EXONEST: The Bayesian Exoplanetary Explorer

The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits plan\'etaires) exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.Comment: 30 pages, 8 figures, 5 tables. Presented at the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017) in Jarinu/SP Brasi

Semiclassical model of ultrafast photoisomerization reactions

In this letter we propose a model which explains ultrafast and efficient photoisomerization reactions as driven by transitions between quasistationary states of one dimensional (1D) double well potential of an excited electronic state. This adiabatic potential is formed as a result of doubly crossing of a decay diabatic potential of the ground electronic state and a bound diabatic potential of the excited state. We calculate the eigenstates and eigenfunctions using the semiclassical connection matrices at the turning and crossing points and the shift matrices between these points. The transitions between the localized in the wells below the adiabatic barrier states are realized by the tunneling and by the double non-adiabatic transitions via the crossing points processes. Surprisingly the behavior with the maximum transition rate keeps going even for the states relatively far above the barrier (2 -4 times the barrier height). Even though a specific toy model is investigated here, when properly interpreted it yields quite reasonable values for a variety of measured quantities, such as a reaction quantum yield, and conversion time.Comment: 9 pages, 5 figures. accepted to Chem. Phys. Letters (2005

Fire, climate and the origins of agriculture: micro-charcoal records of biomass burning during the Last Glacial Interglacial Transition in Southwest Asia

This study investigates changes in climate, vegetation, wildfire and human activity in Southwest Asia during the transition to Neolithic agriculture between ca. 16 and ca. 9 ka. In order to trace the fire history of this region, we use microscopic charcoal from lake sediment sequences, and present two new records: one from south central Turkey (Akgo¨ l) and the other from the southern Levant (Hula). These are interpreted primarily as the result of regional-scale fire events, with the exception of a single large event ca. 13 ka at Akgo¨ l, which phytolith analysis shows was the result of burning of the local marsh vegetation. Comparison between these and other regional micro-charcoal, stable isotope and pollen records shows that wildfires were least frequent when the climate was cold and dry (glacial, Lateglacial Stadial) and the vegetation dominated by chenopod–Artemisia steppe, and that they became more frequent and/or bigger at times of warmer, wetter but seasonally dry climate (Lateglacial Interstadial, early Holocene). Warmer and wetter climates caused an increase in biomass availability, with woody matter appearing to provide the main fuel source in sites from the Levant, while grass fires predominated in the interior uplands of Anatolia. Southwest Asia’s grasslands reached their greatest extent during the early Holocene, and they were maintained by dry-season burning that helped to delay the spread of woodland by up to 3 ka, at the same time as Neolithic settlement became established across this grass parkland landscape. Although climatic changes appear to have acted as the principal ‘pacemaker’ for fire activity through the last glacial–interglacial climatic transition (LGIT), human actions may have amplified shifts in biomass burning. Fire regimes therefore changed markedly during this time period, and both influenced, and were influenced by, the cultural-economic transition from hunter-foraging to agriculture and village lif

Financial development, real sector, and economic growth

This paper evaluates the interdependence between financial development and real sector output and the effect on economic growth. Using panel data for 101 developed and developing countries over the period 1970 to 2010, we show that the effect of financial development on economic growth depends on the growth of private credit relative to the real output growth. The findings also suggest that the effect of financial development on growth becomes negative, if there is rapid growth in private credit not accompanied by growth in real output. Our findings provide empirical evidence that supports the theories that postulate the existence of an optimal level of financial development given by the characteristics of an economy

A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples

<p>Abstract</p> <p>Background</p> <p>Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.</p> <p>Methods</p> <p>To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.</p> <p>Results</p> <p>The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.</p> <p>Conclusions</p> <p>We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.</p

The Soreq Applied Research Accelerator Facility (SARAF) - Overview, Research Programs and Future Plans

The Soreq Applied Research Accelerator Facility (SARAF) is under construction in the Soreq Nuclear Research Center at Yavne, Israel. When completed at the beginning of the next decade, SARAF will be a user facility for basic and applied nuclear physics, based on a 40 MeV, 5 mA CW proton/deuteron superconducting linear accelerator. Phase I of SARAF (SARAF-I, 4 MeV, 2 mA CW protons, 5 MeV 1 mA CW deuterons) is already in operation, generating scientific results in several fields of interest. The main ongoing program at SARAF-I is the production of 30 keV neutrons and measurement of Maxwellian Averaged Cross Sections (MACS), important for the astrophysical s-process. The world leading Maxwellian epithermal neutron yield at SARAF-I ($5\times 10^{10}$ epithermal neutrons/sec), generated by a novel Liquid-Lithium Target (LiLiT), enables improved precision of known MACSs, and new measurements of low-abundance and radioactive isotopes. Research plans for SARAF-II span several disciplines: Precision studies of beyond-Standard-Model effects by trapping light exotic radioisotopes, such as $^6$He, $^8$Li and $^{18,19,23}$Ne, in unprecedented amounts (including meaningful studies already at SARAF-I); extended nuclear astrophysics research with higher energy neutrons, including generation and studies of exotic neutron-rich isotopes relevant to the rapid (r-) process; nuclear structure of exotic isotopes; high energy neutron cross sections for basic nuclear physics and material science research, including neutron induced radiation damage; neutron based imaging and therapy; and novel radiopharmaceuticals development and production. In this paper we present a technical overview of SARAF-I and II, including a description of the accelerator and its irradiation targets; a survey of existing research programs at SARAF-I; and the research potential at the completed facility (SARAF-II).Comment: 32 pages, 31 figures, 10 tables, submitted as an invited review to European Physics Journal

Competing Sound Sources Reveal Spatial Effects in Cortical Processing

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk