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Rural house is a fundamental component of rural settlements, and understanding its construction and development characteristics is crucial for rural land use and development planning. This paper focuses on the spatiotemporal characteristics and influencing factors of Rural Houses Construction Development (RHCD) from 1980 to 2019 with a case study of Qingyuan Town in China. Based on the literature review and filed research, a set of evaluation indicators for RHCD was established. The article calculates RHCD indicators from temporal and spatial dimensions, uses the location entropy method to demonstrate the spatial distribution of indicators, and classifies the RHCD type of 14 villages in Qingyuan Town using clustering algorithms. It also analyzes the influencing factors of spatiotemporal distribution. The results show that the RHCD in Qingyuan Town exhibits typical characteristics of mountainous areas and aligns with the development trends of rural society in China. Population growth, geographical location, and economic development are the primary driving factors for the quantity indicator (Qi), while economic growth, construction technology, industrial development, and policy adjustments are the key factors influencing the form indicator (Fi). In future policy-making, greater emphasis should be placed on optimizing development strategies, improving data and monitoring systems, and integrating administrative strength with actual development needs.

The neglect of endogenous strength is one of the reasons for the lack of sustainability in mountainous rural development and tourism development in China at present. How to incorporate the opinions of villagers in the tourism development process led by the government and other external entities is the main focus of this article. Based on the fieldwork of two typical mountainous villages and a previous rural development evaluation method, this article proposes the villager-satisfaction-based evaluation method for tourism village development, covering rural settlement construction, village esthetics, and economic and social development. “Villager satisfaction” is a crucial indicator obtained by objectifying the subjective opinions of villagers. Finally, the evaluation method was applied in the form of a questionnaire in two villages. The experimental results are correlated with the tourism development patterns of the two villages, verifying the feasibility and effectiveness of the evaluation method. It is expected that this evaluation method will become an effective communication medium between non-professional villagers and the professional tourism development process, thereby promoting the sustainable development of rural areas in the future.

Objective: To evaluation the clinical efficacy and safety of bronchial artery chemoembolization (BACE) combined with apatinib for treatment of advanced central lung squamous cell carcinoma (LSCC). Methods: Forty-seven patients with pathologically diagnosed stage IIIB or IV central LSCC that was not resectable were selected among hospital patients presenting after November 2016. Twenty-one patients were treated with BACE combined with apatinib; the remaining patients served as a control group treated with BACE alone. Objective response rate (ORR) and disease control rate (DCR) were evaluated with postoperative contrast-enhanced CT scans at 3, 6, and 12 months. Progression-free survival (PFS) curves were used to evaluate curative effects. Adverse events were recorded to assess safety. Results: BACE operations were successfully completed in all 47 patients. Significant differences were found at six and 12 months (P < 0.05). Median PFS was 322 days in the observation group and 209 days in the control group: a statistically significant difference (P = 0.042). One-year survival rates were 76.19% and 46.15% for observation and control patients, respectively; this difference was also significant (P = 0.037). Three patients in the observation group received emergency interventional embolization for hemoptysis, and patients with grade III or greater adverse reaction events (AE) accounted for 19.05% of patients (4/21); these subjects improved or were controlled after active treatment. Conclusion: BACE combined with apatinib is effective for treatment of advanced central LSCC, with definite short-term efficacy, controllable risk, and high safety. Investigation with a larger sample size is warranted to confirm study results.

The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p < 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p < 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p < 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.

Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

The relationship between plasma metal elements and cognitive function is unclear, especially in extremely older individuals. This present study aimed to explore the association between plasma metal concentrations and the risk of cognitive impairment (CI) in Chinese extremely older adults. Individuals aged ≥90 years with plasm metal concentration data from the fifth wave of the 2008 Chinese Longitudinal Healthy Longevity Survey were included. Plasma selenium (Se), manganese (Mn), magnesium (Mg), calcium (Ca), iron (Fe), copper (Cu), and zinc (Zn) concentrations were measured using inductively coupled plasma optical emission spectroscopy. Cognitive function was assessed by the Chinese version of the mini-mental state examination. The study enrolled 408 participants. Participants with CI had significantly lower plasma Se, Mn, and Fe levels and higher Ca levels than those with normal cognitive function (  < 0.05). Plasma Se, Mn, Ca, and Fe concentrations were significantly associated with CI risk in both single- and multiple-element logistic regression models. Additionally, the multiple-element model results showed that the adjusted odds ratios for CI were 0.042 (95% confidence interval 0.016-0.109), 0.106 (0.044-0.255), 7.629 (3.211-18.124) and 0.092 (0.036-0.233) for the highest quartiles compared to the lowest quartiles of Se, Mn, Ca, and Fe, respectively. Moreover, subgroup analyses by age, sex, and body mass index suggested a consistent significant correlation (  < 0.05). Therefore, decreased plasma Se, Mn, and Fe and increased plasma Ca levels were associated with CI risk in Chinese older adults. These findings are of great significance for the development of programs to delay cognitive decline in the elderly.

The proteasome activator PA28αβ affects MHC class I antigen presentation by associating with immunoproteasome core particles (iCPs). However, due to the lack of a mammalian PA28αβ-iCP structure, how PA28αβ regulates proteasome remains elusive. Here we present the complete architectures of the mammalian PA28αβ-iCP immunoproteasome and free iCP at near atomic-resolution by cryo-EM, and determine the spatial arrangement between PA28αβ and iCP through XL-MS. Our structures reveal a slight leaning of PA28αβ towards the α3-α4 side of iCP, disturbing the allosteric network of the gatekeeper α2/3/4 subunits, resulting in a partial open iCP gate. We find that the binding and activation mechanism of iCP by PA28αβ is distinct from those of constitutive CP by the homoheptameric Tb PA26 or Pf PA28. Our study sheds lights on the mechanism of enzymatic activity stimulation of immunoproteasome and suggests that PA28αβ-iCP has experienced profound remodeling during evolution to achieve its current level of function in immune response. The proteasome activator PA28αβ affects MHC class I antigen presentation by associating with immunoproteasome core particles (iCPs). Cryo-EM structures of the mammalian PA28αβ -iCP immunoproteasome and free iCP, combined with cross-linking data, reveal the complex architecture and suggest a distinct immunoproteasome activation mechanism.

Background Artificial intelligence (AI) is reshaping the landscape of medical education with unprecedented depth and breadth. As technologies like large language models and natural language processing advance, AI agents with multimodal interaction capabilities—such as intelligent teaching assistants and virtual simulation labs—are demonstrating immense potential. Concurrently, medical students face challenges including a disconnect between theoretical knowledge and clinical practice, excessive cognitive load, and a lack of personalized practical opportunities. Medical education AI agents are poised to address these issues, but their successful integration hinges on student acceptance and adoption. This study aims to fill a gap in the current empirical research by investigating the key psychological mechanisms and behavioral factors that influence medical students’ adoption of AI educational agents. Methods This study constructed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model by integrating four key variables tailored to the medical education context: AI Trust, Perceived Risk, Hedonic Motivation, and Trialability. A cross-sectional survey was conducted with an initial sample of 200 clinical medicine students following their interaction with a custom-developed interactive medical education AI agent. After excluding invalid responses, a final valid sample of 155 participants was retained. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to validate the theoretical model and test the research hypotheses. Results The constructed model demonstrated strong explanatory power, successfully accounting for 85.3% of the variance in students’ behavioral intention (R² = 0.853). Effort Expectancy (β = 0.362, p  < 0.001) and Performance Expectancy (β = 0.297, p  < 0.001) were the strongest direct positive predictors of behavioral intention, with Facilitating Conditions (β = 0.258, p  = 0.002) also showing a significant impact. A noteworthy finding was that Social Influence had no significant effect on behavioral intention (β = 0.038, p  = 0.633). Furthermore, Hedonic Motivation had a significant positive influence on both Effort Expectancy (β = 0.818, p  < 0.001) and Performance Expectancy (β = 0.237, p  < 0.001). AI Trust, Trialability, and lower Perceived Risk also significantly enhanced students’ Performance Expectancy. Conclusions The findings indicate that for medical students, who are highly autonomous professional learners, the intrinsic value of an AI educational tool (i.e., its utility and ease of use) is the dominant factor in their adoption decisions, far outweighing the social influence of peers or authorities. Therefore, the key to successfully promoting such technologies lies in building users’ intrinsic trust, reducing their perceived risks, and providing an engaging, immersive learning experience. These findings provide a solid empirical basis for the optimal design of medical AI educational agents and for strategies to effectively integrate them into the curriculum.

Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale.

Background: Diabetes affects millions of people worldwide annually, and several methods, including medications, are used for its management; glucagon-like peptide-1 receptor agonists (GLP-1RAs) are one such class of medications. The efficacy and safety of GLP-1RAs in treating type 2 diabetes mellitus (T2DM) have been assessed and have been shown to significantly improve time in range (TIR) in several clinical trials. However, presently, there is a lack of real-world evidence on the efficacy of GLP-1RAs in improving TIR. To address this, we investigated the effect of GLP-1RA-based treatment strategies on TIR among patients with T2DM in real-world clinical practice. Methods: This multicenter, retrospective, real-world study included patients with T2DM who had previously used a continuous glucose monitoring (CGM) system and received treatment with GLP-1RAs or oral antidiabetic drugs (OADs). Patients who received OADs served as controls and were matched in a 1:1 ratio to their GLP-1RA counterparts by propensity score matching. The primary endpoint was the TIR after 3–6 months of treatment. Results: According to propensity score matching, 202 patients were equally divided between the GLP-1RA and OAD groups. After 3–6 months of treatment, the TIR values for the GLP-1RA and OAD groups were 76.0% and 65.7%, respectively ( p < 0.001). The GLP-1RA group displayed significantly lower time above range (TAR) and mean glucose values than the OAD group ( p < 0.001). Subgroup analysis revealed that, compared with the administration of liraglutide, the administration of semaglutide and polyethylene glycol loxenatide (PEG-Loxe) significantly improved TIR over 3–6 months of treatment ( p < 0.05). Conclusion: These real-world findings indicate that GLP-1RA-based treatment strategies could be superior to oral treatment strategies for improving TIR among patients with T2DM and that once-weekly GLP-1RA may be more effective than a once-daily GLP-1RA. Clinical trial registration: http://www.chinadrugtrials.org.cn/index.html , identifier number ChiCTR2300073697.