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Journal articles on the topic 'Models of disorder'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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1

Csahok, Z., and T. Vicsek. "Traffic models with disorder." Journal of Physics A: Mathematical and General 27, no. 16 (August 21, 1994): L591—L596. http://dx.doi.org/10.1088/0305-4470/27/16/005.

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2

Mack, Avram H. "Models for Mental Disorder." Journal of Nervous and Mental Disease 203, no. 12 (December 2015): 977. http://dx.doi.org/10.1097/nmd.0000000000000403.

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3

Haslam, Nick. "Categorical Versus Dimensional Models of Mental Disorder: The Taxometric Evidence." Australian & New Zealand Journal of Psychiatry 37, no. 6 (December 2003): 696–704. http://dx.doi.org/10.1080/j.1440-1614.2003.01258.x.

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Objective: To review studies of the categorical versus dimensional status of mental disorders that employ taxometric methodology. Method: A comprehensive qualitative review of all published taxometric studies of psychopathology. Results: Categorical and dimensional models each receive well-replicated support for some groups of mental disorders. Studies favour categorical models for melancholia, eating disorders, pathological dissociation, and schizotypal and antisocial personality disorders. Dimensional models tend to be favoured for the broad neurotic spectrum – general depression, generalized anxiety, posttraumatic stress disorder – and for borderline personality disorder. Conclusions: Taxometric research clarifies the latent structure of psychopathology in ways that have implications for the classification, assessment, explanation and conceptualization of mental disorder
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4

Jenkins, Rachel. "Models for Mental Disorder, Conceptual Models in Psychiatry." International Clinical Psychopharmacology 3, no. 1 (January 1988): 91–92. http://dx.doi.org/10.1097/00004850-198801000-00014.

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5

Olivier, B. "Animal models of panic disorder." Behavioural Pharmacology 8, no. 6 (November 1997): 661. http://dx.doi.org/10.1097/00008877-199711000-00066.

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6

Kato, Tadafumi, Mie Kubota, and Takaoki Kasahara. "Animal models of bipolar disorder." Neuroscience & Biobehavioral Reviews 31, no. 6 (January 2007): 832–42. http://dx.doi.org/10.1016/j.neubiorev.2007.03.003.

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7

Silva, Rhayra Xavier do Carmo, Sueslene Prado Rocha, Anderson Manoel Herculano, Monica Gomes Lima-Maximino, and Caio Maximino. "Animal models for panic disorder." Psychology & Neuroscience 13, no. 1 (March 2020): 1–18. http://dx.doi.org/10.1037/pne0000177.

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8

Galvez, Juan F., Ives C. Passos, Flavio P. Kapczinski, and Jair C. Soares. "Staging Models in Bipolar Disorder." FOCUS 13, no. 1 (January 2015): 19–24. http://dx.doi.org/10.1176/appi.focus.130110.

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9

Trull, Timothy J. "Dimensional models of personality disorder." Current Opinion in Psychiatry 13, no. 2 (March 2000): 179–84. http://dx.doi.org/10.1097/00001504-200003000-00007.

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10

D’Agostino, Alessandra, Mario Rossi Monti, and Vladan Starcevic. "Models of borderline personality disorder." Current Opinion in Psychiatry 31, no. 1 (January 2018): 57–62. http://dx.doi.org/10.1097/yco.0000000000000374.

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11

Stinchcombe, Robin B. "Disorder in non-equilibrium models." Journal of Physics: Condensed Matter 14, no. 7 (February 8, 2002): 1473–87. http://dx.doi.org/10.1088/0953-8984/14/7/306.

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12

Simon, P., and F. Ricci-Tersenghi. "Coupled Ising models with disorder." Journal of Physics A: Mathematical and General 33, no. 34 (August 16, 2000): 5985–91. http://dx.doi.org/10.1088/0305-4470/33/34/304.

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13

Daugherty, Darryl, Tairi Roque-Urrea, John Urrea-Roque, Jessica Troyer, Stephen Wirkus, and Mason A. Porter. "Mathematical models of bipolar disorder." Communications in Nonlinear Science and Numerical Simulation 14, no. 7 (July 2009): 2897–908. http://dx.doi.org/10.1016/j.cnsns.2008.10.027.

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14

Garrido, P. L., and J. Marro. "Kinetic lattice models of disorder." Journal of Statistical Physics 74, no. 3-4 (February 1994): 663–86. http://dx.doi.org/10.1007/bf02188575.

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15

Xiang, Ting, Zhuo-Ying Tao, Li-Fan Liao, Shuang Wang, and Dong-Yuan Cao. "Animal Models of Temporomandibular Disorder." Journal of Pain Research Volume 14 (May 2021): 1415–30. http://dx.doi.org/10.2147/jpr.s303536.

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16

TRIVEDI, NANDINI, AMIT GHOSAL, and MOHIT RANDERIA. "RECENT PROGRESS ON MODELS OF HIGHLY DISORDERED SUPERCONDUCTORS." International Journal of Modern Physics B 15, no. 10n11 (May 10, 2001): 1347–58. http://dx.doi.org/10.1142/s0217979201005829.

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We show, using a Bogoliubov-de Gennes (BdG) mean field theory, that the local pairing amplitude Δ(r) becomes highly inhomogeneous with increasing disorder in an s-wave superconductor. The probability distribution P(Δ) is peaked about the BCS value at low disorder, but with increasing disorder, progressively develops into a broad distribution with significant build up of weight near Δ≈0. At high disorder, the system is found to form superconducting "islands" separated by a non-superconducting sea. Surprisingly, a finite energy gap persists into the highly disordered state in spite of many sites having negligible pairing amplitude and is understood in detail within the BdG framework. Once the pairing amplitude becomes inhomogeneous, the role of quantum phase fluctuations becomes crucial in driving a superconductor-insulator transition at a critical disorder. The insulator is unusual as it has a finite gap for all disorder strengths in marked contrast to the Anderson insulator in non-interacting systems. We treat the phase fluctuations within a self consistent harmonic approximation and obtain the superfluid stiffness as a function of disorder, which agrees well with our earlier quantum Monte Carlo studies.
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17

Sharma, Amita, and Willem J. M. I. Verbeke. "Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models." PLOS ONE 16, no. 5 (May 10, 2021): e0251365. http://dx.doi.org/10.1371/journal.pone.0251365.

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Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of anxiety and fear. Excessive anxiety can make an individual avoid work, school, family get-togethers, and other social situations that in turn might amplify these symptoms. According to the World Health Organization (WHO), one in thirteen persons globally suffers from anxiety. It is high time to understand the roles of various clinical biomarker measures that can diagnose the types of anxiety disorders. In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders—Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social Anxiety Disorder (SAD) and Panic Disorder (PD). We used several machine learning models and extracted the variable importance contributing to a type of anxiety disorder. The study uses a sample of 11,081 Dutch citizens’ data collected by the Lifelines, Netherlands. The results show that there are significant and low correlations among GAD, AP, PD and SAD and we extracted the variable importance hierarchy of biomarkers with respect to each type of anxiety disorder which will be helpful in designing the experimental setup for clinical trials related to influence of biomarkers on type of anxiety disorder.
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18

Fisher, Peter L., and Adrian Wells. "Conceptual Models of Generalized Anxiety Disorder." Psychiatric Annals 41, no. 2 (February 1, 2011): 127–32. http://dx.doi.org/10.3928/00485713-20110203-11.

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19

Petri, Alberto. "Lattice models of disorder with order." Brazilian Journal of Physics 33, no. 3 (September 2003): 521–25. http://dx.doi.org/10.1590/s0103-97332003000300013.

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20

Man, J., A. Hudson, D. Ashton, and D. Nutt. "Animal Models for Obsessive-Compulsive Disorder." Current Neuropharmacology 2, no. 2 (April 1, 2004): 169–81. http://dx.doi.org/10.2174/1570159043476792.

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21

EVANS, JEFF. "Prescribing Models Shape Personality Disorder Tx." Clinical Psychiatry News 33, no. 6 (June 2005): 26. http://dx.doi.org/10.1016/s0270-6644(05)70426-4.

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22

RAPOPORT, JUDITH L. "ANIMAL MODELS OF OBSESSIVE COMPULSIVE DISORDER." Clinical Neuropharmacology 15 (1992): 261A—262A. http://dx.doi.org/10.1097/00002826-199201001-00136.

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23

Handley, SL. "Animal models of obsessive-compulsive disorder." Behavioural Pharmacology 8, no. 6 (November 1997): 650. http://dx.doi.org/10.1097/00008877-199711000-00042.

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24

Rauch, Scott L., and Michael A. Jenike. "Neurobiological Models of Obsessive-Compulsive Disorder." Psychosomatics 34, no. 1 (January 1993): 20–32. http://dx.doi.org/10.1016/s0033-3182(93)71924-6.

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25

Reichman, David R. "On Stochastic Models of Dynamic Disorder†." Journal of Physical Chemistry B 110, no. 38 (September 2006): 19061–65. http://dx.doi.org/10.1021/jp061992j.

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26

Bratiotis, Christiana, and Gail Steketee. "Hoarding Disorder: Models, Interventions, and Efficacy." FOCUS 13, no. 2 (April 2015): 175–83. http://dx.doi.org/10.1176/appi.focus.130202.

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27

Olivier, B. "Animal models in obsessive compulsive disorder." International Clinical Psychopharmacology 7 (June 1992): 27–30. http://dx.doi.org/10.1097/00004850-199206001-00007.

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28

Valvassori, Samira. "Modeling Bipolar Disorder in Animal Models." Biological Psychiatry 87, no. 9 (May 2020): S14. http://dx.doi.org/10.1016/j.biopsych.2020.02.063.

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29

Meshalkina, Daria A., Marina N. Kizlyk, Elana V. Kysil, Adam D. Collier, David J. Echevarria, Murilo S. Abreu, Leonardo J. G. Barcellos, et al. "Zebrafish models of autism spectrum disorder." Experimental Neurology 299 (January 2018): 207–16. http://dx.doi.org/10.1016/j.expneurol.2017.02.004.

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30

Killeen, Peter R. "Models of attention-deficit hyperactivity disorder." Behavioural Processes 162 (May 2019): 205–14. http://dx.doi.org/10.1016/j.beproc.2019.01.001.

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31

Wu, F. Y., and K. Y. Lin. "Spin models with multiple disorder points." Physics Letters A 130, no. 6-7 (July 1988): 335–37. http://dx.doi.org/10.1016/0375-9601(88)90223-x.

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32

Abramowitz, Jonathan S., Steven Taylor, Dean McKay, and Brett J. Deacon. "Animal Models of Obsessive-Compulsive Disorder." Biological Psychiatry 69, no. 9 (May 2011): e29-e30. http://dx.doi.org/10.1016/j.biopsych.2010.10.034.

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33

Batchelor, M. T., and J. M. J. van Leeuwen. "Disorder solutions of lattice spin models." Physica A: Statistical Mechanics and its Applications 154, no. 3 (January 1989): 365–83. http://dx.doi.org/10.1016/0378-4371(89)90256-2.

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34

McNeill, Rhiannon V., Georg C. Ziegler, Franziska Radtke, Matthias Nieberler, Klaus-Peter Lesch, and Sarah Kittel-Schneider. "Mental health dished up—the use of iPSC models in neuropsychiatric research." Journal of Neural Transmission 127, no. 11 (May 7, 2020): 1547–68. http://dx.doi.org/10.1007/s00702-020-02197-9.

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Abstract Genetic and molecular mechanisms that play a causal role in mental illnesses are challenging to elucidate, particularly as there is a lack of relevant in vitro and in vivo models. However, the advent of induced pluripotent stem cell (iPSC) technology has provided researchers with a novel toolbox. We conducted a systematic review using the PRISMA statement. A PubMed and Web of Science online search was performed (studies published between 2006–2020) using the following search strategy: hiPSC OR iPSC OR iPS OR stem cells AND schizophrenia disorder OR personality disorder OR antisocial personality disorder OR psychopathy OR bipolar disorder OR major depressive disorder OR obsessive compulsive disorder OR anxiety disorder OR substance use disorder OR alcohol use disorder OR nicotine use disorder OR opioid use disorder OR eating disorder OR anorexia nervosa OR attention-deficit/hyperactivity disorder OR gaming disorder. Using the above search criteria, a total of 3515 studies were found. After screening, a final total of 56 studies were deemed eligible for inclusion in our study. Using iPSC technology, psychiatric disease can be studied in the context of a patient’s own unique genetic background. This has allowed great strides to be made into uncovering the etiology of psychiatric disease, as well as providing a unique paradigm for drug testing. However, there is a lack of data for certain psychiatric disorders and several limitations to present iPSC-based studies, leading us to discuss how this field may progress in the next years to increase its utility in the battle to understand psychiatric disease.
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35

Hughes, Julian C. "Models for mental disorder: Conceptual models in psychiatry (2nd ed.)." Journal of Psychosomatic Research 41, no. 5 (November 1996): 495. http://dx.doi.org/10.1016/s0022-3999(96)00125-0.

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36

Dtchetgnia Djeundam, S. R., R. Yamapi, G. Filatrella, and T. C. Kofane. "Dynamics of Disordered Network of Coupled Hindmarsh–Rose Neuronal Models." International Journal of Bifurcation and Chaos 26, no. 03 (March 2016): 1650048. http://dx.doi.org/10.1142/s0218127416500486.

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We investigate the effects of disorder on the synchronized state of a network of Hindmarsh–Rose neuronal models. Disorder, introduced as a perturbation of the neuronal parameters, destroys the network activity by wrecking the synchronized state. The dynamics of the synchronized state is analyzed through the Kuramoto order parameter, adapted to the neuronal Hindmarsh–Rose model. We find that the coupling deeply alters the dynamics of the single units, thus demonstrating that coupling not only affects the relative motion of the units, but also the dynamical behavior of each neuron; Thus, synchronization results in a structural change of the dynamics. The Kuramoto order parameter allows to clarify the nature of the transition from perfect phase synchronization to the disordered states, supporting the notion of an abrupt, second order-like, dynamical phase transition. We find that the system is resilient up to a certain disorder threshold, after that the network abruptly collapses to a desynchronized state. The loss of perfect synchronization seems to occur even for vanishingly small values of the disorder, but the degree of synchronization (as measured by the Kuramoto order parameter) gently decreases, and the completely disordered state is never reached.
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37

Pittenger, Christopher. "37.5 PATHOPHYSIOLOGICALLY GROUNDED MODELS OF TIC DISORDERS: HISTAMINE DYSREGULATION IN TOURETTE'S DISORDER." Journal of the American Academy of Child & Adolescent Psychiatry 55, no. 10 (October 2016): S317. http://dx.doi.org/10.1016/j.jaac.2016.07.338.

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38

Cohen, Hagit, and Rachel Yehuda. "Gender Differences in Animal Models of Posttraumatic Stress Disorder." Disease Markers 30, no. 2-3 (2011): 141–50. http://dx.doi.org/10.1155/2011/734372.

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Epidemiological studies report higher prevalence rates of stress-related disorders such as acute stress disorder and post-traumatic stress disorder (PTSD) in women than in men following exposure to trauma. It is still not clear whether this greater prevalence in woman reflects a greater vulnerability to stress-related psychopathology. A number of individual and trauma-related characteristics have been hypothesized to contribute to these gender differences in physiological and psychological responses to trauma, differences in appraisal, interpretation or experience of threat, coping style or social support. In this context, the use of an animal model for PTSD to analyze some of these gender-related differences may be of particular utility. Animal models of PTSD offer the opportunity to distinguish between biological and socio-cultural factors, which so often enter the discussion about gender differences in PTSD prevalence.In this review, we present and discuss sex-differences in behavioral, neurochemical, neurobiological and pharmacological findings that we have collected from several different animal studies related to both basal conditions and stress responses. These models have used different paradigms and have elicited a range of behavioral and physiological manifestations associated with gender. The overall data presented demonstrate that male animals are significantly more vulnerable to acute and chronic stress, whereas females are far more resilient. The stark contradiction between these findings and contemporary epidemiological data regarding human subjects is worthy of further study. The examination of these gender-related differences can deepen our understanding of the risk or the pathophysiology of stress-related disorders.
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39

Waters, A. M., B. P. Bradley, and K. Mogg. "Biased attention to threat in paediatric anxiety disorders (generalized anxiety disorder, social phobia, specific phobia, separation anxiety disorder) as a function of ‘distress’versus‘fear’ diagnostic categorization." Psychological Medicine 44, no. 3 (April 17, 2013): 607–16. http://dx.doi.org/10.1017/s0033291713000779.

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BackgroundStructural models of emotional disorders propose that anxiety disorders can be classified into fear and distress disorders. Sources of evidence for this distinction come from genetic, self-report and neurophysiological data from adults. The present study examined whether this distinction relates to cognitive processes, indexed by attention bias towards threat, which is thought to cause and maintain anxiety disorders.MethodDiagnostic and attention bias data were analysed from 435 children between 5 and 13 years of age; 158 had principal fear disorder (specific phobia, social phobia or separation anxiety disorder), 75 had principal distress disorder (generalized anxiety disorder, GAD) and 202 had no psychiatric disorder. Anxious children were a clinic-based treatment-seeking sample. Attention bias was assessed on a visual-probe task with angry, neutral and happy faces.ResultsCompared to healthy controls, children with principal distress disorder (GAD) showed a significant bias towards threat relative to neutral faces whereas children with principal fear disorder showed an attention bias away from threat relative to neutral faces. Overall, children displayed an attention bias towards happy faces, irrespective of diagnostic group.ConclusionsOur findings support the distinction between fear and distress disorders, and extend empirically derived structural models of emotional disorders to threat processing in childhood, when many anxiety disorders begin and predict lifetime impairment.
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40

Racine, S. E., K. M. Culbert, S. A. Burt, and K. L. Klump. "Advanced paternal age at birth: phenotypic and etiologic associations with eating pathology in offspring." Psychological Medicine 44, no. 5 (June 24, 2013): 1029–41. http://dx.doi.org/10.1017/s0033291713001426.

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BackgroundAdvanced paternal age at birth has been linked to several psychiatric disorders in offspring (e.g. schizophrenia) and genetic mechanisms are thought to underlie these associations. This study is the first to investigate whether advanced paternal age at birth is associated with eating disorder risk using a twin study design capable of examining both phenotypic and genetic associations.MethodIn a large, population-based sample of female twins aged 8–17 years in mid-puberty or beyond (n = 1722), we investigated whether advanced paternal age was positively associated with disordered eating symptoms and an eating disorder history [i.e. anorexia nervosa (AN), bulimia nervosa (BN) or binge eating disorder (BED)] in offspring. Biometric twin models examined whether genetic and/or environmental factors underlie paternal age effects for disordered eating symptoms.ResultsAdvanced paternal age was positively associated with disordered eating symptoms and an eating disorder history, where the highest level of pathology was observed in offspring born to fathers ⩾40 years old. The results were not accounted for by maternal age at birth, body mass index (BMI), socio-economic status (SES), fertility treatment or parental psychiatric history. Twin models indicated decreased genetic, and increased environmental, effects on disordered eating with advanced paternal age.ConclusionsAdvanced paternal age increased risk for the full spectrum of eating pathology, independent of several important covariates. However, contrary to leading hypotheses, environmental rather than genetic factors accounted for paternal age–disordered eating associations. These data highlight the need to explore novel (potentially environmental) mechanisms underlying the effects of advanced paternal age on offspring eating disorder risk.
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41

Ufer, Kristian, Reinhard Kleeberg, and Thomas Monecke. "Quantification of stacking disordered Si–Al layer silicates by the Rietveld method: application to exploration for high-sulphidation epithermal gold deposits." Powder Diffraction 30, S1 (April 22, 2015): S111—S118. http://dx.doi.org/10.1017/s0885715615000111.

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Hydrothermally altered rocks hosting precious metal deposits frequently contain stacking disordered layer silicates. X-ray diffraction analysis using the Rietveld method can be used to determine mineral abundances in these rocks if suitable disorder models are applied. It is shown here that disorder models of kaolinite and pyrophyllite can be described by a recursive calculation of structure factors. This permits the physically sound refinement of real structure parameters of these disordered minerals and the determination of mineral abundances. Even mixtures containing two disordered Si–Al layer silicates can be quantified reliably. The developed disorder models can now be implemented in routine phase analysis, allowing the quantification of large numbers of samples to identify mineralogical gradients surrounding ore deposits.
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42

Friedberg, Naomi L., and William J. Lyddon. "Self-Other Working Models and Eating Disorders." Journal of Cognitive Psychotherapy 10, no. 3 (January 1996): 193–202. http://dx.doi.org/10.1891/0889-8391.10.3.193.

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In this study, Bartholomew’s (1990) four-category model of attachment (secure, preoccupied, dismissing, and fearful) was used to test Guidano’s (1987) notion that the personal cognitive organization (P.C. Org.) of individuals with eating disorders is characterized by an enmeshed, preoccupied working model of attachment. Consistent with this characterization, Bartholomew’s preoccupied and secure attachment dimensions were found to significantly discriminate a clinical eating disorder sample (n = 17) from normal subjects (n = 27).
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43

Dotsenko, Vladimir S., Xuan Son Nguyen, and Raoul Santachiara. "Models WDn in the presence of disorder and the coupled models." Nuclear Physics B 613, no. 3 (October 2001): 445–71. http://dx.doi.org/10.1016/s0550-3213(01)00392-3.

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44

Simon, P. "Coupled minimal models with and without disorder." Nuclear Physics B 515, no. 3 (April 1998): 624–64. http://dx.doi.org/10.1016/s0550-3213(98)00016-9.

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45

Roth, Deborah A., and Richard G. Heimberg. "COGNATIVE-BEHAVIORAL MODELS OF SOCIAL ANXIETY DISORDER." Psychiatric Clinics of North America 24, no. 4 (December 2001): 753–71. http://dx.doi.org/10.1016/s0193-953x(05)70261-6.

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46

O'Shea, K. Sue, and Melvin G. McInnis. "Neurodevelopmental origins of bipolar disorder: iPSC models." Molecular and Cellular Neuroscience 73 (June 2016): 63–83. http://dx.doi.org/10.1016/j.mcn.2015.11.006.

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47

Schultz, Robert T., David W. Evans, and Monica Wolff. "Neuropsychological Models of Childhood Obsessive-Compulsive Disorder." Child and Adolescent Psychiatric Clinics of North America 8, no. 3 (July 1999): 513–31. http://dx.doi.org/10.1016/s1056-4993(18)30167-6.

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48

Szechtman, Henry, Susanne E. Ahmari, Richard J. Beninger, David Eilam, Brian H. Harvey, Henriette Edemann-Callesen, and Christine Winter. "Obsessive-compulsive disorder: Insights from animal models." Neuroscience & Biobehavioral Reviews 76 (May 2017): 254–79. http://dx.doi.org/10.1016/j.neubiorev.2016.04.019.

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49

Adamec, R. "Animal models of post traumatic stress disorder." Behavioural Pharmacology 8, no. 6 (November 1997): 639. http://dx.doi.org/10.1097/00008877-199711000-00020.

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50

Workman, Joanna L., and Randy J. Nelson. "Potential animal models of seasonal affective disorder." Neuroscience & Biobehavioral Reviews 35, no. 3 (January 2011): 669–79. http://dx.doi.org/10.1016/j.neubiorev.2010.08.005.

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