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We report on the implementation of an automated platform for detecting the presence of an antibody biomarker for human papillomavirus-associated oropharyngeal cancer from a single droplet of serum, in which a nanostructured photonic crystal surface is used to amplify the output of a fluorescence-linked immunosorbent assay. The platform is comprised of a microfluidic cartridge with integrated photonic crystal chips that interfaces with an assay instrument that automates the introduction of reagents, wash steps, and surface drying. Upon assay completion, the cartridge interfaces with a custom laser-scanning instrument that couples light into the photonic crystal at the optimal resonance condition for fluorescence enhancement. The instrument is used to measure the fluorescence intensity values of microarray spots corresponding to the biomarkers of interest, in addition to several experimental controls that verify correct functioning of the assay protocol. In this paper, we report both dose-response characterization of the system using anti-E7 antibody introduced at known concentrations into serum and characterization of a set of clinical samples from which results were compared with a conventional enzyme-linked immunosorbent assay performed in microplate format. The demonstrated capability represents a simple, rapid, automated, and high-sensitivity method for the multiplexed detection of protein biomarkers from a low-volume test sample. [ABSTRACT FROM PUBLISHER]

A crucial problem that involves feature-based image registration algorithms is how to reliably establish the correspondence between the features detected in the sensed image and those detected in the reference image. Generally, most existing methods only use spatial relations or feature similarity, or a simple combination of them, to solve this problem, and all have some limitations. In this paper, a new feature-matching strategy is developed. It is realized by introducing a function whose independent variable is the match matrix, which describes the correspondence of the features, to combine spatial relations and organically feature similarity, and its global maximum is assumed to be reached if the sensed image is completely aligned with the reference image. Thus, the feature correspondence can be estimated by finding the maximum of the function. Two approaches are devised to solve the optimization problem. One is based on the branch-and-bound strategy to yield a global optimal solution, and the other uses an iterative algorithm that combines graduated assignment and variable metric methods to search for a local optimal solution with low computational complexity. The proposed method can work without the limitations of feature type, similarity criterion, and transform model, and its performance is evaluated using a variety of real images. Compared with some existing methods, it is fast and robust, and has the highest accuracy. [ABSTRACT FROM AUTHOR]

The atomic force microscope (AFM) has been widely used to manipulate nanoparticles, nanowires, and nanotubes for applications such as nanostructure building, nanocharacterization, and biomanipulation. However, conventional AFM-based nanomanipulation is inefficient because of the serial scan–manipulation–scan process involved. In this paper, high-efficiency automated nanomanipulation with a parallel imaging/manipulation force microscope (PIMM) is presented. With the PIMM, image scan and nanomanipulation can be performed in parallel through the collaboration between two cantilevers: one cantilever acts as an imaging sensor and the other is used as a manipulating tool. Two automated manipulation schemes were introduced for normal- and high-speed image scanning, respectively. An automated parallel manipulation task is managed by system control software with multithread through a procedure of dynamic image processing, task planning, two-tip collaboration, and a controlled pushing manipulation with amplitude feedback from the cantilevers. The efficiency of automated parallel nanomanipulation with normal-speed image scanning was validated by building nanoparticle patterns. [ABSTRACT FROM PUBLISHER]

The tortuosity of retinal blood vessels is a diagnostic parameter assessed by ophthalmologists on the basis of examples and experience; no quantitative model is specified in clinical practice. All quantitative measures proposed to date for automatic image analysis purposes are functions of the curvature of the vessel skeleton. We suggest in this paper that curvature may not be the only quantity involved in modeling tortuosity, and that vessel thickness, or caliber, may also play a role. To support this statement, we devise a novel measure of tortuosity, depending on both curvature and thickness, and test it with 200 vessels selected by our clinical author from the public digital retinal images for vessel extraction database. Results are in good accordance with clinical judgment. Comparative experiments show performance similar to or better than that of four measures reported in the literature. We conclude that there is reasonable evidence supporting the investigation of tortuosity models incorporating more measurements than just skeleton curvature, and specifically vessel caliber. [ABSTRACT FROM AUTHOR]

Navigation is a primary requirement for mobile robots as they encounter hazards during operation. To improve safety for robotic deployments, hazard alert systems could be integrated into their spatial environments to reduce robotic computational load and hardware requirements, while providing benefits to visually-impaired human users in the area. Modified tactile paving markers known as the Passive Auto-Tactile Heuristic (PATH) tiles are configured for robotic usage to aid in their navigation and hazard avoidance along with a specialized Tactile Sensor Module, while providing tactile cues to visually-impaired human users. This paper proposes a novel algorithm for generating standardized layouts of ‘safe routes’ from site plans of the robot’s workspace as input. The creation of ‘safe routes’ is implemented by extracting site boundaries and outlines of interior obstacles of an input site plan, generating of the site’s Medial Axis (MA) through its connectivity graph and overlaying the corresponding PATH tile types. This implementation is explored using the Rhinoceros3D CAD program, Grasshopper development platform and associated plugins for processing the site plan and visualization of the eventual routes. The algorithm is tested on 5 sites of varying spatial functions for validation.

Irrigation forms one of the primary components of agriculture and food production. Due to outdated techniques in developing and underdeveloped countries, a huge volume of water is wasted in this process. In this article, we have devised a fuzzy rule-base irrigation controller prototype to put a check on this water wastage by providing an optimal irrigating environment for farming. The prototype smart automatic irrigation controller (SAIC) has two operational units, namely, wireless sensor unit (WSU) and wireless information processing unit (WIPU). The purpose of the WSU is to measure weather and soil conditions and calculate the actual water loss due to evapotranspiration. The WIPU processes this calculated value and performs the necessary control action to regulate the actuators supplying the right amount of water to the farm. An exhaustive rule-base combination model is stacked in the lookup table for decision making. The prototype model is first simulated and then validated in the field for checking the performance efficiency. The simulated results showed capabilities to compensate for water loss by almost 100%. The controller achieved a 27% reduction in water usage and a 40% increase in crop yield. The prototype is connected to a cloud server for data repository and remote access and control. The device is efficient, low cost, and user friendly so that the end-users can it with ease and comfort. The model is innovative and unique in the sense that it can the irrigation scheduling for all types of crops, across all climatic conditions for all soil types upon feeding the proper soil–crop–growth stage combinations in the inference engine. [ABSTRACT FROM AUTHOR]

Objective: This study's objective is to develop and validate a fast automated 3-D segmentation method for cardiac magnetic resonance imaging (MRI). The segmentation algorithm automatically reconstructs cardiac MRI DICOM data into a 3-D model (i.e., direct volumetric segmentation), without relying on prior statistical knowledge. Methods: A novel 3-D active contour method was employed to detect the left ventricular cavity in 33 subjects with heterogeneous heart diseases from the York University database. Papillary muscles were identified and added to the chamber using a convex hull of the left ventricle and interpolation. The myocardium was then segmented using a similar 3-D segmentation method according to anatomic information. A multistage approach was taken to determine the method's efficacy. Results: Our method demonstrated a significant improvement in segmentation performance when compared to manual segmentation and other automated methods. Conclusion and Significance: A true 3-D reconstruction technique without the need for training datasets or any user-driven segmentation has been developed. In this method, a novel combination of internal and external energy terms for active contour was utilized that exploits histogram matching for improving the segmentation performance. This method takes advantage of full volumetric imaging, does not rely on prior statistical knowledge, and employs a convex-hull interpolation to include the papillary muscles. [ABSTRACT FROM PUBLISHER]

We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD. [ABSTRACT FROM PUBLISHER]

Current research on sleep using experimental animals is limited by the expense and time-consuming nature of traditional EEG/EMG recordings. We present here an alternative, noninvasive approach utilizing piezoelectric films configured as highly sensitive motion detectors. These film strips attached to the floor of the rodent cage produce an electrical output in direct proportion to the distortion of the material. During sleep, movement associated with breathing is the predominant gross body movement and, thus, output from the piezoelectric transducer provided an accurate respiratory trace during sleep. During wake, respiratory movements are masked by other motor activities. An automatic pattern recognition system was developed to identify periods of sleep and wake using the piezoelectric generated signal. Due to the complex and highly variable waveforms that result from subtle postural adjustments in the animals, traditional signal analysis techniques were not sufficient for accurate classification of sleep versus wake. Therefore, a novel pattern recognition algorithm was developed that successfully distinguished sleep from wake in approximately 95% of all epochs. This algorithm may have general utility for a variety of signals in biomedical and engineering applications. This automated system for monitoring sleep is noninvasive, inexpensive, and may be useful for large-scale sleep studies including genetic approaches towards understanding sleep and sleep disorders, and the rapid screening of the efficacy of sleep or wake promoting drugs. [ABSTRACT FROM AUTHOR]