Studies of the Decay B+- -> D_CP K+-

We report studies of the decay B+- -> D_CP K+-, where D_CP denotes neutral D mesons that decay to CP eigenstates. The analysis is based on a 29.1/fb data sample of collected at the \Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric e+ e- storage ring. Ratios of branching fractions of Cabibbo-suppressed to Cabibbo-favored processes involving D_CP are determined to be B(B- -> D_1 K-)/B(B- -> D_1 pi-)=0.125 +- 0.036 +- 0.010 and B(B- -> D_2 K-)/B(B- -> D_2 pi-)=0.119 +- 0.028 +- 0.006, where indices 1 and 2 represent the CP=+1 and CP=-1 eigenstates of the D0 - anti D0 system, respectively. We also extract the partial rate asymmetries for B+- -> D_CP K+-, finding A_1 = 0.29 +- 0.26 +- 0.05 and A_2 = -0.22 +- 0.24 +- 0.04.Comment: 10 pages, 2 figures, submitted to Physical Review Letter

Two Notch Ligands, Dll1 and Jag1, Are Differently Restricted in Their Range of Action to Control Neurogenesis in the Mammalian Spinal Cord

Notch signalling regulates neuronal differentiation in the vertebrate nervous system. In addition to a widespread function in maintaining neural progenitors, Notch signalling has also been involved in specific neuronal fate decisions. These functions are likely mediated by distinct Notch ligands, which show restricted expression patterns in the developing nervous system. Two ligands, in particular, are expressed in non-overlapping complementary domains of the embryonic spinal cord, with Jag1 being restricted to the V1 and dI6 progenitor domains, while Dll1 is expressed in the remaining domains. However, the specific contribution of different ligands to regulate neurogenesis in vertebrate embryos is still poorly understood.In this work, we investigated the role of Jag1 and Dll1 during spinal cord neurogenesis, using conditional knockout mice where the two genes are deleted in the neuroepithelium, singly or in combination. Our analysis showed that Jag1 deletion leads to a modest increase in V1 interneurons, while dI6 neurogenesis was unaltered. This mild Jag1 phenotype contrasts with the strong neurogenic phenotype detected in Dll1 mutants and led us to hypothesize that neighbouring Dll1-expressing cells signal to V1 and dI6 progenitors and restore neurogenesis in the absence of Jag1. Analysis of double Dll1;Jag1 mutant embryos revealed a stronger increase in V1-derived interneurons and overproduction of dI6 interneurons. In the presence of a functional Dll1 allele, V1 neurogenesis is restored to the levels detected in single Jag1 mutants, while dI6 neurogenesis returns to normal, thereby confirming that Dll1-mediated signalling compensates for Jag1 deletion in V1 and dI6 domains.Our results reveal that Dll1 and Jag1 are functionally equivalent in controlling the rate of neurogenesis within their expression domains. However, Jag1 can only activate Notch signalling within the V1 and dI6 domains, whereas Dll1 can signal to neural progenitors both inside and outside its domains of expression

Sheldon Spectrum and the Plankton Paradox: Two Sides of the Same Coin : A trait-based plankton size-spectrum model

The Sheldon spectrum describes a remarkable regularity in aquatic ecosystems: the biomass density as a function of logarithmic body mass is approximately constant over many orders of magnitude. While size-spectrum models have explained this phenomenon for assemblages of multicellular organisms, this paper introduces a species-resolved size-spectrum model to explain the phenomenon in unicellular plankton. A Sheldon spectrum spanning the cell-size range of unicellular plankton necessarily consists of a large number of coexisting species covering a wide range of characteristic sizes. The coexistence of many phytoplankton species feeding on a small number of resources is known as the Paradox of the Plankton. Our model resolves the paradox by showing that coexistence is facilitated by the allometric scaling of four physiological rates. Two of the allometries have empirical support, the remaining two emerge from predator-prey interactions exactly when the abundances follow a Sheldon spectrum. Our plankton model is a scale-invariant trait-based size-spectrum model: it describes the abundance of phyto- and zooplankton cells as a function of both size and species trait (the maximal size before cell division). It incorporates growth due to resource consumption and predation on smaller cells, death due to predation, and a flexible cell division process. We give analytic solutions at steady state for both the within-species size distributions and the relative abundances across species

Diffusion patterns of new anti-diabetic drugs into hospitals in Taiwan: the case of Thiazolidinediones for diabetes

<p>Abstract</p> <p>Background</p> <p>Diffusion of new drugs in the health care market affects patients' access to new treatment options and health care expenditures. We examined how a new drug class for diabetes mellitus, thiazolidinediones (TZDs), diffused in the health care market in Taiwan.</p> <p>Methods</p> <p>Assuming that monthly hospital prescriptions of TZDs could serve as a micro-market to perform drug penetration studies, we retrieved monthly TZD prescription data for 580 hospitals in Taiwan from Taiwan's National Health Insurance Research Database for the period between March 1, 2001 and December 31, 2005. Three diffusion parameters, time to adoption, speed of penetration (monthly growth on prescriptions), and peak penetration (maximum monthly prescription) were evaluated. Cox proportional hazards model and quantile regressions were estimated for analyses on the diffusion parameters.</p> <p>Results</p> <p>Prior hospital-level pharmaceutical prescription concentration significantly deterred the adoption of the new drug class (HR: 0.02, 95%CI = 0.01 to 0.04). Adoption of TZDs was slower in district hospitals (HR = 0.43, 95%CI = 0.24 to 0.75) than medical centers and faster in non-profit hospitals than public hospitals (HR = 1.79, 95%CI = 1.23 to 2.61). Quantile regression showed that penetration speed was associated with a hospital's prior anti-diabetic prescriptions (25%Q: 18.29; 50%Q: 25.57; 75%Q: 30.97). Higher peaks were found in hospitals that had adopted TZD early (25%Q: -40.33; 50%Q: -38.65; 75%Q: -32.29) and in hospitals in which the drugs penetrated more quickly (25%Q: 16.53; 50%Q: 24.91; 75%Q: 31.50).</p> <p>Conclusions</p> <p>Medical centers began to prescribe TZDs earlier, and they prescribed more TZDs at a faster pace. The TZD diffusion patterns varied among hospitals depending accreditation level, ownership type, and prescription volume of Anti-diabetic drugs.</p

Diffusion patterns of new anti-diabetic drugs into hospitals in Taiwan: the case of Thiazolidinediones for diabetes

<p>Abstract</p> <p>Background</p> <p>Diffusion of new drugs in the health care market affects patients' access to new treatment options and health care expenditures. We examined how a new drug class for diabetes mellitus, thiazolidinediones (TZDs), diffused in the health care market in Taiwan.</p> <p>Methods</p> <p>Assuming that monthly hospital prescriptions of TZDs could serve as a micro-market to perform drug penetration studies, we retrieved monthly TZD prescription data for 580 hospitals in Taiwan from Taiwan's National Health Insurance Research Database for the period between March 1, 2001 and December 31, 2005. Three diffusion parameters, time to adoption, speed of penetration (monthly growth on prescriptions), and peak penetration (maximum monthly prescription) were evaluated. Cox proportional hazards model and quantile regressions were estimated for analyses on the diffusion parameters.</p> <p>Results</p> <p>Prior hospital-level pharmaceutical prescription concentration significantly deterred the adoption of the new drug class (HR: 0.02, 95%CI = 0.01 to 0.04). Adoption of TZDs was slower in district hospitals (HR = 0.43, 95%CI = 0.24 to 0.75) than medical centers and faster in non-profit hospitals than public hospitals (HR = 1.79, 95%CI = 1.23 to 2.61). Quantile regression showed that penetration speed was associated with a hospital's prior anti-diabetic prescriptions (25%Q: 18.29; 50%Q: 25.57; 75%Q: 30.97). Higher peaks were found in hospitals that had adopted TZD early (25%Q: -40.33; 50%Q: -38.65; 75%Q: -32.29) and in hospitals in which the drugs penetrated more quickly (25%Q: 16.53; 50%Q: 24.91; 75%Q: 31.50).</p> <p>Conclusions</p> <p>Medical centers began to prescribe TZDs earlier, and they prescribed more TZDs at a faster pace. The TZD diffusion patterns varied among hospitals depending accreditation level, ownership type, and prescription volume of Anti-diabetic drugs.</p

Optimizing Combination Therapies with Existing and Future CML Drugs

Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2]