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The eukaryotic ribosome is a large ribonucleoprotein complex consisting of four types of ribosomal RNA (rRNA) and approximately 80 ribosomal proteins (RPs), forming the 40S and 60S subunits. In all living cells, its primary function is to produce proteins by converting messenger RNA (mRNA) into polypeptides. In addition to their canonical role in protein synthesis, RPs are crucial in controlling vital cellular processes such as cell cycle progression, cellular proliferation, differentiation, DNA damage repair, genome structure maintenance, and the cellular stress response. Viruses, as obligate intracellular parasites, depend completely on the machinery of the host cell for their replication and survival. During viral infection, RPs have been demonstrated to perform a variety of extra-ribosomal activities, which are especially important in viral disease processes. These functions cover a wide range of activities, ranging from controlling inflammatory responses and antiviral immunity to promoting viral replication and increasing viral pathogenicity. Deciphering the regulatory mechanisms used by RPs in response to viral infections has greatly expanded our understanding of their functions outside of the ribosome. Furthermore, these findings highlight the promising role of RPs as targets for the advancement of antiviral therapies and the development of novel antiviral approaches. This review comprehensively examines the many functions of RPs outside of the ribosome during viral infections and provides a foundation for future research on the host–virus interaction. [ABSTRACT FROM AUTHOR]

Background: Attention deficits are notable in Lewy body dementia (LBD) and in Alzheimer's disease (AD). In this study, we combined functional magnetic resonance imaging (fMRI) and electroencephalograph (EEG) to detect neural correlates of attention dysfunctions in LBD and AD. Methods: We recruited 33 patients with LBD, 15 patients with AD and 19 elderly healthy controls. The participants performed the modified Attention Network Task (ANT) to investigate the attention dysfunctions. Results: We found that LBD had alerting attention deficits and AD showed apparent orienting attention dysfunctions, while LBD and AD maintained relatively normal executive/conflict attention. Based on source-level EEG analyses, LBD had frontal-central deficits for alerting attention while AD showed inferior frontal and precentral impairments for orienting attention. In addition, the insular and inferior frontal areas were hyper-activated in LBD and AD for executive/conflict attention. Apart from these areas, LBD showed activity in the complementary temporal-central-occipital network for the modified ANT task. Furthermore, the oscillational sources for the ANT effects indicated that the alpha and theta bands were partly impaired in dementia patients. Conclusions: In summary, using source-localised EEG, we found that attention dysfunctions in LBD and AD engaged different neural networks. [ABSTRACT FROM AUTHOR]

This study looked at how desalinated seawater, which has low minerals and high boron, could affect bone health. Prior research suggests that low mineral water may harm bone health and boron could be beneficial, but the overall impact on bone health is still unclear. Eighty-nine-week-old male Balb/C mice were allocated into eight groups and administered either tap water or purified water with varying boron concentrations (0, 5, 40, and 200 mg/L). They were kept in an environment mimicking tropical conditions (35–40 °C, 70–80% humidity) and underwent daily treadmill exercise for 13 weeks. At the 14th week, serum, femora, and lumbar vertebrae were collected for mineral metabolism, bone biomarker, microstructure, and biomechanics evaluation. Boron exposure improved bone formation, microstructure, and biomechanics initially but the benefits weakened with higher levels of exposure (p < 0.05). Co-exposure to purified water elevated serum boron but weakened the promotion of boron on bone minerals and the bone benefits of boron compared to tap water (p < 0.05). Thus, when studying the health effects of boron in desalinated seawater, it is crucial to look at various health effects beyond bone health. Furthermore, it is important to consider the mineral composition of drinking water when using boron for bone health benefits. [ABSTRACT FROM AUTHOR]

The consumption of low-mineral water has been increasing worldwide. Drinking low-mineral water is associated with cardiovascular disease, osteopenia, and certain neurodegenerative diseases. However, the specific mechanism remains unclear. The liver metabolic alterations in rats induced by drinking purified water for 3 months were investigated with a metabolomics-based strategy. Compared with the tap water group, 74 metabolites were significantly changed in the purified water group (6 increased and 68 decreased), including 29 amino acids, 11 carbohydrates, 10 fatty acids, 7 short chain fatty acids (SCFAs), and 17 other biomolecules. Eight metabolic pathways were significantly changed, namely aminoacyl–tRNA biosynthesis; nitrogen metabolism; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; histidine metabolism; biosynthesis of unsaturated fatty acids; butanoate metabolism; and glycine, serine and threonine metabolism. These changes suggested that consumption of purified water induced negative nitrogen balance, reduced expression of some polyunsaturated fatty acids and SCFAs, and disturbed energy metabolism in rats. These metabolic disturbances may contribute to low-mineral-water-associated health risks. The health risk of consuming low-mineral water requires attention. [ABSTRACT FROM AUTHOR]

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system's carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the most prevalent malignancy in women. With the improvement of medical treatment, breast cancer has become one of the solid tumors with the best curative effect. However, triple-negative breast cancer is not sensitive to conventional treatment due to its high invasiveness, resulting in a poorer prognosis than other types. The antibodies of programmed death receptor 1 and its ligands represent a new option as immunotherapy for patients with triple negative breast cancer. However, some recent clinical data suggest that a large proportion of patients exhibit primary or acquired resistance to treatment with programmed death receptor antibodies. In this review, we discuss the mechanisms that lead to resistance and also summarize potential strategies to overcome the resistance, improving the therapeutic efficacy of programmed death receptor 1 and its ligand-based antibodies in triple negative breast cancer. Triple-negative breast cancer (TNBC) is characterized by a high rate of systemic metastasis, insensitivity to conventional treatment and susceptibility to drug resistance, resulting in a poor patient prognosis. The immune checkpoint inhibitors (ICIs) represented by antibodies of programmed death receptor 1 (PD-1) and programmed death receptor ligand 1 (PD-L1) have provided new therapeutic options for TNBC. However, the efficacy of PD-1/PD-L1 blockade monotherapy is suboptimal immune response, which may be caused by reduced antigen presentation, immunosuppressive tumor microenvironment, interplay with other immune checkpoints and aberrant activation of oncological signaling in tumor cells. Therefore, to improve the sensitivity of TNBC to ICIs, suitable patients are selected based on reliable predictive markers and treated with a combination of ICIs with other therapies such as chemotherapy, radiotherapy, targeted therapy, oncologic virus and neoantigen-based therapies. This review discusses the current mechanisms underlying the resistance of TNBC to PD-1/PD-L1 inhibitors, the potential biomarkers for predicting the efficacy of anti-PD-1/PD-L1 immunotherapy and recent advances in the combination therapies to increase response rates, the depth of remission and the durability of the benefit of TNBC to ICIs. [ABSTRACT FROM AUTHOR]

With the application of distributed generation and the development of smart grid technology, micro-grid, an economic and stable power grid, tends to play an important role in the demand side management. Because micro-grid technology and demand response have been widely applied, what Demand Response actions can realize the economic operation of micro-grid has become an important issue for utilities. In this proposed work, operation optimization modeling for micro-grid is done considering distributed generation, environmental factors and demand response. The main contribution of this model is to optimize the cost in the context of considering demand response and system operation. The presented optimization model can reduce the operation cost of micro-grid without bringing discomfort to the users, thus increasing the consumption of clean energy effectively. Then, to solve this operational optimization problem, genetic algorithm is used to implement objective function and DR scheduling strategy. In addition, to validate the proposed model, it is employed on a smart micro-grid from Tianjin. The obtained numerical results clearly indicate the impact of demand response on economic operation of micro-grid and development of distributed generation. Besides, a sensitivity analysis on the natural gas price is implemented according to the situation of China, and the result shows that the natural gas price has a great influence on the operation cost of the micro-grid and effect of demand response. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP) and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA). The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data. [ABSTRACT FROM AUTHOR]

With the application and the rapid advancement of smart grid technology, the practical application and operation status of multi-energy complementary microgrids have been widely investigated. In the paper presented, the optimal operation of a solar unit, a storage battery and combined cooling, heating and power is studied via an economic optimization model implemented in General Algebraic Modeling Systems (GAMS). The model represents an optimization strategy for the economic operation of a microgrid considering demand response programs in different scenarios, and it is intended for the targets of minimizing the operating cost of the microgrid and maximizing the efficiency of renewable energy utilization. In addition, a multi-time electricity price response model based on user behavior and satisfaction is established, and the core value of the model is to describe the mechanism and effect of participation in electricity price demand response. In order to verify the accuracy of the model proposed, we design the dispatch strategy of a microgrid under different states considering demand response, and use genetic algorithm to solve the optimization problems. On the other hand, the application of methodology to a real case study in Suzhou demonstrates the effectiveness of this model to solve the economic dispatch of the microgrid’s renewable energy park. [ABSTRACT FROM AUTHOR]