The Rho GTPases form a subgroup of the Ras superfamily of 20- to 30-kD GTP-binding proteins that have been shown to regulate a wide spectrum of cellular functions. These proteins are ubiquitously expressed across the species, from yeast to man. The mammalian Rho-like GTPases comprise at least 10 distinct proteins: RhoA, B, C, D, and E; Rac1 and 2; RacE; Cdc42Hs, and TC10. A comparison of the amino acid sequences of the Rho proteins from various species has revealed that they are conserved in primary structure and are 50%–55% homologous to each other. Like all members of the Ras superfamily, the Rho GTPases function as molecular switches, cycling between an inactive GDP-bound state and an active GTP-bound state. Until recently, members of the Rho subfamily were believed to be involved primarily in the regulation of cytoskeletal organization in response to extracellular growth factors. However, research from a number of laboratories over the past few years has revealed that the Rho GTPases play crucial roles in diverse cellular events such as membrane trafficking, transcriptional regulation, cell growth control, and development. Consequently, a major challenge has been to unravel the underlying molecular mechanisms by which the Rho GTPases mediate these various activities. Many targets of the Rho GTPases have now been identified and further characterization of some of them has provided major insights toward our understanding of Rho GTPase function at the molecular level. This review aims to summarize the general established principles about the Rho GTPases and some of the more recent exciting findings, hinting at novel, unanticipated functions of the Rho GTPases.

/pdf/rho-gtpases-and-signaling-networks-48wqq41h57.pdf

Rho GTPases and signaling networks

We have constructed a series of strains to facilitate the generation and analysis of clones of genetically distinct cells in developing and adult tissues of Drosophila. Each of these strains carries an FRT element, the target for the yeast FLP recombinase, near the base of a major chromosome arm, as well as a gratuitous cellautonomous marker. Novel markers that carry epitope tags and that are localized to either the cell nucleus or cell membrane have been generated. As a demonstration of how these strains can be used to study a partic ular gene, we have analyzed the developmental role of the Drosophila EGF receptor homolog. Moreover, we have shown that these strains can be utilized to identify new mutations in mosaic animals in an efficient and unbiased way, thereby providing an unprecedented opportunity to perform systematic genetic screens for mutations affecting many biological processes. SUMMARY

/pdf/analysis-of-genetic-mosaics-in-developing-and-adult-2kzltuts2u.pdf

Analysis of genetic mosaics in developing and adult Drosophila tissues

Identification of complementary DNAs encoding the human glucocorticoid receptor predicts two protein forms, of 777 (alpha) and 742 (beta) amino acids, which differ at their carboxy termini. The proteins contain a cysteine/lysine/arginine-rich region which may define the DNA-binding domain. Pure radiolabelled glucocorticoid receptor, synthesized in vitro, is immunoreactive and possesses intrinsic steroid-binding activity characteristic of the native glucocorticoid receptor.

/pdf/primary-structure-and-expression-of-a-functional-human-9s709u9sel.pdf

Primary structure and expression of a functional human glucocorticoid receptor cDNA

Selection against deleterious alleles maintained by mutation may cause a reduction in the amount of genetic variability at linked neutral sites. This is because a new neutral variant can only remain in a large population for a long period of time if it is maintained in gametes that are free of deleterious alleles, and hence are not destined for rapid elimination from the population by selection. Approximate formulas are derived for the reduction below classical neutral values resulting from such background selection against deleterious mutations, for the mean times to fixation and loss of new mutations, nucleotide site diversity, and number of segregating sites. These formulas apply to random-mating populations with no genetic recombination, and to populations reproducing exclusively asexually or by self-fertilization. For a given selection regime and mating system, the reduction is an exponential function of the total mutation rate to deleterious mutations for the section of the genome involved. Simulations show that the effect decreases rapidly with increasing recombination frequency or rate of outcrossing. The mean time to loss of new neutral mutations and the total number of segregating neutral sites are less sensitive to background selection than the other statistics, unless the population size is of the order of a hundred thousand or more. The stationary distribution of allele frequencies at the neutral sites is correspondingly skewed in favor of rare alleles, compared with the classical neutral result. Observed reductions in molecular variation in low recombination genomic regions of sufficiently large size, for instance in the centromere-proximal regions of Drosophila autosomes or in highly selfing plant populations, may be partly due to background selection against deleterious mutations.

/pdf/the-effect-of-deleterious-mutations-on-neutral-molecular-49m5u8w40l.pdf

The effect of deleterious mutations on neutral molecular variation.

The genetic basis of inbreeding depression and of the related phenomenon, heterosis, has been a puzzle for many decades. Based on recent studies in many species, the authors argue that both phenomena are predominantly caused by the presence of recessive deleterious mutations in populations. Inbreeding depression — the reduced survival and fertility of offspring of related individuals — occurs in wild animal and plant populations as well as in humans, indicating that genetic variation in fitness traits exists in natural populations. Inbreeding depression is important in the evolution of outcrossing mating systems and, because intercrossing inbred strains improves yield (heterosis), which is important in crop breeding, the genetic basis of these effects has been debated since the early twentieth century. Classical genetic studies and modern molecular evolutionary approaches now suggest that inbreeding depression and heterosis are predominantly caused by the presence of recessive deleterious mutations in populations.

The genetics of inbreeding depression.

Studies with Drosophila, in particular with Drosophila melanogaster, have contributed more to our understanding of heredity than studies with any other eukaryotes. It is genetically the best known "higher" organism; this means not only that a greater number of mutations are known in Drosophila than in other organisms, but also that we have an unrivalled ability to manipulate its chromosomes and mutations to experimental advantage, Drosophila is the organism of choice for studies of development, behaviour and evolution as well as the formal mechanisms of genetics. Current research ranges from problems in "pure genetics" to sophisticated studies in neurobiology and ecology. Surprisingly, this is the first attempt since 1925 to publish a comprehensive account of the biology and genetics of Drosophila. It aims to collate the dauntingly large literature on the subject and to make more accessible the private language ot the Drosophilist. In this way the work will be of value both to established research workers and to biologists in general. This remarkable and ambitious work has been divided into three series of volumes. Volumes la, lb and lc deal with the formal genetics of Drosophila, Volume 2 deals with its biology and development and Volume 3 with its evolution and ecology. These volumes will prove an invaluable source of reference for Drosophilists, advanced students of genetics and for biologists, entomologists and zoologists with an interest in Drosophila research.

The Genetics and biology of Drosophila

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of nonexpert stakeholders a challenge. Here, we explore the applicability of deep learning approaches by adapting a pretrained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the U.K. where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides, and tunneling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exist to construct a balanced training data set, and the deformation signals are slower and more localized than in previous applications. We propose three enhancement methods to tackle these problems: 1) spatial interpolation with modified matrix completion; 2) a synthetic training data set based on the characteristics of the real U.K. velocity map; and 3) enhanced overwrapping techniques. Using velocity maps spanning 2015–2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides, and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

/pdf/detecting-ground-deformation-in-the-built-environment-using-1qskzq4tci.pdf

Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

James Thompson

Papers

The Genetics and biology of Drosophila

Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network

Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data.