Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network.

Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach:•An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network.•A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity.•Predictions for each time point are based on data from a wider, 600-minute, time window.•We evaluate model performance on a dataset with 10 min granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods.

The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes.

The IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

eCAT: Online electronic lab notebook for scientific research.

BACKGROUND: eCAT is an electronic lab notebook (ELN) developed by Axiope Limited. It is the first online ELN, the first ELN to be developed in close collaboration with lab scientists, and the first ELN to be targeted at researchers in non-commercial institutions. eCAT was developed in response to feedback from users of a predecessor product. By late 2006 the basic concept had been clarified: a highly scalable web-based collaboration tool that possessed the basic capabilities of commercial ELNs, i.e. a permissions system, controlled sharing, an audit trail, electronic signature and search, and a front end that looked like the electronic counterpart to a paper notebook. RESULTS: During the development of the beta version feedback was incorporated from many groups including the FDA's Center for Biologics Evaluation & Research, Uppsala University, Children's Hospital Boston, Alex Swarbrick's lab at the Garvan Institute in Sydney and Martin Spitaler at Imperial College. More than 100 individuals and groups worldwide then participated in the beta testing between September 2008 and June 2009. The generally positive response is reflected in the following quote about how one lab is making use of eCAT: "Everyone uses it as an electronic notebook, so they can compile the diverse collections of data that we generate as biologists, such as images and spreadsheets. We use to it to take minutes of meetings. We also use it to manage our common stocks of antibodies, plasmids and so on. Finally, perhaps the most important feature for us is the ability to link records, reagents and experiments." CONCLUSION: By developing eCAT in close collaboration with lab scientists, Axiope has come up with a practical and easy-to-use product that meets the need of scientists to manage, store and share data online. eCAT is already being perceived as a product that labs can continue to use as their data management and sharing grows in scale and complexity.

Event-related fMRI of word classification and successful word recognition in subjects at genetically enhanced risk of schizophrenia.

BACKGROUND: Verbal declarative memory is a core deficit in schizophrenia patients, seen to a lesser extent in unaffected biological relatives. Neuroimaging studies suggest volumetric differences and aberrant function in prefrontal and temporal regions in schizophrenia patients compared to controls. These deficits are also reflected in the small number of similar investigations in unaffected biological relatives. However, it is unclear the extent to which dysfunction is genetically mediated or a feature of the established illness. METHOD: Event-related blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to measure brain activation in 68 biological relatives of schizophrenia patients (of whom 27 experienced transient or isolated psychotic symptoms) and 21 controls during verbal classification and recognition. RESULTS: During word classification, the high-risk group showed a greater response relative to controls in the right inferior frontal gyrus. During correct recognition (relative to correct rejection), the high-risk group showed significantly greater response relative to controls in the right cerebellum. When the high-risk group was split into those with (HR+) and without (HR-) psychotic symptoms, the increased response in the right inferior frontal gyrus was only seen when the HR+ were compared to controls. The greater cerebellar response was seen when both HR groups were compared to controls. CONCLUSIONS: Activation increases in the right inferior frontal gyrus and cerebellum in high-risk subjects compared to controls during a relatively low-load memory task are likely to represent compensation for genetically mediated abnormalities. This is consistent with a leftward shift of the inverted 'U' load-response model of cognitive function in schizophrenia.

Functional imaging as a predictor of schizophrenia.

BACKGROUND: Prospective studies of young individuals at high risk of schizophrenia allow the investigation of whether neural abnormalities predate development of illness and, if present, have the potential to identify those who may become ill. METHODS: We studied young individuals with at least two relatives with the disorder. At baseline functional magnetic resonance imaging (fMRI) scan, none met criteria for any psychiatric disorder, but four subjects subsequently developed schizophrenia. We report the baseline functional imaging findings in these subjects performing a sentence completion task compared with normal control subjects (n = 21) and those at high risk with (n = 21) and without (n = 41) psychotic symptoms who have not developed the disorder. RESULTS: High-risk subjects who became ill demonstrated increased activation of the parietal lobe, decreased activation of the anterior cingulate, and smaller increases in activation with increasing task difficulty in the right lingual gyrus and bilateral temporal regions. The hypothesized predictive power of parietal activation was supported only in combination with lingual gyrus activity, which gave a positive predictive value in this sample of .80. CONCLUSIONS: Although these findings should be considered cautiously, as only four subjects who had an fMRI scan subsequently became ill, they suggest functional abnormalities are present in high-risk subjects who later became ill, which distinguish them not only from normal control subjects but also those at high risk who had not developed the disorder. These differences are detectable with fMRI and may have clinical utility.

Functional disconnectivity in subjects at high genetic risk of schizophrenia.

Schizophrenia is a highly heritable psychotic disorder. It has been suggested that deficits of the established state arise from abnormal interactions between brain regions. We sought to examine whether such connectivity abnormalities would be present in subjects at high genetic risk for the disorder. Functional connectivity analysis was carried out on functional MRI images from 21 controls and 69 high risk subjects performing the Hayling sentence completion task; 27 high risk subjects reported isolated psychotic symptoms, the remaining high risk subjects and controls did not. There were no significant differences in task performance between the groups. Based on previous findings we hypothesized: (i) state-related differences in connectivity between dorsolateral prefrontal cortex and lateral temporal lobe; (ii) genetically mediated reductions in a medial prefrontal-thalamic-cerebellar network; and (iii) increased prefrontal-parietal connectivity in high risk subjects (to a greater extent in those with isolated psychotic symptoms). Connectivity analysis was performed in two ways: with and without variance associated with task effects modelled and removed from the data. We did not find evidence to support our first hypothesis with either analysis method. However, consistent with hypothesis (ii), decreased connectivity between right medial prefrontal regions and contralateral cerebellum was found. This was only statistically significant in the analysis with task effects modelled and removed from the data. Finally, consistent with hypothesis (iii), increased connectivity between the left parietal and left prefrontal regions in high risk subjects was found in both analyses. These results, all in a situation uncontaminated by the effects of anti-psychotic medication, performance differences and prolonged illness, suggest there are abnormalities in functional connectivity over and above those attributable to task effects in high risk subjects. These connectivity abnormalities may underlie the diverse deficits seen in the established condition and the more subtle deficits seen in close relatives of those with the disorder.

Repeatability of motor and working-memory tasks in healthy older volunteers: assessment at functional MR imaging.

PURPOSE: To prospectively determine the repeatability of functional magnetic resonance (MR) imaging brain activation tasks in a group of healthy older male volunteers. MATERIALS AND METHODS: Local research ethics committee approval and informed consent were obtained. Sixteen men with a mean age of 69 years +/- 3 (standard deviation) performed finger-tapping and N-back (number of screens back) working-memory tasks. Each subject underwent MR imaging three times in weekly intervals. Within-subject task repeatability was analyzed in terms of the number of voxels classified as activated (activation extent), the mean activation amplitude, and (for finger tapping) the center of the mass of the activated region. A repeatability index was calculated to compare test-retest repeatability between subjects and between functional MR imaging tasks. Within-session, between-session, and between-subject variability was assessed by using analysis of variance testing of activation amplitude and extent. RESULTS: Nine of the 16 subjects generated useful data at all three MR imaging-functional task sessions. At single-subject, single-session analysis, cortical activation was identified in most subjects and at most sessions. The centers of the masses of motor cortex activation were highly reproducible (within 3 mm). Patterns of activation were qualitatively repeatable, but there was substantial variability in the amplitudes and extents of activated regions. Within-session coefficients of variation (CVs) for left- versus right-hand and right- versus left-hand finger tapping were, respectively, 65% and 43% for activation amplitude and 75% and 121% for activation extent. The between-session CVs for activation amplitude were similar to the within-session values, whereas between-session CVs for activation extent were much greater than within-session values, up to 206%. CONCLUSION: The generally poor quantitative task repeatability highlights the need for further methodologic developments before much reliance can be placed on functional MR imaging results of single-session experiments.

The functional anatomy of inspection time: an event-related fMRI study.

Twenty healthy young adults underwent functional magnetic resonance imaging (fMRI) of the brain while performing a visual inspection time task. Inspection time is a forced-choice, two-alternative visual backward-masking task in which the subject is briefly shown two parallel vertical lines of markedly different lengths and must decide which is longer. As stimulus duration decreases, performance declines to chance levels. Individual differences in inspection time correlate with higher cognitive functions. An event-related design was used. The hemodynamic (blood oxygenation level-dependent; BOLD) response was computed as both a function of the eight levels of stimulus duration, from 6 ms (where performance is almost at chance) to 150 ms (where performance is nearly perfect), and a function of the behavioral responses. Random effects analysis showed that the difficulty of the visual discrimination was related to bilateral activation in the inferior fronto-opercular cortex, superior/medial frontal gyrus, and anterior cingulate gyrus, and bilateral deactivation in the posterior cingulate gyrus and precuneus. Examination of the time courses of BOLD responses showed that activation was related specifically to the more difficult, briefer stimuli and that deactivation was found across most stimulus levels. Functional connectivity suggested the existence of two networks. One comprised the fronto-opercular area, intrasylvian area, medial frontal gyrus, and the anterior cingulate cortex (ACC), possibly associated with processing of visually degraded percepts. A posterior network of sensory-related and associative regions might subserve processing of a visual discrimination task that has high processing demands and combines several fundamental cognitive domains. fMRI can thus reveal information about the neural correlates of mental events which occur over very short durations.

Axiope tools for data management and data sharing.

Many areas of biological research generate large volumes of very diverse data. Managing this data can be a difficult and time-consuming process, particularly in an academic environment where there are very limited resources for IT support staff such as database administrators. The most economical and efficient solutions are those that enable scientists with minimal IT expertise to control and operate their own desktop systems. Axiope provides one such solution, Catalyzer, which acts as flexible cataloging system for creating structured records describing digital resources. The user is able specify both the content and structure of the information included in the catalog. Information and resources can be shared by a variety of means, including automatically generated sets of web pages. Federation and integration of this information, where needed, is handled by Axiope's Mercat server. Where there is a need for standardization or compatibility of the structures usedby different researchers this canbe achieved later by applying user-defined mappings in Mercat. In this way, large-scale data sharing can be achieved without imposing unnecessary constraints or interfering with the way in which individual scientists choose to record and catalog their work. We summarize the key technical issues involved in scientific data management and data sharing, describe the main features and functionality of Axiope Catalyzer and Axiope Mercat, and discuss future directions and requirements for an information infrastructure to support large-scale data sharing and scientific collaboration.

Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience.

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.