Effects of hydration status on cognitive performance and mood

Although it is well known that water is essential for human homeostasis and survival, only recently have we begun to understand its role in the maintenance of brain function. Herein, we integrate emerging evidence regarding the effects of both dehydration and additional acute water consumption on cognition and mood. Current findings in the field suggest that particular cognitive abilities and mood states are positively influenced by water consumption. The impact of dehydration on cognition and mood is particularly relevant for those with poor fluid regulation, such as the elderly and children. We critically review the most recent advances in both behavioural and neuroimaging studies of dehydration and link the findings to the known effects of water on hormonal, neurochemical and vascular functions in an attempt to suggest plausible mechanisms of action. We identify some methodological weaknesses, including inconsistent measurements in cognitive assessment and the lack of objective hydration state measurements as well as gaps in knowledge concerning mediating factors that may influence water intervention effects. Finally, we discuss how future research can best elucidate the role of water in the optimal maintenance of brain health and function.

Mood and recall of autobiographical memory: the effect of focus of self-knowledge

Mood state facilitates recall of affectively congruent memories (i.e., mood-congruent recall). Mood state may also promote motivation to alleviate a negative affective state, leading to retrieval of affectively incongruent memories (i.e., mood incongruent recall). The present study demonstrates that the focus of self-knowledge influences the occurrence of both mood-congruent recall and mood-incongruent recall. Three experiments found that mood-congruent recall occurred when participants recalled their experiences from a self-aspect that was related to the elicitor of moods, whereas mood-incongruent recall occurred when they recalled their experiences from a self-aspect that was unrelated to the elicitor of moods. These results suggest that the nature of the self-aspect from which persons recall their experiences determines whether mood-congruent or mood-incongruent recall occurs.

Effects of structure of self-knowledge on mood-incongruent effect

Some people recall more positive memories in negative moods than in neutral moods, which is called mood-incongruent effect. Although previous research suggested that structure of self-knowledge influences mood-incongruent effect (Sakaki, 2004), it is possible that motivation for mood-regulation mediates relation between structure of self-knowledge and mood-incongruent effect. The present study aimed at exploring this possibility by using self-complexity. In Study 1, participants with higher self-complexity, whose self-knowledge has more self-aspects with a higher level of differentiation, recalled more positive memories in negative moods (compared to neutral moods) than participants with lower self-complexity, whose self-knowledge has a fewer self-aspects with larger overlap. Study 1 also revealed that these effects hold even when the motivation for mood-regulation was partialed out. Study 2 examined mood-incongruent effect under positive moods, in which participants are unlikely motivated to alter their moods, and it was found that participants with higher self-complexity recalled more negative memories in positive moods (compared to neutral moods) than participants with lower self-complexity.

Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed.

Sources, sinks, and transatlantic transport of North African dust aerosol: a multimodel analysis and comparison with remote sensing data

This study evaluates model-simulated dust aerosols over North Africa and the North Atlantic from five global models that participated in the Aerosol Comparison between Observations and Models phase II model experiments. The model results are compared with satellite aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-viewing Wide Field-of-view Sensor, dust optical depth (DOD) derived from MODIS and MISR, AOD and coarse-mode AOD (as a proxy of DOD) from ground-based Aerosol Robotic Network Sun photometer measurements, and dust vertical distributions/centroid height from Cloud Aerosol Lidar with Orthogonal Polarization and Atmospheric Infrared Sounder satellite AOD retrievals. We examine the following quantities of AOD and DOD: (1) the magnitudes over land and over ocean in our study domain, (2) the longitudinal gradient from the dust source region over North Africa to the western North Atlantic, (3) seasonal variations at different locations, and (4) the dust vertical profile shape and the AOD centroid height (altitude above or below which half of the AOD is located). The different satellite data show consistent features in most of these aspects; however, the models display large diversity in all of them, with significant differences among the models and between models and observations. By examining dust emission, removal, and mass extinction efficiency in the five models, we also find remarkable differences among the models that all contribute to the discrepancies of model-simulated dust amount and distribution. This study highlights the challenges in simulating the dust physical and optical processes, even in the best known dust environment, and stresses the need for observable quantities to constrain the model processes.

Observing wind, aerosol particles, clouds and precipitation: Finland's new ground-based remote-sensing network

The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

Situation assessment through multi-modal sensing of dynamic environments to support cognitive robot control

Awareness of emerging situations in a dynamic operational environment of a robotic assistive device is an essential capability of such a cognitive system, based on its effective and efficient assessment of the prevailing situation. This allows the system to interact with the environment in a sensible (semi)autonomous / pro-active manner without the need for frequent interventions from a supervisor. In this paper, we report a novel generic Situation Assessment Architecture for robotic systems directly assisting humans as developed in the CORBYS project. This paper presents the overall architecture for situation assessment and its application in proof-of-concept Demonstrators as developed and validated within the CORBYS project. These include a robotic human follower and a mobile gait rehabilitation robotic system. We present an overview of the structure and functionality of the Situation Assessment Architecture for robotic systems with results and observations as collected from initial validation on the two CORBYS Demonstrators.

Interventions in social networks: impact on mood and network dynamics

Results from two studies on longitudinal friendship networks are presented, exploring the impact of a gratitude intervention on positive and negative affect dynamics in a social network. The gratitude intervention had been previously shown to increase positive affect and decrease negative affect in an individual but dynamic group effects have not been considered. In the first study the intervention was administered to the whole network. In the second study two social networks are considered and in each only a subset of individuals, initially low/high in negative affect respectively received the intervention as `agents of change'. Data was analyzed using stochastic actor based modelling techniques to identify resulting network changes, impact on positive and negative affect and potential contagion of mood within the group. The first study found a group level increase in positive and a decrease in negative affect. Homophily was detected with regard to positive and negative affect but no evidence of contagion was found. The network itself became more volatile along with a fall in rate of change of negative affect. Centrality measures indicated that the best broadcasters were the individuals with the least negative affect levels at the beginning of the study. In the second study, the positive and negative affect levels for the whole group depended on the initial levels of negative affect of the intervention recipients. There was evidence of positive affect contagion in the group where intervention recipients had low initial level of negative affect and contagion in negative affect for the group where recipients had initially high level of negative affect.

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Improving mood with psychoanalytic and cognitive therapies (IMPACT): a pragmatic effectiveness superiority trial to investigate whether specialised psychological treatment reduces the risk for relapse in adolescents with moderate to severe unipolar depression: study protocol for a randomised controlled trial

Background Up to 70% of adolescents with moderate to severe unipolar major depression respond to psychological treatment plus Fluoxetine (20-50 mg) with symptom reduction and improved social function reported by 24 weeks after beginning treatment. Around 20% of non responders appear treatment resistant and 30% of responders relapse within 2 years. The specific efficacy of different psychological therapies and the moderators and mediators that influence risk for relapse are unclear. The cost-effectiveness and safety of psychological treatments remain poorly evaluated. Methods/Design Improving Mood with Psychoanalytic and Cognitive Therapies, the IMPACT Study, will determine whether Cognitive Behavioural Therapy or Short Term Psychoanalytic Therapy is superior in reducing relapse compared with Specialist Clinical Care. The study is a multicentre pragmatic effectiveness superiority randomised clinical trial: Cognitive Behavioural Therapy consists of 20 sessions over 30 weeks, Short Term Psychoanalytic Psychotherapy 30 sessions over 30 weeks and Specialist Clinical Care 12 sessions over 20 weeks. We will recruit 540 patients with 180 randomised to each arm. Patients will be reassessed at 6, 12, 36, 52 and 86 weeks. Methodological aspects of the study are systematic recruitment, explicit inclusion criteria, reliability checks of assessments with control for rater shift, research assessors independent of treatment team and blind to randomization, analysis by intention to treat, data management using remote data entry, measures of quality assurance, advanced statistical analysis, manualised treatment protocols, checks of adherence and competence of therapists and assessment of cost-effectiveness. We will also determine whether time to recovery and/or relapse are moderated by variations in brain structure and function and selected genetic and hormone biomarkers taken at entry. Discussion The objective of this clinical trial is to determine whether there are specific effects of specialist psychotherapy that reduce relapse in unipolar major depression in adolescents and thereby costs of treatment to society. We also anticipate being able to utilise psychotherapy experience, neuroimaging, genetic and hormone measures to reveal what techniques and their protocols may work best for which patients.

Enabling actor model for crowd sensing and IoT

The cloud is playing a very important role in wireless sensor network, crowd sensing and IoT data collection and processing. However, current cloud solutions lack of some features that hamper the innovation a number of other new services. We propose a cloud solution that provides these missing features as multi-cloud and device multi-tenancy relying in a whole different fully distributed paradigm, the actor model.

Global climatology of surface water temperatures of large lakes by remote sensing

Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake-mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 (inline image = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 (inline image = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 (inline image) of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake-mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake-mean open water phase and the spring and fall 0 °C air temperature transition days, (inline image = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter-lake variation in the start and end of the open water phase.

Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360 deg) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.