On a minimal model for estimating climate sensitivity

In a recent issue of this journal, Loehle (2014) presents a "minimal model" for estimating climate sensitivity, identical to that previously published by Loehle and Scafetta (2011). The novelty in the more recent paper lies in the straightforward calculation of an estimate of transient climate response based on the model and an estimate of equilibrium climate sensitivity derived therefrom, via a flawed methodology. We demonstrate that the Loehle and Scafetta model systematically underestimates the transient climate response, due to a number of unsupportable assumptions regarding the climate system. Once the flaws in Loehle and Scafetta's model are addressed, the estimates of transient climate response and equilibrium climate sensitivity derived from the model are entirely consistent with those obtained from general circulation models, and indeed exclude the possibility of low climate sensitivity, directly contradicting the principal conclusion drawn by Loehle. Further, we present an even more parsimonious model for estimating climate sensitivity. Our model is based on observed changes in radiative forcings, and is therefore constrained by physics, unlike the Loehle model, which is little more than a curve-fitting exercise

Automorphic Equivalence within Gapped Phases of Quantum Lattice Systems

Gapped ground states of quantum spin systems have been referred to in the physics literature as being `in the same phase' if there exists a family of Hamiltonians H(s), with finite range interactions depending continuously on $s \in [0,1]$, such that for each $s$, H(s) has a non-vanishing gap above its ground state and with the two initial states being the ground states of H(0) and H(1), respectively. In this work, we give precise conditions under which any two gapped ground states of a given quantum spin system that 'belong to the same phase' are automorphically equivalent and show that this equivalence can be implemented as a flow generated by an $s$-dependent interaction which decays faster than any power law (in fact, almost exponentially). The flow is constructed using Hastings' 'quasi-adiabatic evolution' technique, of which we give a proof extended to infinite-dimensional Hilbert spaces. In addition, we derive a general result about the locality properties of the effect of perturbations of the dynamics for quantum systems with a quasi-local structure and prove that the flow, which we call the {\em spectral flow}, connecting the gapped ground states in the same phase, satisfies a Lieb-Robinson bound. As a result, we obtain that, in the thermodynamic limit, the spectral flow converges to a co-cycle of automorphisms of the algebra of quasi-local observables of the infinite spin system. This proves that the ground state phase structure is preserved along the curve of models $H(s), 0\leq s\leq 1$.Comment: Updated acknowledgments and new email address of S

Lieb-Robinson Bounds for Harmonic and Anharmonic Lattice Systems

We prove Lieb-Robinson bounds for the dynamics of systems with an infinite dimensional Hilbert space and generated by unbounded Hamiltonians. In particular, we consider quantum harmonic and certain anharmonic lattice systems

Simulation of truncated normal variables

We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure.Comment: This 1992 paper appeared in 1995 in Statistics and Computing and the gist of it is contained in Monte Carlo Statistical Methods (2004), but I receive weekly requests for reprints so here it is

The statistical mechanics of complex signaling networks : nerve growth factor signaling

It is becoming increasingly appreciated that the signal transduction systems used by eukaryotic cells to achieve a variety of essential responses represent highly complex networks rather than simple linear pathways. While significant effort is being made to experimentally measure the rate constants for individual steps in these signaling networks, many of the parameters required to describe the behavior of these systems remain unknown, or at best, estimates. With these goals and caveats in mind, we use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. To establish the usefulness of our approach, we have applied our methods towards modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. Using our approach, we are able to extract predictions that are highly specific and accurate, thereby enabling us to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. We show that extracting biologically relevant predictions from complex signaling models appears to be possible even in the absence of measurements of all the individual rate constants. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems wherein particular ''soft'' combinations of parameters can be varied over wide ranges without impacting the final output and demonstrating that a few ''stiff'' parameter combinations center around the paramount regulatory steps of the network. We refer to this property -- which is distinct from robustness -- as ''sloppiness.''Comment: 24 pages, 10 EPS figures, 1 GIF (makes 5 multi-panel figs + caption for GIF), IOP style; supp. info/figs. included as brown_supp.pd

Lieb-Robinson Bounds for the Toda Lattice

We establish locality estimates, known as Lieb-Robinson bounds, for the Toda lattice. In contrast to harmonic models, the Lieb-Robinson velocity for these systems do depend on the initial condition. Our results also apply to the entire Toda as well as the Kac-van Moerbeke hierarchy. Under suitable assumptions, our methods also yield a finite velocity for certain perturbations of these systems

Development stage-specific proteomic profiling uncovers small, lineage specific proteins most abundant in the Aspergillus Fumigatus conidial proteome

Background The pathogenic mold Aspergillus fumigatus is the most frequent infectious cause of death in severely immunocompromised individuals such as leukemia and bone marrow transplant patients. Germination of inhaled conidia (asexual spores) in the host is critical for the initiation of infection, but little is known about the underlying mechanisms of this process. Results To gain insights into early germination events and facilitate the identification of potential stage-specific biomarkers and vaccine candidates, we have used quantitative shotgun proteomics to elucidate patterns of protein abundance changes during early fungal development. Four different stages were examined: dormant conidia, isotropically expanding conidia, hyphae in which germ tube emergence has just begun, and pre-septation hyphae. To enrich for glycan-linked cell wall proteins we used an alkaline cell extraction method. Shotgun proteomic resulted in the identification of 375 unique gene products with high confidence, with no evidence for enrichment of cell wall-immobilized and secreted proteins. The most interesting discovery was the identification of 52 proteins enriched in dormant conidia including 28 proteins that have never been detected in the A. fumigatus conidial proteome such as signaling protein Pil1, chaperones BipA and calnexin, and transcription factor HapB. Additionally we found many small, Aspergillus specific proteins of unknown function including 17 hypothetical proteins. Thus, the most abundant protein, Grg1 (AFUA_5G14210), was also one of the smallest proteins detected in this study (M.W. 7,367). Among previously characterized proteins were melanin pigment and pseurotin A biosynthesis enzymes, histones H3 and H4.1, and other proteins involved in conidiation and response to oxidative or hypoxic stress. In contrast, expanding conidia, hyphae with early germ tubes, and pre-septation hyphae samples were enriched for proteins responsible for housekeeping functions, particularly translation, respiratory metabolism, amino acid and carbohydrate biosynthesis, and the tricarboxylic acid cycle. Conclusions The observed temporal expression patterns suggest that the A. fumigatus conidia are dominated by small, lineage-specific proteins. Some of them may play key roles in host-pathogen interactions, signal transduction during conidial germination, or survival in hostile environments

The age-redshift relation for Luminous Red Galaxies in the Sloan Digital Sky Survey

We present a detailed analysis of 17,852 quiescent, Luminous Red Galaxies (LRGs) selected from Sloan Digital Sky Survey (SDSS) Data Release Seven (DR7) spanning a redshift range of 0.0 < z < 0.4. These galaxies are co-added into four equal bins of velocity dispersion and luminosity to produce high signal-to-noise spectra (>100A^{-1}), thus facilitating accurate measurements of the standard Lick absorption-line indices. In particular, we have carefully corrected and calibrated these indices onto the commonly used Lick/IDS system, thus allowing us to compare these data with other measurements in the literature, and derive realistic ages, metallicities ([Z/H]) and alpha-element abundance ratios ([alpha/Fe]) for these galaxies using Simple Stellar Population (SSP) models. We use these data to study the relationship of these galaxy parameters with redshift, and find little evidence for evolution in metallicity or alpha-elements (especially for our intermediate mass samples). This demonstrates that our subsamples are consistent with pure passive evolving (i.e. no chemical evolution) and represent a homogeneous population over this redshift range. We also present the age-redshift relation for these LRGs and clearly see a decrease in their age with redshift (5 Gyrs over the redshift range studied here) which is fully consistent with the cosmological lookback times in a concordance Lambda CDM universe. We also see that our most massive sample of LRGs is the youngest compared to the lower mass galaxies. We provide these data now to help future cosmological and galaxy evolution studies of LRGs, and provide in the appendices of this paper the required methodology and information to calibrate SDSS spectra onto the Lick/IDS system.Comment: 26 pages, with several appendices containing data. Accepted for publication in MNRA

Plasma Dynamics

Contains reports on ten research projects divided into two sections.National Science Foundation (Grant ENG79-07047)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Department of Energy (Contract DE-ACO2-78ET51013)U.S. Department of Energy (Contract DE-ASO2-78ET53073.AO02)U.S. Department of Energy (Contract ET-78-S-02-4682)U.S. Department of Energy (Contract DE-AS02-78ET53074)U.S. Department of Energy (Contract DE-ASO2-78ET53050)U.S. Department of Energy (Contract DE-AS02-78ET51002)U.S. Department of Energy (Contract DE-ASO2-78ET53076

Structure-based classification and ontology in chemistry

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p