Short-term in vitro responses of human peripheral blood monocytes to ferritic stainless steel fiber networks.

Beneficial effects on bone-implant bonding may accrue from ferromagnetic fiber networks on implants which can deform in vivo inducing controlled levels of mechanical strain directly in growing bone. This approach requires ferromagnetic fibers that can be implanted in vivo without stimulating undue inflammatory cell responses or cytotoxicity. This study examines the short-term in vitro responses, including attachment, viability, and inflammatory stimulation, of human peripheral blood monocytes to 444 ferritic stainless steel fiber networks. Two types of 444 networks, differing in fiber cross section and thus surface area, were considered alongside austenitic stainless steel fiber networks, made of 316L, a widely established implant material. Similar high percent seeding efficiencies were measured by CyQuant® on all fiber networks after 48 h of cell culture. Extensive cell attachment was confirmed by fluorescence and scanning electron microscopy, which showed round monocytes attached at various depths into the fiber networks. Medium concentrations of lactate dehydrogenase (LDH) and tumor necrosis factor alpha (TNF-α) were determined as indicators of viability and inflammatory responses, respectively. Percent LDH concentrations were similar for both 444 fiber networks at all time points, whereas significantly lower than those of 316L control networks at 24 h. All networks elicited low-level secretions of TNF-α, which were significantly lower than that of the positive control wells containing zymosan. Collectively, the results indicate that 444 networks produce comparable responses to medical implant grade 316L networks and are able to support human peripheral blood monocytes in short-term in vitro cultures without inducing significant inflammatory or cytotoxic effects.

Characterising the neutron and gamma responses of the NPL liquid scintillation detectors.

The absolute responses of the NPL liquid scintillation spectrometers to monoenergetic neutrons and gammas were measured at various energies in the ranges 1.2 - 17 MeV approximately for neutrons and 0.28 - 1.8 MeV for gammas. Additional measurements of the proton light output function were also carried out. Calculated responses were then obtained for the larger detector using the programs NRESP7 and PHRESP, and compared with the absolute measurements. Finally, response matrices for this detector were generated using responses calculated at closely spaced energies.

Room temperature all-silicon photonic crystal nanocavity light emitting diode at sub-bandgap wavelengths

Silicon is now firmly established as a high performance photonic material. Its only weakness is the lack of a native electrically driven light emitter that operates CW at room temperature, exhibits a narrow linewidth in the technologically important 1300-1600 nm wavelength window, is small and operates with low power consumption. Here, an electrically pumped all-silicon nano light source around 1300-1600 nm range is demonstrated at room temperature. Using hydrogen plasma treatment, nano-scale optically active defects are introduced into silicon, which then feed the photonic crystal nanocavity to enhance the electrically driven emission in a device via Purcell effect. A narrow (Δλ=0.5 nm) emission line at 1515 nm wavelength with a power density of 0.4mW/cm2 is observed, which represents the highest spectral power density ever reported from any silicon emitter. A number of possible improvements are also discussed, that make this scheme a very promising light source for optical interconnects and other important silicon photonics applications. © 2012 by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Enhancement of room temperature sub-bandgap light emission from silicon photonic crystal nanocavity by Purcell effect

We report the enhancement of sub-bandgap photoluminescence from silicon via the Purcell effect. We couple the defect emission from silicon, which is believed to be due to hydrogen incorporation into the lattice, to a photonic crystal (PhC) nanocavity. We observe an up to 300-fold enhancement of the emission at room temperature at 1550 nm, as compared to an unpatterned sample, which is then comparable to the silicon band-edge emission. We discuss the possibility of enhancing this emission even further by introducing additional defects by ion implantation, or by treating the silicon PhC nanocavity with hydrogen plasma. © 2011 Elsevier B.V.

Empathic neural responses are modulated by the perceived fairness of others.

The neural processes underlying empathy are a subject of intense interest within the social neurosciences. However, very little is known about how brain empathic responses are modulated by the affective link between individuals. We show here that empathic responses are modulated by learned preferences, a result consistent with economic models of social preferences. We engaged male and female volunteers in an economic game, in which two confederates played fairly or unfairly, and then measured brain activity with functional magnetic resonance imaging while these same volunteers observed the confederates receiving pain. Both sexes exhibited empathy-related activation in pain-related brain areas (fronto-insular and anterior cingulate cortices) towards fair players. However, these empathy-related responses were significantly reduced in males when observing an unfair person receiving pain. This effect was accompanied by increased activation in reward-related areas, correlated with an expressed desire for revenge. We conclude that in men (at least) empathic responses are shaped by valuation of other people's social behaviour, such that they empathize with fair opponents while favouring the physical punishment of unfair opponents, a finding that echoes recent evidence for altruistic punishment.

Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum.

Food preferences are acquired through experience and can exert strong influence on choice behavior. In order to choose which food to consume, it is necessary to maintain a predictive representation of the subjective value of the associated food stimulus. Here, we explore the neural mechanisms by which such predictive representations are learned through classical conditioning. Human subjects were scanned using fMRI while learning associations between arbitrary visual stimuli and subsequent delivery of one of five different food flavors. Using a temporal difference algorithm to model learning, we found predictive responses in the ventral midbrain and a part of ventral striatum (ventral putamen) that were related directly to subjects' actual behavioral preferences. These brain structures demonstrated divergent response profiles, with the ventral midbrain showing a linear response profile with preference, and the ventral striatum a bivalent response. These results provide insight into the neural mechanisms underlying human preference behavior.

Controlling Sub-nm Gaps in Plasmonic Dimers using Graphene

Graphene is used as the thinnest possible spacer between gold nanoparticles and a gold substrate. This creates a robust, repeatable, and stable sub-nanometre gap for massive plasmonic field enhancements. White light spectroscopy of single 80 nm gold nanoparticles reveals plasmonic coupling between the particle and its image within the gold substrate. While for a single graphene layer, spectral doublets from coupled dimer modes are observed shifted into the near infra-red, these disappear for increasing numbers of layers. These doublets arise from plasmonic charge transfer, allowing the direct optical measurement of out-of-plane conductivity in such layered systems. Gating the graphene can thus directly produce plasmon tuning.

Using sub-word-level information for confidence estimation with conditional random field models

The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.

Enhanced infra-red emission from sub-millimeter microelectromechanical systems micro hotplates via inkjet deposited carbon nanoparticles and fullerenes

In this paper, we demonstrate a micro-inkjet printing technique as a reproducible post-process for the deposition of carbon nanoparticles and fullerene adlayers onto fully CMOS compatible micro-electro-mechanical silicon-on-insulator infrared (IR) light sources to enhance their infrared emission. We show experimentally a significant increase in the infrared emission efficiency of the coated emitters. We numerically validate these findings with models suggesting a dominant performance increase for wavelengths <5.5 μm. Here, the bimodal size distribution in the diameter of the carbon nanoparticles, relative to the fullerenes, is an effective mediator towards topologically enhanced emittance of our miniaturised emitters. A 90% improvement in IR emission power density has been shown which we have rationalised with an increase in the mean thickness of the deposited carbon nanoparticle adlayer. © 2013 AIP Publishing LLC.