A Latent Parameter Node-Centric Model for Spatial Networks

Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density) may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social). We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models

Ontologies for the study of neurological disease

We have begun work on two separate but related ontologies for the study of neurological diseases. The first, the Neurological Disease Ontology (ND), is intended to provide a set of controlled, logically connected classes to describe the range of neurological diseases and their associated signs and symptoms, assessments, diagnoses, and interventions that are encountered in the course of clinical practice. ND is built as an extension of the Ontology for General Medical Sciences — a high-level candidate OBO Foundry ontology that provides a set of general classes that can be used to describe general aspects of medical science. ND is being built with classes utilizing both textual and axiomatized definitions that describe and formalize the relations between instances of other classes within the ontology itself as well as to external ontologies such as the Gene Ontology, Cell Ontology, Protein Ontology, and Chemical Entities of Biological Interest. In addition, references to similar or associated terms in external ontologies, vocabularies and terminologies are included when possible. Initial work on ND is focused on the areas of Alzheimer’s and other diseases associated with dementia, multiple sclerosis, and stroke and cerebrovascular disease. Extensions to additional groups of neurological diseases are planned. The second ontology, the Neuro-Psychological Testing Ontology (NPT), is intended to provide a set of classes for the annotation of neuropsychological testing data. The intention of this ontology is to allow for the integration of results from a variety of neuropsychological tests that assay similar measures of cognitive functioning. Neuro-psychological testing is an important component in developing the clinical picture used in the diagnosis of patients with a range of neurological diseases, such as Alzheimer’s disease and multiple sclerosis, and following stroke or traumatic brain injury. NPT is being developed as an extension to the Ontology for Biomedical Investigations

PPI Questionnaire on Adaptive Wearable Appropriateness as an Autistic Intervention

Autism Spectrum Condition (ASC) is a life-long diagnosis, which has a subset of features including hyper-, seeking- and/or hypo-reactivity to sensory inputs or unusual interests (APA, 2013). These qualities are evident across environmental (e.g. response to specific sounds, visual fascination with lights or movements) and physiological domains (e.g. anxiety, respiration or euthermia). Scholars report that ninety (90%) of autistic adults experience sensory issues causing significant barriers at school/work (Leekam et al., 2007). As part of a larger PhD Research Project, this pilot study establishes designs, processes and measures that may establish if autistic individuals find value utilising adaptive/wearable interventions that possibly alter, redirect and/or attenuate disruptive stimuli. This study incorporates benign information not yet containing practical data, other than to provision and trial space where real data is nominally present. This pilot loads systems functionality for future use (e.g. consent, demographic collection, measures, post-mortem/survey feedback, storage, sorting, query, statistical analyses and reporting). Finally, this pilot provisions a follow-on and full-fledge Participant Public Involvement (PPI) designed to exploit data from focus group and co-produced surveys/designs. In turn, these may be used to inform an as-yet-to-be developed interventional prototype. Hence, the forthcoming PPI—by leveraging this pilot—aims to describe what degree sensory distractions occur among adolescent and adult ASC participants. Both pilot and PPI aspire to whether focus, anxiety and attentional concerns are perceived as negative issues and if individuals prefer assistance (vis à vis assistive wearables) to reduce anxiety, distractions and increase focus at school and at work (Bagley et al., 2016). This study results yield promise; in that, a subsequent PPI can be leveraged to obtain co-designed autistic data leading to a randomised clinical trial

Sound Impairment Effect on Cognitive Skill Performance

Autism Spectrum Condition (ASC) is a life-long diagnosis, which has a subset of individualized characteristics consisting of hyper-, seeking- and/or hypo-reactivity to sensory inputs or unusual interests (APA, 2013). These sensitivities are evident in both environmental (e.g. apparent response to specific sounds, visual fascination with lights or movements) and physiological domains (e.g. anxiety, respiration or euthermia). As part of a larger PhD Research Project (SensorAble), this pilot study proposes that autistic individuals who exhibit greater distractibility and reduced focus/attention resulting stimuli may benefit from interventions that alter, redirect and/or attenuate stimuli. In particular, Irrelevant-Sound Effect (ISE) consisting of un-targeted and/or modulated sonics cause greater disruption of performance of simultaneous and visual simple tasks compared to baseline ISE that are merely directed. Using gold-standard Stroop experiments, data collected among neurotypical (NT) and ASC individuals at baseline and at various ISE modes result in greater reaction time (RT) improvements among ASC than NT participants. In this study, which focuses on aural distractibility only, data supports that signal processing may provide a gateway to enhancing focus and attention while reduce distractibility and anxiety in other domains

The Relationship Between Disperal Ability and Geographic Range Size

There are a variety of proposed evolutionary and ecological explanations for why some species have more extensive geographical ranges than others. One of the most common explanations is variation in species’ dispersal ability. However, the purported relationship between dispersal distance and range size has been subjected to few theoretical investigations, and empirical tests reach conflicting conclusions. We attempt to reconcile the equivocal results of previous studies by reviewing and synthesizing quantitative dispersal data, examining the relationship between average dispersal ability and range size for different spatial scales, regions and taxonomic groups. We use extensive data from marine taxa whose average dispersal varies by seven orders of magnitude. Our results suggest dispersal is not a general determinant of range size, but can play an important role in some circumstances. We also review the mechanistic theories proposed to explain a positive relationship between range size and dispersal and explore their underlying rationales and supporting or refuting evidence. Despite numerous studies assuming a priori that dispersal influences range size, this is the first comprehensive conceptual evaluation of these ideas. Overall, our results indicate that although dispersal can be an important process moderating species’ distributions, increased attention should be paid to other processes responsible for range size variation

Scenarios for Offshore Wind Power Production for Central California Call Areas

In response to the growing interest in offshore wind energy development in California, the U.S. Bureau of Ocean Energy Management delineated three Call Areas for potential leasing. This study provides a comprehensive characterization and comparison of offshore wind power potential within the two Central California Call Areas (Diablo Canyon and Morro Bay) using 12-and 15-MW turbines under different inter-turbine spacing and wind farm size scenarios. Our analysis shows similar daily and seasonal patterns of wind power produced within the Call Areas, which peak in spring and during evening hours. Per-turbine power production is higher in the Morro Bay Call Area due to slightly higher hub-height wind speeds, whereas total power production is higher in the Diablo Canyon Call Area due to its larger size. Turbine type had a negligible impact on average power production per-unit-area because while larger turbines produce more power, they require greater inter-turbine spacing. Combined power production from the two fully built out Call Areas could equal nearly a quarter of California\u27s current annual electrical energy production. A commercial-scale wind farm with a realized power output of 960 MW would require a footprint of at least half of the Morro Bay Call Area or at least a quarter of the Diablo Canyon Call Area. These results provide guidance on offshore wind development over the Central California Coast, and the framework demonstrated here could be applied to other wind data sets in other regions

Experimental investigation of taxon-specific response of alkaline phosphatase activity in natural freshwater phytoplankton

It is widely accepted that alkaline phosphatase activity (APA) is an efficient indicator of phosphate limitation in freshwater phytoplankton communities. In this study, we investigated whether the response in APA to phosphate limitation differs among the taxa in a mixed phytoplankton assemblage. We used the new enzyme-labeled fluorescence (ELF) technique, which allows microscopic detection of phosphate limitation in individual cells of multiple species. The most prominent findings of this study were that alkaline phosphatase (AP) was induced in many, but not all taxa and that different taxa, as well as different cells within a single taxon, experienced different degrees of phosphate stress under the same environmental conditions. Our approach was to manipulate the limiting nutrient in a natural freshwater phytoplankton community by incubating lake water in the laboratory. We induced nitrogen (N) or phosphate limitation through additions of inorganic nutrients. Both the ELF assay and bulk APA indicated that the lake phytoplankton were not phosphate limited at the start of the experiment. During the experiment, several chlorophyte taxa (e.g., Eudorina and an unidentified solitary spiny coccoid) were driven to phosphate limitation when inorganic N was added, as evidenced by a higher percentage of ELF-labeled cells relative to controls, whereas other chlorophyte taxa such as Actinastrum and Dicryosphaerium were not phosphate stressed under these conditions. In the phosphate-limited treatments, little or no ELF labeling was observed in any cyanobacterial taxa. Furthermore, all taxa observed after the ELF labeling procedure (>10-mum fraction) were labeled with ELF at least on one occasion, demonstrating the wide applicability of the ELF method. By using ELF labeling in tandem with bulk APA, the resolution and analysis of phosphate limitation was increased, allowing the identification of specific phosphate-stressed taxa

Spatial and Temporal Variation of Offshore Wind Power and its Values Along the Central California Coast

The analysis of the spatiotemporal variability of wind power remains limited during the planning stage of an offshore wind farm. This study provides a framework to investigate how offshore wind power varies along the Central California Coast over diurnal and seasonal time scales, which is critical for reliability and functionality of the grid system. We find that offshore wind power in this region peaks during evening hours across all seasons and maximizes in spring and summer. The timing of peak offshore wind power production better aligns with that of peak demand across California than solar and land-based wind power production, highlighting its potential to fill the supply gap when demand is high and power production from other renewable energy sources is low. We further assess the value of offshore wind power using demand-based and wholesale market metrics. Both metrics indicate high potential value of offshore wind power over most areas in this region. Finally, we show that the estimate of power production is significantly biased when using mean wind speeds that do not account for temporal variability, leading to potentially inaccurate predictions about locations that are expected to produce the most power. These results reiterate the importance in considering spatiotemporal variability in wind power for accurately calculating the value of offshore wind development