Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Alternative positional FEM applied to thermomechanical impact of truss structures

This paper presents a formulation to deal with dynamic thermomechanical problems by the finite element method. The proposed methodology is based on the minimum potential energy theorem written regarding nodal positions, not displacements, to solve the mechanical problem. The thermal problem is solved by a regular finite element method. Such formulation has the advantage of being simple and accurate. As a solution strategy, it has been used as a natural split of the thermomechanical problem, usually called isothermal split or isothermal staggered algorithm. Usual internal variables and the additive decomposition of the strain tensor have been adopted to model the plastic behavior. Four examples are presented to show the applicability of the technique. The results are compared with other authors` numerical solutions and experimental results. (C) 2010 Elsevier B.V. All rights reserved.

A simple method for non-linear analysis of steel fiber reinforced concrete

This paper proposes a physical non-linear formulation to deal with steel fiber reinforced concrete by the finite element method. The proposed formulation allows the consideration of short or long fibers placed arbitrarily inside a continuum domain (matrix). The most important feature of the formulation is that no additional degree of freedom is introduced in the pre-existent finite element numerical system to consider any distribution or quantity of fiber inclusions. In other words, the size of the system of equations used to solve a non-reinforced medium is the same as the one used to solve the reinforced counterpart. Another important characteristic of the formulation is the reduced work required by the user to introduce reinforcements, avoiding ""rebar"" elements, node by node geometrical definitions or even complex mesh generation. Bounded connection between long fibers and continuum is considered, for short fibers a simplified approach is proposed to consider splitting. Non-associative plasticity is adopted for the continuum and one dimensional plasticity is adopted to model fibers. Examples are presented in order to show the capabilities of the formulation.

A discussion on volume change in the plastic phase

This technical note discusses the possibility of using a more simplified scheme to estimate the plastic multiplier when some material shows volume changes, e.g. soil, balsa wood foam and other similar materials. Two procedures regarding volume changes during the plastic phase are discussed here. The first one is the classic procedure applied to non-associative plasticity, for which a Drucker-Prager-like surface is adopted to represent the plastic potential. For the second procedure, the plastic potential is not explicitly known, however, its orthogonal direction is chosen respecting a plastic volume change parameter similar to Poisson`s ratio. Copyright (C) 2007 John Wiley & Sons, Ltd.

Recognition of noisy images by PLL networks

We use networks composed of three phase-locked loops (PLLs), where one of them is the master, for recognizing noisy images. The values of the coupling weights among the PLLs control the noise level which does not affect the successful identification of the input image. Analytical results and numerical tests are presented concerning the scheme performance. (c) 2008 Elsevier B.V. All rights reserved.

Multiple synchronous states in static delay-free mutually connected PLL networks

In many engineering applications, the time coordination of geographically separated events is of fundamental importance, as in digital telecommunications and integrated digital circuits. Mutually connected (MC) networks are very good candidates for some new types of application, such as wireless sensor networks. This paper presents a study on the behavior of MC networks of digital phase-locked loops (DPLLs). Analytical results are derived showing that, even for static networks without delays, different synchronous states may exist for the network. An upper bound for the number of such states is also presented. Numerical simulations are used to show the following results: (i) the synchronization precision in MC DPLLs networks; (ii) the existence of synchronous states for the network does not guarantee its achievement and (iii) different synchronous states may be achieved for different initial conditions. These results are important in the neural computation context. as in this case, each synchronous state may be associated to a different analog memory information. (C) 2010 Elsevier B.V. All rights reserved.

The viability of three probiotic organisms grown with yoghurt starter cultures during storage for 21 days at 4 degrees C

This study investigated the viability of probiotic (Lactobacillus acidophilus LA5, Lactobacillus rhamnosus LBA and Bifidobacterium animalis subsp. lactis BL-04) in milk fermented with Lactobacillus delbrueckii subsp. bulgaricus LB340 and Streptococcus thermophilus TAO (yoghurt - Y). Each probiotic strain was grown separately in co-culture with Y and in blends of different combinations. Blends affected fermentation time(s), pH and firmness during storage at 4 degrees C. The product made with Y plus B. animalis subsp. lactis and L. rhamnosus had counts of viable cells at the end of shelf life that met the minimum required to achieve probiotic effect. However, L. acidophilus and L. delbrueckii subsp. bulgaricus were inhibited.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

The game to play: expanding the co-opetition proposal through the strategic games matrix

Purpose - Using Brandenburger and Nalebuff`s 1995 co-opetition model as a reference, the purpose of this paper is to seek to develop a tool that, based on the tenets of classical game theory, would enable scholars and managers to identify which games may be played in response to the different conflict of interest situations faced by companies in their business environments. Design/methodology/approach - The literature on game theory and business strategy are reviewed and a conceptual model, the strategic games matrix (SGM), is developed. Two novel games are described and modeled. Findings - The co-opetition model is not sufficient to realistically represent most of the conflict of interest situations faced by companies. It seeks to address this problem through development of the SGM, which expands upon Brandenburger and Nalebuff`s model by providing a broader perspective, through incorporation of an additional dimension (power ratio between players) and three novel, respectively, (rival, individualistic, and associative). Practical implications - This proposed model, based on the concepts of game theory, should be used to train decision- and policy-makers to better understand, interpret and formulate conflict management strategies. Originality/value - A practical and original tool to use game models in conflict of interest situations is generated. Basic classical games, such as Nash, Stackelberg, Pareto, and Minimax, are mapped on the SGM to suggest in which situations they Could be useful. Two innovative games are described to fit four different types of conflict situations that so far have no corresponding game in the literature. A test application of the SGM to a classic Intel Corporation strategic management case, in the complex personal computer industry, shows that the proposed method is able to describe, to interpret, to analyze, and to prescribe optimal competitive and/or cooperative strategies for each conflict of interest situation.

Development of an IgG4-Based Predictor of Endemic Pemphigus Foliaceus (Fogo Selvagem)

Fogo selvagem (FS) is mediated by pathogenic, predominantly IgG4, anti-desmoglein 1 (Dsg1) autoantibodies and is endemic in Limao Verde, Brazil. IgG and IgG subclass autoantibodies were tested in a sample of 214 FS patients and 261 healthy controls by Dsg1 ELISA. For model selection, the sample was randomly divided into training (50%), validation (25%), and test (25%) sets. Using the training and validation sets, IgG4 was chosen as the best predictor of FS, with index values above 6.43 classified as FS. Using the test set, IgG4 has sensitivity of 92% (95% confidence interval (95% CI): 82-95%), specificity of 97% (95% CI: 89-100%), and area under the curve of 0.97 ( 95% CI: 0.94-1.00). The IgG4 positive predictive value (PPV) in Limao Verde (3% FS prevalence) was 49%. The sensitivity, specificity, and PPV of IgG anti-Dsg1 were 87, 91, and 23%, respectively. The IgG4-based classifier was validated by testing 11 FS patients before and after clinical disease and 60 Japanese pemphigus foliaceus patients. It classified 21 of 96 normal individuals from a Limao Verde cohort as having FS serology. On the basis of its PPV, half of the 21 individuals may currently have preclinical FS and could develop clinical disease in the future. Identifying individuals during preclinical FS will enhance our ability to identify the etiological agent(s) triggering FS.

Incidental Encoding Strategies Did Not Improve Contextual Memory in Parkinson`s Disease Patients

Background. This study investigated the performance of patients with idiopathic Parkinson`s disease (PD) without dementia for incidental recognition memory and the effect of encoding strategies on contextual memory. Methods. The authors studied 21 patients with PD (ages 60-85, 12 women; Hoehn and Yahr I-III, Activities of Daily Living 70%-100%) and 22 healthy controls (ages 60-84, 18 women). Participants completed the vocabulary subtest of the Wechsler Adult Intelligence Scale and the Wisconsin Card Sorting Test (WCST). To assess the incidental recognition memory for item (object) and context (location of the object), participants of each group were assigned to 1 of 2 encoding conditions: (a) an incidental associative instruction to bind the object to its location or (b) a nonassociative, nonspecific instruction. Results. PD patients showed performance comparable to the control group`s on the vocabulary subtest and WCST. In contrast to controls, PD patients were unable to take advantage of the associative encoding instruction, which also had a deleterious effect on item recognition. Conclusion. This sample of participants with PD showed diminished item and context recognition memory and an impaired ability to use incidental memory encoding strategy, suggesting a compromised cognitive reserve. The fact that these alterations occurred in early stages of PD, and prior to more general cognitive alterations such as executive dysfunction, should be considered in the management of patients by using specific cognitive rehabilitation interventions.

Fear of heights: cognitive performance and postural control

Fear of heights, or acrophobia, is one of the most frequent subtypes of specific phobia frequently associated to depression and other anxiety disorders. Previous evidence suggests a correlation between acrophobia and abnormalities in balance control, particularly involving the use of visual information to keep postural stability. This study investigates the hypotheses that (1) abnormalities in balance control are more frequent in individuals with acrophobia even when not exposed to heights, that (2) acrophobic symptoms are associated to abnormalities in visual perception of movement; and that (3) individuals with acrophobia are more sensitive to balance-cognition interactions. Thirty-one individuals with specific phobia of heights and thirty one non-phobic controls were compared using dynamic posturography and a manual tracking task. Acrophobics had poorer performance in both tasks, especially when carried out simultaneously. Previously described interference between posture control and cognitive activity seems to play a major role in these individuals. The presence of physiologic abnormalities is compatible with the hypothesis of a non-associative acquisition of fear of heights, i.e., not associated to previous traumatic events or other learning experiences. Clinically, this preliminary study corroborates the hypothesis that vestibular physical therapy can be particularly useful in treating individuals with fear of heights.

Kallmann syndrome and mirror movements: White matter quantitative evaluation with magnetic resonance imaging

Kallmann syndrome (KS), characterized by the association of hypogonadotropic hypogonadism and anosmia, may present many other phenotypic abnormalities, including neurologic features as involuntary movements, called mirror movements (MM). MM etiology probably involves a complex mechanism comprising corticospinal tract abnormal development associated with deficient contralateral motor cortex inhibitory system. In this study, in order to address previous hypotheses concerning MM etiology, we identified and quantified white matter (WM) alterations in 21 KS patients, comparing subjects with and without MM and 16 control subjects, using magnetization transfer ratio (MTR) and T2 relaxometry (R2). Magnetization transfer and 12 double-echo images were acquired in a 1.5 T system. MTR and R2 were calculated pixel by pixel to initially create individual maps, and then, group average maps, co-registered with MNI305 stereotaxic coordinate system. After analysis of selected regions of interest, we demonstrated areas with higher 12 relaxation time and lower MTR values in KS patients, with and without MM, differently involving corticospinal tract projection, frontal lobes and corpus callosum. Higher MTR was observed only in pyramidal decussation when compared in both groups of patients with controls. In conclusion, we demonstrated that patients with KS have altered WM areas, presenting in a different manner in patients with and without MM. These data suggest axonal loss or disorganization involving abnormal pyramidal tracts and other associative/connective areas, relating to the presence or absence of MM. We also found a different pattern of alteration in pyramidal decussation, which can represent the primary area of neuronal disarrangement. (C) 2010 Elsevier B.V. All rights reserved.