A new growing method for simplex-based endmember extraction algorithms (EEAs), called simplex growing algorithm (SGA), is presented in this paper. It is a sequential algorithm to find a simplex with the maximum volume every time a new vertex is added. In order to terminate this algorithm a recently developed concept, virtual dimensionality (VD), is implemented as a stopping rule to determine the number of vertices required for the algorithm to generate. The SGA improves one commonly used EEA, the N-finder algorithm (N-FINDR) developed by Winter, by including a process of growing simplexes one vertex at a time until it reaches a desired number of vertices estimated by the VD, which results in a tremendous reduction of computational complexity. Additionally, it also judiciously selects an appropriate initial vector to avoid a dilemma caused by the use of random vectors as its initial condition in the N-FINDR where the N-FINDR generally produces different sets of final endmembers if different sets of randomly generated initial endmembers are used. In order to demonstrate the performance of the proposed SGA, the N-FINDR and two other EEAs, pixel purity index, and vertex component analysis are used for comparison

Due to advances of hyperspectral imaging sensors many unknown and subtle targets that cannot be resolved by multispectral imagery can now be uncovered by hyperspectral imagery. These targets generally cannot be identified by visual inspection or prior knowledge, but yet provide crucial and vital information for data exploitation. One such type of targets is anomalies which have recently received considerable interest in hyperspectral image analysis. Many anomaly detectors have been developed and most of them are based on the most widely used Reed-Yu's algorithm, called RX detector (RXD). However, a key issue in making RX detector-like anomaly detectors effective is how to effectively utilize the spectral information provided by data samples, e.g., sample covariance matrix used by RXD. Recently, a dual window-based eigen separation transform (DWEST) was developed to address this issue. This paper extends the concept of DWEST to develop a new approach, to be called multiple-window anomaly detection (MWAD) by making use of multiple windows to perform anomaly detection adaptively. As a result, MWAD is able to detect anomalies of various sizes using multiple windows so that local spectral variations can be characterized and extracted by different window sizes. By virtue of this newly developed MWAD, many existing RXD-like anomaly detectors including DWEST can be derived as special cases of MWAD.

Chronotherapeutics involve the administration of treatments according to circadian rhythms. Circadian timing of anti-cancer medications has been shown to improve treatment tolerability up to fivefold and double efficacy in experimental and clinical studies. However, the physiological and the molecular components of the circadian timing system (CTS), as well as gender, critically affect the success of a standardized chronotherapeutic schedule. In addition, a wrongly timed therapy or an excessive drug dose disrupts the CTS. Therefore, a non-invasive approach to accurately detect and monitor circadian rhythms is needed for a dynamic assessment of the CTS in order to personalize chronomodulated drug delivery schedule in cancer patients. Since core body temperature is a robust circadian biomarker, we recorded temperature at multiple locations on the skin of the upper chest and back of controls and cancer patients continuously. Variability in the circadian phase existed among patch locations in individual subjects over the course of 2–6 days, demonstrating the need to monitor multiple skin temperature locations to determine the precise circadian phase. Additionally, we observed that locations identified by infrared imaging as relatively cool had the largest 24 h temperature variations. Disruptions in skin temperature rhythms during treatment were found, pointing to the need to continually assess circadian timing and personalize chronotherapeutic schedules.

While a number of the consequences of mast cell degranulation within tissues have been documented including tissue-specific changes such as bronchospasm and the subsequent cellular infiltrate, there is little known about the immediate effects of mast cell degranulation on the associated vasculature, critical to understanding the evolution of mast cell dependent inflammation. To characterize the microcirculatory events that follow mast cell degranulation. Perturbations in dermal blood flow, temperature and skin color were analyzed using laser-speckle contrast imaging, infrared and polarized-light colorimetry following cold-hand immersion (CHI) challenge in patients with cold-induced urticaria compared to the response in healthy controls. Evidence for mast cell degranulation was established by documentation of serum histamine levels and the localized release of tryptase in post-challenge urticarial biopsies. Laser-speckle contrast imaging quantified the attenuated response to cold challenge in patients on cetirizine. We found that the histamine-associated vascular response accompanying mast cell degranulation is rapid and extensive. At the tissue level, it is characterized by a uniform pattern of increased blood flow, thermal warming, vasodilation, and recruitment of collateral circulation. These vascular responses are modified by the administration of an antihistamine. Monitoring the hemodynamic responses within tissues that are associated with mast cell degranulation provides additional insight into the evolution of the acute inflammatory response and offers a unique approach to assess the effectiveness of treatment intervention.