Regional Distribution Research Articles

Lamb Wave TDTE Super-Resolution Imaging Assisted by Deep Learning

Abstract Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy imaging. Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method (SCF-TFM), the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.

Serial and regional assessment of the right ventricular molecular and functional response to pressure-loading.

Right ventricular(RV) function determines outcomes in RV pressure-loading. A better understanding of the time-course and regional distribution of RV remodeling may help optimize targets and timing for therapeutic intervention. We sought to characterize RV remodeling between zero and 6-weeks after initiation of RV pressure-loading. Thirty-six rats were randomized to either sham surgery or to pulmonary artery banding(PAB). After echocardiography and conductance catheter studies, groups of rats were euthanized at 1-week, 3-weeks and 6-weeks after sham surgery, or induction of RV pressure-loading, for RV histological, RNA and molecular analysis. A vigorous inflammatory response characterized by increased RV inflammatory cytokines, chemokines and macrophage markers was observed at 1-week following PAB. Metabolic changes, TGF-β1 canonical signaling, collagenous fibrosis deposition and apoptosis were already significantly increased by 1-week after PAB. Genes marking fibroblast activation were upregulated at 1-week but not 6-week post-PAB surgery. Mitochondrial dysfunction as evidenced by increased PDK activity and decreased PDH phosphorylation significantly at 6-week post PAB. These processes preceded the development of overt myocardial hypertrophy and impaired echo parameters of systolic and diastolic function which occurred significantly from 3-weeks after PAB. RV myocardial inflammation, metabolic shift, metabolic gene transcription and pro-fibrotic signaling occur early after initiation of pressure-loading when RV pressures are only moderately elevated, before the development of overt myocardial hypertrophy and dysfunction, suggesting that adaptive hypertrophy and maladaptive remodeling occur simultaneously. These results suggest that therapeutic intervention to reduce adverse RV remodeling may be needed earlier and at lower thresholds than currently employed.

Prevalence of schizophrenia spectrum disorders in the Lazio region, Italy: use of an algorithm based on health administrative databases

BackgroundMental healthcare provision is undergoing substantial reconfiguration in many regions of the world. Such changes require a broad, evidence-based approach incorporating epidemiological data and information on local needs. The objective of this study was to estimate the prevalence of schizophrenia spectrum disorders (SSD) in the Lazio region and its geographical distribution using regional administrative healthcare databases.MethodsCases of SSD (15–64 years old) were identified using an algorithm based on data from the hospital discharge registry (ICD IX CM: 295, 297, 298 [excl. 298.0], 299) and the ticket exemption database [code 044], between 2006 and 2019. We calculated crude, age- and gender-specific prevalence estimates on December 31, 2019. We also calculated age- and gender-adjusted prevalence to compare prevalence in different regional areas.ResultsWe identified 18,371 cases. The overall prevalence was 5.03 per 1000 population (95% CI 4.96–5.10). Age-adjusted prevalence estimates were 4.18 (95% CI 4.09–4.27) per 1000 for women and 5.92 (95% CI 5.81–6.04) per 1000 for men. The prevalence was higher among older age groups, in both genders. There were differences in prevalence within the region, ranging from 4.25/1000 in the province of Viterbo to 5.42/1000 in Rome and 6.02/1000 in the province of Frosinone. When we analysed the subcategories of SSD, the three most frequent conditions were schizophrenia, schizoaffective disorder, and psychosis NOS. In general, the prevalence was higher in men for all the conditions but delusional disorders and brief psychosis.ConclusionsOur results show that the overall prevalence of SSD among adults in the Lazio region is similar to those published in previous reviews, but an uneven regional distribution was observed. While possible underestimation must be considered, administrative databases represent a valuable source of information for epidemiological surveillance and healthcare planning.

Research on the Social Media Marketing on Beauty Product: Based on Questionnaire Method

With the rapid development of the Internet, network broadcast and product marketing advertisements are gradually integrated into our lives. As the main force of consumption, women's consumption behavior has also become the focus of attention. This paper takes the social platform marketing of beauty products as an example, and conducts a directional analysis of consumer behavior through the research method of questionnaire survey. The survey results show that the main group of online consumption is concentrated in young people. From the perspective of regional distribution, most consumers come from southeast China, that is, the more economically developed areas, and the first-tier cities are mostly. Consumers prefer actual demand and are influenced by Internet celebrities and online comments. The research results of this paper can be used as a reference for cosmetics companies to formulate more targeted marketing strategies, help cosmetics brands to improve sales and performance, and provide references for the direction of social network publicity.

Analysis of the employment structure of ukraine’s population during the war

Structural shifts in employment in the modern Ukrainian economy are caused by factors such as the military situation, technological progress, demographic changes, and economic instability. These changes affect the sectoral, gender, and regional distribution of workers and require adaptation from labor market participants and social policies to ensure the stability and efficiency of employment. Addressing structural shifts in employment in Ukraine requires a comprehensive approach, which includes stimulating the development of new economic sectors, supporting entrepreneurship, modernizing education and training, and providing social protection for workers during reorientation and retraining processes. Such an approach will help achieve a balanced state of supply and demand in the labor market, thereby ensuring long-term societal protection and well-being.

MMG4: Recognition of G4-Forming Sequences Based on Markov Model.

G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.

Regional ventilation dynamics of electrical impedance tomography validated with four-dimensional computed tomography: single-center, prospective, observational study

BackgroundThe dynamic regional accuracy of electrical impedance tomography has not yet been validated. We aimed to compare the regional accuracy of electrical impedance tomography with that of four-dimensional computed tomography during dynamic ventilation.MethodsThis single-center, prospective, observational study conducted in a general intensive care unit included adult patients receiving mechanical ventilation from July 2021 to February 2024. The patients were mechanically ventilated passively and underwent electrical impedance tomography and four-dimensional computed tomography on the same day.ResultsOverall, 45 patients were analyzed. The correlation coefficients in regional dynamic ventilation between four-dimensional computed tomography and electrical impedance tomography in each region were 0.963, 0.963, 0.835 (ventral, central, and dorsal, respectively) in the right lung and 0.947, 0.927, 0.823 (ventral, central, and dorsal, respectively) in the left lung. The correlation coefficient was low when the regional ventilation distribution detected by the electrical impedance tomography was < 2%. After excluding nine patients with a regional ventilation distribution of < 2%, the ventral, central, and dorsal correlation coefficients were 0.963, 0.963, and 0.946 in the right lung and 0.942, 0.924, and 0.951, respectively, in the left lung.ConclusionsRegional ventilation using electrical impedance tomography during dynamic ventilation was highly accurate and consistent with the time phase compared to four-dimensional computed tomography. Given the high correlation between these modalities, they can contribute significantly to further studies on regional ventilation dynamics.Trial registration number ClinicalTrials.gov (No. UMIN00044386).

Assessing the Adequacy of Earthquake Catalog Sampling for Long-Term Seismicity in Low-to-Moderate Seismic Regions: A Geodetic Perspective

Abstract Seismic hazard assessment in low-to-moderate seismicity regions can benefit from the knowledge of surface deformation rates to better constrain earthquake recurrence models. This, however, amounts to assuming that the known seismicity rate, generally observed over historical times (i.e., up to a few centuries in Europe), provides a representative sample of the underlying long-term activity. We here investigate how this limited sampling can affect the estimated seismic hazard and whether it can explain the disagreement between the seismic moment loading rate as seen by nowadays Global Navigation Satellite Systems (GNSS) measurements and the seismic moment release rate by past earthquakes, as is sometimes observed in regions with limited activity. We approach this issue by running simulations of earthquake time series over very long timescales that account for temporal clustering and the known magnitude–frequency distribution in such regions, and that those are constrained to a seismic moment rate balance between geodetic and seismicity estimates at very long timescales. We show that, in the example of southeastern Switzerland, taken here as a case study, this sampling issue can indeed explain this disagreement, although it is likely that other phenomena, including aseismic deformation and changes in strain rate due to erosional and/or glacial rebound, may also play a significant role in this mismatch.

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Evaluation of Water Quality and Eutrophication of Typical Lakes in Southeast Hubei, China

Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed.

Impact of Rhizosphere Biostimulation on Cd Transport and Isotope Fractionation in Cd-Tolerant and Hyperaccumulating Plants Based on MC-ICP-MS and NanoSIMS.

Phytoremediation efficiency can be enhanced by regulating rhizosphere processes, and the Cd isotope is a useful approach for deciphering Cd transport processes in soil-plant systems. However, the effects of adsorption and complexation on Cd isotope fractionation during the rhizosphere processes remain unclear. Here, we cultivated the Cd hyperaccumulator Sedum alfredii and Cd-tolerance Sedum spectabile in three different soils with citric acid applied as a degradable rhizosphere biostimulant. Cellular elemental distributions in the tissues and Cd isotope compositions were determined through NanoSIMS and MC-ICP-MS, respectively. Cd precipitation/adsorption on cell walls and intracellular regional distribution were the main mechanisms of Cd tolerance in S. spectabile. Plant roots became enriched with heavier Cd isotopes relative to the surrounding soils upon increasing secretion of rhizosphere organic acids. This indicates that organic matter with O and N functional groups preferentially chelates heavy Cd isotopes. In addition, Cd isotope fractionation between roots and shoots varies within the three soils, which could be due to the influence of protein and metallothionein contents in roots and leaves. The finding indicates that sulfur-containing ligands preferentially chelate light Cd isotopes. This study suggests that organic ligands play a vital role in Cd isotope fractionation and consequent hyperaccumulation of soil-plant systems.

A single-cell 3D dynamic volume control system for chondrocytes.

In articular cartilage, zone-specific cellular morphology is a typical characteristic of cartilage tissue, which is related with chondrocyte function, inflammation and osteoarthritis (OA). Chondrocyte hypertrophic phenotype is a criticle physiological process which indicates a hallmark of chondrocyte terminal differentiation and bone formation. Thus, developing a in vitro cell culture system for dynamic regulation of single chondrocyte volume at a three-dimensional (3D) level is particularly necessary for understanding how physical cues of matrix microenvironment regulate chondrocyte fate and the degeneration of articular cartilage. Here, based on the soft lithography techniques, we have constructed well-defined single-cell 3D dynamic volume control system to recapitulate the physiological matrix microenvironment of single chondrocyte niche. The results of finite element analysis indicated that the stress and strain distribution in the cell culture region is homogeneous during the stretching process. Additionally, 3D dynamic volume expansion and compression of single cells in physiological or hyperphysiological can be realized in this cell culture system. Our device for single-cell 3D dynamic culture provides a microphysiological culture system for chondrocytes to explore the mechanisms of cartilage hypertrophy, as well as develops a new paradigm for functional cartilage tissue engineering and regenerative medicine.

Using UAV RGB Images for Assessing Tree Species Diversity in Elevation Gradient of Zao Mountains

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower.

Seasonal dynamics and regional distribution patterns of CO2 and CH4 in the north-eastern Baltic Sea

Abstract. Significant research has been carried out in the last decade to describe the CO2 system dynamics in the Baltic Sea. However, there is a lack of knowledge in this field in the NE Baltic Sea, which is the main focus of the present study. We analysed the physical forcing and hydrographic background in the study year (2018) and tried to elucidate the observed patterns of surface water CO2 partial pressure (pCO2) and methane concentrations (cCH4). Surface water pCO2 and cCH4 were continuously measured during six monitoring cruises onboard R/V Salme, covering the Northern Baltic Proper (NBP), the Gulf of Finland (GoF), and the Gulf of Riga (GoR) and all seasons in 2018. The general seasonal pCO2 pattern showed oversaturation in autumn–winter (average relative CO2 saturation 1.2) and undersaturation in spring–summer (average relative CO2 saturation 0.5), but it locally reached the saturation level during the cruises in April, May, and August in the GoR and in August in the GoF. The cCH4 was oversaturated during the entire study period, and the seasonal course was not well exposed on the background of high variability. Surface water pCO2 and cCH4 distributions showed larger spatial variability in the GoR and GoF than in the NBP for all six cruises. We linked the observed local maxima to river bulges, coastal upwelling events, fronts, and occasions when vertical mixing reached the seabed in shallow areas. Seasonal averaging over the CO2 flux suggests a weak sink for atmospheric CO2 for all basins, but high variability and the long periods between cruises (temporal gaps in observation) preclude a clear statement.

Regional distribution of anaplastic thyroid cancer in the United States

Abstract Introduction/Objective Anaplastic thyroid cancer (ATC) is an aggressive rare malignancy found in less than 2% of thyroid cancer patients. Its low occurrence rate presents challenges for collecting sufficiently large cohorts at a single institution, making statistical analysis about the disease’s spread, geographical distribution, and prevalence difficult. Nationwide data lakes such as Oracle EHR Real-World Data (ORWD, formerly Cerner Real World Data) offer a boost in cohort sizes and an opportunity to study ATC’s geographical distribution and assess regional differences. Methods/Case Report We searched ATC distribution across US regions defined by ZIP codes recorded in ORWD using multiple coding parameters, including ICD9, ICD10, and SNOMED as well as direct text searches for “anaplastic thyroid carcinoma”. We analyzed the geographical associations defined by ORWD (region) with sex, race, and age of ATC onset using the Fisher’s exact test. We combined 10 regions into 4 representative groups because of the small sample size. The groups are West Coast including the Rockies, Midwest from North Dakota to Texas, East Coast, and New England. Results (if a Case Study enter NA) Search for ATC condition codes in ORWD produced only 200 region-coded patient records across the US using multiple methods to try and identify cases. The mid-section of the US and coastal regions shows the highest numbers of ATC patients while New England and South regions show the lowest. While more women have ATC overall, the East Coast group has significantly more males (p value = 0.04). ATC is a predominantly white disease (79.8% overall) with black population increasing in East Coast group and other races (Asian/Undeclared) in West Coast (p-value &amp;lt; 0.001). There appears to be no significant association between age of ATC onset and the geographical groups. Conclusion The data shows geographic differences in distribution of ATC although this preliminary analysis neither explains the nature of these differences nor allows for a clearer view. The rarity of the disease complicates data analysis. This study was also limited by number of cases retrievable from the database itself and lack of granularity in coding available for ATC to find cases of interest, highlighting the difficulty of accurate tumor classifications in large data lakes. Further research into geographical distribution and environmental factors (radioactivity, pollution) of thyroid cancer using more advanced tools, such as GeoSpark, are planned.

The mechanism of degradation and failure in NiO/ β -Ga2O3 heterojunction diodes induced by the high-energy ion irradiation

This work investigated the single-event effects (SEE) of NiO/β-Ga2O3 heterojunction diodes (HJDs) irradiated by 1.86 GeV tantalum ions with linear energy transfer over 80 MeV cm2/mg. The HJDs exhibited radiation responses with the early single-event leakage current (SELC) degradation until the fatal single-event burnout (SEB) failure, which was far below their breakdown voltages. Meanwhile, the surface morphology revealed the SELC damage expressed as burnout of topside NiO and metal stacks, while the SEB damage was observed as a burned hole in the β-Ga2O3 epitaxial layer. According to technology computer aided design simulations, the thicker p-type region in HJDs could further alleviate the electric field crowding effect exacerbated by the heavy-ion strike because of the extension of charge distribution in the p-type region. The SEB threshold was raised to 250 V by thickening the NiO layer to 300 nm. As for the SELC degradation process along with the burnout of topside stacks in HJDs, we supposed the probable reason was the intolerance of NiO to the high electric field under the SEE. This paper analyzed the SEE mechanism in β-Ga2O3 diodes and paved the way for heavy-ion irradiation hardening.

Dimethyl sulfide (DMS) climatologies, fluxes, and trends – Part 1: Differences between seawater DMS estimations

Abstract. Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most atmospheric models that represent aerosol processes depend on regional or global distributions of seawater DMS concentrations and sea–air flux parameterizations to estimate its emissions. In this study, we analyse the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method following Hulswar et al. (2022) (hereafter referred to as H22) and two of which are proxy-based parameterization methods following Galí et al. (2018) (hereafter referred to as G18) and Wang et al. (2020a) (hereafter referred to as W20). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a, mixed-layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high-DMS region in austral summer. The parameterization-based methods suggest positive long-term trends in seawater DMS (3.82±0.79 % per decade for G18 and 2.13±0.32 % per decade for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea–air fluxes and, hence, the impact of DMS on the radiative budget are sensitive to the estimate used.

Spatial and Single-Cell Transcriptomics Reveals the Regional Division of the Spatial Structure of MASH Fibrosis.

To elucidate the regional distribution of metabolic dysfunction-associated steatohepatitis (MASH) fibrosis within the liver and to identify potential therapeutic targets for MASH fibrosis. Liver sections from healthy controls, patients with simple steatosis and MASH patients were analysed using spatial transcriptomics integrated with single-cell RNA-seq. Spatial transcriptomics analysis of liver tissues revealed that the fibrotic region (Cluster 9) was primarily distributed in lobules, with some fibrosis also found in the surrounding area. Integration of the single-cell-sequencing data set (GSE189175) showed a greater proportion of inflammatory cells (Kupffer cells and T cells) and myofibroblasts in MASH. Six genes, showing high- or low-specific expression in Cluster 9, namely, ADAMTSL2, PTGDS, S100A6, PPP1R1A, ASS1 and G6PC, were identified in combination with pathology. The average expression levels of ADAMTSL2, PTGDS and S100A6 on the pathological HE staining map were positively correlated with the increase in the degree of fibrosis and aligned strongly with the distribution of fibrosis. ADAMTSL2+ myofibroblasts play a role in TNF signalling pathways and in the production of ECM structural components. Pseudotime analysis indicated that in the early stages of MASH, infiltration by T cells and Kupffer cells triggers a significant inflammatory response. Subsequently, this inflammation leads to the activation of hepatic stellate cells (HSCs), transforming them into myofibroblasts and promoting the development of liver fibrosis. This study is the first to characterise lineage-specific changes in gene expression, subpopulation composition, and pseudotime analysis in MASH fibrosis and reveals potential therapeutic targets for this condition.

Developing a New Method to Rapidly Map Eucalyptus Distribution in Subtropical Regions Using Sentinel-2 Imagery

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (CIEC) and for eucalyptus–broadleaf forest separation (CIEB), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for CIEC and 1.29 for CIEB) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions.

Decoding China's new-type industrialization: Insights from an XGBoost-SHAP analysis

Integrating machine learning and explainable artificial intelligence offers superior interpretability for indicator selection compared with traditional evaluation models. This study utilizes the XGBoost-SHAP model to assess the influencing factors, development trends, and spatiotemporal evolution of China's new-type industrialization. The results demonstrate that digitalization emerges as the most critical factor, contributing 41.23% to the weight of indicators, followed by innovation drive at 34.31%, greenization at 16.38%, and aspects of servitization and fusion. China's new industrialization level shows a positive trend, with an accelerated growth rate post-2020, despite significant regional disparities in industrial indices. A spatiotemporal evolution identified a three-tier regional distribution: the eastern coastal, central plain, and western plateau regions. Notably, the improvement in new-type industrialization has expanded from coastal to inland regions, with significant progress observed in the southwestern region, represented by Sichuan Province. Additionally, the analysis revealed that China's level of new industrialization is converging toward the Hu Huanyong Line, suggesting that it may be influenced by population distribution density and natural environmental factors. The research results provide a scientific basis and decision-making guidance for evaluating the dynamics of China's new industrialization.