Anderson-like Transition for a Class of Random Sparse Models in d >= 2 Dimensions

We show that the Kronecker sum of d >= 2 copies of a random one-dimensional sparse model displays a spectral transition of the type predicted by Anderson, from absolutely continuous around the center of the band to pure point around the boundaries. Possible applications to physics and open problems are discussed briefly.

Twinlike models for kinks and compactons in flat and warped spacetime

This work deals with the presence of twinlike models in scalar field theories. We show how to build distinct scalar field theories having the same extended solution, with the same energy density and linear stability. Here, however, we start from a given but generalized scalar field theory, and we construct the corresponding twin model, which also engenders generalized dynamics. We investigate how the twinlike models arise in both flat and curved spacetimes. In the curved spacetime, we consider a braneworld model with the warp factor controlling the spacetime geometry with a single extra dimension of infinite extent. In particular, we study linear stability in both flat and curved spacetimes, and in the case of curved spacetime-in both the gravity and the scalar field sectors-for the two braneworld models. DOI: 10.1103/PhysRevD.86.125021

Plasma corticosterone levels in mouse models of pain

Background: Pain markedly activates the hypothalamic-pituitary-adrenal (HPA) axis and increases plasma corticosterone release interfering significantly with nociceptive behaviour as well as the mechanism of action of analgesic drugs. Aims/Methods: In the present study, we monitored the time course of circulating corticosterone in two mouse strains (C57Bl/6 and Balb/C) under different pain models. In addition, the stress response was investigated following animal handling, intrathecal (i.t.) manipulation and habituation to environmental conditions commonly used in nociceptive experimental assays. We also examined the influence of within-cage order of testing on plasma corticosterone. Results: Subcutaneous injection of capsaicin precipitated a prompt stress response whereas carrageenan and complete Freund's adjuvant induced an increased corticosterone release around the third hour post-injection. However, carrageenan induced a longer increased corticosterone in C57Bl/6 mice. In partial sciatic nerve ligation, neuropathic pain model corticosterone increased only in the first days whereas mechanical hypersensitivity remained much longer. Animal handling also represents an important stressor whereas the i.t. injection per se does not exacerbate the handling-induced stress response. Moreover, the order of testing animals from the same cage does not interfere with plasma corticosterone levels in the intrathecal procedure. Animal habituation to the testing apparatus also does not reduce the immediate corticosterone increase as compared with non-habituated mice. Conclusion: Our data indicate that HPA axis activation in acute and chronic pain models is time dependent and may be dissociated from evoked hyperalgesia. Therefore, HPA-axis activation represents an important variable to be considered when designing experimental assays of persistent pain as well as for interpretation of data.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Holographic field theory models of dark energy in interaction with dark matter

We discuss two Lagrangian interacting dark energy models in the context of the holographic principle. The potentials of the interacting fields are constructed. The models are compared with CMB distance information, baryonic acoustic oscillations, lookback time and the Constitution supernovae sample. For both models, the results are consistent with a nonvanishing interaction in the dark sector of the Universe and the sign of coupling is consistent with dark energy decaying into dark matter, alleviating the coincidence problem-with more than 3 standard deviations of confidence for one of them. However, this is because the noninteracting holographic dark energy model is a bad fit to the combination of data sets used in this work as compared to the cosmological constant with cold dark matter model, so that one needs to introduce the interaction in order to improve this model.

Percutaneous sciatic nerve block with tramadol induces analgesia and motor blockade in two animal pain models

Local anesthetic efficacy of tramadol has been reported following intradermal application. Our aim was to investigate the effect of perineural tramadol as the sole analgesic in two pain models. Male Wistar rats (280-380 g; N = 5/group) were used in these experiments. A neurostimulation-guided sciatic nerve block was performed and 2% lidocaine or tramadol (1.25 and 5 mg) was perineurally injected in two different animal pain models. In the flinching behavior test, the number of flinches was evaluated and in the plantar incision model, mechanical and heat thresholds were measured. Motor effects of lidocaine and tramadol were quantified and a motor block score elaborated. Tramadol, 1.25 mg, completely blocked the first and reduced the second phase of the flinching behavior test. In the plantar incision model, tramadol (1.25 mg) increased both paw withdrawal latency in response to radiant heat (8.3 +/- 1.1, 12.7 +/- 1.8, 8.4 +/- 0.8, and 11.1 +/- 3.3 s) and mechanical threshold in response to von Frey filaments (459 +/- 82.8, 447.5 +/- 91.7, 320.1 +/- 120, 126.43 +/- 92.8 mN) at 5, 15, 30, and 60 min, respectively. Sham block or contralateral sciatic nerve block did not differ from perineural saline injection throughout the study in either model. The effect of tramadol was not antagonized by intraperitoneal naloxone. High dose tramadol (5 mg) blocked motor function as well as 2% lidocaine. In conclusion, tramadol blocks nociception and motor function in vivo similar to local anesthetics.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Female fertility in a Guzerat dairy subpopulation: Heterogeneity of variance components for calving intervals

The objectives of the present study were to determine if variance components of calving intervals varied with age at calving and if considering calving intervals as a longitudinal trait would be a useful approach for fertility analysis of Zebu dairy herds. With these purposes, calving records from females born from 1940 to 2006 in a Guzerat dairy subpopulation in Brazil were analyzed. The fixed effects of contemporary groups, formed by year and farm at birth or at calving, and the regressions of age at calving, equivalent inbreeding coefficient and day of the year on the studied traits were considered in the statistical models. In one approach, calving intervals (Cl) were analyzed as a single trait, by fitting a statistical model on which both animal and permanent environment effects were adjusted for the effect of age at calving by random regression. In a second approach, a four-trait analysis was conducted, including age at first calving (AFC) and three different female categories for the calving intervals: first calving females; young females (less than 80 months old, but not first calving); or mature females (80 months old or more). Finally, a two-trait analysis was performed, also including AFC and Cl, but calving intervals were regarded as a single trait in a repeatability model. Additionally, the ranking of sires was compared among approaches. Calving intervals decreased with age until females were about 80 months old, remaining nearly constant after that age. A quasi-linear increase of 11.5 days on the calving intervals was observed for each 10% increase in the female's equivalent inbreeding coefficient. The heritability of AFC was 0.37. For Cl. the genetic-phenotypic variance ratios ranged from 0.064 to 0.141, depending on the approach and on ages at calving. Differences among genetic variance components for calving intervals were observed along the animal's lifetime. Those differences confirmed the longitudinal aspect of that trait, indicating the importance of such consideration when accessing fertility of Zebu dairy females, especially in situations where the available information relies on their calving intervals. Spearman rank correlations among approaches ranged from 0.90 to 0.95, and changes observed in the ranking of sires suggested that the genetic progress of the population could be affected by the approach chosen for the analysis of calving intervals. (C) 2012 Elsevier ay. All rights reserved.

RefZ Facilitates the Switch from Medial to Polar Division during Spore Formation in Bacillus subtilis

During sporulation, Bacillus subtilis redeploys the division protein FtsZ from midcell to the cell poles, ultimately generating an asymmetric septum. Here, we describe a sporulation-induced protein, RefZ, that facilitates the switch from a medial to a polar FtsZ ring placement. The artificial expression of RefZ during vegetative growth converts FtsZ rings into FtsZ spirals, arcs, and foci, leading to filamentation and lysis. Mutations in FtsZ specifically suppress RefZ-dependent division inhibition, suggesting that RefZ may target FtsZ. During sporulation, cells lacking RefZ are delayed in polar FtsZ ring formation, spending more time in the medial and transition stages of FtsZ ring assembly. A RefZ-green fluorescent protein (GFP) fusion localizes in weak polar foci at the onset of sporulation and as a brighter midcell focus at the time of polar division. RefZ has a TetR DNA binding motif, and point mutations in the putative recognition helix disrupt focus formation and abrogate cell division inhibition. Finally, chromatin immunoprecipitation assays identified sites of RefZ enrichment in the origin region and near the terminus. Collectively, these data support a model in which RefZ helps promote the switch from medial to polar division and is guided by the organization of the chromosome. Models in which RefZ acts as an activator of FtsZ ring assembly near the cell poles or as an inhibitor of the transient medial ring at midcell are discussed.

Mayaro Virus Infection in Amazonia: A Multimodel Inference Approach to Risk Factor Assessment

Background: Arboviral diseases are major global public health threats. Yet, our understanding of infection risk factors is, with a few exceptions, considerably limited. A crucial shortcoming is the widespread use of analytical methods generally not suited for observational data - particularly null hypothesis-testing (NHT) and step-wise regression (SWR). Using Mayaro virus (MAYV) as a case study, here we compare information theory-based multimodel inference (MMI) with conventional analyses for arboviral infection risk factor assessment. Methodology/Principal Findings: A cross-sectional survey of anti-MAYV antibodies revealed 44% prevalence (n = 270 subjects) in a central Amazon rural settlement. NHT suggested that residents of village-like household clusters and those using closed toilet/latrines were at higher risk, while living in non-village-like areas, using bednets, and owning fowl, pigs or dogs were protective. The "minimum adequate" SWR model retained only residence area and bednet use. Using MMI, we identified relevant covariates, quantified their relative importance, and estimated effect-sizes (beta +/- SE) on which to base inference. Residence area (beta(Village) = 2.93 +/- 0.41; beta(Upland) = -0.56 +/- 0.33, beta(Riverbanks) = -2.37 +/- 0.55) and bednet use (beta = -0.95 +/- 0.28) were the most important factors, followed by crop-plot ownership (beta = 0.39 +/- 0.22) and regular use of a closed toilet/latrine (beta = 0.19 +/- 0.13); domestic animals had insignificant protective effects and were relatively unimportant. The SWR model ranked fifth among the 128 models in the final MMI set. Conclusions/Significance: Our analyses illustrate how MMI can enhance inference on infection risk factors when compared with NHT or SWR. MMI indicates that forest crop-plot workers are likely exposed to typical MAYV cycles maintained by diurnal, forest dwelling vectors; however, MAYV might also be circulating in nocturnal, domestic-peridomestic cycles in village-like areas. This suggests either a vector shift (synanthropic mosquitoes vectoring MAYV) or a habitat/habits shift (classical MAYV vectors adapting to densely populated landscapes and nocturnal biting); any such ecological/adaptive novelty could increase the likelihood of MAYV emergence in Amazonia.

Latent residual analysis in binary regression with skewed link

Model diagnostics is an integral part of model determination and an important part of the model diagnostics is residual analysis. We adapt and implement residuals considered in the literature for the probit, logistic and skew-probit links under binary regression. New latent residuals for the skew-probit link are proposed here. We have detected the presence of outliers using the residuals proposed here for different models in a simulated dataset and a real medical dataset.

Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.

Psychiatric comorbidities in temporal lobe epilepsy: Possible relationships between psychotic disorders and involvement of limbic circuits

Objective: Mounting evidence suggests that the limbic system is pathologically involved in cases of psychiatric comorbidities in temporal lobe epilepsy (TLE) patients. Our objective was to develop a conceptual framework describing how neuropathological and connectivity changes might contribute to the development of psychosis and to the potential neurobiological mechanisms that cause schizophrenia-like psychosis in TLE patients. Methods: In this review, clinical and neuropathological findings, especially brain circuitry of the limbic system, were examined together to enhance our understanding of the association between TLE and psychosis. Finally, the importance of animal models in epilepsy and psychiatric disorders was discussed. Conclusions: TLE and psychiatric symptoms coexist more frequently than chance would predict. Damage and deregulation among critical anatomical regions, such as the hippocampus, amygdala, thalamus, and the temporal, frontal and cingulate cortices, might predispose TLE brains to psychosis. Studies of the effects of kindling and injection of neuroactive substances on behavior and electrophysiological patterns may offer a model of how limbic seizures in humans increase the vulnerability of TLE patients to psychiatric symptoms.

Secondary production of sandy beach macrofauna: An evaluation of predictive models

During the last three decades, several predictive models have been developed to estimate the somatic production of macroinvertebrates. Although the models have been evaluated for their ability to assess the production of macrobenthos in different marine ecosystems, these approaches have not been applied specifically to sandy beach macrofauna and may not be directly applicable to this transitional environment. Hence, in this study, a broad literature review of sandy beach macrofauna production was conducted and estimates obtained with cohort-based and size-based methods were collected. The performance of nine models in estimating the production of individual populations from the sandy beach environment, evaluated for all taxonomic groups combined and for individual groups separately, was assessed, comparing the production predicted by the models to the estimates obtained from the literature (observed production). Most of the models overestimated population production compared to observed production estimates, whether for all populations combined or more specific taxonomic groups. However, estimates by two models developed by Cusson and Bourget provided best fits to measured production, and thus represent the best alternatives to the cohort-based and size-based methods in this habitat. The consistent performance of one of these Cusson and Bourget models, which was developed for the macrobenthos of sandy substrate habitats (C&B-SS), shows that the performance of a model does not depend on whether it was developed for a specific taxonomic group. Moreover, since some widely used models (e.g., the Robertson model) show very different responses when applied to the macrofauna of different marine environments (e.g., sandy beaches and estuaries), prior evaluation of these models is essential.