Future directions for the management of pain in osteoarthritis.

Osteoarthritis (OA) is the predominant form of arthritis worldwide, resulting in a high degree of functional impairment and reduced quality of life owing to chronic pain. To date, there are no treatments that are known to modify disease progression of OA in the long term. Current treatments are largely based on the modulation of pain, including NSAIDs, opiates and, more recently, centrally acting pharmacotherapies to avert pain. This review will focus on the rationale for new avenues in pain modulation, including inhibition with anti-NGF antibodies and centrally acting analgesics. The authors also consider the potential for structure modification in cartilage/bone using growth factors and stem cell therapies. The possible mismatch between structural change and pain perception will also be discussed, introducing recent techniques that may assist in improved patient phenotyping of pain subsets in OA. Such developments could help further stratify subgroups and treatments for people with OA in future

Rapid identification of the medicinal plant Taraxacum formosanum and distinguishing of this plant from its adulterants by ribosomal DNA internal transcribed spacer (ITS) based DNA barcode

Original identification of medicinal plants is essential for quality control. In this study, the internal transcribed spacer 2 (ITS2) nuclear ribosomal DNA served as a DNA barcode and was amplified by allele-specific PCR. This approach was exploited to differentiate Taraxacum formosanum from five related adulterants. Using a set of designed PCR primers, a highly specific 223 bp PCR product of T. formosanum was successfully amplified by PCR. However, no similar DNA fragment was amplified from any of the other adulterants. This indicates that, our allele specific primers have high specificity and can accurately discriminate T. formosanum from its adulterant plants.Key words: Medicinal plant, polymerase chain reaction (PCR), authentication, Taraxacum formosanum, traditional Chinese medicinal, internal transcribed spacers 2 (ITS2)

Heralded quantum entanglement between two crystals

Quantum networks require the crucial ability to entangle quantum nodes. A prominent example is the quantum repeater which allows overcoming the distance barrier of direct transmission of single photons, provided remote quantum memories can be entangled in a heralded fashion. Here we report the observation of heralded entanglement between two ensembles of rare-earth-ions doped into separate crystals. A heralded single photon is sent through a 50/50 beamsplitter, creating a single-photon entangled state delocalized between two spatial modes. The quantum state of each mode is subsequently mapped onto a crystal, leading to an entangled state consisting of a single collective excitation delocalized between two crystals. This entanglement is revealed by mapping it back to optical modes and by estimating the concurrence of the retrieved light state. Our results highlight the potential of rare-earth-ions doped crystals for entangled quantum nodes and bring quantum networks based on solid-state resources one step closer.Comment: 10 pages, 5 figure

Efficient and long-lived quantum memory with cold atoms inside a ring cavity

Quantum memories are regarded as one of the fundamental building blocks of linear-optical quantum computation and long-distance quantum communication. A long standing goal to realize scalable quantum information processing is to build a long-lived and efficient quantum memory. There have been significant efforts distributed towards this goal. However, either efficient but short-lived or long-lived but inefficient quantum memories have been demonstrated so far. Here we report a high-performance quantum memory in which long lifetime and high retrieval efficiency meet for the first time. By placing a ring cavity around an atomic ensemble, employing a pair of clock states, creating a long-wavelength spin wave, and arranging the setup in the gravitational direction, we realize a quantum memory with an intrinsic spin wave to photon conversion efficiency of 73(2)% together with a storage lifetime of 3.2(1) ms. This realization provides an essential tool towards scalable linear-optical quantum information processing.Comment: 6 pages, 4 figure

Quantum teleportation between light and matter

Quantum teleportation is an important ingredient in distributed quantum networks, and can also serve as an elementary operation in quantum computers. Teleportation was first demonstrated as a transfer of a quantum state of light onto another light beam; later developments used optical relays and demonstrated entanglement swapping for continuous variables. The teleportation of a quantum state between two single material particles (trapped ions) has now also been achieved. Here we demonstrate teleportation between objects of a different nature - light and matter, which respectively represent 'flying' and 'stationary' media. A quantum state encoded in a light pulse is teleported onto a macroscopic object (an atomic ensemble containing 10^12 caesium atoms). Deterministic teleportation is achieved for sets of coherent states with mean photon number (n) up to a few hundred. The fidelities are 0.58+-0.02 for n=20 and 0.60+-0.02 for n=5 - higher than any classical state transfer can possibly achieve. Besides being of fundamental interest, teleportation using a macroscopic atomic ensemble is relevant for the practical implementation of a quantum repeater. An important factor for the implementation of quantum networks is the teleportation distance between transmitter and receiver; this is 0.5 metres in the present experiment. As our experiment uses propagating light to achieve the entanglement of light and atoms required for teleportation, the present approach should be scalable to longer distances.Comment: 23 pages, 8 figures, incl. supplementary informatio

Entanglement of spin waves among four quantum memories

Quantum networks are composed of quantum nodes that interact coherently by way of quantum channels and open a broad frontier of scientific opportunities. For example, a quantum network can serve as a `web' for connecting quantum processors for computation and communication, as well as a `simulator' for enabling investigations of quantum critical phenomena arising from interactions among the nodes mediated by the channels. The physical realization of quantum networks generically requires dynamical systems capable of generating and storing entangled states among multiple quantum memories, and of efficiently transferring stored entanglement into quantum channels for distribution across the network. While such capabilities have been demonstrated for diverse bipartite systems (i.e., N=2 quantum systems), entangled states with N > 2 have heretofore not been achieved for quantum interconnects that coherently `clock' multipartite entanglement stored in quantum memories to quantum channels. Here, we demonstrate high-fidelity measurement-induced entanglement stored in four atomic memories; user-controlled, coherent transfer of atomic entanglement to four photonic quantum channels; and the characterization of the full quadripartite entanglement by way of quantum uncertainty relations. Our work thereby provides an important tool for the distribution of multipartite entanglement across quantum networks.Comment: 4 figure

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

<p>Abstract</p> <p>Background</p> <p>Prediction of protein structural classes (<it>α</it>, <it>β</it>, <it>α </it>+ <it>β </it>and <it>α</it>/<it>β</it>) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%.</p> <p>Results</p> <p>We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the <it>chaos game representation </it>is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using <it>recurrence quantification analysis</it>, <it>K-string based information entropy </it>and <it>segment-based analysis</it>. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at <url>http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/</url>.</p> <p>Conclusion</p> <p>The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of <it>α </it>helices and <it>β </it>strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.</p

The impact of SARS on hospital performance

© 2008 Chu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Impedance of nanometer thickness ferromagnetic Co40Fe40B20 films

Nanocrystalline Co40Fe40B20 films, with film thickness tf = 100 nm, were deposited on glass substrates by the magnetron sputtering method at room temperature. During the film deposition period, a dc magnetic field, h = 40 Oe, was applied to introduce an easy axis for each film sample: one with h||L and the other with h||w, where L and w are the length and width of the film. Ferromagnetic resonance (FMR), ultrahigh frequency impedance (IM), dc electrical resistivity (ρ), and magnetic hysteresis loops (MHL) of these films were studied. From the MHL and r measurements, we obtain saturation magnetization 4πMs = 15.5 kG, anisotropy field Hk = 0.031 kG, and r = 168 mW.cm. From FMR, we can determine the Kittel mode ferromagnetic resonance (FMR-K) frequency fFMRK = 1,963 MHz. In the h||L case, IM spectra show the quasi-Kittel-mode ferromagnetic resonance (QFMR-K) at f0 and the Walker-mode ferromagnetic resonance (FMR-W) at fn, where n = 1, 2, 3, and 4. In the h||w case, IM spectra show QFMR-K at F0 and FMR-W at Fn. We find that f0 and F0 are shifted from fFMRK, respectively, and fn = Fn. The in-plane spin-wave resonances are responsible for those relative shifts