Models for Chronology Selection

In this paper, we derive an expression for the grand canonical partition function for a fluid of hot, rotating massless scalar field particles in the Einstein universe. We consider the number of states with a given energy as one increases the angular momentum so that the fluid rotates with an increasing angular velocity. We find that at the critical value when the velocity of the particles furthest from the origin reaches the speed of light, the number of states tends to zero. We illustrate how one can also interpret this partition function as the effective action for a boosted scalar field configuration in the product of three dimensional de Sitter space and $S^1$. In this case, we consider the number of states with a fixed linear momentum around the $S^1$ as the particles are given more and more boost momentum. At the critical point when the spacetime is about to develop closed timelike curves, the number of states again tends to zero. Thus it seems that quantum mechanics naturally enforces the chronology protection conjecture by superselecting the causality violating field configurations from the quantum mechanical phase space.Comment: 20 pages, Late

Contribution to understanding the mathematical structure of quantum mechanics

Probabilistic description of results of measurements and its consequences for understanding quantum mechanics are discussed. It is shown that the basic mathematical structure of quantum mechanics like the probability amplitudes, Born rule, commutation and uncertainty relations, probability density current, momentum operator, rules for including the scalar and vector potentials and antiparticles can be obtained from the probabilistic description of results of measurement of the space coordinates and time. Equations of motion of quantum mechanics, the Klein-Gordon equation, Schrodinger equation and Dirac equation are obtained from the requirement of the relativistic invariance of the space-time Fisher information. The limit case of the delta-like probability densities leads to the Hamilton-Jacobi equation of classical mechanics. Many particle systems and the postulates of quantum mechanics are also discussed.Comment: 21 page

DPRESS: Localizing estimates of predictive uncertainty

<p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it>_ucan be estimated as the non-cross-validated error <it>s</it>_t*for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it>_t*is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it>_t*and <it>γ</it>_t*are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p

Psip1/Ledgf p52 Binds Methylated Histone H3K36 and Splicing Factors and Contributes to the Regulation of Alternative Splicing

Increasing evidence suggests that chromatin modifications have important roles in modulating constitutive or alternative splicing. Here we demonstrate that the PWWP domain of the chromatin-associated protein Psip1/Ledgf can specifically recognize tri-methylated H3K36 and that, like this histone modification, the Psip1 short (p52) isoform is enriched at active genes. We show that the p52, but not the long (p75), isoform of Psip1 co-localizes and interacts with Srsf1 and other proteins involved in mRNA processing. The level of H3K36me3 associated Srsf1 is reduced in Psip1 mutant cells and alternative splicing of specific genes is affected. Moreover, we show altered Srsf1 distribution around the alternatively spliced exons of these genes in Psip1 null cells. We propose that Psip1/p52, through its binding to both chromatin and splicing factors, might act to modulate splicing

Patterns of Plant Biomass Partitioning Depend on Nitrogen Source

Nitrogen (N) availability is a strong determinant of plant biomass partitioning, but the role of different N sources in this process is unknown. Plants inhabiting low productivity ecosystems typically partition a large share of total biomass to belowground structures. In these systems, organic N may often dominate plant available N. With increasing productivity, plant biomass partitioning shifts to aboveground structures, along with a shift in available N to inorganic forms of N. We tested the hypothesis that the form of N taken up by plants is an important determinant of plant biomass partitioning by cultivating Arabidopsis thaliana on different N source mixtures. Plants grown on different N mixtures were similar in size, but those supplied with organic N displayed a significantly greater root fraction. 15N labelling suggested that, in this case, a larger share of absorbed organic N was retained in roots and split-root experiments suggested this may depend on a direct incorporation of absorbed amino acid N into roots. These results suggest the form of N acquired affects plant biomass partitioning and adds new information on the interaction between N and biomass partitioning in plants

Conserved Expression of the Glutamate NMDA Receptor 1 Subunit Splice Variants during the Development of the Siberian Hamster Suprachiasmatic Nucleus

Glutamate neurotransmission and the N-methyl-D-aspartate receptor (NMDAR) are central to photic signaling to the master circadian pacemaker located in the hypothalamic suprachiasmatic nucleus (SCN). NMDARs also play important roles in brain development including visual input circuits. The functional NMDAR is comprised of multiple subunits, but each requiring the NR1 subunit for normal activity. The NR1 can be alternatively spliced to produce isoforms that confer different functional properties on the NMDAR. The SCN undergoes extensive developmental changes during postnatal life, including synaptogenesis and acquisition of photic signaling. These changes are especially important in the highly photoperiodic Siberian hamster, in which development of sensitivity to photic cues within the SCN could impact early physiological programming. In this study we examined the expression of NR1 isoforms in the hamster at different developmental ages. Gene expression in the forebrain was quantified by in situ hybridization using oligonucleotide probes specific to alternatively spliced regions of the NR1 heteronuclear mRNA, including examination of anterior hypothalamus, piriform cortex, caudate-putamen, thalamus and hippocampus. Gene expression analysis within the SCN revealed the absence of the N1 cassette, the presence of the C2 cassette alone and the combined absence of C1 and C2 cassettes, indicating that the dominant splice variants are NR1-2a and NR1-4a. Whilst we observe changes at different developmental ages in levels of NR1 isoform probe hybridization in various forebrain structures, we find no significant changes within the SCN. This suggests that a switch in NR1 isoform does not underlie or is not produced by developmental changes within the hamster SCN. Consistency of the NR1 isoforms would ensure that the response of the SCN cells to photic signals remains stable throughout life, an important aspect of the function of the SCN as a responder to environmental changes in quality/quantity of light over the circadian day and annual cycle

Satellite-based terrestrial production efficiency modeling

Production efficiency models (PEMs) are based on the theory of light use efficiency (LUE) which states that a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at the canopy level. Challenges remain however in the application of the PEM methodology to global net primary productivity (NPP) monitoring. The objectives of this review are as follows: 1) to describe the general functioning of six PEMs (CASA; GLO-PEM; TURC; C-Fix; MOD17; and BEAMS) identified in the literature; 2) to review each model to determine potential improvements to the general PEM methodology; 3) to review the related literature on satellite-based gross primary productivity (GPP) and NPP modeling for additional possibilities for improvement; and 4) based on this review, propose items for coordinated research