Quantum control of electromagnetically induced transparency dispersion via atomic tunneling in a double-well Bose-Einstein condensate

Electromagnetically induced transparency (EIT) is an important tool for controlling light propagation and nonlinear wave mixing in atomic gases with potential applications ranging from quantum computing to table top tests of general relativity. Here we consider EIT in an atomic Bose-Einstein condensate (BEC) trapped in a double-well potential. A weak probe laser propagates through one of the wells and interacts with atoms in a three-level Lambda configuration. The well through which the probe propagates is dressed by a strong control laser with Rabi frequency Omega(mu), as in standard EIT systems. Tunneling between the wells at the frequency g provides a coherent coupling between identical electronic states in the two wells, which leads to the formation of interwell dressed states. The macroscopic interwell coherence of the BEC wave function results in the formation of two ultranarrow absorption resonances for the probe field that are inside of the ordinary EIT transparency window. We show that these new resonances can be interpreted in terms of the interwell dressed states and the formation of a type of dark state involving the control laser and the interwell tunneling. To either side of these ultranarrow resonances there is normal dispersion with very large slope controlled by g. We discuss prospects for observing these ultranarrow resonances and the corresponding regions of high dispersion experimentally.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Role of dietary fats in modulating cardiometabolic risk during moderate weight gain : a randomized double-blind overfeeding trial (LIPOGAIN study)

BACKGROUND: Whether the type of dietary fat could alter cardiometabolic responses to a hypercaloric diet is unknown. In addition, subclinical cardiometabolic consequences of moderate weight gain require further study. METHODS AND RESULTS: In a 7-week, double-blind, parallel-group, randomized controlled trial, 39 healthy, lean individuals (mean age of 27±4) consumed muffins (51% of energy [%E] from fat and 44%E refined carbohydrates) providing 750 kcal/day added to their habitual diets. All muffins had identical contents, except for type of fat; sunflower oil rich in polyunsaturated fatty acids (PUFA diet) or palm oil rich in saturated fatty acids (SFA diet). Despite comparable weight gain in the 2 groups, total: high-density lipoprotein (HDL) cholesterol, low-density lipoprotein:HDL cholesterol, and apolipoprotein B:AI ratios decreased during the PUFA versus the SFA diet (-0.37±0.59 versus +0.07±0.29, -0.31±0.49 versus +0.05±0.28, and -0.07±0.11 versus +0.01±0.07, P=0.003, P=0.007, and P=0.01 for between-group differences), whereas no significant differences were observed for other cardiometabolic risk markers. In the whole group (ie, independently of fat type), body weight increased (+2.2%, P<0.001) together with increased plasma proinsulin (+21%, P=0.007), insulin (+17%, P=0.003), proprotein convertase subtilisin/kexin type 9, (+9%, P=0.008) fibroblast growth factor-21 (+31%, P=0.04), endothelial markers vascular cell adhesion molecule-1, intercellular adhesion molecule-1, and E-selectin (+9, +5, and +10%, respectively, P<0.01 for all), whereas nonesterified fatty acids decreased (-28%, P=0.001). CONCLUSIONS: Excess energy from PUFA versus SFA reduces atherogenic lipoproteins. Modest weight gain in young individuals induces hyperproinsulinemia and increases biomarkers of endothelial dysfunction, effects that may be partly outweighed by the lipid-lowering effects of PUFA. CLINICAL TRIAL REGISTRATION URL: http://ClinicalTrials.gov. Unique identifier: NCT01427140.

Double stranded DNA-binding studies of potential Rhodium(ll) antitumor complexes

In 1952, Dwyer and coworkers began testing a series of metal complexes for potential inhibition of cancer cell proliferation in animals.[l] The complexes tested were unsuitable for such studies due to their high toxicity. Therefore, no further work was done on the project. However, in 1965, Rosenberg and coworkers revisited the possibility of potential metal-based drugs. Serendipitously, they discovered that cis-diamminedichloroplatinum(lI) (cisplatin) inhibits cell division in E. coli.[2] Further studies of this and other platinum compounds revealed inhibition of tumor cell lines sarcoma 180 and leukemia LI2l0 in mice.[l] Cisplatin was approved by the Food and Drug Administration in 1970 as a chemical chemotherapeutic agent in the treatment of cancer. The drug has primarily been used in the treatment of testicular and ovarian cancers, although the powerful chemotherapeutic properties of the compound indicate use against a variety of other cancers.[3] The toxicity of this compound, however, warrants the development of other metal-based potential antitumor agents. The success of cisplatin, a transition-metal-based chemotherapeutic, opened the doors to a host of research on the antitumor effects of other transition-metal complexes. Beginning in the 1970s, researchers looked to rhodium for potential use in antitumor complexes. Dirhodium complexes with bridging equatorial ligands (Figure I) were the primary focus for this research. The overwhelming majority of these complexes were dirhodium(II) carboxylate complexes, containing two rhodium(II) centers, four equatorial ligands in a lantero formation around the metal center, and an axial ligand on either end. The family of complexes in Figure 1 will be referred to as dirhodium(II) carboxylate complexes. The dirhodium centers are each d? with a metal-metal bond between them. Although d? atoms are paramagnetic, the two unpaired electrons pair to make the complex diamagnetic. The basic formula of the dirhodium(lI) carboxylate complexes is Rh?(RCOO)?(L)? with R being methyl, ethyl, propyl, or butyl groups and L being water or the solvent in which the complex was crystalized. Of these dirbodium(II) carboxylate complexes, our research focuses on Rb la and two other similar complexes Rh2 and Rh3 (Figure 2). Rh2 is an activated form of Rhla, with four acetonitrile groups in place of two of the bidentate acetate ligands. Rh3 is similar to Rhla, with trifluoromethyl groups in place of the methyl groups on the acetate ligands.