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We describe a small population of remarkable plants found in Shuangjiang County, SW Yunnan, as Tuberowithania pengiana gen. & sp. nov., a unique genus within the subtribe Withaninae, tribe Physalideae of the Solanaceae family. The new genus and species feature a distinctive combination of morphological traits, including a large clumped tuberous root, urceolate and densely pubescent dark purplish flowers, densely pubescent purplish filaments, and yellowish‐white, ovoid berries snugly covered by 10‐ribbed accrescent calyx. Molecular phylogenetic analysis, based on a combined matrix of four DNA markers (nrITS, trnL‐F, waxy, LFY), revealed that T. pengiana is deeply nested within the subtribe Withaninae. It forms a sister relationship with Archiphysalis chamaesarachoides, although with weak support. [ABSTRACT FROM AUTHOR]

Traditional villages are encountering contemporary problems in terms of preservation and comprehension as a result of their complex spatial morphologies, which are distinguished by their extensive cultural and architectural legacy, including distinctive architectural styles and historical significance. This study provides evidence that reinforces the unique characteristics of these settlements, supported by particular data and findings. The primary objective of this study is to employ a quantitative approach in order to assess and conserve the spatial morphology of the aforementioned villages. This will be achieved through the utilization of a novel methodology. The utilization of oblique photography technology, which involves taking photos from an angled perspective, along with the incorporation of many quantitative indices, enables the classification and evaluation of traditional villages. The indices encompass measurements such as length/width and perimeter/area ratios, shape index measurements for assessing form complexity, and fractal dimensions for quantifying pattern complexity. This methodology is employed to examine five discrete villages located in Yunnan Province, thereby uncovering distinctive village layouts and boundary characteristics and providing valuable insights into spatial cohesion. This approach offers a full analysis of traditional village morphologies, thus providing useful insights for the preservation of history and the protection of villages. [ABSTRACT FROM AUTHOR]

With the continuous development of thermal infrared remote sensing technology and the maturation of remote sensing inversion algorithms based on surface temperatures, identifying high-temperature anomalous areas by inverting surface temperatures has become an crucial approach to finding geothermal potential areas. The eastern region of Longyang in western Yunnan Province is renowned for geothermal resources, though the distribution area of geothermal potential remains unknown. Therefore, this study used Landsat-8 TIRS data and four surface temperature inversion algorithms, namely, mono-window algorithm, single-channel algorithm, Du split window algorithm (SWD), and Jiménez-Muñoz split window algorithm (SWJ), to explore the astern region of Longyang. The inversion results were compared with Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) results for analysis and cross-validation to select the optimal algorithm. A multi-view remote sensing temperature anomaly information extraction method was adopted. Moreover, the overall threshold method, the fracture structure buffer method, and the joint analysis of diurnal temporal data were combined for the reduction of the thermal anomaly area as well as for comprehensively defining the geothermal prospective area in the study area. The results demonstrated that the mono-window algorithm had the highest accuracy with a Pearson coefficient of 0.77, which is more suitable for the surface temperature inversion in Longyang area. Furthermore, three geothermal anomalies (A, B, and C) were identified in the study area, with larger thermal anomaly in A and C, but a smaller one in B. All three areas had hot spring points verified, with A and C exhibiting more significant development potential. The research results provide a reliable methodological basis for the development of geothermal resources in the region. [ABSTRACT FROM AUTHOR]

Anchor cables and piles are commonly adopted to improve the stability of slopes under seismic loading. System reliability analysis (SRA) is essential for assessing the stability of slopes stabilized with anchor cables and piles (SSCPs) due to the various uncertainties and complex interactions in geological and geotechnical engineering. A new SRA approach for SSCPs was proposed by considering the effect of the failure of local anchor cables and piles on the system. The failure event of the SSCP was decomposed into multiple failure events with different numbers and sequences of failed anchor cables and piles. The system failure probability (PSF) of the SSCP was calculated by summing the probabilities of all mutually exclusive failure events according to the total probability formulation. An example slope with weak layers in Yunnan Province, China, was employed to illustrate the proposed approach. The results show that this approach does not need to determine the correlation coefficients of multiple failure modes of SSCPs and is able to take into account the effect of the failure of the local anchor cables and piles on the SSCP system. The failure of the local anchor cables and piles significantly increases the failure probability of the remaining anchor cables, piles, and slopes due to stress redistribution, which may lead to cascading failures of anchor cables and piles failing one after another. The proposed approach provides a unique value for PSF rather than a range with upper and lower bounds, which is more precise and convenient for assessing the system reliability of SSCPs. The failure probability of SSCPs could be underestimated if the failure of local anchor cables and piles is ignored. [ABSTRACT FROM AUTHOR]

The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series studies incorporating sliding windows are currently lacking. Therefore, using National Polar-Orbiting Partnership's visible infrared imaging radiometer suite (NPP-VIIRS) night-time light remote sensing (NTLRS) data, VNP46A2, toponym, and Yunnan census statistical data, this study proposes a sliding-window-based NTLRS time series detection and analysis method. We extracted ethnic minority areas on the PyCharm platform using ethnic minority population proportion data and toponym and excluding data representing interference from urban areas. We used a sliding window approach to analyze NTLRS time series data of each ethnic group and calculated the cosine similarity between the NTL brightness curve of original data and the sliding window analysis result. The cosine similarity was greater than 0.96 from 2018 to 2020; we also conducted a field trip to the 2019 Torch Festival to demonstrate the applicability of the employed method. Finally, the temporal and spatial pattern of the Torch Festival was analyzed using the festival in Yunnan Province as an example. Results showed that the Torch Festival, mostly celebrated by the Yi ethnic group, was usually held on the 24th (and ranged from the 22nd to 26th) day in the sixth month of the lunar calendar (LC) every year. We found that during the Torch Festival, the greater the increase in the percentage of NTL brightness reduction in the main urban area of Kunming, the greater the percentage of ethnic minorities' NTL brightness. The width of the sliding window can be adjusted appropriately according to the research objective, with these results showing good continuity. Our study presents a new application of the sliding window approach in the field of remote sensing, suitable for research into festivals related to night lights and fire all over the world. [ABSTRACT FROM AUTHOR]

Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas. [ABSTRACT FROM AUTHOR]

The geographical environment of Yunnan Province in China and Han migration during the Ming Dynasty contributed to the development of the Biantun culture. Biantun toponyms (BTT) record the integration process between the Central Plains and native Yunnan cultures. The GIS analysis method of toponyms was used in this study to reproduce the settlement characteristics of BTT and the spatial development of the Biantun culture in the Ming and Qing Dynasties. In addition, we have developed a toponymical landscape index to represent the degree of spatial integration between the BTT and ethnic minority toponyms in Yunnan and explore the spatial characteristics of the integration of Han immigrants and local ethnic minorities. The results show that the spatial distribution of the BTT is consistent with the sites selection of the Tuntian (屯田) in Yunnan during the Ming and Qing Dynasties, and the centroids of BTT spread to outskirts and intermontane area from central towns. In the Dali, Kunming, Qujing and other regions, the distribution characteristics of the integrated of BTT and ethnic minority toponyms reflect a higher degree of Sinicization in the central urban areas. Exploring the evolution of Biantun cultural development through the spatial characteristics of toponymical landscapes can help adjust policies for the development and protection of Biantun cultural resources. [ABSTRACT FROM AUTHOR]

Alcohol use disorder (AUD) is one of the most serious public health problems worldwide. The OPRM1 and ALDH2 genes are important factors in the reward and alcohol metabolism pathways, and their DNA methylation patterns are closely related to AUD and are population-specific. Chinese Han people are the most populous ethnic group in the world, and this group experiences severe AUD. No epigenetic study on OPRM1 and ALDH2 has been performed in Chinese Han patients with AUD. To investigate whether methylation patterns of OPRM1 and ALDH2 are associated with susceptibility to AUD in Chinese Han males. DNA methylation of the OPRM1 and ALDH2 promoters was studied in Chinese Han males with AUD in Yunnan Province (N = 50 controls, N = 90 individuals with AUD) using the bisulfite pyrosequencing method. In the AUD group, compared with the control group, OPRM1 was hypermethylated(p <.01) but there was no significant difference in the methylation level of ALDH2 (p >.05). 9 CpG sites of OPRM1 (p <.05) and 2 CpG sites of ALDH2 (p >.01) were hypermethylated. Smoking promoted AUD-mediated hypermethylation of OPRM1, in which 3 CpG sites showed significant hypermethylation (p <.01). Age had no significant effect on the DNA methylation levels of these two genes. Our study demonstrates that DNA hypermethylation of the OPRM1 and ALDH2 promoter regions is associated with an increased risk of AUD, which may help to explain the pathogenesis and progression of AUD. [ABSTRACT FROM AUTHOR]

• Landsat images were classified using the Google Earth Engine platform. • Wetland classification result accuracy was high compared to control data. • Dianchi Basin wetlands areas increased overall from 1988 to 2020. • The study methodology can be used to fill data gaps where historical data are missing. Wetlands are transitional zones between terrestrial and aquatic ecosystems. They have the potential to continuously provide human beings with food, raw materials, and other substances. Also, wetland landscape pattern changes have profound impacts on the climate of plateau areas and accelerate the rate of climate change, so it is crucial to extract long time series of plateau wetland information. Concurrent with large-scale urbanization, industrial development and construction, and rapid population increases, the lakeside wetlands in the Dianchi Lake Basin are changing rapidly. However, scholars have not yet extracted long time series wetland information for this area, so the long-term evolution of these wetlands cannot be clarified. In this study, we used the Google Earth Engine (GEE) platform to extract wetland information for the Dianchi Basin from 1988 to 2020 from Landsat data and generated 33 wetland maps by applying a random forest classification model to identify trends and a confusion matrix to assess accuracy. The results showed that the overall trend of wetland area from 1988 to 2020 showed an increasing area, and the total wetland area increased by 31.11 km2 (+9.67 %). Furthermore, the overall accuracy of most years exceeded 80 %, the QADI was no higher than 0.2, the user accuracy and producer accuracy were higher than 80 % and 70 %, respectively, for swamps and water bodies, and the remaining non-wetland categories also achieved robust classification accuracy. Thus, the results of this study can compensate for the lack of information on wetland changes in the region over the past 33 years and provide data support and a scientific basis for sustainable wetland development in the plateau. [ABSTRACT FROM AUTHOR]

In the original publication of this article [1] we noticed the Fig. 4 was incorrect. The correct Fig. 4 is as below. [ABSTRACT FROM AUTHOR]

The Yunnan province has one of the most serious outbreaks of the plague epidemic in China. Small mammals and fleas are risk factors for the occurrence of plague in commensal plague foci. Understanding the relationship between fleas and small mammals will help control fleas and prevent the onset of the plague. Four hundred and twenty-one small mammals, belonging to 9 species, were captured. Of these, 170 small mammals (40.4%) were found infested with fleas. A total of 992 parasitic fleas (including 5 species) were collected. The number of Leptopsylla segnis and Xenopsylla cheopis accounted for 91.03% (903/992). The final multiple hurdle negative binomial regression model showed that when compared with Rattus tanezumi, the probability of flea infestation with Mus musculus as well as other host species decreased by 58% and 99%, respectively, while the number of flea infestations of the other host species increased by 4.71 folds. The probability of flea prevalence in adult hosts increased by 74%, while the number of fleas decreased by 76%. The number of flea infestations in small male mammals increased by 62%. The number of fleas in small mammals weighing more than 59 g has been multiplied by about 4. R. tanezumi is the predominant species in households in the west Yunnan province, while L.segnis and X. cheopis were dominant parasitic fleas. There is a strong relationship between the abundance of fleas and the characteristics of small mammals (e.g. Species, age, sex, and body weight). [ABSTRACT FROM AUTHOR]

Aiming at the problem that the estimation of electric power consumption (EPC) by using night-time light (NTL) data is mostly concentrated in large areas, a method for estimating EPC in rural areas is proposed. Rural electric power consumption (REPC) is a key indicator of the national socio-economic development. Despite an improved quality of life in rural areas, there is still a big gap between electricity consumption between rural residents and urban residents in China. The experiment takes REPC as the research target, selects Dehong (DH) Dai Jingpo Autonomous Prefecture of Yunnan Province as an example, and uses the NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite from 2012 to 2017, toponym and points-of-interest (POI) data as the main data source. By performing kernel density estimation to extract the urban center and rural area boundaries in the prefecture, and combining the county-level boundary data and electric power data, a linear regression model of the total rural NTL intensity and REPC is estimated. Finally, according to the model, the EPC in ethnic minority rural areas is estimated at a 1-km spatial resolution. The results show that the NPP-REPC model can simulate REPC within a small average error (17.8%). Additionally, there are distinct spatial differences of REPC in ethnic minority areas. [ABSTRACT FROM AUTHOR]

Background: The barriers to access diagnosis and receive treatment, in addition to insufficient case identification and reporting, lead to tuberculosis (TB) spreads in communities, especially among hard-to-reach populations. This study evaluated a community-based active case finding (ACF) strategy for the detection of tuberculosis cases among high-risk groups and general population in China between 2013 and 2015. Methods: This retrospective cohort study conducted an ACF in ten communities of Dongchuan County, located in northeast Yunnan Province between 2013 and 2015; and compared to 136 communities that had passive case finding (PCF). The algorithm for ACF was: 1) screen for TB symptoms among community enrolled residents by home visits, 2) those with positive symptoms along with defined high-risk groups underwent chest X-ray (CXR), followed by sputum microscopy confirmation. TB incidence proportion and the number needed to screen (NNS) to detect one case were calculated to evaluate the ACF strategy compared to PCF, chi-square test was applied to compare the incidence proportion of TB cases' demography and the characteristics for detected cases under different strategies. Thereafter, the incidence rate ratio (IRR) and multiple Fisher's exact test were applied to compare the incidence proportion between general population and high-risk groups. Patient and diagnostic delays for ACF and PCF were compared by Wilcoxon rank sum test. Results: A total of 97 521 enrolled residents were visited with the ACF cumulatively, 12.3% were defined as high-risk groups or had TB symptoms. Sixty-six new TB patients were detected by ACF. There was no significant difference between the cumulative TB incidence proportion for ACF (67.7/100000 population) and the prevalence for PCF (62.6/100000 population) during 2013 to 2015, though the incidence proportion in ACF communities decreased after three rounds active screening, concurrent with the remained stable prevalence in PCF communities. The cumulative NNS were 34, 39 and 29 in HIV/AIDS infected individuals, people with positive TB symptoms and history of previous TB, respectively, compared to 1478 in the general population. The median patient delay under ACF was 1 day (Interquartile range, IQR: 0–27) compared to PCF with 30 days (IQR: 14–61). Conclusions: This study confirmed that massive ACF was not effective in general population in a moderate TB prevalence setting. The priority should be the definition and targeting of high-risk groups in the community before the screening process is launched. The shorter time interval of ACF between TB symptoms onset and linkage to healthcare service may decrease the risk of TB community transmission. Furthermore, integrated ACF strategy in the National Project of Basic Public Health Service may have long term public health impact. [ABSTRACT FROM AUTHOR]

[Display omitted] • The position of the magma heat source was firstly reported in the city of Ruili. • Geothermal anomalies are distributed along NE–SW oriented faults. • The geothermal anomalies on the NE side exhibited greater significance. • Day-Night LST integration improves geothermal anomaly detection. Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources. [ABSTRACT FROM AUTHOR]