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Doctors working in healthcare are operating in complex adaptive systems that are unpredictable and have complex problems requiring new and unique skills. The Medical Council of New Zealand has specified a scope of practice for doctors involved in health system leadership, and there are several programmes of study that exist in Aotearoa New Zealand (Aotearoa NZ) to gain skills in this domain. It is crucial at this time of change that we understand why doctors as leaders and governors improve outcomes, the importance of training future medical leaders and how we validate these skills as well as the environment in which they operate. As we begin to reorganise our health system, the question we ask is when will we organise our system to recognise, develop and value these skills?

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The purpose of this thesis was to summarize the relevant research for the evaluation of a S.T.E.A.M.-based, grassroots, neuroscience outreach group called ‘NW Noggin’. This paper discusses the benefits and skills of S.T.E.M. in learning, the benefits and skills of S.T.E.A.M. in learning, educational disparities within the United States, educational outreach groups how they are currently assessed as well as more innovative arts-based and inquiry-based alternative assessments of student knowledge. This paper then focuses on the nonprofit NW Noggin to discuss their goals and activities and possible options for program assessment. This thesis also discusses how they fulfill their civic duty as an educational outreach group, both to underserved communities and to their own outreach volunteers. This thesis will hopefully provide a solid foundation for outreach assessment, both for NW Noggin and other S.T.E.M./S.T.E.A.M. outreach groups, while encouraging a more interdisciplinary approach to inclusive education.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Introduction: The HER2+ tumor immune microenvironment is composed of macrophages, natural killer cells, and tumor infiltrating lymphocytes, which produce pro-inflammatory cytokines. Determining the effect of T-cells on HER2+ cancer cells during therapy could guide immunogenic therapies that trigger antibody-dependent cellular cytotoxicity. This study utilized longitudinal in vitro time-resolved microscopy imaging to measure T-cell influence on trastuzumab in HER2+ breast cancer.Methods: Fluorescently-labeled breast cancer cells (BT474, SKBR3, MDA-MB-453, and MDA-MB-231) were co-cultured with CD4+ T-cells (Jurkat cell line) and longitudinally imaged to quantify cancer cell viability when treated with trastuzumab (10, 25, 50 and 100 g/mL). The presence and timing of T-cell co-culturing was manipulated to determine immune stimulation of trastuzumab-treated HER2+ breast cancer. HER2 and TNF- expression were evaluated with western blot and ELISA, respectively. Significance was calculated using a two-tailed parametric t-test.Results: The viability of HER2+ cancer cells significantly decreased when exposed to 25 g/mL trastuzumab and T-cells, compared to cancer cells exposed to trastuzumab without T-cells (p = 0.01). The presence of T-cells significantly increased TNF- expression in trastuzumab-treated cancer cells (p = 0.02). Conversely, cancer cells treated with TNF- and trastuzumab had a similar decrease in viability as trastuzumab-treated cancer cells co-cultured with T-cells (p = 0.49).Conclusions: The presence of T-cells significantly increases the efficacy of targeted therapies and suggests trastuzumab may trigger immune mediated cytotoxicity. TNF- expression suggests cytokines may interact with trastuzumab-induced HER2 receptor blockade. Examining molecular mechanisms of breast cancer immune infiltration has the potential to improve response to targeted therapies.

Introduction The HER2 + tumor immune microenvironment is composed of macrophages, natural killer cells, and tumor infiltrating lymphocytes, which produce pro-inflammatory cytokines. Determining the effect of T-cells on HER2 + cancer cells during therapy could guide immunogenic therapies that trigger antibody-dependent cellular cytotoxicity. This study utilized longitudinal in vitro time-resolved microscopy to measure T-cell influence on trastuzumab in HER2 + breast cancer. Methods Fluorescently-labeled breast cancer cells (BT474, SKBR3, MDA-MB-453, and MDA-MB-231) were co-cultured with CD4 + T-cells (Jurkat cell line) and longitudinally imaged to quantify cancer cell viability when treated with or without trastuzumab (10, 25, 50 and 100 μg/mL). The presence and timing of T-cell co-culturing was manipulated to determine immune stimulation of trastuzumab-treated HER2 + breast cancer. HER2 and TNF-α expression were evaluated with western blot and ELISA, respectively. Significance was calculated using a two-tailed parametric t-test. Results The viability of HER2 + cancer cells significantly decreased when exposed to 25 μg/mL trastuzumab and T-cells, compared to cancer cells exposed to trastuzumab without T-cells (p = 0.01). The presence of T-cells significantly increased TNF-α expression in trastuzumab-treated cancer cells (p = 0.02). Conversely, cancer cells treated with TNF-α and trastuzumab had a similar decrease in viability as trastuzumab-treated cancer cells co-cultured with T-cells (p = 0.32). Conclusions The presence of T-cells significantly increases the efficacy of targeted therapies and suggests trastuzumab may trigger immune mediated cytotoxicity. Increased TNF-α receptor expression suggest cytokines may interact with trastuzumab to create a state of enhanced response to therapy in HER2 + breast cancer, which has potential to reducing tumor burden.

SummaryA significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

The study by Fyfe and colleagues on the effects of home insulation on hospital admission1 contributes to an existing body of knowledge of the adverse effects of poor housing on health.2 We know this to be true yet the real growth in health expenditure is at the end of life, at the margin of benefit, in high …

Mann’s response to Smith and colleagues’ article on lean thinking highlights the problems with adapting production practices to health.12 The marriage of medical autonomy to various forms of operations management (in this case lean thinking) is unquestionably one of the greatest challenges facing all of …

The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. Although several molecular mechanisms of chemotherapeutic resistance are well studied, a quantitative understanding of the dynamics of resistant subpopulations within a heterogeneous tumor cell population remains elusive. While mathematical models describing the dynamics of heterogeneous cancer cell populations have been proposed, few have been experimentally validated due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently isolate drug resistant subpopulations. In this work, we address this problem with a combined experimental and modeling system that uses drug sensitivity data to reveal the composition of multiple subpopulations differing in their level of drug resistance. We calibrate time-resolved dose-response data to three mathematical models to interrogate the models’ ability to capture the dynamics of drug. All three models demonstrated an increase in population level resistance following drug exposure. The candidate models were compared by Akaike information criterion and the model selection criteria identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity. To validate the ability of this model to identify the composition of subpopulations, we mixed wild-type MCF-7 and MCF-7/ADR resistant cells at various proportions and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell subtypes, with the measured proportions falling within the 95 percent confidence intervals on the parameter estimations and at an R-squared value of 0.986. To the best of our knowledge, this contribution represents the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors., This study applied principles that describe the growth dynamics of species within an ecosystem in a novel attempt to understand the growth of tumors. At low cell densities, cooperative interactions among cancer cells may influence growth in a manner reminiscent of the ecological “Allee effect,” in contrast to conventional logistic growth models.

Free and total concentrations of cefazolin were compared in plasma and interstitial fluid during continuous intravenous infusion therapy. Seven patients, median age 53 (25-74) years, were administered a constant infusion of cefazolin at a mean (+/-s.d.) dose of 3.5 g (+/-1.1) per 24 h for greater than or equal to5 days. Four blisters were induced on the forearm of each patient for sampling of interstitial fluid. Free concentrations in plasma and interstitial fluid were similar, and correlated better than total concentrations (r(2) = 0.82, P = 0.005 versus r(2) = 0.54, P = 0.056). In all patients, the free concentrations in the interstitial fluid were at least two-fold the MIC90 for Staphylococcus aureus.

Although several molecular mechanisms of chemotherapeutic resistance are well studied, the ability to model and predict the dynamic development of resistance in cancer remains elusive. The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. Understanding of the dynamic progression of resistance in vitro is significant both in optimizing clinical dosing schedules and in probing the fundamental rules governing cancer cell population dynamics. While mathematical models to predict the dynamics of heterogeneous cancer cell populations have been proposed, none have been experimentally tested or linked to an observable resistant phenotype. There exists a need to integrate predictive modeling with measurable phenotypic time-resolved cell population characteristics. In this work, we describe and model in vitro phenotypically heterogeneous cancer cell populations cultured in a homogeneous microenvironment. MCF-7 cells were treated with a 24-hour pulse of doxorubicin and monitored over a fifteen-week period. Samples were counted each week, drug sensitivity was assayed with an AOPI live-dead reagent, and DNA content was measured. Phase images taken each week were used to classify cells according to morphology and determine subpopulation frequency. Drug sensitivity data were used to calibrate three mathematical models to interrogate the models’ ability to capture time-resolved response to drug. While all three models capture a trajectory in which population level resistance increases following drug exposure and eventually attenuates, results indicated that a multi-state model which incorporates the role of heterogeneity and cellular plasticity is superior at capturing the phenotypically observed response. Here we describe a two state model that provides an experimentally derived estimate of how cancer cells populate resistant and sensitive states over time following a drug perturbation. Drug sensitivity, proliferation rate, DNA content, and morphology frequency shift were quantified with proliferation rate dropping, the proportion of cells with 4N or higher DNA content increasing, and the proportion of cells displaying typical cuboidal morphology decreasing following doxorubicin treatment. Within approximately 6-7 weeks, proliferation rates, DNA content, and morphology distributions return to baseline, consistent with observed alterations in drug sensitivity. In future work, this approach could be extended to other cancer cell lines and eventually have a direct clinical application. This contribution is the first work to our knowledge that combines experimental time-resolved drug sensitivity data with a mathematical model of resistance development. This work aims to provide a platform for future investigation into the dynamic role of phenotypic heterogeneity in the development of chemotherapeutic resistance in all aspects of cancer research and therapy. Citation Format: Kaitlyn E. Johnson, Grant Howard, Amy Brock, Thomas Yankeelov. Experimentally derived multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-011. doi:10.1158/1538-7445.AM2017-LB-011

The further that I read into Whittle and colleagues’ editorial on patients’ optimism about percutaneous coronary interventions,1 the more the suspense grew. Imagine my surprise when I found no real reference to the …

Within the resin transfer molding (RTM) process, flow is generally characterized by the progression of a distinct, nonuniform flow front into the preform as a function of time. The flow front progression introduces unsaturated characteristics into RTM flow fields. As a result, the definition of an effective in-plane permeability (K eff ) is used to determine the permeability of actual preforms as they fill with fluid. This K eff expression expands upon the original definition of Darcy's low by generalizing its applicability to unsaturated creeping flows. Results of experimentally obtained K eff for flow in single-layer preforms have been detailed for two common RTM materials, a random mat and a 3-D weave, in Part 1. In this paper (Part 2), we characterize the unsaturated and saturated permeabilities of multiple-layer preforms constructed from the random mat and 3-D weave materials characterized in Part 1. This work identifies the apparent permeability characteristics of a specific unsaturated multiple-layer flow that demonstrates behavior inherent to this important class of heterogeneous flows. Also, parallels are drawn between the unsaturated permeability behavior of complex 3-D weave materials and multiple-layer preforms.

A three-dimensionally woven fabric is proposed as a standard reference material for permeability characterization. The 3-D woven fabric requires care in cutting and handling, although it is more robust than 2-D woven or braided fabrics. If prepared carefully, the permeability of the 3-D woven fabric can be measured reproducibly within 15% in either radial flow or saturated 1-D flow geometries. The material was characterized for permeability in radial, unsaturated and saturated 1-D, and through-thickness flow geometries. The transient results demonstrated the importance of structural heterogeneity on the unsaturated flow behavior, and agree qualitatively with a simplistic model of flow in heterogeneous unsaturated porous media. The effects of heterogeneity were manifested in the proposed SRM by an increasing trend in the “unsaturated permeability.” Experiments were also conducted with a random mat that displayed transient flows dominated by wicking. The effects of wicking on the macroscopic flow behavior were manifested by transients in the “unsaturated permeability” in which a decreasing trend was observed.

The NRL Optical Sciences Division has initiated a multi-year effort to develop and demonstrate an airborne net-centric suite of multi-intelligence (multi-INT) sensors and exploitation systems for real-time target detection and targeting product dissemination. The goal of this Net-centric Multi-Intelligence Fusion Targeting Initiative (NCMIFTI) is to develop an airborne real-time intelligence gathering and targeting system that can be used to detect concealed, camouflaged, and mobile targets. The multi-INT sensor suite will include high-resolution visible/infrared (EO/IR) dual-band cameras, hyperspectral imaging (HSI) sensors in the visible-to-near infrared, short-wave and long-wave infrared (VNIR/SWIR/LWIR) bands, Synthetic Aperture Radar (SAR), electronics intelligence sensors (ELINT), and off-board networked sensors. Other sensors are also being considered for inclusion in the suite to address unique target detection needs. Integrating a suite of multi-INT sensors on a single platform should optimize real-time fusion of the on-board sensor streams, thereby improving the detection probability and reducing the false alarms that occur in reconnaissance systems that use single-sensor types on separate platforms, or that use independent target detection algorithms on multiple sensors. In addition to the integration and fusion of the multi-INT sensors, the effort is establishing an open-systems net-centric architecture that will provide a modular “plug and play” capability for additional sensors and system components and provide distributed connectivity to multiple sites for remote system control and exploitation.

The performance of any scene-adaptive Nonuniformity Correction (NUC) algorithm is fundamentally limited by the quality of the scene-based predicted value of each pixel. TARID-based composite imagery can serve as a scene-based pixel predictor with improved robustness, and reduced noise than that of more common scene-based pixel predictors. These improved properties result in dramatically faster algorithm convergence, generating corrected imagery with reduced spatial noise due to intrinsic nonuniform or inoperative pixels in a Focal Plane Array.

A general method is described to improve the operational resolution of an Electro-Optic (EO) imaging sensor using multiple frames of an image sequence. This method only assumes the constituent video has some ambient motion between the sensor and stationary background, and the optical image is electronically captured and digitally recorded by a staring focal plane detector array. Compared to alternative techniques that may require externally controlled or measured dither motion, this approach offers significantly enhanced operational resolution with substantially relaxed constraints on sensor stabilization, optics, and exposure time.

This paper outlines a generalized image reconstruction approach to improve the resolution of an Electro-Optic (EO) imaging sensor using multiple frames of an image sequence. This method only assumes the constituent video has some ambient motion between the sensor and stationary background, and the optical image is physically captured by a staring focal plane array.

Many imaging systems consist of a combination of distinct electro-optic sensors that are constrained to view the same scene through a common aperture or from a common platform. Oftentimes, a spatial registration of one sensor's image is required to conform to the slightly disparate imaging geometry of a different sensor on the same platform. This is generally achieved through a judicious selection of image tie-points and geometric transformation model. This paper outlines a procedure to improve any such registration technique by leveraging the temporal motion within a pair of video sequences and requiring an additional constraint of minimizing the disparity in optical flow between registered video sequences.

Current target acquisition models are for monochrome imagery systems (single detector). The increasing interest in multispectral infrared systems and color daylight imagers highlights the need for models that describe the target acquisition process for color systems (2 or more detectors). This study investigates the detection of simple color targets in a noise color background.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper outlines a generalized image reconstruction approach that improves the resolution of an Electro-Optic (EO) imaging sensor based on multiple frame exposures during a temporal window of video. Such an approach is innovative in that it does not depend on controlled micro dithering of the camera, nor require the set of exposures to maintain any strictly defined transformation. It suffices to assume such video is physically captured by a focal plane array, and loosely requires some relative motion between sensor and subject.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A multi-spectral imaging system can be defined as a combination of electro-optic imagers that are mechanically constrained to view the same scene. Subsequent processing of the output imagery invariably requires a spatial registration of one spectral band image to geometrically conform to the imagery from a different sensor. This paper outlines a procedure to leverage motion estimation of a pair of video sequences to determine a transformation that minimizes the disparity in optical flow between the sequences.

Increases in the power of personal computers and the availability of infrared focal plane array cameras allows new options in the development of real-time color fusion system for human visualization. This paper describes on-going development of an inexpensive, real-time PC=based infrared color visualization system. The hardware used in the system is all COTS, making it relatively inexpensive to maintain and modify. It consists of a dual Pentium II PC, with fast digital storage and up to five PCI frame-grabber cards. The frame-grabbers cards allow data to be selected from RS-170 (analog) or RS-422 (digital) cameras. Software allows the system configuration to be changed on the fly, so cameras can be swapped at will and new cameras can be added to the system in a matter of minutes. The software running on the system reads up to five separate images from the frame-grabber cards. These images are then digitally registered using a rubber-sheeting algorithm to reshape and shift the images. The registered data, from two or three cameras, is then processed by the selected fusion algorithm to produce a color-fused image, which is then displayed in real-time. The real-time capability of this system allows interactive laboratory testing of issues such as band selection, fusion algorithm optimization, and visualization trade-offs.

Sensor fusion of up to three disparate imagers can readily be achieved by assigning each component video stream to a separate channel any standard RBG color monitor such as with television or personal computer systems. Provided the component imagery is pixel registered, such a straightforward systems can provide improved object-background separation, yielding quantifiable human-factors performance improvement compared to viewing monochrome imagery of a single sensor. Consideration is given to appropriate dynamic range management of the available color gamut, and appropriate color saturation in the presence of imager noise.

The emergence of new infrared sensor technologies and the availability of powerful, inexpensive computers have made many new imaging applications possible. Researchers working in the area of traditional image processing are showing an increased interest in working with infrared imagery. However, because of the inherent differences between infrared and visible phenomenology, a number of fundamental problems arise when trying to apply traditional processing methods to the infrared. Furthermore, the technologies required to image in the infrared are currently much less mature than comparable camera technologies used in visible imaging. Also infrared sensors need to capture six to eight additional bits of additional dynamic range over the eight normally used for visible imaging. Over the past half-century, visible cameras have become highly developed and can deliver images that meet engineering standards compatible with image displays. Similar image standards are often not possible in the infrared for a number of technical and phenomenological reasons. The purpose of this paper is to describe some of these differences and discuss a related topic known as image preprocessing. This is an area of processing that roughly lies between traditional image processing and image generation; because the camera images are less than ideal, additional processing is needed to perform necessary functions such as dynamic range management, non-uniformity correction, resolution enhancement, or color processing. A long-range goal for the implementation of these algorithms is to move them on-chip as analog retina-like or cortical-like circuits, thus achieving extraordinary reductions in power dissipation, size, and cost. Because this area of research is relatively new and still evolving, the discussion in this paper is limited to only a partial overview of the topic.