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Abstract Triboelectric nanogenerator (TENG) operates on the principle of utilizing contact electrification and electrostatic induction. However, visualization and standardized quantification of surface charges for triboelectric materials remain challenging. Here, we report a surface charge visualization and standardized quantification method using electrostatic surface potential measured by Kevin probe and the iterative regularization strategy. Moreover, a tuning strategy on surface charge is demonstrated based on the corona discharge with a three-electrode design. The long-term stability and dissipation mechanisms of the injected negative or positive charges demonstrate high dependence on deep carrier traps in triboelectric materials. Typically, we achieved a 70-fold enhancement on the output voltage (~135.7 V) for the identical polytetrafluoroethylene (PTFE) based TENG (neg-PTFE/PTFE or posi-PTFE/PTFE triboelectric pair) with stable surface charge density (5% decay after 140 days). The charged PTFE was demonstrated as a robot e-skins for non-contact perception of object geometrics. This work provides valuable tools for surface charge visualization and quantification, giving a new strategy for a deeper understanding of contact electrification.

Abstract Background Deciphering the assembly rules of microbial communities is vital for a mechanistic understanding of the general principles driving microbiome structures and functions. In this study, a null modeling-based framework was implemented to infer the assembly rules of bacterial community in oat silages harvested in southern China starting from the grain-filling stage through to full ripening. Results Most silages displayed “inferior” or “very inferior” fermentation quality. The fermentation qualities of silages tended to further decrease with the delay of harvest. Lactobacillus, Pediococcus, unclassified_f_Enterobacteriaceae, and Hafnia–Obesumbacterium constituted the predominated genera in silages. Delaying harvest increased the proportions of Hafnia–Obesumbacterium. Null model analysis revealed that stochastic processes were the primary contributor to the assembly of rare subcommunity during silage fermentation. The succession of abundant subcommunity was controlled both by stochastic and deterministic processes. Deterministic processes, more specifically, heterogeneous selection, were more prominent in the assembly of abundant bacteria in silages with the delay of harvest. Linear regression analysis indicated the important roles of DM, WSC and pH in the assembly of abundant subcommunity. Conclusion This study, from the ecological perspectives, revealed the ecological processes controlling the bacterial community assembly in silage, providing new insights into the mechanisms underlying the construction of silage bacterial community. Graphical Abstract

Abstract This study combined applied PICRUSt2 and BugBase tools to evaluate the effects of these two strains on the fermentation characteristics, microbial community, potential microbial risk and hygienic quality of whole-crop corn (WCC) silage. Fresh WCC harvested at the dough stage was ensiled with distilled water (CON), Leuconostoc lactis (LS) and Weissella confusa (WA) for 2, 4, 8, 15 and 30 days. After ensiling, all WCC silages presented desirable fermentation with high lactic acid and Lactobacillus proportions, low pH and ammonia nitrogen levels and absent butyric acid. Ensiling decreased the complexity of bacterial co-occurrence networks, and the Lc. lactis and W. confusa inoculation further decreased the complexity. The inoculation of W. confusa suppressed the most pathogenic pathways and related modules associated with zoonosis. In bacterial phenotype predicted analysis, although CON had lower proportions of ‘Potentially pathogenic’ than fresh material, this undesirable phenotype declined to negligible levels via LS and WA inoculation. Even for well-fermented WCC silages, the risk of pathogens remained after 30 days of ensiling. Therefore, WA could be developed as a promising fast start-up inoculant for reducing pathogenic contamination and improving hygienic quality of silage. Graphical Abstract

Operating conditions of film capacitors are complex, and the problem of film insulation failure caused by repetitively pulsed voltage is becoming ever serious. Degradation of the film under repetitively pulsed voltage cannot be accurately evaluated by the average breakdown electric field. In this paper, the effects of pulsed electric field and pulse repetition frequency on the breakdown in biaxially oriented polypropylene (BOPP) films are investigated. Three phases of BOPP degradation are proposed based on the voltage amplitude, i.e., maintenance (M), decline (D), and near-zero (N). Evolution of the BOPP film from degradation to breakdown at different frequencies is presented. Meanwhile, transition of discharge mode and elemental composition of the film are analyzed. Experimental results show continuous heat generation under repetitive microsecond pulses is the dominant factor for degradation of BOPP film. The number of applied pulses and the repetitive stressing time decrease exponentially with increase of frequency. This research can be contributed to the safe and reliable operation of capacitors.

The “The Use of Lactobacillus in Forage Storage and Processing” Special Issue (SI) in Fermentation has brought together groundbreaking research and innovative methodologies in the realm of forage preservation [...]

Oil monitoring plays an important role in early maintenance of mechanical equipment on account of the fact that lubricating oil contains a large amount of wear information. However, due to extreme industrial environment and long-term service, the data history and the sample size of lubricating oil are very limited. Therefore, to address problems due to a lack of oil samples, this paper proposes a new prediction strategy that fuses the domain shifts with uncertainty (DSU) method and long short-term memory (LSTM) method. The proposed DSU-LSTM model combines the advantages of the DSU model, such as increasing data diversity and uncertainty, reducing the impact of independent or identical domains on neural network training, and mitigating domain changes between different oil data histories, with the advantages of LSTM in predicting time series, thereby improving prediction capability. To validate the proposed method, a case study with real lubricating oil data is conducted, and comparisons are given by calculating the root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) with LSTM, support vector machine (SVM), and DSU-SVM models. The results illustrate the effectiveness of the proposed DSU-LSTM method for lubricating oil, and the robustness of the prediction model can be improved as well.

This work aimed to assess microbial inoculants (Lactiplantibacillus plantarum and Lentilactobacillus buchneri), chemical additives (natamycin and hexanoic acid), and their combination on fermentation characteristics and aerobic stability in total mixed ration (TMR) silage. The TMR consisted of 30% water bamboo shell (WBS), 10% alfalfa, 20% rice straw, and 40% concentrate. There were six treatments as follows: (1) deionized water (control, CON). (2) lactic acid bacteria (Lactiplantibacillus plantarum + Lentilactobacillus buchneri; LPB, 1 × 106 cfu/g FW). (3) natamycin (NT, 0.02 g/kg FW). (4) hexanoic acid (HA, 0.02 g/kg FW). (5) lactic acid bacteria + natamycin (SLNT, 0.02 g/kg FW). (6) lactic acid bacteria + hexanoic acid (SLHA, 0.02 g/kg FW). After fermentation, laboratory silos (10 L) were opened to assess fermentation quality, followed by a 6-day aerobic stability test. The results showed that all silages were well fermented with high lactic acid (LA) content, low ammonia nitrogen (NH3-N), and negligible butyric acid (BA) levels. Among all silages, SLNT silage exhibited the greatest LA, acetic acid (AA) levels, LAB counts, and the lowest pH and NH3-N. For aerobic stability, all additives significantly (p < 0.05) enhanced aerobic stability, delayed (p < 0.05) the decrease in LA and water-soluble carbohydrates (WSC) and the increase in pH, and significantly (p < 0.05) minimized yeast proliferation. The SLNT silage showed the best aerobic stability, with SLHA, NT, HA, and LPB following. In conclusion, SLNT is recommended as the optimal additive in improving the fermentation quality and aerobic stability of TMR silage, with SLHA, NT, HA, and LPB following.

Silage quality and aerobic stability are the key factors affecting the utilization efficiency of silage feed, and ensiling density stands as the fundamental principle of silage making. The experiment presented here evaluates the effects of ensiling density on the silage quality and aerobic stability of alfalfa silage. In this experiment, alfalfa was harvested, wilted, chopped, and subsequently packed into 10 L laboratory silos. The ensiling densities were set to 800 g/L, 700 g/L, and 600 g/L, respectively, with three replicates in each group. Sampling and analysis were carried out at 45 days of silage and 8 days of aerobic exposure. The results showed that ensiling density significantly (p < 0.05) affected the content of ammonia nitrogen, Flieg score, the counts of yeast, and mold. After 45 days of ensiling, 800 g/L silage had the highest contents of dry matter, water-soluble carbohydrates, crude protein, lactic acid, and total organic acids, and the lowest pH and ammonia nitrogen compared to the 700 g/L and 600 g/L silage (p < 0.05). The Flieg score of 800 g/L silage was higher than those of the 700 g/L and 600 g/L silage. The counts of yeast and mold of 800 g/L silage was significantly lower than those of the 700 g/L and 600 g/L silage. During aerobic exposure, a consistent decrease in lactic acid and an increase in pH were observed among all silages. The aerobic stability of 800 g/L silage (156 h) was significantly higher than that in the 700 g/L (136 h) and 600 g/L silage (111 h). It was suggested that the increasing ensiling density above 800 g/L was an effective method to improve both the silage quality and the aerobic stability of alfalfa silage.

This study proposes a novel model that integrates the generative adversarial network (GAN) with the value at risk (VaR) measuring method. The objective is to investigate the efficacy of the VaR method in addressing the issue of excessive financialization in enterprises. Firstly, the related concepts and calculation principles of VaR in the financial field are explained, and the autoregressive conditional heteroscedastic (ARCH) family-based VaR calculation method and the basic structure of GAN under the deep learning are introduced. Then, the GAN algorithm is employed to optimize and train the initialization network, transformation network, and structure network of the GAN algorithm. Finally, the optimized GAN is applied to the VaR measurement of 300 stocks in Shanghai and Shenzhen stock market. GAN demonstrates the ability to handle unbalanced data samples, sample minority data, and fit the overall distribution of minority samples. GAN introduces a groundbreaking method for data processing, and its integration with manual efforts yields significant improvements in practical applications. Moreover, (JAN demonstrates a positive impact on data set training, offering reliable potential for advancement and serving as a valuable point of reference. In conclusion, the combination of GAN under deep learning with VaR showcases a dependable practicability in assessing the risks associated with excessive financialization. [ABSTRACT FROM AUTHOR]

Objective(s): Wnt5a, which regulates the activities of osteoblasts and osteoclasts, is reportedly overexpressed in osteoarthritis (OA) tissues. The purpose of this study was to elucidate its role in the development of OA by deleting Wnt5a in osteocalcin (OCN)-expressing cells. Materials and Methods: Knee OA was induced by anterior cruciate ligament transection (ACLT) in OCN-Cre;Wnt5afl/fl knockout (Wnt5a-cKO) mice and control littermates. Eight weeks after surgery, histological changes, cell apoptosis, and matrix metabolism of cartilage were evaluated by toluidine blue, TUNEL staining, and im-immunohistochemistry analyses, respectively. In addition, the subchondral bone microarchitecture of mice was examined by micro-computed tomography (micro-CT). Results: Histological scores show substantial cartilage degeneration occurred in ACLT knees, coupled with decreased collagen type II expression and enhanced matrix metalloproteinase 13 expression, as well as higher proportions of apoptotic cells. Micro-CT results show that ACLT resulted in decreased bone mineral density, bone volume/trabecular volume, trabecular number, and structure model index of subchondral bones in both Wnt5a-cKO and control littermates; although Wnt5a-cKO mice display lower BMD and BV/TV values, no significant difference was observed between Wnt5a-cKO and control mice for any of these values. Conclusion: Our findings indicate that Wnt5a deficiency in OCN-expressing cells could not prevent an osteoarthritic phenotype in a mouse model of post-traumatic OA. [ABSTRACT FROM AUTHOR]

Background: CD276 is a promising immune checkpoint molecule with significant therapeutic potential. Several clinical trials are currently investigating CD276-targeted therapies. Purpose: This study aims to assess the prognostic significance of CD276 expression levels and to predict its expression using a radiomic approach in breast cancer (BC). Methods: A cohort of 840 patients diagnosed with BC from The Cancer Genome Atlas was included in this study. The Cancer Imaging Archive provided 98 magnetic resonance imaging (MRI) scans, which were randomly allocated to training and validation datasets in a 7:3 ratio. The association between CD276 expression and patient survival was assessed using Cox regression analysis. Feature selection was performed using the maximum relevance minimum redundancy algorithm and recursive feature elimination. Subsequently, support vector machine (SVM) and logistic regression (LR) models were constructed to predict CD276 expression. Results: The expression of CD276 was found to be elevated in BC. It was an independent risk factor for overall survival (hazard ratio = 1.579, 95 % CI: 1.054–2.366). There were eight radiomic features selected in total. In both the training and validation subsets, the SVM and LR models demonstrated favorable predictive abilities with AUC values of 0.744 and 0.740 for the SVM model and 0.742 and 0.735 for the LR model. These results indicate that the radiomic models efficiently differentiate the CD276 expression status. Conclusions: CD276 expression levels can have an impact on cancer prognosis. The MRI-based radiomic signature described in this study can discriminate the CD276 expression status.

Background and purpose: For patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer, trastuzumab treatment can prolong the overall survival and significantly improve the prognosis of patients. However, the reference original research trastuzumab (Herceptin®) is more expensive. Biosimilars have comparable efficacy and safety profiles while increasing patient access to treatment. This clinical trial aimed to evaluate the efficacy, pharmacokinetics, safety and immunogenicity of the trastuzumab biosimilar AK-HER2 compared to trastuzumab (Herceptin®) in patients with HER2-positive metastatic breast cancer. Methods: This multi-center, randomised, double-blind phase Ⅲ clinical trial was conducted in 43 subcenters in China. This study complied with the research protocol, the ethical principles stated in the Declaration of Helsinki and the quality management standards for drug clinical trials. It was approved by the hospital's medical ethics committee. The clinical trial registration agency is the State Food and Drug Administration (clinical trial approval number: 2015L04224; clinical trial registration number: CTR20170516). Written informed consent was obtained from subjects before enrollment. Enrolled patients were randomly assigned to the AK-HER2 group and the control group, respectively receiving AK-HER2 or trastuzumab (initial loading dose 8 mg/kg, maintenance dose 6 mg/kg, every 3 weeks as a treatment cycle, total treatment time is 16 cycles) in combination with docetaxel (75 mg/m2, treatment duration is at least 9 cycles). The primary endpoint of this clinical trial was the objective response rate (ORR9) between the AK-HER2 group and the control group in the 9th cycle. Secondary efficacy endpoints included ORR16, disease control rate (DCR), clinical benefit rate (CBR), progression-free survival (PFS) and 1-year survival rate. In this study, 100 subjects (AK-HER2 group to control group=1:1) were randomly selected for blood sample collection after the 6th cycle of medication, The collection time points were 45 minutes after infusion (the end of administration), 4, 8, 24, 72, 120, 168, 336, and 504 hours after the end of administration. After collection, blood samples were analyzed by PK parameter set (PKPS). Other evaluation parameters included safety and immunogenicity assessment. Results: A total of 550 patients with HER2-positive metastatic breast cancer were enrolled in this clinical trial between Sep. 2017 and Mar. 2021. In the AK-HER2 group (n=237), 129 subjects in the experimental group achieved complete response (CR) or partial response (PR), and the ORR9 was 54.4%. There were 134 subjects in the control group (n=241) who achieved CR or PR, and the ORR9 was 55.6%. The ORR9 ratio between the AK-HER2 group and the control group was 97.9% [90% confidence interval (CI): 85.4%-112.2%, P=0.784], which was not statistically significant. In all secondary efficacy endpoints, no statistically significant differences were observed between the two groups. We conducted a mean ratio analysis of pharmacokinetics (PK) parameters between the AK-HER2 group and the control group, and the results suggested that the pharmacokinetic characteristics of the two drugs are similar. The incidence of treatment emergent adverse event (TEAE) leading to drug reduction or suspension during trastuzumab treatment was 3.6% (10 cases) in the AK-HER2 group and 8.1% (22 cases) in the control group. There was statistically significant difference between the two groups (P=0.027). The incidence rate was significantly lower in the AK-HER2 group than in the control group, and there was no statistically significant difference among the other groups. The differences in the positive rates of anti-drug antibodies (ADA) and neutralizing antibodies (NAB) between groups were of no statistical significance (P=0.385 and P=0.752). Conclusion: In patients with HER2-positive metastatic breast cancer, AK-HER2 was comparable to the trastuzumab (Herceptin®) in terms of drug efficacy, pharmacokinetics, safety and immunogenicity.

A tumor-targeting fluorescent probe has attracted increasing interest in fluorescent imaging for the noninvasive detection of cancers in recent years. Sulfonamide-containing naphthalimide derivatives (SN-2NI, SD-NI) were synthesized by the incorporation of N-butyl-4-ethyldiamino-1,8-naphthalene imide (NI) into sulfonamide (SN) and sulfadiazine (SD) as the tumor-targeting groups, respectively. These derivatives were further characterized by mass spectrometry (MS), nuclear magnetic resonance spectroscopy (1H NMR), Fourier transform infrared spectroscopy (FT-IR), ultraviolet–visible spectroscopy (UV), and a fluorescence assay. In vitro properties, including cell cytotoxicity and the cell uptake of tumor cells, were also evaluated. Sulfonamide-containing naphthalimide derivatives possessed low cell cytotoxicity to B16F10 melanoma cells. Moreover, SN-2NI and SD-NI can be taken up highly by B16F10 cells and then achieve good green fluorescent images in B16F10 cells. Therefore, sulfonamide-containing naphthalimide derivatives can be considered to be the potential probes used to target fluorescent imaging in tumors.

Background: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU)., Study Design: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis., Results: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96., Conclusions: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

BACKGROUND Hypertriglyceridemia-induced acute pancreatitis (HTG-AP), representing 10% of all acute pancreatitis cases, is characterized by younger onset age and more severe progression, often leading to higher ICU admission rates. This condition poses a significant challenge due to its rapid progression and the potential for severe complications, including multiple organ failure. HTG-AP is distinct from other forms of pancreatitis, such as those caused by cholelithiasis or alcohol, in terms of clinical presentation and outcomes. It's essential to identify early markers that can predict the severity of HTG-AP to improve patient management and outcomes. MATERIAL AND METHODS This study divided 127 HTG-AP patients into mild acute pancreatitis (MAP, n=71) and moderate-to-severe acute pancreatitis (MSAP/SAP, n=56) groups. Blood biological indicators within the first 24 hours of admission were analyzed. Risk factors for HTG-AP progression were determined using binary logistic regression and ROC curves. RESULTS Elevated levels of HCT, NLR, TBI, DBI, AST, Cre, and AMS were noted in the MSAP/SAP group, with lower levels of LYM, Na⁺, Ca²⁺, ApoA, and ApoB compared to the MAP group (p<0.05). NEUT%, Ca²⁺, ApoA, and ApoB were significantly linked with HTG-AP severity. Their combined ROC analysis yielded an area of 0.81, with a sensitivity of 61.8% and specificity of 90%. CONCLUSIONS NEUT%, Ca²⁺, ApoA, and ApoB are significant risk factors for progressing to MSAP/SAP in HTG-AP. Their combined assessment provides a reliable predictive measure for early intervention in patients at risk of severe progression.