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Memory loss is a part of normative aging. Many older adults commonly forget to take prescribed medication, which can have an adverse effect on health. Therefore, it is important to provide older adults with a medication reminder service. There are several mobile apps and devices capable of reminding medication, but their user interfaces and operations are usually unfriendly to seniors. Considering the above demands and shortcomings, we proposed a conversation-based medication management system (CMMS). The CMMS uses a companion robot and the cloud to create medication reminders and check medication adherence. We implemented the CMMS in our ASCC (Advanced Sensing, Computation and Control) companion robot. To evaluate the CMMS, we tested our system through the mobile app end with 23 human subjects and the robot end with 15 human subjects. The feedback from the post-test survey shows that the convenience, usefulness and total rating of our system is 8.217, 8.696 and 8.478 out of 10 from the mobile app end and 9.000, 8.933 and 8.533 out of 10 from the robot end, respectively. The System Usability Scale (SUS) score of our system is 81.333 from the robot end users, which means the participants had a high satisfaction level when using the system.

International audience; Tropospheric anthropogenic aerosols contribute the second-largest forcing to climate change, but with high uncertainty owing to their spatio-temporal variability and complicated optical properties. In this Review, we synthesize understanding of aerosol observations and their radiative and climate effects. Aerosols offset about one-third of the warming effect by anthropogenic greenhouse gases. Yet, in regions and seasons where the absorbing aerosol fraction is high — such as South America and East and South Asia — substantial atmospheric warming can occur. The internal mixing and the vertical distribution of aerosols, which alters both the direct effect and aerosol-cloud interactions, might further enhance this warming. Despite extensive research in aerosol-cloud interactions, there is still at least a 50% spread in total aerosol forcing estimates. This ongoing uncertainty is linked, in part, to the poor measurement of anthropogenic and natural aerosol absorption, as well as the little-understood effects of aerosols on clouds. Next-generation, space-borne, multi-angle polarization and active remote sensing, combined with in situ observations, offer opportunities to better constrain aerosol scattering, absorption and size distribution, thus, improving models to refine estimates of aerosol forcing and climate effects.

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)-based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by ~27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

Surface remote sensing of aerosol properties provides “ground truth” for satellite and model validation, and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually have different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory. The ensemble is constructed using aerosol optical depth (AOD) products from five satellite sensors. The optimal locations are solved sequentially by minimizing the total analysis error variance, which means that observations at these locations will reduce the background error variance to the largest extent. The location determined by the algorithm is further verified to have larger spatial representativeness than some other arbitrary location. In addition to the existing active AERONET sites, the 40 selected optimal locations are mostly concentrated on regions with both high AOD inhomogeneity and its spatial representativeness, namely the Sahel, South Africa, East Asia and North Pacific Islands. These places should be the focuses of establishing future AERONET sites in order to further reduce the uncertainty in the monthly mean AOD. Observations at these locations contribute to approximately 50% of the total background uncertainty reduction.

The current study examined relationships between laterality in cerebral oxygenation (L-COX), sleep-disordered breathing (SDB), and daytime function in 16 adults with mild cognitive impairment (MCI). All participants underwent two nights of diagnostic polysomnography. Using dual-cerebral oximetry, L-COX was defined by differences ≥4% in right- versus left-sided percent cerebral oxyhemoglobin saturation. Eight patients had SDB. L-COX was found in five patients, but only on nights with SDB. Greater L-COX was associated more severe SDB: higher frequency of apneas + hypopneas per hour ( r = 0.66, p < 0.01), desaturations per hour ( r = 0.73, p < 0.01), and percent time with oxygen saturation r = 0.65, p < 0.01). Greater laterality, but not severity of SDB, was associated with poorer functional ability (Lawton Instrumental Activities of Daily Living Scale: r = −0.83, p = 0.02), lower cognitive function (Mini-Mental State Examination: r = −0.76, p = 0.03), and greater daytime sleepiness (Epworth Sleepiness Scale: r = 0.85, p < 0.001). L-COX associated with SDB suggests disruptions in cerebral autoregulation and need for aggressive treatment of SDB in individuals with MCI. [Res Gerontol Nurs. 2018; 11(6):282–292.]

Surface remote sensing of aerosol properties provides "ground truth" for satellite and model validation, and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually have different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory. The ensemble is constructed using aerosol optical depth (AOD) products from five satellite sensors. The optimal locations are solved sequentially by minimizing the total analysis error variance, which means that observations at these locations will reduce the background error variance to the largest extent. The location determined by the algorithm is further verified to have larger spatial representativeness than some other arbitrary location. In addition to the existing active AERONET sites, the 40 selected optimal locations are mostly concentrated on regions with both high AOD inhomogeneity and its spatial representativeness, namely the Sahel, South Africa, East Asia and North Pacific Islands. These places should be the focuses of establishing future AERONET sites in order to further reduce the uncertainty in the monthly mean AOD. Observations at these locations contribute to approximately 50% of the total background uncertainty reduction.

Background: Cognitive complaints (“chemo brain”) are reported frequently after breast cancer treatment but little is known about its incidence and causes. Before we can intervene we need to be able to diagnose it properly. Mini Mental state examination (MMSE) can detect advanced Alzheimer's but won't help in detecting mild cognitive disturbances. We used Montreal Cognitive Assessment for Telephone (MoCA-T) and full scale Montreal Cognitive Assessment for Telephone (MoCA) to diagnose mild cognitive impairments in patients having normal MMSE. Methods: 20 breast cancer survivors aged 50 years or older completed MoCA-T and MoCA, a year after they completed their treatment for breast cancer, describing the impact of their treatment regimen on their short term memory and ability to think and concentrate. On Day 5, they underwent a standardized laboratory protocol that assessed both behavioral and electroencephalographic (EEG) indicators of memory consolidation. Results: Patient characteristics: 20 postmenopausal breast cancer survivors aged 50 or above participated in the study a year after they completed their therapy. We are reporting preliminary results of 10 patients. Patients had stage I-III disease (stage I – One patient, Stage II – five patients, and stage III- four patients). All of them received various chemo regimens e.g. Cytoxan, methotrexate, 5-FU (CMF), Taxol, Trastuzumab (TH), Adriamycin (AC), taxotere, carboplatin (TCH). 8/10 patients received adjuvant radiation. 1/10 patient underwent reconstruction later. 7/10 patients received hormonal therapy with aromatase inhibitors. Results of MoCA –T and MoCA: MoCA-T, tests attention and concentration, executive functions, memory, language, conceptual thinking, calculations, and orientation. Possible scores range from 0-22 and scores 26 is normal but 5/10 patients had abnormal MoCA and 3/10 patients had impaired MoCA-T signifying that almost 50%patients develop mild cognitive impairment after breast cancer treatment. During memory consolidation, EEG contained less theta and frequent bursts of alpha waves which is commonly seen in patients with neuropathic pain and insomnia. MoCA-T and MoCA results for the patientsPatient no.MoCA-TMoCAPtient 00817*23*Patient 0102126Patient 0111923*Patient 01216*27Patient 0132027Patient 01517*21*Patient 0161824*Patient 0172025*Patient 0191926Patient 0202129*presents affected patient Conclusions: The study signifies that mild cognitive impairment from breast cancer treatment (or “chemo brain”) was frequently reported and MoCA-T and MoCA tests were able to show even mild cognitive impairment in these patients. We are compiling our full data for EEG but the early results show that the patients had less theta but had frequent bursts of alpha waves, a pattern seen commonly in patients with insomnia and neuropathic pain. They retain fewer items and take more time responding to items as compared to normal people. We need more studies to diagnose and treat mild cognitive impairment (“chemo brain”) in breast survivors as most of these patients are still working and can be a valuable part of the community. Citation Format: Razaq WA, Tanaka T, Carlson B, Wenger M, Friedman J, Benbrook D, Craft M. Diagnosing cognitive impairment (“chemo brain”) in breast cancer survivors [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-20-09.

Data for this study was acquired from three separate stakeholder focus group sessions involving nurse case managers (n = 5), social agency caseworkers (n = 5), and rural outreach providers (n = 5). Participants across all groups were asked to address the question: “When it comes to your work, what would you want a smart robot assistant to do for you?” Data from the three sessions were combined, transcribed verbatim, coded, and analyzed for thematic content. Three shared themes emerged, including health monitoring, behavioral intervention, and healthcare literacy. Relative to health monitoring, participants desired a robot that possessed functions in the form of “taking vital signs,” and “tracking water and food intake.” There was also a thematic agreement regarding behavioral intervention capabilities. Most notably, advisory stakeholders acknowledged a need for a smart robotic assistant to provide geriatric care recipients with “an alert or reminder to take medication.” This was viewed as an essential intervention for improving medication adherence. Healthcare literacy emerged as a final theme among advisory groups. In particular, participants noted that a smart robot should assist with bi-directional communication and translation of health care information and instructions as a way to “minimize impediments of care due to language barriers.” Findings will be further used to highlight how future integration of robotic health assistants represents a viable solution in helping geriatric healthcare workers work effectively alongside machines to meet the diverse care needs of older adults in both urban and rural settings.

International audience; Satellite- and ground-based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474-member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground-based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root-mean-square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root-mean-square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space-time variability.

Objectives To examine the association between performance-based neurocognitive and patient-reported cognitive function tests and identify characteristics that may explain observed discrepancies as a means to advance intervention development. Sample & setting 40 adults diagnosed with a primary brain tumor (PBT) (high-grade, n = 35) were recruited from two academic neuro-oncology clinics in North Carolina. Methods & variables Eligibility included a Mini-Mental State Examination score of 24 or greater, having completed cancer treatment, and having tumor stability. Participants completed performance-based neurocognitive and patient-reported cognitive function, demographic, and symptom assessment tests at one time point. Results Neurocognitive impairments included executive control, memory, and attention. Age, time since diagnosis, and tumor- or treatment-specific variables were not associated with neurocognitive or patient-reported cognitive function. Those reporting worse cognitive impairment tended also to report greater severity of PBT-specific and depressive symptoms. Implications for nursing Patient-reported cognitive concerns warrant additional assessment for potential interventions to maintain function.

The epigraph in poet Jean Valentine's book Break the Glass: "A pencil/for a wing bone" (by Lorine Neidecker) leads us to consider the way writing allows for transcendence. Similarly, in her work, Meta Kušar brushes out stars with a comb and then finds "an accomplice/combed/in this hollowed-out place". From the two countries US and Slovenia of such vastly different sizes, these poets, both influenced by such luminaries as Emily Dickinson and Marina Tsvetaeva, have carved out intimate spiritually enriching spaces where consciousness meets the sublime. Focusing on Kušar's view of Heraclitus as a teacher who "understood invisible strings are stronger than visible ones," I will explore the thematic, literary and stylistic connections between these two literary stars, as well as some of their differences in how they cultivate a poetics of the invisible that illuminates the mysterious underworld of the human soul as it negotiates the political, philosophical and ethical realms of contemporary existence. Epigraf "A pencil/for a wing bone" (Svinčnik / za lopatico) v pesniški zbirki Break the Glass pesnice Jean Valentine (gre za verz Lorine Neidecker) spodbuja k razmisleku o tem, kako pisanje omogoča transcendenco. Podobno v svojem delu Meta Kušar razčeše zvezde z glavnikom in nato najde "zaveznika / počesanega / v tej votli deželi". Pesnici, ki prihajata iz dveh po velikosti tako različnih držav, kot sta ZDA in Slovenija, in na kateri so vplivale zvezdnice, kot sta Emily Dickinson in Marina Cvetajeva, sta ustvarili intimne in duhovno oplajajoče prostore, v katerih se zavest sreča s sublimnostjo. Avtorica se osredotoča na pogled Kušarjeve, da je Heraklit učitelj, ki "je razumel, da so nevidne vezi močnejše od vidnih", ter proučuje tematske, literarne in slogovne povezave med tema dvema literarnima zvezdama, pa tudi nekatere razlike pri ustvarjanju poetike nevidnega, ki osvetljuje skrivnostno podzemlje človeške duše med obvladovanjem politične, filozofske in etične sfere sodobnega obstoja.

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

Background: In mild cognitive impairment (MCI), sleep disordered breathing (SDB) contributes to the severity of symptoms by lowering arterial oxygenation (SaO2); potentially, leading to disruptions in in cerebral blood flow and oxygenation. In this pilot study, we examined associations between SDB severity, laterality in cerebral oxygenation, and MCI symptoms. Methods: Sixteen subjects underwent 2-nights of polysomnography. An apnea-hypopnea index (AHI) >5/hour, desaturations/hour asleep (DI), and percentage time with SaO2

This study pilot-tested a 12-week, comprehensive, resident-centered fall prevention program aimed to lower falls in nursing home residents in Oklahoma. Staff from 52 nursing homes received a training on evidence-based fall prevention strategies and fall-risk assessment. Content was present using motivational scenarios that encouraged situational problems solving. Rate of falls, including falls with major injury) were collected for 3 months before (roll-in), during (treatment), and following (sustainability). Nursing homes completing the project (n=29) showed significant decline in falls, including falls with major injury. Staff acceptance and project implementation varied across nursing homes and was related to organizational policies that did not adapt care based on residents needs on any given day. Adaptability, not just knowledge, is important for ensuring the safety of residents. Future work will focus on the role of leadership in promoting more open systems for delivering care.

Chemotherapy destroys cells indiscriminantly and releases proinflammatory factors into the bloodstream that can adhere to endothelial cells (ECs); resulting in phenotypical changes consistent with “accelerated vascular aging”. This pilot study examined associations between markers of EC integrity, vascular aging, and cognition in 15 female breast cancer survivors 12–18 months after chemotherapy (median age: 57 years) and 2 non-cancer controls (58/59 years). EC integrity was evaluated using frequency-dependent electrical impedance (Z4000Hz) and levels of apoptosis (caspase-3/7), inflammation (NFkB activation), and oxidative stress (NRF2 activation) in cultured human endothelial cells (EC) exposed to the subject’s serum. Vascular aging was characterized by low serum insulin growth factor ([IGF1a]

Because of the importance of clouds in modulating Earth’s energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast for CA and in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supports the conclusion that they are describing cloud variations with these climate modes.

Spectral dependence of aerosol single-scattering albedo (SSA) has been used to infer aerosol composition. In particular, aerosol mixtures dominated by dust absorption will have monotonically increasing SSA with wavelength while that dominated by black carbon absorption has monotonically decreasing SSA spectra. However, by analyzing SSA measured at four wavelengths, 440, 675, 870, and 1020 nm from the Aerosol Robotic Network data set, we find that the SSA spectra over East Asia are frequently peaked at 675 nm. In these cases, we suggest that SSA spectral curvature, defined as the negative of the second derivative of SSA as a function of wavelength, can provide additional information on the composition of these aerosol mixtures. Aerosol SSA spectral curvatures for East Asia during fall and winter are considerably larger than those found in places primarily dominated by biomass burning or dust aerosols. SSA curvature is found to increase as the SSA magnitude decreases. The curvature increases with coarse mode fraction (CMF) to a CMF value of about 0.4, then slightly decreases or remains constant at larger CMF. Mie calculations further verify that the strongest SSA curvature occurs at approx. 40% dust fraction, with 10% scattering aerosol fraction. The nonmonotonic SSA spectral dependence is likely associated with enhanced absorption in the shortwave by dust, absorption by black carbon at longer wavelengths, and also the flattened absorption optical depth spectral dependence due to the increased particle size.

Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique e Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500 m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations.

Advances in breast cancer treatment have markedly increased survivorship over the past three decades, with over 3.1 million survivors expected to live into their 70s and 80s. Without symptom relief interventions, nearly 35% of these survivors will have life-altering and distressing cognitive symptoms. This pilot study explored associations between serum markers of vascular aging, laterality in cerebral oxygenation, and severity of cognitive impairment in women, 12-18 months after chemotherapy for stage 2/3 invasive ductal breast cancer. Fifteen women (52-84 years) underwent a brief cognitive assessment (Montreal Cognitive Assessment [MOCA]) and blood draws to assess markers of vascular aging (interleukin-6 [IL-6], tumor necrosis factor alpha [TNF-α], C-reactive protein [CRP], and insulin growth factor-1 [IGF-1]). All underwent a computer-based test protocol that is known to increase blood flow within the frontal lobes. Percent cerebral oxyhemoglobin saturation (rcSO2) was recorded during and after testing. Laterality in rcSO2 was defined by ≥ 3% difference between left and right rcSO2 (|rcSO2 meanRIGHT - meanLEFT|). Eight participants had MOCA scores between 21 and 25 points, suggestive of mild cognitive impairment. Neither CRP (r = -.24) nor IL-6 (r = .34) nor TNF-α (r = .002) were associated with MOCA scores. Higher IL-6 was associated with greater laterality (r = .41). MOCA scores were significantly lower in subjects with laterality in rcSO2 than in those without laterality (F(1,14) = 13.5, p = 003). Lower IGF-1 was significantly associated with greater laterality (r = - .66, p = .007) and lower cognitive function (r = .58). These findings suggest that persistent cognitive impairment is associated with phenotypical changes consistent with accelerated vascular aging.

Amnestic Mild Cognitive Impairment (aMCI), a condition in which the memory functions of cognition are significantly impaired, is an established risk factor for Alzheimer's disease. Electroencephalography (EEG) is a tool capable of measuring the dynamics of the brain's neural networks, and is thus an important means in analysis and understanding of aMCI. In this proof-of-concept study, we compared the brain activation patterns of ten aMCI subjects with those of four healthy subjects during sleep by employing a 64-channel EEG data collection system. The power spectrum was analyzed to identify sleep stages, while spectral topography and source imaging techniques were employed to study the fluctuating patterns of the brain. Results of this study show an increase in activation power across all sleep stages in the delta and theta frequency bands alongside a decrease in alpha band activity for aMCI subjects. Source imaging analysis of the resting EEG identified default mode network, which becomes decoupled as sleep stages deepen. In the proof-of-concept study, our exploratory analysis demonstrated the feasibility of imaging dynamic network organization using EEG in aMCI.

Federal funding from the Child Care and Development Fund (CCDF) enables states to offset the cost of child care for eligible low-income families to support parental employment and promote healthy child development. The federal government sets a benchmark for provider payment rates in order to ensure that low-income families have access to child care settings comparable to those available to non-CCDF families. In order to meet this “equal access” objective, states need reliable information and valid methods to estimate benchmark prices in local child care markets. Although all 50 states face the challenge of meeting the federal benchmark, limited research on best practices is available to guide them. This case study of Colorado's child care market prices highlights key challenges to states in using survey data to set provider payment rates, offering policy and practice implications for sampling, collecting price data, and estimating valid child care benchmark prices.

The Aerosol Robotic Network (AERONET) has been providing high-quality retrievals of aerosol optical properties from the surface at worldwide locations for more than a decade. Many sites have continuous and consistent records for more than 10 years, which enables the investigation of long-term trends in aerosol properties at these locations. In this study, we present the results of a trend analysis at selected stations with long data records. In addition to commonly studied parameters such as aerosol optical depth (AOD) and Ångström exponent (AE), we also focus on inversion products including absorption aerosol optical depth (ABS), single-scattering albedo (SSA) and the absorption Ångström exponent (AAE). Level 2.0 quality assured data are the primary source. However, due to the scarcity of level 2.0 inversion products resulting from the strict AOD quality control threshold, we have also analyzed level 1.5 data, with some quality control screening to provide a reference for global results. Two statistical methods are used to detect and estimate the trend: the Mann–Kendall test associated with Sen's slope and linear least-squares fitting. The results of these statistical tests agree well in terms of the significance of the trend for the majority of the cases. The results indicate that Europe and North America experienced a uniform decrease in AOD, while significant (>90%) increases in these two parameters are found for North India and the Arabian Peninsula. The AE trends turn out to be different for North America and Europe, with increases for the former and decreases for the latter, suggesting opposite changes in fine/coarse-mode fraction. For level 2.0 inversion parameters, Beijing and Kanpur both experienced an increase in SSA. Beijing also shows a reduction in ABS, while the SSA increase for Kanpur is mainly due the increase in scattering aerosols. Increased absorption and reduced SSA are found at Solar_Village. At level 1.5, most European and North American sites also show positive SSA and negative ABS trends, although the data are more uncertain. The AAE trends are less spatially coherent due to large uncertainties, except for a robust increase at three sites in West Africa, which suggests a possible reduction in black carbon. Overall, the trends do not exhibit obvious seasonality for the majority of parameters and stations.

Nocturia is a bothersome symptom that increases with age, resulting in sleep disruption, an increased risk of falls, and a greater likelihood of rating one's health as poor. It is often a symptom of conditions that cause low volume voiding, overproduction of urine across the day or only at night and a symptom of a sleep disorder. Nocturia affects quality of life and has an impact on aging in place, thus assessment and treatment are essential. Behavioral treatments should be explored first, keeping in mind what the affected older adult defines as the desired outcomes of treatment.

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Nino over Indonesia and central South America is also captured with the EOF technique.

The nominal Global Aerosol Climatology Project (GACP) record of aerosol optical thickness (AOT) and Angstrom exponent (AE) over the oceans is extended by 6 months to cover the period from August 1981 through December 2005. The most recent 4-year segment reveals no significant short-term tendencies in globally and hemispherically averaged AOTs and AEs. This finding is consistent with contemporaneous MODIS and MISR results and the accumulating evidence of a gradual transition from global brightening to global dimming. We also analyze the retrieval implications of allowing the imaginary part of the aerosols refractive index Im( m ) to change over the duration of the GACP record. Our sensitivity study shows that increasing Im( m ) from 0.003 during the 4-year pre-Pinatubo period up to 0.007 during the most recent 4-year segment of GACP data eliminates the previously identified long-term decreasing AOT trend. Should this long-term trend in Im( m ) be real then it would cause the global absorption AOT over the oceans to more than double and the global single-scattering albedo to decrease from ∼0.95 to ∼0.88. Such changes could make tropospheric aerosols significant contributors to the recent surface temperature increase.

The dominant interannual El Niño–Southern Oscillation (ENSO) phenomenon and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENSO signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, an ENSO-removal method is developed through which the ENSO signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations are isolated at mid- and low latitudes in the climate parameter fields from observed and reanalysis datasets. This paper addresses the long-term global warming trend (GW); a companion paper concentrates on Pacific pan-decadal variability (PDV). The warming that occurs in the Pacific basin (approximately 0.4 K in the twentieth century) is much weaker than in surrounding regions and the other two ocean basins (approximately 0.8 K). The modest warming in the Pacific basin is likely due to its dynamic nature on the interannual and decadal time scales and/or the leakage of upper ocean water through the Indonesian Throughflow. Based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), a comprehensive atmospheric structure associated with the GW trend is given. Significant discrepancies exist between the two datasets, especially in the tightly coupled dynamics and water vapor fields. The dynamics fields based on NCEP–NCAR, which show a change in the Walker Circulation, are consistent with the GW change in the surface temperature field. However, intensification in the Hadley Circulation is associated with GW trend in ERA-40 instead.

The spatiotemporal structure of Pacific pan-decadal variability (PDV) is isolated in global long-term surface temperature (ST) datasets and reanalysis atmospheric parameter fields from which El Niño–Southern Oscillation (ENSO) effects have been removed. Empirical orthogonal function (EOF) and combined EOF analysis of the resulting time series identify PDV as one of two primary modes of long-term variability, the other being a global warming (GW) trend, which is addressed in a companion paper (Part I). In this study, it is shown that one of several PDV interdecadal regime shifts occurred during the 1990s. This significant change in the Pacific basin is comparable but antiphase to the well-known 1976 climate regime shift and is consistent with the observed changes in biosystems and ocean circulation. A comprehensive picture of PDV as manifested in the troposphere and at the surface is described. In general, the PDV spatial patterns in different parameter fields share some similarities with the patterns associated with ENSO, but important differences exist. First, the PDV circulation pattern is shifted westward by about 20° and is less zonally extended than that for ENSO. The westward shift of the PDV wave train produces a different North American teleconnection pattern that is more west–east oriented. The lack of a strong PDV surface temperature (ST) signal in the west equatorial Pacific and the relatively strong ST signal in the subtropical regions are consistent with an atmospheric overturning circulation response that differs from the one associated with ENSO. The analysis also suggests that PDV is a combination of decadal and/or interdecadal oscillations interacting through teleconnections.

OBJECTIVES: To explore differences in cerebral oxygen reserves during sleep in old and young adults. DESIGN: Descriptive cross-sectional study. SETTING: General clinical research center. PARTICIPANTS: Nine old (aged 65–84) and 10 young (aged 21–39) adults. MEASUREMENTS: Subjects were monitored during the first nightly sleep cycle using standard polysomnography, including measures of arterial oxyhemoglobin saturation (SaO2). Changes in regional cerebral oxyhemoglobin saturation (rcSO2) were used to estimate cerebral oxygen reserves. General linear models were used to test group differences in the change in SaO2 and rcSO2 during sleep.

A number of passive satellite instruments have been used to develop global climatologies of terrestrial tropospheric aerosols by analyzing the properties of sunlight reflected by the atmosphere–surface system. The outcome of these efforts are several climatologies which all purport to represent the same aerosol characteristics such as optical thickness and size. However, the quantitative differences between these climatologies have been found to far exceed the corresponding individual uncertainty claims. The magnitude of these differences is alarming and necessitates a detailed critical assessment and integrated analysis that would go far beyond simple intercomparisons of various satellite products and comparisons of satellite aerosol optical thickness results with ground-based sun-photometer data. This paper outlines the framework for a global long-term satellite climatology of aerosol properties based on a consistent combination of previous, current, and near-future satellite retrievals. We also discuss potential future strategies for deriving a much improved aerosol climatology from Earth-orbiting satellites.