A fungal pathogen secretes a cell wall-associated ß-N-acetylhexosaminidase that is co-expressed with chitinases to contribute to infection of insects.

BACKGROUND: ß-N-acetylhexosaminidases (HEXs) are widely distributed in fungi and involved in cell wall chitin metabolism and utilization of chitin-containing substrates. However, details of the fungal pathogens-derived HEXs in the interaction with their hosts remain limited. RESULTS: An insect nutrients-induced ß-N-acetylhexosaminidase, BbHex1, was identified from the entomopathogenic fungus Beauveria bassiana, which was involved in cell wall modification and degradation of insect cuticle. BbHex1 was localized to cell wall and secreted, and displayed enzyme activity to degrade the chitinase-hydrolyzed product (GlcNAc)2. Disruption of BbHex1 resulted in a significant decrease in the level of cell wall chitin in the presence of insect nutrients and during infection of insects, with impaired ability to penetrate insect cuticle, accompanying downregulated cell wall metabolism-involved and cuticle-degrading chitinase genes. However, the opposite phenotypes were examined in the gene overexpression strain. Distinctly altered cell wall structures caused by BbHex1 mutation and overexpression led to the easy activation and evasion (respectively) of insect immune response during fungal infection. As a result, BbHex1 contributed to fungal virulence. Bioinformatics analysis revealed that promoters of some co-expressed chitinase genes with the BbHex1 promoter shared conserved transcription factors Skn7, Msn2 and Ste12, and CreA-binding motifs, implying co-regulation of those genes with BbHex1. CONCLUSION: These data support a mechanism that the fungal pathogen specifically expresses BbHex1, which is co-expressed with chitinases to modify cell wall for evasion of insect immune recognition and to degrade insect cuticle, and contributes to the fungal virulence against insects. © 2024 Society of Chemical Industry.

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.

Robust Gradient Hydrogel-Loaded Nanofiber Fleshy Artificial Skin Via A Coupled Microfluidic Electrospinning-Reactive Coating Strategy.

Skin regeneration attracts tremendous interest due to the important role of skin for human protection and beauty. Thus, methods allowing artificial skin to be carried out in a controllable fashion are potentially important for wound healing, which involves an intersection of materials, medicine, biology, and other disciplines. Herein, aiming at a new general methodology for fleshy materials, a new hydrogel-loaded hydrophobic-hydrophilic nanofiber fleshy artificial skin is designed and fabricated. The gradient hydrogel-loaded nanofiber artificial skin integrates both advantages of nanofiber and hydrogel, exhibiting fleshy feature (comparability to real skin in terms of appearance, texture, and function), excellent air permeability, compatibility, and good mechanical and antibacterial property. Interestingly, the efficient transport channels are formed throughout the hydrogel-loaded nanofiber structure, which is beneficial for water absorption and transfer. These advantages enable the establishment of a moist and favorable microenvironment; thus, greatly accelerating wound healing process. This work couples microfluidic electrospinning with reactive coating technique, which is in favor of material design and fabrication with controllable and uniform structures. The hydrogel-loaded nanofiber fleshy artificial skin shows comparability to real skin in terms of beauty, texture, and function, which would definitely provide new opportunities for the further optimization and upgrading of artificial skin.

Efficient CsPbBr3 Perovskite Light-Emitting Diodes via Novel Multi-Step Ligand Exchange Strategy Based on Zwitterionic Molecules.

Perovskite nanocrystals have absorbed increasing interest, especially in the field of optoelectronics, owing to their unique characteristics, including their tunable luminescence range, robust solution processability, facile synthesis, and so on. However, in practice, due to the inherent instability of the traditional long-chain insulating ligands surrounding perovskite quantum dots (PeQDs), the performance of the as-fabricated QLED is relatively disappointing. Herein, the zwitterion 3-(decyldimethylammonio)propanesulfonate (DLPS) with the capability of double passivating perovskite quantum dots could effectively replace the original long-chain ligand simply through a multistep post-treatment strategy to finally inhibit the formation of defects. It was indicated from theexperimental results that the DLPS, as one type of ligand with the bimolecular ion, was very adavntageous in replacing long-chain ligands and further suppressing the formation of defects. Finally, the perovskite quantum dots with greatly enhanced PLQY as high as 98% were effectively achieved. Additionally, the colloidal stability of the corresponding PeQDs has been significantly enhanced, and a transparent colloidal solution was obtained after 45 days under ambient conditions. Finally, the as-fabricated QLEDs based on the ligand-exchanged PeQDs exhibited a maximum brightness of 9464 cd/m2 and an EQE of 12.17%.

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.

ACE2 improves endothelial cell function and reduces acute lung injury by downregulating FAK expression.

Endothelial cell (EC) barrier dysfunction and increased adhesion of immune inflammatory cells to ECs crucially contribute to acute lung injury (ALI). Angiotensin-converting enzyme 2 (ACE2) is an essential regulator of the renin-angiotensin system (RAS) and exerts characteristic vasodilatory and anti-inflammatory effects. SARS-COV-2 infects the lungs by binding to ACE2, which can lead to dysregulation of ACE2 expression, further leading to ALI with predominantly vascular inflammation and eventually to more severe acute respiratory distress syndrome (ARDS). Therefore, restoration of ACE2 expression represents a valuable therapeutic approach for SARS-COV-2-related ALI/ARDS. In this study, we used polyinosinic-polycytidylic acid (Poly(I:C)), a double-stranded RNA analog, to construct a mouse ALI model that mimics virus infection. After Poly(I:C) exposure, ACE2 was downregulated in mouse lung tissues and in cultured ECs. Treatment with DIZE, an ACE2-activating compound, upregulated ACE2 expression and relieved ALI in mice. DIZE also improved barrier function and reduced the number of THP-1 monocytes adhering to cultured ECs. Focal adhesion kinase (FAK) and phosphorylated FAK (p-FAK) levels were increased in lung tissues of ALI mice as well as in Poly(I:C)-treated ECs in vitro. Both DIZE and the FAK inhibitor PF562271 decreased FAK/p-FAK expression in both ALI models, attenuating ALI severity in vivo and increasing barrier function and reducing monocyte adhesion in cultured ECs. Furthermore, in vivo experiments using ANG 1-7 and the MAS inhibitor A779 corroborated that DIZE-mediated ACE2 activation stimulated the activity of the ANG 1-7/MAS axis, which inhibited FAK/p-FAK expression in the mouse lung. These findings provide further evidence that activation of ACE2 in ECs may be a valuable therapeutic strategy for ALI.

Lipid droplets' functional protein caveolin-2 is associated with lipid metabolism-related molecule FABP5 and EMT marker E-cadherin in oral epithelial dysplasia.

AIMS: To explore the accumulation of lipid droplets (LDs) and its relationship with lipid metabolism, and epithelial-mesenchymal transition (EMT) in the carcinogenesis processes in the oral cavity. METHODS: LDs were stained by oil red O. Forty-eight oral squamous cell carcinomas (OSCC), 78 oral potentially malignant disorders (OPMDs) and 25 normal tissue sections were included to explore the LDs surface protein caveolin-2 and perilipin-3, lipid metabolism-related molecule FABP5 and EMT biomarker E-cadherin expression by immunohistochemical staining. RESULTS: The accumulation of LDs was observed in OPMDs and OSCCs compared with normal tissues (p<0.05). In general, an increasing trend of caveolin-2, perilipin-3 and FABP5 expression was detected from the normal to OPMDs to OSCC groups (p<0.05). Additionally, caveolin-2, perilipin-3 and FABP5 expression were positively correlated with epithelial dysplasia in OPMDs, whereas E-cadherin positivity was negatively correlated with histopathological grade in both OPMDs and OSCC, respectively. A negative correlation of caveolin-2 (p<0.01, r =-0.1739), and FABP5 (p<0.01, r =-0.1880) with E-cadherin expression was detected. The caveolin-2 (p<0.0001, r=0.2641) and perilipin-3 (p<0.05, r=0.1408) staining was positively correlated with FABP5. Increased caveolin-2 expression was related to local recurrence and worse disease-free survival (p<0.05). CONCLUSION: In the oral epithelial carcinogenesis process, LDs begin to accumulate early in the precancerous stage. LDs may be the regulator of FABP5-associated lipid metabolism and may closely related to the process of EMT; caveolin-2 could be the main functional protein.

The Potential of Lipid Droplet-associated Genes as Diagnostic and Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma.

OBJECTIVE: The role of lipid droplets (LDs) and lipid droplet-associated genes (LD-AGs) remains unclear in head and neck squamous cell carcinoma (HNSCC). This study aimed to investigate LDs in HNSCC and identify LD-AGs essential for the diagnosis and prognosis of HNSCC patients. METHODS: The LDs in the HNSCC and normal cell lines were stained with oil red O. Bioinformatic analysis was used to find LD-AGs in HNSCC that had diagnostic and prognostic significance. RESULTS: LDs accumulation was increased in HNSCC cell lines compared with normal cell lines (P<0.05). Fifty-three differentially expressed genes, including 34 upregulated and 19 downregulated, were found in HNSCC based on the TCGA platform (P<0.05). Then, 53 genes were proved to be functionally enriched in lipid metabolism and LDs. Among them, with an AUC value > 0.7, 34 genes demonstrated a high predictive power. Six genes (AUP1, CAV1, CAV2, CAVIN1, HILPDA, and SQLE) out of 34 diagnostic genes were linked to overall survival in patients with HNSCC (P<0.05). The significant prognostic factors AUP1, CAV1, CAV2, and SQLE were further identified using the univariate and multivariate cox proportional hazard models (P<0.05). The protein expression of CAV2 and SQLE was significantly increased in the HNSCC tissue compared to normal tissues (P<0.05). Finally, the knockdown of the four LD-AGs decreased LDs accumulation, respectively. CONCLUSIONS: Increased LDs accumulation was a hallmark of HNSCC, and AUP1, CAV1, CAV2, and SQLE were discovered as differentially expressed LD-AGs with diagnostic and prognostic potential in HNSCC.

Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network.

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data's temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

Lake eutrophication prediction based on improved MIMO-DD-3Q Learning.

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

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.

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.

Location estimation based on feature mode matching with deep network models.

Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion.

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.

Data augmentation based semi-supervised method to improve COVID-19 CT classification.

The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.

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

Objective: Many studies have explored the roles of microRNAs (miRs) in myocardial ischemia/reperfusion injury (MI/RI), while the function of miR-214-3p in MI/RI remained obscure. This study aims to unravel the regulatory mechanism of miR-214-3p in MI/RI via targeting histone demethylase lysine demethylase 3A (KDM3A). Methods: MI/RI rat model was established by ligating the left anterior descending coronary artery. MiR-214-3p and KDM3A expression in myocardial tissues of MI/RI rats was examined. Then, the serum oxidative stress factors, inflammatory factors, pathological changes of myocardial tissues, cardiomyocyte apoptosis, and fibrosis of myocardial tissues were detected in MI/RI rats intervening with miR-214-3p or KDM3A expression. The targeting relation between miR-214-3p and KDM3A was validated. Results: MiR-214-3p was low-expressed while KDM3A was high-expressed in MI/RI rat model. Up-regulated miR-214-3p or down-regulated KDM3A protected against MI/RI via mitigating serum oxidative response, reducing the levels of inflammatory factors, alleviating the pathological changes of myocardial tissues, and decreasing cardiomyocyte apoptosis and fibrosis of myocardial tissue. KDM3A amplification reversed the therapeutic effects of elevated miR-214-3p on MI/RI. KDM3A was targeted by miR-214-3p. Conclusion: miR-214-3p hinders cardiomyocyte apoptosis and myocardial injury in MI/RI rats via regulating KDM3A. Thus, miR-214-3p may function as a potential candidate for MI/RI treatment.

Molecular Regulation of Flexible Composite Solid-Solid Phase Change Materials with Controllable Isotropic Thermal Conductivity for Thermal Energy Storage.

In recent years, graphene has been introduced into phase change materials (PCMs) to improve thermal conductivity to enhance the heat transfer efficiency in thermal energy storage. However, graphenes tend to aggregate in PCMs, leading to the low thermal conductivity efficient enhancement (TCEE), anisotropic thermal conductivity, and deterioration of mechanical performance of PCMs. In this work, we fabricated biomimetic thermally conductive solid-solid PCMs (SSPCMs) by facile blending of the graphene into well-designed polyurethane SSPCMs, in which the graphene established a controllable and highly efficient isotropic thermally conductive pathway based on the π-π stacking between the graphene and the polymer aromatic ring segment. The as-fabricated SSPCMs showed high TCEE (156.78%), excellent flexibility (328% elongation at break), high enthalpy value (>101 J/g), and solid-solid phase transition properties, under 2% loading of graphene. The proportion of in-plane to through-plane thermal conductivity can be adjusted by an elaborate design of the aromatic ring segment in polyurethane SSPCMs. We further demonstrated mechanical flexibility and photothermal property of the composites to reveal their potential in practical 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.

Catalytic Oxidation of n-Decane, n-Hexane, and Propane over Pt/CeO2 Catalysts.

Pt species with different chemical states and structures were supported on CeO2 by solution reduction (Pt/CeO2-SR) and wet impregnation (Pt/CeO2-WI) and investigated in catalytic oxidation of n-decane (C10H22), n-hexane (C6H14), and propane (C3H8). Characterization by X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, H2-temperature programming reduction, and oxygen temperature-programmed desorption showed that Pt0 and Pt2+ existed on Pt nanoparticles of the Pt/CeO2-SR sample, which promoted redox, oxygen adsorption, and activation. On Pt/CeO2-WI, Pt species were highly dispersed on CeO2 as the Pt-O-Ce structure, in which surface oxygen decreased significantly. The Pt/CeO2-SR catalyst presents high activity in oxidation of C10H22 with a rate of 0.164 µmol min-1 m-2 at 150 °C. The rate increased with oxygen concentration. Moreover, Pt/CeO2-SR presents high stability on feed stream containing 1000 ppm C10H22 at gas hour space velocity = 30,000 h-1 as low as 150 °C for 1800 min. The low activity and stability of Pt/CeO2-WI were probably related to its low availability of surface oxygen. In situ Fourier transform infrared results showed that the adsorption of alkane occurred through the interaction with Ce-OH. The adsorption of C6H14 and C3H8 was much weaker than that of C10H22, which resulted in the decrease in activity for C6H14 and C3H8 oxidation of Pt/CeO2 catalysts.

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.