scholarly journals Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model

2008 ◽  
Vol 2 (1) ◽  
pp. 023542 ◽  
Author(s):  
Sarol Lee
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Logistic Regression ◽  
Geographic Information System ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hazard Analysis ◽  
Remote Sensing Data ◽  
Landslide Hazard ◽  
Optical Remote Sensing

scholarly journals Geographic Distribution of Desert Locusts in Africa, Asia and Europe Using Multiple Sources of Remote-Sensing Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3593
Author(s):  
Chaoliang Chen ◽  
Jing Qian ◽  
Xi Chen ◽  
Zengyun Hu ◽  
Jiayu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Regression Model ◽  
Geographic Distribution ◽  
Logistic Regression Model ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Desert Locust ◽  
Sensing Data ◽  
Potential Habitats

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 107 km2 and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 107 km2 and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.

Pretreatment metabolomic markers associated with therapeutic responses to FOLFOX with bevacizumab in chemotherapy-naive patients with colorectal cancer.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 540-540
Author(s):  
Yusuke Tanigawara ◽  
Shinji Sugimoto ◽  
Kei Muro
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Treatment Response ◽  
Logistic Regression Model ◽  
Hazard Analysis ◽  
Serum Levels ◽  
Multivariate Logistic Regression Model ◽  
Pretreatment Serum ◽  
Therapeutic Responses

540 Background: FOLFOX with bevacizumab (BV) is a standard treatment for metastatic colorectal cancer (mCRC); however, its clinical response is around 50% and there is no way to predict responders prior to therapy. In this study, we attempted to identify new biomarkers associated with positive therapeutic responses by means of a comprehensive metabolomic analysis of patient serum. Methods: Serum collected from 68 mCRC patients, who were registered in a phase II study of first-line FOLFOX with BV treatment (Nishina, JJCO 2013) was used to conduct a comprehensive metabolomic quantification analysis using a capillary electrophoresis time-of-flight mass spectrometry. Responders (Rs: n = 37) and non-responders (NRs: n = 31) were defined as those achieving a status of CR/PR and SD/PD, respectively, which were assessed by an extramural review board using RECIST. Statistical analyses were performed using a logistic regression model for treatment response and Cox proportional hazard analysis for overall survival (OS). Results: Among 470 annotated endogenous metabolites, cysteine-glutathione disulphide (CSG), gamma-glutamyl cysteine (gGC) and hypoxanthine (HPX) were identified as significant (p < 0.05) metabolites associated with positive therapeutic responses by both response and survival analyses. Patients were divided into two groups according to cutoff values of pretreatment serum levels of these metabolites. The actual response rate in the high CSG, gGC and HPX group were 86, 69 and 8% whereas those in the low group were 40, 26 and 64%, respectively. The hazard ratio (HR) for OS in CSG, gGC and HPX were 0.28 (p < 0.01), 0.37 (p < 0.01) and 2.39 (p < 0.05), respectively. Using CSG, gGC and HPX, we have developed a multivariate logistic regression model to predict Rs/NRs and survival benefit based on the three metabolite levels in pretreatment serum. Sensitivity, specificity, ROC AUC and HR of OS between Rs and NRs were 89%, 65%, 0.83 and 0.36 (p = 0.002), respectively. Conclusions: We identified three novel pretreatment serum metabolomic markers that are associated with treatment response to FOLFOX with BV in chemotherapy-naive mCRC patients. Clinical trial information: UMIN000001490.

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