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In this paper, we leverage on a case study about the governance of groundwater and the hazard of land subsidence in Yunlin, Taiwan, to address the state's political strategies of producing 'hydro‐social territories'. Our goal is to examine how local agents and their knowledge of the socio‐technological system shape groundwater use, with particular attention on how that system has changed due to the governmentalized regulation of groundwater resources as a means to mitigate land subsidence. Drawing from the local agrarian development and its tube well irrigation networks that underlie the groundwater‐scape in this research, the paper demonstrates that the 'hydro‐social territory' itself is complicated, as local farmers who rely on groundwater are never fully controlled by the state, regardless of whether or not they use groundwater for agricultural cultivation or other purposes, yet at the same time, the intention of governmentalizing the local groundwater regime is concealed and has become more concrete with the introduction of new sciences and technologies. [ABSTRACT FROM AUTHOR]

Background: Incidentally discovered adrenal lesions known as adrenal incidentalomas (AI) are being encountered with increasing frequency due to the widespread use of abdominal computed tomography (CT). The aim of this study was to identify the clinical predictors of malignancy in AI and to evaluate the accuracy of a recently proposed risk stratification algorithm. Methods: A retrospective analysis of 96 patients presenting with AI between 2004 and 2014 was undertaken; 66 patients underwent adrenalectomy, and 30 were managed non‐operatively. Univariate analysis including patient demographics, CT features of tumour size, density and heterogeneity was performed. Hormonal parameters including 24‐h urinary‐free cortisol and serum dehydroepiandrosterone sulphate (DHEAS) were also included. A Cleveland Clinic risk stratification model utilizing adrenal size and density was evaluated. Results: The overall rate of malignancy was 8%. On univariate analysis, the following preoperative variables were predictive of malignancy – tumour size on pathology (P = 0.0031) and CT (P = 0.0016), heterogeneity on CT imaging (P = 0.0036), a relative percentage washout of less than 40% (P = 0.0178), elevated 24‐h urinary‐free cortisol levels (P = 0.0176), elevated DHEAs (P = 0.0061) and younger age at presentation (P < 0.0001). Evaluation of the Cleveland Clinic algorithm found an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.52–1.00). Conclusion: CT characteristics of tumour size, density and heterogeneity are significantly associated with malignancy in AI and applied together reliably exclude malignancy. The risk stratification algorithm utilizing size and density alone may fail to identify some smaller adrenal cancers. [ABSTRACT FROM AUTHOR]