Collagen IVα345 dysfunction in glomerular basement membrane diseases. II. Crystal structure of the α345 hexamer
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Boudko, Sergei P. (ORCID:0000000338564859)↗
SEARCH · Engineering Papers
Results for “disease”
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First-in-class SAM-competitive G9a inhibitor FLAV-27 as a disease-modifying therapy for Alzheimer disease
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Bellver-Sanchis, Aina↗
Somatic Mutation and Autoimmune Disease: Insight from the World of Orphan and Rare Diseases
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Viktor Stolc↗
Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study
Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.
60 APPLIED LIFE SCIENCES↗
Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning
Abstract Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.
Hwang, Gyujoon↗
Crosstalk between cyclophilins and T lymphocytes in coronary artery disease
Highlights: • CD147 receptor expression is elevated in T lymphocytes from CAD patients. • Intracellular CypA and B expression are augmented in T lymphocytes from CAD patients. • CypA, B and C, and IL-1β and IL-6 serum levels are increased in patients with CAD. • CypA and B levels in T lymphocytes are correlated with CypA and B serum levels, respectively. Cardiovascular diseases and atherosclerosis are currently some of the most widespread diseases of our time. Within cardiovascular disease, coronary artery disease and underlying atherosclerosis were recently linked with systemic and local inflammation. Cyclophilins participate in the initiation and progression of these inflammatory-related diseases. Cyclophilins are released into the extracellular space upon inflammatory stimuli and participate in the pathology of cardiovascular diseases. The cell surface receptor for extracellular cyclophilins, the CD147 receptor, also contributes to coronary artery disease pathogenesis. Nevertheless, the physiological relevance of cyclophilin's family and their receptor in cardiovascular diseases remains unclear. The present study aimed to better understand the role of cyclophilins in cardiovascular artery disease and their relationship with inflammation. Hence, cyclophilins and pro-inflammatory interleukins were measured in the serum of 30 subjects (divided into three groups according to coronary artery disease status: 10 patients with acute coronary syndrome, 10 patients with chronic coronary artery disease, and 10 control volunteers). In addition, cyclophilin levels and CD147 receptor expression were measured in T lymphocytes purified from these subjects. Cyclophilin A, B, and C, pro-inflammatory interleukins, and CD147 membrane expression were significantly elevated in patients with coronary artery disease.
60 APPLIED LIFE SCIENCES↗
New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer’s disease
Abstract Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer’s disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer’s disease. However, there is currently no effective CypD inhibitor for Alzheimer’s disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood–brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer’s disease mitochondrial and glycolytic bioenergetics in Alzheimer’s disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer’s disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer’s disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer’s disease- and amyloid-β-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer’s disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer’s disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.
Neurosciences & Neurology↗
Warning Signs of Potential Black Swan Outbreaks in Infectious Disease
Black swan events in infectious disease describe rare but devastatingly large outbreaks. While experts are skeptical that such events are predictable, it might be possible to identify the warning signs of a black swan event. Specifically, following the initiation of an outbreak, key differentiating features could serve as alerts. Such features could be derived from meta-analyses of large outbreaks for multiple infectious diseases. We hypothesized there may be common features among the pathogen, environment, and host epidemiological triad that characterize an infectious disease black swan event. Using Los Alamos National Laboratory’s tool, Analytics for Investigation of Disease Outbreaks, we investigated historical disease outbreak information and anomalous events for several infectious diseases. By studying 32 different infectious diseases and global outbreaks, we observed that in the past 20–30 years, there have been potential black swan events in the majority of infectious diseases analyzed. Importantly, these potential black swan events cannot be attributed to the first introduction of the disease to a susceptible host population. This paper describes our observations and perspectives and illustrates the value of broad analysis of data across the infectious disease realm, providing insights that may not be possible when we focus on singular infectious agents or diseases. Data analytics could be developed to warn health authorities at the beginning of an outbreak of an impending black swan event. Such tools could complement traditional epidemiological modeling to help forecast future large outbreaks and facilitate timely warning and effective, targeted resource allocation for mitigation efforts.
60 APPLIED LIFE SCIENCES↗
Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022
Background/aims Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration’s Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval. Methods Using the Food and Drug Administration’s Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined. Results One hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug’s therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%). Discussion In the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as surrogate efficacy endpoints in accelerated approvals, but also in traditionally approved rare disease drugs. The choice of primary efficacy endpoints varied by therapeutic area, approval pathway, and understanding of disease etiology.
Hong, Kyungwan [Rare Diseases Team, Office of New ↗
Evidence Report: Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation Exposure
Occupational radiation exposure from the space environment may result in non-cancer or non-CNS degenerative tissue diseases, such as cardiovascular disease, cataracts, and respiratory or digestive diseases. However, the magnitude of influence and mechanisms of action of radiation leading to these diseases are not well characterized. Radiation and synergistic effects of radiation cause DNA damage, persistent oxidative stress, chronic inflammation, and accelerated tissue aging and degeneration, which may lead to acute or chronic disease of susceptible organ tissues. In particular, cardiovascular pathologies such as atherosclerosis are of major concern following gamma-ray exposure. This provides evidence for possible degenerative tissue effects following exposures to ionizing radiation in the form of the GCR or SPEs expected during long-duration spaceflight. However, the existence of low dose thresholds and dose-rate and radiation quality effects, as well as mechanisms and major risk pathways, are not well-characterized. Degenerative disease risks are difficult to assess because multiple factors, including radiation, are believed to play a role in the etiology of the diseases. As additional evidence is pointing to lower, space-relevant thresholds for these degenerative effects, particularly for cardiovascular disease, additional research with cell and animal studies is required to quantify the magnitude of this risk, understand mechanisms, and determine if additional protection strategies are required.The NASA PEL (Permissive Exposure Limit)s for cataract and cardiovascular risks are based on existing human epidemiology data. Although animal and clinical astronaut data show a significant increase in cataracts following exposure and a reassessment of atomic bomb (A-bomb) data suggests an increase in cardiovascular disease from radiation exposure, additional research is required to fully understand and quantify these adverse outcomes at lower doses (less than 0.5 gray (SI unit for ionizing radiation dosage, i.e. one joule of radiation energy per one kilogram of matter)) to facilitate risk prediction. This risk has considerable uncertainty associated with it, and no acceptable model for projecting degenerative tissue risk is currently available. In particular, risk factors such as obesity, alcohol, and tobacco use can act as confounding factors that contribute to the large uncertainties. The PELs could be violated under certain scenarios, including following a large SPE (solar proton event) or long-term GCR (galactic cosmic ray) exposure. Specifically, for a Mars mission, the accumulated dose is sufficiently high that epidemiology data and preliminary risk estimates suggest a significant risk for cardiovascular disease. Ongoing research in this area is intended to provide the evidence base for accurate risk quantification to determine criticality for extended duration missions. Data specific to the space radiation environment must be compiled to quantify the magnitude of this risk to decrease the uncertainty in current PELs and to determine if additional protection strategies are required. New research results could lead to estimates of cumulative radiation risk from CNS and degenerative tissue diseases that, when combined with the cancer risk, may have major negative impacts on mission design, costs, schedule, and crew selection. The current report amends an earlier report (Human Research Program Requirements Document, HRP-47052, Rev. C, dated Jan 2009) in order to provide an update of evidence since 2009.
Patel, Zarana↗
Network-medicine framework for studying disease trajectories in U.S. veterans
A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.
60 APPLIED LIFE SCIENCES↗
Multiscale Airborne Infectious Disease Transmission
Airborne disease transmission is central to many scientific disciplines, including agriculture, veterinary biosafety, medicine, and public health. Legal and regulatory standards are in place to prevent agricultural, nosocomial, and community airborne disease transmission. However, the overall importance of the airborne pathway is underappreciated; e.g., the U.S. National Library of Medicine’s Medical Subjects Headings (MESH) thesaurus lacks an airborne disease transmission indexing term. This has practical consequences, as airborne precautions to control epidemic disease spread may not be taken when airborne transmission is important but unrecognized. Publishing clearer practical methodological guidelines for surveillance studies and disease outbreak evaluations could help address this situation. To inform future work, this paper highlights selected, well-established airborne transmission events, largely cases replicated in multiple, independently conducted scientific studies. Methodologies include field experiments, modeling, epidemiology studies, disease outbreak investigations, and mitigation studies. Collectively, this literature demonstrates that airborne viruses, bacteria, and fungal pathogens have the ability to cause disease in plants, animals, and humans over multiple distances, from near range (<5 m) to continental (>500 km) in scale. The plausibility and implications of undetected airborne disease transmission are discussed, including the notable underreporting of disease burdens for several airborne-transmitted diseases.
59 BASIC BIOLOGICAL SCIENCES↗
Dose-finding studies in drug development for rare genetic diseases
The small patient populations inherent to rare genetic diseases present many challenges to the traditional drug development paradigm. One major challenge is generating sufficient data in early phase studies to inform dose selection for later phase studies and dose optimization for clinical use of the drug. However, optimizing the benefit-risk profile of drugs through appropriate dose selection during drug development is critical for all drugs, including those being developed to treat rare diseases. Recognizing the challenges of conducting dose finding studies in rare disease populations and the importance of dose selection and optimization for successful drug development, we assessed the dose-finding studies and analyses conducted for drugs recently approved for rare genetic diseases. Of the 40 marketing applications for new molecular entity (NME) drugs and biologics approved by the United States Food and Drug Administration for rare genetic diseases from 2015 to 2020, 21 (53%) of the development programs conducted at least one dedicated dose-finding study. In addition, the majority of drug development programs conducted clinical studies in healthy subjects and included population pharmacokinetic and exposure–response analyses; some programs also conducted clinical studies in patient populations other than the disease for which the drug was initially approved. The majority of primary endpoints utilized in dedicated dose-finding studies were biomarkers, and the primary endpoint of the safety and efficacy study matched the primary endpoint used in the dose finding study in 9 of 13 (69%) drug development programs where primary study endpoints were assessed. Our study showed that NME drug development programs for rare genetic diseases utilize multiple data sources for dosing information, including studies in healthy subjects, population pharmacokinetic analyses, and exposure–response analyses. In addition, our results indicate that biomarkers play a key role in dose-finding studies for rare genetic disease drug development programs. Our findings highlight the need to develop study designs and methods to allow adequate dose-finding efforts within rare disease drug development programs that help overcome the challenges presented by low patient prevalence and other factors. Furthermore, the frequent reliance on biomarkers as endpoints for dose-finding studies underscores the importance of biomarker development in rare diseases.
60 APPLIED LIFE SCIENCES↗
MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis
Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain, spinal cord and optic nerve leading to demyelination. Focal demyelination is associated with relapsing-remitting multiple sclerosis, while progressive forms of the disease show axonal degeneration and neuronal loss. The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity. MicroRNAs (miRNAs) are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases. A review of recent studies with the experimental autoimmune encephalomyelitis animal model (mostly female mice 6–12 weeks of age) has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset (asymptomatic) stage when assessed in blood plasma and urine exosomes, and spinal cord tissue. The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes, brain and spinal cord tissue, and at the post-peak (chronic) stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue. Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease. Interestingly, experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a, miR-23b, miR-497, miR-26a, and miR-20b, or by suppression of miR-182, miR-181c, miR-223, miR-155, and miR873. Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course. Additionally, studies should be performed with male mice of a similar age, and with aged male and female mice.
59 BASIC BIOLOGICAL SCIENCES↗
Simulating nationwide coupled disease and fear spread in an agent-based model
Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission within a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental models to ensure that introducing additional disease states does not prevent the emergence of multiple waves in these simpler models. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity of fear and behavior change. Our results show that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory in compartmental models. In EpiCast, the combination of non-local fear spread via broadcasters and strong behavioral responses by fearful individuals generally leads to multiple epidemic waves, an outcome that occurs only within a narrow parameter range when fear spreads purely through local contact. Accounting for the coupled spread of fear and disease is critical for understanding disease dynamics and designing timely, targeted responses to emerging infectious threats.
60 APPLIED LIFE SCIENCES↗
Mondo: integrating disease terminology across communities
Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.
biomedical informatics↗
Bark morphological and chemical features are differentially correlated with disease resistance and yield in hybrid poplar taxa
In the southeastern United States, the establishment of short-rotation intensively cultured plantations of hybrid poplar has been hindered by its susceptibility to stem cankers. We evaluated the tradeoffs between biomass yield and disease tolerance in hybrid poplar genotypes belonging to P. deltoides × P. maximowiczii (DM), P. deltoides × P. nigra (DN), P. trichocarpa × P. maximowiczii (TM), and P. deltoides × P. deltoides (DD) taxa. We hypothesized that canker resistant genotypes will have thicker bark but bark thickness and biomass yield will be negatively correlated. After two growing seasons, the DD genotypes developed thicker bark compared to the genotypes of other taxa and bark thickness was not correlated with biomass yield in the DD genotypes (R 2 = 0.002). However, in the TM, DM, and DN genotypes, bark thickness was negatively correlated with biomass yield (R 2 = 0.33–0.77). Disease incidence studies revealed that the DM genotypes were most susceptible to canker whereas no disease was detected in DD genotypes. Furthermore, bark analysis conducted by Fourier transform infrared spectroscopy coupled with multivariate analysis showed that that DD genotypes to be chemically separate from the three hybrid genotypes and that bark chemistry was correlated with canker disease incidence. Taken together, these results reveal that it is possible to generate hybrid poplar genotypes with thicker bark, disease resistance, and higher biomass yields. This insight should guide further efforts to develop genetically improved hybrid poplar genotypes, both in terms of biomass yield and disease tolerance, for cultivation in the southeastern United States. Hybrid poplar cultivation in southeastern United States is hindered by its susceptibility to stem cankers. We evaluated tradeoffs between yield and canker disease resistance in various hybrid poplar genotypes. After two growing seasons, the DD genotypes showed disease resistance and developed thicker bark that was chemically distinct from the other genotypes. Bark thickness was not correlated with yield in the DD genotypes but was negatively correlated with yield in the other genotypes. These results will guide the development of hybrid poplar genotypes that are both disease resistant and high yielding for cultivation in the southeastern United States.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission
Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk—a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease “sparks” that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks.
54 ENVIRONMENTAL SCIENCES↗