986 resultados para Applied Behavioral Analysis


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Sphingomonas wittichii RW1 is a bacterium isolated for its ability to degrade the xenobiotic compounds dibenzodioxin and dibenzofuran (DBF). A number of genes involved in DBF degradation have been previously characterized, such as the dxn cluster, dbfB, and the electron transfer components fdx1, fdx3, and redA2. Here we use a combination of whole genome transcriptome analysis and transposon library screening to characterize RW1 catabolic and other genes implicated in the reaction to or degradation of DBF. To detect differentially expressed genes upon exposure to DBF, we applied three different growth exposure experiments, using either short DBF exposures to actively growing cells or growing them with DBF as sole carbon and energy source. Genome-wide gene expression was examined using a custom-made microarray. In addition, proportional abundance determination of transposon insertions in RW1 libraries grown on salicylate or DBF by ultra-high throughput sequencing was used to infer genes whose interruption caused a fitness loss for growth on DBF. Expression patterns showed that batch and chemostat growth conditions, and short or long exposure of cells to DBF produced very different responses. Numerous other uncharacterized catabolic gene clusters putatively involved in aromatic compound metabolism increased expression in response to DBF. In addition, only very few transposon insertions completely abolished growth on DBF. Some of those (e.g., in dxnA1) were expected, whereas others (in a gene cluster for phenylacetate degradation) were not. Both transcriptomic data and transposon screening suggest operation of multiple redundant and parallel aromatic pathways, depending on DBF exposure. In addition, increased expression of other non-catabolic genes suggests that during initial exposure, S. wittichii RW1 perceives DBF as a stressor, whereas after longer exposure, the compound is recognized as a carbon source and metabolized using several pathways in parallel.

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BACKGROUND Job satisfaction of nurses is a determinant factor in the quality and organizational adaptation of clinical management models in the current socio-economic context. The aim of this study was to construct and validate a questionnaire to measure job satisfaction of nurses in the Clinical Management Units in the Andalusian Public Health System. METHODS Clinimetric and cross-sectional study with a sample of 314 nurses of two university hospitals from Seville. Nurses were surveyed in 2011, from March to June. We used the Font Roja questionnaire adapted to our study variables. We performed analyses of correlations, reliability and construct validity, using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to test the a priori model. RESULTS The end questionnaire consists of 10 items, whose internal consistency was 0.75, with a percentage of variance explaining of 63.67%. CFA confirmed 4 dimensions (work environment, work relationships, motivation, and recognition): significant χ2 (p < .001); χ2/gl = 2.013; GFI= 0.958, RMR = 0.055 y RMSEA = 0.057; AGFI = 0.927, NFI = 0.878, TLI = 0.902, CFI =0.933 e IFI = 0.935; AIC = 132.486 y ECVI = 0.423. CONCLUSION This new questionnaire (G_Clinic) improves clinimetric values of the Font Roja questionnaire, because it reduces the number of items, improves the reliability of the dimensions, increases the value of variance explained, and allows knowing job satisfaction of nurses in clinical managementt.

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As stated in Aitchison (1986), a proper study of relative variation in a compositional data set should be based on logratios, and dealing with logratios excludes dealing with zeros. Nevertheless, it is clear that zero observations might be present in real data sets, either because the corresponding part is completelyabsent –essential zeros– or because it is below detection limit –rounded zeros. Because the second kind of zeros is usually understood as “a trace too small to measure”, it seems reasonable to replace them by a suitable small value, and this has been the traditional approach. As stated, e.g. by Tauber (1999) and byMartín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000), the principal problem in compositional data analysis is related to rounded zeros. One should be careful to use a replacement strategy that does not seriously distort the general structure of the data. In particular, the covariance structure of the involvedparts –and thus the metric properties– should be preserved, as otherwise further analysis on subpopulations could be misleading. Following this point of view, a non-parametric imputation method isintroduced in Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000). This method is analyzed in depth by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2003) where it is shown that thetheoretical drawbacks of the additive zero replacement method proposed in Aitchison (1986) can be overcome using a new multiplicative approach on the non-zero parts of a composition. The new approachhas reasonable properties from a compositional point of view. In particular, it is “natural” in the sense thatit recovers the “true” composition if replacement values are identical to the missing values, and it is coherent with the basic operations on the simplex. This coherence implies that the covariance structure of subcompositions with no zeros is preserved. As a generalization of the multiplicative replacement, in thesame paper a substitution method for missing values on compositional data sets is introduced

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Introduction: Discrimination of species-specific vocalizations is fundamental for survival and social interactions. Its unique behavioral relevance has encouraged the identification of circumscribed brain regions exhibiting selective responses (Belin et al., 2004), while the role of network dynamics has received less attention. Those studies that have examined the brain dynamics of vocalization discrimination leave unresolved the timing and the inter-relationship between general categorization, attention, and speech-related processes (Levy et al., 2001, 2003; Charest et al., 2009). Given these discrepancies and the presence of several confounding factors, electrical neuroimaging analyses were applied to auditory evoked-potential (AEPs) to acoustically and psychophysically controlled non-verbal human and animal vocalizations. This revealed which region(s) exhibit voice-sensitive responses and in which sequence. Methods: Subjects (N=10) performed a living vs. man-made 'oddball' auditory discrimination task, such that on a given block of trials 'target' stimuli occurred 10% of the time. Stimuli were complex, meaningful sounds of 500ms duration. There were 120 different sound files in total, 60 of which represented sounds of living objects and 60 man-made objects. The stimuli that were the focus of the present investigation were restricted to those of living objects within blocks where no response was required. These stimuli were further sorted between human non-verbal vocalizations and animal vocalizations. They were also controlled in terms of their spectrograms and formant distributions. Continuous 64-channel EEG was acquired through Neuroscan Synamps referenced to the nose, band-pass filtered 0.05-200Hz, and digitized at 1000Hz. Peri-stimulus epochs of continuous EEG (-100ms to 900ms) were visually inspected for artifacts, 40Hz low-passed filtered and baseline corrected using the pre-stimulus period . Averages were computed from each subject separately. AEPs in response to animal and human vocalizations were analyzed with respect to differences of Global Field Power (GFP) and with respect to changes of the voltage configurations at the scalp (reviewed in Murray et al., 2008). The former provides a measure of the strength of the electric field irrespective of topographic differences; the latter identifies changes in spatial configurations of the underlying sources independently of the response strength. In addition, we utilized the local auto-regressive average distributed linear inverse solution (LAURA; Grave de Peralta Menendez et al., 2001) to visualize and statistically contrast the likely underlying sources of effects identified in the preceding analysis steps. Results: We found differential activity in response to human vocalizations over three periods in the post-stimulus interval, and this response was always stronger than that to animal vocalizations. The first differential response (169-219ms) was a consequence of a modulation in strength of a common brain network localized into the right superior temporal sulcus (STS; Brodmann's Area (BA) 22) and extending into the superior temporal gyrus (STG; BA 41). A second difference (291-357ms) also followed from strength modulations of a common network with statistical differences localized to the left inferior precentral and prefrontal gyrus (BA 6/45). These two first strength modulations correlated (Spearman's rho(8)=0.770; p=0.009) indicative of functional coupling between temporally segregated stages of vocalization discrimination. A third difference (389-667ms) followed from strength and topographic modulations and was localized to the left superior frontal gyrus (BA10) although this third difference did not reach our spatial criterion of 12 continuous voxels. Conclusions: We show that voice discrimination unfolds over multiple temporal stages, involving a wide network of brain regions. The initial stages of vocalization discrimination are based on modulations in response strength within a common brain network with no evidence for a voice-selective module. The latency of this effect parallels that of face discrimination (Bentin et al., 2007), supporting the possibility that voice and face processes can mutually inform one another. Putative underlying sources (localized in the right STS; BA 22) are consistent with prior hemodynamic imaging evidence in humans (Belin et al., 2004). Our effect over the 291-357ms post-stimulus period overlaps the 'voice-specific-response' reported by Levy et al. (Levy et al., 2001) and the estimated underlying sources (left BA6/45) were in agreement with previous findings in humans (Fecteau et al., 2005). These results challenge the idea that circumscribed and selective areas subserve con-specific vocalization processing.

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A new quantitative approach of the mandibular sexual dimorphism, based on computer-aided image analysis and elliptical Fourier analysis of the mandibular outline in lateral view is presented. This method was applied to a series of 117 dentulous mandibles from 69 male and 48 female individuals native of Rhenish countries. Statistical discriminant analysis of the elliptical Fourier harmonics allowed the demonstration of a significant sexual dimorphism in 97.1% of males and 91.7% of females, i.e. in a higher proportion than in previous studies using classical metrical approaches. This original method opens interesting perspectives for increasing the accuracy of sex identification in current anthropological practice and in forensic procedures.

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The application of Discriminant function analysis (DFA) is not a new idea in the studyof tephrochrology. In this paper, DFA is applied to compositional datasets of twodifferent types of tephras from Mountain Ruapehu in New Zealand and MountainRainier in USA. The canonical variables from the analysis are further investigated witha statistical methodology of change-point problems in order to gain a betterunderstanding of the change in compositional pattern over time. Finally, a special caseof segmented regression has been proposed to model both the time of change and thechange in pattern. This model can be used to estimate the age for the unknown tephrasusing Bayesian statistical calibration

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BACKGROUND: It is unknown why patients with extensive ulcerative colitis (UC) have a higher risk of colorectal cancer compared with patients with left-sided UC. This study characterizes the inflammatory processes in left-sided UC, pancolitis, and UC-associated dysplasia at the transcriptional level to identify potential biomarkers and transcripts of importance for the carcinogenic behavior of chronic inflammation. METHODS: The Affymetrix GeneChip Human Genome U133 Plus 2.0 was applied on colonic biopsies from UC patients with left-sided UC, pancolitis, dysplasia, and controls. Reverse transcription polymerase chain reaction and immunohistochemistry were performed for validating selected transcripts in the initial cohort and in 2 independent cohorts of patients with UC. Microarray data were analyzed by principal component analysis, and reverse transcription polymerase chain reaction and immunohistochemistry data by the Wilcoxon's rank-sum test. RESULTS: The principal component analysis results revealed separate clusters for left-sided UC, pancolitis, dysplasia, and controls. Close clustering of dysplastic and pancolitic samples indicated similarities in gene expression. Indeed, 101 and 656 parallel upregulated and downregulated transcripts, respectively, were identified in specimens from dysplasia and pancolitis. Validation of selected transcripts hereof identified insulin receptor alpha (INSRA) and MAP kinase interacting serine/threonine kinase 2 (MKNK2) with an enhanced expression in dysplasia compared with left-sided UC and controls, whereas laminin γ2 (LAMC2) was found with a lower expression in dysplasia compared with the remaining 3 groups. CONCLUSIONS: This study demonstrates pancolitis and left-sided UC as distinct inflammatory processes at the transcriptional level, and identifies INSRA, MKNK2, and LAMC2 as potential critical transcripts in the inflammation-driven preneoplastic process of UC.

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Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

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Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

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A procedure based on quantum molecular similarity measures (QMSM) has been used to compare electron densities obtained from conventional ab initio and density functional methodologies at their respective optimized geometries. This method has been applied to a series of small molecules which have experimentally known properties and molecular bonds of diverse degrees of ionicity and covalency. Results show that in most cases the electron densities obtained from density functional methodologies are of a similar quality than post-Hartree-Fock generalized densities. For molecules where Hartree-Fock methodology yields erroneous results, the density functional methodology is shown to yield usually more accurate densities than those provided by the second order Møller-Plesset perturbation theory

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Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

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Because of the various matrices available for forensic investigations, the development of versatile analytical approaches allowing the simultaneous determination of drugs is challenging. The aim of this work was to assess a liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform allowing the rapid quantification of colchicine in body fluids and tissues collected in the context of a fatal overdose. For this purpose, filter paper was used as a sampling support and was associated with an automated 96-well plate extraction performed by the LC autosampler itself. The developed method features a 7-min total run time including automated filter paper extraction (2 min) and chromatographic separation (5 min). The sample preparation was reduced to a minimum regardless of the matrix analyzed. This platform was fully validated for dried blood spots (DBS) in the toxic concentration range of colchicine. The DBS calibration curve was applied successfully to quantification in all other matrices (body fluids and tissues) except for bile, where an excessive matrix effect was found. The distribution of colchicine for a fatal overdose case was reported as follows: peripheral blood, 29 ng/ml; urine, 94 ng/ml; vitreous humour and cerebrospinal fluid, < 5 ng/ml; pericardial fluid, 14 ng/ml; brain, < 5 pg/mg; heart, 121 pg/mg; kidney, 245 pg/mg; and liver, 143 pg/mg. Although filter paper is usually employed for DBS, we report here the extension of this alternative sampling support to the analysis of other body fluids and tissues. The developed platform represents a rapid and versatile approach for drug determination in multiple forensic media.

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In rodents and nonhuman primates subjected to spinal cord lesion, neutralizing the neurite growth inhibitor Nogo-A has been shown to promote regenerative axonal sprouting and functional recovery. The goal of the present report was to re-examine the data on the recovery of the primate manual dexterity using refined behavioral analyses and further statistical assessments, representing secondary outcome measures from the same manual dexterity test. Thirteen adult monkeys were studied; seven received an anti-Nogo-A antibody whereas a control antibody was infused into the other monkeys. Monkeys were trained to perform the modified Brinkman board task requiring opposition of index finger and thumb to grasp food pellets placed in vertically and horizontally oriented slots. Two parameters were quantified before and following spinal cord injury: (i) the standard 'score' as defined by the number of pellets retrieved within 30 s from the two types of slots; (ii) the newly introduced 'contact time' as defined by the duration of digit contact with the food pellet before successful retrieval. After lesion the hand was severely impaired in all monkeys; this was followed by progressive functional recovery. Remarkably, anti-Nogo-A antibody-treated monkeys recovered faster and significantly better than control antibody-treated monkeys, considering both the score for vertical and horizontal slots (Mann-Whitney test: P = 0.05 and 0.035, respectively) and the contact time (P = 0.008 and 0.005, respectively). Detailed analysis of the lesions excluded the possibility that this conclusion may have been caused by differences in lesion properties between the two groups of monkeys.

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We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the Music Information Retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms.This article first presents a series of experiments carried outwith two state-of-the-art methods for cover song identification.We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or Dynamic Time Warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best-performing ones are finally applied to the newly proposed method. Multipleevaluations of this one confirm a large increase in identificationaccuracy when comparing it with alternative state-of-the-artapproaches.

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A simple wipe sampling procedure was developed for the surface contamination determination of ten cytotoxic drugs: cytarabine, gemcitabine, methotrexate, etoposide phosphate, cyclophosphamide, ifosfamide, irinotecan, doxorubicin, epirubicin and vincristine. Wiping was performed using Whatman filter paper on different surfaces such as stainless steel, polypropylene, polystyrol, glass, latex gloves, computer mouse and coated paperboard. Wiping and desorption procedures were investigated: The same solution containing 20% acetonitrile and 0.1% formic acid in water gave the best results. After ultrasonic desorption and then centrifugation, samples were analysed by a validated liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in selected reaction monitoring mode. The whole analytical strategy from wipe sampling to LC-MS/MS analysis was evaluated to determine quantitative performance. The lowest limit of quantification of 10 ng per wiping sample (i.e. 0.1 ng cm(-2)) was determined for the ten investigated cytotoxic drugs. Relative standard deviation for intermediate precision was always inferior to 20%. As recovery was dependent on the tested surface for each drug, a correction factor was determined and applied for real samples. The method was then successfully applied at the cytotoxic production unit of the Geneva University Hospitals pharmacy.