Self organization of large-scale structures in nature - either coherent structures like crystals, or incoherent dynamic structures like clouds - is governed by long-range interactions. In many problems, hydrodynamics and electrostatics are the source of such long-range interactions. The tuning of electrostatic interactions has helped to elucidate when coherent crystalline structures or incoherent amorphous structures form in colloidal systems. However, there is little understanding of self organization in situations where both electrostatic and hydrodynamic interactions are present. We present a minimal two-component oil-in-oil model system where we can control the strength and lengthscale of the electrohydrodynamic interactions by tuning the amplitude and frequency of the imposed electric field. As a function of the hydrodynamic lengthscale, we observe a rich phenomenology of exotic structure and dynamics, from incoherent cloud-like structures and chaotic droplet dynamics, to polyhedral droplet phases, to coherent droplet arrays.
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http://dx.doi.org/10.1038/srep00738 | DOI Listing |
Genes Dev
January 2025
Oncode Institute, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW), University Medical Center Utrecht, Utrecht 3584 CT, the Netherlands;
Enhancers are tissue-specific regulatory DNA elements that can activate transcription of genes over distance. Their target genes most often are located in the same contact domain-chromosomal entities formed by cohesin DNA loop extrusion and typically flanked by CTCF-bound boundaries. Enhancers shared by multiple unrelated genes are underexplored but may be more common than anticipated.
View Article and Find Full Text PDFPLoS Pathog
January 2025
Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom.
Upon infection, human papillomavirus (HPV) manipulates host cell gene expression to create an environment that is supportive of a productive and persistent infection. The virus-induced changes to the host cell's transcriptome are thought to contribute to carcinogenesis. Here, we show by RNA-sequencing that oncogenic HPV18 episome replication in primary human foreskin keratinocytes (HFKs) drives host transcriptional changes that are consistent between multiple HFK donors.
View Article and Find Full Text PDFRSC Adv
January 2025
Dipartimento di Scienze e Innovazione Tecnologica, Università Del Piemonte Orientale A. Avogadro Viale T. Michel 11 15121 Alessandria Italy
A novel synthesis of a nanometric MCM-41 from biogenic silica obtained from rice husk is here presented. CTABr and Pluronic F127 surfactants were employed as templating agents to promote the formation of a long-range ordered 2D-hexagonal structure with cylindrical pores and to limit the particle growth at the nanoscale level thus resulting in a material with uniform particle size of 20-30 nm. The physico-chemical properties of this sample (RH-nanoMCM) were investigated through a multi-technique approach, including PXRD, Si MAS NMR, TEM, -potential and N physisorption analysis at 77 K.
View Article and Find Full Text PDFNat Commun
January 2025
School of Environment, Tsinghua University, Beijing, 100084, P. R. China.
Modulation of electronic spin states in cobalt-based catalysts is an effective strategy for molecule activations. Crystalline-amorphous interfaces often exhibit unique catalytic properties due to disruptions of long-range order and alterations in electronic structure. However, the mechanisms of molecule activation and spin states at interfaces remain elusive.
View Article and Find Full Text PDFBiomolecules
January 2025
School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.
Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, especially when homology transfer cannot provide information about a protein's structure, is a significant step toward addressing the protein structure prediction challenge. Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins.
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