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COVID-19: famotidine, histamine, mast cells, and mechanisms [eprint]

Summary

SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain obscure. We explore several plausible avenues of activity including antiviral and host-mediated actions. We propose that the principal famotidine mechanism of action for COVID-19 involves on-target histamine receptor H2 activity, and that development of clinical COVID-19 involves dysfunctional mast cell activation and histamine release.
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Summary

SARS-CoV-2 infection is required for COVID-19, but many signs and symptoms of COVID-19 differ from common acute viral diseases. Currently, there are no pre- or post-exposure prophylactic COVID-19 medical countermeasures. Clinical data suggest that famotidine may mitigate COVID-19 disease, but both mechanism of action and rationale for dose selection remain...

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The 2017 Buffalo Area Icing and Radar Study (BAIRS II)

Published in:
MIT Lincoln Laboratory Report ATC-447

Summary

The second Buffalo Area Icing and Radar Study (BAIRS II) was conducted during the winter of 2017. The BAIRS II partnership between Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL), the National Research Council of Canada (NRC), and Environment and Climate Change Canada (ECCC) was sponsored by the Federal Aviation Administration (FAA). It is a follow-up to the similarly sponsored partnership of the original BAIRS conducted in the winter of 2013. The original BAIRS provided in situ verification and validation of icing and hydrometeors, respectively, within the radar domain in support of a hydrometeor-classification-based automated icing hazard algorithm. The BAIRS II motivation was to: --Collect additional in situ verification and validation data, --Probe further dual polarimetric radar features associated with icing hazard, --Provide foundations for additions to the icing hazard algorithm beyond hydrometeor classifications, and --Further characterize observable microphysical conditions in terms of S-band dual polarimetric radar data. With BAIRS II, the dual polarimetric capability is provided by multiple Next Generation Weather Radar (NEXRAD) S-band radars in New York State, and the verification of the icing hazard with microphysical and hydrometeor characterizations is provided by NRC's Convair-580 instrumented research plane during five icing missions covering about 21 mission hours. The ability to reliably interpret the NEXRAD dual polarization radar-sensed thermodynamic phase of the hydrometeors (solid, liquid, mix) in the context of cloud microphysics and precipitation physics makes it possible to assess the icing hazard potential to aviation. The challenges faced are the undetectable nature of supercooled cloud droplets (for Sband) and the isotropic nature of Supercooled Large Drops (SLD). The BAIRS II mission strategy pursued was to study and probe radar-identifiable, strongly anisotropic crystal targets (dendrites and needles) with which supercooled water (and water saturated conditions) are physically linked as a means for dual polarimetric detection of icing hazard. BAIRS II employed superior optical array probes along with state and microphysical instrumentation; and, using again NEXRAD-feature-guided flight paths, was able to make advances from the original BAIRS helpful to the icing algorithm development. The key findings that are given thorough treatment in this report are: --Identification of the radar-detectable "crystal sandwich" structure from two anisotropic crystal types stratified by in situ air temperature in association with varying levels of supercooled water --with layer thicknesses observed to 2 km, --over hundred-kilometer scales matched with the mesoscale surveillance of the NEXRAD radars, --Development and application of a multi-sensor cloud phase algorithm to distinguish between liquid phase, mixed phase, and glaciated (no icing) conditions for purposes of a "truth" database and improved analysis in BAIRS II, --Development of concatenated hydrometeor size distributions to examine the in situ growth of both liquid and solid hydrometeors over a broad size spectrum; used, in part, to demonstrate differences between maritime and continental conditions, and --The Icing Hazard Levels (IHL) algorithm’s verification in icing conditions is consistent with previous work and, new, is documented to perform well when indicating "glaciated" (no icing) conditions.
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Summary

The second Buffalo Area Icing and Radar Study (BAIRS II) was conducted during the winter of 2017. The BAIRS II partnership between Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL), the National Research Council of Canada (NRC), and Environment and Climate Change Canada (ECCC) was sponsored by the Federal Aviation...

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Kawasaki disease and multisystem inflammatory syndrome in children: an antibody-induced mast cell activation hypothesis [eprint]

Published in:
The Lancet, manuscript submitted May 2020.

Summary

Multisystem Inflammatory Syndrome in Children (MIS-C, previously designated as Pediatric Multisystem Inflammatory Syndrome - PMIS) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections. Kawasaki Disease (KD, previously called mucocutaneous lymph node syndrome) is one of the most common vasculitides of childhood. KD presents with similar symptoms to MIS-C especially in severe forms such as Kawasaki Disease Shock Syndrome (KDSS). The cause of KD is currently unknown; KD has features similar to those associated with viral infection. The leading hypothesis is that a ubiquitous infectious agent can induce KD in a genetically susceptible patient. This hypothesis is supported by the presence of IgA plasma cells identified in inflamed tissues and coronary arteries of KD patients. Associations between KD and multiple pathogens have been reported, including: adenovirus, human bocavirus, coronavirus, human coronavirus 229E, human coronavirus (HCoV-NH) NL63, cytomegalovirus, dengue, enterovirus, Epstein–Barr virus, human herpesvirus 6, human lymphotropic virus, human rhinovirus, influenza, measles, parvovirus B19, parainfluenza virus type 2, respiratory syncytial virus (RSV), rotavirus, varicella zoster (chicken pox), torque teno virus, Staphylococcus aureus, and Streptococcus. Postinfluenza vaccination KD has also been reported. The seasonality and temporal clustering of KD further support an infectious etiology. A mild cold may precede the onset of KD and up to one third of patients have concurrent, confirmed infections at the time of KD diagnosis. The aggregate of these pathogen associations with KD support the rejection of the hypothesis that KD is caused by a single infectious agent. The alternative hypothesis is that KD is associated with multiple infectious agents. We hypothesize that MIS-C may be atypical KD or a KD-like disease associated with SARS-CoV-2 as a result of antibody dependent enhancement activation of mast cells. We further hypothesize that KD and MIS-C may be induced in part by histamine and other inflammatory molecules released from activation of mast cells by Fc receptor bound pathogen antibodies resulting in a hyperinflammatory response.
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Summary

Multisystem Inflammatory Syndrome in Children (MIS-C, previously designated as Pediatric Multisystem Inflammatory Syndrome - PMIS) is appearing in infants, children, and young adults in association with COVID-19 (coronavirus disease 2019) infections. Kawasaki Disease (KD, previously called mucocutaneous lymph node syndrome) is one of the most common vasculitides of childhood. KD...

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Medical countermeasures analysis of 2019-nCoV and vaccine risks for antibody-dependent enhancement (ADE)

Published in:
https://www.preprints.org/manuscript/202003.0138/v1

Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages in lungs, and immune dysregulation including immunosuppression. Methods: All major SARS-CoV-2 proteins were characterized using an amino acid residue variation analysis method. Results predict that most SARS-CoV-2 proteins are evolutionary constrained, with the exception of the spike (S) protein extended outer surface. Results were interpreted based on known SARS-like coronavirus virology and pathophysiology, with a focus on medical countermeasure development implications. Findings: Non-neutralizing antibodies to variable S domains may enable an alternative infection pathway via Fc receptor-mediated uptake. This may be a gating event for the immune response dysregulation observed in more severe COVID-19 disease. Prior studies involving vaccine candidates for FCoV SARS-CoV-1 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) demonstrate vaccination-induced antibody-dependent enhancement of disease (ADE), including infection of phagocytic antigen presenting cells (APC). T effector cells are believed to play an important role in controlling coronavirus infection; pan-T depletion is present in severe COVID-19 disease and may be accelerated by APC infection. Sequence and structural conservation of S motifs suggests that SARS and MERS vaccine ADE risks may foreshadow SARS-CoV-2 S-based vaccine risks. Autophagy inhibitors may reduce APC infection and T-cell depletion. Amino acid residue variation analysis identifies multiple constrained domains suitable as T cell vaccine targets. Evolutionary constraints on proven antiviral drug targets present in SARS-CoV-1 and SARS-CoV-2 may reduce risk of developing antiviral drug escape mutants. Interpretation: Safety testing of COVID-19 S protein-based B cell vaccines in animal models is strongly encouraged prior to clinical trials to reduce risk of ADE upon virus exposure.
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Summary

Background: In 80% of patients, COVID-19 presents as mild disease. 20% of cases develop severe (13%) or critical (6%) illness. More severe forms of COVID-19 present as clinical severe acute respiratory syndrome, but include a T-predominant lymphopenia, high circulating levels of proinflammatory cytokines and chemokines, accumulation of neutrophils and macrophages...

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Automated discovery of cross-plane event-based vulnerabilities in software-defined networking

Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the control plane towards unwanted states that bypass network security restrictions (i.e., cross-plane attacks). Unfortunately, because of the complex interplay among controllers, apps, and data plane inputs, at present it is difficult to systematically identify and analyze these cross-plane vulnerabilities. We present EVENTSCOPE, a vulnerability detection tool that automatically analyzes SDN control plane event usage, discovers candidate vulnerabilities based on missing event-handling routines, and validates vulnerabilities based on data plane effects. To accurately detect missing event handlers without ground truth or developer aid, we cluster apps according to similar event usage and mark inconsistencies as candidates. We create an event flow graph to observe a global view of events and control flows within the control plane and use it to validate vulnerabilities that affect the data plane. We applied EVENTSCOPE to the ONOS SDN controller and uncovered 14 new vulnerabilities.
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Summary

Software-defined networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers' functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the...

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Wind information requirements for NextGen applications phase 7 report

Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter fixes down to 7,000' AGL regardless of the forecast quality provided. Below 7,000' AGL, RTA performance significantly degraded demonstrating around 80% compliance under the best forecast and operating conditions. This report also provides a comprehensive lexicon of aviation and air traffic control related "wind" terms.
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Summary

This report details the Required Time of Arrival (RTA) performance of B757 aircraft arriving at various meter fixes across a range of altitudes from 33,000' down to 3,000' above ground level (AGL). The system tested demonstrated less than ±10 second arrival error in at least 95% of flights at meter...

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75,000,000,000 streaming inserts/second using hierarchical hypersparse GraphBLAS matrices [e-print]

Summary

The SuiteSparse GraphBLAS C-library implements high performance hypersparse matrices with bindings to a variety of languages (Python, Julia, and Matlab/Octave). GraphBLAS provides a lightweight in-memory database implementation of hypersparse matrices that are ideal for analyzing many types of network data, while providing rigorous mathematical guarantees, such as linearity. Streaming updates of hypersparse matrices put enormous pressure on the memory hierarchy. This work benchmarks an implementation of hierarchical hypersparse matrices that reduces memory pressure and dramatically increases the update rate into a hypersparse matrices. The parameters of hierarchical hypersparse matrices rely on controlling the number of entries in each level in the hierarchy before an update is cascaded. The parameters are easily tunable to achieve optimal performance for a variety of applications. Hierarchical hypersparse matrices achieve over 1,000,000 updates per second in a single instance. Scaling to 31,000 instances of hierarchical hypersparse matrices arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update rate of 75,000,000,000 updates per second. This capability allows the MIT SuperCloud to analyze extremely large streaming network data sets.
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Summary

The SuiteSparse GraphBLAS C-library implements high performance hypersparse matrices with bindings to a variety of languages (Python, Julia, and Matlab/Octave). GraphBLAS provides a lightweight in-memory database implementation of hypersparse matrices that are ideal for analyzing many types of network data, while providing rigorous mathematical guarantees, such as linearity. Streaming updates...

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AI data wrangling with associative arrays [e-print]

Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical representations of these data enables data translation and analysis optimization within and across steps. Associative array algebra provides a mathematical foundation that naturally describes the tabular structures and set mathematics that are the basis of databases. Likewise, the matrix operations and corresponding inference/training calculations used by neural networks are also well described by associative arrays. More surprisingly, a general denormalized form of hierarchical formats, such as XML and JSON, can be readily constructed. Finally, pivot tables, which are among the most widely used data analysis tools, naturally emerge from associative array constructors. A common foundation in associative arrays provides interoperability guarantees, proving that their operations are linear systems with rigorous mathematical properties, such as, associativity, commutativity, and distributivity that are critical to reordering optimizations.
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Summary

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical...

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FirmFuzz: automated IOT firmware introspection and analysis

Published in:
2nd Workshop on the Internet of Things Security and Privacy, IoT S&P '19, 15 November 2019.

Summary

While the number of IoT devices grows at an exhilarating pace their security remains stagnant. Imposing secure coding standards across all vendors is infeasible. Testing individual devices allows an analyst to evaluate their security post deployment. Any discovered vulnerabilities can then be disclosed to the vendors in order to assist them in securing their products. The search for vulnerabilities should ideally be automated for efficiency and furthermore be device-independent for scalability. We present FirmFuzz, an automated device-independent emulation and dynamic analysis framework for Linux-based firmware images. It employs a greybox-based generational fuzzing approach coupled with static analysis and system introspection to provide targeted and deterministic bug discovery within a firmware image. We evaluate FirmFuzz by emulating and dynamically analyzing 32 images (from 27 unique devices) with a network accessible from the host performing the emulation. During testing, FirmFuzz discovered seven previously undisclosed vulnerabilities across six different devices: two IP cameras and four routers. So far, 4 CVE's have been assigned.
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Summary

While the number of IoT devices grows at an exhilarating pace their security remains stagnant. Imposing secure coding standards across all vendors is infeasible. Testing individual devices allows an analyst to evaluate their security post deployment. Any discovered vulnerabilities can then be disclosed to the vendors in order to assist...

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Cultivating professional technical skills and understanding through hands-on online learning experiences

Published in:
2019 IEEE Learning with MOOCS, LWMOOCS, 23-25 October 2019.

Summary

Life-long learning is necessary for all professions because the technologies, tools and skills required for success over the course of a career expand and change. Professionals in science, technology, engineering and mathematics (STEM) fields face particular challenges as new multi-disciplinary methods, e.g. Machine Learning and Artificial Intelligence, mature to replace those learned in undergraduate or graduate programs. Traditionally, industry, professional societies and university programs have provided professional development. While these provide opportunities to develop deeper understanding in STEM specialties and stay current with new techniques, the constraints on formal classes and workshops preclude the possibility of Just-In-Time Mastery Learning, particularly for new domains. The MIT Lincoln Laboratory Supercomputing Center (LLSC) and MIT Supercloud teams have developed online course offerings specifically designed to provide a way for adult learners to build their own educational path based on their immediate needs, problems and schedules. To satisfy adult learners, the courses are formulated as a series of challenges and strategies. Using this perspective, the courses incorporate targeted theory supported by hands-on practice. The focus of this paper is the design of Mastery, Just-in-Time MOOC courses that address the full space of hands-on learning requirements, from digital to analog. The discussion centers on the design of project-based exercises for professional technical education courses. The case studies highlight examples from courses that incorporate practice ranging from the construction of a small radar used for real world data collection and processing to the development of high performance computing applications.
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Summary

Life-long learning is necessary for all professions because the technologies, tools and skills required for success over the course of a career expand and change. Professionals in science, technology, engineering and mathematics (STEM) fields face particular challenges as new multi-disciplinary methods, e.g. Machine Learning and Artificial Intelligence, mature to replace...

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