Optics clustered to output unique solutions: a multi-laser facility for combined single molecule and ensemble microscopy

Optics clustered to output unique solutions (OCTOPUS) is a microscopy platform that combines single molecule and ensemble imaging methodologies. A novel aspect of OCTOPUS is its laser excitation system, which consists of a central core of interlocked continuous wave and pulsed laser sources, launched into optical fibres and linked via laser combiners. Fibres are plugged into wall-mounted patch panels that reach microscopy end-stations in adjacent rooms. This allows multiple tailor-made combinations of laser colours and time characteristics to be shared by different end-stations minimising the need for laser duplications. This setup brings significant benefits in terms of cost effectiveness, ease of operation, and user safety. The modular nature of OCTOPUS also facilitates the addition of new techniques as required, allowing the use of existing lasers in new microscopes while retaining the ability to run the established parts of the facility. To date, techniques interlinked are multi-photon/multicolour confocal fluorescence lifetime imaging for several modalities of fluorescence resonance energy transfer (FRET) and time-resolved anisotropy, total internal reflection fluorescence, single molecule imaging of single pair FRET, single molecule fluorescence polarisation, particle tracking, and optical tweezers. Here, we use a well-studied system, the epidermal growth factor receptor network, to illustrate how OCTOPUS can aid in the investigation of complex biological phenomena.     

Fast k most similar neighbor classifier for mixed data (tree k-MSN)

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. For this reason, many fast k-NN classifiers have been developed, some of them are based on a tree structure, which is created during a preprocessing phase using the prototypes in T. Then, in a search phase, the tree is traversed to find the nearest neighbor. The speed up is obtained, while the exploration of some parts of the tree is avoided using pruning rules which are usually based on the triangle inequality. However, in soft sciences as Medicine, Geology, Sociology, etc., the prototypes are usually described by numerical and categorical attributes (mixed data), and sometimes the comparison function for computing the similarity between prototypes does not satisfy metric properties. Therefore, in this work an approximate fast k most similar neighbor classifier, for mixed data and similarity functions that do not satisfy metric properties, based on a tree structure (Tree k-MSN) is proposed. Some experiments with synthetic and real data are presented.     

Neutronic characterization of the MEGAPIE target

The MEGAPIE project aimed to design, build and operate a liquid metal spallation neutron target of about 1 MW beam power in the SINQ facility at the Paul Scherrer Institut (Villigen, Switzerland). This project is an important step in the roadmap towards the demonstration of the accelerator driven system (ADS) concept and high power liquid metal targets in general. Following the design phase, an experimental program was defined to provide a complete characterization of the facility by performing a “mapping” of the neutron flux at different points, from the center of the target to the beam lines. The neutronic performance of the target was studied using different experimental techniques with the goals of validating the Monte Carlo codes used in the design of the target; additionally, the performance was compared with the solid lead targets used before and after the MEGAPIE experiment.     

High-dose transdermal nicotine and naltrexone: Effects on nicotine withdrawal, urges, smoking, and effects of smoking.

Although treatment with transdermal nicotine replacement (TNR) has improved smoking abstinence rates, higher doses of TNR could improve effects on urge to smoke, nicotine withdrawal, and reinforcement from smoking, and naltrexone might further reduce reinforcement and urges. A laboratory investigation with 134 smokers using a 3 × 2 parallel-group design evaluated the effects of TNR (42-mg, 21-mg, or 0-mg patch) as crossed with a single dose of naltrexone (50 mg) versus placebo on urge to smoke, withdrawal, and responses to an opportunity to smoke (intake, subjective effects) after 10 hr of deprivation. Urge and withdrawal were assessed both prior to and after cigarette cue exposure. Only 42 mg TNR, not 21 mg, prevented urge to smoke, heart rate change, and cue-elicited increase in withdrawal. Both 21 and 42 mg TNR blocked cue-elicited drop in heart rate and arterial pressure. Naltrexone reduced cue-elicited withdrawal symptoms but not urges to smoke or deprivation-induced withdrawal prior to cue exposure. Neither medication significantly affected carbon monoxide intake or subjective effects of smoking except that 42 mg TNR resulted in lower subjective physiological activation. No interaction effects were found, and no results differed by gender. Results suggest that starting smokers with 42 mg TNR may increase comfort during initial abstinence, but limited support is seen for naltrexone during smoking abstinence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bleaching of Knitted Cotton Fabric Applying Ozone

Several samples of knitted cotton fabric in raw state were impregnated with a solution of industrial wetting detergent and treated directly with ozone gas in a closed chamber. Working conditions for the variables: time, pH of the impregnation solution, residual pickup and feed flow rate of oxygen (for ozone generation) were investigated to identify the effects on whiteness degree and fabric strength. The application of ozone proved to be an efficient and promising technology with low water usage.     

Correction to: Online Identity Crisis: Identity Issues in Online Communities

Minds and Machines - An amendment to this paper has been published and can be accessed via the original article.     

Olive mill wastewaters decontamination based on organo-nano-clay composites

Green composites for environmental applications were successfully prepared by intercalation of the biosurfactant Quillaja saponin onto montmorillonite mineral clay on varying pH and surfactant/clay ratio. Equilibrium adsorption isotherms were constructed and the system was characterized by performing TGA and XRD analyses.The efficiency of the surfactant-modified clay in the removal of the organic content present in olive mill wastewaters (OMW) was evaluated by means of spectrophotometric measurements. The interest for this cogent issue comes from the consideration that, despite their high pollutant content, OMW can be considered as a potential resource of several organic compounds which can be recovered for a wide array of pharmaceutical and industrial applications. Two different strategies were proposed. In the first one, dried surfactant/clay hybrids were added to the batch samples under continuous stirring, while in the second alternative approach the organoclays were packed in chromatography column filled with multiple alternate layers of sand and organoclay.These studies revealed the efficacy of the methods used and suggested that the modification of the montmorillonite clay substrate significantly improves the performance of the clay.     

Web-based environmental simulation: bridging the gap between scientific modeling and decision-making

Data availability in environmental sciences is growing rapidly. Conventional monitoring systems are collecting data at increasing spatial and temporal resolutions; satellites provide a constant stream of global observations, and citizen scientist generate local data with electronic gadgets and cheap devices. There is a need to process this stream of heterogeneous data into useful information, both for science and for decision-making. Advances in networking and computer technologies increasingly enable accessing, combining, processing, and visualizing these data. This Feature reflects upon the role of environmental models in this process. We consider models as the primary tool for data processing, pattern identification, and scenario analysis. As such, they are an essential element of science-based decision-making. The new technologies analyzed here have the potential to turn the typical top-down flow of information from scientists to users into a much more direct, interactive approach. This may accelerate the dissemination of environmental information to a larger community of users. It may also facilitate harvesting feedback, and evaluating simulations and predictions from different perspectives. However, the evolution poses challenges, not only to model development but also to the communication of model results and their assumptions, shortcomings, and errors.