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Threonine (Thr), an essential amino acid for animals and the limiting amino acid in swine and poultry diets, which plays a vital role in the modulation of nutritional metabolism, macromolecular biosynthesis, and gut homeostasis. Current evidence supports that the supplementation of Thr leads to benefits in terms of energy metabolism. Threonine is not only an important component of gastrointestinal mucin, but also acts as a nutritional modulator that influences the intestinal immune system via complex signaling networks, particularly mitogen-activated protein kinase (MAPK) and the target of the rapamycin (TOR) signal pathway. Threonine is also recognized as an indispensable nutrient for cell growth and proliferation. Hence, optimization of Thr requirement may exert a favorable impact on the factors linked to health and diseases in animals. This review focuses on the latest reports of Thr in metabolic pathways and nutritional regulation, as well as the relationship between Thr and relevant physiological functions.

The emergence of multimedia data has enriched people’s lives and work and has penetrated into education, finance, medical, military, communications, and other industries. The text data takes up a small space, and the network transmission speed is fast. However, due to its richness, the multimedia data makes it occupy an ample space. Some high-definition multimedia information even reaches the GB level, and the multimedia data network transmission is relatively slow. Compared with the traditional scalar data, the multimedia data better describes the characteristics of the transaction, but at the same time, the multimedia data itself has a large capacity and must be compressed. Nodes of wireless multimedia sensor networks have limited ability to process data. Traditional data compression schemes require high processing power of nodes and are not suitable for sensor networks. Therefore, distributed video codec scheme in recent years becomes one of the hot multimedia sensor network technologies, which is a simple coding scheme, coding complexity of decoding performance. In this paper, distributed video codec and its associated knowledge based on the study present a distributed video coding scheme and its improvements. Aiming at the problem that the traditional distributed video coding scheme cannot accurately decode the motion severe region and the boundary region, a distributed video coding algorithm based on gradient-domain ROI is proposed, which can enhance the coding efficiency of the severe motion region and improve the decoded image while reducing the code rate and quality, ultimately reducing sensor node energy consumption.

Pricing catastrophe (CAT) bonds in incomplete markets poses persistent challenges, particularly in converting risk from the real-world measure to the pricing measure. The commonly used Wang transform focuses on distorting the loss severity distribution, which may underestimate catastrophe risk. This paper proposes a new distortion operator based on the Esscher transform that distorts the aggregate loss distribution rather than focusing solely on the severity or frequency components. The proposed approach provides more comprehensive risk adjustment, making it well-suited for the distributional characteristics of catastrophic loss indicators. Its applicability is demonstrated via an application to Chinese earthquake data. Monte Carlo simulation was used to compare pricing results via the distortion of different components. By reformulating the proposed distortion method into the form of a distortion operator and comparing it with the Wang transform, this paper demonstrates that the proposed approach offers significantly enhanced analytical tractability for complex distributions. It enables a more transparent analysis of the transformed distribution and its implications for bond pricing mechanisms.

Cell migration is crucial for physiological and pathological processes such as morphogenesis, wound repair, immune response and cancer invasion/metastasis. There are many factors affecting cell migration, and the regulatory mechanisms are complex. Rac1 is a GTP-binding protein with small molecular weight belonging to the Rac subfamily of the Rho GTPase family. As a key molecule in regulating cell migration, Rac1 participates in signal transduction from the external cell to the actin cytoskeleton and promotes the establishment of cell polarity which plays an important role in cancer cell invasion/metastasis. In this review, we firstly introduce the molecular structure and activity regulation of Rac1, and then summarize the role of Rac1 in cancer invasion/metastasis and other physiological processes. We also discuss the regulatory mechanisms of Rac1 in cell migration and highlight it as a potential target in cancer therapy. Finally, the current state as well as the future challenges in this area are considered. Understanding the role and the regulatory mechanism of Rac1 in cell migration can provide fundamental insights into Rac1-related cancer progression and further help us to develop novel intervention strategies for cancer therapy in clinic.

As a novel type of energy storage element, supercapacitors have been extensively used in power systems, transportation and industry due to their high power density, long cycle life and good low-temperature performance. The health status of supercapacitors is of vital importance to the safe operation of the entire energy storage system. In order to improve the prediction accuracy of the remaining useful life (RUL) of supercapacitors, this paper proposes a method based on the Harris hawks optimization (HHO) algorithm and long short-term memory (LSTM) recurrent neural networks (RNNs). The HHO algorithm has the advantages of a wide global search range and a high convergence speed. Therefore, the HHO algorithm is used to optimize the initial learning rate of LSTM RNNs and the number of hidden-layer units, so as to improve the stability and reliability of the system. The root mean square error (RMSE) between the predicted result and the observed result reduced to 0.0207, 0.026 and 0.0341. The prediction results show that the HHO-LSTM has higher accuracy and robustness than traditional LSTM and GRU (gate recurrent unit) models.

Inflammation can be induced by chronic infection, inflammatory diseases and physicochemical factors. Chronic inflammation is estimated to contribute to approximately 25% of human cancers. Under inflammatory conditions, inflammatory and epithelial cells release reactive oxygen (ROS) and nitrogen species (RNS), which are capable of causing DNA damage, including the formation of 8-oxo-7,8-dihydro-2′-deoxyguanosine and 8-nitroguanine. We reported that 8-nitroguanine was clearly formed at the sites of cancer induced by infectious agents including Helicobacter pylori, inflammatory diseases including Barrett’s esophagus, and physicochemical factors including asbestos. DNA damage can lead to mutations and genomic instability if not properly repaired. Moreover, DNA damage response can also induce high mobility group box 1-generating inflammatory microenvironment, which is characterized by hypoxia. Hypoxia induces hypoxia-inducible factor and inducible nitric oxide synthase (iNOS), which increases the levels of intracellular RNS and ROS, resulting DNA damage in progression with poor prognosis. Furthermore, tumor-producing inflammation can induce nuclear factor-κB, resulting in iNOS-dependent DNA damage. Therefore, crosstalk between DNA damage and inflammation may play important roles in cancer development. A proposed mechanism for the crosstalk may explain why aspirin decreases the long-term risk of cancer mortality.

Background Osteoarthritis (OA) is a common degenerative joint disease that significantly impacts the quality of life, especially among older adults. Testosterone, a critical hormone for musculoskeletal health, has been suggested to influence OA pathogenesis. However, the relationship between low testosterone levels and OA risk remains underexplored in large, representative populations. This study aimed to investigate the association between low testosterone levels and OA risk using data from the National Health and Nutrition Examination Survey (NHANES, 2011–2016). Methods This cross-sectional analysis included 4,548 participants from NHANES, a nationally representative U.S. dataset. Testosterone levels were categorized as low or normal, with low testosterone defined as < 300 ng/dL for men and population-based cutoffs for women. The presence of OA was determined through self-reported physician diagnosis. Multivariable logistic regression models were used to examine the association between testosterone levels and OA risk, adjusting for demographic, socioeconomic, lifestyle, and clinical factors. Restricted cubic spline (RCS) analysis was conducted to evaluate non-linear relationships. Subgroup analyses were performed to assess consistency across key demographic and clinical strata. Results Among the 4,548 participants, 812 (17.9%) were diagnosed with OA. Participants with OA were older, more likely to be female, and exhibited higher rates of obesity and hyperlipidemia. In fully adjusted models, low testosterone levels were significantly associated with increased OA risk (OR, 1.22; 95% CI, 1.02–1.46; P  = 0.028). RCS analysis indicated a non-linear relationship, with a steep increase in OA risk at lower testosterone levels, suggesting a threshold effect. Subgroup analyses demonstrated consistent associations across demographic and clinical groups without significant interactions. Conclusion Low testosterone levels are independently associated with an increased risk of OA in the U.S. population. These findings underscore the potential role of hormonal health in OA pathogenesis and highlight the need for longitudinal studies to clarify causal pathways. The observed non-linear relationship suggests that maintaining optimal testosterone levels may be important for joint health, and testosterone replacement therapy could be explored as a preventative strategy for individuals with testosterone deficiency.

With the rapid development of the new energy industry, supercapacitors have become key devices in the field of energy storage. To forecast the remaining useful life (RUL) of supercapacitors, we introduce a new technology that integrates variational mode decomposition (VMD) with a bidirectional long short-term memory (BiLSTM) neural network. Firstly, the aging experiments of supercapacitors under various temperatures and voltages were carried out to obtain aging data. Then, VMD was implemented to decompose the aging data, which helped to eliminate disturbances, including capacity recovery and test errors. Then, the hyperparameters of BiLSTM were adjusted, employing the sparrow search algorithm (SSA) to improve the consistency between the input data and the network structure. After obtaining the optimal hyperparameters of BiLSTM, the decomposed aging data were input into BiLSTM for prediction. The experimental results showed that the VMD-SSA-BiLSTM model proposed in this paper has high prediction accuracy and high robustness under different temperatures and voltages, with an average RMSE of 0.112519, a decrease of 44.3% compared to BiLSTM, and a minimum of 0.031426.

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer.

Hepatocellular carcinoma (HCC) is one of the most common human malignancies worldwide with very poor prognosis. Resistance to targeted therapeutic drugs such as sorafenib remains one of the major challenges in clinical treatment. In the present study, PARP1 was found to be highly expressed in human embryonic stem cells, but progressively decreased upon specified hepatic differentiation. Reactivation of PARP1 expression was also detected in HCC residual tumors after sorafenib treatment in xenograft mouse model, indicating the potential important roles of PARP1 in stem cell pluripotency and HCC sorafenib treatment resistance. Overexpression of PARP1 was frequently observed in HCC patients, and closely associated with poor clinical outcome. Treatment of Sorafenib induced activation of DNA damage repair signaling, which is highly active and essential for maintenance of stem cell pluripotency in HCC residual tumors. PARP inhibitor Olaparib extensively suppressed the DNA damage repair signaling, and significantly inhibited the global pluripotent transcriptional network. The repression of key pluripotent transcriptional factors and DNA damage repair signaling by Olaparib was mainly through CHD1L-mediated condensation of the chromatin structure at their promotor regions. The global reshaping of the pluripotent transcriptome by Olaparib might reinforce Sorafenib in eliminating HCC residual tumors and enhance therapeutic efficiency.