Urban floods analysis by using remote sensing imageries in Asian: systematic review

Floods are not extraordinary natural disasters; in fact, it happens every year in Asian countries. In the future, climate change will increase the likelihood of flooding in most urban areas due to extreme events and rising sea levels. The objective of this article is to provide an overview and systematic literature on the remote sensing approach in order to examine and analyse urban floods in Asian countries. This paper highlights various aspects of remote sensing and its applications in flood studies in Asian countries particularly. For mapping and analysing urban floods, remote sensing data and methods such as high-resolution data from optical and SAR satellites are vital. This technology is able to analyse and display visualizations of a geographical area because the action of identifying and understanding the possibility of flood risk areas accurately is very important to reduce the risk during a disaster. The finding shows that remote sensing technology has been used in Asian countries widely in terms of surveying, identifying, classifying, mapping, and monitoring natural resources, environment, and disasters in the context of flood risk analysis. The majority of Asian nations employ remote sensing imagery from optical and microwave satellite sensors. Remote sensing technology is capable of disaster monitoring, mitigation, damage assessment, security, preventive, training, information exchange, and regional and worldwide collaboration. The right procedures through this technology should be made to mitigate a disaster for achieving the resiliency of cities. Every level of project design, execution, and monitoring should put in a valiant effort to address the existing flood disasters

Bottle house: A case study of transdisciplinary research for tackling global challenges

This work was done in collaboration with colleagues from the institute of Engineering sciences and Architecture Research Institute The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Globalisation has brought a number of challenges to the fore, particularly those problems which require collaboration, innovation and capability development between nations. There are some complex issues piquing the attention of researchers with respect to sustainable development, such as, waste management, climate change, and access to amenities, housing or education. Non-Governmental Organisations, Institutions, governments and others working in the field of international development have been grappling with these difficulties for decades. However, it is becoming apparent that many of these difficulties require multifaceted solutions, particularly in Low and Middle Income countries (LMIC) where it is difficult to consolidate gains and fund schemes. Development work can sometimes be disjointed and inefficient, impairing the capability of local communities and inhibiting sustainable and innovative approaches. Transdisciplinary collaboration is reliably a more efficient way of tackling some of the most pertinacious challenges. This paper presents findings from a transdisciplinary research project focussed on developing resources and capacity for the construction of affordable homes in a low income community in Nigeria. The project explored the suitability of using upcycled materials such as plastic bottles and agricultural waste in construction. Using a user-centred, co-creation methodology, a team of experts from the UK and Nigeria worked with local entrepreneurs to build a prototype home. The study explores the functionality of the home and the sustainability of project. The findings demonstrate the benefits of tackling global challenges from a transdisciplinary perspective. This has implications for researchers focused on developing technical solutions for low-income communities

The loss of histone H3 lysine 9 acetylation due to dSAGA-specific dAda2b mutation influences the expression of only a small subset of genes

In Drosophila, the dADA2b-containing dSAGA complex is involved in histone H3 lysine 9 and 14 acetylation. Curiously, although the lysine 9- and 14-acetylated histone H3 levels are drastically reduced in dAda2b mutants, these animals survive until a late developmental stage. To study the molecular consequences of the loss of histone H3 lysine 9 and 14 acetylation, we compared the total messenger ribonucleic acid (mRNA) profiles of wild type and dAda2b mutant animals at two developmental stages. Global gene expression profiling indicates that the loss of dSAGA-specific H3 lysine 9 and 14 acetylation results in the expression change (up- or down-regulation) of a rather small subset of genes and does not cause a general transcription de-regulation. Among the genes up-regulated in dAda2b mutants, particularly high numbers are those which play roles in antimicrobial defense mechanisms. Results of chromatin immunoprecipitation experiments indicate that in dAda2b mutants, the lysine 9-acetylated histone H3 levels are decreased both at dSAGA up- and down-regulated genes. In contrast to that, in the promoters of dSAGA-independent ribosomal protein genes a high level of histone H3K9ac is maintained in dAda2b mutants. Our data suggest that by acetylating H3 at lysine 9, dSAGA modifies Pol II accessibility to specific promoters differently

Most Networks in Wagner's Model Are Cycling

In this paper we study a model of gene networks introduced by Andreas Wagner in the 1990s that has been used extensively to study the evolution of mutational robustness. We investigate a range of model features and parameters and evaluate the extent to which they influence the probability that a random gene network will produce a fixed point steady state expression pattern. There are many different types of models used in the literature, (discrete/continuous, sparse/dense, small/large network) and we attempt to put some order into this diversity, motivated by the fact that many properties are qualitatively the same in all the models. Our main result is that random networks in all models give rise to cyclic behavior more often than fixed points. And although periodic orbits seem to dominate network dynamics, they are usually considered unstable and not allowed to survive in previous evolutionary studies. Defining stability as the probability of fixed points, we show that the stability distribution of these networks is highly robust to changes in its parameters. We also find sparser networks to be more stable, which may help to explain why they seem to be favored by evolution. We have unified several disconnected previous studies of this class of models under the framework of stability, in a way that had not been systematically explored before

Crk and CrkL adaptor proteins: networks for physiological and pathological signaling

The Crk adaptor proteins (Crk and CrkL) constitute an integral part of a network of essential signal transduction pathways in humans and other organisms that act as major convergence points in tyrosine kinase signaling. Crk proteins integrate signals from a wide variety of sources, including growth factors, extracellular matrix molecules, bacterial pathogens, and apoptotic cells. Mounting evidence indicates that dysregulation of Crk proteins is associated with human diseases, including cancer and susceptibility to pathogen infections. Recent structural work has identified new and unusual insights into the regulation of Crk proteins, providing a rationale for how Crk can sense diverse signals and produce a myriad of biological responses

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

The in vitro activity of the tyrosine kinase inhibitor STI571 in BCR-ABL positive chronic myeloid leukaemia cells: synergistic interactions with anti-leukaemic agents.

Chronic myeloid leukaemia is typically characterised by the presence of dysregulated BCR-ABL tyrosine kinase activity, which is central to the oncogenic feature of being resistant to a wide range of cytotoxic agents. We have investigated whether the inhibition of this tyrosine kinase by the novel compound STI571 (formerly CGP57148B) would render K562, KU812 cell lines and chronic myeloid leukaemia-progenitor cells sensitive to induction of cell kill. Proliferation assays showed STI571 to be an effective cytotoxic agent in chronic myeloid leukaemia-derived cell lines (IC(50) on day 5 of 4.6 microg ml(-1) and 3.4 microg ml(-1) for K562 and KU812 respectively) and in leukaemic blast cells (per cent viability on day 3 at 4 microg ml(-1): 55.5+/-8.7 vs 96.4+/-3.7%). STI571 also appeared to specifically target bcr-abl expressing cells, as results from colony forming assays using the surviving cell fraction from STI571-treated peripheral CD34(+) chronic myeloid leukaemia blast cells, indicated a reduction in the expansion of colonies of myeloid lineage, but no effect on normal colony formation. Our data also showed synergy between STI571 and other anti-leukaemic agents; as an example, there were significant increases in per cent cell kill in cell lines cultured with both STI571 and etoposide compared to the two alone (per cent cell kill on day 3: 73.7+/-11.3 vs 44.5+/-8.7 and 17.8+/-7.0% in cultures with STI571 and etoposide alone respectively; P<0.001). This study confirms the central oncogenic role of BCR-ABL in the pathogenesis of chronic myeloid leukaemia, and highlights the role of targeting this tyrosine kinase as a useful tool in the clinical management of the disease

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them