DeadEasy Mito-Glia: Automatic Counting of Mitotic Cells and Glial Cells in Drosophila

Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists

The outlook of building information modeling for sustainable development

As human needs evolve, information technologies and natural environments require a wider perspective of sustainable development, especially when examining the built environment that impacts the central of social-ecological systems. The objectives of the paper are (a) to review the status and development of building information modeling (BIM) in regards to the sustainable development in the built environment, and (b) to develop a future outlook framework that promotes BIM in sustainable development. Seven areas of sustainability were classified to analyze forty-four BIM guidelines and standards. This review examines the use of BIM in sustainable development, focusing primarily on certain areas of sustainability, such as project development, design, and construction. The developed framework describes the need for collaboration with the multiple disciplines for the future adoption and use of BIM for the sustainable development. It also considers the integration between “BIM and green assessment criteria”; and “BIM and renewable energy” to address the shortcomings of the standards and guidelines

Streamlining Digital Modeling and Building Information Modelling (BIM) Uses for the Oil and Gas Projects

The oil and gas industry is a technology-driven industry. Over the last two decades, it has heavily made use of digital modeling and associated technologies (DMAT) to enhance its commercial capability. Meanwhile, the Building Information Modelling (BIM) has grown at an exponential rate in the built environment sector. It is not only a digital representation of physical and functional characteristics of a facility, but it has also made an impact on the management processes of building project lifecycle. It is apparent that there are many similarities between BIM and DMAT usability in the aspect of physical modeling and functionality. The aim of this study is to streamline the usage of both DMAT and BIM whilst discovering valuable practices for performance improvement in the oil and gas projects. To achieve this, 28 BIM guidelines, 83 DMAT academic publications and 101 DMAT vendor case studies were selected for review. The findings uncover (a) 38 BIM uses; (b) 32 DMAT uses and; (c) 36 both DMAT and BIM uses. The synergy between DMAT and BIM uses would render insightful references into managing efficient oil and gas’s projects. It also helps project stakeholders to recognise future investment or potential development areas of BIM and DMAT uses in their projects

An Isolated Stellar-Mass Black Hole Detected Through Astrometric Microlensing

We report the first unambiguous detection and mass measurement of an isolated stellar-mass black hole (BH). We used the Hubble Space Telescope (HST) to carry out precise astrometry of the source star of the long-duration (t_E ~ 270 days), high-magnification microlensing event MOA-2011-BLG-191/OGLE-2011-BLG-0462, in the direction of the Galactic bulge. HST imaging, conducted at eight epochs over an interval of six years, reveals a clear relativistic astrometric deflection of the background star's apparent position. Ground-based photometry shows a parallactic signature of the effect of the Earth's motion on the microlensing light curve. Combining the HST astrometry with the ground-based light curve and the derived parallax, we obtain a lens mass of 7.1 +/- 1.3 M_Sun and a distance of 1.58 +/- 0.18 kpc. We show that the lens emits no detectable light, which, along with having a mass higher than is possible for a white dwarf or neutron star, confirms its BH nature. Our analysis also provides an absolute proper motion for the BH. The proper motion is offset from the mean motion of Galactic-disk stars at similar distances by an amount corresponding to a transverse space velocity of ~45 km/s, suggesting that the BH received a modest natal 'kick' from its supernova explosion. Previous mass determinations for stellar-mass BHs have come from radial-velocity measurements of Galactic X-ray binaries, and from gravitational radiation emitted by merging BHs in binary systems in external galaxies. Our mass measurement is the first ever for an isolated stellar-mass BH using any technique

Time series analysis of all share price index& sector indices

In the recent past, stock market trading became one of the most important factors in a country. Colombo Stock Exchange (CSE) is the main stock exchange in Sri Lanka and All Share Price Index (ASPI) is a main index used by CSE. ASPI indicates the price fluctuations of all the listed companies and covers all the traded companies during a market day. The CSE market has been divided in to 20 sectors, based on the nature of the business. Out of the 20 sector indices "Bank Finance and Insurance" (BFI) sector has become one of the most important and influential indicator on economy of Sri Lanka and it has a high influence on ASPI as well. Thus, forecasting ASPI and BFI is very important for the decision maker. Hence this study was carried out to study, the pattern of time series of ASPI and BFI and to forecast values of ASPI and BFI. The data considered for this study was from 3r d January 2000 to 30t h January 2009 which accommodate to 2177 daily data points in each index. The result found that the original ASPI series depicts a similar trend pattern to "S Curve”. The SLR/US $ Exchange Rate does not have an impact on ASPI. As a result a univariate time series Autoregressive Integrated Moving Average (ARIMA (2, 1,2)) model was fitted to predict values of ASPI. The validity of the model was confirmed using various statistic tests. And found the predicted values of ASPI were below 5%. Thus, this model is recommended to forecast ASPI. Thus, BFI sector has a high influence on ASPI with the most number of companies coming under it. Hence, tried to fit a combined model to predict values of BFI. The Granger causality test and co integration test confirmed that BFI is influenced by 10 sector indices and with a lag length of 3 for each sector index. Hence, a Vector Auto regression (VAR) model was identified as the predictive model to forecast values of BFI. The percentage error for the forecasted values of BFI also varied between 0.44% and 4.83% ensuring the suitability of the model. Thus, this model is also recommended to forecast BFI

An efficient tool for genetic experiments: agarose gel image analysis

Repeatability of many genetic analysis experiments can be improved by the application of signal processing and image-processing tools. Most of the experiments in genetics result in some sort of digital signal or image patterns that are subjectively analyzed by the geneticists. This paper presents an example project where image-processing techniques are applied for automation of most of the routine processes of agarose gel image analysis. Result of applying this program has shown that the time taken for the analysis has been reduced by about 70% and the result is reproducible. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved