The effects of memantine on prepulse inhibition.

Reduced prepulse inhibition (PPI) of startle provides evidence of deficient sensorimotor gating in several disorders, including schizophrenia. The role of NMDA neurotransmission in the regulation of PPI is unclear, due to cross-species differences in the effects of NMDA antagonists on PPI. Recent reports suggest that drug effects on PPI differ in subgroups of normal humans that differ in the levels of baseline PPI or specific personality domains; here, we tested the effects of these variables on the sensitivity of PPI to the NMDA antagonist, memantine. PPI was measured in male Sprague-Dawley rats, after treatment with memantine (0, 10 or 20 mg/kg, s.c.). Baseline PPI was then measured in 37 healthy adult men. Next, subjects were tested twice, in a double-blind crossover design, comparing either (1) placebo vs 20 mg of the NMDA antagonist memantine (n=19) or (2) placebo vs 30 mg memantine (n=18). Tests included measures of acoustic startle amplitude, PPI, autonomic indices and subjective self-rating scales. Memantine had dose- and interval-dependent effects on PPI in rats. Compared with vehicle, 10 mg/kg increased short-interval (10-20 ms) PPI, and 20 mg/kg decreased long-interval (120 ms) PPI. In humans, memantine caused dose-dependent effects on psychological and somatic measures: 20 mg was associated with increased ratings of happiness, and 30 mg was associated with increased ratings of dizziness. PPI at the 120 ms prepulse interval was increased by 20 mg, but not 30 mg of memantine. Subgroups most sensitive to the PPI-enhancing effects of memantine were those with low baseline PPI, or with personality scale scores suggestive of high novelty seeking, high sensation seeking, or high disinhibition. NMDA blockade with memantine appears to have dose- and interval-dependent effects on sensorimotor gating in rats and humans, particularly among specific subgroups of normal human subjects. These findings are discussed as they relate to consistencies across other studies in humans, as well as apparent inconsistencies in the NMDA regulation of PPI across species

The application of statistical network models in disease research

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Host social structure is fundamental to how infections spread and persist, and so the statistical modelling of static and dynamic social networks provides an invaluable tool to parameterise realistic epidemiological models. We present a practical guide to the application of network modelling frameworks for hypothesis testing related to social interactions and epidemiology, illustrating some approaches with worked examples using data from a population of wild European badgers Meles meles naturally infected with bovine tuberculosis. Different empirical network datasets generate particular statistical issues related to non-independence and sampling constraints. We therefore discuss the strengths and weaknesses of modelling approaches for different types of network data and for answering different questions relating to disease transmission. We argue that statistical modelling frameworks designed specifically for network analysis offer great potential in directly relating network structure to infection. They have the potential to be powerful tools in analysing empirical contact data used in epidemiological studies, but remain untested for use in networks of spatio-temporal associations. As a result, we argue that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio-logging studies. Furthermore, we encourage improved integration of statistical network approaches into epidemiological research to facilitate the generation of novel modelling frameworks and help extend our understanding of disease transmission in natural populations.M.J.S. is funded by a NERC standard grant (NE/M004546/1) awarded to R.A.M., D.P.C., D.J.H. and M.B., with the APHA team at Woodchester Park, UK (lead scientist is R.J.D.) as project partners

Social structure contains epidemics and regulates individual roles in disease transmission in a group-living mammal

This is the final version. Available from Wiley via the DOI in this record. Data accessibility: The original weighted adjacency matrix for the high‐density population of European badgers, as well as code used for simulating networks and disease simulations can be found online https://doi.org/10.5061/dryad.49n3878.Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high-density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for disease dynamics with important management implications for socially structured populations.Natural Environment Research Council (NERC

Children and older adults exhibit distinct sub-optimal cost-benefit functions when preparing to move their eyes and hands

"© 2015 Gonzalez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited"Numerous activities require an individual to respond quickly to the correct stimulus. The provision of advance information allows response priming but heightened responses can cause errors (responding too early or reacting to the wrong stimulus). Thus, a balance is required between the online cognitive mechanisms (inhibitory and anticipatory) used to prepare and execute a motor response at the appropriate time. We investigated the use of advance information in 71 participants across four different age groups: (i) children, (ii) young adults, (iii) middle-aged adults, and (iv) older adults. We implemented 'cued' and 'non-cued' conditions to assess age-related changes in saccadic and touch responses to targets in three movement conditions: (a) Eyes only; (b) Hands only; (c) Eyes and Hand. Children made less saccade errors compared to young adults, but they also exhibited longer response times in cued versus non-cued conditions. In contrast, older adults showed faster responses in cued conditions but exhibited more errors. The results indicate that young adults (18 -25 years) achieve an optimal balance between anticipation and execution. In contrast, children show benefits (few errors) and costs (slow responses) of good inhibition when preparing a motor response based on advance information; whilst older adults show the benefits and costs associated with a prospective response strategy (i.e., good anticipation)

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Chemistry-driven changes strongly influence climate forcing from vegetation emissions

Biogenic volatile organic compounds (BVOCs) affect climate via changes to aerosols, aerosol-cloud interactions (ACI), ozone and methane. BVOCs exhibit dependence on climate (causing a feedback) and land use but there remains uncertainty in their net climatic impact. One factor is the description of BVOC chemistry. Here, using the earth-system model UKESM1, we quantify chemistry’s influence by comparing the response to doubling BVOC emissions in the pre-industrial with standard and state-of-science chemistry. The net forcing (feedback) is positive: ozone and methane increases and ACI changes outweigh enhanced aerosol scattering. Contrary to prior studies, the ACI response is driven by cloud droplet number concentration (CDNC) reductions from suppression of gas-phase SO2 oxidation. With state-of-science chemistry the feedback is 43% smaller as lower oxidant depletion yields smaller methane increases and CDNC decreases. This illustrates chemistry’s significant influence on BVOC’s climatic impact and the more complex pathways by which BVOCs influence climate than currently recognised

Cavity Induced Interfacing of Atoms and Light

This chapter introduces cavity-based light-matter quantum interfaces, with a single atom or ion in strong coupling to a high-finesse optical cavity. We discuss the deterministic generation of indistinguishable single photons from these systems; the atom-photon entanglement intractably linked to this process; and the information encoding using spatio-temporal modes within these photons. Furthermore, we show how to establish a time-reversal of the aforementioned emission process to use a coupled atom-cavity system as a quantum memory. Along the line, we also discuss the performance and characterisation of cavity photons in elementary linear-optics arrangements with single beam splitters for quantum-homodyne measurements.Comment: to appear as a book chapter in a compilation "Engineering the Atom-Photon Interaction" published by Springer in 2015, edited by A. Predojevic and M. W. Mitchel

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

Reconceptualizing Profit-Orientation in Management: A Karmic View on "Return on Investment" Calculations

From the perspective of the present day, Puritan-inspired capitalism seems to have succeeded globally, including in India. Connected to this, short-term profit-orientation in management seems to constrain the scope of different management approaches in a tight ideological corset. This article discusses the possibility of replacing this Puritan doctrine with the crucial elements of Indian philosophy: Karma and samsara. In doing so, the possibility of revising the guiding principles in capitalist management becomes conceivable, namely the monetary focus of profit-orientation and its short-term orientation. This perspective allows a detachment of the concept of profit from the realm of money, as the seemingly only objectifiable measure of profit. Furthermore it allows a removal of the expectation that every "investment" has to directly "pay off". A karmic view offers management a possible facility for being more caring about the needs and fates of other stakeholders, as profit-orientation would no longer be attached as a factual constraint to merely accumulate money. (author's abstract