Analysis of Industrial Granular Flow Applications by Using Advanced Collision Models

Granular flow phenomena are frequently encountered in the design of process and industrial plants in the traditional fields of the chemical, nuclear and oil industries as well as in other activities such as food and materials handling. Multi-phase flow is one important branch of the granular flow. Granular materials have unusual kinds of behavior compared to normal materials, either solids or fluids. Although some of the characteristics are still not well-known yet, one thing is confirmed: the particle-particle interaction plays a key role in the dynamics of granular materials, especially for dense granular materials. At the beginning of this thesis, detailed illustration of developing two models for describing the interaction based on the results of finite-element simulation, dimension analysis and numerical simulation is presented. The first model is used to describing the normal collision of viscoelastic particles. Based on some existent models, more parameters are added to this model, which make the model predict the experimental results more accurately. The second model is used for oblique collision, which include the effects from tangential velocity, angular velocity and surface friction based on Coulomb's law. The theoretical predictions of this model are in agreement with those by finite-element simulation. I n the latter chapters of this thesis, the models are used to predict industrial granular flow and the agreement between the simulations and experiments also shows the validation of the new model. The first case presents the simulation of granular flow passing over a circular obstacle. The simulations successfully predict the existence of a parabolic steady layer and show how the characteristics of the particles, such as coefficients of restitution and surface friction affect the separation results. The second case is a spinning container filled with granular material. Employing the previous models, the simulation could also reproduce experimentally observed phenomena, such as a depression in the center of a high frequency rotation. The third application is about gas-solid mixed flow in a vertically vibrated device. Gas phase motion is added to coherence with the particle motion. The governing equations of the gas phase are solved by using the Large eddy simulation (LES) and particle motion is predicted by using the Lagrangian method. The simulation predicted some pattern formation reported by experiment.

Personality traits are associated with acute major depression across the age spectrum.

Objectives: Psychological predictors, such as personality traits, have aroused growing interest as possible predictors of late-life depression outcome in old age. It remains, however, unclear whether the cross-sectional relationship between personality traits and depression occurrence reported in younger samples is also present in the elderly. Methods: Comparisons amongst 79 outpatients with DSM-IV major depression and 102 healthy controls included assessment of the five-factor model of personality (NEO PI-R), socio-demographic variables, physical health status, as well as depression features. Two sub-groups were considered, defined as young (25-50 years) and old (60-85 years) patients. Results: Depressed patients showed significantly higher levels of Neuroticism and lower levels of Extraversion, Openness to Experience and Conscientiousness compared to controls. Sequential logistic regression models confirmed that the combination of increased physical burden, levels of dependency, and increased Neuroticism strongly predicts the occurrence of acute depressive symptoms. In contrast, the levels of Neuroticism did not allow for differentiating late-life from young age depression. Increased physical burden and decreased depression severity were the main predictors for this distinction. Conclusion: Our data indicate that personality factors and depression are related, independently of patients' age. Differences in this relationship are mainly due to the intensity of depressive symptoms rather than the patients' life period. They also stress the need to consider physical health, level of dependency and severity of symptoms when studying the relationship between personality traits and mood disorders.

Efficacy of a dilemma-focused intervention for unipolar depression: study protocol for a multicenter randomized controlled trial

Background Depression is one of the more severe and serious health problems because of its morbidity, disabling effects and for its societal and economic burden. Despite the variety of existing pharmacological and psychological treatments, most of the cases evolve with only partial remission, relapse and recurrence. Cognitive models have contributed significantly to the understanding of unipolar depression and its psychological treatment. However, success is only partial and many authors affirm the need to improve those models and also the treatment programs derived from them. One of the issues that requires further elaboration is the difficulty these patients experience in responding to treatment and in maintaining therapeutic gains across time without relapse or recurrence. Our research group has been working on the notion of cognitive conflict viewed as personal dilemmas according to personal construct theory. We use a novel method for identifying those conflicts using the repertory grid technique (RGT). Preliminary results with depressive patients show that about 90% of them have one or more of those conflicts. This fact might explain the blockage and the difficult progress of these patients, especially the more severe and/or chronic. These results justify the need for specific interventions focused on the resolution of these internal conflicts. This study aims to empirically test the hypothesis that an intervention focused on the dilemma(s) specifically detected for each patient will enhance the efficacy of cognitive behavioral therapy (CBT) for depression. Design A therapy manual for a dilemma-focused intervention will be tested using a randomized clinical trial by comparing the outcome of two treatment conditions: combined group CBT (eight, 2-hour weekly sessions) plus individual dilemma-focused therapy (eight, 1-hour weekly sessions) and CBT alone (eight, 2-hour group weekly sessions plus eight, 1-hour individual weekly sessions). Method Participants are patients aged over 18 years meeting diagnostic criteria for major depressive disorder or dysthymic disorder, with a score of 19 or above on the Beck depression inventory, second edition (BDI-II) and presenting at least one cognitive conflict (implicative dilemma or dilemmatic construct) as assessed using the RGT. The BDI-II is the primary outcome measure, collected at baseline, at the end of therapy, and at 3- and 12-month follow-up; other secondary measures are also used. Discussion We expect that adding a dilemma-focused intervention to CBT will increase the efficacy of one of the more prestigious therapies for depression, thus resulting in a significant contribution to the psychological treatment of depression. Trial registration ISRCTN92443999; ClinicalTrials.gov Identifier: NCT01542957.

What interventions facilitate client progress through the assimilation model? A task analysis of interventions in the psychodynamic treatment of depression.

A variation of task analysis was used to build an empirical model of how therapists may facilitate client assimilation process, described in the Assimilation of Problematic Experiences Scale. A rational model was specified and considered in light of an analysis of therapist in-session performances (N = 117) drawn from six inpatient therapies for depression. The therapist interventions were measured by the Comprehensive Psychotherapeutic Interventions Rating Scale. Consistent with the rational model, confronting interventions were particularly useful in helping clients elaborate insight. However, rather than there being a small number of progress-related interventions at lower levels of assimilation, therapists' use of interventions was broader than hypothesized and drew from a wide range of therapeutic approaches. Concerning the higher levels of assimilation, there was insufficient data to allow an analysis of the therapist's progress-related interventions.

Tracing the associations between sex, the atypical and the combined atypical-melancholic depression subtypes: A path analysis.

BACKGROUND: Numerous studies have examined determinants leading to preponderance of women in major depressive disorder (MDD), which is particularly accentuated for the atypical depression subtype. It is thus of interest to explore the specific indirect effects influencing the association between sex and established depression subtypes. METHODS: The data of 1624 subjects with a lifetime diagnosis of MDD derived from the population-based PsyCoLaus data were used. An atypical (n=256), a melancholic (n=422), a combined atypical and melancholic features subtype (n=198), and an unspecified MDD group (n=748) were constructed according to the DSM-IV specifiers. Path models with direct and indirect effects were applied to the data. RESULTS: Partial mediation of the female-related atypical and combined atypical-melancholic depression subtypes was found. Early anxiety disorders and high emotion-orientated coping acted as mediating variables between sex and the atypical depression subtype. In contrast, high Body Mass Index (BMI) served as a suppression variable, also concerning the association between sex and the combined atypical-melancholic subtype. The latter association was additionally mediated by an early age of MDD onset and early/late anxiety disorders. LIMITATIONS: The use of cross-sectional data does not allow causal conclusions. CONCLUSIONS: This is the first study that provides evidence for a differentiation of the general mechanisms explaining sex differences of overall MDD by depression subtypes. Determinants affecting the pathways begin early in life. Since some of them are primarily of behavioral nature, the present findings could be a valuable target in mental health care.

The influence of primary care clinic characteristics on the quality of care for depression in patients with different comorbidity profiles

Dans les services de première ligne, la majorité des personnes atteintes de dépression souffrent également d’autres maladies chroniques comorbides. Offrir des soins de haute qualité à ces patients représente un défi important pour les intervenants en première ligne ainsi que pour le système de santé. Il y a des raisons de croire que les contextes organisationnels dans lesquels les intervenants pratiquent ont une influence importante sur les soins. Cependant, peu d’études ont examiné directement la façon dont les caractéristiques des cliniques facilitent ou entravent les soins offerts aux patients atteints de dépression et de différents types de maladies chroniques comorbides. L’objectif général de ce projet de recherche était donc de mieux comprendre comment différentes caractéristiques des cliniques de première ligne influencent la qualité des soins pour la dépression chez des patients ayant différents profils de comorbidité. La thèse comporte deux études. Tout d'abord, nous avons effectué une revue systématique examinant les relations entre la comorbidité physique chronique et la qualité des soins pour la dépression dans les services de première ligne afin de clarifier la nature de ces relations et d’identifier les facteurs qui pourraient influer sur ces relations. Ensuite, nous avons effectué une étude aux méthodes mixtes ayant deux volets : (a) un volet quantitatif examinant les relations entre la qualité des soins pour la dépression, les profils de comorbidité des patients, et les caractéristiques des cliniques de première ligne par le biais d’analyses de régression multiniveaux de données issues de deux enquêtes, et (b) un volet qualitatif basé sur une étude de cas explorant les perceptions des professionnels des services de première ligne sur les facteurs organisationnels pouvant influencer la qualité des soins offerts aux patients souffrant de dépression et d’autres maladies chroniques comorbides. Les résultats de ces études ont montré que, bien que la qualité des soins de la dépression en soins primaires soit sous-optimale, certains sous-groupes de patients dépressifs sont plus à risque que d’autres de recevoir des soins pour la dépression inadéquats, notamment des patients ayant uniquement des comorbidités chroniques physiques. Cependant, plusieurs caractéristiques des cliniques de première ligne semblent faciliter l’offre de soins de qualité aux patients atteints de dépression et de maladies chroniques comorbides : les normes et les valeurs liées au travail d'équipe et le soutien mutuel, l'accès au soutien des professionnels ayant une expertise en santé mentale, l’utilisation des algorithmes de traitement et d’autres outils d’aide à la décision pour la dépression, et l’absence d’obstacles liés aux modèles de rémunération inadéquats. Une des façons dont ces caractéristiques favorisent la qualité est en facilitant la circulation des connaissances dans les cliniques de première ligne. Nos résultats suggèrent que des interventions organisationnelles ciblées sont nécessaires pour améliorer la qualité des soins pour la dépression que reçoivent les patients ayant des maladies chroniques comorbides. Ces interventions peuvent viser différents domaines organisationnels (ex : caractéristiques structurelles/stratégiques, sociales, technologies et épistémiques) mais doivent aussi prendre en compte comment les éléments de chaque domaine interagissent et comment ils pourraient influencer les soins pour des patients ayant des profils de comorbidité différents.

The depression of tropical snow lines at the Last Glacial Maximum: what can we learn from climate and model experiments?

Analyses of simulations of the last glacial maximum (LGM) made with 17 atmospheric general circulation models (AGCMs) participating in the Paleoclimate Modelling Intercomparison Project, and a high-resolution (T106) version of one of the models (CCSR1), show that changes in the elevation of tropical snowlines (as estimated by the depression of the maximum altitude of the 0 °C isotherm) are primarily controlled by changes in sea-surface temperatures (SSTs). The correlation between the two variables, averaged for the tropics as a whole, is 95%, and remains >80% even at a regional scale. The reduction of tropical SSTs at the LGM results in a drier atmosphere and hence steeper lapse rates. Changes in atmospheric circulation patterns, particularly the weakening of the Asian monsoon system and related atmospheric humidity changes, amplify the reduction in snowline elevation in the northern tropics. Colder conditions over the tropical oceans combined with a weakened Asian monsoon could produce snowline lowering of up to 1000 m in certain regions, comparable to the changes shown by observations. Nevertheless, such large changes are not typical of all regions of the tropics. Analysis of the higher resolution CCSR1 simulation shows that differences between the free atmospheric and along-slope lapse rate can be large, and may provide an additional factor to explain regional variations in observed snowline changes.

Effects of prenatal and postnatal depression, and maternal stroking, at the glucocorticoid receptor gene

In animal models, prenatal and postnatal stress is associated with elevated hypothalamic–pituitary axis (HPA) reactivity mediated via altered glucocorticoid receptor (GR) gene expression. Postnatal tactile stimulation is associated with reduced HPA reactivity mediated via increased GR gene expression. In this first study in humans to examine the joint effects of prenatal and postnatal environmental exposures, we report that GR gene (NR3C1) 1-F promoter methylation in infants is elevated in the presence of increased maternal postnatal depression following low prenatal depression, and that this effect is reversed by self-reported stroking of the infants by their mothers over the first weeks of life.

Assessment of inbreeding depression in a Guzerat dairy herd: Effects of individual increase in inbreeding coefficients on production and reproduction

Influences of inbreeding on daily milk yield (DMY), age at first calving (AFC), and calving intervals (CI) were determined on a highly inbred zebu dairy subpopulation of the Guzerat breed. Variance components were estimated using animal models in single-trait analyses. Two approaches were employed to estimate inbreeding depression: using individual increase in inbreeding coefficients or using inbreeding coefficients as possible covariates included in the statistical models. The pedigree file included 9,915 animals, of which 9,055 were inbred, with an average inbreeding coefficient of 15.2%. The maximum inbreeding coefficient observed was 49.45%, and the average inbreeding for the females still in the herd during the analysis was 26.42%. Heritability estimates were 0.27 for DMY and 0.38 for AFC. The genetic variance ratio estimated with the random regression model for CI ranged around 0.10. Increased inbreeding caused poorer performance in DMY, AFC, and CI. However, some of the cows with the highest milk yield were among the highly inbred animals in this subpopulation. Individual increase in inbreeding used as a covariate in the statistical models accounted for inbreeding depression while avoiding overestimation that may result when fitting inbreeding coefficients.

Inbreeding depression on production and reproduction traits of buffaloes from Brazil

The objective of this study was to evaluate the influence of inbreeding depression on traits of buffaloes from Brazil. Specifically, the traits studied were body weight at 205 and 365 days of age, average daily gain from birth to 205 days (ADG_205), average daily gain between 205 and 365 days (ADG205_365) in Mediterranean buffaloes, and milk yield, lactation length, age of first calving and calving intervals in Murrah buffaloes. Inbreeding effects on the traits were determined by fitting four regression models (linear, quadratic, exponential and Michaelis-Menten) about the errors generated by the animal model. The linear model was only significant (P<0.05) for growth traits (exception of ADG205_365). The exponential and Michaelis-Menten models were significant (P<0.01) for all the studied traits while the quadratic model was not significant (P>0.05) for any of the traits. Weight at 205 and 365 days of age decreased 0.25kg and 0.39kg per 1% of increase in inbreeding, respectively. The inbred animals (F=0.25) produced less milk than non-inbred individuals: 50.4kg of milk. Moreover, calving interval increased 0.164 days per 1% of increase in inbreeding. Interestingly, inbreeding had a positive effect on age at first calving and lactation length, decreasing age of first calving and increasing lactation length. © 2012 Japanese Society of Animal Science.

Relationship between body image disturbance and incidence of depression: the SUN prospective cohort

[EN] Background: Body image disturbance is an increasing problem in Western societies and is associated with a number of mental health outcomes including anorexia, bulimia, body dysmorphia, and depression. The aim of this study was to assess the association between body image disturbance and the incidence of depression. Methods: This study included 10,286 participants from a dynamic prospective cohort of Spanish university graduates, who were followed-up for a median period of 4.2 years (Seguimiento Universidad de Navarra – the SUN study). The key characteristic of the study is the permanently open recruitment that started in 1999. The baseline questionnaire included information about body mass index (BMI) and the nine figure schemes that were used to assess body size perception. These variables were grouped according to recommended classifications and the difference between BMI and body size perception was considered as a proxy of body image disturbance. A subject was classified as an incident case of depression if he/she was initially free of depression and reported a physician-made diagnosis of depression and/or the use of antidepressant medication in at least one of the follow-up questionnaires. The association between body image disturbance and the incidence of depression was estimated by calculating the multivariable adjusted Odds Ratio (OR) and its 95% Confidence Interval (95% CI), using logistic regression models. Results: The cumulative incidence of depression during follow-up in the cohort was 4.8%. Men who underestimated their body size had a high percentage of overweight and obesity (50.1% and 12.6%, respectively), whereas women who overestimated their body size had a high percentage of underweight (87.6%). The underestimation exhibited a negative association with the incidence of depression among women (OR: 0.72, 95% CI: 0.54 – 0.95), but this effect disappeared after adjusting for possible confounding variables. The proportion of participants who correctly perceived their body size was high (53.3%) and gross misperception was seldom found, with most cases selecting only one silhouette below (42.7%) or above (2.6%) their actual BMI. Conclusion: We found no association between body image disturbance and subsequent depression in a cohort of university graduates in Spain.

Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Transgenic mice as a tool for depression research: examples from the endocannabinoid and the corticotropin-releasing hormone system

Major depression belongs to the most serious and widespread psychiatric disorders in today’s society. There is a great need for the delineation of the underlying molecular mechanisms as well as for the identification of novel targets for its treatment. In this thesis, transgenic mice of the endocannabinoid and the corticotropin-releasing hormone (CRH) system were investigated to determine the putative role of these systems for depression-like phenotypes in mice. In the first part of the thesis, we found that the endocannabinoid system was prominently involved in a brain region-specific and temporally controlled manner in acute as well as in chronic stress processing. Genetic deletion in combination with pharmacological intervention revealed the importance of a fully functional endocannabinoid system for efficient neuroendocrine and behavioral stress coping. Accordingly, cannabinoid type 1 (CB1) receptor-deficient mice displayed several depression-like symptoms and molecular alterations, including “behavioral despair”, stress hormone hypersecretion and decreased glucocorticoid receptor and brain-derived neurotrophic factor expression in the hippocampus. However, the endocannabinoid system was dispensable for the efficacy of currently used antidepressant drugs. To facilitate future endocannabinoid research, a transgenic mouse was generated, which overexpressed the CB1 receptor protein fused to a fluorescent protein. In the second part of the thesis, conditional brain region-specific CRH overexpressing mice were evaluated as a model for pathological chronic CRH hyperactivation. Mutant mice showed aberrant neuroendocrine and behavioral stress coping and hyperarousal due to CRH-induced activation of the noradrenergic system in the brain. Mutant mice appeared to share similarities with naturally occurring endogenous CRH activation in wild-type mice and were sensitive to acute pharmacological blockade of CRH receptor type 1 (CRH-R1). Thus, CRH overexpressing mice serve as an ideal in vivo tool to evaluate the efficacy of novel CRH-R1 antagonists. Together, these findings highlight the potential of transgenic mice for the understanding of certain endo-phenotypes (isolated symptoms) of depression and their molecular correlates.

Elevated level of metabotropic glutamate receptor 2/3 in the prefrontal cortex in major depression

Clinical, postmortem and preclinical research strongly implicates dysregulation of glutamatergic neurotransmission in major depressive disorder (MDD). Recently, metabotropic glutamate receptors (mGluRs) have been proposed as attractive targets for the discovery of novel therapeutic approaches against depression. The aim of this study was to examine mGluR2/3 protein levels in the prefrontal cortex (PFC) from depressed subjects. In addition, to test whether antidepressants influence mGluR2/3 expression we also studied levels of mGluR2/3 in fluoxetine-treated monkeys. Postmortem human prefrontal samples containing Brodmann's area 10 (BA10) were obtained from 11 depressed and 11 psychiatrically healthy controls. Male rhesus monkeys were treated chronically with fluoxetine (dose escalated to 3mg/kg, p.o.; n=7) or placebo (n=6) for 39 weeks. The mGluR2/3 immunoreactivity was investigated using Western blot method. There was a robust (+67%) increase in the expression of the mGlu2/3 protein in the PFC of depressed subjects relative to healthy controls. The expression of mGlu2/3 was unchanged in the PFC of monkeys treated with fluoxetine. Our findings provide the first evidence that mGluR2/3 is elevated in the PFC in MDD. This observation is consistent with reports showing that mGluR2/3 antagonists exhibit antidepressant-like activity in animal models and demonstrates that these receptors are promising targets for the discovery of novel antidepressants.

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.