Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data.

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500-850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001-0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603-778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R2 = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R2 = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R2 = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R2 = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m3 compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.

Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning.

Citrus has become a pivotal industry for the rapid development of agriculture and increasing farmers' incomes in the main production areas of southern China. Knowing how to diagnose and control citrus huanglongbing has always been a challenge for fruit farmers. To promptly recognize the diagnosis of citrus huanglongbing, a new classification model of citrus huanglongbing was established based on MobileNetV2 with a convolutional block attention module (CBAM-MobileNetV2) and transfer learning. First, the convolution features were extracted using convolution modules to capture high-level object-based information. Second, an attention module was utilized to capture interesting semantic information. Third, the convolution module and attention module were combined to fuse these two types of information. Last, a new fully connected layer and a softmax layer were established. The collected 751 citrus huanglongbing images, with sizes of 3648 × 2736, were divided into early, middle, and late leaf images with different disease degrees, and were enhanced to 6008 leaf images with sizes of 512 × 512, including 2360 early citrus huanglongbing images, 2024 middle citrus huanglongbing images, and 1624 late citrus huanglongbing images. In total, 80% and 20% of the collected citrus huanglongbing images were assigned to the training set and the test set, respectively. The effects of different transfer learning methods, different model training effects, and initial learning rates on model performance were analyzed. The results show that with the same model and initial learning rate, the transfer learning method of parameter fine tuning was obviously better than the transfer learning method of parameter freezing, and that the recognition accuracy of the test set improved by 1.02~13.6%. The recognition accuracy of the citrus huanglongbing image recognition model based on CBAM-MobileNetV2 and transfer learning was 98.75% at an initial learning rate of 0.001, and the loss value was 0.0748. The accuracy rates of the MobileNetV2, Xception, and InceptionV3 network models were 98.14%, 96.96%, and 97.55%, respectively, and the effect was not as significant as that of CBAM-MobileNetV2. Therefore, based on CBAM-MobileNetV2 and transfer learning, an image recognition model of citrus huanglongbing images with high recognition accuracy could be constructed.

Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images.

Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.

Quantitative assessment of the relative contributions of climate change and human activities to NPP changes in the Southwest Karst area of China.

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m-2·year-1 and 2.29 gC·m-2·year-1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change.

Spatio-temporal monitoring of marsh vegetation phenology and its response to hydro-meteorological factors using CCDC algorithm with optical and SAR images: In case of Honghe National Nature Reserve, China.

Vegetation phenology is a sensitive indicator which can comprehensively reflect the response of wetland vegetation to external environment changes. However, the time-series monitoring wetland vegetation phenological changes and clarifying its response to hydrology and meteorology still face great challenges. To fill these research gaps, this paper proposed a novel time-series approach for monitoring phenological change of marsh vegetation in Honghe National Nature Reserve (HNNR), Northeast China, using continuous change detection and classification (CCDC) algorithm and Landsat and Sentinel-1 SAR images from 1985 to 2021. We evaluated the spatio-temporal response relationship of phenological characteristics to hydro-meteorological factors by combining CCDC algorithm with partial least squares regression (PLSR). Finally, this study further explored the intra-annual loss and restoration of marsh vegetation in response to hydro-meteorological factors using the transfer entropy (TE) and CCDC-MLSR model constructed by CCDC and Multiple Linear Stepwise Regression (MLSR) algorithms. We found that the bimodal trajectory of phenology reflects two growth processes of marsh vegetation in one year, and high-frequency and high-amplitude loss occurred in shallow-water and deep-water marsh vegetation from April to October, resulting in the loss area within the year was significantly greater than the recovery area. We confirmed that the CCDC algorithm could track the evolution trajectory of time-series phenology of marsh vegetation. We further revealed that precipitation, temperature and frequency of water-level changes are the main driving factors for the spatio-temporal phenological evolution of different marsh vegetation. This study verified the effect of alternative changes of hydrology and climate on loss and recovery of marsh vegetation in each growth stage. The results of this study provide a scientific basis for wetland protection, ecological restoration, and sustainable development.

Methylation in the promoter regions of WT1, NKX6-1 and DBC1 genes in cervical cancer tissues of Uygur women in Xinjiang.

This study aimed to explore: 1) DNA methylation in the promoter regions of Wilms tumor gene 1 (WT1), NK6 transcription factor related locus 1 gene (NKX6-1) and Deleted in bladder cancer 1 (DBC1) gene in cervical cancer tissues of Uygur women in Xinjiang, and 2) the correlation of gene methylation with the infection of HPV16/18 viruses. We detected HPV16/18 infection in 43 normal cervical tissues, 30 cervical intraepithelial neoplasia lesions (CIN) and 48 cervical cancer tissues with polymerase chain reaction (PCR) method. Methylation in the promoter regions of the WT1, NKX6-1 and DBC1 genes in the above-mentioned tissues was measured by methylation-specific PCR (MSP) and cloning sequencing. The expression level of these three genes was measured by real-time PCR (qPCR) in 10 methylation-positive cervical cancer tissues and 10 methylation-negative normal cervical tissues. We found that the infection of HPV16 in normal cervical tissues, CIN and cervical cancer tissues was 14.0, 36.7 and 66.7%, respectively. The infection of HPV18 was 0, 6.7 and 10.4%, respectively. The methylation rates of WT1, NKX6-1 and DBC1 genes were 7.0, 11.6 and 23.3% in normal cervical tissues, 36.7, 46.7 and 30.0% in CIN tissues, and 89.6, 77.1 and 85.4% in cervical cancer tissues. Furthermore, WT1, NKX6-1 and DBC1 genes were hypermethylated in the high-grade squamous intraepithelial lesion (CIN2, CIN3) and in the cervical cancer tissues with infection of HPV16/18 (both P< 0.05). The expression of WT1, NKX6-1 and DBC1 was significantly lower in the methylation-positive cervical cancer tissues than in methylation-negative normal cervical tissues. Our findings indicated that methylation in the promoter regions of WT1, NKX6-1 and DBC1 is correlated with cervical cancer tumorigenesis in Uygur women. The infection of HPV16/18 might be correlated with methylation in these genes. Gene inactivation caused by methylation might be related to the incidence and development of cervical cancer.

Methylation in the promoter regions of WT1, NKX6-1 and DBC1 genes in cervical cancer tissues of Uygur women in Xinjiang

Abstract This study aimed to explore: 1) DNA methylation in the promoter regions of Wilms tumor gene 1 (WT1), NK6 transcription factor related locus 1 gene (NKX6-1) and Deleted in bladder cancer 1 (DBC1) gene in cervical cancer tissues of Uygur women in Xinjiang, and 2) the correlation of gene methylation with the infection of HPV16/18 viruses. We detected HPV16/18 infection in 43 normal cervical tissues, 30 cervical intraepithelial neoplasia lesions (CIN) and 48 cervical cancer tissues with polymerase chain reaction (PCR) method. Methylation in the promoter regions of the WT1, NKX6-1 and DBC1 genes in the above-mentioned tissues was measured by methylation-specific PCR (MSP) and cloning sequencing. The expression level of these three genes was measured by real-time PCR (qPCR) in 10 methylation-positive cervical cancer tissues and 10 methylation-negative normal cervical tissues. We found that the infection of HPV16 in normal cervical tissues, CIN and cervical cancer tissues was 14.0, 36.7 and 66.7%, respectively. The infection of HPV18 was 0, 6.7 and 10.4%, respectively. The methylation rates of WT1, NKX6-1 and DBC1 genes were 7.0, 11.6 and 23.3% in normal cervical tissues, 36.7, 46.7 and 30.0% in CIN tissues, and 89.6, 77.1 and 85.4% in cervical cancer tissues. Furthermore, WT1, NKX6-1 and DBC1 genes were hypermethylated in the high-grade squamous intraepithelial lesion (CIN2, CIN3) and in the cervical cancer tissues with infection of HPV16/18 (both P< 0.05). The expression of WT1, NKX6-1 and DBC1 was significantly lower in the methylation-positive cervical cancer tissues than in methylation-negative normal cervical tissues. Our findings indicated that methylation in the promoter regions of WT1, NKX6-1 and DBC1 is correlated with cervical cancer tumorigenesis in Uygur women. The infection of HPV16/18 might be correlated with methylation in these genes. Gene inactivation caused by methylation might be related to the incidence and development of cervical cancer.

Down-regulation of IFITM1 and its growth inhibitory role in cervical squamous cell carcinoma.

BACKGROUND: Cervical cancer is a major cause of death in women worldwide. Interferon-induced transmembrane protein 1 (IFITM1) is involved in antivirus defense, cell adhesion, and carcinogenesis in different tissues. However, the role of IFITM1 gene in cervical squamous cell cancer is unclear. METHODS: To explore the role of IFITM1 in carcinogenesis of cervical cancer, we investigated the expression of IFITM1 gene in cervical squamous cell carcinoma. IFITM1 mRNA level was measured by real-time quantitative RT-PCR in cervical cancer tissues and their adjacent normal tissues. IFITM1 protein level was measured by immunohistochemistry. Methylation in the IFITM1 gene promoter was detected by methylation-specific PCR. We then transfected HeLa cells with IFITM1 expression vector or control vector. IFITM1 expression was examined; cell migration and invasion were analyzed by wound healing assay and matrigel-coated transwell migration assays, respectively. HeLa cell proliferation was measured by cell counting kit-8 assay and cell cycle analysis. Cell apoptosis was analyzed by Annexin V/propidium iodide double staining assay. RESULTS: The difference in IFITM1 protein expression between samples from chronic cervicitis and cervical carcinoma was statistically significant (P < 0.01). Ki-67 and PCNA protein expression levels were significantly higher in cervical cancer tissues than in their corresponding cervicitis tissues (P < 0.05 and P < 0.001, respectively). IFITM1 mRNA level was significantly lower in cervical cancer tissues than in normal cervical tissues (P < 0.05). Methylation of the IFITM1 gene promoter was significantly higher in cervical cancer than in normal cervical tissues (P < 0.05). Transfection of the IFITM1 pcDNA3.1 construct decreased cell migration and invasion of HeLa cells, inhibited cell proliferation, and increased cell apoptosis. CONCLUSION: IFITM1 gene expression may reduce the proliferation, migration, and invasion of cervical squamous cancer cells.

Variants of human papillomaviruses 16 (HPV16) in Uigur women in Xinjiang, China.

BACKGROUND: Persistent infection of high-risk human papillomaviruses 16 (HPV16) has been considered as the leading cause of cervical cancer. In this study we assessed HPV16 sequence variation and genetic diversity of HPV16 variants in cervical cancer in Uigur women in Xinjiang, China. We analyzed the nucleotide sequences of the open reading frames of E6 and E7, and part of the open reading frames of L1 of HPV16 in Uigur women. METHODS: Biopsies of histologically confirmed HPV16 infections with cervical cancer were obtained from 43 Uigur women in Xinjiang, China. E6, E7 and L1 genes of HPV16 of all samples were amplified and sequenced; the sequences were used in phylogenetic analysis of HPV16 variants. RESULTS: Our analysis revealed nine nucleotide changes in E6 (five changes), E7 (one change) and L1 (three changes) gene. The most frequently observed variations were T350G (79.1 %). One variation T295G (D64E) at E6 were detected in 6 cases (KT959536, KT959542, KT959546, KT959550, KT959553, KT959558). Deletion (464Asp) along with insertion (448Ser) were observed in L1 (100 %). Most variants were European lineage (97.7 %); only one belongs to Asia variants with common T178G (D25E) in E6 and A647G (N29S) in E7. CONCLUSION: The most prevalent HPV16 variants in the Uigur women we studied were of the European lineage. Our results indicate that HPV16 European lineage may serve as a harmful factor associated with the development and progression of cervical cancer.