therapeutics for ischemic heart diseases.

Multi-Channel Fusion Classification Method Based on Time-Series Data.

Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster-Shafer evidence theory (D-S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.

The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.

Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.