One of the most important aspect of molecular and computational biology is the reconstruction of evolutionary relationships. The area is well explored after decades of intensive research. Despite this fact there remains a need for good and efficient algorithms that are capable of reconstructing the evolutionary relationship in reasonable time.Since the problem is computationally intractable, exact algorithms are used only for small groups of species. In the Maximum Parsimony approach the time of computation grows so fast when number of sequences increases, that in practice it is possible to find the optimal solution for instances containing about 20 sequences only.It is this reason that in practical applications, heuristic methods are used. In this paper, parallel adaptive memory programming algorithms based on Maximum Parsimony and some known neighborhood search methods for phylogenetic tree construction are proposed, and the results of computational experiments are presented. The proposed algorithms achieve a superlinear speedup and find solutions of good quality.
This study investigates the agreement between two methods used for the assessment of human balance system. Static posturography based on tensometers, using a commercially available platform, is used as the reference method. An alternative approach is a portable prototype MediPost system that utilises inertial sensors developed by the authors. Both approaches determine the movement of the subject's centre of mass, quantifying this movement in terms of angular speed. Data for the evaluation of agreement were obtained from 205 subjects, with each subject simultaneously tested with both methods. During the tests, the subject performed a set of standard procedures involving quiet standing in an upright position. In order to verify the agreement between the evaluated methods, the Bland-Altman, concordance correlation and intraclass correlation coefficients were used. In addition, the trajectories of the centre of gravity were compared. The obtained results show good agreement between the verified methods, even though they are based on different physical phenomena.
Balance disorders are a growing problem worldwide. Thus, there is an increasing need to provide an inexpensive and feasible alternative to standard posturographic platforms (SP) used for the assessment of balance and to provide a possible solution for telemonitoring of patients. A novel mobile posturography (MP) MediPost device was developed to address these issues. This prospective study used a Modified Clinical Test of Sensory Interaction on Balance to evaluate healthy individuals and patients with a unilateral vestibular disorder through SP and MP simultaneously. The control group included 65 healthy volunteers, while the study group included 38 patients diagnosed with a unilateral vestibular deficit. The angular velocity values obtained from both methods were compared by intraclass correlation coefficients (ICC) and Bland–Altman plot analysis. Diagnostic capabilities were measured in terms of sensitivity and specificity. The ICC between the two methods for conditions 2–4 was indicative of excellent reliability, with the ICC > 0.9 (p < 0.001), except for Condition 1 (standing stance, eyes open) ICC = 0.685, p < 0.001, which is indicative of moderate reliability. ROC curve analysis of angular velocity for condition 4 represents the most accurate differentiating factor with AUC values of 0.939 for SP and 0.953 for MP. This condition also reported the highest sensitivity, specificity, PPV, and NPV values with 86.4%, 87.7%, 80%, and 90.5% for SP, and 92.1%, 84.6%, 77.8%, and 94.8% for MP, respectively. The newly developed MediPost device has high sensitivity and specificity in distinguishing between healthy individuals and patients with a unilateral vestibular deficit.
This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.
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