Do Newton's G and Milgrom's a_0 vary with cosmological epoch ?

In the scalar tensor gravitational theories Newton's constant G_N evolves in the expanding universe. Likewise, it has been speculated that the acceleration scale a_0 in Milgrom's modified Newtonian dynamics (MOND) is tied to the scale of the cosmos, and must thus evolve. With the advent of relativistic implementations of the modified dynamics, one can address the issue of variability of the two gravitational ''constants'' with some confidence. Using TeVeS, the Tensor-Vector-Scalar gravitational theory, as an implementation of MOND, we calculate the dependence of G_N and a_0 on the TeVeS parameters and the coeval cosmological value of its scalar field, \phi_c. We find that G_N, when expressed in atomic units, is strictly nonevolving, a result fully consistent with recent empirical limits on the variation of G_N. By contrast, we find that a_0 depends on \phi_c and may thus vary with cosmological epoch. However, for the brand of TeVeS which seems most promising, a_0 variation occurs on a timescale much longer than Hubble's, and should be imperceptible back to redshift unity or even beyond it. This is consistent with emergent data on the rotation curves of disk galaxies at significants redshifts.Comment: 9 pages, RevTe

Antigen and Thapsigargin Promote Influx of Ca2+ in Rat Basophilic RBL-2H3 Cells by Ostensibly Similar Mechanisms That Allow Filling of Inositol 1,4,5-Trisphosphate-Sensitive and Mitochondrial Ca2+ Stores

In single, Fura 2-loaded RBL-2H3 cells, antigen and thapsigargin depleted the same intracellular pool of Ca2+ in the absence of external Ca2+; provision of external Ca2+ induced immediate increases in levels of free Ca2+ ([Ca2+](i)). These increases were dependent on the presence of external Ca2+ and, presumably, on influx of Ca2+ across the cell membrane. Both stimulants enhanced intracellular accumulation of 45Ca2+ through ostensibly similar mechanisms because accumulation was blocked to similar extents by various multivalent cations or by depolarization with K+. Because thapsigargin blocked reuptake of Ca2+ into inositol 1,4,5-trisphosphate sensitive stores, uptake occurred independently of the refilling of these stores. Uptake was dependent instead on sequestration of 45Ca2+ in a pool of high capacity that was insensitive to thapsigargin, caffeine, GTP and inositol 1,4,5-trisphosphate but sensitive to ionomycin and mitochondrial inhibitors. The existence of an inositol 1,4,5-trisphosphate-insensitive pool was also apparent in permeabilized cells; at 0.1 μM [Ca2+](i), uptake of 45Ca2+ was largely confined (\u3e 80%) to the inositol 1,4,5-trisphosphate-sensitive pool, but at 2 μM [Ca2+](i) uptake was largely (\u3e 60%) into the inositol 1,4,5-trisphosphate-insensitive pool. Provision of mitochondrial inhibitors along with thapsigargin to block uptake into both pools, did not impair the thapsigargin-induced increase in [Ca2+](i) or influx of Ca2+, as indicated by changes in Fura 2 fluorescence, but did block the intracellular accumulation of 45Ca2+. The studies illustrate the utility of simultaneous measurements of [Ca2+](i) and 45Ca2+ uptake for a full accounting of Ca2+ homoeostasis as exemplified by the ability to distinguish between influx and mitochondrial uptake of Ca2+

Preferred frame parameters in the tensor-vector-scalar theory of gravity and its generalization

The Tensor-Vector-Scalar theory of gravity, which was designed as a relativistic implementation to the modified dynamics paradigm, has fared quite well as an alternative to dark matter, on both galactic and cosmological scales. However, its performance in the solar system, as embodied in the post-Newtonian formalism, has not yet been fully investigated. Tamaki has recently attempted to calculate the preferred frame parameters for TeVeS, but ignored the cosmological value of the scalar field, thus concluding that the Newtonian potential must be static in order to be consistent with the vector equation. We show that when the cosmological value of the scalar field is taken into account, there is no constraint on the Newtonian potential; however, the cosmological value of the scalar field is tightly linked to the vector field coupling constant K, preventing the former from evolving as predicted by its equation of motion. We then proceed to investigate the post-Newtonian limit of a generalized version of TeVeS, with {\AE}ther type vector action, and show that its \beta,\gamma and \xi parameters are as in GR, while solar system constraints on the preferred frame parameters \alpha_1 and \alpha_2 can be satisfied within a modest range of small values of the scalar and vector fields coupling parameters, and for values of the cosmological scalar field consistent with evolution within the framework of existing models.Comment: 16 pages, 2 figures Figures and corresponding discussion replaced; added reference

Assigning Diagnosis Codes Using Medication History

Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. We propose using patients' medication data to assign diagnosis codes. We present a proof-of-concept study using medical data from an American dataset (MIMIC-III) and Danish nationwide registers to train a machine-learning-based model that predicts an extensive collection of diagnosis codes for multiple levels of aggregation over a disease hierarchy. We further suggest a specialized loss function designed to utilize the innate hierarchical nature of the disease hierarchy. We evaluate the proposed method on a subset of 567 disease codes. Moreover, we investigate the technique's generalizability and transferability by (1) training and testing models on the same subsets of disease codes over the two medical datasets and (2) training models on the American dataset while evaluating them on the Danish dataset, respectively. Results demonstrate the proposed method can correctly assign diagnosis codes on multiple levels of aggregation from the disease hierarchy over the American dataset with recall 70.0% and precision 69.48% for top-10 assigned codes; thereby being comparable to text-based techniques. Furthermore, the specialized loss function performs consistently better than the non-hierarchical state-of-the-art version. Moreover, results suggest the proposed method is language and dataset-agnostic, with initial indications of transferability over subsets of disease codes