Activity Database Research Articles

AuroraWatch UK: An Automated Aurora Alert System

The AuroraWatch UK aurora alert service uses a network of magnetometers from across the United Kingdom to measure the disturbance in the Earth's magnetic field caused by the aurora borealis (northern lights). The service has been measuring disturbances in the Earth's magnetic field from the UK and issuing auroral visibility alerts to its subscribers, since September 2000. These alerts have four levels, corresponding to the magnitude of disturbance measured, which indicate from where in the UK an auroral display might be seen. In the following, we describe the AuroraWatch UK system in detail and reprocess the historical magnetometer data using the current alert algorithm to compile an activity database. This data set is composed of over 150,000 h (99.94% data availability) of magnetic disturbance measurements, including nearly 9,000 h of enhanced geomagnetic activity.

Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach

Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation’s impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging—with the potential to reduce fuel consumption by 45% in our “Optimistic” case or increase it by 30% in our “Pessimistic” case. Second, implementing automation on urban roads could potentially result in larger fuel savings compared with highway automation because of the driving features of urban roads. Through scenario analysis, we showed that the proposed framework can be used for refined assessments as better data on vehicle-level fuel efficiency and travel demand impacts of automation become available.

Report on paediatric care in the Moroccan military mission in the Gaza Strip

This report aims to describe the epidemiologic findings of the workload associated with paediatric consultation of the Moroccan Military field hospital in the Gaza Strip. The patient Administration System and Biostatistics Activity database was reviewed and queried for all patients < 15 years of age who were consulted in the Moroccan Army hospital in the Gaza Strip between November 2012 to February 2013. Paediatric consultations during the study period were 7420 (19.9%) out of 37 227 consultations. The largest proportion of children were from 2 to 13 years old (3066 of 7420 children; 41.3%). Combined, pulmonary and gastroenterology diseases were the main causes for consultations (60.4%). Paediatric patients with threatening life injuries form part of the primary responsibility of military medical facilities during wartime. The findings may guide future military medical care planning, particularly in war zones suffering a lack of basic infrastructure, with special a need for medical supplies and drugs.

PENERAPAN TIME-DRIVEN ACTIVITY-BASED COSTING PADA PERHITUNGAN HARGA POKOK PRODUK USAHA JASA UNIXON

The purpose of this paper is to calculate branding consultant cost using Time-Driven Activity-Based Costing (TDABC). TDABC calculate cost based on activity that is controlled by time. The method that is used in this research is a case study in Unixon, a small business that works in the field of branding, using data from 2016. Interviews were conducted. The cost and activity database was analyzed. The results of this research show that TDABC can be applied in this Company because it qualified the requirements; diversity in product and different time consumption ratio for each product. TDABC provided more accurate cost information by calculating all costs; cost to make and cost to sell. Implementation of TDABC also show time capacity utilization by comparing actual labor hours and work hours that is controlled by government regulation. By understanding this condition, the owner is able to evaluate the amount of job that is done, time consumption, and employee who is involved. To implement TDABC, the Company must consider each steps needed from identifying activity, assign cost to activity, calculate tariff per activity, to calculate cost of product.

Metabolomic Studies of Indonesian Jamu Medicines: Prediction of Jamu Efficacy and Identification of Important Metabolites.

In order to obtain a better understanding why some Jamu formulas can be used to treat a specific disease, we performed metabolomic studies of Jamu by taking into consideration the biologically active compounds existing in plants used as Jamu ingredients. A thorough integration of information from omics is expected to provide solid evidence-based scientific rationales for the development of modern phytomedicines. This study focused on prediction of Jamu efficacy based on its component metabolites and also identification of important metabolites related to each efficacy group. Initially, we compared the performance of Support Vector Machines and Random Forest to predict the Jamu efficacy with three different data pre-processing approaches, such as no filtering, Single Filtering algorithm, and a combination of Single Filtering algorithm and feature selection using Regularized Random Forest. Both classifiers performed very well and according to 5-fold cross-validation results, the mean accuracy of Support Vector Machine with linear kernel was slightly better than Random Forest. It can be concluded that machine learning methods can successfully relate Jamu efficacy with metabolites. In addition, we extended our analysis by identifying important metabolites from the Random Forest model. The inTrees framework was used to extract the rules and to select important metabolites for each efficacy group. Overall, we identified 94 significant metabolites associated to 12 efficacy groups and many of them were validated by published literature and KNApSAcK Metabolite Activity database.

Evaluation of exposure factors to support development of generic recreational reuse scenarios for land reclamation activities

ABSTRACTRecreational reuse of contaminated lands can be a sustainable and cost-effective approach to address the increasing demand on land availability for community development as well as for ecologically valuable conservation areas. Conservatively, residential exposure defaults are often applied for evaluation of an alternative future land use. A risk-based fit-for-purpose approach is preferred in evaluating contaminated sites for reuse purposes. The consideration of exposure factors relevant to the anticipated land use is an important step in performing these assessments. Data from two time activity databases, the Consolidated Human Activity Database (CHAD) and the American Time Use Survey (ATUS), were analyzed for activities that might occur at recreational sites for two important exposure factors: duration and frequency of site activities. The information in these databases indicated that the majority of “doers” spent a total time less than 4–6 h per day on these activities. Only a very small percentage of the survey populations participated in these recreational activities. Limited information on activity frequency was reported in the two data sets. This analysis supports modification to the duration of activities at recreational reuse sites as compared to residential defaults, and provides a basis for the development of alternate values.

Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity

Abstract We examined the performance of several popular machine learning methods including multiple linear regression (MLR), support vector regression (SVR) and Gaussian process regression (GPR) for quantitative predictions of estrogen receptor (ER) binding affinity. Particular attention is devoted to compiling an accurate and precise dataset of 1589 experimental binding affinities (logRBA) from the Estrogenic Activity Database to train and validate the models. Issues related to accuracy/precision of experimental data, choice of binding affinity data measured on human ER or across species are addressed. The SVR and GPR models performed the best with root-mean-square-errors below 1 log unit that are comparable to experimental precision. We further examined the use of functional group analysis, nearest-neighbour distance and confidence interval estimates to flag large outliers. This work provides a test set of precise experimental data that may be used in future benchmarking studies of other machine learning models or free energy calculations.

Assessment of Circadian Rhythmicity of Respiratory Determinants Related to Diurnal Activities of Children and Adolescents: A Case Study in the City of Isfahan

Background: Diurnal sequences of activities conducted at several locations visited by individuals have an impact on population exposure to air pollution. However, data on individual's movement with a fine time resolution is rare. Methods: In the current study, 399 children and adolescents (aged 11–18 years) from Isfahan city were asked to recall their 24-hour diary during winter and spring 2014-2015. Daily ventilation rates for individuals were calculated using Consolidated Human Activity Database (CHAD) and were subject to cosinor analysis. Results: There was a significant circadian rhythm in inhalation rate, outdoor time-spent and ambient NO2 pollution. The ANOVA of rhythm parameters showed a significant difference (P < 0.05) between gender groups and day types, whereas the difference between age groups and seasons was not significant. Analysis of results showed that increased NO2 pollution was concurrent with increased inhalation rate and outdoor time-spent. Inhalation rates obtained for population groups were 9.3, 11.6, 9.0 and 11.3 L/min for high school girls, high school boys, elementary girls and elementary boys, respectively. Elementary boys were at higher risk of exposure to air pollution. Boys spent more time outdoors and in traffic than girls. Respondents spent 89% of their time inside and 82% of their inside time at home. They also spent 10% of their time during a year in school. Respondents were exposed to cooking generated pollutants 11 times a week. Among them, 30% were exposed to second-hand smoke, and 86.3% reported in very good health. Conclusion: We concluded that actual exposure levels may be underestimated when the simple risk assessment method is implemented without the survey of fine time resolution spatiotemporal activity data.

A prospective study of outcome of acute physiology and chronic health evaluation ii score in critically ill surgical patients

Background: The acute physiology score is determined from the most deranged (worst) physiologic value, for example, the lowest blood pressure or the highest respiratory rate, during the initial 24 h after Intensive Care Unit (ICU) admission. The aim of present study is to apply Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring system to surgical patients who have been critically ill preoperatively requiring elective surgical intervention or who underwent extensive elective surgery thereby requiring post-operative critical care monitoring and treatment in the post-operative ward or ICU or surgical ward. Materials and Methods: This prospective study was carried out on critically ill surgical patients from August 2012 to December 2013 in M. G. Hospital, Department of Surgery attached to Dr. S.N. Medical College and Associated Group of Hospitals, Jodhpur. The APACHE II score in the first 24 h of admission or operation and expected risks of death was calculated. Results: This study observed that 67.74% recovered male and 81.58% female and mortality rate increase when APACHE II score of patients increases. From the 100 patients enrolled the mean age was 47.38 ± 18.37 the overall median APACHE II score for all critically ill surgical patient was 9 with a range of 2–44 (minimum 2 and maximum 44). There was a significant difference between the APACHE II score of survivors and non-survivors. Conclusion: This scoring still provides a basic idea and uniform comparison of critical patients, thus help in research activities database validation. Few pitfalls related in the present study could have been avoided had there been large number of cases and more so of specific procedure-related patients necessitating critical care.

Developing and Implementing a Web-Based Departmental Faculty Scholarly and Service Activity Database

Developing and Implementing a Web-Based Departmental Faculty Scholarly and Service Activity Database

Use of population exposure frequency distributions to simulate effects of policy interventions on NO2 exposure

Health effects of air pollution on individuals depend on their personal exposure, but few modelling tools are available which can predict how the distribution of personal exposures within a city will change in response to policies to reduce emissions both indoors and outdoors. We describe a new probabilistic modelling framework (INDAIR-2/EXPAIR), which provides predictions of the personal exposure frequency distribution (PEFD) across a city to assess the effects of both reduced emissions from home sources and reduced roadside concentrations on population exposure. The model uses a national time activity database, which gives the percentage of each population group in different residential and non-residential micro-environments, and links this, for the home, to predictions of concentrations from a three-compartment model, and for non-residential microenvironments to empirical indoor/outdoor ratios. This paper presents modelled PEFDs for NO2 in the city of Leicester, for children, the elderly, and office workers, comparing results in different seasons and on different days of the week. While the mean NO2 population exposure was close to, or below the urban background concentration, the 95%ile of the PEFD was well above the urban background concentration. The relationship between both mean and 95%ile PEFD and urban background concentrations was strongly influenced by air exchange rate. The 24 h mean PEFD showed relative small differences between the population groups, with both removal of home sources and reductions of roadside concentrations on roads with a high traffic density having similar effects in reducing mean exposure. In contrast, the 1 h maximum of the PEFD was significantly higher for children and the elderly than for office workers, and showed a much greater response to reduced home emissions in these groups. The results demonstrate the importance of understanding the dynamics of NO2 exposure at a population level within different groups, if the benefits of policy interventions are to be accurately assessed.

Method to evaluate the quality of herbal medicines based on the dynamic changes of chemical compounds and pharmacological activity

Quality control has been one of the key scientific issues in the modernization of traditional Chinese medicine. This study explored a novel method for quality evaluation of herbal medicines. High-performance liquid chromatography fingerprints and the osteoblast proliferation activity of 18 batches of Achyranthes bidentata, which were prepared with salt, were determined to establish a chromatographic database and an activity database. Correlation analyses of these databases were performed using partial least squares to obtain regression coefficients (positive and negative correlation coefficients). Then, the sums of the products of the positive and negative correlation peak areas and the corresponding coefficients, respectively, were calculated for each sample. The absolute value of the ratios of the sums of the positive and negative products were calculated, our studies showed that this ratio was significantly correlated with the proliferation activity, particularly when the activity was in the best and worst ranges. Therefore, we developed a parameter that was used to evaluate the quality of samples osteoblast proliferation activity. The quality of another ten batches of samples was assessed to verify this method. The results indicated that this method can be used for quality evaluation of herbal medicines according to the dynamic changes in the chemical compounds and activity.

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

Discriminative sparse projections for activity-based person recognition

In this paper, we propose an activity-based person recognition approach based on discriminative sparse projections (DSPs) and ensemble metric learning. Unlike gait recognition where only the walking activity is utilized for human identification, we aim to recognize people from more types of activities such as eating, drinking, running, and so on. Our motivation is inspired by the fact that people do not always walk in person identification systems and gait recognition could fail to work in this scenario. Given each video clip, we first extract the binary human body mask in each frame by using background substraction. Then, we propose a DSP method to project these body masks into a low-dimensional subspace and cluster them into a number of clusters simultaneously. Subsequently, each video clip is pooled as a histogram feature for activity representation. Lastly, we propose an orthogonal ensemble metric learning (OEML) method to learn a distance metric to exploit more discriminative information for recognition. Experimental results on five benchmark activity databases are presented to show the efficacy of our proposed approach. Moreover, we apply our approach to gait recognition and also achieve competitive results with the state-of-the-art methods on the widely used CASIA-B gait dataset.

An analysis of baseline data from the PROUD study: an open-label randomised trial of pre-exposure prophylaxis.

BackgroundPre-exposure prophylaxis (PrEP) has proven biological efficacy to reduce the sexual acquisition of the human immunodeficiency virus (HIV). The PROUD study found that PrEP conferred higher protection than in placebo-controlled trials, reducing HIV incidence by 86 % in a population with seven-fold higher HIV incidence than expected. We present the baseline characteristics of the PROUD study population and place the findings in the context of national sexual health clinic data.MethodsThe PROUD study was designed to explore the real-world effectiveness of PrEP (tenofovir-emtricitabine) by randomising HIV-negative gay and other men who have sex with men (GMSM) to receive open-label PrEP immediately or after a deferral period of 12 months. At enrolment, participants self-completed two baseline questionnaires collecting information on demographics, sexual behaviour and lifestyle in the last 30 and 90 days. These data were compared to data from HIV-negative GMSM attending sexual health clinics in 2013, collated by Public Health England using the genitourinary medicine clinic activity database (GUMCAD).ResultsThe median age of participants was 35 (IQR: 29–43). Typically participants were white (81 %), educated at a university level (61 %) and in full-time employment (72 %). Of all participants, 217 (40 %) were born outside the UK. A sexually transmitted infection (STI) was reported to have been diagnosed in the previous 12 months in 330/515 (64 %) and 473/544 (87 %) participants reported ever having being diagnosed with an STI. At enrolment, 47/280 (17 %) participants were diagnosed with an STI. Participants reported a median (IQR) of 10 (5–20) partners in the last 90 days, a median (IQR) of 2 (1–5) were condomless sex acts where the participant was receptive and 2 (1–6) were condomless where the participant was insertive. Post-exposure prophylaxis had been prescribed to 184 (34 %) participants in the past 12 months. The number of STI diagnoses was high compared to those reported in GUMCAD attendees.ConclusionsThe PROUD study population are at substantially higher risk of acquiring HIV infection sexually than the overall population of GMSM attending sexual health clinics in England. These findings contribute to explaining the extraordinary HIV incidence rate during follow-up and demonstrate that, despite broad eligibility criteria, the population interested in PrEP was highly selective.Trial registrationCurrent Controlled TrialsISRCTN94465371. Date of registration: 28 February 2013.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1286-4) contains supplementary material, which is available to authorized users.

Combined Bioinformatic and Rational Design Approach To Develop Antimicrobial Peptides against Mycobacterium tuberculosis.

Drug-resistant pathogens are a growing problem, and novel strategies are needed to combat this threat. Among the most significant of these resistant pathogens is Mycobacterium tuberculosis, which is an unusually difficult microbial target due to its complex membrane. Here, we design peptides for specific activity against M. tuberculosis using a combination of "database filtering" bioinformatics, protein engineering, and de novo design. Several variants of these peptides are structurally characterized to validate the design process. The designed peptides exhibit potent activity (MIC values as low as 4 μM) against M. tuberculosis and also exhibit broad activity against a host of other clinically relevant pathogenic bacteria such as Gram-positive bacteria (Streptococcus) and Gram-negative bacteria (Escherichia coli). They also display excellent selectivity, with low cytotoxicity against cultured macrophages and lung epithelial cells. These first-generation antimicrobial peptides serve as a platform for the design of antibiotics and for investigating structure-activity relationships in the context of the M. tuberculosis membrane. The antimicrobial peptide design strategy is expected to be generalizable for any pathogen for which an activity database can be created.

A multiple regression model-based chemical biology approach to dissect signal transduction pathways downstream of cytokine receptors

In this paper, we propose a statistical model-based chemical biology approach to extract new biological insights from the compound activity database. We developed and characterised a small-molecule kinase inhibitor library for their ability to inhibit JAK1, 2 and 3 kinase in vitro. These compounds were also tested for their ability to suppress STAT1/5 phosphorylation induced by GM-CSF, interferon (IFN)-γ or interleukin (IL)-2 in primary human peripheral blood mononuclear cells (PBMC). Correlation analysis between the in-vitro and cell-based potencies of the inhibitors was performed by using a multiple-linear regression model. The regression p value for three JAK kinases confirmed the known individual contributions of JAK kinases to signalling pathway downstream of GM-CSF and IFN-γ receptor. Interestingly, the model suggests a previously under-appreciated role played by JAK2 downstream of IL-2 receptor activation. This study demonstrates the potential use of chemical biology approach in generating biological hypotheses when facilitated with proper statistical modelling techniques.

Building Construction Information System Using GIS

The stages of building construction vary with respect to activities and procedures. Execution schedules and two-dimensional (2D) drawings are commonly used to identify hazards in construction. Geographic information systems (GISs) can provide stakeholders, construction engineers and contractors with the required data during building construction. Planners can visualise 2D drawings and three-dimensional (3D) models using a GIS model that logically links their components with the activities in the schedule to establish the construction sequence. This study suggests the use of a GIS in building information modelling to construct a building construction information system by integrating 2D drawings and 3D models with the activities database in the GIS model. GIS modelling can be used to generate and update schedules, databases and geospatial analyses within a single platform, which is a major requirement in the construction industry. Combining a GIS model in construction with its surrounding topography by linking 2D drawings and databases of activities or components of a building can produce a safe execution sequence. If a hazardous situation is identified during the analytical review process, it can be prevented within the GIS platform prior to the actual implementation. If defects are identified during and after construction, the cause of the defects and suitable mitigation can be determined. The methodology is tested using a real-life project in Riyadh in the Kingdom of Saudi Arabia. The results conclude that GIS modelling can be employed to establish an information system for all stages of building construction and to create an archived system for efficient project documentation.

Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Abstract The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.