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Concerns that infection with SARS-CoV-2, the etiological agent of COVID-19, may cause new-onset diabetes persist amidst an evolving research landscape, and precise risk assessment is hampered by at times conflicting evidence. Here, leveraging comprehensive single-cell analyses of in vitro SARS-CoV-2-infected human pancreatic islets, we demonstrate that productive infection is strictly dependent on the SARS-CoV-2 entry receptor ACE2 and targets all pancreatic cell types. Importantly, the infection remains highly circumscribed, largely non-cytopathic, and despite high viral burden in infected subsets, promotes only modest cellular perturbations and inflammatory responses. Similar experimental outcomes are also observed after islet infection with endemic coronaviruses. Thus, the limits of pancreatic SARS-CoV-2 infection, even under in vitro conditions of enhanced virus exposure, do not support the proposition that in vivo targeting of beta cells by SARS-CoV-2 precipitates new-onset diabetes. If restricted pancreatic damage accrued by COVID-19 increases cumulative diabetes risk, however, remains to be evaluated. Funding: These efforts were supported by JDRF 3-PDF-2018-575-A-N (V.v.d.H.); NIH/NIDDK R01DK12392, NIH/NIAID P01AI042288 and NIH/NIAID U54AI142766-S1 (M.A.A.); NIH/NIAID Center of Excellence for Influenza Research and Response/Center for Research for Influenza Pathogenesis and Transmission contract # 75N93019R00028, NIH/NIAID U19AI135972 (supplement), Defense Advanced Research Projects Agency HR0011-19-2-0020, JPB Foundation, and Open Philanthropy Project # 2020-215611 (5384), Anonymous (A.G.-S.); NIH/NIAID R01AI151029 and NIA/NIAID U01AI150748 (B.R.R.); NIH/NIDDK R01DK130425 (M.S.); and NIH/NIAID R01AI134971, NIH/NIDDK U01DK123716, NIH/NIDDK U01DK104162, NIH/NIDDK P30DK020541 and NIH/NIDDK R01DK130425 (D.H.). Funding: The AG-S laboratory has received research support from Pfizer, Senhwa Biosciences, Kenall Manufacturing, Avimex, Johnson & Johnson, Dynavax, 7Hills Pharma, Pharmamar, ImmunityBio, Accurius, Nanocomposix, Hexamer, N-fold LLC, Model Medicines and Merck, outside of the reported work. Declaration of Interests: AG-S has consulting agreements for the following companies involving cash and/or stock: Vivaldi Biosciences, Contrafect, 7Hills Pharma, Avimex, Vaxalto, Pagoda, Accurius, Esperovax, Farmak, Applied Biological Laboratories and Pfizer, outside of the reported work. AG-S is inventor on patents and patent applications on the use of antivirals and vaccines for the treatment and prevention of virus infections and cancer, owned by the Icahn School of Medicine at Mount Sinai, New York, outside of the reported work. All other authors declare no conflict of interest. Ethics Approval Statement: Our study is considered “not human subjects research” since all donor islet preparations were provided as de-identified tissue specimens by a commercial purveyor

Semiquantitative immunohistochemical methods applied to tumor proliferation under suitable quality control conditions are a powerful prognostic tool for assessing breast carcinomas. The monoclonal antibody MIB-1, reactive in formalin fixed, paraffin embedded tissues allows quantitation of proliferation index. Antigen restoration systems, including appropriate citrate solution and microwave conditions, are required for MIB-1 immunohistochemical detection. We have determined that the sequential use of trypsin followed by low temperature (80°C) citrate buffer exposure is an efficient pretreatment protocol for MIB-1 demonstration. (The J Histotechnol 22:103, 1999)

The U.S. Air Force is consistently evolving to support current and future operations through the planning and execution of intelligence, surveillance and reconnaissance (ISR) missions. However, it is a challenge to maintain a precise awareness of current and emerging ISR capabilities to properly prepare for future conflicts. We present a decisionsupport tool for acquisition managers to empirically compare ISR capabilities and approaches to employing them, thereby enabling the DoD to acquire ISR platforms and sensors that provide the greatest return on investment. We have developed an analysis environment to perform modeling and simulation-based experiments to objectively compare alternatives. First, the analyst specifies an operational scenario for an area of operations by providing terrain and threat information; a set of nominated collections; sensor and platform capabilities; and processing, exploitation, and dissemination (PED) capacities. Next, the analyst selects and configures ISR collection strategies to generate collection plans. The analyst then defines customizable measures of effectiveness or performance to compute during the experiment. Finally, the analyst empirically compares the efficacy of each solution and generates concise reports to document their conclusions, providing traceable evidence for acquisition decisions. Our capability demonstrates the utility of using a workbench environment for analysts to design and run experiments. Crafting impartial metrics enables the acquisition manager to focus on evaluating solutions based on specific military needs. Finally, the metric and collection plan visualizations provide an intuitive understanding of the suitability of particular solutions. This facilitates a more agile acquisition strategy that handles rapidly changing technology in response to current military needs.

To extend modern simulation-based training environments to incorporate tailored simulation-based training experiences to facilitate accelerated skills development and sustainment, we are developing a system for Enhancing Simulation-based Training Adversary Tactics via Evolution (ESTATE). The system consists of an off-line adaptation engine to extract a model of the trainee based on past performance and generate tailored challenge problems for on-line training. Off-line adaptation is performed using evolutionary algorithms (EAs) to search through the space of challenges to exploit fundamental weaknesses in trainee strategy and tactics. The full-scope prototype ESTATE system is targeting simulation-based training systems within the Deployable Virtual Training Environment (DVTE) to support the squad-level training of U.S. Marines. This report documents the Option 1 period of the effort.

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology transition goals.

Recent military operations have demonstrated the use by adversaries of non-traditional or asymmetric military tactics to offset US military might. Rogue nations with links to trans-national terrorists have created a highly unpredictable and potential dangerous environment for US military operations. Several characteristics of these threats include extremism in beliefs, global in nature, non-state oriented, and highly networked and adaptive, thus making these adversaries less vulnerable to conventional military approaches. Additionally, US forces must also contend with more traditional state-based threats that are further evolving their military fighting strategies and capabilities. What are needed are solutions to assist our forces in the prosecution of operations against these diverse threat types and their atypical strategies and tactics. To address this issue, we present a system that allows for the adaptation of a multi-resolution adversarial model. The developed model can then be used to support both training and simulation based acquisition requirements to effectively respond to such an adversary. The described system produces a combined adversarial model by merging behavior modeling at the individual level with aspects at the group and organizational level via network analysis. Adaptation of this adversarial model is performed by means of an evolutionary algorithm to build a suitable model for the chosen adversary.