A solid state light-matter interface at the single photon level

Coherent and reversible mapping of quantum information between light and matter is an important experimental challenge in quantum information science. In particular, it is a decisive milestone for the implementation of quantum networks and quantum repeaters. So far, quantum interfaces between light and atoms have been demonstrated with atomic gases, and with single trapped atoms in cavities. Here we demonstrate the coherent and reversible mapping of a light field with less than one photon per pulse onto an ensemble of 10 millions atoms naturally trapped in a solid. This is achieved by coherently absorbing the light field in a suitably prepared solid state atomic medium. The state of the light is mapped onto collective atomic excitations on an optical transition and stored for a pre-programmed time up of to 1 mu s before being released in a well defined spatio-temporal mode as a result of a collective interference. The coherence of the process is verified by performing an interference experiment with two stored weak pulses with a variable phase relation. Visibilities of more than 95% are obtained, which demonstrates the high coherence of the mapping process at the single photon level. In addition, we show experimentally that our interface allows one to store and retrieve light fields in multiple temporal modes. Our results represent the first observation of collective enhancement at the single photon level in a solid and open the way to multimode solid state quantum memories as a promising alternative to atomic gases.Comment: 5 pages, 5 figures, version submitted on June 27 200

Identification of Potent EGFR Inhibitors from TCM Database@Taiwan

Overexpression of epidermal growth factor receptor (EGFR) has been associated with cancer. Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells. Hence, we employed Traditional Chinese Medicine Database (TCM Database@Taiwan) (http://tcm.cmu.edu.tw) to identify potential EGFR inhibitor. Multiple Linear Regression (MLR), Support Vector Machine (SVM), Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarities Indices Analysis (CoMSIA) models were generated using a training set of EGFR ligands of known inhibitory activities. The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid, and all had higher binding affinities than the control Iressa®. The TCM candidates had interactions with Asp855, Lys716, and Lys728, all which are residues of the protein kinase binding site. Validated MLR (r² = 0.7858) and SVM (r² = 0.8754) models predicted good bioactivity for the TCM candidates. In addition, the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship (3D-QSAR) map derived from the CoMFA (q² = 0.721, r² = 0.986) and CoMSIA (q² = 0.662, r² = 0.988) models. The steric field, hydrophobic field, and H-bond of the 3D-QSAR map were well matched by each TCM candidate. Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain. Based on the different structures, H-bonds were formed at either Asp855 or Lys716/Lys728. The compounds remained stable throughout molecular dynamics (MD) simulation. Based on the results of this study, 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University (CMU98-TCM)China Medical University (CMU99-TCM)China Medical University (CMU99-S-02)China Medical University (CMU99-ASIA-25)China Medical University (CMU99-ASIA-26)China Medical University (CMU99-ASIA-27)China Medical University (CMU99-ASIA-28)Asia UniversityTaiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005

Asymmetric neurotransmitter release enables rapid odor lateralization in Drosophila

In Drosophila, most individual olfactory receptor neurons (ORNs) project bilaterally to both sides of the brain1,2. Having bilateral rather than unilateral projections may represent a useful redundancy. However, bilateral ORN projections to the brain should also compromise the ability to lateralize odors. Nevertheless, walking or flying Drosophila reportedly turn toward their more strongly stimulated antenna3-5. Here we show that each ORN spike releases ~40% more neurotransmitter from the axon branch ipsilateral to the soma, as compared to the contralateral branch. As a result, when an odor activates the antennae asymmetrically, ipsilateral central neurons begin to spike a few milliseconds before contralateral neurons, and ipsilateral central neurons also fire at a 30-50% higher rate. We show that a walking fly can detect a 5% asymmetry in total ORN input to its left and right antennal lobes, and can turn toward the odor in less time than it requires the fly to complete a stride. These results demonstrate that neurotransmitter release properties can be tuned independently at output synapses formed by a single axon onto two target cells with identical functions and morphologies. Our data also show that small differences in spike timing and spike rate can produce reliable differences in olfactory behavior

MCL-1 antagonism enhances the anti-invasive effects of dasatinib in pancreatic adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies. It is phenotypically heterogeneous with a highly unstable genome and provides few common therapeutic targets. We found that MCL1, Cofilin1 (CFL1) and SRC mRNA were highly expressed by a wide range of these cancers, suggesting that a strategy of dual MCL-1 and SRC inhibition might be efficacious for many patients. Immunohistochemistry revealed that MCL-1 protein was present at high levels in 94.7% of patients in a cohort of PDACs from Australian Pancreatic Genome Initiative (APGI). High MCL1 and Cofilin1 mRNA expression was also strongly predictive of poor outcome in the TCGA dataset and in the APGI cohort. In culture, MCL-1 antagonism reduced the level of the cytoskeletal remodeling protein Cofilin1 and phosphorylated SRC on the active Y416 residue, suggestive of reduced invasive capacity. The MCL-1 antagonist S63845 synergized with the SRC kinase inhibitor dasatinib to reduce cell viability and invasiveness through 3D-organotypic matrices. In preclinical murine models, this combination reduced primary tumor growth and liver metastasis of pancreatic cancer xenografts. These data suggest that MCL-1 antagonism, while reducing cell viability, may have an additional benefit in increasing the antimetastatic efficacy of dasatinib for the treatment of PDAC

Testing the theory of immune selection in cancers that break the rules of transplantation

Modification of cancer cells likely to reduce their immunogenicity, including loss or down-regulation of MHC molecules, is now well documented and has become the main support for the concept of immune surveillance. The evidence that these modifications, in fact, result from selection by the immune system is less clear, since the possibility that they may result from reorganized metabolism associated with proliferation or from cell de-differentiation remains. Here, we (a) survey old and new transplantation experiments that test the possibility of selection and (b) survey how transmissible tumours of dogs and Tasmanian devils provide naturally evolved tests of immune surveillance

Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/

Differences in Efficacy and Safety of Pharmaceutical Treatments between Men and Women: An Umbrella Review

Being male or female is an important determinant of risks for certain diseases, patterns of illness and life expectancy. Although differences in risks for and prognoses of several diseases have been well documented, sex-based differences in responses to pharmaceutical treatments and accompanying risks of adverse events are less clear. The objective of this umbrella review was to determine whether clinically relevant differences in efficacy and safety of commonly prescribed medications exist between men and women. We retrieved all available systematic reviews of the Oregon Drug Effectiveness Review Project published before January 2010. Two persons independently reviewed each report to identify relevant studies. We dually abstracted data from the original publications into standardized forms. We synthesized the available evidence for each drug class and rated its quality applying the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Findings, based on 59 studies and data of more than 250,000 patients suggested that for the majority of drugs no substantial differences in efficacy and safety exist between men and women. Some clinically important exceptions, however, were apparent: women experienced substantially lower response rates with newer antiemetics than men (45% vs. 58%; relative risk 1.49, 95% confidence interval 1.35–1.64); men had higher rates of sexual dysfunction than women while on paroxetine for major depressive disorder; women discontinued lovastatin more frequently than men because of adverse events. Overall, for the majority of drugs sex does not appear to be a factor that has to be taken into consideration when choosing a drug treatment. The available body of evidence, however, was limited in quality and quantity, confining the range and certainty of our conclusions

Notch1 deficiency decreases hepatic lipid accumulation by induction of fatty acid oxidation

Notch signaling pathways modulate various cellular processes, including cell proliferation, differentiation, adhesion, and communication. Recent studies have demonstrated that Notch1 signaling also regulates hepatic glucose production and lipid synthesis. However, the effect of Notch1 signaling on hepatic lipid oxidation has not yet been directly investigated. To define the function of Notch1 signaling in hepatic lipid metabolism, wild type mice and Notch1 deficient antisense transgenic (NAS) mice were fed a high-fat diet. High-fat diet-fed NAS mice exhibited a marked reduction in hepatic triacylglycerol accumulation compared with wild type obese mice. The improved fatty liver was associated with an increased expression of hepatic genes involved in fatty acid oxidation. However, lipogenic genes were not differentially expressed in the NAS liver, suggesting lipolytic-specific regulatory effects by Notch1 signaling. Expression of fatty acid oxidative genes and the rate of fatty acid oxidation were also increased by inhibition of Notch1 signaling in HepG2 cells. In addition, similar regulatory effects on lipid accumulation were observed in adipocytes. Taken together, these data show that inhibition of Notch1 signaling can regulate the expression of fatty acid oxidation genes and may provide therapeutic strategies in obesity-induced hepatic steatosisopen0