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Objective. Mounting evidence has elaborated the implication of long noncoding RNAs (lncRNAs) in tumorigenesis of several cancers, including glioma. However, little was known about the mechanism of lncRNA maternally expressed gene 3 (MEG3) in the development and progression of glioma. This work is designed to explore the effect of MEG3 on glioma progression and its possible mechanism. Methods. Expressions of lncRNA-MEG3 and SMARCB1 were detected in human glioblastoma U87 and U251 cell lines. Gain and loss of function of MEG3 or/and miR-6088 was performed in U87 and U251 cells to observe its effect on cell proliferation and migration as well as on epithelial-mesenchymal transition (EMT) related markers. Luciferase reporter gene assay was employed to inspect the interactions among MEG3, miR-6088, and SMARCB1. Results. MEG3 and SMARCB1 expressions were downregulated in glioma cells. Transfection of pcDNA3.1-MEG3 or pcDNA3.1-SMARCB1 plasmids could clearly block cell proliferation, migration, and EMT progression. MEG3 functions as a sponge for miR-6088, while SMARCB1 is a downstream protein of miR-6088. Transfection of miR-6088 mimic or si-SMARCB1 could obviously reverse the favorable effect of pcDNA3.1-MEG3 on glioma progression. Conclusion. Collectively, the evidence in this study indicated that MEG3 was downregulated in glioma cells and inhibited proliferation and migration of glioma cells via regulating miR-6088/SMARCB1 axis.

Selenium (Se) is an indispensable trace element with versatile functions in antioxidant defense in poultry. In our previous study, we synthesized a novel type of biogenic selenium nanoparticle based on alginate oligosaccharides (SeNPs-AOS), and found that the particles are sized around 80 nm with an 8% Se content, and the dietary addition of 5 mg/kg of SeNPs-AOS could effectively alleviate the deleterious effects of heat stress (HS) in broilers, but it is still unclear whether SeNPs-AOS can improve the meat quality. Therefore, the aim of this study was to evaluate the protective effects of SeNPs-AOS on breast meat quality in heat-stressed broilers, and explore the relevant mechanisms. Birds at the age of 21 days were randomly divided into four groups with six replicates per group (eight broilers per replicate) according to a 2 × 2 experimental design, using HS (33 ± 2 °C, 10 h/day vs. thermoneutral, TN, under 23 ± 1.5 °C) and SeNPs-AOS (5 mg/kg feed vs. no inclusion) as variables. The results showed that dietary SeNPs-AOS decreased the cooking loss ( < 0.05), freezing loss ( < 0.001), and shear force ( < 0.01) of breast muscle in heat-stressed broilers. The non-targeted metabolomics analysis of the breast muscle identified 78 differential metabolites between the HS and HS + SeNPs-AOS groups, mainly enriched in the arginine and proline metabolism, β-alanine metabolism, D-arginine and D-ornithine metabolism, pantothenate, and CoA biosynthesis pathways ( < 0.05). Meanwhile, supplementation with SeNPs-AOS increased the levels of the total antioxidant capacity (T-AOC), the activities of catalase (CAT) and glutathione peroxidase (GSH-Px), and decreased the content of malondialdehyde (MDA) in the breast muscle ( < 0.05) in broilers under HS exposure. Additionally, SeNPs-AOS upregulated the mRNA expression of , , , ( ), ( , , ( , , ( ), ( ), and ( ( < 0.05), while it downregulated Kelch-like ECH-associated pro-36 tein 1 ( ) and prostaglandin-endoperoxide Synthase 2 ( ) expression ( < 0.05) in broilers under HS. These findings demonstrated that the dietary addition of SeNPs-AOS mitigated HS-induced oxidative damage and metabolite changes in the breast muscle of broilers, which may be related to the regulation of the Nrf2 signaling pathway and selenoprotein synthesis. In addition, SeNPs-AOS upregulated the breast muscle gene expression of anti-ferroptosis-related molecules in broilers under HS, suggesting that SeNPs-AOS can be used as novel Se supplements against HS in broilers.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation was trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of 10 - 5 , showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop a non-recursive convolutional neural networks (CNNs) that maps system parameters and a redundant time encoding directly to excitation-energy-transfer populations in the Fenna-Matthews-Olson complex. The encoding-modified logistic plus \\(\\) functions-normalizes time and resolves fast, transitional, and quasi-steady regimes, while physics-informed labels enforce population conservation and inter-site consistency. Trained only on \\(0 7 ps\\) reference trajectories generated with a Lindblad model in QuTiP, the network accurately predicts \\(0100 ps\\) dynamics across a range of reorganization energies, bath rates, and temperatures. Beyond \\(20 ps\\), the absolute relative error remains below 0.05, demonstrating stable long-time extrapolation. By avoiding step-by-step recursion, the method suppresses error accumulation and generalizes across timescales. These results show that redundant time encoding enables data-efficient inference of long-time quantum dissipative dynamics in realistic pigment-protein complexes, and may aid the data-driven design of light-harvesting materials.

Selenium (Se) is an indispensable trace element with versatile functions in antioxidant defense in poultry. In our previous study, we synthesized a novel type of biogenic selenium nanoparticle based on alginate oligosaccharides (SeNPs-AOS), and found that the particles are sized around 80 nm with an 8% Se content, and the dietary addition of 5 mg/kg of SeNPs-AOS could effectively alleviate the deleterious effects of heat stress (HS) in broilers, but it is still unclear whether SeNPs-AOS can improve the meat quality. Therefore, the aim of this study was to evaluate the protective effects of SeNPs-AOS on breast meat quality in heat-stressed broilers, and explore the relevant mechanisms. Birds at the age of 21 days were randomly divided into four groups with six replicates per group (eight broilers per replicate) according to a 2 × 2 experimental design, using HS (33 ± 2 °C, 10 h/day vs. thermoneutral, TN, under 23 ± 1.5 °C) and SeNPs-AOS (5 mg/kg feed vs. no inclusion) as variables. The results showed that dietary SeNPs-AOS decreased the cooking loss (p < 0.05), freezing loss (p < 0.001), and shear force (p < 0.01) of breast muscle in heat-stressed broilers. The non-targeted metabolomics analysis of the breast muscle identified 78 differential metabolites between the HS and HS + SeNPs-AOS groups, mainly enriched in the arginine and proline metabolism, β-alanine metabolism, D-arginine and D-ornithine metabolism, pantothenate, and CoA biosynthesis pathways (p < 0.05). Meanwhile, supplementation with SeNPs-AOS increased the levels of the total antioxidant capacity (T-AOC), the activities of catalase (CAT) and glutathione peroxidase (GSH-Px), and decreased the content of malondialdehyde (MDA) in the breast muscle (p < 0.05) in broilers under HS exposure. Additionally, SeNPs-AOS upregulated the mRNA expression of CAT, GPX1, GPX3, heme oxygenase-1 (HO-1), masculoaponeurotic fibrosarcoma G (MafG), MafK, selenoprotein W (SELENOW), SELENOK, ferritin heavy polypeptide-1 (FTH1), Ferroportin 1 (Fpn1), and nuclear factor erythroid 2-related factor 2 (Nrf2) (p < 0.05), while it downregulated Kelch-like ECH-associated pro-36 tein 1 (Keap1) and prostaglandin-endoperoxide Synthase 2 (PTGS2) expression (p < 0.05) in broilers under HS. These findings demonstrated that the dietary addition of SeNPs-AOS mitigated HS-induced oxidative damage and metabolite changes in the breast muscle of broilers, which may be related to the regulation of the Nrf2 signaling pathway and selenoprotein synthesis. In addition, SeNPs-AOS upregulated the breast muscle gene expression of anti-ferroptosis-related molecules in broilers under HS, suggesting that SeNPs-AOS can be used as novel Se supplements against HS in broilers.

Predicting future physical behavior from limited theoretical simulation data is an emerging research paradigm driven by the integration of artificial intelligence and quantum physics. In this work, charge transport (CT) behavior was predicted over extended time scales using a deep learning model-the long short-term memory (LSTM) network with an error-threshold training method-in the photosystem II reaction center (PSII-RC). Theoretical simulation data within 8 fs were used to train the modified LSTM network, yielding distinct predictions with differences on the order of \\(10^-4\\) over prolonged periods compared to the training set collection time. The results highlight the potential of LSTM to uncover the underlying physics governing CT beyond conventional quantum physical methods. These findings warrant further investigation to fully explore the scope and efficacy of LSTM in advancing our understanding of photosynthesis at the molecular scale.

Predicting future physical behavior through the limited theoretical simulation data available is an emerging research paradigm resulted by the integration of artificial intelligence technology and quantum physics. In this work, the charge-transport(CT) behavior was forecasted over a long time by a deep learning model, the long short-term memory (LSTM) network with error threshold training method in the photosynthesis II reaction center (PSII-RC). The theoretical simulation data within 8 fs was fed to the modified LSTM network for training, which brings out a distinct prediction with difference of \\(10^-4\\) orders of magnitude over a long time period compared to the collection time for training sets. The results indicate the potential of employing LSTM to reveal the physics governing CT in addition to quantum physical methods. The implications of this work warrant further investigation to fully elucidate the scope and efficacy of LSTM for advancing our understanding of photosynthesis at the molecular scale.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of \\(10^-5\\), showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of \\(10^-5\\), showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

In this paper, we study Cohen-Grossberg neural networks (CGNN) with time-varying delay. Based on Halanay inequality and continuation theorem of the coincidence degree, we obtain some sufficient conditions ensuring the existence, uniqueness, and global exponential stability of periodic solution. Our results complement previously known results.