King Abdullah University of Science and Technology

About: King Abdullah University of Science and Technology is a education organization based out in Jeddah, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences

Abstract: Mycobacterium tuberculosis drug resistance (DR) challenges effective tuberculosis disease control. Current molecular tests examine limited numbers of mutations, and although whole genome sequencing approaches could fully characterise DR, data complexity has restricted their clinical application. A library (1,325 mutations) predictive of DR for 15 anti-tuberculosis drugs was compiled and validated for 11 of them using genomic-phenotypic data from 792 strains. A rapid online ‘TB-Profiler’ tool was developed to report DR and strain-type profiles directly from raw sequences. Using our DR mutation library, in silico diagnostic accuracy was superior to some commercial diagnostics and alternative databases. The library will facilitate sequence-based drug-susceptibility testing.

Optimization of photoelectrochemical water splitting performance on hierarchical TiO2 nanotube arrays

Abstract: In this paper, we show that by varying the voltages during two-step anodization the morphology of the hierarchical top-layer/bottom-tube TiO2 (TiO2 NTs) can be finely tuned between nanoring/nanotube, nanopore/nanotube, and nanohole–nanocave/nanotube morphologies. This allows us to optimize the photoelectrochemical (PEC) water splitting performance on the hierarchical TiO2 NTs. The optimized photocurrent density and photoconversion efficiency in this study, occurring on the nanopore/nanotube TiO2 NTs, were 1.59 mA cm−2 at 1.23 V vs. RHE and 0.84% respectively, which are the highest values ever reported on pristine TiO2 materials under illumination of AM 1.5G. Our findings contribute to further improvement of the energy conversion efficiency of TiO2-based devices.

Important Flavonoids and Their Role as a Therapeutic Agent.

Abstract: Flavonoids are phytochemical compounds present in many plants, fruits, vegetables, and leaves, with potential applications in medicinal chemistry. Flavonoids possess a number of medicinal benefits, including anticancer, antioxidant, anti-inflammatory, and antiviral properties. They also have neuroprotective and cardio-protective effects. These biological activities depend upon the type of flavonoid, its (possible) mode of action, and its bioavailability. These cost-effective medicinal components have significant biological activities, and their effectiveness has been proved for a variety of diseases. The most recent work is focused on their isolation, synthesis of their analogs, and their effects on human health using a variety of techniques and animal models. Thousands of flavonoids have been successfully isolated, and this number increases steadily. We have therefore made an effort to summarize the isolated flavonoids with useful activities in order to gain a better understanding of their effects on human health.

Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection

Abstract: Android has been a major target of malicious applications (malapps). How to detect and keep the malapps out of the app markets is an ongoing challenge. One of the central design points of Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, it imparts a significant responsibility to the app developers with regard to accurately specifying the requested permissions and to the users with regard to fully understanding the risk of granting certain combinations of permissions. Android permissions requested by an app depict the app’s behavioral patterns. In order to help understanding Android permissions, in this paper, we explore the permission-induced risk in Android apps on three levels in a systematic manner. First, we thoroughly analyze the risk of an individual permission and the risk of a group of collaborative permissions. We employ three feature ranking methods, namely, mutual information, correlation coefficient, and T-test to rank Android individual permissions with respect to their risk. We then use sequential forward selection as well as principal component analysis to identify risky permission subsets. Second, we evaluate the usefulness of risky permissions for malapp detection with support vector machine, decision trees, as well as random forest. Third, we in depth analyze the detection results and discuss the feasibility as well as the limitations of malapp detection based on permission requests. We evaluate our methods on a very large official app set consisting of 310 926 benign apps and 4868 real-world malapps and on a third-party app sets. The empirical results show that our malapp detectors built on risky permissions give satisfied performance (a detection rate as 94.62% with a false positive rate as 0.6%), catch the malapps’ essential patterns on violating permission access regulations, and are universally applicable to unknown malapps (detection rate as 74.03%).