What is a Good Pattern of Life Model? Guidance for Simulations

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each

StateSim: Lessons Learned from 20 Years of A Country Modeling and Simulation Toolset

A holy grail for military, diplomatic, and intelligence analysis is a valid set of software agent models that act as the desired ethno-political factions so that one can test the effects of alternative courses of action in different countries. This article explains StateSim, a country modeling approach that synthesizes best-of-breed theories from across the social sciences and that has helped numerous organizations over 20 years to study insurgents, gray zone actors, and other societal instabilities. The country modeling literature is summarized (Sect 1.1) and synthetic inquiry is contrasted with scientific inquiry (Sect. 1.2 and 2). Section 2 also explains many fielded StateSim applications and 100s of past acceptability tests and validity assessments. Section 3 then describes how users now construct and run ‘first pass’ country models within hours due to the StateSim Generator, while Section 4 offers two country analyses that illustrate this approach. The conclusions explain lessons learned

Rich Socio-Cognitive Agents for Immersive Training Environments: Case of NonKin Village

Demand is on the rise for scientifically based human-behavior models that can be quickly customized and inserted into immersive training environments to recreate a given society or culture. At the same time, there are no readily available science model-driven environments for this purpose (see survey in Sect. 2). In researching how to overcome this obstacle, we have created rich (complex) socio-cognitive agents that include a large number of social science models (cognitive, sociologic, economic, political, etc) needed to enhance the realism of immersive, artificial agent societies. We describe current efforts to apply model-driven development concepts and how to permit other models to be plugged in should a developer prefer them instead. The current, default library of behavioral models is a metamodel, or authoring language, capable of generating immersive social worlds. Section 3 explores the specific metamodels currently in this library (cognitive, socio-political, economic, conversational, etc.) and Sect. 4 illustrates them with an implementation that results in a virtual Afghan village as a platform-independent model. This is instantiated into a server that then works across a bridge to control the agents in an immersive, platform-specific 3D gameworld (client). Section 4 also provides examples of interacting in the resulting gameworld and some of the training a player receives. We end with lessons learned and next steps for improving both the process and the gameworld. The seeming paradox of this research is that as agent complexity increases, the easier it becomes for the agents to explain their world, their dilemmas, and their social networks to a player or trainee

Tuberculosis diagnostics and biomarkers: needs, challenges, recent advances, and opportunities

Tuberculosis is unique among the major infectious diseases in that it lacks accurate rapid point-of-care diagnostic tests. Failure to control the spread of tuberculosis is largely due to our inability to detect and treat all infectious cases of pulmonary tuberculosis in a timely fashion, allowing continued Mycobacterium tuberculosis transmission within communities. Currently recommended gold-standard diagnostic tests for tuberculosis are laboratory based, and multiple investigations may be necessary over a period of weeks or months before a diagnosis is made. Several new diagnostic tests have recently become available for detecting active tuberculosis disease, screening for latent M. tuberculosis infection, and identifying drug-resistant strains of M. tuberculosis. However, progress toward a robust point-of-care test has been limited, and novel biomarker discovery remains challenging. In the absence of effective prevention strategies, high rates of early case detection and subsequent cure are required for global tuberculosis control. Early case detection is dependent on test accuracy, accessibility, cost, and complexity, but also depends on the political will and funder investment to deliver optimal, sustainable care to those worst affected by the tuberculosis and human immunodeficiency virus epidemics. This review highlights unanswered questions, challenges, recent advances, unresolved operational and technical issues, needs, and opportunities related to tuberculosis diagnostics

Tuberculosis Diagnostics and Biomarkers: Needs, Challenges, Recent Advances, and Opportunities

Tuberculosis is unique among the major infectious diseases in that it lacks accurate rapid point-of-care diagnostic tests. Failure to control the spread of tuberculosis is largely due to our inability to detect and treat all infectious cases of pulmonary tuberculosis in a timely fashion, allowing continued Mycobacterium tuberculosis transmission within communities. Currently recommended gold-standard diagnostic tests for tuberculosis are laboratory based, and multiple investigations may be necessary over a period of weeks or months before a diagnosis is made. Several new diagnostic tests have recently become available for detecting active tuberculosis disease, screening for latent M. tuberculosis infection, and identifying drug-resistant strains of M. tuberculosis. However, progress toward a robust point-of-care test has been limited, and novel biomarker discovery remains challenging. In the absence of effective prevention strategies, high rates of early case detection and subsequent cure are required for global tuberculosis control. Early case detection is dependent on test accuracy, accessibility, cost, and complexity, but also depends on the political will and funder investment to deliver optimal, sustainable care to those worst affected by the tuberculosis and human immunodeficiency virus epidemics. This review highlights unanswered questions, challenges, recent advances, unresolved operational and technical issues, needs, and opportunities related to tuberculosis diagnostic

Machine learning workflows identify a microRNA signature of insulin transcription in human tissues

Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic β-cell function. Studies to identify specific microRNA(s) associated with human (pro-)endocrine gene expression are needed. We profiled microRNAs and key pancreatic genes in 353 human tissue samples. Machine learning workflows identified microRNAs associated with (pro-)insulin transcripts in a discovery set of islets (n = 30) and insulin-negative tissues (n = 62). This microRNA signature was validated in remaining 261 tissues that include nine islet samples from individuals with type 2 diabetes. Top eight microRNAs (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, and -429-3p) were confirmed to be associated with and predictive of (pro-)insulin transcript levels. Use of doxycycline-inducible microRNA-overexpressing human pancreatic duct cell lines confirmed the regulatory roles of these microRNAs in (pro-)endocrine gene expression. Knockdown of these microRNAs in human islet cells reduced (pro-)insulin transcript abundance. Our data provide specific microRNAs to further study microRNA-mRNA interactions in regulating insulin transcription

International Diabetes Federation guideline for management of postmeal glucose: a review of recommendations

Diabetes is a significant and growing concern, with over 246 million people around the world living with the disease and another 308 million with impaired glucose tolerance. Depending on the resources of different nations, intervention has generally focused on optimizing overall glycaemic control as assessed by glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG) values. Nevertheless, increasing evidence supports the importance of controlling all three members of the glucose triad, namely HbA1c, FPG and postmeal glucose (PMG) in order to improve outcome in diabetes. As part of its global mission to promote diabetes care and prevention and to find a cure, the International Diabetes Federation (IDF) recently developed a guideline that reviews evidence to date on PMG and the development of diabetic complications. Based on an extensive database search of the literature, and guided by a Steering and Development Committee including experts from around the world, the IDF Guideline for Management of Postmeal Glucose offers recommendations for appropriate clinical management of PMG. These recommendations are intended to help clinicians and organizations in developing strategies for effective management of PMG in individuals with Type 1 and Type 2 diabetes. The following review highlights the recommendations of the guideline, the supporting evidence provided and the major conclusions drawn. The full guideline is available for download at http://www.idf.org

Dapagliflozin: a sodium glucose cotransporter 2 inhibitor in development for type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a growing worldwide epidemic. Patients face lifelong therapy to control hyperglycemia and prevent the associated complications. There are many medications, with varying mechanisms, available for the treatment of T2DM, but almost all target the declining insulin sensitivity and secretion that are associated with disease progression. Medications with such insulin-dependent mechanisms of action often lose efficacy over time, and there is increasing interest in the development of new antidiabetes medications that are not dependent upon insulin. One such approach is through the inhibition of renal glucose reuptake. Dapagliflozin, the first of a class of selective sodium glucose cotransporter 2 inhibitors, reduces renal glucose reabsorption and is currently under development for the treatment of T2DM. Here, we review the literature relating to the preclinical and clinical development of dapagliflozin