Magnifying superlens in the visible frequency range

In this communication we introduce a new design of the magnifying superlens and demonstrate it in the experiment.Comment: 3pages, 1 figur

The Optimal Decoupled Liabilities: A General Analysis

The “decoupled” liability system awards the plaintiff an amount that differs from what the defendant pays. The previous approach to the optimal decoupling design is based on the assumption of complete information, which results in an optimal liability for the defendant “as much as he can afford.” This extreme conclusion may hinder the acceptability of the decoupling system. This paper proposes an alternative design based on the assumption that agents in the post-accident subgame have asymmetric information. Our model indicates that the optimal penalty faced by the defendant is generally greater than the optimal award to the plaintiff. When the potential harm is sufficiently large, the optimal penalty can be approximated by a multiple of the harm, but the plaintiff receives only a finite amount of the damages regardless of the loss suffered. Such a decoupling scheme deters frivolous lawsuits without reducing the defendants’ incentives to exercise care. Additionally, this paper derives comparative static results concerning how the trial costs of the plaintiff and defendant affect the optimal design of decoupling.

Approximate gauge symmetry of composite vector bosons

It can be shown in a solvable field theory model that the couplings of the composite vector bosons made of a fermion pair approach the gauge couplings in the limit of strong binding. Although this phenomenon may appear accidental and special to the vector boson made of a fermion pair, we extend it to the case of bosons being constituents and find that the same phenomenon occurs in more an intriguing way. The functional formalism not only facilitates computation but also provides us with a better insight into the generating mechanism of approximate gauge symmetry, in particular, how the strong binding and global current conservation conspire to generate such an approximate symmetry. Remarks are made on its possible relevance or irrelevance to electroweak and higher symmetries.Comment: Correction of typos. The published versio

Carbonate mineral saturation states in the East China Sea: present conditions and future scenarios

To assess the impact of rising atmospheric CO₂ and eutrophication on the carbonate chemistry of the East China Sea shelf waters, saturation states (Ω) for two important biologically relevant carbonate minerals – calcite (Ω_c) and aragonite (Ω_a) – were calculated throughout the water column from dissolved inorganic carbon (DIC) and total alkalinity (TA) data collected in spring and summer of 2009. Results show that the highest Ω_c (∼9.0) and Ω_a (∼5.8) values were found in surface water of the Changjiang plume area in summer, whereas the lowest values (Ω_c = ∼2.7 and Ω_a = ∼1.7) were concurrently observed in the bottom water of the same area. This divergent behavior of saturation states in surface and bottom waters was driven by intensive biological production and strong stratification of the water column. The high rate of phytoplankton production, stimulated by the enormous nutrient discharge from the Changjiang, acts to decrease the ratio of DIC to TA, and thereby increases Ω values. In contrast, remineralization of organic matter in the bottom water acts to increase the DIC to TA ratio, and thus decreases Ω values. The projected result shows that continued increases of atmospheric CO₂ under the IS92a emission scenario will decrease Ω values by 40–50% by the end of this century, but both the surface and bottom waters will remain supersaturated with respect to calcite and aragonite. Nevertheless, superimposed on such Ω decrease is the increasing eutrophication, which would mitigate or enhance the Ω decline caused by anthropogenic CO₂ uptake in surface and bottom waters, respectively. Our simulation reveals that, under the combined impact of eutrophication and augmentation of atmospheric CO₂, the bottom water of the Changjiang plume area will become undersaturated with respect to aragonite (Ω_a = ∼0.8) by the end of this century, which would threaten the health of the benthic ecosystem

Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

This work was supported by a United States Department of Agriculture-Cooperative State Research, Education, and Extension Service grant (no. 2009-35318-05032), a Biotechnology Research grant (no. 2007-BRG-1223) from the North Carolina Biotechnology Center, and a startup fund from the Golden LEAF Foundation to the Biomanufacturing Research Institute and Technology Enterprise (BRITE).Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.Publisher PDFPeer reviewe

IL-33 ameliorates Alzheimer’s disease-like pathology and cognitive decline

Alzheimer’s disease (AD) is a devastating condition with no known effective treatment. AD is characterized by memory loss as well as impaired locomotor ability, reasoning, and judgment. Emerging evidence suggests that the innate immune response plays a major role in the pathogenesis of AD. In AD, the accumulation of β-amyloid (Aβ) in the brain perturbs physiological functions of the brain, including synaptic and neuronal dysfunction, microglial activation, and neuronal loss. Serum levels of soluble ST2 (sST2), a decoy receptor for interleukin (IL)-33, increase in patients with mild cognitive impairment, suggesting that impaired IL-33/ST2 signaling may contribute to the pathogenesis of AD. Therefore, we investigated the potential therapeutic role of IL-33 in AD, using transgenic mouse models. Here we report that IL-33 administration reverses synaptic plasticity impairment and memory deficits in APP/PS1 mice. IL-33 administration reduces soluble Aβ levels and amyloid plaque deposition by promoting the recruitment and Aβ phagocytic activity of microglia; this is mediated by ST2/p38 signaling activation. Furthermore, IL-33 injection modulates the innate immune response by polarizing microglia/macrophages toward an antiinflammatory phenotype and reducing the expression of proinflammatory genes, including IL-1β, IL-6, and NLRP3, in the cortices of APP/PS1 mice. Collectively, our results demonstrate a potential therapeutic role for IL-33 in AD

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications

A new mechanism for a naturally small Dirac neutrino mass

A mechanism is proposed in which a right-handed neutrino zero mode and a right-handed charged lepton zero mode can be localized at the same place along an extra compact dimension while having markedly different spreads in their wave functions: a relatively narrow one for the neutrino and a rather broad one for the charged lepton. In their overlaps with the wave function for the left-handed zero modes, this mechanism could produce a natural large hierarchy in the effective Yukawa couplings in four dimensions, and hence a large disparity in masses.Comment: 6 pages (2 with figures), twocolumn forma