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Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ˜15 mm and average duration of ˜115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ˜50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates. [ABSTRACT FROM AUTHOR]

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, deformable image registration was performed between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, new DVFs can be generated, which, when applied on the reference image, lead to new volumetric images. A volumetric image can then be reconstructed from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. The algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. The training data were generated using a realistic and dynamic mathematical phantom with ten breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9%±2.4%. The average 3D tumor localization error is 0.8±0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 s (range: 0.17 and 0.35 s). Conclusions: The authors have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image. [ABSTRACT FROM AUTHOR]

Myosin-Is are molecular motors that link cellular membranes to the actin cytoskeleton, where they play roles in mechano-signal trans duction and membrane trafficking. Some myosin Is are proposed to act as force sensors, dynamically modulating their motile proper ties in response to changes in tension. In this study, we examined force sensing by the widely expressed myosin-l isoform, myolb, which is alternatively spliced in its light chain binding domain (LCBD), yielding proteins with lever arms of different lengths. We found the actin-detachment kinetics of the splice isoforms to be extraordinarily tension-sensitive, with the magnitude of tension sensitivity to be related to LCBD splicing. Thus, in addition to reg ulating step-size, motility rates, and myosin activation, the LCBD is a key regulator of force sensing. We also found that myol b is sub stantially more tension-sensitive than other myosins with similar length lever arms, indicating that different myosins have different tension-sensitive transitions. [ABSTRACT FROM AUTHOR]

The article reports on the development of a method for the detection and quantification of zolpidem carboxylic acid (ZCA), the major urinary metabolite of zolpidem. Zolpidem has been implicated in both drug-facilitated sexual assault and drink spiking and can be detected in urine. The method involves extraction of metabolites and analysis using gas chromatography-mass spectrometry. It was concluded that the method is rapid, simple, and can be used by laboratory technicians.

The article explores the important role that economic change, size and location played in shaping local crime rates in Australia. It presents an analysis of the study using data for local government areas (LGAs) in the mainland eastern states which reveals that local levels of crime are affected in different ways through structural change and changes in economic activity had a stronger impact on the crime rates of small and medium-sized towns. It also notes that the relationship between economic change and crime was assessed by using separate regression models for violent and property crime.

The article discusses the role of drug detection in the law enforcement in Australia. It notes that detection of drugs play an important role in many areas of society including sport, suspicious deaths and violent crimes. It reveals that an increase in drug -related problems in the society and the advances in the technology have prompted Australia to start using the drug testing technology in the criminal justices system in much wider scale. It presents the history of drug detection and cites that urinalysis is the method to test illicit drugs to identify drug related offenders. It emphasizes that there are available less invasive method but have limitations and the capacity for new drug detection technologies to differentiate between use and intoxication is another issue.

Purpose To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. Methods and materials Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. Results Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. Conclusion Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods. [ABSTRACT FROM AUTHOR]