The Enhanced Tool-Kit of Two Groups of Wild Bearded Capuchin Monkeys in the Caatinga: Tool Making, Associative Use, and Secondary Tools

The use of stones to crack open encapsulated fruit is widespread among wild bearded capuchin monkeys (Cebus libidinosus) inhabiting savanna-like environments. Some populations in Serra da Capivara National Park (Piaui, Brazil), though, exhibit a seemingly broader toolkit, using wooden sticks as probes, and employing stone tools for a variety of purposes. Over the course of 701.5 hr of visual contact of two wild capuchin groups we recorded 677 tool use episodes. Five hundred and seventeen of these involved the use of stones, and 160 involved the use of sticks (or other plant parts) as probes to access water, arthropods, or the contents of insects` nests. Stones were mostly used as ""hammers""-not only to open fruit or seeds, or smash other food items, but also to break dead wood, conglomerate rock, or cement in search of arthropods, to dislodge bigger stones, and to pulverize embedded quartz pebbles (licking, sniffing, or rubbing the body with the powder produced). Stones also were used in a ""hammer-like"" fashion to loosen the soil for digging out roots and arthropods, and sometimes as ""hoes"" to pull the loosened soil. In a few cases, we observed the re-utilization of stone tools for different purposes (N = 3), or the combined use of two tools-stones and sticks (N = 4) or two stones (N = 5), as sequential or associative tools. On three occasions, the monkeys used smaller stones to loosen bigger quartz pebbles embedded in conglomerate rock, which were subsequently used as tools. These could be considered the first reports of secondary tool use by wild capuchin monkeys. Am. J. Primatol. 71:242-251, 2009. (c) 2008 Wiley-Liss, Inc.

ASSOCIATIVE SYMMETRY BY PIGEONS AFTER FEW-EXEMPLAR TRAINING

The present experiment investigated whether pigeons can show associative symmetry on a two-alternative matching to-sample procedure The procedure consisted of a within subject sequence of training and testing with reinforcement and It provided (a) exemplars of symmetrical responding and (b) all prerequisite discriminations among test samples and comparisons After pigeons had learned two arbitrary matching tasks (A B and C D) they were given a reinforced symmetry test for half of the baseline relations (B1-A1 and D1-C1) To control for the effects of reinforcement during testing two novel nonsymmetrical responses were concurrently reinforced using the other baseline stimuli (D2-A2 and B2-C2) Pigeons matched at chance on both types of relations thus indicating no evidence for symmetry These symmetrical and nonsymmetrical relations were then directly trained in order to provide exemplars of symmetry and all prerequisite discriminations for a second test The symmetrical test relations were now B2-A2 and D2-C2 and the nonsymmetrical relations were D1-A1 and B1-C1 On this test 1 pigeon showed clear evidence of symmetry 2 pigeons showed weak evidence and 1 pigeon showed no evidence The previous training of all prerequisite discriminations among stimuli and the within subject control for testing with reinforcement seem to have set favorable conditions for the emergence of symmetry in nonhumans However the variability across subjects shows that methodological variables still remain to be controlled

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Functional coupling of sensorimotor and associative areas during a catching ball task: a qEEG coherence study

Abstract Background Catching an object is a complex movement that involves not only programming but also effective motor coordination. Such behavior is related to the activation and recruitment of cortical regions that participates in the sensorimotor integration process. This study aimed to elucidate the cortical mechanisms involved in anticipatory actions when performing a task of catching an object in free fall. Methods Quantitative electroencephalography (qEEG) was recorded using a 20-channel EEG system in 20 healthy right-handed participants performed the catching ball task. We used the EEG coherence analysis to investigate subdivisions of alpha (8-12 Hz) and beta (12-30 Hz) bands, which are related to cognitive processing and sensory-motor integration. Results Notwithstanding, we found the main effects for the factor block; for alpha-1, coherence decreased from the first to sixth block, and the opposite effect occurred for alpha-2 and beta-2, with coherence increasing along the blocks. Conclusion It was concluded that to perform successfully our task, which involved anticipatory processes (i.e. feedback mechanisms), subjects exhibited a great involvement of sensory-motor and associative areas, possibly due to organization of information to process visuospatial parameters and further catch the falling object.

Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012

The Dorsolateral Periaqueductal Gray and Its Role in Mediating Fear Learning to Life Threatening Events

The dorsolateral column of the periaqueductal gray (dlPAG) integrates aversive emotional experiences and represents an important site responding to life threatening situations, such as hypoxia, cardiac pain and predator threats. Previous studies have shown that the dorsal PAG also supports fear learning; and we have currently explored how the dlPAG influences associative learning. We have first shown that N-methyl-D-aspartate (NMDA) 100 pmol injection in the dlPAG works as a valuable unconditioned stimulus (US) for the acquisition of olfactory fear conditioning (OFC) using amyl acetate odor as conditioned stimulus (CS). Next, we revisited the ascending projections of the dlPAG to the thalamus and hypothalamus to reveal potential paths that could mediate associative learning during OFC. Accordingly, the most important ascending target of the dlPAG is the hypothalamic defensive circuit, and we were able to show that pharmacological inactivation using beta-adrenoceptor blockade of the dorsal premammillary nucleus, the main exit way for the hypothalamic defensive circuit to thalamo-cortical circuits involved in fear learning, impaired the acquisition of the OFC promoted by NMDA stimulation of the dlPAG. Moreover, our tracing study revealed multiple parallel paths from the dlPAG to several thalamic targets linked to cortical-hippocampal-amygdalar circuits involved in fear learning. Overall, the results point to a major role of the dlPAG in the mediation of aversive associative learning via ascending projections to the medial hypothalamic defensive circuit, and perhaps, to other thalamic targets, as well. These results provide interesting perspectives to understand how life threatening events impact on fear learning, and should be useful to understand pathological fear memory encoding in anxiety disorders.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Higher identities for the ternary commutator

We use computer algebra to study polynomial identities for the trilinear operation [a, b, c] = abc - acb - bac + bca + cab - cba in the free associative algebra. It is known that [a, b, c] satisfies the alternating property in degree 3, no new identities in degree 5, a multilinear identity in degree 7 which alternates in 6 arguments, and no new identities in degree 9. We use the representation theory of the symmetric group to demonstrate the existence of new identities in degree 11. The only irreducible representations of dimension <400 with new identities correspond to partitions 2(5), 1 and 2(4), 1(3) and have dimensions 132 and 165. We construct an explicit new multilinear identity for partition 2(5), 1 and we demonstrate the existence of a new non-multilinear identity in which the underlying variables are permutations of a(2)b(2)c(2)d(2)e(2) f.

Generalizations of Lie Algebras

The generalizations of Lie algebras appeared in the modern mathematics and mathematical physics. In this paper we consider recent developments and remaining open problems on the subject. Some of that developments have been influenced by lectures given by Professor Jaime Keller in his research seminar. The survey includes Lie superalgebras, color Lie algebras, Lie algebras in symmetric categories, free Lie tau-algebras, and some generalizations with non-associative enveloping algebras: tangent algebras to analytic loops, bialgebras and primitive elements, non-associative Hopf algebras.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Listening to music in the first, but not the last 1.5 km of a 5-km running trial alters pacing strategy and improves performance

We examined the effects of listening to music on attentional focus, rating of perceived exertion (RPE), pacing strategy and performance during a simulated 5-km running race. 15 participants performed 2 controlled trials to establish their best baseline time, followed by 2 counterbalanced experimental trials during which they listened to music during the first (M-start) or the last (M-finish) 1.5 km. The mean running velocity during the first 1.5 km was significantly higher in M-start than in the fastest control condition (p < 0.05), but there was no difference in velocity between conditions during the last 1.5 km (p > 0.05). The faster first 1.5 m in M-start was accompanied by a reduction in associative thoughts compared with the fastest control condition. There were no significant differences in RPE between conditions (p > 0.05). These results suggest that listening to music at the beginning of a trial may draw the attentional focus away from internal sensations of fatigue to thoughts about the external environment. However, along with the reduction in associative thoughts and the increase in running velocity while listening to music, the RPE increased linearly and similarly under all conditions, suggesting that the change in velocity throughout the race may be to maintain the same rate of RPE increase.

Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis

Background: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal Findings: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/Significance: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Counter-ion effect on surfactant-DNA gel particles as controlled DNA delivery systems

The ability to entrap drugs within vehicles and subsequently release them has led to new treatments for a number of diseases. Based on an associative phase separation and interfacial diffusion approach, we developed a way to prepare DNA gel particles without adding any kind of cross-linker or organic solvent. Among the various agents studied, cationic surfactants offered particularly efficient control for encapsulation and DNA release from these DNA gel particles. The driving force for this strong association is the electrostatic interaction between the two components, as induced by the entropic increase due to the release of the respective counter-ions. However, little is known about the influence of the respective counter-ions on this surfactant-DNA interaction. Here we examined the effect of different counter-ions on the formation and properties of the DNA gel particles by mixing DNA (either single-(ssDNA) or double-stranded (dsDNA)) with the single chain surfactant dodecyltrimethylammonium (DTA). In particular, we used as counter-ions of this surfactant the hydrogen sulfate and trifluoromethane sulfonate anions and the two halides, chloride and bromide. Effects on the morphology of the particles obtained, the encapsulation of DNA and its release, as well as the haemocompatibility of these particles are presented, using counter-ion structure and DNA conformation as controlling parameters. Analysis of the data indicates that the degree of counter-ion dissociation from the surfactant micelles and the polar/hydrophobic character of the counter-ion are important parameters in the final properties of the particles. The stronger interaction with amphiphiles for ssDNA than for dsDNA suggests the important role of hydrophobic interactions in DNA.

New microsatellite markers for garlic, Allium sativum (Alliaceae)

Premise of the study: A new set of microsatellite or simple sequence repeat (SSR) markers for garlic, an important medicinal spice, was developed to aid studies of genetic diversity and to define efficient strategies for germplasm conservation. Methods and Results: Using a (CT)(8)- and (GT)(8)-enriched library, a total of 16 SSR loci were developed and optimized in garlic. Ten loci were found to be polymorphic after screening 75 accessions. The parameters used to characterize the loci were observed and expected heterozygosity, number of alleles, Shannon Index, and polymorphism information content (PIC). A total of 44 alleles were identified, with an average of 4.4 alleles per loci. The vast majority of loci were moderate to highly informative according to PIC and the Shannon Index. Conclusion: The new SSR markers have the potential to be informative tools for genetic diversity, allele mining, mapping and associative studies, and in the management and conservation of garlic collections.