Exceptionally Long Distance Transport of Volcanic Ash: Implications for Stratigraphy, Hazards and the Sourcing of Distal Tephra Deposits

Cryptotephras (tephra not visible to the naked eye) form the foundation of the tephrostratigraphic frameworks used in Europe to date and correlate widely distributed geologic, paleoenvironmental and archaeological records. Pyne-O'Donnell et al. (2012) established the potential for developing a similar crypto-tephrostratigraphy across eastern North America by identifying multiple tephra, including the White River Ash (east; WRAe), St. Helens We and East Lake, in a peat core located in Newfoundland. Following on from this work, several ongoing projects have examined additional peat cores from Michigan, New York State, Maine, Nova Scotia and Newfoundland to build a tephrostratigraphic framework for this region. Using the precedent set by recent research by Jensen et al.(in press) that correlated the Alaskan WRAe to the European cryptotephra AD860B, unknown tephras identified in this work were not necessarily assumed to be from "expected" source areas (e.g. the Cascades). Here we present several examples of the preservation of tephra layers with an intercontinental distribution (i.e. WRAe and Ksudach 1), from relatively small magnitude events (i.e. St. Helens layer T, Mono Crater), and the first example of a Mexican ash in the NE (Volcan Ceboruco, Jala pumice). There are several implications of the identification of these units. These far-travelled ashes: (1) highlight the need to consider "ultra" distal source volcanoes for unknown cryptotephra deposits,. (2) present an opportunity for physical volcanologists to examine why some eruptions have an exceptional distribution of ash that is not necessarily controlled by the magnitude of the event. (3) complicate the idea of using tephrostratigraphic frameworks to understand the frequency of eruptions towards aiding hazard planning and prediction (e.g. Swindles et al., 2011). (4) show that there is a real potential to link tropical and mid to high-latitude paleoenvironmental records. Jensen et al. (in press) Transatlantic correlation of the Alaskan White River Ash. Geology. Pyne-O'Donnell et al. (2012). High-precision ultra-distal Holocene tephrochronology in North America. Quaternary Science Reviews, 52, 6-11. Swindles et al. (2011). A 7000 yr perspective on volcanic ash clouds affecting northern Europe. Geology, 39, 887-890.

Anti-biofilm activity of ultrashort cinnamic acid peptide derivatives against medical device-related pathogens

The threat of antimicrobial resistance has placed increasing emphasis on the development of innovative approaches to eradicate multidrug-resistant pathogens. Biofilm-forming microorganisms, for example, Staphylococcus epidermidis and Staphylococcus aureus, are responsible for increased incidence of biomaterial infection, extended hospital stays and patient morbidity and mortality. This paper highlights the potential of ultrashort tetra-peptide conjugated to hydrophobic cinnamic acid derivatives. These peptidomimetic molecules demonstrate selective and highly potent activity against resistant biofilm forms of Gram-positive medical device-related pathogens. 3-(4-Hydroxyphenyl)propionic)-Orn-Orn-Trp-Trp-NH2 displays particular promise with minimum biofilm eradication concentration (MBEC) values of 125 µg/ml against methicillin sensitive (ATCC 29213) and resistant (ATCC 43300) S. aureus and activity shown against biofilm forms of Escherichia coli (MBEC: 1000 µg/ml). Kill kinetics confirms complete eradication of established 24-h biofilms at MBEC with 6-h exposure. Reduced cell cytotoxicity, relative to Gram-positive pathogens, was proven via tissue culture (HaCaT) and haemolysis assays (equine erythrocytes).

Existing in nature as part of the immune response, antimicrobial peptides display great promise for exploitation by the pharmaceutical industry in order to increase the library of available therapeutic molecules. Ultrashort variants are particularly promising for translation as clinical therapeutics as they are more cost-effective, easier to synthesise and can be tailored to specific functional requirements based on the primary sequence allowing factors such as spectrum of activity to be varied.

Patriotism and nationalism

A review of developments since the late middle ages, built round the distinction between national consciousness and nationalism.

Factors influencing success of clinical genome sequencing across a broad spectrum of disorders

To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges.

Hierarchical Demand Response for Peak Reduction using Dantzig‐Wolfe Decomposition

Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Residential electrical vehicle charging strategies: the good, the bad and the ugly

In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Hydrogel antimicrobial capture coatings for endotracheal tubes; a pharmaceutical strategy designed to prevent ventilator-associated pneumonia.

This paper presents a novel strategy for the prevention of ventilator-associatedpneumonia that involves coating poly(vinyl chloride, PVC) endotracheal tubes (ET) withhydrogels that may be subsequently used to entrap nebulized antimicrobial solutions. Candidatehydrogels were prepared containing a range of ratios of hydroxyethyl methacrylate (HEMA) andmethacrylic acid (MAA) from 100:0 to 70:30 using free radical polymerization and, whenrequired, simultaneous attachment to PVC was performed. The mechanical properties, glasstransition temperatures, swelling kinetics, uptake of gentamicin from an aqueous medium, andgentamicin release were characterized. Increasing the MAA content of the hydrogels significantlydecreased the ultimate tensile strength, % elongation at break, Young’s modulus, and increasedthe glass transition temperature, the swelling ratio, and gentamicin uptake. Microbial(Staphylococcus aureus and Pseudomonas aeruginosa) adherence to control (drug-free) hydrogelswas observed; however, while adherence to gentamicin-containing p(HEMA) occurred, noadherence occurred to gentamicin-containing HEMA:MAA copolymers. Antimicrobialpersistence of gentamicin-containing hydrogels was examined by determining the zone ofinhibition against each microorganism on successive days. Hydrogel composition affected the observed antimicrobial persistence,with the hydrogel composed of 70:30 HEMA:MAA exhibiting >20 days persistence against S. aureus and P. aeruginosa,respectively. To simulate clinical use, the hydrogels (coated onto PVC) were first exposed to a nebulized solution of gentamicin(4 mL, 80 mg for 20 min), and then to nebulized bacteria (4 mL ca. 1 × 109 colony forming units mL−1, 30 min). Viable bacteriawere not observed on the gentamicin-treated p(HEMA: MAA) copolymers, whereas growth was observed on gentamicin-treatedp(HEMA). In light of the excellent antimicrobial activity and physicochemical properties, p(HEMA: MAA) copolymerscomposed of ratios of 80:20 or 70:30 HEMA: MAA were identified as potentially useful coatings of endotracheal tubes to be usedin conjunction with the clinical nebulization of gentamicin and designed for the prevention of ventilator-associated pneumonia