Tracking environmental trends in the Great Bay Estuarine System through comparisons of historical and present-day green and red algal community structure and nutrient content

Monitoring macroalgae populations is an effective means of detecting long term water quality changes in estuarine systems. To investigate the environmental status of New Hampshire’s Great Bay National Estuarine Research Reserve, this study assessed the abundance/distribution of macrophytes, particularly Gracilaria and Ulva species, relative to eutrophication patterns; compared historical (1970s-1990s) and current algal biomass/cover at several sites; and compared Ulva and Gracilaria tissue N/P content to ambient and historical levels. Ulva and Gracilaria biomass/cover have increased significantly at several sites. Cover by Ulva species, at seasonal maxima, was over 90 times the value recorded in the 1970s at Lubberland Creek, and exceeded 50% at all sites in the upper estuary. Gracilaria cover was greater than 25% at Depot Road in the upper estuary, whereas the historical measure was 1%. Sequencing of ITS2, rbcL and CO1 revealed the presence of previously undetected Ulva and Gracilaria species, including Gracilaria vermiculophylla (Ohmi) Papenfuss, an invasive species of Asian origin. Gracilaria vermiculophylla has exceeded G. tikvahiae as the dominant Gracilaria species in Great Bay. Historical voucher specimen screening suggests G. vermiculophylla was introduced as recently as 2003. Nitrogen and phosphorus levels are elevated in the estuary. We should expect continued seasonal nuisance algal blooms

The impact of poor asthma control among asthma patients treated with inhaled corticosteroids plus long-acting β2-agonists in the United Kingdom : a cross-sectional analysis

This study was sponsored by Boehringer Ingelheim Ltd UK, which was involved in all stages of the study conduct and analysis and also funded all costs associated with the development of the manuscript. The authors acknowledge Kantar Health and Errol J Philip for providing medical writing support. Editorial assistance and medical writing support was also provided by Michelle Rebello, PhD, and Suchita Nath-Sain, PhD, of Cactus Communications. This study was sponsored by Boehringer Ingelheim Ltd., UK, which also funded all costs associated with the development of the manuscript. Author Correction, npj Primary Care Respiratory Medicine 27, Article number: 65 (2017) doi:10.1038/s41533-017-0063-5, 05 December 2017 Correction to:npj Primary Care Respiratory Medicine (2017); doi:10.1038/s41533-017-0014-1; Published 09 March 2017Peer reviewedPublisher PD

Programmable active pixel sensor to investigate neural interactions within the retina

Detection of the visual scene by the eye and the resultant neural interactions of the retina-brain system give us our perception of sight. We have developed an Active Pixel Sensor (APS) to be used as a tool for both furthering understanding of these interactions via experimentation with the retina and to make developments towards a realisable retinal prosthesis. The sensor consists of 469 pixels in a hexagonal array. The pixels are interconnected by a programmable neural network to mimic lateral interactions between retinal cells. Outputs from the sensor are in the form of biphasic current pulse trains suitable to stimulate retinal cells via a biocompatible array. The APS will be described with initial characterisation and test results

Chondros crispus Stackhouse

The Northwest Atlantic Chondrus fishery is the basis of a phycocolloid industry. Harvesting is centered in the Gulfs of Maine and St. Lawrence where the species is an ecological dominant. The extensive ecological-physiological data base is reviewed as is the biosystematics. Annual catch peaked in 1974 (approx. 50,000 t) and troughed in 1983 (approx. 18,000 t). Catch decline was due/in part, to a decrease in crop demand caused by international marketplace competition from other carra-geenophytes. Harvesting and handling techniques are described. Resource management structure is outlined, as are stock assessment and assessment science programs. The international aspect of resource utilization is discussed

Comparative Analysis of Requirements Change Prediction Models: Manual, Linguistic, and Neural Network

Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input

Assembly Time Modeling Through Connective Complexity Metrics

This paper presents an approach for the development of surrogate models predicting the assembly time of a system based on complexity metrics of the physical system architecture when detailed geometric information is unavailable. A convention for modelling physical architecture is presented, followed by a sample of 10 analysed systems used for training and three systems used for validation. These systems are evaluated on complexity metrics developed from graph theoretic measures. An example model is developed based on a series of regressions of trends observed within the sample data. This is validated against the systems that are not used to develop the model. The model developed uses average path length, part count and path length density to approximate assembly time within the standard deviation of the subjective variation possible in Boothroyd and Dewhurst design for assembly (DFA) analysis. While the specific example model developed is generalisable only to systems similar to those in the sample set, the capability to develop mappings between physical architecture and assembly time in early-stage design is demonstrated

Large-scale multielectrode recording and stimulation of neural activity

Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer's Disease Progression

Background: One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer's disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes. Methodology/Principal Findings: We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis. Conclusions/Significance: Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation. © 2012 Arefin et al

Development of a Novel Pinned Connection for Cold-Formed Steel Trusses

Cold-formed steel trusses are a popular form of construction for light-weight buildings, particularly portal frame structures, for which spans up to 25m are increasingly common. In these long span trusses, providing high strength connections with sufficient elastic stiffness is a current limitation to developing cost-effective solutions. A novel pin-jointed truss connection named the Howick Rivet Connector (HRC) has been tested, firstly in a T-joint arrangement, then in a truss assemblage to determine its reliable strength and stiffness. Results showed that the HRC performs similarly to a bolted connection in terms of failure modes observed and loads reached. Additionally, the process of installing the HRC creates a bearing fit, eliminating slip due to tolerances. The elastic stiffness and proportionality limit of trusses with HRCs installed was shown to be appreciably greater than similarly dimensioned conventional screwed systems. Finite element (FE) models of both T-joints and trusses tested showed good agreement with experimental results, particularly in the transition from elastic to inelastic behaviour. The peak loads predicted from the FE models were however not accurately determined. To better predict this, it is recommended that the HRC forming and installation process be modelled to capture geometric irregularities and inelastic distributions which were idealised

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions