Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making

Male breast cancer: is the scenario changing

<p>Abstract</p> <p>Background</p> <p>The overall incidence of male breast cancer is around 1% of all breast cancers and is on the rise. In this review we aim to present various aspects of male breast cancer with particular emphasis on incidence, risk factors, patho-physiology, treatment, prognostic factors, and outcome.</p> <p>Methods</p> <p>Information on all aspects of male breast cancer was gathered from available relevant literature on male breast cancer from the MEDLINE database over the past 32 years from 1975 to 2007. Various reported studies were scrutinized for emerging evidence. Incidence data were also obtained from the IARC, Cancer Mondial database.</p> <p>Conclusion</p> <p>There is a scenario of rising incidence, particularly in urban US, Canada and UK. Even though more data on risk factors is emerging about this disease, more multi-institutional efforts to pool data with large randomized trials to show treatment and survival benefits are needed to support the existing vast emerging knowledge about the disease.</p

Major abdominal surgery in Jehovah’s Witnesses

Introduction Patients who are Jehovah’s Witnesses pose difficult ethical and moral dilemmas for surgeons because of their refusal to receive blood and blood products. This article outlines the personal experiences of six Jehovah’s Witnesses who underwent major abdominal surgery at a single institution and also summarises the literature on the perioperative care of these patients. Methods The patients recorded their thoughts and the dilemmas they faced during their surgical journey. We also reviewed the recent literature on the ethical principles involved in treating such patients and strategies recommended to make surgery safer. Results All patients were supported in their decision making by the clinical team and the Hospital Liaison Committee for Jehovah’s Witnesses. The patients recognised the ethical and moral difficulties experienced by clinicians in this setting. However, they described taking strength from their belief in Jehovah. A multitude of techniques are available to minimise the risk associated with major surgery in Jehovah’s Witness patients, many of which have been adopted to minimise unnecessary use of blood products in general. Nevertheless, the risks of catastrophic haemorrhage and consequent mortality remain an unresolved issue for the treating team. Conclusions Respect for a patient’s autonomy in this setting is the overriding ethical principle, with detailed discussion forming an important part of the preparation of a Jehovah’s Witness for major abdominal surgery. Clinicians must be diligent in the documentation of the patient’s wishes to ensure all members of the team can abide by these

Theoretical frameworks for the study of structuring processes in group decision support systems

Most theoretical perspectives used to explain the use and effects of communication and decision support technologies assume someform of technological a&amp;rminism. lnwnsisten&amp;s in the research jindings have prompted theorists to reject the assumptions of technological determinism in favor of an emergent perspective. To date, only adaptive structuration theo y CAST) offers the promise of satisfying two requirements for exphnation based on an emergent perspective: recursivify and unique effects. The current article reviews the application of AST to the study of a relatively recent technology in the workplace--group decision support systems (GDSS). Next it discusses AST&apos;s chal- lenge to capture, dynamically and precisely, GDSS processes and outcomes. In response to these concerns, self-organizing systems theory (SOST) is reviewed and applied to problematic areas in GDSS research with the aim of advancing AST

Altered activity–rest patterns in mice with a human autosomal-dominant nocturnal frontal lobe epilepsy mutation in the β2 nicotinic receptor

High-affinity nicotinic receptors containing β2 subunits (β2^*) are widely expressed in the brain, modulating many neuronal processes and contributing to neuropathologies such as Alzheimer's disease, Parkinson's disease and epilepsy. Mutations in both the α4 and β2 subunits are associated with a rare partial epilepsy, autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE). In this study, we introduced one such human missense mutation into the mouse genome to generate a knock-in strain carrying a valine-to-leucine mutation β2V287L. β2^(V287L) mice were viable and born at an expected Mendelian ratio. Surprisingly, mice did not show an overt seizure phenotype; however, homozygous mice did show significant alterations in their activity–rest patterns. This was manifest as an increase in activity during the light cycle suggestive of disturbances in the normal sleep patterns of mice; a parallel phenotype to that found in human ADNFLE patients. Consistent with the role of nicotinic receptors in reward pathways, we found that β2^(V287L) mice did not develop a normal proclivity to voluntary wheel running, a model for natural reward. Anxiety-related behaviors were also affected by the V287L mutation. Mutant mice spent more time in the open arms on the elevated plus maze suggesting that they had reduced levels of anxiety. Together, these findings emphasize several important roles of β2^* nicotinic receptors in complex biological processes including the activity–rest cycle, natural reward and anxiety

Serotype-specific differences in inhibition of reovirus infectivity by human-milk glycans are determined by viral attachment protein σ1

AbstractHuman milk contains many bioactive components, including secretory IgA, oligosaccharides, and milk-associated proteins. We assessed the antiviral effects of several components of milk against mammalian reoviruses. We found that glucocerebroside (GCB) inhibited the infectivity of reovirus strain type 1 Lang (T1L), whereas gangliosides GD3 and GM3 and 3′-sialyllactose (3SL) inhibited the infectivity of reovirus strain type 3 Dearing (T3D). Agglutination of erythrocytes mediated by T1L and T3D was inhibited by GD3, GM3, and bovine lactoferrin. Additionally, α-sialic acid, 3SL, 6′-sialyllactose, sialic acid, human lactoferrin, osteopontin, and α-lactalbumin inhibited hemagglutination mediated by T3D. Using single-gene reassortant viruses, we found that serotype-specific differences segregate with the gene encoding the viral attachment protein. Furthermore, GD3, GM3, and 3SL inhibit T3D infectivity by blocking binding to host cells, whereas GCB inhibits T1L infectivity post-attachment. These results enhance an understanding of reovirus cell attachment and define a mechanism for the antimicrobial activity of human milk

From sparse to dense and from assortative to disassortative in online social networks

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.Comment: 10 pages, 7 figures and 2 table

Lactation and neonatal nutrition: defining and refining the critical questions.

This paper resulted from a conference entitled "Lactation and Milk: Defining and refining the critical questions" held at the University of Colorado School of Medicine from January 18-20, 2012. The mission of the conference was to identify unresolved questions and set future goals for research into human milk composition, mammary development and lactation. We first outline the unanswered questions regarding the composition of human milk (Section I) and the mechanisms by which milk components affect neonatal development, growth and health and recommend models for future research. Emerging questions about how milk components affect cognitive development and behavioral phenotype of the offspring are presented in Section II. In Section III we outline the important unanswered questions about regulation of mammary gland development, the heritability of defects, the effects of maternal nutrition, disease, metabolic status, and therapeutic drugs upon the subsequent lactation. Questions surrounding breastfeeding practice are also highlighted. In Section IV we describe the specific nutritional challenges faced by three different populations, namely preterm infants, infants born to obese mothers who may or may not have gestational diabetes, and infants born to undernourished mothers. The recognition that multidisciplinary training is critical to advancing the field led us to formulate specific training recommendations in Section V. Our recommendations for research emphasis are summarized in Section VI. In sum, we present a roadmap for multidisciplinary research into all aspects of human lactation, milk and its role in infant nutrition for the next decade and beyond