Redefining governance: a critical analysis of sustainability transformation in e-governance

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Inactivation kinetics of selected pathogenic and non-pathogenic bacteria by aqueous ozone to validate minimum usage in purified water

Ozone is often used as an antimicrobial agent at the final step in purified water processing. When used in purified bottled water manufacturing, residual ozone should not exceed 0.4 mg/L, per US-FDA regulations. These regulations require the control of Escherichia coli and other coliform bacteria; however, non-coliform pathogens can contaminate bottled water. Hence, it is prudent to test the efficacy of ozone against such pathogens to determine if the regulated ozone level adequately ensures the safety of the product. Inactivation of selected pathogenic and non-pathogenic bacteria in purified water was investigated as a function of ozone dose, expressed in Ct units (mg O3*min/L). Bacterial species tested were Enterococcus faecium, E. coli (two serotypes), Listeria monocytogenes (three strains), Pseudomonas aeruginosa, and Salmonella enterica (three serovars). Resulting dose (Ct)-response (reduction in populations' log10 CFU/mL) relationships were mostly linear with obvious heteroscedasticity. This heteroscedastic relationship required developing a novel statistical approach to analyze these data so that the lower bound of the dose-response relationships can be determined and appropriate predictive models for such a bound can be formulated. An example of this analysis was determining the 95%-confidence lower bound equation for the pooled dose-responses of all tested species; the model can be presented as follows: Logpopulationreduction = 3.80Ct + 1.84. Based on this relationship, application ozone at a Ct of 0.832 and 21°C achieves ≥ 5-log reduction in the population of any of the tested pathogenic and non-pathogenic bacteria. This dose can be implemented by applying ozone at 0.832 mg/L for 1 min, 0.416 mg/L for 2 min, or other combinations. The study also proved the suitability of E. faecium ATCC 8459 as a surrogate strain for the pathogens tested in the current study for validating water decontamination processes by ozone. In conclusion, the study findings can be usefully implemented in processing validation of purified water and possibly other water types.

Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

Harnessing Code Interpreters for Enhanced Predictive Modeling: A Case Study on High-Density Lipoprotein Level Estimation in Romanian Diabetic Patients

Diabetes is a condition accompanied by the alteration of body parameters, including those related to lipids like triglyceride (TG), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped under the term dyslipidemia and are considered a risk factor for cardiovascular events. In the present work, we analyzed the complex relationships between twelve parameters (disease status, age, sex, body mass index, systolic blood pressure, diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) of patients with diabetes from Romania. An initial prospective analysis showed that HDL is inversely correlated with most of the parameters; therefore, we further analyzed the dependence of HDLs on the other factors. The analysis was conducted with the Code Interpreter plugin of ChatGPT, which was used to build several models from which Random Forest performed best. The principal predictors of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model all parameters, showing that blood pressure and HbA1c can be predicted based on the other parameters with the least error, while the less predictable parameters were TG and LDL levels. By conducting the present study using the ChatGPT Code Interpreter, we show that elaborate analysis methods are at hand and easy to apply by researchers with limited computational resources. The insight that can be gained from such an approach, such as what we obtained on HDL level predictors in diabetes, could be relevant for deriving novel management strategies and therapeutic approaches.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

A Model for Multi-Institutional Clinical Data Repository

Creating a sustainable model for clinical data infrastructure requires the inclusion of key stakeholders, harmonization of their needs and constraints, integration with data governance considerations, conforming to FAIR principles while maintaining data safety and data quality, and maintaining financial health for contributing organizations and partners. This paper reflects on Columbia University's 30+ years of experiences in designing and developing clinical data infrastructure that synergizes both patient care and clinical research missions. We define the desiderata for a sustainable model and make recommendations of best practices to achieve a sustainable model.

Why do employees actively work overtime? The motivation of employees' active overtime in China

Introduction

Previous studies have defined "workaholic" effort as "bad effort" while work engagement is defined as "good effort." Active overtime is a mapping of work effort, but at this stage there is still relatively little exploration of the motivation behind "good effort" in the Chinese context.

Methods

This study explores the reasons that promote employees' initiative to perform overtime work in Chinese enterprises based on the two-factor theory. The study mainly used data empirical research approaches, including exploratory factor analysis, validation factor analysis, and data modeling. The questionnaire scale was developed based on factors that have been proven to be of high reliability and validity. The data are mainly for employees who are currently employed in Chinese companies.

Results and discussion

We received a total of 1741 valid questionnaires, which provided a good database for this study. The results of the study show that both motivational and hygiene factors can positively promote employees' motivation to intentionally work overtime to a certain extent. Among them, overtime culture, institutional agreement, good physical office environment, career growth, financial rewards, and work challenges can positively promote motivation to work overtime. Work stress can increase the frequency and intensity of overtime work, but negatively promote motivation to work overtime. The study helps to improve enterprise management, optimize work design, and enhance psychological satisfaction.

Cervical vestibular evoked myogenic potentials in healthy children: Normative values for bone and air conduction

Objectives

To characterize cervical vestibular evoked myogenic potentials (c-VEMPs) in bone conduction (BC) and air conduction (AC) in healthy children, to compare the responses to adults and to provide normative values according to age and sex.

Design

Observational study in a large cohort of healthy children (n = 118) and adults (n = 41). The c-VEMPs were normalized with the individual EMG traces, the amplitude ratios were modeled with the Royston-Wright method.

Results

In children, the amplitude ratios of AC and BC c-VEMP were correlated (r = 0.6, p < 0.001) and their medians were not significantly different (p = 0.05). The amplitude ratio was higher in men than in women for AC (p = 0.04) and BC (p = 0.03). Children had significantly higher amplitude ratios than adults for AC (p = 0.01) and BC (p < 0.001). Normative values for children are shown. Amplitude ratio is age-dependent for AC more than for BC. Confidence limits of interaural amplitude ratio asymmetries were less than 32%. Thresholds were not different between AC and BC (88 ± 5 and 86 ± 6 dB nHL, p = 0.99). Mean latencies for AC and BC were for P-wave 13.0 and 13.2 msec and for N-wave 19.3 and 19.4 msec.

Conclusion

The present study provides age- and sex-specific normative data for c-VEMP for children (6 months to 15 years of age) for AC and BC stimulation. Up to the age of 15 years, c-VEMP responses can be obtained equally well with both stimulation modes. Thus, BC represents a valid alternative for vestibular otolith testing, especially in case of air conduction disorders.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics' Common Data Model

Background

Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure.

Objective

The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) data and the population-level estimation method.

Methods

Using electronic health record–based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses.

Results

The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02).

Conclusions

Although we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years.

Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study

Background

In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems.

Objective

This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal.

Methods

A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient’s cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization.

Results

For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision.

Conclusions

The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.

What the Coronavirus Disease 2019 (COVID-19) Pandemic Has Reinforced: The Need for Accurate Data

The COVID-19 pandemic has challenged the United States’ existing national public health informatics infrastructure. This report details the factors that have contributed to COVID-19 data inaccuracies and reporting delays and their effect on the modeling and monitoring of the COVID-19 pandemic.

Measuring the Masses: A Proposed Template for Post-Event Medical Reporting (Paper 4)

BACKGROUND

Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting.

METHODS

Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources.

FINDINGS

Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain.

DISCUSSION

The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as "overlays," depending on the event category and type.

CONCLUSIONS

This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.

Measuring the Masses: Domains Driving Data Collection and Analysis for the Health Outcomes of Mass Gatherings (Paper 3)

INTRODUCTION

Without a robust evidence base to support recommendations for medical services at mass gatherings (MGs), levels of care will continue to vary and preventable morbidity and mortality will exist. Accordingly, researchers and clinicians publish case reports and case series to capture and explain some of the health interventions, health outcomes, and host community impacts of MGs. Streamlining and standardizing post-event reporting for MG medical services and associated health outcomes could improve inter-event comparability, thereby supporting and promoting growth of the evidence base for this discipline. The present paper is focused on theory building, proposing a set of domains for data that may support increasingly comprehensive, yet lean, reporting on the health outcomes of MGs. This paper is paired with another presenting a proposal for a post-event reporting template.

METHODS

The conceptual categories of data presented are based on a textual analysis of 54 published post-event medical case reports and a comparison of the features of published data models for MG health outcomes.

FINDINGS

A comparison of existing data models illustrates that none of the models are explicitly informed by a conceptual lens. Based on an analysis of the literature reviewed, four data domains emerged. These included: (i) the Event Domain, (ii) the Hazard and Risk Domain, (iii) the Capacity Domain, and (iv) the Clinical Domain. These domains mapped to 16 sub-domains.

DISCUSSION

Data modelling for the health outcomes related to MGs is currently in its infancy. The proposed illustration is a set of operationally relevant data domains that apply equally to small, medium, and large-sized events. Further development of these domains could move the MG community forward and shift post-event health outcomes reporting in the direction of increasing consistency and comprehensiveness.

CONCLUSION

Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

PPGSynth: An Innovative Toolbox for Synthesizing Regular and Irregular Photoplethysmography Waveforms

Photoplethysmography (PPG) is increasingly used in digital health, exceptionally in smartwatches. The PPG signal contains valuable information about heart activity, and there is lots of research interest in its means and analysis for cardiovascular diseases. Unfortunately, to our knowledge, there is no arrhythmic PPG dataset publicly available—this paper attempt to provide a toolbox that can generate synthesized arrhythmic PPG signals. The model of a single PPG pulse in this toolbox utilizes two combined Gaussian functions. This toolbox supports synthesizing PPG waveform with regular heartbeats and three irregular heartbeats: compensation, interpolation, and reset. The user can generate a large amount of PPG data with a certain irregularity, with different sampling frequency, time length, and a range of noise types (Gaussian noise and multi-frequency noise) can be added to the synthesized PPG which can all be modified from the interface, and different types of arrhythmic PPGs (as calculated by the model) generated. The generation for large PPG datasets that simulate PPG collected from real humans could be used for testing the robustness of developed algorithms that are targeting arrhythmic PPG signals. Our PPG synthesis tool is publicly available.

A Review of Predictive Analytics Solutions for Sepsis Patients

BACKGROUND

Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.

OBJECTIVE

To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality.

METHODS AND RESULTS

We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24-198,833) and feature size (range: 2-285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes.

CONCLUSION

Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.

HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

Asthma and allergy as risk factors for suicidal behavior among young adults

An association between allergic disease, depression and suicidality has been reported.

OBJECTIVE

To explore the relationships between suicidality and asthma, allergy, internet addiction, stress, sleep quality, pain/discomfort, and depression, among emerging adults.

PARTICIPANTS

929 college students completed an online survey between October 2015 and April 2017.

METHODS

A cross-sectional study using multivariate analysis techniques was implemented.

RESULTS

Using structural equation modeling, we found that allergies and stress were directly related to pain/discomfort; pain/discomfort was associated to poor sleep, depression, and suicidality. Sleep quality was also affected by stress; while sleep, stress, pain/discomfort, and internet addiction were directly related to depression (all p < .05). Ultimately, four factors impacted suicidality: stress, pain/discomfort, depression, and, indirectly, sleep quality (all p < .05). Although allergy had some effects, these did not reach statistical significance (p < .09).

CONCLUSION

Findings suggest that allergy might impact suicidality indirectly through increased pain/discomfort, poor sleep, and depression.

Effects of multiple scattering encountered for various small-angle scattering model functions

The means by which multiple scattering contributions in experimental small-angle scattering data can be estimated, and how they can be included in the data analysis, are reviewed and discussed. The multiple scattering effects for a range of relevant model scattering functions are calculated using semi-analytically derived solutions to Hankel transforms as well as Monte Carlo simulations.

In small-angle scattering theory and data modeling, it is generally assumed that each scattered ray – photon or neutron – is only scattered once on its path through the sample. This assumption greatly simplifies the interpretation of the data and is valid in many cases. However, it breaks down under conditions of high scattering power, increasing with sample concentration, scattering contrast, sample path length and ray wavelength. For samples with a significant scattering power, disregarding multiple scattering effects can lead to erroneous conclusions on the structure of the investigated sample. In this paper, the impact of multiple scattering effects on different types of scattering pattern are determined, and methods for assessing and addressing them are discussed, including the general implementation of multiple scattering effects in structural model fits. The modification of scattering patterns by multiple scattering is determined for the sphere scattering function and the Gaussian function, as well as for different Sabine-type functions, including the Debye–Andersen–Brumberger (DAB) model and the Lorentzian scattering function. The calculations are performed using the semi-analytical convolution method developed by Schelten & Schmatz [J. Appl. Cryst. (1980 ▸). 13, 385–390], facilitated by analytical expressions for intermediate functions, and checked with Monte Carlo simulations. The results show how a difference in the shape of the scattering function plotted versus momentum transfer q results in different multiple scattering effects at low q, where information on the particle mass and radius of gyration is contained.

BRIDG: a domain information model for translational and clinical protocol-driven research

Background

It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data.

Objective

To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG.

Methods

BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models.

Results

With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains.

Interpretation

The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)

Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.

A Data-Driven Approach to Risk-Based Source Data Verification

Source data verification (SDV) is the process of confirming that reliable, accurate information collected from participants during a clinical trial has been reported successfully to the trial's sponsor by investigators conducting the study. Over the past 15 years or so, there has been considerable discussion in the literature of alternate (reduced and risk-based) approaches to the traditional 100% SDV approach, but these discussions have been theoretical rather than data driven. This research therefore employed data from studies conducted by the authors' company to answer the following research question: Can historical data and simulation methodology be employed to understand the risks (unidentified problems) and benefits (cost reductions) of specific reduced SDV scenarios? The methodological approach was based upon a 2010 paper published in the Drug Information Journal that proposed 4 hypothetical risk-based monitoring approaches. The paper's authors proposed well-thought-out and defined scenarios that were readily replicated in simulation algorithms. These scenarios therefore facilitated the exploration of whether real data could be used to simulate reduced SDV scenarios. These data came from 30 trials that had utilized electronic data capture and were completed between 2005 and 2010. Findings revealed that real study data can successfully be used to simulate reduced SDV scenarios, bringing a data-driven analytical approach to the determination of efficient and effective approaches to reduced SDV, hence translating our theoretical understanding to data-driven methodology.

Signal-response modeling of partial hormone feedback networks

BACKGROUND

Endocrine feedback control networks are typically complex and contain multiple hormones, pools, and compartments. The hormones themselves commonly interact via multiple pathways and targets within the networks, and a complete description of such relationships may involve hundreds of parameters. In addition, it is often difficult, if not impossible, to collect experimental data pertaining to every component within the network. Therefore, the complete simultaneous analysis of such networks is challenging. Nevertheless, an understanding of these networks is critical for furthering our knowledge of hormonal regulation in both physiologic and pathophysiologic conditions.

METHODS

We propose a novel approach for the analysis of dose-response relationships of subsets of hormonal feedback networks. The algorithm and signal-response quantification (SRQuant) software is based on convolution integrals, and tests whether several discretely measured input signals can be individually delayed, spread in time, transformed, combined, and discretely convolved with an elimination function to predict the time course of the concentration of an output hormone. Signal-response quantification is applied to examples from the endocrine literature to demonstrate its applicability to the analysis of the different endocrine networks.

RESULTS

In one example, SRQuant determines the dose-response relationship by which one hormone regulates another, highlighting its advantages over other traditional methods. In a second example, for the first time (to the best of our knowledge), we show that the secretion of glucagon may be jointly controlled by the β and the δ cells.

CONCLUSION

We have developed a novel convolution integral-based approach, algorithm, and software (SRQuant) for the analysis of dose-response relationships within subsets of complex endocrine feedback control networks.