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Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. [ABSTRACT FROM AUTHOR]

Using coal-based solid waste (CSWM) to develop artificial soil (AS) can solve the shortage of planting soil in mine ecological restoration and realize the resource utilization of solid waste, which is a very promising research direction. This study used three common CSWM, coal gangue (CG), fly ash (FA), and desulfurization gypsum (DG), to prepare AS. The physicochemical properties of AS, such as bulk density, specific gravity, porosity, field water capacity, available nutrient content, pH value and EC value, were studied. Simultaneously, Elymus dahuricus was utilized in pot experiments to explore the planting performance of AS. Results show that, as the CG content decreased, the specific gravity of AS decreased, and the porosity increased. Both field capacity and saturation capacity demonstrated a notable upward trend. The results of pot experiments showed that plant growth was best when the CG content in AS was 80% and the FA and DG content was 20%. In comparison to the control group consisting of pure CG, the plant biomass in the optimal experimental group exhibited a significant increase of 20.48%. This study verified the feasibility of making AS by combining various CSWM, and provides a new pathway for ecological restoration and resource utilization in mines. [ABSTRACT FROM AUTHOR]

To improve the durability of pumice lightweight aggregate concrete applied in cold and drought areas, sodium silicate-modified waste tire rubber powder is used to treat the pumice lightweight aggregate concrete. The pumice lightweight aggregate concrete studied is mainly used in river lining structures. It will be eroded by water flow and the impact of ice and other injuries, resulting in reduced durability, and the addition of modified rubber will reduce the damage. The durability, including mass loss rate and relative dynamic elastic modulus of pumice lightweight aggregate concrete with different sodium silicate dosages and rubber power particle sizes, is analyzed under freeze-thaw cycles, and the microstructure is further characterized by using microscopic test methods such as nuclear magnetic resonance tests, ultra-depth 3D microscope tests, and scanning electron microscopy tests. The results showed that the durability of pumice lightweight aggregate concrete is significantly improved by the addition of modified waste tire rubber powder, and the optimum durability is achieved when using 2 wt% sodium silicate modified rubber power with a particle size of 20, and then the mass loss rate decreased from 4.54% to 0.77% and the relative dynamic elastic modulus increased from 50.34% to 64.87% after 300 freeze-thaw cycles compared with other samples. The scanning electron microscopy test result showed that the surface of rubber power is cleaner after the modification of sodium silicate, so the bonding ability between rubber power and cement hydration products is improved, which further improved the durability of concrete under the freeze-thaw cycle. The results of the nuclear magnetic resonance test showed that the pore area increased with the number of freeze-thaw cycles, and the small pores gradually evolved into large pores. The effect of sodium silicate on the modification of rubber power with different particle sizes is different. After the treatment of 2 wt% sodium silicate, the relationship between the increased rate of pore area and the number of freezing-thawing cycles is 23.8/times for the pumice lightweight aggregate concrete containing rubber power with a particle size of 20 and 35.3/times for the pumice lightweight aggregate concrete containing a particle size of 80 rubber power, respectively. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies with a high lethality rate. ZMIZ2 is a transcriptional co-activator implicated in various human diseases. However, the role and molecular mechanism of ZMIZ2 in HCC remains to be elucidated. Methods: The expression and prognostic value of ZMIZ2 in HCC was excavated from public databases and explored by bioinformatic analysis. Then the expression of ZMIZ2 and related genes was further validated by quantitative RT-PCR, western blotting, and immunohistochemistry. Loss and gain-of-function experiments were performed in vitro and in vivo to investigate the function of ZMIZ2 in HCC. In addition, transcriptome sequencing and immunoprecipitation was conducted to explore the potential molecular mechanisms of ZMIZ2. Results: ZMIZ2 was highly expressed in HCC and associated with poor prognosis. Silencing ZMIZ2 significantly inhibited HCC cell proliferation, cell cycle process, migration, and invasion in vitro, and also inhibited the progression of HCC in vivo. Additionally, ZMIZ2 expression was correlated with immune cell infiltration in HCC samples. Somatic mutation analysis showed that ZMIZ2 and TP53 mutations jointly affected the progression of HCC. Mechanistically, ZMIZ2 interacted with LEF1 to regulate malignant progression of HCC by activating the Wnt/β-catenin pathway. Conclusion: ZMIZ2 was overexpressed in HCC and associated with poor prognosis. The overexpression of ZMIZ2 was corelated with malignant phenotype, and it facilitated HCC progression via LEF1-mediated activation of the Wnt/β-catenin pathway. Furthermore, ZMIZ2 could be served as a prognostic biomarker and a new therapeutic target for HCC. [ABSTRACT FROM AUTHOR]

BACKGROUND: Agaricus bisporus with a completely white appearance is popular with consumers. However, A. bisporus is susceptible to senescence and spoilage, which results in browning and oxidative tissue damage, with limited shelf life. This study investigated the effects of shortwave ultraviolet (UV‐C, 1.0 kJ m−2) irradiation on quality features, enzymatic browning, and defense properties of A. bisporus stored at 4 °C for 21 days. RESULTS: After storage, UV‐C irradiated A. bisporus had increased quality of sensory, color and hardness characteristics; it exhibited higher content of total phenolic, brown melanin precursors, including γ‐glutaminyl‐4‐hydroxybenzene, γ‐glutaminyl‐3,4‐dihydroxybenzene and tyrosine, lower accumulation of malondiadehyde, hydrogen peroxide and superoxide radical, and maintained membrane integrity in comparison to control samples. Besides, A. bisporus treated by UV‐C showed lower degree of browning and higher level of self‐defense capacity, which may be ascribed to the enhancement in activities of superoxide dismutase, catalase, phenylalanine ammonia lyase, chitinase and β‐1,3‐glucanase activity, while inhibiting polyphenol oxidase activity during storage. CONCLUSION: These results indicate that the higher self‐defense capacity with UV‐C treatment might be the mechanism associated with the delay of senescence in Agaricus bisporus. Therefore, UV‐C treatment is suggested as a potential practical application in mushrooms. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) is a widely applied technique in practical applications that involve complex data structures. MIL can be broadly categorized into two types: traditional methods and those based on deep learning. These approaches have yielded significant results, especially regarding their problem-solving strategies and experiment validation, providing valuable insights for researchers in the MIL field. However, considerable knowledge is often trapped within the algorithm, leading to subsequent MIL algorithms that rely solely on the model's data fitting to predict unlabeled samples. This results in a significant loss of knowledge and impedes the development of more powerful models. In this article, we propose a novel data-driven knowledge fusion for deep MIL (DKMIL) algorithm. DKMIL adopts a completely different idea from existing deep MIL methods by analyzing the decision-making of key samples in the dataset (referred to as the data-driven) and using the knowledge fusion module designed to extract valuable information from these samples to assist the model's learning. In other words, this module serves as a new interface between data and the model, providing strong scalability and enabling prior knowledge from existing algorithms to enhance the model's learning ability. Furthermore, to adapt the downstream modules of the model to more knowledge-enriched features extracted from the data-driven knowledge fusion (DDKF) module, we propose a two-level attention (TLA) module that gradually learns shallow-and deep-level features of the samples to achieve more effective classification. We will prove the scalability of the knowledge fusion module and verify the efficiency of the proposed architecture by conducting experiments on 62 datasets across five categories.

Pesticides play an important role in the development of agriculture, as they can prevent and control crop diseases and pests, improve crop yield and quality. However, the abuse and improper use of pesticides can lead to negative impacts such as environmental pollution and pest resistance issues. There is an urgent need to develop green, safe, and efficient pesticides. In this work, natural product arecoline was selected as parent structure, a series of arecoline derivatives were designed, synthesized, and systematically investigated antiviral activities against tobacco mosaic virus (TMV). These compounds were found to have good to excellent anti-TMV activities for the first time. The antiviral activities of 4a, 4 h, 4 l, 4p, 6a, 6c, and 6f are higher than that of ningnanmycin. Compounds 4 h (EC 50 value 146 µg/mL) and 4p (EC 50 value 161 µg/mL) with simple structures and excellent activities emerged as new antiviral candidates. We chose 4 h to further investigate the antiviral mechanism, which revealed that it can cause virus fragmentation by acting on the viral coat protein (CP). We further validated this result through molecular docking. These compounds also displayed broad-spectrum fungicidal activities against 8 plant pathogenic fungi. This work lays the theoretical foundation for the application of arecoline derivatives in the agricultural field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Reprogramming of lipid metabolism during hepatocarcinogenesis is not well elucidated. Here, we aimed to explore pivotal RNA-binding motif proteins (RBMs) in lipid metabolism and their therapeutic potential in hepatocellular carcinoma (HCC). Through bioinformatic analysis, we identified RBM45 as a critical gene of interest among differentially expressed RBMs in HCC, with significant prognostic relevance. RBM45 influenced the malignant biological phenotype and lipid metabolism of HCC cells. Mechanically, RBM45 promotes de novo lipogenesis in HCC by directly targeting two key enzymes involved in long-chain fatty acid synthesis, ACSL1 and ACSL4. RBM45 also targets Rictor, which has been demonstrated to modulate lipid metabolism profoundly. RBM45 also aided lipid degradation through activating a key fatty acid β oxidation enzyme, CPT1A. Thus, RBM45 boosted lipid synthesis and decomposition, indicating an enhanced utility of lipid fuels in HCC. Clinically, body mass index was positively correlated with RBM45 in human HCCs. The combination of a PI3K/AKT/mTOR pathway inhibitor in vitro or Sorafenib in orthotopic liver cancer mouse models with shRBM45 has a more significant therapeutic effect on liver cancer than the drug alone. In summary, our findings highlight the versatile roles of RBM45 in lipid metabolism reprogramming and its therapeutic potential in HCC. Lipids induced RBM45 expression. In turn, RBM45 promoted the utility of lipid in HCCs through accelerating both de novo lipogenesis and fatty acid β oxidation, which required the participation of Rictor, a core component of mTORC2 that has been demonstrated to modulate lipid metabolism potently, as well as ACSL1/ACSL4, two key enzymes of long-chain fatty acid synthesis. When the first-line chemotherapy drug sorafenib is combined with a PI3K/AKT/mTOR pathway inhibitor (MK2206 is an AKT inhibitor, rapamycin is a mTOR inhibitor, and inhibiting RBM45 can significantly inhibit Rictor), cell cycle, proliferation, lipid metabolism reprogramming, and hepatocarcinogenesis can be significantly inhibited, while apoptosis can be significantly enhanced., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)