Detecting peatland vegetation patterns with multi-temporal field spectroscopy

doi: 10.1080/15481603.2022.2152303Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojankka and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R-2 was 42.6-48.0% (root mean square error (RMSE) 0.153-0.193), and for PFT 2D %-cover 53.9-69.8% (RMSE 8.2-17.6%), AGB 43.1-61.5% (RMSE 86.2-165.5 g/m(2)) and LAI 46.3-51.3% (RMSE 0.220-0.464 m(2)/m(2)). The multi-temporal data of the whole season increased R-2 by 13.7-24.6%-points and 10.2-33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojankka the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojankka, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.Peer reviewe

Remote sensing phenology of two Chinese northern Sphagnum bogs under climate drivers during 2001 and 2018

Boreal peatlands, of which Sphagnum bogs are one of the main types, play essential roles in the terrestrial soil carbon pool. Vegetation phenology is a sensitive indicator that reveals the underlying processes as well as responses to climate change, while currently there remain knowledge gaps in exploring and monitoring the longterm bog vegetation phenology due to insufficient remote sensing application experiences. In this study, we investigated three remotely sensed vegetation phenological parameters, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in two bogs located in norther China by using double-logistic reconstructed MOD13Q1-EVI from 2001 to 2018, which were evaluated by the flux phenology. Also combing with meteorological data to detect interactions between vegetation phenology and climate change. The results showed that remotely sensed EOS had 8-day time lags with flux phenological date, while that outperformed SOS. Bog vegetation generally with a life pattern of SOS at the 108th day of year (doy) and EOS at the 328th doy, though the life cycle of individual vegetation groups varies among different vegetation communities. There was no significant delayed (or extended) trend in each phenological features in bogs. Precipitation and minimum temperature (monthly and annual) were the driving forces for bog vegetation growth (R2 0.9, P < 0.01), and other features presented weaker correlations. Overall, this study determined the remote sensing phenology and climate drivers in two Chinese bogs, we suggested that vegetation phenology alternation should be concerned when carry on ecological processes and carbon dynamics researches in peatlands.Peer reviewe

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojänkkä and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R2 was 42.6–48.0% (root mean square error (RMSE) 0.153–0.193), and for PFT 2D %-cover 53.9–69.8% (RMSE 8.2–17.6%), AGB 43.1–61.5% (RMSE 86.2–165.5 g/m2) and LAI 46.3–51.3% (RMSE 0.220–0.464 m2/m2). The multi-temporal data of the whole season increased R2 by 13.7–24.6%-points and 10.2–33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojänkkä the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojänkkä, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes

Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems

Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions

Chemoradiotherapy-Induced CD4+ and CD8+ T-Cell Alterations to Predict Patient Outcomes in Esophageal Squamous Cell Carcinoma

Purpose and Objectives: Chemoradiotherapy (CRT) is an important component of treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Recent research findings support the role of CRT in activating an anti-tumor immune response. However, predictors of CRT efficacy are not fully understood. The aim of this study was to measure CRT-induced changes to lymphocyte subpopulations and to evaluate the prognostic value of lymphocyte alterations for patients with ESCC.Materials and Methods: In total, this pilot study enrolled 64 patients with ESCC who received neo-adjuvant CRT or definitive CRT. Peripheral blood samples were collected before and during treatment and were analyzed by flow cytometry for CD19, CD3, CD4, CD8, CD56, and CD16. Relationships between lymphocyte subset alterations and overall survival (OS) and progression-free survival (PFS) were evaluated using the log-rank test and a Cox regression model.Results: The median follow-up period was 11.8 months (range, 4.0–20.2 months). Compared to pre-treatment specimens, post-treatment blood samples had decreased proportions of CD19+ B-cells and increased proportions of CD3+ and CD8+ T-cells (all P &lt; 0.05). Univariate and multivariate analysis showed that increased CD4+ T-cell ratios after CRT independently predicted superior PFS (hazard ratio [HR] = 0.383; 95% confidence interval [CI] = 0.173–0.848, P = 0.017) and that increased CD8+ T-cell ratios predicted improved OS (HR = 0.258; 95% CI = 0.083–0.802, P = 0.019). Patients with both increased CD4+ and CD8+ ratios had a superior PFS and OS, compared to patients with an increased CD4+ ratio only or CD8+ ratio only or neither (1-year PFS rate 63 vs. 25%, 1-year OS rate 80 vs. 62%, P = 0.005 and 0.025, respectively).Conclusions: CRT-induced increases in CD4+ and CD8+ T-cell ratios are reliable biomarker predictors of survival in patients with ESCC

Association Analysis of IL-17A and IL-17F Polymorphisms in Chinese Han Women with Breast Cancer

Background: Research into the etiology of breast cancer has recently focused on the role of the immunity and inflammation. The proinflammatory cytokines IL-17A and IL-17F can mediate inflammation and cancer. To evaluate the influences of IL-17A and IL-17F gene polymorphisms on the risk of sporadic breast cancer, a case-control study was conducted in Chinese Han women. Methodology and Principal Findings: We genotyped three single-nucleotide polymorphisms (SNPs) in IL-17A (rs2275913, rs3819025 and rs3748067) and five SNPs in IL-17F (rs7771511, rs9382084, rs12203582, rs1266828 and rs763780) to determine the haplotypes in 491 women with breast cancer and 502 healthy individuals. The genotypes were determined using the SNaPshot technique. The differences in the genotypic distribution between breast cancer patients and healthy controls were analyzed with the Chi-square test for trends. For rs2275913 in IL-17A, the frequency of the AA genotype was higher in patients than controls (P = 0.0016). The clinical features analysis demonstrated significant associations between IL-17 SNPs and tumor protein 53 (P53), progesterone receptor (PR), human epidermal growth factor receptor 2 (Her-2) and triple-negative (ER-/PR-/Her-2-) status. In addition, the haplotype analysis indicated that the frequency of the haplotype A rs2275913G rs3819025G rs3748067, located in the IL-17A linkage disequilibrium (LD) block, was higher in patients than in controls (P = 0.0471 after correction for multiple testing)

Pathogenesis of gallbladder adenomyomatosis and its relationship with early-stage gallbladder carcinoma: an overview

The exact pathogenesis of gallbladder adenomyomatosis is still lacking and some controversies over its diagnosis and treatment exist. Originally recognized as a precancerous lesion, adenomyomatosis is currently recognized by recent studies as a benign alteration of the gallbladder that is often associated with cholecystitis and cholecystolithiasis. Gallbladder carcinoma is an extremely malignant disease with a 5-year survival rate of less than 5%. Therefore, it is important to diagnose, differentiate, and confirm the relationship between adenomyomatosis and early-stage gallbladder carcinoma. However, the early clinical symptoms of adenomyomatosis are extremely similar to those of gallbladder stones and cholecystitis, increasing the difficulty to identify and treat this disease. This article summarizes the research progress on gallbladder adenomyomatosis, aiming to improve the understanding of the pathogenesis of adenomyomatosis and further provide insight for its clinical diagnosis and treatment

Clinical and prognostic value of preoperative hydronephrosis in upper tract urothelial carcinoma: a systematic review and meta-analysis

Background. Epidemiological studies have reported various results relating preoperative hydronephrosis to upper tract urothelial carcinoma (UTUC). However, the clinical significance and prognostic value of preoperative hydronephrosis in UTUC remains controversial. The aim of this study was to provide a comprehensive meta-analysis of the extent of the possible association between preoperative hydronephrosis and the risk of UTUC. Methods. We searched PubMed, ISI Web of Knowledge, and Embase to identify eligible studies written in English. Summary odds ratios (ORs) or hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using fixed-effects or random-effects models. Results. Nineteen relevant studies, which had a total of 5,782 UTUC patients enrolled, were selected for statistical analysis. The clinicopathological and prognostic relevance of preoperative hydronephrosis was evaluated in the UTUC patients. The results showed that all tumor stages, lymph node status and tumor location, as well as the risk of cancer-specific survival (CSS), overall survival (OS), recurrence-free survival (RFS) and metastasis-free survival (MFS) were significantly different between UTUC patients with elevated preoperative hydronephrosis and those with low preoperative hydronephrosis. High preoperative hydronephrosis indicated a poor prognosis. Additionally, significant correlations between preoperative hydronephrosis and tumor grade (high grade vs. low grade) were observed in UTUC patients; however, no significant difference was observed for tumor grading (G1 vs. G2 + G3 and G1 + G2 vs. G3). In contrast, no such correlations were evident for recurrence status or gender in UTUC patients. Conclusions. The results of this meta-analysis suggest that preoperative hydronephrosis is associated with increased risk and poor survival in UTUC patients. The presence of preoperative hydronephrosis plays an important role in the carcinogenesis and prognosis of UTUC

Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems

Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions.Peer reviewe