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Apathy is a common condition that involves diminished initiative, diminished interest and diminished emotional expression or responsiveness. It is highly prevalent in the context of a variety of neuropsychiatric disorders and is related to poor health outcomes. Presence of apathy is associated with cognitive and functional decline in dementia. Despite its negative impact on health, there is no definitive treatment for apathy, a clinical reality that may be due in part to lack of knowledge about assessment, neuropsychological features and neurobiological underpinnings. Here, we review and synthesize evidence from clinical, epidemiological, neuropsychological, peripheral biomarker and neuroimaging research. Apathy is a common feature of depression and cognitive disorders and is associated with impairment in executive function. Neuropsychological and neuroimaging studies point to dysfunction of brain circuitry involving the prefrontal cortex, especially the dorsolateral prefrontal cortex circuit, the dorsomedial prefrontal cortex circuit, and the ventromedial prefrontal cortex circuit. However, inconsistent findings, particularly in neuroimaging may be due to heterogeneity of apathy symptoms (with a need to better elucidate subtypes), neuropsychiatric comorbidities, the severity of cognitive impairment and other factors. These factors need to be accounted for in future studies so that biomarker research can make progress. On the whole, the literature on apathy has identified likely neurocognitive, peripheral biomarker and neuroimaging targets for understanding apathy, but also points to the need to address methodological issues that will better inform future studies. In turn, as we learn more about the underpinning of apathy and its subtypes, subsequent research can focus on new neurally based interventions that will strengthen the clinical management of apathy in the context of its comorbidities.

Objective:Late life depression (LLD) refers to a diagnosis of major depressive disorder in people older than 60, and has been linked to significant cognitive impairment and increased risk of Alzheimer's disease. Although anxiety and depression are highly comorbid, the impact of anxiety on cognition in LLD is far less researched. This is important given that over 20% of middle aged and older adults endorse clinically significant chronic worry. Generalized anxiety disorder in older adults with major depression is associated with poorer cognition and worse treatment outcomes compared with those without anxiety. Therefore, the purpose of the study is to examine the role of anxiety on memory in LLD. We hypothesized that presence of anxiety among older depressed adults would be associated with worse cognitive performance over time.Participants and Methods:Participants included 124 individuals (69.4% female, 90.3% Caucasian) aged 60 or above (M = 71.5, SD = 7.4) who met criteria for major depression, single episode or recurrent. They completed the State Trait Anxiety Inventory, Montgomery Asberg Depression Rating Scale, and a measure of verbal episodic memory (WMS-IV Logical Memory) as part of a larger neuropsychological battery. Data were collected from baseline to three years as part of a larger NIMH-supported longitudinal study. Two-level linear mixed-effect models were fitted to predict memory. State and trait anxiety were used as time-varying predictors. The between-person (level 2) and within-person (level 1) effects of anxiety on memory were assessed controlling for the time trend, age, education, gender, race, and change in depression over time.Results:Plot trajectories across variables revealed a negative correlation such that as anxiety decreased, memory improved over time. Hierarchical linear mixed-effect models revealed that average state anxiety was a marginally significant between-person (level2) predictor for memory [B=-0.041, t(128)=-1.8, p=0.083]. Individuals with greater average state anxiety were more likely to experience memory decline compared to those with lower average state anxiety. In addition, the within-person effect (level 1) of state anxiety was significant [B=-0.096, t(253)=-2.7, p=0.007]. As an individual's anxiety increased over time, their memory declined. Trait anxiety showed a significant within-person effect on memory [B=-0.087, t(254)=-2.0, p=0.048], but a non-significant between-person effect [B=-0.005, t(124)=-0.06, p=0.95].Conclusions:Anxiety appears to increase the risk of memory decline in older adults with major depression, a cohort who are already at risk of cognitive decline. Changes in anxiety increased risk of memory decline even when accounting for changes in depression over time. Although the causal link between anxiety and cognitive impairment remains unclear, it is possible that anxiety and worry may compete for cognitive resources necessary for demanding tasks and situations, detracting from abilities, such as attention and working memory. Older adults with depression may also have difficulty coping adaptively with anxiety, which may negatively affect cognition. Finally, presence of anxiety may represent a form of mild behavioral impairment, a prodrome of cognitive decline leading to dementia. Overall, the present study highlights the negative impact of anxiety on memory performance, indicating that treatment interventions targeting anxiety in older adults are essential to help prevent cognitive decline.

The Antarctic Mesoscale Prediction System (AMPS) is a real-time numerical weather prediction (NWP) system covering Antarctica that has served a remarkable range of groups and activities for a decade. It employs the Weather Research and Forecasting model (WRF) on varying-resolution grids to generate numerical guidance in a variety of tailored products. While its priority mission has been to support the forecasters of the U.S. Antarctic Program, AMPS has evolved to assist a host of scientific and logistical needs for an international user base. The AMPS effort has advanced polar NWP and Antarctic science and looks to continue this into another decade. To inform those with Antarctic scientific and logistical interests and needs, the history, applications, and capabilities of AMPS are discussed.

The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

AbstractObjectivesThe relationships among depression, personality factors, and cognitive decline in the elderly are complex. Depressed elders score higher in neuroticism than nondepressed older individuals. Presence of neuroticism worsens cognitive decline in depressed older adults. Yet little is known about changes in neuroticism among older adults being treated for depression and the impact of these changes on cognitive decline. DesignLongitudinal observational study. SettingAcademic Health Center. ParticipantsWe examined 68 participants in the neurobiology of late-life depression (LLD) study to test the hypothesis that older depressed subjects with more improvement in neuroticism would experience less cognitive decline compared with those with less change in neuroticism. MeasurementsWe measured neuroticism using the NEO-Personality Inventory-Revised at baseline and 1 year. Study psychiatrists measured depression using the Montgomery–Åsberg depression rating scale (MADRS). Global cognitive performance was measured using the Consortium to Establish a Registry for Alzheimer’s disease (CERAD) battery at baseline and annually over 3 years. Regression models of 1-year change in neuroticism and 3-year change in CERAD included sex, age, race, education, and 1-year change in MADRS score as covariates. ResultsWe found that among older adults, 1-year change in neuroticism was inversely associated with 3-year change in CERAD total score. ConclusionsOur findings challenge the notion of longitudinal stability of measures of personality, especially among older depressed individuals. They highlight the importance of repeated personality assessment, especially of neuroticism, in the management of LLD. Future studies in larger samples followed for longer periods are needed to confirm our results and to extend them to examine both cognitive change and development of dementia.

The neurobiological basis of neuroticism in late-life depression (LLD) is understudied. We hypothesized that older depressed subjects scoring high in measures of neuroticism would have smaller hippocampal and prefrontal volumes compared with non-neurotic older depressed subjects and with nondepressed comparison subjects based on previous research. Non-demented subjects were recruited and were either depressed with high neuroticism (n = 65), depressed with low neuroticism (n = 36), or never depressed (n = 27). For imaging outcomes focused on volumetric analyses, we found no significant between-group differences in hippocampal volume. However, we found several frontal lobe regions for which depressed subjects with high neuroticism scores had smaller volumes compared with non-neurotic older depressed subjects and with nondepressed comparison subjects, controlling for age and gender. These regions included the frontal pole, medial orbitofrontal cortex, and left pars orbitalis. In addition, we found that non-neurotic depressed subjects had a higher volume of non-white matter hypointensities on T1-weighted images (possibly related to cerebrovascular disease) than did neurotic depressed subjects. Our finding that depressed subjects low in neuroticism had higher volumes of non-white matter hypointensities is consistent with prior literature on “vascular depression.” In contrast, the finding that those high in neuroticism had smaller frontal volume than depressed subjects low in neuroticism and never-depressed subjects highlight the importance of frontal circuitry in the subgroup of older depressed individuals with comorbid neuroticism. Together, these results implicate different neural mechanisms in older neurotic and non-neurotic depressed groups and suggest that multiple biological pathologies may lead to different clinical expressions of LLD.

Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing.

At the East Antarctic deep ice core drilling site Dome C, daily precipitation measurements were initiated in 2006 and are being continued until today. The amounts and stable isotope ratios of the precipitation samples as well as crystal types are determined. Within the measuring period, the two years 2009 and 2010 showed striking contrasting temperature and precipitation anomalies, particularly in the winter seasons. The reasons for these anomalies are analysed using data from the mesoscale atmospheric model WRF (Weather Research and Forecasting Model) run under the Antarctic Mesoscale Prediction System (AMPS). 2009 was relatively warm and moist due to frequent warm air intrusions connected to amplification of Rossby waves in the circumpolar westerlies, whereas the winter of 2010 was extremely dry and cold. It is shown that while in 2010 a strong zonal atmospheric flow was dominant, in 2009 an enhanced meridional flow prevailed, which increased the meridional transport of heat and moisture onto the East Antarctic plateau and led to a number of high-precipitation/warming events at Dome C. This was also evident in a positive (negative) SAM (Southern Annular Mode) index and a negative (positive) ZW3 (zonal wave number three) index during the winter months of 2010 (2009). Changes in the frequency or seasonality of such event-type precipitation can lead to a strong bias in the air temperature derived from stable water isotopes in ice cores.

This study evaluates the sensitivity of winter precipitation to numerous aspects of a bulk, mixed-phase microphysical parameterization found in three widely used mesoscale models [the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), the Rapid Update Cycle (RUC), and the Weather Research and Forecast (WRF) model]. Sensitivities of the microphysics to primary ice initiation, autoconversion, cloud condensation nuclei (CCN) spectra, treatment of graupel, and parameters controlling the snow and rain size distributions are tested. The sensitivity tests are performed by simulating various cloud depths (with different cloud-top temperatures) using flow over an idealized two-dimensional mountain. The height and width of the two-dimensional barrier are designed to reproduce an updraft pattern with extent and magnitude consistent with documented freezing-drizzle cases. By increasing the moisture profile to saturation at low temperatures, a deep, precipitating snow cloud is also simulated. Upon testing the primary sensitivities of the microphysics scheme in two dimensions as reported in the present study, the MM5 with the modified scheme will be tested in multiple case studies and the results will be compared to observations in a forthcoming companion paper, Part II. The key results of this study are 1) the choice of ice initiation schemes is relatively unimportant for deep precipitating snow clouds but more important for shallow warm clouds having cloud-top temperature greater than -13°C, 2) the assumed snow size distribution and associated snow diffusional growth along with the assumed graupel size distribution and method of transforming rimed snow into graupel have major impacts on the mass of cloud water and formation of freezing drizzle, and 3) a proper simulation of drizzle using a single-moment scheme and exponential size distribution requires an increase in the rain intercept parameter, thereby reducing rain terminal velocities to values more characteristic of drizzle.

Herein, a summary of the authors’ experiences with 36-h real-time explicit (4 km) convective forecasts with the Advanced Research Weather Research and Forecasting Model (WRF-ARW) during the 2003–05 spring and summer seasons is presented. These forecasts are compared to guidance obtained from the 12-km operational Eta Model, which employed convective parameterization (e.g., Betts–Miller–Janjić). The results suggest significant value added for the high-resolution forecasts in representing the convective system mode (e.g., for squall lines, bow echoes, mesoscale convective vortices) as well as in representing the diurnal convective cycle. However, no improvement could be documented in the overall guidance as to the timing and location of significant convective outbreaks. Perhaps the most notable result is the overall strong correspondence between the Eta and WRF-ARW guidance, for both good and bad forecasts, suggesting the overriding influence of larger scales of forcing on convective development in the 24–36-h time frame. Sensitivities to PBL, land surface, microphysics, and resolution failed to account for the more significant forecast errors (e.g., completely missing or erroneous convective systems), suggesting that further research is needed to document the source of such errors at these time scales. A systematic bias is also noted with the Yonsei University (YSU) PBL scheme, emphasizing the continuing need to refine and improve physics packages for application to these forecast problems.