Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors.

With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient's body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient.

Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra.

Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.

Feature analysis of cell nuclear chromatin distribution in support of cervical cytology.

Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist's experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.

Proteinase inhibitor from ginkgo seeds is a member of the plant nonspecific lipid transfer protein gene family.

A 9-kD proteinase inhibitor was isolated from the seeds of ginkgo (Ginkgo biloba) and purified to homogeneity. This protein was revealed to partial-noncompetitively inhibit the aspartic acid proteinase pepsin and the cysteine proteinase papain (inhibition constant = 10(-5)-10(-4) m). The cDNA of the inhibitor was revealed to contain a 357-bp open reading frame encoding a 119-amino acid protein with a potential signal peptide (27 residues), indicating that this protein is synthesized as a preprotein and secreted outside the cells. Semiquantitative reverse transcription-polymerase chain reaction revealed that this gene expresses only in seeds, not in stems, leaves, and roots, suggesting that the protein is involved in seed development and/or germination. The inhibitor showed about 40% sequence homology with type-I nonspecific lipid transfer protein (nsLTP1) from other plant species. Actually, this inhibitor exerted both lipid transfer activity and lipid-binding activity, while the protein did not show any antifungal and antibacterial activities. Furthermore, the site-directed mutagenesis study using a recombinant ginkgo nsLTP1 revealed that proline (Pro)-79 and phenylalanine-80 are important on phospholipid transfer activity and that Pro-79 and isoleucine-82 are essential for the binding activity toward cis-unsaturated fatty acids. On the other hand, the alpha-helical content of P79A and F80A mutants was significantly lower than that of the wild-type protein. It was noteworthy that the papain-inhibitory activity of P79A and F80A mutants was elevated twice as much as that of the wild-type protein. In summary, we concluded that Pro-79 plays a critical role in both the lipid transfer and binding activities of ginkgo nsLTP1.