Equilibrium Demand Elasticities across Quality Segments

Empirical studies find substantial differences in demand elasticities and associated markups among products of different quality. This paper analyzes the theoretical determinants of such variation. We present a simple model that allows for horizontal and vertical differentiation and accounts for endogenous entry. We find that most economic forces in our model, such as consumers’ price sensitivity, the scope for product differentiation, and sunk costs of entry, are likely to induce lower equilibrium demand elasticities for higher quality products. In contrast, other economic forces, such as marginal cost of production and the distribution (across consumers) of the willingness to pay for quality, may induce the opposite pattern. These results provide an organizing framework through which empirical findings may be interpreted, and may also help to predict variation in demand elasticities for markets in which empirical estimates of elasticities are unavailable or infeasible to obtain.

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Abstract- RNA-binding proteins (RBPs) play diverse roles in eukaryotic RNA processing. Despite their pervasive functions in coding and non-coding RNA biogenesis and regulation, elucidating the specificities that define protein-RNA interactions remains a major challenge. Here, we describe a novel model-based approach — RNAMotifModeler to identify binding consensus of RBPs by integrating sequence features and RNA secondary structures. Using RNA sequences derived from Cross-linking immunoprecipitation (CLIP) followed by high-throughput sequencing for SRSF1 proteins, we identified a purine-rich octamer ‘AGAAGAAG ’ in a highly singlestranded RNA context, which is consistent with previous knowledge. The successful implementation on SRSF1 CLIPseq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins

Prioritization of disease microRNAs through a human phenome-microRNAome network

<p>Abstract</p> <p>Background</p> <p>The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.</p> <p>Results</p> <p>Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.</p> <p>Conclusions</p> <p>We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.</p

miR2Disease: a manually curated database for microRNA deregulation in human disease

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org

Optimal Tax Mix with Income Tax Non-compliance *

Abstract Although developing countries face high levels of income inequality, they rely more on consumption taxes, which tend to be linear and are less effective for redistribution than a non-linear income tax. One explanation for this pattern is that the consumption taxes are generally more enforceable in these economies. This paper studies the optimal combination of a linear consumption tax, with a non-linear income tax, for redistributive purposes. In our model, households might not comply with the income tax code by reporting income levels that differ from their true income. However, the consumption tax is fully enforceable. We derive a formula for the optimal income tax schedule as a function of the consumption tax rate, the recoverable elasticities, and the moments of the taxable income distribution. Our equation differs from those of JEL: D31, D63, D82, H21, H23, H26, I30, O2

The scientific payload of the Ultraviolet Transient Astronomy Satellite (ULTRASAT)

The Ultraviolet Transient Astronomy Satellite (ULTRASAT) is a space-borne near UV telescope with an unprecedented large field of view (200 sq. deg.). The mission, led by the Weizmann Institute of Science and the Israel Space Agency in collaboration with DESY (Helmholtz association, Germany) and NASA (USA), is fully funded and expected to be launched to a geostationary transfer orbit in Q2/3 of 2025. With a grasp 300 times larger than GALEX, the most sensitive UV satellite to date, ULTRASAT will revolutionize our understanding of the hot transient universe, as well as of flaring galactic sources. We describe the mission payload, the optical design and the choice of materials allowing us to achieve a point spread function of ~10arcsec across the FoV, and the detector assembly. We detail the mitigation techniques implemented to suppress out-of-band flux and reduce stray light, detector properties including measured quantum efficiency of scout (prototype) detectors, and expected performance (limiting magnitude) for various objects.Comment: Presented in the SPIE Astronomical Telescopes + Instrumentation 202

MicroRNA Promoter Identification in Arabidopsis Using Multiple Histone Markers

A microRNA is a small noncoding RNA molecule, which functions in RNA silencing and posttranscriptional regulation of gene expression. To understand the mechanism of the activation of microRNA genes, the location of promoter regions driving their expression is required to be annotated precisely. Only a fraction of microRNA genes have confirmed transcription start sites (TSSs), which hinders our understanding of the transcription factor binding events. With the development of the next generation sequencing technology, the chromatin states can be inferred precisely by virtue of a combination of specific histone modifications. Using the genome-wide profiles of nine histone markers including H3K4me2, H3K4me3, H3K9Ac, H3K9me2, H3K18Ac, H3K27me1, H3K27me3, H3K36me2, and H3K36me3, we developed a computational strategy to identify the promoter regions of most microRNA genes in Arabidopsis, based upon the assumption that the distribution of histone markers around the TSSs of microRNA genes is similar to the TSSs of protein coding genes. Among 298 miRNA genes, our model identified 42 independent miRNA TSSs and 132 miRNA TSSs, which are located in the promoters of upstream genes. The identification of promoters will provide better understanding of microRNA regulation and can play an important role in the study of diseases at genetic level

Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs

Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Abstract Background RNA-binding proteins (RBPs) play diverse roles in eukaryotic RNA processing. Despite their pervasive functions in coding and noncoding RNA biogenesis and regulation, elucidating the sequence specificities that define protein-RNA interactions remains a major challenge. Recently, CLIP-seq (Cross-linking immunoprecipitation followed by high-throughput sequencing) has been successfully implemented to study the transcriptome-wide binding patterns of SRSF1, PTBP1, NOVA and fox2 proteins. These studies either adopted traditional methods like Multiple EM for Motif Elicitation (MEME) to discover the sequence consensus of RBP's binding sites or used Z-score statistics to search for the overrepresented nucleotides of a certain size. We argue that most of these methods are not well-suited for RNA motif identification, as they are unable to incorporate the RNA structural context of protein-RNA interactions, which may affect to binding specificity. Here, we describe a novel model-based approach--RNAMotifModeler to identify the consensus of protein-RNA binding regions by integrating sequence features and RNA secondary structures. Results As an example, we implemented RNAMotifModeler on SRSF1 (SF2/ASF) CLIP-seq data. The sequence-structural consensus we identified is a purine-rich octamer 'AGAAGAAG' in a highly single-stranded RNA context. The unpaired probabilities, the probabilities of not forming pairs, are significantly higher than negative controls and the flanking sequence surrounding the binding site, indicating that SRSF1 proteins tend to bind on single-stranded RNA. Further statistical evaluations revealed that the second and fifth bases of SRSF1octamer motif have much stronger sequence specificities, but weaker single-strandedness, while the third, fourth, sixth and seventh bases are far more likely to be single-stranded, but have more degenerate sequence specificities. Therefore, we hypothesize that nucleotide specificity and secondary structure play complementary roles during binding site recognition by SRSF1. Conclusion In this study, we presented a computational model to predict the sequence consensus and optimal RNA secondary structure for protein-RNA binding regions. The successful implementation on SRSF1 CLIP-seq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins.</p