A Bayesian approach based on Kalman filter frameworks for bullet identification.

When a bullet is fired from a barrel, random imperfections in the interior surface of the barrel imprint 3-D micro structures on the bullet surface that are seen as striations. Despite being random and non-stationary in nature, these striations are known to be consistently reproduced in a unique pattern on every bullet. This is a key idea in bullet identification. Common procedures in the field of automatic bullet identification include extraction of a feature profile from bullet image, profile smoothing and comparison of profiles using normalized cross correlation. Since the cross correlation based comparison is susceptible to high-frequency noise and nonlinear baseline drift, profile smoothing is a critical step in bullet identification. In previous work, we considered bullet images as nonlinear non-stationary processes and applied ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. Using EEMD, each bullet average profile was decomposed into several scales known as intrinsic mode functions (IMFs). By choosing an appropriate range of scales, the resultant smoothed profile contained less high-frequency noise and no nonlinear baseline drift. But the procedure of choosing the proper number of IMFs to reduce the high-frequency noise effect was manual. This poses a problem in comparison of bullets whose images contained less or more noise in comparison to others because their useful information may be present in the corresponding discarded IMFs. Moreover, another problem arises when the bullet type changes. In this case manual inspection is needed once more to figure out which range of IMFs contain less high-frequency noise for this particular type of bullet. In this paper, we propose a novel combination of EEMD and Bayesian Kalman filter to solve these problems. First the bullet images are rotated using Radon transform. The rotated images are averaged column-wise to acquire averaged 1-D profiles. The nonlinear baseline drifts of averaged profiles are removed using EEMD algorithm. The profiles are then processed by a Kalman filter that is designed to automatically and optimally reduce the effect of high-frequency noise. Using Expectation Maximization (EM) technique, for each averaged profile, the parameters of Kalman filter are reconfigured to optimally suppress the high-frequency noise in each averaged profile. This work is the first effort that practically implements the Kalman filter for optimal denoising of firearm image profiles. In addition, we believe that Euclidean distance metric can help the normalized cross-correlation based comparison. Therefore, in this paper, we propose a comparison metric that is invariant to start and endpoints of firearm image profiles. This metric combines the prized properties of both Euclidean and normalized cross-correlation metrics in order to improve identification results. The proposed algorithm was evaluated on a database containing 180 2-D gray-scale images acquired from bullets fired from different AK-47 assault rifles. Although the proposed method needs more calculations in comparison to conventional methods, the experiments showed that it attained better results compared with the conventional methods and the previous method based on EMD in the field of automatic bullet identification.

Alternative treatment of ovarian cysts with Tribulus terrestris extract: a rat model.

Tribulus terrestris has long been used in traditional medicine to treat impotency and improve sexual functions in man. The aim of this study was to evaluate the efficiency of T. terrestris extract in the treatment of polycystic ovary (PCO) in Wistar rat. Estradiol valerate was injected to 15 mature Wistar rats to induce PCO. Rats were randomly divided into three groups (control, low-dose and high-dose groups) of five each and received 0, 5 and 10 mg of T. terrestris extract, respectively.Treatments began on days 50 and 61 after estradiol injection; at the same time, vaginal smear was prepared. The ovaries were removed on day 62, and histological sections were prepared accordingly. The number and diameter of corpora lutea, thickness of the theca interna layer and the number of all follicles were evaluated in both ovaries. In comparison with the control group, the number of corpora lutea and primary and secondary follicles significantly increased following T. terrestris treatment; however, the number of ovarian cysts significantly decreased. It can be concluded that T. terrestris have a luteinizing effect on ovarian cysts, which may relate to its gonadotropin-like activity; also, a high dose of the extract can efficiently remove ovarian cysts and resume ovarian activity.

Exemplars from an acute care geriatric psychiatry unit.

The exemplars in this article reflect caring contexts and creative nursing solutions to dementia, depression, and addiction, common mental health problems afflicting elderly patients and for which inpatient evaluation and treatment are necessitated. Optimal functioning and quality of life for elderly individuals depend substantially upon both physical and mental capacity. The coexistence of mental and physical illness leads to rapid impairment of functioning and interrupts the individual's zest for living. Although in most cases dementia is irreversible, other treatable comorbid conditions like delirium can exacerbate suffering and decline. Conversely, mental disorders, like depression and addiction, can amplify the negative effects associated with other health conditions, causing excess disability and mortality, and are associated with older individuals having the highest suicide rate of any age group in the United States. Nurses are well positioned to identify mental health problems and humanely treat primary and secondary symptoms associated with these disorders in their elderly patients. A document to guide medical professionals' assessment of mental disorders is now available (Spitzer et al., 1994). Remaining attentive to early identification of high-risk individuals and mobilizing resources in their behalf will substantially contribute to their well-being. There is ample research evidence on the benefits and efficacy of mental health interventions (Lebowitz, 1994). Much of the challenge and hard work for nurses lies in getting to know the patient, grasping what is happening for the individual and determining which treatment interventions will be most effective given the present circumstances surrounding the illness episode (Benner, 1984).