Macroeconomic implications of virtual shopping : a theoretical approach

Recently, parallel to developments in the communication technology, online shopping has become increasingly popular for many products, like books, CDs, software, and computers. Most analysts conjecture that the future will witness a wider basket of products and a higher trade volume via the Internet. This paper investigates the economic implications of Internet shopping in a Ricardian equilibrium framework. First, it shows the necessary and sufficient condition for the shift to Internet shopping. Next, it indicates that macroeconomic variables like consumption and income rise when this shift takes place. Thus, this paper shows that the economic implications of Internet shopping will be higher than the current experience and Internet shopping will become an important element of the `new economy\' when the bulky part of the shopping is done via the Internet.

Macroeconomic implications of virtual shopping:a theoretical approach

Recently, parallel to developments in the communication technology, online shopping has become increasingly popular for many products, like books, CDs, software, and computers. Most analysts conjecture that the future will witness a wider basket of products and a higher trade volume via the Internet. This paper investigates the economic implications of Internet shopping in a Ricardian equilibrium framework. First, it shows the necessary and sufficient condition for the shift to Internet shopping. Next, it indicates that macroeconomic variables like consumption and income rise when this shift takes place. Thus, this paper shows that the economic implications of Internet shopping will be higher than the current experience and Internet shopping will become an important element of the `new economy\' when the bulky part of the shopping is done via the Internet

Intelligent image-based in situ single-cell isolation

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.Peer reviewe

PRMT1 and PRMT8 regulate retinoic acid-dependent neuronal differentiation with implications to neuropathology.

Retinoids are morphogens and have been implicated in cell fate commitment of embryonic stem cells (ESCs) to neurons. Their effects are mediated by RAR and RXR nuclear receptors. However, transcriptional cofactors required for cell and gene-specific retinoid signaling are not known. Here we show that protein arginine methyl transferase (PRMT) 1 and 8 have key roles in determining retinoid regulated gene expression and cellular specification in a multistage neuronal differentiation model of murine ESCs. PRMT1 acts as a selective modulator, providing the cells with a mechanism to reduce the potency of retinoid signals on regulatory "hotspots." PRMT8 is a retinoid receptor target gene itself and acts as a cell type specific transcriptional coactivator of retinoid signaling at later stages of differentiation. Lack of either of them leads to reduced nuclear arginine methylation, dysregulated neuronal gene expression, and altered neuronal activity. Importantly, depletion of PRMT8 results in altered expression of a distinct set of genes, including markers of gliomagenesis. PRMT8 is almost entirely absent in human glioblastoma tissues. We propose that PRMT1 and PRMT8 serve as a rheostat of retinoid signaling to determine neuronal cell specification in a context-dependent manner and might also be relevant in the development of human brain malignancy

Fast and efficient QTL mapper for thousands of molecular phenotypes

In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing