Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Social interaction as a heuristic for combinatorial optimization problems

We investigate the performance of a variant of Axelrod's model for dissemination of culture-the Adaptive Culture Heuristic (ACH)-on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents' strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N(1/4) so that the number of agents must increase with the fourth power of the problem size, N proportional to F(4), to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F(6) which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

High performance gold nanorods and silver nanocubes in surface-enhanced Raman spectroscopy of pesticides

The behavior of Au nanorods and Ag nanocubes as analytical sensors was evaluated for three different classes of herbicides. The use of such anisotropic nanoparticles in surface-enhanced Raman scattering (SERS) experiments allows the one to obtain the spectrum of crystal violet dye in the single molecule regime, as well as the pesticides dichlorophenoxyacetic acid (2,4-D), trichlorfon and ametryn. Such metallic substrates show high SERS performance at low analyte concentrations making them adequate for use as analytical sensors. Density functional theory (DFT) calculations of the geometries and vibrational wavenumbers of the adsorbates in the presence of silver or gold atoms were used to elucidate the nature of adsorbate-nanostructure bonding in each case and support the enhancement patterns observed in each SERS spectrum.

Validation of dot blot hybridization and denaturing high performance liquid chromatography as reliable methods for TP53 codon 72 genotyping in molecular epidemiologic studies

Background: Mutations in TP53 are common events during carcinogenesis. In addition to gene mutations, several reports have focused on TP53 polymorphisms as risk factors for malignant disease. Many studies have highlighted that the status of the TP53 codon 72 polymorphism could influence cancer susceptibility. However, the results have been inconsistent and various methodological features can contribute to departures from Hardy-Weinberg equilibrium, a condition that may influence the disease risk estimates. The most widely accepted method of detecting genotyping error is to confirm genotypes by sequencing and/or via a separate method. Results: We developed two new genotyping methods for TP53 codon 72 polymorphism detection: Denaturing High Performance Liquid Chromatography (DHPLC) and Dot Blot hybridization. These methods were compared with Restriction Fragment Length Polymorphism (RFLP) using two different restriction enzymes. We observed high agreement among all methodologies assayed. Dot-blot hybridization and DHPLC results were more highly concordant with each other than when either of these methods was compared with RFLP. Conclusions: Although variations may occur, our results indicate that DHPLC and Dot Blot hybridization can be used as reliable screening methods for TP53 codon 72 polymorphism detection, especially in molecular epidemiologic studies, where high throughput methodologies are required.

Compton suppression instrumental neutron activation analysis performance in determining trace- and minor-element contents in foodstuff

In 2003-2004, several food items were purchased from large commercial outlets in Coimbra, Portugal. Such items included meats (chicken, pork, beef), eggs, rice, beans and vegetables (tomato, carrot, potato, cabbage, broccoli, lettuce). Elemental analysis was carried out through INAA at the Technological and Nuclear Institute (ITN, Portugal), the Nuclear Energy Centre for Agriculture (CENA, Brazil), and the Nuclear Engineering Teaching Lab of the University of Texas at Austin (NETL, USA). At the latter two, INAA was also associated to Compton suppression. It can be concluded that by applying Compton suppression (1) the detection limits for arsenic, copper and potassium improved; (2) the counting-statistics error for molybdenum diminished; and (3) the long-lived zinc had its 1115-keV photopeak better defined. In general, the improvement sought by introducing Compton suppression in foodstuff analysis was not significant. Lettuce, cabbage and chicken (liver, stomach, heart) are the richest diets in terms of human nutrients.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Elderly online: Effects of a digital inclusion program in cognitive performance

There is little empirical data about the impact of digital inclusion on cognition among older adults. This paper aimed at investigating the effects of a digital inclusion program in the cognitive performance of older individuals who participated in a computer learning workshop named ""Idosos On-Line`` (Elderly Online). Forty-two aged individuals participated in the research study: 22 completed the computer training workshop and 20 constituted the control group. All subjects answered a sociodemographic questionnaire and completed the Addenbrooke`s cognitive examination, revised (ACE-R), which examines five cognitive domains: orientation and attention, memory, verbal fluency, language, and visuo-spatial skills. It was noted that the experimental group`s cognitive performance significantly improved after the program, particularly in the language and memory domains, when compared to the control group. These findings suggest that the acquisition of new knowledge and the use of a new tool, that makes it possible to access the Internet, may bring gains to cognition. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Short Cognitive Performance Test: Diagnostic Accuracy and Education Bias in Older Brazilian Adults

The ""Short Cognitive Performance Test"" (Syndrom Kurztest, SKT) is a cognitive screening battery designed to detect memory and attention deficits. The aim of this study was to evaluate the diagnostic accuracy of the SKT as a screening tool for mild cognitive impairment (MCI) and dementia. A total of 46 patients with Alzheimer`s disease (AD), 82 with MCI, and 56 healthy controls were included in the study. Patients and controls were allocated into two groups according to educational level (< 8 years or > 8 years). ROC analyses suggested that the SKT adequately discriminates AD from non-demented subjects (MCI and controls), irrespective of the education group. The test had good sensitivity to discriminate MCI from unimpaired controls in the sub-sample of individuals with more than 8 years of schooling. Our findings suggest that the SKT is a good screening test for cognitive impairment and dementia. However, test results must be interpreted with caution when administered to less-educated individuals.

Effects of branched-chain amino acid (BCAA) supplementation on endurance exercise performance of pregnant rats

Aims. - The present study evaluated the effects of BCAA supplementation on exercise performance of pregnant rats. Methods. - In order to assess these effects, Wistar rats were divided into four groups: sedentary not-supplemented (SNS, n = 8); sedentary supplemented (SS, n = 8); trained not-supplemented (TNS, n = 8) and trained supplemented (TS, n = 8). All groups were submitted to the endurance test until exhaustion (ET) and post-effort lactate (PEL) determination before pregnancy (ET-B and PEL-B) and at the 19th day of pregnancy (ET-19 and PEL-19). Results. - The endurance training significantly increased the ET time to exhaustion (p<0.05). Regardless of BCAA supplementation, both endurance trained groups (TS and TNS) showed a longer time to exhaustion, assessed by ET, compared with the sedentary groups (SS and SNS) (p < 0.05). In the TNS, ET-19 time to exhaustion decreased when compared with the period before pregnancy. On the other hand, ET-19 time to exhaustion was not affected in the TS at the end of the pregnancy period. In addition, TS showed a marked PEL-19 reduction when compared with PEL-B. The data presented herein suggest that BCAA supplementation plays an ergogenic role in the maintenance of exercise performance during pregnancy in rats. (C) 2008 Elsevier Masson SAS. All rights reserved.

Relationship Between Cortisol Levels and Memory Performance may be Modulated by the Presence or Absence of Cognitive Impairment: Evidence from Healthy Elderly, Mild Cognitive Impairment and Alzheimer`s Disease Subjects

An inverted U-shape function between cortisol levels and memory performance has been reported in studies on both young animals and humans. Yet little is known about this relationship in normal aging or in older subjects with cognitive impairment. This issue is particularly significant since increased levels of cortisol have been reported in Alzheimer`s disease (AD). The present study examined the association between cortisol levels and visual memory performance in healthy subjects as well as in individuals presenting mild cognitive impairment (MCI) or AD. Salivary cortisol was measured in 40 healthy elderly subjects, 31 individuals with amnestic MCI, and 40 subjects with mild probable AD. Memory performance was evaluated using the Brief Cognitive Screening Battery. Higher cortisol levels were associated with better memory performance in healthy elderly (p = 0.005), while higher cortisol levels were correlated with poorer memory performance in MCI subjects (p = 0.011). No correlation between cortisol and memory was found in the AD group (p > 0.05). These results suggest that the relationship between cortisol levels and memory performance in the aging process could vary according to the presence or absence of cognitive impairment.

Impact of cancer-related symptom synergisms on health-related quality of life and performance status

To identify the impact of multiple symptoms and their co-occurrence on health-related quality of life (HRQOL) dimensions and performance status (PS), 115 outpatients with cancer, who were not receiving active cancer treatment and were recruited from, a university hospital in Sao Paulo, Brazil completed the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30, the Beck Depression Inventory, and the Brief Pain Inventory. Karnofsky Performance Status scores also were completed. Application of TwoStep Cluster analysis resulted in two distinct patient subgroups based on 113 patient experiences with pain, depression, fatigue, insomnia, constipation, lack of appetite, dyspnea, nausea, vomiting, and diarrhea. One group had multiple and severe symptom subgroup and another had Less symptoms and with lower severity. Multiple and severe symptoms had worse PS, role functioning, and physical, emotional, cognitive, social, and overall HRQOL. Multiple and severe symptom subgroup was also six times as likely as lower severity to have poor role functioning;five times more likely to have poor emotional;four times more likely to have poor PS, physical, and overall HRQOL, and three times as likely to have poor cognitive and social HRQOL, independent of gender, age, level of education, and economic condition. Classification and Regression Tree analyses were undertaken to identify which co-occurring symptoms would best determine reduction in HRQOL and PS. Pain and fatigue were identified as indicators of reduction on physical HRQOL and PS. Fatigue and insomnia were associated with reduction in cognitive; depression and pain in social; and fatigue and constipation in role functioning. Only depression was associated with reduction in overall HRQOL. These data demonstrate that there is a synergic effect among distinct cancer symptoms that result in reduction in HRQOL dimensions and PS.

Role of beta-alanine supplementation on muscle carnosine and exercise performance

ARTIOLI, G. G., B. GUALANO, A. SMITH, J. STOUT, and A. H. LANCHA, JR. Role of beta-Alanine Supplementation on Muscle Carnosine and Exercise Performance. Med. Sci. Sports Exerc., Vol. 42, No. 6, pp. 1162-1173, 2010. In this narrative review, we present and discuss the current knowledge available on carnosine and beta-alanine metabolism as well as the effects of beta-alanine supplementation on exercise performance. Intramuscular acidosis has been attributed to be one of the main causes of fatigue during intense exercise. Carnosine has been shown to play a significant role in muscle pH regulation. Carnosine is synthesized in skeletal muscle from the amino acids L-histidine and beta-alanine. The rate-limiting factor of carnosine synthesis is beta-alanine availability. Supplementation with beta-alanine has been shown to increase muscle carnosine content and therefore total muscle buffer capacity, with the potential to elicit improvements in physical performance during high-intensity exercise. Studies on beta-alanine supplementation and exercise performance have demonstrated improvements in performance during multiple bouts of high-intensity exercise and in single bouts of exercise lasting more than 60 s. Similarly, beta-alanine supplementation has been shown to delay the onset of neuromuscular fatigue. Although beta-alanine does not improve maximal strength or (V) over dotO(2max), some aspects of endurance performance, such as anaerobic threshold and time to exhaustion, can be enhanced. Symptoms of paresthesia may be observed if a single dose higher than 800 mg is ingested. The symptoms, however, are transient and related to the increase in plasma concentration. They can be prevented by using controlled release capsules and smaller dosing strategies. No important side effect was related to the use of this amino acid so far. In conclusion, beta-alanine supplementation seems to be a safe nutritional strategy capable of improving high-intensity anaerobic performance.