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This paper employs the slack-based measure method and an extended Luenberger productivity indicator to estimate and decompose the atmospheric environmental performance under the constraints of energy and atmospheric pollutant emissions [i.e., the growth of the atmospheric environment total factor productivity (AETFP)] of the “three regions and ten urban agglomerations” (TRTAs) in China. Specifically, undesirable output is considered as both carbon and air pollutant emissions, i.e., CO2, SO2, and NOx emissions. Also, based on the proposed approach, we identify the different paths of the technical change as a crucial driver of the AETFP growth. Furthermore, using the spatial econometric model with a symmetric geographical distance weight matrix and an asymmetric economic geography weight matrix, we investigate the effect of different types of environmental regulation on the AETFP growth to verify the Porter hypothesis in China. The results show that the main drivers of China’s atmospheric environment inefficiency are air pollutant emissions (SO2 and NOx), carbon emissions, and fossil energy use. Spatially, the environment inefficiency presents a decreasing trend from northern China to southern China. The improved performance of SO2 emissions made more contributions to the AETFP growth during China’s 11th “Five-Year Plan” period (2006–2010), while NOx emissions has a marginal positive effect on the AETFP growth is marginal. Despite the differences in the technical change across regions, the technical progress offsets the negative impact of declining technical efficiency on the AETFP growth. Overall, energy-saving and emission-reduction policies and technologies in TRTAs exert a decisive influence on the AETFP growth. In particular, the spatial econometric results indicate that the market-motivated environmental regulation has a positive effect on the AETFP growth and thus conforms to the Porter hypothesis in China but does not cause the “race-to-the-bottom” effect among local governments, while the command-and-control oriented regulation leads to a “race-to-the-bottom” effect and undermines the AETFP growth.

As the centre of human activity and being under the threat of climate change, cities are considered to be major components in the implementation of climate change mitigation and CO2 emission reduction strategies. Inventories of cities' emissions serve as the foundation for the analysis of emissions characteristics and policymaking. China is the world's top energy consumer and CO2 emitter, and it is facing great potential harm from climate change. Consequently, China is taking increasing responsibility in the fight against global climate change. Many energy/emissions control policies have been implemented in China, most of which are designed at the national level. However, cities are at different stages of industrialization and have distinct development pathways; they need specific control policies designed based on their current emissions characteristics. This study is the first to construct emissions inventories for 182 Chinese cities. The inventories are constructed using 17 fossil fuels and 47 socioeconomic sectors. These city-level emissions inventories have a scope and format consistent with China's national/provincial inventories. Some socioeconomic data of the cities, such as GDP, population, industrial structures, are included in the datasets as well. The dataset provides transparent, accurate, complete, comparable, and verifiable data support for further city-level emissions studies and low-carbon/sustainable development policy design. The dataset also offers insights for other countries by providing an emissions accounting method with limited data.

Phytoplasmas are wall-less, phloem-restricted bacteria responsible for numerous significant plant diseases worldwide. An increasing body of evidence indicates that phytoplasmas can coexist with other pathogens in mixed infections, including various 16Sr group phytoplasmas, ‘Candidatus Liberibacter’ species, viruses, spiroplasmas, fungi, and other difficult-to-culture phloem-limited bacteria. These interactions challenge established views regarding the causes, detection, and management of plant diseases. This review consolidates existing knowledge on the diversity and epidemiology of phytoplasma-related mixed infections, with a particular emphasis on documented co-infections across various host plants and regions, especially in tropical and subtropical areas. Mixed infections affect disease severity, symptom expression, vector behavior, and pathogen dissemination, highlighting the limitations of pathogen-specific diagnostic and control strategies. The necessity for tools to detect multiple pathogens, enhanced understanding of pathogen–pathogen and host–pathogen interactions, and comprehensive surveillance systems is emphasized. Ultimately, breeding for resistance must consider the complexities of natural co-infections to ensure effective protection of crops. Addressing the challenges presented by phytoplasma-related mixed infections is crucial for developing resilient and sustainable plant health strategies in the face of increasing ecological and agricultural pressures.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, with anti-double-stranded DNA (anti-dsDNA) antibodies as its serological biomarkers. However, conventional anti-dsDNA antibody detection methods, which mainly rely on antibody-binding assays, often suffer from limited sensitivity and specificity, cumbersome procedures, and poor suitability for accurate clinical analysis. Herein, we developed an integrated detection system combining a circularly permuted fluorescent protein (cpFP)-based biosensor with a microfluidic chip for rapid and reliable anti-dsDNA antibody detection. The biosensor, cpR-dsAb-C1, was engineered from mApple by inserting an affinity peptide identified through phage display, enabling specific recognition of the variable region of anti-dsDNA antibodies. The biosensor exhibited good sensitivity, specificity, and anti-interference capability. Furthermore, integration of cpR-dsAb-C1 with a polydimethylsiloxane (PDMS)-based microfluidic chip yielded a microfluidic detection platform with good linearity for rapid antibody analysis. Clinical validation showed significantly higher anti-dsDNA antibody levels in patients with SLE than in healthy controls, and the results were consistent with those obtained using routine clinical methods, with an accuracy exceeding 95%. Overall, this system provides a promising low-cost, efficient, and accurate strategy for the early diagnosis and dynamic monitoring of SLE.

CRISPR/Cas genome editing is a simple, cost effective, and highly specific technique for introducing genetic variations. In mammalian cells, CRISPR/Cas can facilitate non-homologous end joining, homology- directed repair, and single-base exchanges. Cas9/Cas12a nuclease, dCas9 transcriptional regulators, base editors, PRIME editors and RNA editing tools are widely used in basic research. Currently, a variety of CRISPR/Cas-based therapeutics are being investigated in clinical trials. Among many new findings that have advanced the field, we highlight a few recent advances that are relevant to CRISPR/Cas-based gene therapies for monogenic human genetic diseases.

The excessive accumulation of extracellular matrix (ECM) is a key feature of liver fibrosis and the activated hepatic stellate cells (HSCs) are the major producer of ECM proteins. However, the precise mechanisms and target molecules that are involved in liver fibrosis remain unclear. In this study, we reported that activating transcription factor 3 (ATF3) was over-expressed in mice and human fibrotic livers, in activated HSCs and injured hepatocytes (HCs). Both in vivo and in vitro study have revealed that silencing ATF3 reduced the expression of pro-fibrotic genes and inhibited the activation of HSCs, thus alleviating the extent of liver fibrosis, indicating a potential protective role of ATF3 knockdown. However, ATF3 was not involved in either the apoptosis or proliferation of HCs. In addition, our data illustrated that increased nuclear localization of ATF3 promoted the transcription of fibrogenic genes and lnc-SCARNA10, which functioned as a novel positive regulator of TGF-β signaling in liver fibrogenesis by recruiting SMAD3 to the promoter of these genes. Interestingly, further study also demonstrated that lnc-SCARNA10 promoted the expression of ATF3 in a TGF-β/SMAD3-dependent manner, revealing a TGF-β/ATF3/lnc-SCARNA10 axis that contributed to liver fibrosis by activating HSCs. Taken together, our data provide a molecular mechanism implicating induced ATF3 in liver fibrosis, suggesting that ATF3 may represent a useful target in the development of therapeutic strategies for liver fibrosis.

Cobalt disulfide (CoS2) features highly active catalytic sites and is regarded as a promising candidate for electrocatalytic hydrogen evolution. In this study, molybdenum-doped cobalt disulfide (CoS2:Mo) was synthesized via a facile hydrothermal approach. XRD analysis confirms that the obtained samples crystallize in a cubic pyrite structure, with diffraction peaks consistently shifting towards lower angles. SEM characterization reveals that the samples exhibit microrod-like morphologies with an average size of approximately 1 μm. Integrated analyses from XRD, XPS, and EDS mapping demonstrate that Mo is uniformly distributed across the surface and successfully doped into the CoS2 lattice. Electrochemical measurements indicate that the CoS2:Mo sample delivers a low overpotential of 122 mV and a Tafel slope of 128 mV dec−1 at a current density of 10 mA cm−2 in alkaline media, significantly surpassing the performance of pure CoS2 and MoS2. Moreover, the CoS2:Mo exhibits an enhanced double-layer capacitance, with a Cdl value of 2.72 mF cm−2, superior to that of pure CoS2 (1.63 mF cm−2) and MoS2 (0.31 mF cm−2). Mo doping enhances conductivity and active sites, thereby boosting electrocatalysis. This work presents an effective strategy for the development of cost-efficient and high-performance non-precious metal electrocatalysts.

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost.

To investigate the impact of exercise adherence on life satisfaction among Chinese university students and to explore the mediating roles of motor-skill competence and health literacy. The ultimate goals are to provide a comprehensive understanding of the mechanisms underlying the relationship between physical activity and well-being, and to inform the development of targeted interventions that promote holistic student development. A total of 15,031 valid responses were extracted from a national university-student survey database. All variables were assessed with standardized questionnaires. Data were analyzed with SPSS 27.0. Promoting the physical and mental well-being and holistic development of students has become a policy priority that commands national attention, public concern, and sustained governmental focus, and the PROCESS macro v4.0. Exercise adherence, life satisfaction, motor-skill competence, and health literacy were positively intercorrelated. Exercise adherence significantly and positively predicted university students' life satisfaction. The indirect effect along Path 1-exercise adherence → motor-skill competence → life satisfaction-was 0.003, accounting for 1.1% of the total effect. The indirect effect along Path 2-exercise adherence → health literacy → life satisfaction-was 0.067, accounting for 26.25% of the total effect. The indirect effect along Path 3-exercise adherence → motor-skill competence → health literacy → life satisfaction-was 0.001, accounting for 0.28% of the total effect. The study findings reveal that exercise adherence has a positive direct effect on university students' life satisfaction and also exerts an indirect effect through the sequential mediation of motor-skill competence and health literacy. These results provide empirical evidence of the complex interplay between physical activity, skill development, and mental health.

Due to the slender geometry and low-amplitude vibrations of stayed cables, existing vision-based methods often fail to accurately identify their full-field dynamic parameters, especially the higher-order modes. This paper proposes a novel holographic vision-based method to accurately identify the high-order full-field dynamic parameters and estimate the tension of the stayed cables. Particularly, a full-field optical flow tracking algorithm is proposed to obtain the full-field dynamic displacement information of the stayed cable by tracking the changes in the optical flow field of the continuous motion signal spectral components of holographic feature points. Frequency-domain analysis is applied to extract the natural frequencies and damping ratios, and the vibration frequency method is used to estimate the tension. Additionally, an Eulerian-based amplification algorithm—holographic feature point video magnification (HFPVM)—is proposed for enhancing weak visual motion signals of the stayed cables, so that the morphological motion information of the stayed cables can be visualized. The effectiveness of the proposed method has been validated through experiments on the stayed cable models. Compared with the results obtained using contact sensors, the proposed holographic vision-based method can accurately identify the first five natural frequencies with overall errors below 5% and a maximum deviation of 6.86% in cable tension estimation. The first three normalized holographic mode shapes and dynamic displacement vectors are successfully identified, with the MAC value reaching up to 99.51%. This entirely non-contact vision-based method offers a convenient and low-cost approach for cable tension estimation, and this is also the first study to propose a comprehensive, visual, and quantifiable strategy for periodic or long-term monitoring of cable-supported structures, highlighting its strong potential in practical applications.