Alterations in Lipids and Adipocyte Hormones in Female-to-Male Transsexuals

Testosterone therapy in men and women results in decreased high-density lipoprotein cholesterol (HDL) and increased low-density lipoprotein cholesterol (LDL). We sought to determine whether testosterone therapy has this same effect on lipid parameters and adipocyte hormones in female-to-male (FTM) transsexuals. Twelve FTM transsexuals provided a fasting lipid profile including serum total cholesterol, HDL, LDL, and triglycerides prior to and after 1 year of testosterone therapy (testosterone enanthate or cypionate 50–125mg IM every two weeks). Subjects experienced a significant decrease in mean serum HDL (52 ± 11 to 40 ± 7mg/dL) (P < .001). The mean LDL (P = .316), triglyceride (P = .910), and total cholesterol (P = .769) levels remained unchanged. In a subset of subjects, we measured serum leptin levels which were reduced by 25% but did not reach statistical significance (P =.181) while resistin levels remained unchanged. We conclude that testosterone therapy in FTM transsexuals can promote an increased atherogenic lipid profile by lowering HDL and possibly reduce serum leptin levels. However, long-term studies are needed to determine whether decreases in HDL result in adverse cardiovascular outcomes.National Institutes of Health (M01RR000533

Surface Tension dominates Insect Flight on Fluid Interfaces

Flight on the two-dimensional air-water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in phylogenetically basal aquatic insects like stone flies, the biomechanics of interfacial flight has never been analyzed. Here, we report interfacial flight as an adapted behaviour in water-lily beetles (Galerucella nymphaeae, Linnaeus 1758) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that water-lily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that nonlinear surface tension forces make interfacial flight energetically expensive compared to airborne flight at the relatively high speeds characteristic of water-lily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary-gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air-water interface presents a radically modified force landscape for flapping wing flight compared to air.Comment: 7 figures, 4 supplementary figures, 12 videos (link given in Supplementary Information

Pseudo-scalar Higgs Boson Production at Threshold N3^3LO and N3^3LL QCD

We present the first results on the production of pseudo-scalar through gluon fusion at the LHC to N$^3$LO in QCD taking into account only soft gluon effects. We have used the effective Lagrangian that describes the coupling of pseudo-scalar with the gluons in the large top quark mass limit. We have used recently available quantities namely the three loop pseudo-scalar form factor and the third order universal soft function in QCD to achieve this. Along with the fixed order results, we also present the process dependent resummation coefficient for threshold resummation to N$^3$LL in QCD. Phenomenological impact of these threshold N$^3$LO corrections to pseudo-scalar production at the LHC is presented and their role to reduce the renormalisation scale dependence is demonstrated.Comment: 34 pages, 17 figure

Unparticle physics in diphoton production at the CERN LHC

We have considered the di-photon production with unparticle at LHC. The contributions of spin-0 and spin-2 unparticle to the di-photon production are studied in the invariant mass and other kinematical distributions, along with their dependencies on the model dependent parameters. The signal corresponding to the unparticle is significant for moderate coupling constant values.Comment: 17 pages, 15 eps figure

Bumper catch of mackerel at Panjim, Goa

Mackeral fishery at Panjim during this season was exceptionally good ranging from 14 to 19 cm size. On an average each purse-seine landed a catch of about 3 tonnes of mackerel practically flooding the jetty. The prices crashed to Rs. 10/- per basket weighing 40 kg. Sixty seven purse-seiners , each one with 2 to 4 tonnes of mackerel landed on a single day and on next day 39 purse-seiners landed the catch at a rate of 1.5 to 5 tonnes by each boat

An Impossibility Result for High Dimensional Supervised Learning

We study high-dimensional asymptotic performance limits of binary supervised classification problems where the class conditional densities are Gaussian with unknown means and covariances and the number of signal dimensions scales faster than the number of labeled training samples. We show that the Bayes error, namely the minimum attainable error probability with complete distributional knowledge and equally likely classes, can be arbitrarily close to zero and yet the limiting minimax error probability of every supervised learning algorithm is no better than a random coin toss. In contrast to related studies where the classification difficulty (Bayes error) is made to vanish, we hold it constant when taking high-dimensional limits. In contrast to VC-dimension based minimax lower bounds that consider the worst case error probability over all distributions that have a fixed Bayes error, our worst case is over the family of Gaussian distributions with constant Bayes error. We also show that a nontrivial asymptotic minimax error probability can only be attained for parametric subsets of zero measure (in a suitable measure space). These results expose the fundamental importance of prior knowledge and suggest that unless we impose strong structural constraints, such as sparsity, on the parametric space, supervised learning may be ineffective in high dimensional small sample settings.Comment: This paper was submitted to the IEEE Information Theory Workshop (ITW) 2013 on April 23, 201