Study on the susceptibility of bovine coronavirus to BALB/c mice.

There are no other bovine coronavirus (BCoV) infection models except calves, which makes efficacy evaluation of vaccines and pathogenic mechanism research of BCoV inconvenient owing to their high value and inconvenient operation. This study aimed to establish a mouse model of BCoV infection. BCoV was used to infect 4-week-old male BALB/c mice and the optimal infection conditions were screened, including the following infection routes: gavage, intraperitoneal injection, and tail vein injection at doses of 1 × 108 TCID50, 2 × 108 TCID50 and 4 × 108 TCID50. Using the optimal infection conditions, BALB/c mice were infected with BCoV, and their body weight, blood routine, inflammatory factors, autopsy, virus distribution, and viral load were measured at 1, 3, 5, and 7 days after infection. The results showed that the optimal conditions for infecting BALB/c mice with BCoV HLJ-325 strain were continuous oral gavage for 3 days with a dose of 4 × 108 TCID50. On the 7th day after infection, there was significant extensive consolidation of the lungs and thinning of the colon wall. Significant inflammation was observed in various organs, especially in the colon and alveoli, where a large number of inflammatory cells infiltrate. Both BCoV Ag and nucleic acid are positive in visceral organs. The viral load in the colon and lungs was significantly higher than that in the other organs (p < 0.001). BCoV-infected mice showed a decreasing trend in body weight starting from day 5, and there was a significant difference compared to the control group on days 6 and 7 (p < 0.001). The total number of white blood cells and lymphocytes began to decrease and was significantly lower than that in the control group 24 h after infection (p < 0.001), and gradually returned to the control level. The cytokine TNF-α, IL-1ß, and IL-6 showed an increasing trend, significantly higher than the control group on day 5 and 7 (p < 0.001). These results indicate that the BCoV HLJ-325 strain can infect BALB/c mice and cause inflammatory reactions and tissue lesions. The most significant effect was observed on the seventh day after infection with a dose of 4 × 108 TCID50 and three consecutive gavages. This study established, for the first time, a BALB/c mouse model of BCoV infection, providing a technical means for evaluating the immune efficacy of BCoV vaccines and studying their pathogenic mechanisms.

Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study.

OBJECTIVES: Accurate preoperative prediction of the pathological grade of clear cell renal cell carcinoma (ccRCC) is crucial for optimal treatment planning and patient outcomes. This study aims to develop and validate a deep-learning (DL) algorithm to automatically segment renal tumours, kidneys, and perirenal adipose tissue (PRAT) from computed tomography (CT) images and extract radiomics features to predict the pathological grade of ccRCC. METHODS: In this cross-ethnic retrospective study, a total of 614 patients were divided into a training set (383 patients from the local hospital), an internal validation set (88 patients from the local hospital), and an external validation set (143 patients from the public dataset). A two-dimensional TransUNet-based DL model combined with the train-while-annotation method was trained for automatic volumetric segmentation of renal tumours, kidneys, and visceral adipose tissue (VAT) on images from two groups of datasets. PRAT was extracted using a dilation algorithm by calculating voxels of VAT surrounding the kidneys. Radiomics features were subsequently extracted from three regions of interest of CT images, adopting multiple filtering strategies. The least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and the support vector machine (SVM) for developing the pathological grading model. Ensemble learning was used for imbalanced data classification. Performance evaluation included the Dice coefficient for segmentation and metrics such as accuracy and area under curve (AUC) for classification. The WHO/International Society of Urological Pathology (ISUP) grading models were finally interpreted and visualized using the SHapley Additive exPlanations (SHAP) method. RESULTS: For automatic segmentation, the mean Dice coefficient achieved 0.836 for renal tumours and 0.967 for VAT on the internal validation dataset. For WHO/ISUP grading, a model built with features of PRAT achieved a moderate AUC of 0.711 (95% CI, 0.604-0.802) in the internal validation set, coupled with a sensitivity of 0.400 and a specificity of 0.781. While model built with combination features of the renal tumour, kidney, and PRAT showed an AUC of 0.814 (95% CI, 0.717-0.889) in the internal validation set, with a sensitivity of 0.800 and a specificity of 0.753, significantly higher than the model built with features solely from tumour lesion (0.760; 95% CI, 0.657-0.845), with a sensitivity of 0.533 and a specificity of 0.767. CONCLUSION: Automated segmentation of kidneys and visceral adipose tissue (VAT) through TransUNet combined with a conventional image morphology processing algorithm offers a standardized approach to extract PRAT with high reproducibility. The radiomics features of PRAT and tumour lesions, along with machine learning, accurately predict the pathological grade of ccRCC and reveal the incremental significance of PRAT in this prediction.

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

CRISPR/Cas9-mediated knockout of Bsr-d1 enhances the blast resistance of rice in Northeast China.

KEY MESSAGE: The blast resistance allele of OsBsr-d1 does not exist in most japonica rice varieties of Jilin Province in China. The development of Bsr-d1 knockout mutants via CRISPR/Cas9 enhances broad-spectrum resistance to rice blast in Northeast China. Rice blast is a global disease that has a significant negative impact on rice yield and quality. Due to the complexity and variability of the physiological races of rice blast, controlling rice blast is challenging in agricultural production. Bsr-d1, a negative transcription factor that confers broad-spectrum resistance to rice blast, was identified in the indica rice cultivar Digu; however, its biological function in japonica rice varieties is still unclear. In this study, we analyzed the blast resistance allele of Bsr-d1 in a total of 256 japonica rice varieties from Jilin Province in Northeast China and found that this allele was not present in these varieties. Therefore, we generated Bsr-d1 knockout mutants via the CRISPR/Cas9 system using the japonica rice variety Jigeng88 (JG88) as a recipient variety. Compared with those of the wild-type JG88, the homozygous Bsr-d1 mutant lines KO#1 and KO#2 showed enhanced leaf blast resistance at the seedling stage to several Magnaporthe oryzae (M. oryzae) races collected from Jilin Province in Northeast China. Physiological and biochemical indices revealed that the homozygous mutant lines produced more hydrogen peroxide than did JG88 plants when infected with M. oryzae. Comparative RNA-seq revealed that the DEGs were mainly involved in the synthesis of amide compounds, zinc finger proteins, transmembrane transporters, etc. In summary, our results indicate that the development of Bsr-d1 knockout mutants through CRISPR/Cas9 can enhance the broad-spectrum resistance of rice in Northeast China to rice blast. This study not only provides a theoretical basis for disease resistance breeding involving the Bsr-d1 gene in Northeast China, but also provides new germplasm resources for disease-resistance rice breeding.

Bimetallic Ag/Au nanoclusters encapsulated in ZIF-8 framework: A novel strategy for ratiometric fluorescence detection of doxycycline in food.

This study successfully encapsulated the Ag+-doped Au nanoclusters (Ag/AuNCs) within the ZIF-8 framework to construct a novel Ag/AuNCs@ZIF-8 ratiometric fluorescent probe for the antibiotic doxycycline (DOX) detection. The incorporation of Ag+ contributed to the fluorescence enhancement of the nanoclusters through the "silver effect", consequently improving the stability of the developed bimetallic Ag/AuNCs. Furthermore, the encapsulation of bimetallic Ag/AuNCs within the ZIF-8 framework restricted their intramolecular vibrations, resulting in further amplification of fluorescence intensity at 595 nm. The ZIF-8 also sensitized the restoration of DOX green fluorescence at 515 nm. Within the concentration range of 0.001-20 µg mL-1, the ratio of fluorescence intensity (F515/F595) exhibited a favorable linearity for DOX concentration, with a detection limit of 36.8 ng mL-1. This ratiometric fluorescence approach had the promising potential for accurate and efficient quantitative detection of DOX residue in food and served as a valuable reference for rapid monitoring of food contaminants.

STYXL1 regulates CCT complex assembly and flagellar tubulin folding in sperm formation.

Tubulin-based microtubule is a core component of flagella axoneme and essential for sperm motility and male fertility. Structural components of the axoneme have been well explored. However, how tubulin folding is regulated in sperm flagella formation is still largely unknown. Here, we report a germ cell-specific co-factor of CCT complex, STYXL1. Deletion of Styxl1 results in male infertility and microtubule defects of sperm flagella. Proteomic analysis of Styxl1-/- sperm reveals abnormal downregulation of flagella-related proteins including tubulins. The N-terminal rhodanese-like domain of STYXL1 is important for its interactions with CCT complex subunits, CCT1, CCT6 and CCT7. Styxl1 deletion leads to defects in CCT complex assembly and tubulin polymerization. Collectively, our findings reveal the vital roles of germ cell-specific STYXL1 in CCT-facilitated tubulin folding and sperm flagella development.

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.

BACKGROUND: Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. METHODS: Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. RESULTS: SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). CONCLUSIONS: NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Research progress in the role of non-coding RNAs and embryo implantation. / éžç¼–ç RNAåœ¨èƒšèƒŽç€åºŠä¸­ä½œç”¨çš„ç ”ç©¶è¿›å±•.

Non-coding RNA (ncRNA) refers to RNA that lack the ability to encode protein. Based on their distinct biological characteristics, ncRNA are mainly classified into microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). NcRNA plays a crucial regulatory role in various biological processes. Pregnancy is a highly intricate physiological process that requires successful completion of multiple steps. Embryo implantation, as a key event of pregnancy, which is regulated by numerous factors, including embryo development, endometrial changes, and the maternal-embryo crosstalk. A diverse array of regulatory mechanisms ensures the accomplishment of embryo localization, adhesion, invasion, and ultimately successful implantation. MiRNA, lncRNA, and circRNA are extensively studied ncRNA molecules at present, which play an important role in the physiological and pathological processes associated with embryo implantation through targeting and regulating the expression of multiple cytokine and genes. With advancements in molecular biology technology, it is anticipated that ncRNA will contribute to the prediction and enhancement of clinical pregnancy outcomes from a molecular perspective.

Effect of N-Carbamylglutamate Supplementation on Growth Performance, Jejunal Morphology, Amino Acid Transporters, and Antioxidant Ability of Weaned Pigs.

Weaning is an important period that affects the performance of piglets. However, the regulation of dietary amino acid levels is considered to be an effective way to alleviate the weaning stress of piglets. N-carbamylglutamate (NCG) plays an important role in improving the growth performance and antioxidant capacity of animals. A total of 36 weaned piglets were randomly assigned to two treatment groups, a control group (CON) and a 500 mg/kg NCG group (NCG), and the experiment lasted for 28 days. The results show that the NCG treatment group showed an increased 0-28 days average weight gain and average daily feed intake, and also increased contents of GLU and HDL, and lower SUN in serum, and an upregulation of the expression of the amino acid transporters SNAT2, EAAC1, SLC3A1, and SLC3A2 mRNA in the jejunum (p < 0.05), as well as an increased villus length and VH:CD ratio, and claudin-1, occludin, and ZO-1 mRNA expression in the jejunum (p < 0.05). The NCG treatment group showed an increased content of GSH-Px in serum and T-AOC and SOD in the jejunum, and a lower content of MDA (p < 0.05); and the upregulation of the mRNA expression related to antioxidant enzymes (CAT, SOD1, Gpx4, GCLC, GCLM and Nrf2, AhR, CYP1A1) in the jejunal mucosa (p < 0.05). In addition, compared with the control group, the NCG treatment group saw an upregulation in the mRNA expression of IL-10 and a decrease in the expression of IL-1ß and IL-4 in the jejunal mucosa (p < 0.05). In summary, the results of this study suggest that NCG improved growth performance and jejunal morphology, improved the jejunal transport of amino acids related to the ornithine cycle, and improved the antioxidant capacity in weaned pigs.

High-sensitivity fiber optic temperature sensor based on CTFBG-FPI and Vernier effect.

A novel high-sensitivity temperature sensor based on a chirped thin-core fiber Bragg grating Fabry-Perot interferometer (CTFBG-FPI) and the Vernier effect is proposed and demonstrated. With femtosecond laser direct writing technology, two CTFBG-FPIs with different interferometric cavity lengths are inscribed inside a thin-core fiber to form a Vernier effect system. The two FPIs consist of two pairs of CTFBGs with a full width at half maximum (FWHM) of 66.5 nm staggered in parallel. The interferometric cavity lengths of the two FPIs were designed to be 2 mm and 1.98 mm as the reference arm and sensing arm of the sensor, respectively. The temperature sensitivity of this sensor was measured to be -1.084 nm/°C in a range of 40-90°C. This sensor is expected to play a crucial role in precision temperature measurement applications.

A study on the preoperative risk factors for primary healing failure in the reconstruction of deep sternal wound infection with platelet-rich plasma and negative pressure trauma therapy.

Deep sternal wound infection (DSWI) is a relatively complex wound in wound reconstruction surgery. Because plastic surgeons deal with DSWI patients late. The primary healing (healing by first intention) after reconstruction of DSWI is restricted by many preoperative risk factors. The purpose of this study is to explore and analyse the risk factors of primary healing failure in patients with DSWI treated with platelet-rich plasma (PRP) and negative pressure trauma therapy (NPWT). 115 DSWI patients treated with the PRP and NPWT (PRP + NPWT) modality were retrospectively (2013-2021) analysed. They were divided into two groups according to primary healing results after the first PRP + NPWT treatment. Univariate and multivariate analyses were used to compare the data of the two groups to find out the risk factors and their optimal cut-off values were identified by ROC analysis. The primary healing results, debridement history, wound size, sinus, osteomyelitis, renal function, bacterial culture, albumin (ALB), platelet (PLT) between the two groups were significantly different (P < 0.05). Binary logistic regression showed that osteomyelitis, sinus, ALB and PLT were the risk factors affecting primary healing outcomes (P < 0.05). ROC analysis showed that AUC for ALB in the non-primary healing group was 0.743 (95% CI: 0.650-0.836, P < 0.05) and its optimal cutoff value of 31 g/L was associated with primary healing failure with a sensitivity of 96.9% and specificity of 45.1%. AUC for PLT in the non-primary healing group was 0.670 (95% CI: 0.571 ~ 0.770, P < 0.05) its optimal cutoff value of 293 × 109 /L was associated with primary healing failure with a sensitivity of 72.5% and specificity of 56.3%. In the cases included in this study, the success rate of primary healing of DSWI treated with PRP + NPWT was not affected by the most common preoperative risk factors for wound non-union. It is indirectly confirmed that PRP + NPWT is an ideal treatment. However, it should be noted that it will still be adversely affected by sinus osteomyelitis, ALB and PLT. The patients need to be carefully evaluated and corrected before reconstruction.

Purification Process and In Vitro and In Vivo Bioactivity Evaluation of Pectolinarin and Linarin from Cirsium japonicum.

Pectolinarin and linarin are two major flavone O-glycosides of Cirsium japonicum, which has been used for thousands of years in traditional Chinese medicine. Pharmacological research on pectolinarin and linarin is meaningful and necessary. Here, a process for the purification of pectolinarin and linarin from C. japonicum was established using macroporous resin enrichment followed by prep-HPLC separation. The results show the purity of pectolinarin and linarin reached 97.39% and 96.65%, respectively. The in vitro bioactivities result shows the ORAC values of pectolinarin and linarin are 4543 and 1441 µmol TE/g, respectively, meanwhile their inhibition rate of BSA-MGO-derived AGEs is 63.58% and 19.31% at 2 mg/mL, which is 56.03% and 30.73% in the BSA-fructose system, respectively. The COX-2 inhibition rate at 50 µg/mL of linarin and pectolinarin reached 55.35% and 40.40%, respectively. Furthermore, the in vivo bioassay combining of histopathologic evaluation and biochemical analysis of liver glutamic oxaloacetic transaminase, serum creatinine and TNF-α show pectolinarin can alleviate lipopolysaccharide (LPS)-induced acute liver and kidney injury in mice. Metabolomics analysis shows that pectolinarin attenuates LPS-challenged liver and kidney stress through regulating the arachidonic acid metabolism and glutathione synthesis pathways. Collectively, our work presents a solid process for pectolinarin and linarin purification and has discovered a promising natural therapeutic agent-pectolinarin.

Development of fluorescence sensors with copper-based nanoclusters via Förster resonance energy transfer and the quenching effect for vanillin detection.

Two kinds of copper-based metal fluorescent nanoclusters were successfully prepared by the chemical reduction method; one of them (CuNCs) was synthesized by direct reduction of copper sulfate, and the other (CuAuNCs) was synthesized by the stepwise addition of copper salt and chloroauric acid. CuNCs were used to establish the fluorescence resonance energy transfer (FRET) system with neutral red (NR) due to the supramolecular effect of ß-cyclodextrin (ß-CD) modified on the surface of CuNCs. NR could enter the hydrophobic cavity of ß-CD and narrow the distance between CuNCs and NR, which could lead to FRET. Fluorescence was transferred from CuNCs to NR, resulting in amplification of the NR fluorescence signal, which could be used to detect vanillin. In addition, CuAuNCs with strong fluorescence were used as fluorescent probes to detect vanillin through the quenching mechanism. By comparison, the simplicity of CuNC synthesis and the high selectivity of ß-CD made the FRET method more practical, which may provide a new strategy for assaying vanillin.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

An occupational health survey on health utility and occupational diseases in Chinese university staff to inform cost-utility analysis.

Background: The occupational health of university staff bears great social and economic value for which health utility is an indivisible aspect. Utility is also the primary data for the cost-utility analysis of occupational health programs. Health utility and occupational diseases have not been reported for the university staff in China. In the light of "Healthy China," we conducted this study aiming to (1) estimate the health utility of university staff to inform cost-utility analysis and (2) screen and identify potential occupational diseases for this occupation and examine their impacts on health. Methods: An occupational health survey was conducted in a sample of working-age university staff. Participants were interviewed face-to-face using the WHO Health and Work Performance Questionnaire and the European Quality of Life 5 Dimensions (EQ-5D) instrument to measure health conditions and health utility, respectively. The univariate analysis included the t-test, chi-square test, and correlation techniques. Multivariate generalized linear models were applied to evaluate the significance of each health condition when controlling for other factors. Results: The sample (n = 154) had a mean age of 40.65 years and consisted of slightly more women (51.30%). Participants attained a mean (standard deviation) health utility of 0.945 (0.073). The most affected domain was anxiety/depression with 62 (40.26%) participants reporting problems, followed by pain/discomfort which captured 60 (37.66%) staff with problems. Thus, pain and psychologically related conditions were prevalent. Multivariate models identified two conditions that can significantly reduce the health utility. The psychological/emotional conditions were associated with a utility loss of -0.067 (95%CI: -0.089, -0.045). The pain in body parts other than the head, neck, and back reduced the utility by -0.034 (95%CI: -0.055, -0.014). Conclusion: Working-age staff in Chinese universities may have a lower health utility than the general population. Psychological conditions and musculoskeletal pain appear like occupational diseases. With the health utility data available, economic evaluation of cost-utility should follow up to facilitate the implementation of cost-effective programs.

Quasi-solid electrolyte developed on hierarchical rambutan-like γ-AlOOH microspheres with high ionic conductivity for lithium ion batteries.

Upgrading liquid electrolytes with all-solid-state electrolytes (ASEs) or quasi-solid-state electrolytes (QSEs) for solid-state batteries (SBs) have emerged not only to address the intrinsic disadvantages of traditional liquid lithium ion batteries, but also to offer more possibilities for the development of new battery chemistries. In this work, a novel rambutan-like yolk-shell-structured porous γ-AlOOH microsphere with a large specific surface area of 262.92 m2 g-1 was firstly obtained by a simple hydrothermal synthesis route, which was then utilized as a robust framework to assemble QSE via encapsulating abundant liquid electrolyte (LE). The obtained γ-AlOOH-QSE exhibits a high ionic conductivity of 4.0 × 10-3 S cm-1, a large lithium ion transference number (tLi+) of 0.76, as well as a wide electrochemical window of 4.72 V vs. Li/Li+. Moreover, the assembled cell of LiFePO4/γ-AlOOH-QSE/Li could maintain a high specific capacity of 144.4 mA h g-1 even after 120 cycles with almost negligible capacity decay, which could be mainly attributed to the excellent interfacial compatibility, prominent performance in suppressing lithium dendrite growth upon cycling (rigid characteristic), as well as the high ionic conductivity of γ-AlOOH-QSE (intrinsic advantage). This work could not only expand the applications of QSE with cost-effective aluminum-based oxides with facile fabrication strategy, but also will shed light on the construction of SEs with more integrated QSEs and ASEs in the field of advanced energy storage.

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives.

Broiler sounds can provide feedback on their own body condition, to a certain extent. Aiming at the noise in the sound signals collected in broiler farms, research on evaluating the filtering methods for broiler sound signals from multiple perspectives is proposed, and the best performer can be obtained for broiler sound signal filtering. Multiple perspectives include the signal angle and the recognition angle, which are embodied in three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and prediction accuracy. The signal filtering methods used in this study include Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering and Sparse Decomposition using both thirty atoms and fifty atoms. In analysis of the signal angle, Improved Spectral Subtraction based on multi-taper spectrum estimation achieved the highest average SNR of 5.5145 and achieved the smallest average RMSE of 0.0508. In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. These are significantly higher than those obtained by classifiers on data sets established from sound signals filtered by other methods. Further research shows that after removing the starting noise in the sound signal, Wiener filtering achieved the highest average SNR of 5.6108 and a new RMSE of 0.0551. Finally, in comprehensive analysis of both the signal angle and the recognition angle, this research determined that Wiener filtering is the best broiler sound signal filtering method. This research lays the foundation for follow-up research on extracting classification features from high-quality broiler sound signals to realize broiler health monitoring. At the same time, the research results can be popularized and applied to studies on the detection and processing of livestock and poultry sound signals, which has extremely important reference and practical value.

Modulation of the superficial electronic structure via metal-support interaction for H2 evolution over Pd catalysts.

Electronic interactions can radically enhance the performance of supported metal catalysts and are critical for fundamentally understanding the nature of catalysts. However, at the microscopic level, the details of such interactions tuning the electronic properties of the sites on the metal particle's surface and metal-support interface remain obscure. Herein, we found polarized electronic metal-support interaction (pEMSI) in oxide-supported Pd nanoparticles (NPs) describing the enhanced accumulation of electrons at the surface of NPs (superficial Pd δ-) with positive Pd atoms distributed on the interface (interfacial Pd δ+). More superficial Pd δ- species mean stronger pEMSI resulting from the synergistic effect of moderate Pd-oxide interaction, high structural fluxionality and electron transport activity of Pd NPs. The surface Pd δ- species are responsible for improved catalytic performance for H2 evolution from metal hydrides and formates. These extensive insights into the nature of supported-metal NPs may open new avenues for regulating a metal particle's electronic structure precisely and exploiting high-performance catalysts.

Enhanced and sustainable pretreatment for bioconversion and extraction of resveratrol from peanut skin using ultrasound-assisted surfactant aqueous system with microbial consortia immobilized on cellulose.

In this study, the ultrasound-assisted surfactant aqueous system coupled with microbial consortia immobilized by cellulose has been created as an enhanced and sustainable method for the bioconversion and extraction of resveratrol from peanut skin. Based on central composite design, and several single-factor experiments, we derived the optimal bioconversion and extraction system. Microbial consortia consist of Yeast CICC 1912, Aspergillus oryzae 3.951 and Aspergillus niger 3.3148 were chosen to be immobilized using cellulose. Other treatment conditions include concentration of surfactant as 3% (w/v), temperature as 30 °C, time as 36 h, ultrasonic power as 250 W and liquid to solid ratio as 25:1 mL/g. Under these conditions, we achieved a promising yield of resveratrol 96.58 µg/g, which is 4.02 folds compared to the untreated sample. This sustainable and green method not only enhanced the production of resveratrol but also improved the safety and reliability of the bioconversion and extraction process. Our novel method has shown great potential to realize large-scale bioconversion and extraction of bioactive compounds from plant waste.

Simplification of Carbon Bond Mechanism IV (CBM-IV) under Different Initial Conditions by Using Concentration Sensitivity Analysis.

Carbon Bond Mechanism IV (CBM-IV) is a widely used reaction mechanism in which VOCs are grouped according to the molecular structure. In the present study, we applied a sensitivity analysis on the CBM-IV mechanism to clarify the importance of each reaction under two different initial conditions (urban and low-NO scenarios). The reactions that exert minor influence on the reaction system are then screened out from the mechanism, so that a reduced version of the CBM-IV mechanism under specific initial conditions can be obtained. We found that in a typical urban condition, 11 reactions can be removed from the original CBM-IV mechanism, and the deviation is less than 5% between the results using the original CBM-IV mechanism and the reduced mechanism. Moreover, in a low-NO initial condition, two more reactions, both of which are nitrogen-associated reactions, can be screened out from the reaction mechanism, while the accuracy of the simulation is still maintained. It is estimated that the reduction of the CBM-IV mechanism can save 11-14% of the computing time in the calculation of the chemistry in a box model simulation.