Recognizing Mendelian disorders should improve health care for persons with strong familial risks for common chronic diseases. The Online Mendelian Inheritance in Man (OMIM) database was reviewed to identify Mendelian disorders featuring 17 common chronic diseases, including 9 cardiovascular conditions, diabetes, and 7 common cancers. Mendelian disorders were selected if any one of the 17 diseases was reported in more than two families manifesting in adulthood. Patterns of chronic diseases and modes of inheritance associated with these Mendelian disorders are described. The GeneTests/Reviews database and other websites were reviewed to determine availability of genetic testing and management and prevention recommendations for the selected disorders. Of 2,592 (OMIM) entries reviewed, 188 Mendelian disorders were selected. Most (67.7%) are autosomal dominant disorders. Almost half (45.8%) feature combinations of the chronic diseases under study. At least one gene is known for 68.8% of the selected disorders, and clinical genetic testing is available for 55% of disorders. Guidelines for management and prevention are available for 33.9% of these, ranging from recommendations for supportive care to guidelines for managing affected persons and screening relatives. Significant clinical heterogeneity exists for Mendelian disorders that might present as strong family histories of common chronic diseases. Recognition of the different combinations of diseases within a pedigree, including mode of inheritance and heritable disease risk factors, facilitates diagnosis of these Mendelian disorders. Genetic testing is available for most disorders, which can further clarify the genetic risk, and for some, recommendations for management and prevention are available. However, evidence-based guidelines are needed. Published 2004 Wiley-Liss, Inc.dagger.

Two novel statistical methods are applied to the prediction of transitions between weather regimes. The methods are tested using a long, 6 000-day simulation of a three-layer, quasi-geostrophic (QG3) model on the sphere at T21 resolution. The two methods are the k nearest-neighbor classifier and the random-forest method. Both methods are widely used in statistical classification and machine learning; they are applied here to forecast the break of a regime and subsequent onset of another one. The QG3 model has been previously shown to possess realistic weather regimes in its Northern Hemisphere and preferred transitions between these have been deter- mined. The two methods are applied to the three more robust transitions; they both demonstrate a skill of 35–40% better than random and are thus encouraging for use on real data. Moreover, the random-forest method allows, while keeping the overall skill unchanged, to efficiently adjust the ratio of correctly predicted transitions to false alarms. A long-standing conjecture has associated regime breaks and preferred transitions with distinct directions in the reduced model phase space spanned by a few leading empirical orthogonal functions of its variability. Sensitivity studies for several predic- tors confirm the crucial influence of the exit angle on a preferred transition path. The present results thus support the paradigm of multiple weather regimes and of their association with unstable fixed points of atmospheric dynamics.

Two novel statistical methods are applied to the prediction of transitions between weather regimes. The methods are tested using a long, 6 000-day simulation of a three-layer, quasi-geostrophic (QG3) model on the sphere at T21 resolution. The two methods are the k nearest-neighbor classifier and the random-forest method. Both methods are widely used in statistical classification and machine learning; they are applied here to forecast the break of a regime and subsequent onset of another one. The QG3 model has been previously shown to possess realistic weather regimes in its Northern Hemisphere and preferred transitions between these have been determined. The two methods are applied to the three more robust transitions; they both demonstrate a skill of 35—40% better than random and are thus encouraging for use on real data. Moreover, the random-forest method allows, while keeping the overall skill unchanged, to efficiently adjust the ratio of correctly predicted transitions to false alarms.

A long-standing conjecture has associated regime breaks and preferred transitions with distinct directions in the reduced model phase space spanned by a few leading empirical orthogonal functions of its variability. Sensitivity studies for several predictors confirm the crucial influence of the exit angle on a preferred transition path. The present results thus support the paradigm of multiple weather regimes and of their association with unstable fixed points of atmospheric dynamics.

Two novel statistical methods are applied to the prediction of transitions between weather regimes. The methods are tested using a long, 6 000-day simulation of a three-layer, quasi-geostrophic (QG3) model on the sphere at T21 resolution. The two methods are the k nearest-neighbor classifier and the random-forest method. Both methods are widely used in statistical classification and machine learning; they are applied here to forecast the break of a regime and subsequent onset of another one. The QG3 model has been previously shown to possess realistic weather regimes in its Northern Hemisphere and preferred transitions between these have been determined. The two methods are applied to the three more robust transitions; they both demonstrate a skill of 35—40% better than random and are thus encouraging for use on real data. Moreover, the random-forest method allows, while keeping the overall skill unchanged, to efficiently adjust the ratio of correctly predicted transitions to false alarms.

A long-standing conjecture has associated regime breaks and preferred transitions with distinct directions in the reduced model phase space spanned by a few leading empirical orthogonal functions of its variability. Sensitivity studies for several predictors confirm the crucial influence of the exit angle on a preferred transition path. The present results thus support the paradigm of multiple weather regimes and of their association with unstable fixed points of atmospheric dynamics.

The ability to shift our internal state to meet expected environmental demands fundamentally molds our experience of and interaction with the world, supporting learning, speech comprehension, and social behaviors. Such dynamic attending relies on (1) temporal expectations to guide neurobehavioral state shifts and (2) sufficient state flexibility to allow for these shifts. When the latter goes awry, we may struggle to increment our attention when faced with challenge or decrement arousal despite quiescent conditions. Whereas previous literature on dynamic attention emphasizes the role of temporal expectancy, little research examines the interplay between state flexibility and temporal processing. The present thesis aims to fill this gap in scientific knowledge by addressing both temporal processing and state flexibility in relation to dynamic attention. To do so, we measure sensorimotor synchronization and brain responses to rhythmic auditory stimuli to examine temporal processing, whilst considering the influence of diverse stressors on one’s ability to shift states via self-reported lifestyle factors and emotional wellbeing.

In an experiment using both behavior and electrophysiology, Chapter 1 identifies a potential neural marker of auditory temporal processing. Chapter 2 subsequently provides an integrative review elucidating the influence of psychological and physiological stressors on brain state and state flexibility through allostasis. Chapter 3 then lends support to the arguments outlined in Chapter 2, describing an experiment in which chronic stress burden was found to correlate with resting cortical state. Lastly, a collaborative systematic review in Chapter 4 highlights the therapeutic potential of voluntary regulated breathing as a means of stress-reduction. Cumulatively, this research emphasizes the importance of neurobehavioral state flexibility in dynamic attention and its modulation by varied stressors. This synthesis illuminates the extensive yet underappreciated interconnectedness of brain, body, and behavior and urges a reconceptualization of stress, health, and the brain to lead future research and application.