Topological Corner States in Graphene by Bulk and Edge Engineering

Two-dimensional higher-order topology is usually studied in (nearly) particle-hole symmetric models, so that an edge gap can be opened within the bulk one. But more often deviates the edge anticrossing even into the bulk, where corner states are difficult to pinpoint. We address this problem in a graphene-based $\mathbb{Z}_2$ topological insulator with spin-orbit coupling and in-plane magnetization both originating from substrates through a Slater-Koster multi-orbital model. The gapless helical edge modes cross inside the bulk, where is also located the magnetization-induced edge gap. After demonstrating its second-order nontriviality in bulk topology by a series of evidence, we show that a difference in bulk-edge onsite energy can adiabatically tune the position of the crossing/anticrossing of the edge modes to be inside the bulk gap. This can help unambiguously identify two pairs of topological corner states with nonvanishing energy degeneracy for a rhombic flake. We further find that the obtuse-angle pair is more stable than the acute-angle one. These results not only suggest an accessible way to "find" topological corner states, but also provide a higher-order topological version of "bulk-boundary correspondence"

CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps

Correlation of PK/PD Indices with Resistance Selection for Cefquinome against Staphylococcus aureus in an In Vitro Model

Cefquinome is a fourth-generation Cephalosporin approved for use in animals exclusively. The objective of this study was to explore the relationship of cefquinome pharmacokinetic/pharmacodynamic (PK/PD) indices with resistance selection of Staphylococcus aureus ATCC25923 in an in vitro model. Six dosing regiments of cefquinome at an interval of 24 h for three consecutive times were simulated, resulting in maximum concentrations (Cmax) from 1/2 MIC to 16 MIC and half-lives (t1/2β) of 3 and 6 h, respectively. The in vitro sensitivity of S. aureus was monitored by bacterial susceptibility and dynamic time-kill curve experiments over the six cefquinome concentrations. The correlation between changes in bacterial susceptibility (MIC72/MIC0) and the percentage of time within mutant selection window (MSW) versus dosing interval (TMSW %) was subjected to Gaussian function and regression analysis. The results favored the consensus that time above MIC (T>MIC) was recognized as an important PK/PD parameter of cephalosporins for antibacterial efficiency. Cefquinome reached the maximum killing effect when T>MIC% attained approximately 40%~60%. The subsequent correlation analysis demonstrated that resistant S. aureus ATCC25923 was easy to occur when TMSW% attained an index of about 20% with t1/2β of 3 h after multiple dosing, and 40% with t1/2β of 6 h after multiple dosing