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A fact sheet of best practices and guidelines around HVAC, exhaust systems, BAS, elevators, and space use for retail operators.
A fact sheet of best practices and guidelines around HVAC and space use for commercial office operators.
Abstract not provided.
Abstract not provided.
In one embodiment, a prefabricated container is modified as a medical isolation room by replacing an original door with a clear front door. An intake louver and an adjustable damper are disposed at a lower part of the front end. An exhaust vent is disposed at an upper part of the back wall. An exhaust fan and a HEPA filter are coupled to the exhaust vent. Washable coverings cover interior sides of the container to provide washable, nonslip interior surfaces. The exhaust fan and the adjustable damper at the intake louver are controlled to produce in the medical isolation room a negative air pressure of at least about minus 0.01 inch of water gage (approximately 2.5 pascals) and a displacement ventilation exhaust flow rate through the exhaust vent of at least about 100 cubic feet per minute (cfm) greater than an intake flow rate through the intake louver.
Abstract not provided.
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Objective: We will demonstrate tools that allow mechanistic contraints on disease progression and epidemic spread to play off against interventions, mitigation, and control measures. The fundamental mechanisms of disease progression and epidemic spread provide important constraints on interpreting changing epidemic cases counts with time and geography in the context of on-going interventions, mitigations, and controls. Models such as these that account for the effects of human actions can also allow evaluation of the importance of categories of epidemic and disease controls. Introduction: We present the EpiEarly, EpiGrid, and EpiCast tools for mechanistically-based biological decision support. The range of tools covers coarse-, medium-, and fine-grained models. The coarse-grained, aggregated time-series only data tool (EpiEarly) provides a statistic quantifying epidemic growth potential and associated uncertainties. The medium grained, geographically-resolved model (EpiGrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs. late diagnosis, vaccination, etc. in outbreak control. A fine-grained hybrid-agent epidemic model (EpiCast) with diurnal agent travel and contagion allows the analysis of the importance of contact-networks, travel, and detailed intervention strategies for the control of outbreaks and epidemics. Methods: We use three types of methods for simulation and analysis. They are: (1) Bayesian and regression methods allowing estimation of the basic reproductive number from case-count data; (2) ordinary-differential equation integration with modifications to account for discreteness of disease spread when case counts are small (we include space- and time-dependent effects); and (3) methods that hybridize agent-based travel, mixing, and disease progression with nested-mass action contagion (i.e. not fully agent-based). From the perspective of decision support, the crucial feature of mechanistic infectious epidemiological models is a way to capture the human interventions that determine epidemic outcome. Categorizing types of mitigation into those that change the force of infection, and those that branch disease progression allows a common framework that can be extended from medium-grained models through fine-grained. Our canonical example is our EpiGrid tool which allows for the modulation of the force of infection (i.e. contagion) with time (and potentially space), the vaccination of a susceptible population in a geographically-targeted manner, movement controls, and branching our disease progression model to account for early- vs. late-intervention during host disease progression.ResultsWe will present analysis of diseases that exemplify the various aspects of analysis in support of outbreak and epidemic control. Human and animal diseases relevant to this demonstration include rinderpest, avian influenza, and measles. We will begin with EpiEarly’s estimate of epidemic potential using aggregated time-dependent case-count data. The key observation for EpiEarly is that under a wide range of situations a disease's reproductive number should be generalized to a distribution of possibilities to account for inherent randomness and other factors (including the variability of a disease contact network). We will then continue with a demonstration of EpiGrid’s capabilities for understanding and modelling the role of interventions including contagion control (the force of infection), treatment (changing disease progression and infectiousness depending on treatment), vaccination, culling, and movement controls. We will briefly touch on the capabilities of EpiCast for more detailed analysis of specific intervention strategies. Conclusions: We will demonstrate examples where modeling either contributed or plausibly would contribute to informing epidemic and outbreak control constrained by the possibilities of the underlying epidemic and disease dynamics.
Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) was tasked by the Defense Threat Reduction Agency (DTRA)-Joint Science and Technology Office (JSTO) to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. The long-term, broad objective of this project was to provide information and analysis that can be leveraged from existing and developing national and international disease surveillance systems and methodologies, to create a global disease monitoring system, ultimately providing decision makers with timely information to prepare for and mitigate the spread of disease. Specifically, this project evaluated data streams that are currently being used in surveillance systems and data streams that had the potential for being used in surveillance to enable early disease detection. LANL’s focus was on infectious disease surveillance and the term “biosurveillance” will be used to refer to this scope hereafter. Data stream evaluation was conducted using two different methods: Multi criteria decision analysis (MCDA) using a tool called Logical Decisions ® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a Surveillance Window concept developed at LANL that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. A cross method analysis was performed between the surveillance window based evaluation and the MCDA-based evaluation to identify data stream categories that showed high utility for both methods. In addition, algorithms that can integrate useful data streams to facilitate early disease detection were also examined. This project provided an understanding of data streams relevant to early warning, early detection and monitoring of disease outbreaks. The report describes the results of LANL’s evaluation of 16 data stream categories using the two methods, and recommendations for next steps. The project evolved significantly over its course, and significant improvements in our approaches were made compared to the initial proposed processes. A key outcome of this effort was that LANL was able to bring together several diverse entities involved in disease surveillance and lay the foundation for new collaborations that straddled military and civilian health surveillance. The robust evaluation framework developed by LANL has generated significant interest and there is interest in adopting this framework for various applications both within disease surveillance as well as other public health initiatives. Finally, LANL developed the Biosurveillance Resource Directory (BRD), a relational database that underwent pilot testing by members of the human, plant and animal disease surveillance community. The BRD is intended to be a global resource to facilitate rapid information access.
Life can be defined as a self-sustaining chemical system capable of undergoing Darwinian evolution; a self-bounded, self-replicating, and self-perpetuating entity [1]. This definition should hold for terrestrial as well as extraterrestrial life-forms. Although, it is reasonable to expect that a Mars life-form would be more adaptable to Mars-like conditions than to Earth-like environments, it remains possible that negative ecological or host interactions might occur if Mars microbiota were to be inadvertently released into the terrestrial environment. A biogenic infectious agent can be defined as a self-sustaining chemical system capable of undergoing Darwinian evolution and derives its sustenance from a living cell or from the by-products of cell death. Disease can be de-fined as the detrimental alteration of one or more ordered metabolic processes in a living host caused by the continued irritation of a primary causal factor or factors; disease is a dynamic process [2]. In contrast, an injury is due to an instantaneous event; injury is not a dynamic process [2]. A causal agent of disease is defined as a pathogen, and can be either abiotic or biotic in nature. Diseases incited by biotic pathogens are the exceptions, not the norms, in terrestrial host-microbe interactions. Disease induction in a plant host can be conceptually characterized using the Disease Triangle (Fig. 1) in which disease occurs only when all host, pathogen, and environ-mental factors that contribute to the development of disease are within conducive ranges for a necessary minimum period of time. For example, plant infection and disease caused by the wheat leaf rust fungus, Puccinia recondita, occur only if virulent spores adhere to genetically susceptible host tissues for at least 4-6 hours under favorable conditions of temperature and moisture [3]. As long as one or more conditions required for disease initiation are not available, disease symptoms will not develop.
Circulating microRNAs (miRNAs) are receiving attention as potential biomarkers of various diseases, including cancers, chronic obstructive pulmonary disease, and cardiovascular disease. However, it is unknown whether the levels of circulating miRNAs in a healthy subject might vary with external factors in daily life. In this study, we investigated whether cigarette smoking, a habit that has spread throughout the world and is a risk factor for various diseases, affects plasma miRNA profiles. We determined the profiles of 11 smokers and 7 non-smokers by TaqMan MicroRNA array analysis. A larger number of miRNAs were detected in smokers than in non-smokers, and the plasma levels of two-thirds of the detected miRNAs (43 miRNAs) were significantly higher in smokers than in non-smokers. A principal component analysis of the plasma miRNA profiles clearly separated smokers and non-smokers. Twenty-four of the miRNAs were previously reported to be potential biomarkers of disease, suggesting the possibility that smoking status might interfere with the diagnosis of disease. Interestingly, we found that quitting smoking altered the plasma miRNA profiles to resemble those of non-smokers. These results suggested that the differences in the plasma miRNA profiles between smokers and non-smokers could be attributed to cigarette smoking. In addition, we found that an acute exposure of ex-smokers to cigarette smoke (smoking one cigarette) did not cause a dramatic change in the plasma miRNA profile. In conclusion, we found that repeated cigarette smoking substantially alters the plasma miRNA profile, interfering with the diagnosis of disease or signaling potential smoking-related diseases. - Highlights: • Plasma miRNA profiles were unambiguously different between smokers and non-smokers. • Smoking status might interfere with the diagnosis of disease using plasma miRNAs. • Changes of plasma miRNA profiles may be a signal of smoking-related diseases.
Triosephosphate isomerase (TPI) deficiency is a poorly understood disease characterized by hemolytic anemia, cardiomyopathy, neurologic dysfunction, and early death. TPI deficiency is one of a group of diseases known as glycolytic enzymopathies, but is unique for its severe patient neuropathology and early mortality. The disease is caused by missense mutations and dysfunction in the glycolytic enzyme, TPI. Previous studies have detailed structural and catalytic changes elicited by disease-associated TPI substitutions, and samples of patient erythrocytes have yielded insight into patient hemolytic anemia; however, the neuropathophysiology of this disease remains a mystery. This study combines structural, biochemical, and genetic approaches to demonstrate that perturbations of the TPI dimer interface are sufficient to elicit TPI deficiency neuropathogenesis. The present study demonstrates that neurologic dysfunction resulting from TPI deficiency is characterized by synaptic vesicle dysfunction, and can be attenuated with catalytically inactive TPI. Collectively, our findings are the first to identify, to our knowledge, a functional synaptic defect in TPI deficiency derived from molecular changes in the TPI dimer interface.