This work develops a static analysis to create a model of the behavior of an Android application's GUI. We propose the window transition graph (WTG), a model representing the possible GUI window sequences and their associated events and callbacks. A key component and contribution of our work is the careful modeling of the stack of currently-active windows, the changes to this stack, and the effects of callbacks related to these changes. To the best of our knowledge, this is the first detailed study of this important static analysis problem for Android. We develop novel analysis algorithms for WTG construction and traversal, based on this modeling of the window stack. We also describe an application of the WTG for GUI test generation, using path traversals. The evaluation of the proposed algorithms indicates their effectiveness and practicality.

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Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

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The use of mobile devices and the complexity of their software continue to grow rapidly. This growth presents significant challenges for software correctness and performance. In addition to traditional defects, a key consideration are defects related to the limited resources available on these devices. Resource leaks in an application, due to improper management of resources, can lead to slowdowns, crashes, and negative user experience. Despite a large body of existing work on leak detection, testing for resource leaks remains a challenging problem. We propose a novel and comprehensive approach for systematic testing for resource leaks in Android software. Similar to existing testing techniques, the approach is based on a GUI model, but is focused specifically on coverage criteria aimed at resource leak defects. These criteria are based on neutral cycles: sequences of GUI events that should have a "neutral" effect and should not lead to increases in resource usage. Several important categories of neutral cycles are considered in the proposed test coverage criteria. Experimental evaluation and case studies were performed on eight Android applications. The approach exposed 18 resource leak defects, 12 of which were previously unknown. These results provide motivation for future work on analysis, testing, and prevention of resource leaks in Android software.

A mangiferin aglycon derivative J99745 has been identified as a potent xanthine oxidase (XOD) inhibitor by previous in vitro study. This study aimed to evaluate the hypouricemic effects of J99745 in experimental hyperuricemia mice, and explore the underlying mechanisms. Mice were orally administered 600 mg/kg xanthine once daily for 7 days and intraperitoneally injected 250 mg/kg oxonic acid on the 7th day to induce hyperuricemia. Meanwhile, J99745 (3, 10, and 30 mg/kg), allopurinol (20 mg/kg) or benzbromarone (20 mg/kg) were orally administered to mice for 7 days. On the 7th day, uric acid and creatinine in serum and urine, blood urea nitrogen (BUN), malondialdehyde (MDA) content and XOD activities in serum and liver were determined. Morphological changes in kidney were observed using hematoxylin and eosin (H&E) staining. Hepatic XOD, renal urate transporter 1 (URAT1), glucose transporter type 9 (GLUT9), organic anion transporter 1 (OAT1) and ATP-binding cassette transporter G2 (ABCG2) were detected by Western blot and real time polymerase chain reaction (PCR). The results showed that J99745 at doses of 10 and 30 mg/kg significantly reduced serum urate, and enhanced fractional excretion of uric acid (FEUA). H&E staining confirmed that J99745 provided greater nephroprotective effects than allopurinol and benzbromarone. Moreover, serum and hepatic XOD activities and renal URAT1 expression declined in J99745-treated hyperuricemia mice. In consistence with the ability to inhibit XOD, J99745 lowered serum MDA content in hyperuricemia mice. Our results suggest that J99745 exerts urate-lowering effect by inhibiting XOD activity and URAT1 expression, thus representing a promising candidate as an anti-hyperuricemia agent.

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