Benefits of publicly available data

The National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was designed to examine the health effects of air pollution in the United States. The primary question was whether particulate matter was responsible for the associations between air pollution and daily mortality. Secondary questions concerned measurement error in air pollution and mortality displacement.1 Since then, NMMAPS has been used to answer many important questions in environmental epidemiology...

Agrobacterium-mediated transformation of indica rice cultivars using binary and superbinary vectors

The Agrobacterium-mediated transformation system was extended to two indica cultivars: a widely cultivated breeding line IR-64 and an elite basmati cultivar Karnal Local. Root tips and shoot tips of seedlings, and scutellar-calli derived from mature seeds showed high-efficiency Agrobacterium tumefaciens infection and stable transformation. In addition to the superbinary vector pTOK233 in Agrobacterium strain LBA4404, almost equally high levels of transformation were achieved with a relatively much smaller (13.1 kb) binary vector (pCAMBIA1301) in a supervirulent host strain AGL1. In both cases, as well as in both cultivars, while 60–90% of the infected explants produced calli resistant to the selectable agent hygromycin, 59–75% of such calli tested positive for GUS. A high level (400 μM) of acetosyringone in the preinduction medium for Agrobacterium and a higher level (500 μM) in the cocultivation medium was necessary for an enhancement in transformation frequency of the binary vector to levels comparable to a superbinary. Hygromycin-resistant calli could be produced from all the explants used. Transformants could be regenerated for both cultivars using the superbinary and binary vector, but only for calli of scutellar origin. In addition to the molecular confirmation of hpt and gus gene transfer and transcription, absence of gene sequences outside the transferred DNA (T-DNA) region confirmed absence of any long T-DNA transfer.

Empirical analysis on relationship between traffic conditions and crash occurrences

This paper investigates relationship between traffic conditions and the crash occurrence likelihood (COL) using the I-880 data. To remedy the data limitations and the methodological shortcomings suffered by previous studies, a multiresolution data processing method is proposed and implemented, upon which binary logistic models were developed. The major findings of this paper are: 1) traffic conditions have significant impacts on COL at the study site; Specifically, COL in a congested (transitioning) traffic flow is about 6 (1.6) times of that in a free flow condition; 2)Speed variance alone is not sufficient to capture traffic dynamics’ impact on COL; a traffic chaos indicator that integrates speed, speed variance, and flow is proposed and shows a promising performance; 3) Models based on aggregated data shall be interpreted with caution. Generally, conclusions obtained from such models shall not be generalized to individual vehicles (drivers) without further evidences using high-resolution data and it is dubious to either claim or disclaim speed kills based on aggregated data.

Analysing environmental acoustic data through collaboration and automation

Monitoring environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; online collaboration, manual, automatic and human-in-the loop analysis.

Effects of Mobile Phone Distraction at the Onset of Amber Light: Analysis of Driving Simulator Data

Distraction whilst driving on an approach to a signalized intersection is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. This study examines the decisions of distracted drivers during the onset of amber lights. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of IOWA - National Advanced Driving Simulator. Explanatory variables include age, gender, cell phone use, distance to stop-line, and speed. An iterative combination of decision tree and logistic regression analyses are employed to identify main effects, non-linearities, and interactions effects. Results show that novice (16-17 years) and younger (18-25 years) drivers’ had heightened amber light running risk while distracted by cell phone, and speed and distance thresholds yielded significant interaction effects. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Solutions are needed to combat the use of mobile phones whilst driving.

Business Process Data Compliance

Most approaches to business process compliance are restricted to the analysis of the structure of processes. It has been argued that full regulatory compliance requires information on not only the structure of processes but also on what the tasks in a process do. To this end Governatori and Sadiq[2007] proposed to extend business processes with semantic annotations. We propose a methodology to automatically extract one kind of such annotations; in particular the annotations related to the data schema and templates linked to the various tasks in a business process.

NMR relaxation behavior and quadrupole coupling constants of 39K and 23Na ions in glycerol. Comparisons with 39K tissue data

The quadrupole coupling constants (qcc) for39K and23Na ions in glycerol have been calculated from linewidths measured as a function of temperature (which in turn results in changes in solution viscosity). The qcc of39K in glycerol is found to be 1.7 MHz, and that of23Na is 1.6 MHz. The relaxation behavior of39K and23Na ions in glycerol shows magnetic field and temperature dependence consistent with the equations for transverse relaxation more commonly used to describe the reorientation of nuclei in a molecular framework with intramolecular field gradients. It is shown, however, that τc is not simply proportional to the ratio of viscosity/temperature (ηT). The 39K qcc in glycerol and the value of 1.3 MHz estimated for this nucleus in aqueous solution are much greater than values of 0.075 to 0.12 MHz calculated from T2 measurements of39K in freshly excised rat tissues. This indicates that, in biological samples, processes such as exchange of potassium between intracellular compartments or diffusion of ions through locally ordered regions play a significant role in determining the effective quadrupole coupling constant and correlation time governing39K relaxation. T1 and T2 measurements of rat muscle at two magnetic fields also indicate that a more complex correlation function may be required to describe the relaxation of39K in tissue. Similar results and conclusions are found for23Na.

Leveraging social network data for analytical CRM strategies : the introduction of social BI

The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed.

Impact of garment industries on road safety in metropolitan Dhaka

There are about 4,000 garment industries in Bangladesh, most of them are clustered in and around the capital city. Together they account for 75 percent of the country's export earnings and employ around 1.8 million people which is almost one half of the total industrial workforce of the country. Though it is the most important economy sector of Bangladesh, unplanned and haphazardly built garment factories are also inducing many social, housing and most importantly urban transportation problems which are a great cause of concern. This study investigates the impact of garment industries on transportation, in particular road safety issues of garment workers. Data is collected to identify the locational problems of garment factories, spatial distribution of worker residences, and their travel pattern as well as to assess their walking and road crossing problems. Finally, recommendations are put forward to tackle transport problems arising from these unplanned establishments of export oriented garments industries in Dhaka Metropolitan City.

Development of a data mining-based analysis framework for multi-attribute construction project information

Data mining techniques extract repeated and useful patterns from a large data set that in turn are utilized to predict the outcome of future events. The main purpose of the research presented in this paper is to investigate data mining strategies and develop an efficient framework for multi-attribute project information analysis to predict the performance of construction projects. The research team first reviewed existing data mining algorithms, applied them to systematically analyze a large project data set collected by the survey, and finally proposed a data-mining-based decision support framework for project performance prediction. To evaluate the potential of the framework, a case study was conducted using data collected from 139 capital projects and analyzed the relationship between use of information technology and project cost performance. The study results showed that the proposed framework has potential to promote fast, easy to use, interpretable, and accurate project data analysis.

Tooth fracture risk analysis based on a new finite element dental structure models using micro-CT data

The finite element (FE) analysis is an effective method to study the strength and predict the fracture risk of endodontically-treated teeth. This paper presents a rapid method developed to generate a comprehensive tooth FE model using data retrieved from micro-computed tomography (μCT). With this method, the inhomogeneity of material properties of teeth was included into the model without dividing the tooth model into different regions. The material properties of the tooth were assumed to be related to the mineral density. The fracture risk at different tooth portions was assessed for root canal treatments. The micro-CT images of a tooth were processed by a Matlab software programme and the CT numbers were retrieved. The tooth contours were obtained with thresholding segmentation using Amira. The inner and outer surfaces of the tooth were imported into Solidworks and a three-dimensional (3D) tooth model was constructed. An assembly of the tooth model with the periodontal ligament (PDL) layer and surrounding bone was imported into ABAQUS. The material properties of the tooth were calculated from the retrieved CT numbers via ABAQUS user's subroutines. Three root canal geometries (original and two enlargements) were investigated. The proposed method in this study can generate detailed 3D finite element models of a tooth with different root canal enlargements and filling materials, and would be very useful for the assessment of the fracture risk at different tooth portions after root canal treatments.

Association between prostinogen (KLK15) genetic variants and prostate cancer risk and aggressiveness in Australia and a meta-analysis of GWAS data

Background: Kallikrein 15 (KLK15)/Prostinogen is a plausible candidate for prostate cancer susceptibility. Elevated KLK15 expression has been reported in prostate cancer and it has been described as an unfavorable prognostic marker for the disease. Objectives: We performed a comprehensive analysis of association of variants in the KLK15 gene with prostate cancer risk and aggressiveness by genotyping tagSNPs, as well as putative functional SNPs identified by extensive bioinformatics analysis. Methods and Data Sources: Twelve out of 22 SNPs, selected on the basis of linkage disequilibrium pattern, were analyzed in an Australian sample of 1,011 histologically verified prostate cancer cases and 1,405 ethnically matched controls. Replication was sought from two existing genome wide association studies (GWAS): the Cancer Genetic Markers of Susceptibility (CGEMS) project and a UK GWAS study. Results: Two KLK15 SNPs, rs2659053 and rs3745522, showed evidence of association (p, 0.05) but were not present on the GWAS platforms. KLK15 SNP rs2659056 was found to be associated with prostate cancer aggressiveness and showed evidence of association in a replication cohort of 5,051 patients from the UK, Australia, and the CGEMS dataset of US samples. A highly significant association with Gleason score was observed when the data was combined from these three studies with an Odds Ratio (OR) of 0.85 (95% CI = 0.77-0.93; p = 2.7610 24). The rs2659056 SNP is predicted to alter binding of the RORalpha transcription factor, which has a role in the control of cell growth and differentiation and has been suggested to control the metastatic behavior of prostate cancer cells. Conclusions: Our findings suggest a role for KLK15 genetic variation in the etiology of prostate cancer among men of European ancestry, although further studies in very large sample sets are necessary to confirm effect sizes.