Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF–target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF–target interaction database for humans—TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)—which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF–target interactions in mice, including 6552 TF–target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF–target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets, however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

For contact between rough surfaces of conductors in which a clustered contact spot distribution is dominant through a multiscale process, electrical contact resistance (ECR) is analysed using a smoothed version of Greenwood's model (Jang and Barber 2003 J. Appl. Phys. 94 7215), which is extended to estimate the statistical distribution of contact spots considering the size and the location simultaneously. The application of this statistical method to a contact spot distribution, generated by the finite element method using a fractal surface defined by the random midpoint displacement algorithm, identifies the effect of the clustered contact distribution on ECR, showing that including a finer scale in the fractal contact surface causes the predicted resistance to approach a finite limit. It is also confirmed that the results are close to that of Barber's analogy (Barber 2003 Proc. R. Soc. Lond. A 459 53) regarding incremental stiffness and conductance for elastic contact.

We investigate the effects of nanosized contact spots on the thermal contact resistance ͑TCR͒ in multiscale contacts. As the contact size decreases below the phonon mean free path, the thermal conductivity varies with the size of the contact and is not the same as its bulk counterpart. We take this into account in our model and we calculate the TCR of silicon contacted with other silicon. The TCR increases as the number of nanosized contact spots increases. However, if we do not consider the thermal conductivity reduction as the contact size decreases below the size of the phonon mean free path, there is a finite limit of the TCR. A parametric study on the effects of distance and size of the contact spots is also presented.