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Abstract CD8+ T cells are integral to the effective management of cancer and infectious diseases due to their cytotoxic functions. The efficacy of these cells is profoundly influenced by their metabolic state, which regulates their activation, differentiation, and longevity. Accordingly, the modulation of metabolic pathways within CD8+ T cells is crucial for enhancing the effectiveness of T cell-based immunotherapy. Precise metabolic control is paramount in optimizing therapeutic outcomes and minimizing potential toxicities associated with treatment. Importantly, the potential of exogenous metabolites to augment CD8+ T cell responses is critically evaluated, especially through in vivo evidence that underscores their therapeutic promise. This review also addresses current challenges, including the need for precise control of metabolic modulation to avoid adverse effects, the development of targeted delivery systems to ensure efficient metabolite delivery to CD8+ T cells, and the inherent variability of metabolic states among patients that may influence treatment outcomes. Addressing these hurdles will be crucial for the successful integration of metabolic interventions into established immunotherapeutic regimens.

Abstract The tumor microenvironment (TME) orchestrates a complex interplay between tumor cells and immune cells, crucially modulating the immune response. This review delves into the pivotal role of metabolic reprogramming in the TME, highlighting how tumor-derived metabolites influence T lymphocyte functionality and the efficacy of cancer immunotherapies. Focusing on the diverse roles of these metabolites, we examine how lactate, lipids, amino acids, and other biochemical signals act not only as metabolic byproducts but as regulatory agents that can suppress or potentiate T cell-mediated immunity. By integrating recent findings, we underscore the dual impact of these metabolites on enhancing tumor progression and inhibiting immune surveillance. Furthermore, we propose innovative therapeutic strategies that target metabolic pathways to restore immune function within the TME. The insights provided in this review pave the way for the development of metabolic interventions aimed at enhancing the success of immunotherapies in oncology, offering new hope for precision medicine in the treatment of cancer.

Abstract Salivary extracellular vesicles (EVs) have emerged as key tools for non-invasive diagnostics, playing a crucial role in the early detection and monitoring of diseases. These EVs surpass whole saliva in biomarker detection due to their enhanced stability, which minimizes contamination and enzymatic degradation. The review comprehensively discusses methods for isolating, enriching, quantifying, and characterizing salivary EVs. It highlights their importance as biomarkers in oral diseases like periodontitis and oral cancer, and underscores their potential in monitoring systemic conditions. Furthermore, the review explores the therapeutic possibilities of salivary EVs, particularly in personalized medicine through engineered EVs for targeted drug delivery. The discussion also covers the current challenges and future prospects in the field, emphasizing the potential of salivary EVs in advancing clinical practice and disease management.

Advancements in cancer therapy have increasingly focused on leveraging the synergistic effects of combining immunotherapy with other treatment modalities, facilitated by the use of innovative nanoplatforms. These strategies aim to augment the efficacy of standalone treatments while addressing their inherent limitations. Nanoplatforms enable precise delivery and controlled release of therapeutic agents, which enhances treatment specificity and reduces systemic toxicity. This review highlights the critical role of nanomaterials in enhancing immunotherapy when combined with chemotherapy, radiotherapy, photodynamic therapy, photothermal therapy, and sonodynamic therapy. Additionally, it addresses current challenges, including limited in vivo studies, difficulties in standardizing and scaling production, complexities of combination therapies, lack of comparative analyses, and the need for personalized treatments. Future directions involve refining nanoplatform engineering for improved targeting and minimizing adverse effects, alongside large animal studies to establish the long-term efficacy and safety of these combined therapeutic strategies. These efforts aim to translate laboratory successes into clinically viable treatments, significantly improving therapeutic outcomes and advancing the field of oncology.

In this study, a femtosecond laser with a wavelength of 800 nm was used to modify the surface of a titanium alloy bone scaffold created via selective laser melting (SLM). The outcomes demonstrated that the surface morphology of the bone scaffold after femtosecond laser treatment was micro-nano morphology. The hydrophobic structure of the scaffold was changed into a super-hydrophilic structure, improving the surface roughness, which was highly helpful for osteoblast adhesion and differentiation. The femtosecond laser surface treatment in vitro samples produced a thick layer of hydroxyapatite (HAP) with improved surface bioactivity. The effectiveness of osseointegration and interstitial growth of the specimens treated with the femtosecond laser surface were found to be better when bone scaffolds were implanted into the epiphysis of the tibia of rabbits. As a result, femtosecond laser therapy dramatically enhanced the surface activity of bone scaffolds and their capacity to integrate with the surrounding bone tissues, serving as a trustworthy benchmark for future biological scaffold research.

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data.

In lightweight engineering, mechanical metamaterials (MMs) are suitable for additive manufacturing (AM) due to their geometry complexity. However, it is inevitable to bring about mechanical deviation and local yield for MMs infilled lightweight structures. In the current work, MMs mapped structures, named as MMs-TO-PSLs, are proposed. The design domain is divided into core-region, intermediate-region, and solid-region. Core-regions derived from topology optimization (TO) are mapped with MMs, of which the [100] orientations are aligned with the normal directions of principal stress lines (PSLs). The intermediate-regions are infilled with ribbons according to PSLs. The solid regions are allocated to stress concentrated areas. Finally, near isotropic mechanical response is achieved in core-region from anisotropic MMs, and the serious local deformation is eased. To validate such approach, the mapping strategy is carried out on a beam subjected to three-point bending. Metallic MMs-TO-PSLs and MMs infilled uniform lightweight structure are fabricated by selective laser melting (SLM). Elastoplastic simulations with ductile failure model are proposed to investigate the strengthen mechanism of MMs-TO-PSLs. Experimental and numerical results confirmed the ultimate strength of MMs-TO-PSLs can be 40% larger than conventional MMs infilled uniform structures.

This paper studies a Stackelberg differential game between an upstream region and a downstream region for transboundary pollution control and ecological compensation in a river basin and increases the number of pollutants assumed in the model to multiple. Emission and green innovation investment between upstream and downstream regions in the same basin is a Stackelberg game, and the downstream region provides economic compensation for green innovation investment in the upstream region. The results show that there is an optimal ecological compensation rate, and a Pareto improvement result can be obtained by implementing ecological compensation. Increasing the proportion of ecological compensation can improve the nonvirtuous chain reaction between green innovation investment cost, pollutant transfer rate, and ecological compensation rate. Therefore, it is necessary to establish a joint mechanism composed of the government and the market and formulate a reasonable green innovation subsidy scheme according to the actual situation of the basin, so as to restrict the emergence of this “individual rational” behavior. For river basin areas that can establish a unified management department and organize the implementation of decision-making, the cooperative game is a very effective pollution control decision.

This paper analyzes a dynamic Stackelberg differential game model of watershed transboundary water pollution abatement and discusses the optimal decision-making problem under non-cooperative and cooperative differential game, in which the accumulation effect and depreciation effect of learning-by-doing pollution abatement investment are taken into account. We use dynamic optimization theory to solve the equilibrium solution of models. Through numerical simulation analysis, the path simulation and analysis of the optimal trajectory curves of each variable under finite-planning horizon and long-term steady state were carried out. Under the finite-planning horizon, the longer the planning period is, the lower the optimal emission rate is in equilibrium. The long-term steady-state game under cooperative decision can effectively reduce the amount of pollution emission. The investment intensity of pollution abatement in the implementation of non-cooperative game is higher than that of cooperative game. Under the long-term steady state, the pollution abatement investment trajectory of the cooperative game is relatively stable and there is no obvious crowding out effect. Investment continues to rise, and the optimal equilibrium level at steady state is higher than that under non-cooperative decision making. The level of decline in pollution stock under finite-planning horizon is not significant. Under the condition of long-term steady state, the trajectories of upstream and downstream pollution in the non-cooperative model and cooperative model are similar, but cooperative decision-making model is superior to the non-cooperative model in terms of the period of stabilization and steady state.

This paper studies a stochastic differential game of transboundary pollution abatement between two kinds of ecological compensation and the abatement policy, in which the learning by doing is taken into account. Emission and pollution abatement between upstream and downstream region in the same basin is a Stackelberg game, and the downstream regions provide economic compensation for pollution abatement in the upstream region. We discuss the feedback Nash equilibrium strategies of proportional compensation and investment compensation, and it is found that an appropriate ecological compensation ratio can improve the investment level of pollution abatement in the two regions by accumulating experience in the process of learning by doing. In the long term, the investment compensation mechanism is an effective transboundary pollution abatement measure that can continuously reduce the water pollution stock in the upstream and downstream.

Improving the efficiency of family farms is of great significance to rural revitalization and agricultural modernization in China. In order to find out the development status and shortcomings of family farms in China, and put forward targeted policy recommendations to improve the efficiency of various family farms, this paper applies the DEA model to measure the efficiency of family farms from a micro perspective by using the field survey data of the national family farm demonstration bases of Wuhan and Langxi, China. In addition, the Tobit model is further applied to explore the factors that affect the efficiency of full sample family farms, as well as to compare and analyze the differences in the efficiency in different regions and of different operation types. The results show that the efficiency of family farms is low, the efficiency of family farms in Wuhan is higher than that in Langxi, and the efficiency of breeding family farms is higher than that of planting family farms and mixed family farms. Capital input, farmers’ education level, market channels, brand registration, fertilizer usage and financial credit have positively affected the efficiency of family farms, while government subsidies and natural disasters have had negative effects on it. Specially, the land operating area shows a U-shaped relationship with farm efficiency. The efficiency of planting family farms is positively affected by labor input, while that of breeding and mixed family farms rely more on capital input and financial credit instead.

This data article is related to our recently published article (‘20.8% industrial PERC solar cell: ALD Al2O3 rear surface passivation, efficiency loss mechanisms analysis and roadmap to 24%’, Huang et al., 2017 [1]) where we have presented a systematic evaluation of the overall cell processing and a cost-efficient industrial roadmap for PERC cells. Aside from the information already presented in Huang et al., 2017 [1], here we provide data related to Sectin 3 in Huang et al., 2017 [1] concerning the analysis of the recombination losses׳ mechanisms by PC1D V5.9 and PC2D simulations (Clugston and Basore, 1997, Basore and Cabanas-Holmen, 2011, Cabanas-Holmen and Basore, 2012 and Cabanas-Holmen and Basore, 2012.) [2–5] on our current industrial Al2O3 PERC cell. The data include: i) PC2D simulations on J02, ii) the calculation of series resistance and back surface recombination velocity (BSRV) on the rear side metallization of PERC cell for the case of a point contact, and iii) the PC1D simulation on the cumulative photo-generation and recombination along the distance from the front surface. Finally, the roadmap of the solar cell efficiency for an industrial PERC technology up to 24% is presented, with the aim of providing a potential guideline for industrial researchers.

This data article is related to the recently published article ‘20.8% industrial PERC solar cell: ALD Al2O3 rear surface passivation, efficiency loss mechanisms analysis and roadmap to 24%’ (Huang et al., 2017) [1]. This paper is about passivated emitter and rear cell (PERC) structures and it describes the quality of the Al2O3 rear-surface passivation layer deposited by atomic layer deposition (ALD), in relation to the processing parameters (e.g. pre-clean treatment, deposition temperature, growth per cycle, and film thickness) and to the cell efficiency loss mechanisms. This dataset is made public in order to contribute to the limited available public data on industrial PERC cells, to be used by other researchers.