Relaxing the Big-bang Bound to the Baryon Density

In the standard picture of big-bang nucleosynthesis the yields of D, $^3$He, $^4$He, and $^7$Li only agree with their inferred primordial abundances if the fraction of critical density contributed by baryons is between $0.01h^{-2}$ and $0.02h^{-2}$ ($h$ is the present value of the Hubble constant in units of 100\kms\Mpc^{-1}). This is the basis of the very convincing and important argument that baryons can contribute at most 10\% of critical density and thus cannot close the Universe. Nonstandard scenarios involving decaying particles,$^1$ inhomogeneities in the baryon density,$^2$ and even more exotic ideas$^3$ put forth to evade this bound have been largely unsuccessful.$^4$ We suggest a new way of relaxing the bound: If the tau neutrino has a mass of 20\MeV-30\MeV and lifetime of $200\sec -1000\sec$, and its decay products include electron neutrinos, the bound to the baryon mass density can be loosened by a about factor of $10$. The key is the decay-generated electron antineutrinos: around the time of nucleosynthesis they are captured by protons to produce neutrons, thereby changing the outcome of nucleosynthesis. Experiments at $e^\pm$ colliders should soon be sensitive to a tau-neutrino mass in the required range.Comment: 10p, FERMILAB Pub-94/059A, Figs available on reques

Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition