Multiple Imputation Using Gaussian Copulas

Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use method for generating multiple imputations using a Gaussian copula. The Gaussian copula for multiple imputation (Hoff, 2007) allows scholars to attain estimation results that have good coverage and small bias. The use of copulas to model the dependence among variables will enable researchers to construct valid joint distributions of the data, even without knowledge of the actual underlying marginal distributions. Multiple imputations are then generated by drawing observations from the resulting posterior joint distribution and replacing the missing values. Using simulated and observational data from published social science research, we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. Our results suggest that the Gaussian copula approach has a slightly smaller bias, higher coverage rates, and narrower confidence intervals compared to the other methods. This is especially true when the variables with missing data are not normally distributed. These results, combined with theoretical guarantees and ease-of-use suggest that the approach examined provides an attractive alternative for applied researchers undertaking multiple imputations

Amino acid composition predicts prion activity

Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136

Amino acid composition predicts prion activity

Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136

NanoStreams: A Microserver Architecture for Real-time Analytics on Fast Data Streams

Ever increasing power consumption has created great interest in energy-efficient microserver architectures but they lack the computational, networking and storage power necessary to cope with real-time data analytics. We propose NanoStreams, an integrated architecture comprising an ARM-based microserver, coupled via a novel, low latency network interface, Nanowire, to a Analytics-on-Chip architecture implemented on Field Programmable Gate Array (FPGA) technology; the architecture comprises ARM cores for performing low latency transactional processing, integrated with programmable, energy efficient Nanocore processors for high-throughput streaming analytics. The paper outlines the complete system architecture, hardware level detail, compiler, network protocol, and programming environment. We present experiments withan industrial workload from the financial services sector, comparing a state-of-the-art server based on Intel Sandy Bridge processors, an ARM based Calxeda ECS-1000 microserver and ODROID XU3 node, with the NanoStreams microserver architecture. For end-to-end workload, the NanoStreams microserver achieves energy savings up to 10.7x, 5.87x and 5x compared to the Intel server, Calxeda microserver and ODROID node respectively

WHO Parents Skills Training (PST) programme for children with developmental disorders and delays delivered by Family Volunteers in rural Pakistan: study protocol for effectiveness implementation hybrid cluster randomized controlled trial

Development disorders and delays are recognised as a public health priority and included in the WHO mental health gap action programme (mhGAP). Parents Skills Training (PST) is recommended as a key intervention for such conditions under the WHO mhGAP intervention guide. However, sustainable and scalable delivery of such evidence based interventions remains a challenge. This study aims to evaluate the effectiveness and scaled-up implementation of locally adapted WHO PST programme delivered by family volunteers in rural Pakistan. The study is a two arm single-blind effectiveness implementation-hybrid cluster randomised controlled trial. WHO PST programme will be delivered by 'family volunteers' to the caregivers of children with developmental disorders and delays in community-based settings. The intervention consists of the WHO PST along with the WHO mhGAP intervention for developmental disorders adapted for delivery using the android application on a tablet device. A total of 540 parent-child dyads will be recruited from 30 clusters. The primary outcome is child's functioning, measured by WHO Disability Assessment Schedule - child version (WHODAS-Child) at 6 months post intervention. Secondary outcomes include children's social communication and joint engagement with their caregiver, social emotional well-being, parental health related quality of life, family empowerment and stigmatizing experiences. Mixed method will be used to collect data on implementation outcomes. Trial has been retrospectively registered at ClinicalTrials.gov (NCT02792894). This study addresses implementation challenges in the real world by incorporating evidence-based intervention strategies with social, technological and business innovations. If proven effective, the study will contribute to scaled-up implementation of evidence-based packages for public mental health in low resource settings. Registered with ClinicalTrials.gov as Family Networks (FaNs) for Children with Developmental Disorders and Delays. Identifier: NCT02792894 Registered on 6 July 2016

Effects of Non-Driving Related Tasks During Self-Driving Mode

Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study

An overview of the utilisation of microalgae biomass derived from nutrient recycling of wet market wastewater and slaughterhouse wastewater

Microalgae have high nutritional values for aquatic organisms compared to fish meal, because microalgae cells are rich in proteins, lipids, and carbohydrates. However, the high cost for the commercial production of microalgae biomass using fresh water or artificial media limits its use as fish feed. Few studies have investigated the potential of wet market wastewater and slaughterhouse wastewater for the production of microalgae biomass. Hence, this study aims to highlight the potential of these types of wastewater as an alternative superior medium for microalgae biomass as they contain high levels of nutrients required for microalgae growth. This paper focuses on the benefits of microalgae biomass produced during the phycore-mediation of wet market wastewater and slaughterhouse wastewater as fish feed. The extraction techniques for lipids and proteins as well as the studies conducted on the use of microalgae biomass as fish feed were reviewed. The results showed that microalgae biomass can be used as fish feed due to feed utilisation efficiency, physiological activity, increased resistance for several diseases, improved stress response, and improved protein retention

Splitting It Up: The spduration Split-Population Duration Regression Package for Time-Varying Covariates

We present an implementation of split-population duration regression in the spduration (Beger et al., 2017) package for R that allows for time-varying covariates. The statistical model accounts for units that are immune to a certain outcome and are not part of the duration process the researcher is primarily interested in. We provide insights for when immune units exist, that can significantly increase the predictive performance compared to standard duration models. The package includes estimation and several post-estimation methods for split-population Weibull and log-logistic models. Weprovide an empirical application to data on military coups