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Gas therapy has been proven to be a promising and advantageous treatment option for cancers. Studies have shown that nitric oxide (NO) is one of the smallest structurally significant gas molecules with great potential to suppress cancer. However, there is controversy and concern about its use as it exhibits the opposite physiological effects based on its levels in the tumor. Therefore, the anti-cancer mechanism of NO is the key to cancer treatment, and rationally designed NO delivery systems are crucial to the success of NO biomedical applications. This review summarizes the endogenous production of NO, its physiological mechanisms of action, the application of NO in cancer treatment, and nano-delivery systems for delivering NO donors. Moreover, it briefly reviews challenges in delivering NO from different nanoparticles and the issues associated with its combination treatment strategies. The advantages and challenges of various NO delivery platforms are recapitulated for possible transformation into clinical applications.

A joint manifold learning fusion (JMLF) approach is proposed for nonlinear or mixed sensor modalities with large streams of data. The multimodal sensor data are stacked to form joint manifolds, from which the embedded low intrinsic dimensionalities are discovered for moving targets. The intrinsic low dimensionalities are mapped to resolve the target locations. The JMLF framework is tested on digital imaging and remote sensing image generation scenes with mid-wave infrared (WMIR) data augmented with distributed radio-frequency (RF) Doppler data. Eight manifold learning methods are explored to train the system with the neighborhood preserving embedding showing promise for robust target tracking using video–radio-frequency fusion. The JMLF method shows a 93% improved accuracy as compared to a standard target tracking (e.g., Kalman-filter based) approach.

Accurately measuring the oil content and salt content of crude oil is very important for both estimating oil reserves and predicting the lifetime of an oil well. There are some problems with the current methods such as high cost, low precision, and difficulties in operation. To solve these problems, we present a multifunctional sensor, which applies, respectively, conductivity method and ultrasound method to measure the contents of oil, water, and salt. Based on cross sensitivity theory, these two transducers are ideally integrated for simplifying the structure. A concentration test of ternary solutions is carried out to testify its effectiveness, and then Canonical Correlation Analysis is applied to evaluate the data. From the perspective of statistics, the sensor inputs, for instance, oil concentration, salt concentration, and temperature, are closely related to its outputs including output voltage and time of flight of ultrasound wave, which further identify the correctness of the sensing theory and the feasibility of the integrated design. Combined with reconstruction algorithms, the sensor can realize the content measurement of the solution precisely. The potential development of the proposed sensor and method in the aspect of online test for crude oil is of important reference and practical value.