Phosphoinositide-binding interface proteins involved in shaping cell membranes

The mechanism by which cell and cell membrane shapes are created has long been a subject of great interest. Among the phosphoinositide-binding proteins, a group of proteins that can change the shape of membranes, in addition to the phosphoinositide-binding ability, has been found. These proteins, which contain membrane-deforming domains such as the BAR, EFC/F-BAR, and the IMD/I-BAR domains, led to inward-invaginated tubes or outward protrusions of the membrane, resulting in a variety of membrane shapes. Furthermore, these proteins not only bind to phosphoinositide, but also to the N-WASP/WAVE complex and the actin polymerization machinery, which generates a driving force to shape the membranes

Mouse Apolipoprotein B Editing Complex 3 (APOBEC3) Is Expressed in Germ Cells and Interacts with Dead-End (DND1)

encoded protein, DND1, is able to bind to the 3′-untranslated region (UTR) of messenger RNAs (mRNAs) to displace micro-RNA (miRNA) interaction with mRNA. Thus, one function of DND1 is to prevent miRNA mediated repression of mRNA. We report that DND1 interacts specifically with APOBEC3. APOBEC3 is a multi-functional protein. It inhibits retroviral replication. In addition, recent studies show that APOBEC3 interacts with cellular RNA-binding proteins and to mRNA to inhibit miRNA-mediated repression of mRNA.Here we show that DND1 specifically interacts with another cellular protein, APOBEC3. We present our data which shows that DND1 co-immunoprecipitates APOBEC3 from mammalian cells and also endogenous APOBEC3 from mouse gonads. Whether the two proteins interact directly remains to be elucidated. We show that both DND1 and APOBEC3 are expressed in germ cells and in the early gonads of mouse embryo. Expression of fluorescently-tagged DND1 and APOBEC3 indicate they localize to the cytoplasm and when DND1 and APOBEC3 are expressed together in cells, they sequester near peri-nuclear sites.The 3′-UTR of mRNAs generally encode multiple miRNA binding sites as well as binding sites for a variety of RNA binding proteins. In light of our findings of DND1-APOBEC3 interaction and taking into consideration reports that DND1 and APOBEC3 bind to mRNA to inhibit miRNA mediated repression, our studies implicate a possible role of DND1-APOBEC3 interaction in modulating miRNA-mediated mRNA repression. The interaction of DND1 and APOBEC3 could be one mechanism for maintaining viability of germ cells and for preventing germ cell tumor development

Characterization of Geographical and Meteorological Parameters

[EN]This chapter is devoted to the introduction of some geographical and meteorological information involved in the numerical modeling of wind fields and solar radiation. First, a brief description of the topographical data given by a Digital Elevation Model and Land Cover databases is provided. In particular, the Information System of Land Cover of Spain (SIOSE) is considered. The study is focused on the roughness length and the displacement height parameters that appear in the logarithmic wind profile, as well as in the albedo related to solar radiation computation. An extended literature review and characterization of both parameters are reported. Next, the concept of atmospheric stability is introduced from the Monin–Obukhov similarity theory to the recent revision of Zilitinkevich of the Neutral and Stable Boundary Layers (SBL). The latter considers the effect of the free-flow static stability and baroclinicity on the turbulent transport of momentum and of the Convective Boundary Layers (CBL), more precisely, the scalars in the boundary layer, as well as the model of turbulent entrainment

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

VoICE: A semi-automated pipeline for standardizing vocal analysis across models

The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization “types” by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories