Application of piecewise linear regression in the detection of vegetation greenness trends on the Tibetan Plateau

2014 ◽  
Vol 35 (4) ◽  
pp. 1526-1539 ◽  
Author(s):  
Bin Li ◽  
Li Zhang ◽  
Qin Yan ◽  
Yueju Xue
Keyword(s):  
Tibetan Plateau ◽  
Linear Regression ◽  
Piecewise Linear ◽  
The Tibetan Plateau ◽  
Piecewise Linear Regression ◽  
Vegetation Greenness

scholarly journals Continuous Wetting on the Tibetan Plateau during 1970–2017

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2605 ◽  
Author(s):  
Huamin Zhang ◽  
Mingjun Ding ◽  
Lanhui Li ◽  
Linshan Liu
Keyword(s):  
Tibetan Plateau ◽  
Piecewise Linear ◽  
Growing Season ◽  
Significant Trend ◽  
The Tibetan Plateau ◽  
Intensity Analysis ◽  
Standardized Precipitation Evapotranspiration Index ◽  
Piecewise Linear Regression ◽  
Increasing Trend ◽  
Surrounding Areas

Based on daily observation records at 277 meteorological stations on the Tibetan Plateau (TP) and its surrounding areas during 1970–2017, drought evolution was investigated using the Standardized Precipitation Evapotranspiration Index (SPEI). First, the spatiotemporal changes in the growing season of SPEI (SPEIgs) were re-examined using the Mann–Kendall and Sen’s slope approach—the piecewise linear regression and intensity analysis approach. Then, the persistence of the SPEIgs trend was predicted by the Hurst exponent. The results showed that the SPEIgs on the TP exhibited a significant increasing trend at the rate of 0.10 decade−1 (p < 0.05) and that there is no significant trend shift in SPEIgs (p = 0.37), indicating that the TP tended to undergo continuous wetting during 1970–2017. In contrast, the areas surrounding the TP underwent a significant trend shift from an increase to a decrease in SPEIgs around 1984 (p < 0.05), resulting in a weak decreasing trend overall. Spatially, most of the stations on the TP were characterized by an increasing trend in SPEIgs, except those on the Eastern fringe of TP. The rate of drought/wet changes was relatively fast during the 1970s and 1980s, and gradually slowed afterward on the TP. Finally, the consistent increasing trend and decreasing trend of SPEIgs on the TP and the area East of the TP were predicted to continue in the future, respectively. Our results highlight that the TP experienced a significant continuous wetting trend in the growing season during 1970–2017, and this trend is likely to continue.

Identifying changes in trends in the age standardized incidence of melanoma in Australia

2019 ◽  
Vol 3 (4) ◽  
pp. 250-252 ◽  
Author(s):  
David M Hille
Keyword(s):  
Public Health ◽  
Linear Regression ◽  
Iterative Process ◽  
Linear Trend ◽  
Piecewise Linear ◽  
Health Issue ◽  
Public Health Issue ◽  
Piecewise Linear Regression ◽  
Significant Public Health ◽  
Males And Females

ObjectiveTo identify changes in the linear trend of the age-standardized incidence of melanoma in Australia for all persons, males, and females. MethodsA two-piece piecewise linear regression was fitted to the data. The piecewise breakpoint varied through an iterative process to determine the model that best fits the data.ResultsStatistically significant changes in the trendof the age-standardized incidence of melanoma in Australia were found for all persons, males, and females. The optimal breakpoint for all persons and males was at 1998. For females, the optimal breakpoint was at 2005. The trend after these breakpoints was flatter than prior to the breakpoints, but still positive.ConclusionMelanoma is a significant public health issue in Australia. Overall incidence continues to increase. However, the rate at which the incidence is increasing appears to be decreasing.

scholarly journals Elevational Movement of Vegetation Greenness on the Tibetan Plateau: Evidence from the Landsat Satellite Observations during the Last Three Decades

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 161
Author(s):  
Liheng Lu ◽  
Xiaoqian Shen ◽  
Ruyin Cao
Keyword(s):  
Tibetan Plateau ◽  
Spatial Resolution ◽  
Climate Warming ◽  
Vertical Movement ◽  
The Tibetan Plateau ◽  
Upward Movement ◽  
Vegetation Growth ◽  
Vegetation Greenness ◽  
Mountainous Regions ◽  
Vegetation Activities

The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.

U-Shaped Association Between the Triglycerides-Glucose index and the Risk of Incident Diabetes in Apparently Healthy Population: A Population-based Cohort Study

2020 ◽  
Author(s):  
Xiuping Xuan ◽  
Masahide Hamaguchi ◽  
Qiuli Cao ◽  
Okamura Takuro ◽  
Yoshitaka Hashimoto ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Incident Diabetes ◽  
Smoothing Function ◽  
Healthy Population ◽  
Piecewise Linear Regression ◽  
Tyg Index ◽  
Apparently Healthy

Abstract Background Although the triglycerides-glucose (TyG) index was thought to be a practical predictor of incident diabetes, the association between them has not been well characterized. The study aimed to further examine the association between the TyG index and incident diabetes in Japanese adults. Methods The cases were extracted of the individual participating in the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) study at Murakami Memorial Hospital from 2004 to 2015, and 14297individuals apparently healthy at baseline were included in the study. Cox proportional hazards models were used to evaluate the associations between baseline TyG levels and incident of T2DM, and a two-piecewise linear regression model was use to examine the threshold effect of the baseline TyG on incident diabetes using a smoothing function. The threshold level (i.e., turning point) was determined using trial and error. A log likelihood ratio test was also conducted to compare the one-line linear regression model with a two-piecewise linear model. Results During a median follow-up period of 5.26 (women) and 5.88 (men) years, 47 women and 182 men developed Type 2 diabetes. The risk of diabetes was strongly associated with the baseline TyG index in the fully adjusted model in men but not in women, and no dose-dependent positive relationship between incident diabetes and TyG was observed across TyG tertiles. Intriguingly, two-piecewise linear regression analysis showed a U-shaped association between the TyG index and incident T2DM. The risk of incident diabetes decreased by around 90% in women with TyG < 7.27 (HR: 0.09; P = 0.0435) and 80% in men with TyG < 7.97 (HR 0.21, P = 0.002) with each increment of the TyG index after adjusting for confounders. In contrast, the risk of incident T2DM significantly elevated with the increase in TyG index in men with TyG > 7.97 (HR: 2.42, P < 0.001) and women with TyG > 7.29 (HR 2.76, P = 0.0166). Conclusions A U-shaped association was observed between the TyG index and incident T2DM among healthy individuals, with the TyG threshold of 7.97 in men and 7.27 in women. This information may be useful for reducing incident diabetes by maintaining the TyG index near these thresholds.

scholarly journals Piecewise Linear Regression Based on Plane Clustering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29845-29855 ◽  
Author(s):  
Xubing Yang ◽  
Hongxin Yang ◽  
Fuquan Zhang ◽  
Li Zhang ◽  
Xijian Fan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Piecewise Linear ◽  
Piecewise Linear Regression

Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000-2016 on the Tibetan Plateau

2018 ◽  
Vol 24 (11) ◽  
pp. 5411-5425 ◽  
Author(s):  
Shuai An ◽  
Xiaolin Zhu ◽  
Miaogen Shen ◽  
Yafeng Wang ◽  
Ruyin Cao ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Vegetation Greenness

scholarly journals Network-based piecewise linear regression for QSAR modelling

2019 ◽  
Vol 33 (9) ◽  
pp. 831-844
Author(s):  
Jonathan Cardoso-Silva ◽  
Lazaros G. Papageorgiou ◽  
Sophia Tsoka
Keyword(s):  
Virtual Screening ◽  
Linear Regression ◽  
Predictive Accuracy ◽  
Piecewise Linear ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Piecewise Linear Regression ◽  
Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

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