Cyclic di-GMP differentially tunes a bacterial flagellar motor through a novel class of CheY-like regulators

The flagellar motor is a sophisticated rotary machine facilitating locomotion and signal transduction. Owing to its important role in bacterial behavior, its assembly and activity are tightly regulated. For example, chemotaxis relies on a sensory pathway coupling chemical information to rotational bias of the motor through phosphorylation of the motor switch protein CheY. Using a chemical proteomics approach, we identified a novel family of CheY-like (Cle) proteins in Caulobacter crescentus, which tune flagellar activity in response to binding of the second messenger c-di-GMP to a C-terminal extension. In their c-di-GMP bound conformation Cle proteins interact with the flagellar switch to control motor activity. We show that individual Cle proteins have adopted discrete cellular functions by interfering with chemotaxis and by promoting rapid surface attachment of motile cells. This study broadens the regulatory versatility of bacterial motors and unfolds mechanisms that tie motor activity to mechanical cues and bacterial surface adaptation

Functional Diversity and Structural Disorder in the Human Ubiquitination Pathway

The ubiquitin-proteasome system plays a central role in cellular regulation and protein quality control (PQC). The system is built as a pyramid of increasing complexity, with two E1 (ubiquitin activating), few dozen E2 (ubiquitin conjugating) and several hundred E3 (ubiquitin ligase) enzymes. By collecting and analyzing E3 sequences from the KEGG BRITE database and literature, we assembled a coherent dataset of 563 human E3s and analyzed their various physical features. We found an increase in structural disorder of the system with multiple disorder predictors (IUPred - E1: 5.97%, E2: 17.74%, E3: 20.03%). E3s that can bind E2 and substrate simultaneously (single subunit E3, ssE3) have significantly higher disorder (22.98%) than E3s in which E2 binding (multi RING-finger, mRF, 0.62%), scaffolding (6.01%) and substrate binding (adaptor/substrate recognition subunits, 17.33%) functions are separated. In ssE3s, the disorder was localized in the substrate/adaptor binding domains, whereas the E2-binding RING/HECT-domains were structured. To demonstrate the involvement of disorder in E3 function, we applied normal modes and molecular dynamics analyses to show how a disordered and highly flexible linker in human CBL (an E3 that acts as a regulator of several tyrosine kinase-mediated signalling pathways) facilitates long-range conformational changes bringing substrate and E2-binding domains towards each other and thus assisting in ubiquitin transfer. E3s with multiple interaction partners (as evidenced by data in STRING) also possess elevated levels of disorder (hubs, 22.90% vs. non-hubs, 18.36%). Furthermore, a search in PDB uncovered 21 distinct human E3 interactions, in 7 of which the disordered region of E3s undergoes induced folding (or mutual induced folding) in the presence of the partner. In conclusion, our data highlights the primary role of structural disorder in the functions of E3 ligases that manifests itself in the substrate/adaptor binding functions as well as the mechanism of ubiquitin transfer by long-range conformational transitions. © 2013 Bhowmick et al

Functional mapping of yeast genomes by saturated transposition

Yeast is a powerful model for systems genetics. We present a versatile, time- and labor-efficient method to functionally explore the Saccharomyces cerevisiae genome using saturated transposon mutagenesis coupled to high-throughput sequencing. SAturated Transposon Analysis in Yeast (SATAY) allows one-step mapping of all genetic loci in which transposons can insert without disrupting essential functions. SATAY is particularly suited to discover loci important for growth under various conditions. SATAY (1) reveals positive and negative genetic interactions in single and multiple mutant strains, (2) can identify drug targets, (3) detects not only essential genes, but also essential protein domains, (4) generates both null and other informative alleles. In a SATAY screen for rapamycin-resistant mutants, we identify Pib2 (PhosphoInositide-Binding 2) as a master regulator of TORC1. We describe two antagonistic TORC1-activating and -inhibiting activities located on opposite ends of Pib2. Thus, SATAY allows to easily explore the yeast genome at unprecedented resolution and throughput

Lagrangian Neural Style Transfer for Fluids

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials: http://www.byungsoo.me/project/lnst/index.htm

Progress in observing and modelling the urban boundary layer

The urban boundary layer (UBL) is the part of the atmosphere in which most of the planet’s population now lives, and is one of the most complex and least understood microclimates. Given potential climate change impacts and the requirement to develop cities sustainably, the need for sound modelling and observational tools becomes pressing. This review paper considers progress made in studies of the UBL in terms of a conceptual framework spanning microscale to mesoscale determinants of UBL structure and evolution. Considerable progress in observing and modelling the urban surface energy balance has been made. The urban roughness sub-layer is an important region requiring attention as assumptions about atmospheric turbulence break down in this layer and it may dominate coupling of the surface to the UBL due to its considerable depth. The upper 90% of the UBL (mixed and residual layers) remains under-researched but new remote sensing methods and high resolution modelling tools now permit rapid progress. Surface heterogeneity dominates from neighbourhood to regional scales and should be more strongly considered in future studies. Specific research priorities include humidity within the UBL, high-rise urban canopies and the development of long-term, spatially extensive measurement networks coupled strongly to model development

Triclocarban Mediates Induction of Xenobiotic Metabolism through Activation of the Constitutive Androstane Receptor and the Estrogen Receptor Alpha

Triclocarban (3,4,4′-trichlorocarbanilide, TCC) is used as a broad-based antimicrobial agent that is commonly added to personal hygiene products. Because of its extensive use in the health care industry and resistance to degradation in sewage treatment processes, TCC has become a significant waste product that is found in numerous environmental compartments where humans and wildlife can be exposed. While TCC has been linked to a range of health and environmental effects, few studies have been conducted linking exposure to TCC and induction of xenobiotic metabolism through regulation by environmental sensors such as the nuclear xenobiotic receptors (XenoRs). To identify the ability of TCC to activate xenobiotic sensors, we monitored XenoR activities in response to TCC treatment using luciferase-based reporter assays. Among the XenoRs in the reporter screening assay, TCC promotes both constitutive androstane receptor (CAR) and estrogen receptor alpha (ERα) activities. TCC treatment to hUGT1 mice resulted in induction of the UGT1A genes in liver. This induction was dependent upon the constitutive active/androstane receptor (CAR) because no induction occurred in hUGT1Car−/− mice. Induction of the UGT1A genes by TCC corresponded with induction of Cyp2b10, another CAR target gene. TCC was demonstrated to be a phenobarbital-like activator of CAR in receptor-based assays. While it has been suggested that TCC be classified as an endocrine disruptor, it activates ERα leading to induction of Cyp1b1 in female ovaries as well as in promoter activity. Activation of ERα by TCC in receptor-based assays also promotes induction of human CYP2B6. These observations demonstrate that TCC activates nuclear xenobiotic receptors CAR and ERα both in vivo and in vitro and might have the potential to alter normal physiological homeostasis. Activation of these xenobiotic-sensing receptors amplifies gene expression profiles that might represent a mechanistic base for potential human health effects from exposure to TCC

Developing a research strategy to better understand, observe, and simulate urban atmospheric processes at kilometer to subkilometer scales

A Met Office/Natural Environment Research Council Joint Weather and Climate Research Programme workshop brought together 50 key international scientists from the UK and international community to formulate the key requirements for an Urban Meteorological Research strategy. The workshop was jointly organised by University of Reading and the Met Office

Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)

In the last two decades substantial advances have been made in the understanding of the scientific basis of urban climates. These are reviewed here with attention to sustainability of cities, applications that use climate information, and scientific understanding in relation to measurements and modelling. Consideration is given from street (micro) scale to neighbourhood (local) to city and region (meso) scale. Those areas where improvements are needed in the next decade to ensure more sustainable cities are identified. High-priority recommendations are made in the following six strategic areas: observations, data, understanding, modelling, tools and education. These include the need for more operational urban measurement stations and networks; for an international data archive to aid translation of research findings into design tools, along with guidelines for different climate zones and land uses; to develop methods to analyse atmospheric data measured above complex urban surfaces; to improve short-range, high-resolution numerical prediction of weather, air quality and chemical dispersion through improved modelling of the biogeophysical features of the urban land surface; to improve education about urban meteorology; and to encourage communication across scientific disciplines at a range of spatial and temporal scales

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE