Modeling of Exoplanet Atmospheres

Spectrally characterizing exoplanet atmospheres will be one of the fastest moving astronomical disciplines in the years to come. Especially the upcoming James Webb Space Telescope (JWST) will provide spectral measurements from the near- to mid-infrared of unprecedented precision. With other next generation instruments on the horizon, it is crucial to possess the tools necessary for interpretating observations. To this end I wrote the petitCODE, which solves for the self-consistent atmospheric structures of exoplanets, assuming chemical and radiative-convective equilibrium. The code includes scattering, and models clouds. The code outputs the planet’s observable emission and transmission spectra. In addition, I constructed a spectral retrieval code, which derives the full posterior probability distribution of atmospheric parameters from observations. I used petitCODE to systematically study the atmospheres of hot jupiters and found, e.g., that their structures depend strongly on the type of their host stars. Moreover, I found that C/O ratios around unity can lead to atmospheric inversions. Next, I produced synthetic observations of prime exoplanet targets for JWST, and studied how well we will be able to distinguish various atmospheric scenarios. Finally, I verified the implementation of my retrieval code using mock JWST observations

The number of different types of SVs in simulation datasets.

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Results of six protein complex detection algorithms in weighted Gavin dataset using MIPS gold standard.

<p>Results of six protein complex detection algorithms in weighted Gavin dataset using MIPS gold standard.</p

Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks

<div><p>In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.</p></div

HPOSim: An R Package for Phenotypic Similarity Measure and Enrichment Analysis Based on the Human Phenotype Ontology

<div><p>Background</p><p>Phenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO.</p><p>Results</p><p>HPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA).</p><p>Conclusions</p><p>HPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (<a href="https://sourceforge.net/p/hposim/" target="_blank">https://sourceforge.net/p/hposim/</a>).</p></div

Effect and mechanism of modification treatment on ammonium and phosphate removal by ferric-modified zeolite

<p>In this study, the reason for the decline of ammonium sorption capacity by zeolite after ferric modification and the effect of modification treatment on ammonium and phosphate removal by ferric-modified zeolite were studied. Modification treatment media (Na salt and HCl) and Na concentration (0.1 and 0.9 mol/L) have been investigated. Zeolites have been characterized by SEM, XRD, BET and XRF; meanwhile, CEC and pHpzc have been also determined. Equilibrium batch sorption for ammonium and phosphate individually and kinetics batch sorption for ammonium were conducted. The results showed a decline in sorption capacity or diffusion coefficients for ammonium but an increase for phosphate after ferric modification. The decrease of surface negative charge was the main contributor for the former, but iron loading did not well account for the latter. The performance of 0.1 mol/L Na modification treatment was better than other treatments for ammonium sorption and equal to HCl modification treatment for phosphate sorption, and the enhancement extent increased for ammonium but declined for phosphate when Na concentration increased. The advantage of Na modification treatment for ammonium was due to the enhancement of textural properties and high exchange rate with ammonium.</p

Artificial synthetic graph for illustrating the process of the rough-fuzzy clustering method.

<p>In the figure, the network is made of two communities and node <i>d</i> is overlapping node.</p

Results comparison of the six algorithms in four weighted datasets using MIPS gold standard.

<p>Columns correspond to the following algorithms, ClusterONE, CMC, CFinder, MCL, OSLOM and RFC from left to right in Gavins, Collins, Krogan_core and Krogan_extended weighted datasets, respectively, using MIPS gold standard. Various colors of the same column denote the individual components of the composite score of the algorithm (blue = the clustering-wise sensitivity, purple = geometric accuracy, green = the clustering-wise separation). The total height of each column is the value of the composite score for a special algorithm in a special dataset. Larger scores show the clustering result is better.</p

Results of six protein complex detection algorithms in weighted Gavin dataset using SGD gold standard.

<p>Results of six protein complex detection algorithms in weighted Gavin dataset using SGD gold standard.</p

RFC algorithm flowchart.

<p>In the figure, we briefly give RFC algorithm flowchart to describe the operational process of the algorithm.</p