Damage detection in beams by using artificial neural networks and dynamical parameters

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Niobium Effects on the Recrystallization Behavior on a Full Austenitic 15Cr-15Ni Stainless Steel Investigated Using Adaptive Neural Networks (ANN)

A new series of austenitic stainless steels-Nb stabilized, without Mo additions, non-susceptible to delta ferrite formation and devoid of intemetallic phases (sigma and chi), without deformation induced martensite is being developed, aiming at high temperature applications as well as for corrosive environments. The base steel composition is a 15Cr-15Ni with normal additions of Nb of 0.5, 1.0 and 2 wt%. Mechanical properties, oxidation and corrosion resistance already have been invetigated in previous papers. In this paper, the effects of Nb on the SFE, strain hardening and recrystallization resistance are evaluated with the help of Adaptive Neural Networks (ANN).

Endurance exercise training increases APPL1 expression and improves insulin signaling in the hepatic tissue of diet-induced obese mice, independently of weight loss

Hepatic insulin resistance is the major contributor to fasting hyperglycemia in type 2 diabetes. The protein kinase Akt plays a central role in the suppression of gluconeogenesis involving forkhead box O1 (Foxo1) and peroxisome proliferator-activated receptor gamma co-activator 1 alpha (PGC-1a), and in the control of glycogen synthesis involving the glycogen synthase kinase beta (GSK3 beta) in the liver. It has been demonstrated that endosomal adaptor protein APPL1 interacts with Akt and blocks the association of Akt with its endogenous inhibitor, tribbles-related protein 3 (TRB3), improving the action of insulin in the liver. Here, we demonstrated that chronic exercise increased the basal levels and insulin-induced Akt serine phosphorylation in the liver of diet-induced obese mice. Endurance training was able to increase APPL1 expression and the interaction between APPL1 and Akt. Conversely, training reduced both TRB3 expression and TRB3 and Akt association. The positive effects of exercise on insulin action are reinforced by our findings that showed that trained mice presented an increase in Foxo1 phosphorylation and Foxo1/PGC-1a association, which was accompanied by a reduction in gluconeogenic gene expressions (PEPCK and G6Pase). Finally, exercised animals demonstrated increased at basal and insulin-induced GSK3 beta phosphorylation levels and glycogen content at 24?h after the last session of exercise. Our findings demonstrate that exercise increases insulin action, at least in part, through the enhancement of APPL1 and the reduction of TRB3 expression in the liver of obese mice, independently of weight loss. J. Cell. Physiol. 227: 29172926, 2012. (C) 2011 Wiley Periodicals, Inc.

Health and fitness benefits of a resistance training intervention performed in the workplace

Zavanela, PM, Crewther, BT, Lodo, L, Florindo, AA, Miyabara, EH, and Aoki, MS. Health and fitness benefits of a resistance training intervention performed in the workplace. J Strength Cond Res 26(3): 811-817, 2012-This study examined the effects of a workplace-based resistance training intervention on different health-, fitness-, and work-related measures in untrained men (bus drivers). The subjects were recruited from a bus company and divided into a training (n = 48) and control (n = 48) groups after initial prescreening. The training group performed a 24-week resistance training program, whereas the control group maintained their normal daily activities. Each group was assessed for body composition, blood pressure (BP), pain incidence, muscular endurance, and flexibility before and after the 24-week period. Work absenteeism was also recorded during this period and after a 12-week follow-up phase. In general, no body composition changes were identified in either group. In the training group, a significant reduction in BP and pain incidence, along with improvements in muscle endurance and flexibility were seen after 24 weeks (p < 0.05). There were no changes in these parameters in the control group, and the between-group differences were all significant (p < 0.05). A reduction in worker absenteeism rate was also noted in the training (vs. control) group during both the interventional and follow-up periods (p < 0.05). In conclusion, it was found that a periodized resistance training intervention performed within the workplace improved different aspects of health and fitness in untrained men, thereby potentially providing other work-related benefits. Thus, both employers and employees may benefit from the setup, promotion, and support of a work-based physical activity program involving resistance training.

Using complex networks to quantify consistency in the use of words

In this paper we have quantified the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood. Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according to the consistency of use, the words obeyed a log-normal distribution, in contrast to Zipf's law that applies to the frequency of use. Consistency correlated positively with the familiarity and frequency of use, and negatively with ambiguity and age of acquisition. An inspection of some highly consistent words confirmed that they are used in very limited semantic contexts. A comparison of consistency indices for eight authors indicated that these indices may be employed for author recognition. Indeed, as expected, authors of novels could be distinguished from those who wrote scientific texts. Our analysis demonstrated the suitability of the consistency indices, which can now be applied in other tasks, such as emotion recognition.

ACTIVE NETWORKS THAT MAXIMIZE THE AMOUNT OF INFORMATION TRANSMISSION

This work clarifies the relationship between network circuit (topology) and behavior (information transmission and synchronization) in active networks, e. g. neural networks. As an application, we show how to determine a network topology that is optimal for information transmission. By optimal, we mean that the network is able to transmit a large amount of information, it possesses a large number of communication channels, and it is robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Conformation analysis of a surface loop that controls active site access in the GH11 xylanase A from Bacillus subtilis

Xylanases (EC 3.2.1.8 endo-1,4-glycosyl hydrolase) catalyze the hydrolysis of xylan, an abundant hemicellulose of plant cell walls. Access to the catalytic site of GH11 xylanases is regulated by movement of a short beta-hairpin, the so-called thumb region, which can adopt open or closed conformations. A crystallographic study has shown that the D11F/R122D mutant of the GH11 xylanase A from Bacillus subtilis (BsXA) displays a stable "open" conformation, and here we report a molecular dynamics simulation study comparing this mutant with the native enzyme over a range of temperatures. The mutant open conformation was stable at 300 and 328 K, however it showed a transition to the closed state at 338 K. Analysis of dihedral angles identified thumb region residues Y113 and T123 as key hinge points which determine the open-closed transition at 338 K. Although the D11F/R122D mutations result in a reduction in local inter-intramolecular hydrogen bonding, the global energies of the open and closed conformations in the native enzyme are equivalent, suggesting that the two conformations are equally accessible. These results indicate that the thumb region shows a broader degree of energetically permissible conformations which regulate the access to the active site region. The R122D mutation contributes to the stability of the open conformation, but is not essential for thumb dynamics, i.e., the wild type enzyme can also adapt to the open conformation.

Comparison of multisensory and strength training for postural control in the elderly

Objective: The objective of this study was to analyze the efficacy of multisensory versus muscle strengthening to improve postural control in healthy community-dwelling elderly. Participants: We performed a single-blinded study with 46 community-dwelling elderly allocated to strength (GS, n = 23; 70.18 +/- 4.8 years 22 women and 1 man) and multisensory exercises groups (GM, n = 23; 68.8 +/- 5.9 years; 22 women and 1 man) for 12 weeks. Methods: We performed isokinetic evaluations of muscle groups in the ankle and foot including dorsiflexors, plantar flexors, inversion, and eversion. The oscillation of the center of pressure was assessed with a force platform. Results: The GM group presented a reduction in the oscillation (66.8 +/- 273.4 cm(2) to 11.1 +/- 11.6 cm(2); P = 0.02), which was not observed in the GS group. The GM group showed better results for the peak torque and work than the GS group, but without statistical significance. Conclusion: Although the GM group presented better results, it is not possible to state that one exercise regimen proved more efficacious than the other in improving balance control.

Fear state induced by diazepam withdrawal may be due to the sensitization of the neural substrates of aversion in the dPAG

This work was supported by FAPESP (P.N. 04/02859-0)

Effects of high-intensity intermittent training on carnitine palmitoyl transferase activity in the gastrocnemius muscle of rats

We examined the capacity of high-intensity intermittent training (HI-IT) to facilitate the delivery of lipids to enzymes responsible for oxidation, a task performed by the carnitine palmitoyl transferase (CPT) system in the rat gastrocnemius muscle. Male adult Wistar rats (160-250 g) were randomly distributed into 3 groups: sedentary (Sed, N = 5), HI-IT (N = 10), and moderate-intensity continuous training (MI-CT, N = 10). The trained groups were exercised for 8 weeks with a 10% (HI-IT) and a 5% (MI-CT) overload. The HI-IT group presented 11.8% decreased weight gain compared to the Sed group. The maximal activities of CPT-I, CPT-II, and citrate synthase were all increased in the HI-IT group compared to the Sed group (P < 0.01), as also was gene expression, measured by RT-PCR, of fatty acid binding protein (FABP; P < 0.01) and lipoprotein lipase (LPL; P < 0.05). Lactate dehydrogenase also presented a higher maximal activity (nmol·min-1·mg protein-1) in HI-IT (around 83%). We suggest that 8 weeks of HI-IT enhance mitochondrial lipid transport capacity thus facilitating the oxidation process in the gastrocnemius muscle. This adaptation may also be associated with the decrease in weight gain observed in the animals and was concomitant to a higher gene expression of both FABP and LPL in HI-IT, suggesting that intermittent exercise is a "time-efficient" strategy inducing metabolic adaptation.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.