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Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the best image pairs to derive slope deformation remains difficult. By selecting a location in the east Tibetan Plateau, this work used the co-registration of optically sensed images and correlation (COSI-Corr) method to analyze 402 Sentinel-2 images from August 2015 to February 2020, to quantify temporal patterns of uncertainty in deriving slope deformation. By excluding 66% of the Sentinel-2 images that were contaminated by unfavorable weather, uncertainties were found to fluctuate annually, with the least uncertainty achieved in image pairs of similar dates in different years. Six image pairs with the least uncertainties were selected to derive ground displacement for a moving slope in the study area. Cross-checks among these image pairs showed consistent results, with uncertainties less than 1/10 pixels in length. The findings from this work could help in the selection of the best image pairs to derive reliable slope displacement from large numbers of optical images.

Massive landslides over large regions can be triggered by heavy rainfalls or major seismic events. Mapping regional landslides quickly is important for disaster mitigation. In recent years, deep learning methods have been successfully applied in many fields, including landslide automatic identification. In this work, we proposed a deep learning approach, the ResU-Net, to map regional landslides automatically. This method and a baseline model (U-Net) were collectively tested in Tianshui city, Gansu province, where a heavy rainfall triggered more than 10,000 landslides in July 2013. All models were performed on a 3-band (near infrared, red, and green) GeoEye-1 image with a spatial resolution of 0.5 m. At such a fine spatial resolution, the study area is spatially heterogeneous. The tested study area is 128 km2, 80% of which was used to train models and the remaining 20% was used to validate accuracy of the models. This proposed ResU-Net achieved higher accuracy than the baseline U-Net model in this mountain region, where F1 improved by 0.09. Compared with the U-Net model, this proposed model (ResU-Net) performs better in discriminating landslides from bare floodplains along river valleys and unplanted terraces. By incorporating environmental information, this ResU-Net may also be applied to other landslide mapping, such as landslide susceptibility and hazard assessment.

As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. This study examines the eGDR-frailty link, develops a machine learning predictive model to address this gap, and explores diabetes mellitus (DM) as a mediator, providing new insights for clinical intervention. Using National Health and Nutrition Examination Survey (NHANES) 2005-2010 data, we analyzed glucose disposal and frailty associations. Feature selection used LASSO, and class imbalance was handled by SMOTEN. The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. Confusion matrices visualized performance. Mediation analysis assessed DM's role in the eGDR-frailty relationship. Among 26,282 participants, eGDR negatively correlated with frailty. Higher eGDR significantly reduced frailty risk in subgroups: women, age ≤ 60, normal/high BMI, never/current smokers, and alcohol users. LASSO selected 12 predictors. Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model's predicted probability of frailty. Confusion matrices validated classification accuracy. Mediation analysis showed DM partially mediated the eGDR-frailty relationship: indirect effect β=-0.003 (95% CI -0.003 to -0.002; P < 0.001), mediation proportion = 8.71%. This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM's partial mediating role. The developed machine learning models effectively support early frailty risk assessment and intervention.

Using 730 winning entries in the 2023 “Kyushu Cup” National Study Tour Program Design Competition as examples, this article analyzes characteristics of geography study tour program development in China via GIS and SPSS software. The study results show that the development of geography study tour programs in China is unbalanced in terms of spatial distribution, course content, main body of design, and field trip scope. The following manifestations were observed: first, there are large spatial and administrative differences in the development of Chinese geography study tour programs; second, although geography is a comprehensive discipline that emphasizes the holistic nature of regional studies, the proportion of comprehensive regional study tours is relatively low; third, colleges and universities are the primary developers of geography study tour programs, followed by secondary schools; and finally, the development of Chinese geography study tour programs is dominated by local study tours, which are mainly based on vernacular geography. This study provides new insight into the spatial and administrative disparities in geography study tour programs, highlights the lack of comprehensive regional study tours, and offers practical recommendations for promoting a more balanced and interdisciplinary approach to geography education in China.

Milk tea stores have rapidly expanded in Wuhan due to residents’ increased consumption demand. Therefore, studying the spatial distribution and influencing factors of stores is important for optimizing their layout and promoting economic development. Using milk-tea store data from Amap City’s Point of Interest function and road network data from Baidu HeatMap, we analyzed the spatial distribution characteristics of stores within the third ring road of Wuhan City using ArcGIS. We then examined the influencing factors by combining spatial design network analysis, optimal parameters-based geographical detection, and location-based service big data. Our results revealed the following: (1) The spatial distribution of stores was concentrated in areas with high closeness and betweenness centrality, forming a multi-core “northwest–southeast” distribution pattern with significant spatial positive correlation. (2) The stores’ spatial pattern was influenced by the road network betweenness and the presence of office buildings, shopping malls, shopping centers, and tourism resources. The road network betweenness had the greatest impact on the stores’ spatial distribution, while the kernel density of betweenness presented a “one major and multiple sub-core” structure consistent with that of the stores’ spatial distribution. The kernel density of closeness and betweenness regulated the formation of the stores’ core area and multiple sub-core areas, respectively, and both factors governed the stores’ spatial distribution, which was characterized by a “widely-scattered and sporadically-clustered” pattern. (3) The stores’ distribution was closely associated with the spatial and dynamic population distribution at different times of the day. By demonstrating the big data for the spatial distribution and driving factors of milk tea stores at the urban regional scale, we fill the research gap on the spatial distribution of milk tea stores at the meso-scale. Our results offer insights into the future urban planning of milk tea stores amid the current milk tea craze.

To evaluate ultrasound’s impact on braised rabbit legs, rabbit leg meat was treated at frequencies of 0 (control), 30, 60, 90, and 120 kHz during the low-temperature braising process. A multi-dimensional analytical approach—incorporating scanning electron microscopy (SEM), texture profile analysis (TPA), water-holding capacity (WHC) assessment, flavor compound profiling, and oxidation analysis—was employed to systematically investigate ultrasound’s effects on braised rabbit meat. SEM revealed ultrasound-induced muscle fiber contraction and structural disruption, which directly improved water-holding capacity and texture. Quantification of sodium chloride and amino acids in sample cores demonstrated enhanced mass transfer, particularly at higher frequencies (60–120 kHz). Lipid oxidation (TBARS) and fatty acid profiling (GC-MS) confirmed ultrasound-promoted oxidation, generating significantly increased flavor-active aldehydes. Concurrently, HPLC-MS/MS analysis showed elevated nucleotide levels, indicating accelerated hydrolysis of flavor precursors. Collectively, these results suggest that ultrasound may offer a viable approach to energy-efficient braising while improving texture and flavor profiles towards sustainable meat processing.

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area’s total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county’s geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses’ strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.

Memory and specificity are hallmarks of the adaptive immune system. Contrary to prior belief, innate immune systems can also provide forms of immune memory, such as immune priming in invertebrates and trained immunity in vertebrates. Immune priming can even be specific but differs remarkably in cellular and molecular functionality from the well-studied adaptive immune system of vertebrates. To date, it is unknown whether and how the level of specificity in immune priming can adapt during evolution in response to natural selection. We tested the evolution of priming specificity in an invertebrate model, the beetle Tribolium castaneum . Using controlled evolution experiments, we selected beetles for either specific or unspecific immune priming toward the bacteria Pseudomonas fluorescens, Lactococcus lactis , and 4 strains of the entomopathogen Bacillus thuringiensis . After 14 generations of host selection, specificity of priming was not universally higher in the lines selected for specificity, but rather depended on the bacterium used for priming and challenge. The insect pathogen B. thuringiensis induced the strongest priming effect. Differences between the evolved populations were mirrored in the transcriptomic response, revealing involvement of immune, metabolic, and transcription-modifying genes. Finally, we demonstrate that the induction strength of a set of differentially expressed immune genes predicts the survival probability of the evolved lines upon infection. We conclude that high specificity of immune priming can evolve rapidly for certain bacteria, most likely due to changes in the regulation of immune genes.

Background Programmed cell death ligand 1 (PD-L1) is a potential predictive biomarker of the response to anti-PD-L1/anti- programmed cell death 1 (PD-1) therapy in multiple cancers, including triple negative breast cancer(TNBC). The purpose of this study was to investigate whether PD-L1 expression is homogenous in primary tumors(PTs) and synchronous axillary lymph node metastases(LNMs) of TNBC. Methods PD-L1 expression was immunohistochemically evaluated in 101 TNBC patients’ PTs and paired LNMs. PD-L1 expression in tumor cells and infiltrating immune cells or node lymphocytes in the PTs and associated LNMs was scored separately and was correlated with patients’ clinical parameters and prognoses. Results PD-L1 expression exhibited spatial heterogeneity in both the tumor cells and the infiltrating immune cells or node lymphocytes of PTs and LNMs. The PD-L1 expression levels were significantly higher in the lymphocytes and tumor cells of the LNMs than in the PTs. PD-L1 expression was also more frequent among the LNMs. PD-L1 expression was associated with high grade and more stromal tumor-infiltrating lymphocytes(TILs). Furthermore, the disease-free survival and overall survival were similar between the PT- negative/LNM- positive and PT- positive/LNM- positive patients, both of which exhibited worse disease-free survival(DFS) thanPT -negative/LNM -negative patients. Conclusions The differential expression of PD-L1 between the PTs and LNMs suggests that LNMs PD-L1 status may be used to indicate whether PD-1/PD-L1-targeted therapy would be suitable for a node-positive TNBC patient in the future.

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) play vital roles in human diseases. We aimed to identify the effect of the lncRNA AGAP2 antisense RNA 1 (AGAP2-AS1)/miR-296/notch homolog protein 2 (NOTCH2) axis on the progression and radioresistance of lung cancer. Expression of AGAP2-AS1, miR-296, and NOTCH2 in lung cancer cells and tissues from radiosensitive and radioresistant patients was determined, and the predictive role of AGAP2-AS1 in the prognosis of patients was identified. THP-1 cells were induced and exosomes were extracted, and the lung cancer cells were respectively treated with silenced AGAP2-AS1, exosomes, and exosomes upregulating AGAP2-AS1 or downregulating miR-296. The cells were radiated under different doses, and the biological processes of cells were assessed. Moreover, the natural killing cell-mediated cytotoxicity on lung cancer cells was determined. The relationships between AGAP2-AS1 and miR-296, and between miR-296 and NOTCH2 were verified. AGAP2-AS1 and NOTCH2 increased while miR-296 decreased in radioresistant patients and lung cancer cells. The malignant behaviors of radioresistant cells were promoted compared with the parent cells. Inhibited AGAP2-AS1, macrophage-derived exosomes, and exosomes overexpressing AGAP2-AS1 or inhibiting miR-296 facilitated the malignant phenotypes of radioresistant lung cancer cells. Furthermore, AGAP2-AS1 negatively regulated miR-296, and NOTCH2 was targeted by miR-296. M2 macrophage-derived exosomal AGAP2-AS1 enhances radiotherapy immunity in lung cancer by reducing miR-296 and elevating NOTCH2. This study may be helpful for the investigation of radiotherapy of lung cancer.