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To date, evidence is rare regarding whether and how dietary antioxidants are associated with the risk of periodontitis. This study aimed to investigate the association of composite dietary antioxidant index (CDAI) with periodontitis and tooth loss, using data from the National Health and Nutrition Examination Survey (2009-2014). A cross-sectional analysis was conducted using data from 10,067 adults aged ≥30 years who underwent assessments of periodontal health and the 1 day dietary recall. Based on a crude model and three adjusted models, multivariate regressions were used to examine the relationship between CDAI and periodontitis-related measurements including probing pocket depth, clinical attachment loss and tooth loss. Subgroup analyses and the restricted cubic splines plots were applied to examine the association between CDAI ingredients and periodontitis. For the subjects with high CDAI scores, increased CDAI was associated with significant ( 0.05) reduction of severe periodontitis (odd ratio = 0.663, 95% confidence interval: 0.491-0.896) and increased number of remaining teeth (weighted β[SE] = 1.167[0.211]). However, the protective effect of CDAI on periodontitis vanished ( > 0.05) in active smokers and former smokers. There were threshold levels for β-carotene, Vitamin A, C and E intakes where the risk of periodontitis significantly decreased ( 0.05) above these levels. Increased CDAI was associated with reduced risk of periodontitis and tooth loss for non-smokers. It was recommendable that proper dietary intakes of β-carotene, Vitamin A, C and E would be of benefit for preventive dental care and adjuvant therapies for periodontitis.

China’s rapid urbanization, characterized by extensive land allocations, operates within a framework of binding quotas imposed by upper-level governments, while local governments exercise broad discretion over the zoning of newly transacted land parcels. In this context, investigating the evolving patterns of land supply structure during this period is therefore of critical importance. The central government’s 2018 articulation of the “Houses are for living in, not for speculation” (fangzhubuchao) sought to mitigate housing market speculation and curb potential asset bubbles, including through changes to residential land supply. Using a panel of 266 prefecture-level cities across China, this study employs a generalized difference-in-difference model to examine how housing market regulations affect the industrial sector through adjustments in land supply. To capture cross-city variations in local policy interventions, we construct a measure based on the land price wedge between residential (and commercial) and industrial land derived from a hedonic pricing model, which reflects underlying housing market conditions. The results indicate that a reduction in residential land supply caused by these policies results in a corresponding increase in industrial land supply, while the total land supply remains unchanged. These effects are more pronounced in cities with stringent policy regulations and relaxed urban land quotas. The short-term economic outcomes are inadequate. As of 2023, our analysis reveals no substantial increase in either the number of industrial enterprises or the industrial value added, notwithstanding the augmented industrial land supply. Consequently, these findings identify a secondary determinant of industrial location patterns and provide a scientific basis for designing efficient land-use regulations and sustainable urban development strategies.

Abstract Repairing injured tendon or ligament attachments to bones (enthesis) remains costly and challenging. Despite superb surgical management, the disorganized enthesis newly formed after surgery accounts for high recurrence rates after operations. Tissue engineering offers efficient alternatives to promote healing and regeneration of the specialized enthesis tissue. Load-transmitting functions thus can be restored with appropriate biomaterials and engineering strategies. Interestingly, recent studies have focused more on microstructure especially the arrangement of fibers since Rossetti successfully demonstrated the variability of fiber underspecific external force. In this review, we provide an important update on the current strategies for scaffold-based tissue engineering of enthesis when natural structure and properties are equally emphasized. We firstly described compositions, structures and features of natural enthesis with their special mechanical properties highlighted. Stimuli for growth, development and healing of enthesis widely used in popular strategies are systematically summarized. We discuss the fabrication of engineering scaffolds from the aspects of biomaterials, techniques and design strategies and comprehensively evaluate the advantages and disadvantages of each strategy. At last, this review pinpoints the remaining challenges and research directions to make breakthroughs in further studies. Graphical abstract

Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability protein sequences. Our core innovation lies in: First, integrating Markov Bridges with Direct Preference Optimization (DPO), where energy-based preferences are used to fine-tune the Markov Bridge model. The Markov Bridge initiates optimization from an information-rich prior sequence, providing DPO with a pool of structurally plausible sequence candidates. Second, an explicit energy constraint loss is introduced, which enhances the energy-driven nature of DPO based on prior sequences, enabling the model to effectively learn energy representations from a wealth of prior knowledge and directly predict sequence energy values, thereby capturing quantitative features of the energy landscape. Our evaluations demonstrate that EnerBridge-DPO can design protein complex sequences with lower energy while maintaining sequence recovery rates comparable to state-of-the-art models, and accurately predicts \\( G\\) values between various sequences.

Accurate prediction of enzyme function is crucial for elucidating biological mechanisms and driving innovation across various sectors. Existing deep learning methods tend to rely solely on either sequence data or structural data and predict the EC number as a whole, neglecting the intrinsic hierarchical structure of EC numbers. To address these limitations, we introduce MAPred, a novel multi-modality and multi-scale model designed to autoregressively predict the EC number of proteins. MAPred integrates both the primary amino acid sequence and the 3D tokens of proteins, employing a dual-pathway approach to capture comprehensive protein characteristics and essential local functional sites. Additionally, MAPred utilizes an autoregressive prediction network to sequentially predict the digits of the EC number, leveraging the hierarchical organization of EC classifications. Evaluations on benchmark datasets, including New-392, Price, and New-815, demonstrate that our method outperforms existing models, marking a significant advance in the reliability and granularity of protein function prediction within bioinformatics.

At present, face 3D reconstruction has broad application prospects in various fields, but the research on it is still in the development stage. In this paper, we hope to achieve better face 3D reconstruction quality by combining multi-view training framework with face parametric model Flame, propose a multi-view training and testing model MFNet (Multi-view Flame Network). We build a self-supervised training framework and implement constraints such as multi-view optical flow loss function and face landmark loss, and finally obtain a complete MFNet. We propose innovative implementations of multi-view optical flow loss and the covisible mask. We test our model on AFLW and facescape datasets and also take pictures of our faces to reconstruct 3D faces while simulating actual scenarios as much as possible, which achieves good results. Our work mainly addresses the problem of combining parametric models of faces with multi-view face 3D reconstruction and explores the implementation of a Flame based multi-view training and testing framework for contributing to the field of face 3D reconstruction.

Next-generation polarized mid-infrared imaging systems generally requires miniaturization, integration, flexibility, good workability at room temperature and in severe environments, etc. Emerging two-dimensional materials provide another route to meet these demands, due to the ease of integrating on complex structures, their native in-plane anisotropy crystal structure for high polarization photosensitivity, and strong quantum confinement for excellent photodetecting performances at room temperature. However, polarized infrared imaging under scattering based on 2D materials has yet to be realized. Here we report the systematic investigation of polarized infrared imaging for a designed target obscured by scattering media using an anisotropic tellurium photodetector. Broadband sensitive photoresponse is realized at room temperature, with excellent stability without degradation under ambient atmospheric conditions. Significantly, a large anisotropic ratio of tellurium ensures polarized imaging in a scattering environment, with the degree of linear polarization over 0.8, opening up possibilities for developing next-generation polarized mid-infrared imaging technology. Photodetectors operating within scattering environment can be realized with anisotropic materials. Here, the authors report polarization sensitive photodetectors based on thin tellurium nanosheets with high photoresponsivity of 3.54 × 10 2  A/W, detectivity of ~3.01 × 10 9  Jones in the mid-infrared range and an anisotropic ratio of ∼8 for 2.3 μm illumination to ensure polarized imaging.

With the acceleration of Chinese industrialization, industrial wastewater is discharged in large quantities, leading to a groundwater environment with high ammonia nitrogen characteristics in many places, which seriously endangers people’s health and makes the treatment of ammonia nitrogen by enterprises an urgent issue. Therefore, based on the principle of “no-fault responsibility”, this paper combines China’s pollution trading rights policy and the reward and punishment distribution mechanism to provide a three-party governance model for groundwater ammonia nitrogen treatment under the benefit sharing of emissions trading. By constructing a tripartite evolutionary game model of groundwater ammonia nitrogen pollution treatment among sewage discharge enterprises, third-party governance enterprises and local governments, the role mechanisms of the strategic choices of different participating actors are analyzed. Finally, the validity of the model is verified via simulation, and the influence of key variables on the evolutionary stability of the system and the strategic choices of the participating parties under different situations are discussed. The research results show that setting reasonable reward and punishment allocation coefficients is the basis for promoting active pollution treatment among sewage discharge enterprises and third-party governance enterprises; a change in pollution rights trading revenue is a key factor affecting the strategic choices of the three parties; sewage discharge enterprises show stronger revenue sensitivity than third-party governance enterprises; and an environmental treatment credit system built by the government can effectively enhance the enthusiasm of enterprises to control pollution. Based on the research results of this paper, the participation of third-party governance enterprises in pollution rights trading is explored, which effectively promotes enterprises to actively carry out groundwater ammonia nitrogen treatment and provides a reference for the government to improve the construction of a sustainable development system for the water environment.

The objective of this work was to investigate the sorption behavior of gases, namely CO 2 , CH 4 , and N 2 , by molecules of coal sampled from Linglu mine under different water inclusion rates. To this end, the adsorption, diffusion, adsorption heat, and potential energy distribution characteristics of the gases in the coal pores at different water inclusion rates were analyzed using molecular dynamics and grand canonical ensemble Monte Carlo methods. The results showed that the adsorption relationship of the coal molecules on CO 2 , CH 4 , and N 2 exhibited a downtrend followed by an uptrend when the water content was increased from 0 to 3.6%. The adsorption amount of CO 2 was approximately twice as much as those of CH 4 and N 2 , indicating that the competitive adsorption advantage of CO 2 compared with those of CH 4 and N 2 was unaffected by the water content. The trend in the average heat of adsorption was generally consistent with the trend in the density of coal molecules under different moisture contents. Under the same conditions, the diffusion coefficient within a coal molecule was negatively related to the water content in the system. The layer spacing of the water molecules (2.875 Å) was greater than the liquid–water layer spacing, indicating the formation of a water molecule layer at this point, which inhibited gas adsorption. This study lays a theoretical foundation for further investigating the microscopic mechanism of coal–water interaction.

The excessive use of fossil energy has led to a yearly increase in carbon dioxide and atmospheric pollutant emissions, and climate change has become increasingly prominent, seriously affecting people’s daily lives and physical and mental health. According to statistics, rising temperatures and extreme weather phenomena due to climate change have led to a 68% increase in heat-related deaths today compared to the period between 2000 and 2004, and a 61% increase in the number of days humans face high fire risks in the same period. Currently, in order to achieve synergistic economic and environmental development and enhance the health co-benefits of carbon emission reduction, it is urgent for high-energy-consuming enterprises to make sound low-carbon technology investment decisions. Therefore, in this paper, under the carbon quota and trading policy and carbon tax policy, and considering the existence of low-carbon preferences of consumers, the financial constraints of upstream high energy-consuming enterprises and sufficient funds of downstream retailers, a low-carbon technology investment decision model under intra-supply chain financing is constructed using Stackelberg game theory. Moreover, by applying the inverse induction method, we solve the optimal decision of low-carbon technology investment with three different subsidy methods: no subsidy, cost subsidy and product subsidy. Finally, the validity of the model is verified by numerical simulation, and the effects of different influencing factors on low-carbon technology investment are analyzed. The results show that: (1) the reasonable formulation of carbon trading price, carbon tax rate, cost subsidy ratio and product subsidy coefficient are important factors to promote enterprises’ low-carbon technology investment; (2) the improvement of consumers’ low-carbon preference level and the reduction in repayment interest rate can promote enterprises’ investment; (3) compared with no subsidy, cost subsidy and product subsidy can effectively improve enterprises’ low-carbon technology investment enthusiasm, and the effect of product subsidy is better than that of cost subsidy. The effect of product subsidies is better than that of cost subsidies. This paper aims to provide suggestions for the government to refine low-carbon technology investment incentive policies and for enterprises to optimize low-carbon technology investment decisions, so as to enhance the healthy co-benefits of carbon emission reduction and achieve green and sustainable economic development.