Demonstration of a Flexible Graphene-Based Biosensor for Sensitive and Rapid Detection of Ovarian Cancer Cells.

It is significant to develop an efficient early detection and prediction method for ovarian cancer via a facile and low-cost approach. To address such issues, herein, we develop a novel circulating tumor cell (CTC) detection method to sensitively detect ovarian cancer by using a flexible graphene-based biosensor on polyethylene terephthalate (PET) substrate. The results show that the graphene-based flexible biosensor demonstrates sensitive and rapid detection for ovarian cancer cells: it delivers obvious different responses for cell culture medium and cancer solution, different cancer cells and cancer cell solution with different concentrations; it demonstrates high sensitivity for detecting several tens of ovarian cancer cells per ml; moreover, the flexible graphene biosensor is very suitable for rapid and sensitive detection of ovarian cancer cells within 5 s. This work provides a low-cost and facile graphene biosensor fabrication strategy to sensitively and rapidly detect / identify CTC ovarian cancer cells.

Graphene-Based Biosensors with High Sensitivity for Detection of Ovarian Cancer Cells.

Ovarian cancer has the highest mortality rate in the world. Therefore, it is urgent but still challenging to develop an efficient circulating tumor cell (CTC) detection method to sensitively detect ovarian cancer. To address such issues, herein, for the first time, we present a novel CTC detection method for ovarian cancer cells by designing sensitive and rapid graphene-based biosensors. This graphene-based sensor, consisting of a cell pool and two electrodes, can be prepared by a conventional chip fabrication process. It demonstrates high-sensitivity detection even for several ovarian cancer cells by comparing the electrical signal before and after adding cell solution. Moreover, the graphene-based biosensors can perform rapid detection with good repeatability. This suggests that this novel method is possible to use for the early detection of ovarian cancer with very low CTC cell concentration. This work provides a novel and quick strategy to detect ovarian cancer and further judge or predict the risk of the transfer of ovarian cancer.

High-Temperature-Annealed Flexible Carbon Nanotube Network Transistors for High-Frequency Wearable Wireless Electronics.

Semiconducting single-walled carbon nanotubes (SWNTs) are potential active materials for fast-growing flexible/wearable applications with low-power dissipation, especially suitable for increasingly important radio-frequency (RF) wireless biosensor systems. However, the operation frequency of the existing flexible carbon nanotube field-effect transistors (CNT-FETs) is far below the current state-of-the-art GSM spectrum frequency band (typical 850 MHz) for near-field wireless communication applications. In this paper, we successfully conduct a 900 °C annealing process for the flexible CNT-FETs and hence significantly improve their operation frequency up to 2.1 gigahertz (GHz), making it possible to cover the current GSM spectra for integrated wireless sensor systems. The high-temperature annealing process significantly improves the electrical characteristic of the flexible CNT-FETs by removing the surfactant impurities of the SWNT materials. The obtained flexible CNT-FETs exhibit record transconductance (gm) as high as 48 µS/µm. Despite an applied strain level of 2%, a characteristic frequency of over 1 GHz is observed. Further demonstration of GHz performance is also exhibited for flexible RF integrated circuits (ICs) such as frequency multipliers and mixers, which are the fundamental components for wireless applications. This work offers a new pathway for realizing SWNT-based wearable wireless GHz sensor systems with power efficiency.

A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things.

With limited computing resources and a lack of physical lines of defense, the Internet of Things (IoT) has become a focus of cyberattacks. In recent years, outbreak propagation attacks against the IoT have occurred frequently, and these attacks are often strategical. In order to detect the outbreak propagation as soon as possible, t embedded Intrusion Detection Systems (IDSs) are widely deployed in the IoT. This paper tackles the problem of outbreak detection in adversarial environment in the IoT. A dynamic scheduling strategy based on specific IDSs monitoring of IoT devices is proposed to avoid strategic attacks. Firstly, we formulate the interaction between the defender and attacker as a Stackelberg game in which the defender first chooses a set of device nodes to activate, and then the attacker selects one seed (one device node) to spread the worms. This yields an extremely complex bilevel optimization problem. Our approach is to build a modified Column Generation framework for computing the optimal strategy effectively. The optimal response of the defender's problem is expressed as mixed-integer linear programming (MILPs). It is proved that the solution of the defender's optimal response is a NP-hard problem. Moreover, the optimal response of defenders is improved by an approximate algorithm--a greedy algorithm. Finally, the proposed scheme is tested on some randomly generated instances. The experimental results show that the scheme is effective for monitoring optimal scheduling.

Spatiotemporal access model based on reputation for the sensing layer of the IoT.

Access control is a key technology in providing security in the Internet of Things (IoT). The mainstream security approach proposed for the sensing layer of the IoT concentrates only on authentication while ignoring the more general models. Unreliable communications and resource constraints make the traditional access control techniques barely meet the requirements of the sensing layer of the IoT. In this paper, we propose a model that combines space and time with reputation to control access to the information within the sensing layer of the IoT. This model is called spatiotemporal access control based on reputation (STRAC). STRAC uses a lattice-based approach to decrease the size of policy bases. To solve the problem caused by unreliable communications, we propose both nondeterministic authorizations and stochastic authorizations. To more precisely manage the reputation of nodes, we propose two new mechanisms to update the reputation of nodes. These new approaches are the authority-based update mechanism (AUM) and the election-based update mechanism (EUM). We show how the model checker UPPAAL can be used to analyze the spatiotemporal access control model of an application. Finally, we also implement a prototype system to demonstrate the efficiency of our model.