ANOMALY DETECTION OF EMS HARDWIRED INFRASTRUCTURE USING SUPERVISED AND UNSUPERVISED ARTIFICIAL INTELLIGENCE MACHINE LEARNING

The microgrid currently deployed at Marine Corps Air Station (MCAS) Miramar, California began operations in 2021. It is unique in its efforts to leverage a Verizon, fifth generation technology (5G), non-standalone communications network to provide connectivity between distributed energy resources and the MCAS energy management system (EMS). With this new technology comes additional risks in the form of cyber attacks. Therefore, novel approaches to combat this threat are necessary to protect vital energy assets. In this thesis, we discuss the development of anomalous traffic detection models that use an unsupervised machine learning autoencoder trained on benign data sets captured from an AT&T 5G cellular tower at the Naval Postgraduate School Sea Land Air Military Research facility and Raytheon hardwired Modbus network at the EMS. We created synthetic anomalies for each data set to test our autoencoder and assess its effectiveness at classifying these packets as anomalous or benign. F-score, accuracy, precision, and recall were used as performance metrics. Through experiments conducted with Python and TensorFlow, we demonstrate the autoencoder models can successfully be trained and tested using benign network data using carefully crafted synthetic anomalies. This research establishes a baseline of research for an autoencoder to be used as an effective intrusion detection system to demonstrate the utility and operability of unsupervised machine learning for use in a microgrid.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant Commander, United States NavyONR, Arlington, VA 2221

An intraductal human-in-mouse transplantation model mimics the subtypes of ductal carcinoma in situ

Introduction: Human models of noninvasive breast tumors are limited, and the existing in vivo models do not mimic inter- and intratumoral heterogeneity. Ductal carcinoma in situ (DCIS) is the most common type (80%) of noninvasive breast lesions. The aim of this study was to develop an in vivo model whereby the natural progression of human DCIS might be reproduced and studied. To accomplish this goal, the intraductal human-in-mouse (HIM) transplantation model was developed. The resulting models, which mimicked some of the diversity of human noninvasive breast cancers in vivo, were used to show whether subtypes of human DCIS might contain distinct subpopulations of tumor-initiating cells.Methods The intraductal models were established by injection of human DCIS cell lines (MCF10DCIS.COM and SUM-225), as well as cells derived from a primary human DCIS (FSK-H7), directly into the primary mouse mammary ducts via cleaved nipple. Six to eight weeks after injections, whole-mount, hematoxylin and eosin, and immunofluorescence staining were performed to evaluate the type and extent of growth of the DCIS-like lesions. To identify tumor-initiating cells, putative human breast stem/progenitor subpopulations were sorted from MCF10DCIS.COM and SUM-225 with flow cytometry, and their in vivo growth fractions were compared with the Fisher's Exact test. Results: Human DCIS cells initially grew within the mammary ducts, followed by progression to invasion in some cases into the stroma. The lesions were histologically almost identical to those of clinical human DCIS. This method was successful for growing DCIS cell lines (MCF10DCIS.COM and SUM-225) as well as a primary human DCIS (FSK-H7). MCF10DCIS.COM represented a basal-like DCIS model, whereas SUM-225 and FSK-H7 cells were models for HER-2[super]+ DCIS. With this approach, we showed that various subtypes of human DCIS appeared to contain distinct subpopulations of tumor-initiating cells. Conclusions: The intraductal HIM transplantation model provides an invaluable tool that mimics human breast heterogeneity at the noninvasive stages and allows the study of the distinct molecular and cellular mechanisms of breast cancer progression

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Autologous stem cell transplantation for acute myeloid leukemia in first remission

AbstractWe studied the feasibility, toxicity, and efficacy of a 2-step approach to autologous stem cell transplantation for patients with acute myeloid leukemia in first remission. Step 1 consisted of consolidation chemotherapy including cytarabine 2000 mg/m2 twice daily for 4 days concurrent with etoposide 40 mg/kg by continuous infusion over 4 days. During the recovery from this chemotherapy, peripheral blood stem cells were collected under granulocyte colony-stimulating factor stimulation. Step 2, autologous stem cell transplantation, involved the preparative regimen of busulfan 16 mg/kg followed by etoposide 60 mg/kg and reinfusion of unpurged peripheral blood stem cells. A total of 128 patients were treated. During step 1, there was 1 treatment-related death. A median CD34+ cell dose of 14 (x10(6)/kg) was collected in 3 aphereses. Ten patients suffered relapse before transplantation, and 117 patients (91%) proceeded to transplantation. During step 2, there were 2 treatment-related deaths, and 35 patients subsequently suffered relapse. With median follow-up of 30 months, 5-year disease-free survival for all patients entered in the study is projected to be 55%. By cytogenetic risk group, 5-year disease-free survival is 73% for favorable-risk patients, 51% for intermediate-risk patients, and 0% for poor-risk patients. We conclude that this 2-step approach to autologous transplantation produces excellent stem cell yields and allows a high percentage of patients to receive the intended therapy. Preliminary efficacy analysis is very encouraging, with outcomes that appear superior to those of conventional chemotherapy.Biol Blood Marrow Transplant 2000;6(1):50-7

Exposure Assessment in Cohort Studies of Childhood Asthma

Background: The environment is suspected to play an important role in the development of childhood asthma. Cohort studies are a powerful observational design for studying exposure–response relationships, but their power depends in part upon the accuracy of the exposure assessment. Objective: The purpose of this paper is to summarize and discuss issues that make accurate exposure assessment a challenge and to suggest strategies for improving exposure assessment in longitudinal cohort studies of childhood asthma and allergies. Data synthesis: Exposures of interest need to be prioritized, because a single study cannot measure all potentially relevant exposures. Hypotheses need to be based on proposed mechanisms, critical time windows for effects, prior knowledge of physical, physiologic, and immunologic development, as well as genetic pathways potentially influenced by the exposures. Modifiable exposures are most important from the public health perspective. Given the interest in evaluating gene–environment interactions, large cohort sizes are required, and planning for data pooling across independent studies is critical. Collection of additional samples, possibly through subject participation, will permit secondary analyses. Models combining air quality, environmental, and dose data provide exposure estimates across large cohorts but can still be improved. Conclusions: Exposure is best characterized through a combination of information sources. Improving exposure assessment is critical for reducing measurement error and increasing power, which increase confidence in characterization of children at risk, leading to improved health outcomes.Occupational and Environmental Hygiene, School ofPopulation and Public Health (SPPH), School ofMedicine, Faculty ofReviewedFacult