Learning from text

Bibliography: leaves 19-20Supported in part by the National Institute of Education under contract no. NIE 400-81-003

Explaining the Allocation of Bilateral and Multilateral Environmental Aid to Developing Countries

In this paper we examine how international development assistance for environmental purposes is allocated to developing countries. In particular, we investigate whether there are patterned differences between environmental aid for international public goods projects versus environmental projects having more localized impacts. We empirically investigate these questions using project project level development assistance data.International Development,

Differential Binding of Co(II) and Zn(II) to Metallo-β-Lactamase Bla2 from \u3cem\u3eBacillus anthracis\u3c/em\u3e

In an effort to probe the structure, mechanism, and biochemical properties of metallo-β-lactamase Bla2 from Bacillus anthracis, the enzyme was overexpressed, purified, and characterized. Metal analyses demonstrated that recombinant Bla2 tightly binds 1 equiv of Zn(II). Steady-state kinetic studies showed that mono-Zn(II) Bla2 (1Zn-Bla2) is active, while di-Zn(II) Bla2 (ZnZn-Bla2) was unstable. Catalytically, 1Zn-Bla2 behaves like the related enzymes CcrA and L1. In contrast, di-Co(II) Bla2 (CoCo-Bla2) is substantially more active than the mono-Co(II) analogue. Rapid kinetics and UV−vis, 1H NMR, EPR, and EXAFS spectroscopic studies show that Co(II) binding to Bla2 is distributed, while EXAFS shows that Zn(II) binding is sequential. To our knowledge, this is the first documented example of a Zn enzyme that binds Co(II) and Zn(II) via distinct mechanisms, underscoring the need to demonstrate transferability when extrapolating results on Co(II)-substituted proteins to the native Zn(II)-containing forms

Structure and Metal Binding Properties of ZnuA, a Periplasmic Zinc Transporter from \u3cem\u3eEscherichia coli\u3c/em\u3e

ZnuA is the periplasmic Zn2+-binding protein associated with the high-affinity ATP-binding cassette ZnuABC transporter from Escherichia coli. Although several structures of ZnuA and its homologs have been determined, details regarding metal ion stoichiometry, affinity, and specificity as well as the mechanism of metal uptake and transfer remain unclear. The crystal structures of E. coli ZnuA (Eco-ZnuA) in the apo, Zn2+-bound, and Co2+-bound forms have been determined. ZnZnuA binds at least two metal ions. The first, observed previously in other structures, is coordinated tetrahedrally by Glu59, His60, His143, and His207. Replacement of Zn2+ with Co2+ results in almost identical coordination geometry at this site. The second metal binding site involves His224 and several yet to be identified residues from the His-rich loop that is unique to Zn2+ periplasmic metal binding receptors. Electron paramagnetic resonance and X-ray absorption spectroscopic data on CoZnuA provide additional insight into possible residues involved in this second site. The second site is also detected by metal analysis and circular dichroism (CD) titrations. Eco-ZnuA binds Zn2+ (estimated K d \u3c 20 nM), Co2+, Ni2+, Cu2+, Cu+, and Cd2+, but not Mn2+. Finally, conformational changes upon metal binding observed in the crystal structures together with fluorescence and CD data indicate that only Zn2+ substantially stabilizes ZnuA and might facilitate recognition of ZnuB and subsequent metal transfer

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI

Anomalies in low-energy Gamma-Ray Burst spectra with the Fermi Gamma-Ray Burst Monitor

A Band function has become the standard spectral function used to describe the prompt emission spectra of gamma-ray bursts (GRBs). However, deviations from this function have previously been observed in GRBs detected by BATSE and in individual GRBs from the \textit{Fermi} era. We present a systematic and rigorous search for spectral deviations from a Band function at low energies in a sample of the first two years of high fluence, long bursts detected by the \textit{Fermi} Gamma-Ray Burst Monitor (GBM). The sample contains 45 bursts with a fluence greater than 2$\times10^{-5}$ erg / cm$^{2}$ (10 - 1000 keV). An extrapolated fit method is used to search for low-energy spectral anomalies, whereby a Band function is fit above a variable low-energy threshold and then the best fit function is extrapolated to lower energy data. Deviations are quantified by examining residuals derived from the extrapolated function and the data and their significance is determined via comprehensive simulations which account for the instrument response. This method was employed for both time-integrated burst spectra and time-resolved bins defined by a signal to noise ratio of 25 $\sigma$ and 50 $\sigma$. Significant deviations are evident in 3 bursts (GRB\,081215A, GRB\,090424 and GRB\,090902B) in the time-integrated sample ($\sim$ 7%) and 5 bursts (GRB\,090323, GRB\,090424, GRB\,090820, GRB\,090902B and GRB\,090926A) in the time-resolved sample ($\sim$ 11%).} The advantage of the systematic, blind search analysis is that it can demonstrate the requirement for an additional spectral component without any prior knowledge of the nature of that extra component. Deviations are found in a large fraction of high fluence GRBs; fainter GRBs may not have sufficient statistics for deviations to be found using this method

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm

Long-term dopamine neurochemical monitoring in primates

Many debilitating neuropsychiatric and neurodegenerative disorders are characterized by dopamine neurotransmitter dysregulation. Monitoring subsecond dopamine release accurately and for extended, clinically relevant timescales is a critical unmet need. Especially valuable has been the development of electrochemical fast-scan cyclic voltammetry implementing microsized carbon fiber probe implants to record fast millisecond changes in dopamine concentrations. Nevertheless, these well-established methods have only been applied in primates with acutely (few hours) implanted sensors. Neurochemical monitoring for long timescales is necessary to improve diagnostic and therapeutic procedures for a wide range of neurological disorders. Strategies for the chronic use of such sensors have recently been established successfully in rodents, but new infrastructures are needed to enable these strategies in primates. Here we report an integrated neurochemical recording platform for monitoring dopamine release from sensors chronically implanted in deep brain structures of nonhuman primates for over 100 days, together with results for behavior-related and stimulation-induced dopamine release. From these chronically implanted probes, we measured dopamine release from multiple sites in the striatum as induced by behavioral performance and reward-related stimuli, by direct stimulation, and by drug administration. We further developed algorithms to automate detection of dopamine. These algorithms could be used to track the effects of drugs on endogenous dopamine neurotransmission, as well as to evaluate the long-term performance of the chronically implanted sensors. Our chronic measurements demonstrate the feasibility of measuring subsecond dopamine release from deep brain circuits of awake, behaving primates in a longitudinally reproducible manner. Keywords: striatum; voltammetry; neurotransmitters; chronic implantsNational Institute of Neurological Diseases and Stroke (U.S.) (Grant R01 NS025529)National Institute of Neurological Diseases and Stroke (U.S.) (Grant F32 NS093897)United States. Army Research Office (Contract W911NF-16-1-0474)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01 EB016101