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Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/., Competing Interests: ES and AA are currently employed by IBM, GN and JK were employed by IBM at the time of this work. JK is currently employed by Altos Labs. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nayar, Terrizzano, Seabolt, Agarwal, Boucher, Ruiz, Slizovskiy, Kaufman and Noyes.)

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein.

The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing availability of genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address these challenges, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. In this paper, we describe Functional Genomics Platform (formerly known as OMXWare), a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, the Functional Genomics Platform today contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. The Functional Genomics Platform maps all of the many-to-many connections between each biological entity including the originating genome, gene, protein, and protein domain. Various microbial studies, from infectious disease to environmental health, can benefit from the rich data and connections. We describe the data selection, the pipeline to create and update the Functional Genomics Platform, and the developer tools (Python SDK and REST APIs)which allow researchers to efficiently study microbial life at scale.

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences-some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise.

Importance: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it., Objective: This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels., Design: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast., Results: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective - county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves., Conclusions and Relevance: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Case Description: In Latvia in 2014, acquired idiopathic megaesophagus (AIME) was observed in increased numbers of dogs that consumed varieties of 1 brand of dog food. Within 2 years, 253 dogs were affected. In Australia in November 2017, 6 working dogs that consumed 1 diet of another brand of dog food developed AIME. In total, 145 Australian dogs were affected., Clinical Findings: AIME was diagnosed predominantly in large-breed male dogs (> 25 kg [55 lb]). Regurgitation, weight loss, and occasionally signs consistent with aspiration pneumonia (coughing, dyspnea, or fever) were noted. Most Latvian dogs had mild to severe peripheral polyneuropathies as evidenced by laryngeal paralysis, dysphonia, weakness, and histopathologic findings consistent with distal axonopathy. In Australian dogs, peripheral polyneuropathies were not identified, and histopathologic findings suggested that the innervation of the esophagus and pharynx was disrupted locally, although limited samples were available., Treatment and Outcome: Investigations in both countries included clinical, epidemiological, neuropathologic, and case-control studies. Strong associations between the dog foods and the presence of AIME were confirmed; however, toxicological analyses did not identify a root cause. In Latvia, the implicated dietary ingredients and formulations were unknown, whereas in Australia, extensive investigations were conducted into the food, its ingredients, the supply chain, and the manufacturing facilities, but a cause was not identified., Clinical Relevance: A panel of international multidisciplinary experts concluded that the cause of AIME in both outbreaks was likely multifactorial, with the possibility of individualized sensitivities. Without a sentinel group, the outbreak in Australia may not have been recognized for months to years, as happened in Latvia. A better surveillance system for early identification of pet illnesses, including those associated with pet foods, is needed.

Rapid tests for active SARS-CoV-2 infections rely on reverse transcription polymerase chain reaction (RT-PCR). RT-PCR uses reverse transcription of RNA into complementary DNA (cDNA) and amplification of specific DNA (primer and probe) targets using polymerase chain reaction (PCR). The technology makes rapid and specific identification of the virus possible based on sequence homology of nucleic acid sequence and is much faster than tissue culture or animal cell models. However the technique can lose sensitivity over time as the virus evolves and the target sequences diverge from the selective primer sequences. Different primer sequences have been adopted in different geographic regions. As we rely on these existing RT-PCR primers to track and manage the spread of the Coronavirus, it is imperative to understand how SARS-CoV-2 mutations, over time and geographically, diverge from existing primers used today. In this study, we analyze the performance of the SARS-CoV-2 primers in use today by measuring the number of mismatches between primer sequence and genome targets over time and spatially. We find that there is a growing number of mismatches, an increase by 2% per month, as well as a high specificity of virus based on geographic location.

Background: Widespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit., Results: To facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights., Conclusions: We demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development. Video abstract.

Increasingly available microbial reference data allow interpreting the composition and function of previously uncharacterized microbial communities in detail, via high-throughput sequencing analysis. However, efficient methods for read classification are required when the best database matches for short sequence reads are often shared among multiple reference sequences. Here, we take advantage of the fact that microbial sequences can be annotated relative to established tree structures, and we develop a highly scalable read classifier, PRROMenade, by enhancing the generalized Burrows-Wheeler transform with a labeling step to directly assign reads to the corresponding lowest taxonomic unit in an annotation tree. PRROMenade solves the multi-matching problem while allowing fast variable-size sequence classification for phylogenetic or functional annotation. Our simulations with 5% added differences from reference indicated only 1.5% error rate for PRROMenade functional classification. On metatranscriptomic data PRROMenade highlighted biologically relevant functional pathways related to diet-induced changes in the human gut microbiome., Competing Interests: Declaration of Interests N.H., L.P., E. Siragusa, and F.U. are listed (together or partially) as co-inventors on patent applications currently pending review at the USPTO., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Traditional taxonomy in biology assumes that life is organized in a simple tree. Attempts to classify microorganisms in this way in the genomics era led microbiologists to look for finite sets of 'core' genes that uniquely group taxa as clades in the tree. However, the diversity revealed by large-scale whole genome sequencing is calling into question the long-held model of a hierarchical tree of life, which leads to questioning of the definition of a species. Large-scale studies of microbial genome diversity reveal that the cumulative number of new genes discovered increases with the number of genomes studied as a power law and subsequently leads to the lack of evidence for a unique core genome within closely related organisms. Sampling 'enough' new genomes leads to the discovery of a replacement or alternative to any gene. This power law behaviour points to an underlying self-organizing critical process that may be guided by mutation and niche selection. Microbes in any particular niche exist within a local web of organism interdependence known as the microbiome. The same mechanism that underpins the macro-ecological scaling first observed by MacArthur and Wilson also applies to microbial communities. Recent metagenomic studies of a food microbiome demonstrate the diverse distribution of community members, but also genotypes for a single species within a more complex community. Collectively, these results suggest that traditional taxonomic classification of bacteria could be replaced with a quasispecies model. This model is commonly accepted in virology and better describes the diversity and dynamic exchange of genes that also hold true for bacteria. This model will enable microbiologists to conduct population-scale studies to describe microbial behaviour, as opposed to a single isolate as a representative.

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay., Competing Interests: Competing interestsThe authors were employed by private or academic organizations as described in the author affiliations at the time this work was completed. IBM Corporation, Mars Incorporated, and Bio-Rad Laboratories are members of the Consortium for Sequencing the Food Supply Chain. The authors declare no other competing interests., (© The Author(s) 2019.)

The Spatiotemporal Epidemiologic Modeler (STEM) is an open source software project supported by the Eclipse Foundation and used by a global community of researchers and public health officials working to track and, when possible, control outbreaks of infectious disease in human and animal populations. STEM is not a model or a tool designed for a specific disease; it is a flexible, modular framework supporting exchange and integration of community models, reusable plug-in components, and denominator data, available to researchers worldwide at www.eclipse.org/stem. A review of multiple projects illustrates its capabilities. STEM has been used to study variations in transmission of seasonal influenza in Israel by strains; evaluate social distancing measures taken to curb the H1N1 epidemic in Mexico City; study measles outbreaks in part of London and inform local policy on immunization; and gain insights into H7N9 avian influenza transmission in China. A multistrain dengue fever model explored the roles of the mosquito vector, cross-strain immunity, and antibody response in the frequency of dengue outbreaks. STEM has also been used to study the impact of variations in climate on malaria incidence. During the Ebola epidemic, a weekly conference call supported the global modeling community; subsequent work modeled the impact of behavioral change and tested disease reintroduction via animal reservoirs. Work in Germany tracked salmonella in pork from farm to fork; and a recent doctoral dissertation used the air travel feature to compare the potential threats posed by weaponizing infectious diseases. Current projects include work in Great Britain to evaluate control strategies for parasitic disease in sheep, and in Germany and Hungary, to validate the model and inform policy decisions for African swine fever. STEM Version 4.0.0, released in early 2019, includes tools used in these projects and updates technical aspects of the framework to ease its use and re-use.

A zoonotic disease is a disease that can be passed from animals to humans. Zoonotic viruses may adapt to a human host eventually becoming endemic in humans, but before doing so punctuated outbreaks of the zoonotic virus may be observed. The Ebola virus disease (EVD) is an example of such a disease. The animal population in which the disease agent is able to reproduce in sufficient number to be able to transmit to a susceptible human host is called a reservoir. There is little work devoted to understanding stochastic population dynamics in the presence of a reservoir, specifically the phenomena of disease extinction and reintroduction. Here, we build a stochastic EVD model and explicitly consider the impacts of an animal reservoir on the disease persistence. Our modelling approach enables the analysis of invasion and fade-out dynamics, including the efficacy of possible intervention strategies. We investigate outbreak vulnerability and the probability of local extinction and quantify the effective basic reproduction number. We also consider the effects of dynamic population size. Our results provide an improved understanding of outbreak and extinction dynamics in zoonotic diseases, such as EVD., (© 2017 The Author(s).)

Reports results of a survey of K-12 principals to take inventory of student mental health and wellness needs and the types of programs schools are most often implementing to help students in California's public schools.

This paper uses methods drawn from physics to study the life cycle of viruses. The paper analyzes a model of viral infection and evolution using the "grand canonical ensemble" and formalisms from statistical mechanics and thermodynamics. Using this approach we enumerate all possible genetic states of a model virus and host as a function of two independent pressures-immune response and system temperature. We prove the system has a real thermodynamic temperature, and discover a new phase transition between a positive temperature regime of normal replication and a negative temperature "disordered" phase of the virus. We distinguish this from previous observations of a phase transition that arises as a function of mutation rate. From an evolutionary biology point of view, at steady state the viruses naturally evolve to distinct quasispecies. This paper also reveals a universal relationship that relates the order parameter (as a measure of mutational robustness) to evolvability in agreement with recent experimental and theoretical work. Given that real viruses have finite length RNA segments that encode proteins which determine virus fitness, the approach used here could be refined to apply to real biological systems, perhaps providing insight into immune escape, the emergence of novel pathogens and other results of viral evolution.

Context: Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment., Objective: To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities., Design: The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)-based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature., Results: The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014., Conclusions: The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.

Purpose: The practice of "genomic" (or "personalized") medicine requires the availability of appropriate diagnostic testing. Our study objective was to identify the reasons for health systems to bring next-generation sequencing into their clinical laboratories and to understand the process by which such decisions were made. Such information may be of value to other health systems seeking to provide next-generation sequencing testing to their patient populations., Methods: A standardized open-ended interview was conducted with the laboratory medical directors and/or department of pathology chairs of 13 different academic institutions in 10 different states., Results: Genomic testing for cancer dominated the institutional decision making, with three primary reasons: more effective delivery of cancer care, the perceived need for institutional leadership in the field of genomics, and the premise that genomics will eventually be cost-effective. Barriers to implementation included implementation cost; the time and effort needed to maintain this newer testing; challenges in interpreting genetic variants; establishing the bioinformatics infrastructure; and curating data from medical, ethical, and legal standpoints. Ultimate success depended on alignment with institutional strengths and priorities and working closely with institutional clinical programs., Conclusion: These early adopters uniformly viewed genomic analysis as an imperative for developing their expertise in the implementation and practice of genomic medicine.

Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.

Dengue is a major international public health concern and impacts one-third of the world's population. No specific vaccine and treatment are available for this vector-borne disease. There are four similar but distinct serotypes of dengue viruses (DENV). Infection with one serotype affords life-long immunity to that serotype but only temporary partial immunity, or cross immunity (CI), to others. This increases the risk of developing lethal complications upon re-infection, mainly because of the effect of antibody-dependent enhancement (ADE). There have been multiple studies of the dynamic behavior created by the interplay of ADE and CI using mathematical models. However, models in the literature seldom capture the vector population, which we consider important because combating the mosquito vector is the only way to contain dengue transmission in the absence of vaccines. We therefore propose two differential-equation models of dengue fever (DF) with different levels of complexity and details. Our results support the need for ADE to explain the complexity of the epidemiological data., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: The role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation., Methods: This study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation's Spatiotemporal Epidemiological Modeller (STEM)., Results: Although the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166-2 national subdivisions and with monthly time sampling., Conclusions: The high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Background: Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease., Methodology/principal Findings: Using U.S. ticket data from 2007, we compared a simplified "pipe" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a "gravity" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model., Conclusions/significance: If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.

Background: This paper describes the Spatiotemporal Epidemiological Modeller (STEM) which is an extensible software system and framework for modelling the spatial and temporal progression of multiple diseases affecting multiple populations in geographically distributed locations. STEM is an experiment in developing a software system that can model complex epidemiological scenarios while also being extensible by the research community. The ultimate goal of STEM is to provide a common modelling platform powerful enough to be sufficient for all modelling scenarios and extensible in a way that allows different researchers to combine their efforts in developing exceptionally good models., Results: STEM is a powerful modelling system that allows researchers to model scenarios with unmixed populations that are not uniformly distributed and in which multiple populations exist that are being infected with multiple diseases. It's underlying representational framework, a graph, and its software architecture allow the system to be extended by incorporating software components developed by different researchers., Conclusion: This approach taken in the design of STEM creates a powerful platform for epidemiological research collaboration. Future versions of the system will make such collaborative efforts easy and common.

In this paper, we discuss two important elements to lowering the barrier to creation of a National Health Information Network. The first element is the adoption of standards that will enable interoperability while guarantee open interfaces (and preventing vendor lock-in). The second element is the role of open source. While adoption of open standards by large EMR vendors is critically important to enterprise healthcare providers and payors, the availability of inexpensive (or free) standardized Healthcare Information Technology for small physician practices is critical. By analogy to the emergence of the World Wide Web, a framework for creating inexpensive and open source applications for physicians will be as important to realizing a National Health Information Network as availability of free browser technology was to the growth of the internet.

The relationship between distance and size perception is unclear because of conflicting results of tests investigating the size-distance invariance hypothesis (SDIH), according to which perceived size is proportional to perceived distance. We propose that response bias with regard to measures of perceived distance is at the root of the conflict. Rather than employ the usual method of magnitude estimation, the bias-free two-alternative forced choice (2AFC) method was used to determine the precision (1/sigma) of discriminating depth at different distances. The results led us to define perceptual distance as a bias free power function of physical distance, with an exponent of approximately 0.5. Similar measures involving size differences among stimuli of equal angular size yield the same power function of distance. In addition, size discrimination is noisier than depth discrimination, suggesting that distance information is processed prior to angular size. Size constancy implies that the perceived size is proportional to perceptual distance. Moreover, given a constant relative disparity, depth constancy implies that perceived depth is proportional to the square of perceptual distance. However, the function relating the uncertainties of depth and of size discrimination to distance is the same. Hence, depth and size constancy may be accounted for by the same underlying law.

Recently there has been increased focus on the need to modernize the healthcare information infrastructure in the United States. The U.S. healthcare industry is by far the largest in the world in both absolute dollars and in percentage of GDP (more than $1.5 trillion, or 15 percent of GDP). It is also fragmented and complex. These difficulties, coupled with an antiquated infrastructure for the collection of and access to medical data, lead to enormous inefficiencies and sources of error. Consumer, regulatory, and governmental pressure drive a growing consensus that the time has come to modernize the U.S. healthcare information infrastructure (HII). While such transformation may be disruptive in the short term, it will, in the future, significantly improve the quality, expediency, efficiency, and successful delivery of healthcare while decreasing costs to patients and payers and improving the overall experiences of consumers and providers. The launch of a national health infrastructure initiative in the United States in May 2004--with the goal of providing an electronic health record for every American within the next decade--will eventually transform the healthcare industry in general, just as information technology (IT) has transformed other industries in the past. The key to this successful outcome will be based on the way we apply IT to healthcare data and the services delivered through IT. This must be accomplished in a way that protects individuals and allows competition but gives caregivers reliable and efficient access to the data required to treat patients and to improve the practice of medical science. This paper describes key IT solutions and technologies that address the challenges of creating a nation-wide healthcare IT infrastructure. Furthermore we discuss the emergence of new electronic healthcare services and the current efforts of IBM Research, Software Group, and Healthcare Life Sciences to realize this new vision for healthcare.

A fast spin-echo sequence weighted with a time constant that defines the magnetic relaxation of spins under the influence of a radio-frequency field (T1(rho)) was used in six subjects to measure magnetic resonance (MR) relaxation times in the knee joint with a 1.5-T MR imager. A quantitative comparison of T2- and T1(rho)-weighted MR images was also performed. Substantial T1(rho) dispersion was demonstrated in human articular cartilage, but muscle did not demonstrate much dispersion. T1(rho)-weighted images depicted a chondral lesion with 25% better signal-difference-to-noise ratios than comparable T2-weighted images. This technique may depict cartilage and muscular abnormalities.

Objective: To develop a magnetic resonance imaging (MRI) technique to non-invasively map water volume fraction (WVF) in articular cartilage. Special emphasis was placed on spatial resolution and temporal considerations, aimed at creating a procedure feasible for eventual human studies., Design: Absolute proton density MR images of intact, ex vivo bovine patellae were calculated from fully T(1) relaxed, short echo time images. This was accomplished through compensation for T(2) decay with calculated T(2) maps. Calibration of the signal intensity in the image was accomplished with the use of H2O:D2O phantoms, where the WVF was varied from 0.95 to 0.75. Application of the calibration curve to the entire image yielded images that represent WVF on a pixel by pixel basis. Calculations of water content by weight were performed by considering the density of the solid content., Results: Using four echo time points, experiments comparing MR images from single-echo and multi-echo spin echo sequences yielded similar results. T(2) decreased with depth through the cartilage, with a maximum at the articular surface of approx 100 ms, and a approximately 50 ms minimum at the bone/cartilage interface. The WVF through the depth of the cartilage showed a similar trend, decreasing from 0.9 at the surface, to 0.7 at the bone/cartilage interface. Translation to a weight percent yielded approximately 86% weight at the surface, trending down to approximately 63% at the bone/cartilage interface, with an average of 74.5% for five patellae. These MRI derived values were compared to the measured weight of water in excised cartilage plugs from the same patellae and showed remarkably close agreement., Conclusion: We have demonstrated that MRI can non-invasively map WVF in cartilage in a pixel by pixel manner. This was accomplished in a time span that was clinically feasible, allowing the routine use of this method in a clinical setting. Moreover, this procedure employed standard MRI equipment and pulse sequences, avoiding the need for hardware modifications and using simple post processing methods. However, baseline studies need to be performed prior to incorporation into a standard radiological evaluation. Implications in the diagnosis of osteoarthritis (OA) are discussed., (Copyright 2001 OsteoArthritis Research Society International.)

An old explanation of the moon illusion holds that various cues place the horizon moon at an effectively greater distance than the elevated moon. Although both moons have the same angular size, the horizon moon must be perceived as larger. More recent explanations hold that differences in accommodation or other factors cause the elevated moon to appear smaller. As a result of this illusory difference in size, the elevated moon appears to be more distant than the horizon moon. These two explanations, both based on the geometry of stereopsis, lead to two diametrically opposed hypotheses. That is, a depth interval at a long distance is associated with a smaller binocular disparity, whereas an equal depth interval at a smaller distance is associated with a larger disparity. We conducted experiments involving artificial moons and confirmed the hypothesis that the horizon moon is at a greater perceptual distance. Moreover, when a moon of constant angular size was moved closer it was also perceived as growing smaller, which is consistent with the older explanation. Although Emmert's law does not predict the size-distance relationship over long distances, we conclude that the horizon moon is perceived as larger because the perceptual system treats it as though it is much farther away. Finally, we observe that recent explanations substitute perceived size for angular size as a cue to distance. Thus, they imply that perceptions cause perceptions.

Proton and sodium MR relaxation times of bovine articular cartilage specimens were measured as a function of proteoglycan (PG) depletion and as a function of mechanical compression. Proton and sodium relaxation times of normal cartilage were compared with relaxation times of PG-depleted cartilage to evaluate the significance of PG depletion-induced changes in MR relaxation parameters. These comparisons were conducted for both uncompressed and mechanically compressed states. The mechanical compressions were performed with an MR-compatible pressure cell and evaluated dynamically via interleaved one-dimensional proton and sodium MR projection imaging. The comparisons indicate that sodium relaxation parameters are sensitive to PG depletion when cartilage is in a mechanically compressed state or an uncompressed state. In contrast, proton relaxation parameters do not change significantly with PG depletion when cartilage is in an uncompressed state. However, during mechanical compression, proton T2 becomes sensitive to PG depletion. These results support the potential of sodium magnetic resonance imaging (MRI) as a possible modality for obtaining imaging contrast related to PG depletion. The results also indicate the potential of proton MRI to provide such contrast if the image acquisition is conducted in conjunction with a mechanical compression via physical exercise.J. Magn. Reson Imaging 10:961-967, 1999., (Copyright 1999 Wiley-Liss, Inc.)

The design of a pressure cell that compresses a cartilage specimen in one dimension within an imaging magnet is presented. One-dimensional projection images in a direction perpendicular to the articular surface of the cartilage specimen were used to generate a uniaxial confined deformation creep curve for normal and trypsin-degraded cartilage specimens during a continuous 0.690 MPa (100 psi) pressure application. The resulting curves are shown to fit a two time constant viscoelastic model well and also indicate that the elastic modulus of cartilage decreases and the deformation rate increases upon trypsin proteolysis. Furthermore, cartilage permeability is shown as a function of cartilage strain for both the normal and trypsin-degraded case. Several two-dimensional slice-selective images were collected both before and after 80 minutes of continuous compression. These images were used to evaluate the relative changes in the spin-lattice, T1, and spin-spin, T2, relaxation time constant maps for both normal and degraded cartilage specimens in response to compression. The results of this study demonstrate the utility of a novel, non-magnetic, cartilage compression device and also support the validity of a simple two-component rheological model of articular cartilage.

One of the first effects of degenerative osteoarthritis is the loss of proteoglycans from the matrix of articular cartilage. Using a model of osteoarthritic change where the cartilage has been enzymatically degraded with trypsin, the sodium NMR characteristics of the cartilage were determined as a function of changes in the proteoglycan content. The results demonstrate that the single quantum sodium signal decreases as the proteoglycan content of the cartilage matrix decreases. In addition, the relaxation characteristics of the sodium change such that the T1 is longer, the T2s is longer, and the T2f is shorter. Short echo-time, T1-weighted sodium images are presented which demonstrate that this information may be utilized to detect the loss of proteoglycans from articular cartilage.

The effects of mechanical compression on the multiple quantum coherences generated from sodium ions in articular cartilage were investigated. Cartilage samples obtained from bovine patellae were studied during compression at 0.7 MPa (100 psi) for 1 hour. The double quantum filtered spectra showed marked lineshape changes in the compressed samples. Compression did not seem to influence the lineshapes of the single quantum and triple quantum filtered spectra significantly. We found that the residual quadrupolar interaction was reduced in the compressed samples. Changes in the ordering of collagen fibers may be responsible for the observed effect.

Spin-lattice relaxation in the rotating frame (T1rho) dispersion spectroscopy and imaging were used to study normal and enzymatically degraded bovine articular cartilage. Normal specimens demonstrate significant T1rho "dispersion" (approximately 60 to approximately 130 ms) in the 100 Hz to 9 kHz frequency range. Proteoglycan-degraded specimens have 33% greater T1rho values than collagen-degraded or normal samples. T1rho-weighted images reveal structure not found in conventional T1- or T2-weighted images. Our results suggest that T1rho measurements are selectively sensitive to proteoglycan content. The potential of this method in distinguishing the early degenerative changes in cartilage associated with osteoarthritis is discussed.

A folded cortical source of neuromagnetic fields, similar in configuration to the visual cortex, was simulated. Cortical activity was modelled by different distributions of independent current dipoles. The map of the summed fields of the dipoles of this cruciform model changed, depending upon the statistical distribution of the electrical activity of the dipoles and its geometry. Arrays of dipoles of random orientations and strengths produced field patterns that could be interpreted as due to moving neural currents, although the geometry of the neural tissue remained unchanged and the average activity remained approximately constant. The field topography at any instant was apparently unrelated to the depth or orientation of the underlying structure, thus raising questions about how to interpret topographic MEG and EEG displays. Furthermore, asynchronous activity (defined as independent directions and magnitudes of activity of the dipoles) did not result in less field power than when the dipoles were synchronized, i.e., when the direction of current flow was correlated across all of the dipoles within the cruciform structure. Therefore, in this model 'alpha blockage' cannot be mimicked by desynchronization. More generally, for the cruciform or any other symmetrically folded and active cortical sheet, 'blockage' cannot be attributed to desynchronization. The same is true for the EEG except that smooth unfolded sheets of radially oriented dipoles would result in enhancement of voltage due to synchronization. Such radial dipoles do not contribute to the MEG. Blockage was simulated by reducing the amount of activity within different portions of the synchronized cruciform model. This resulted in a dramatic increase in the net field because attenuation broke the symmetry of the synchronized cruciform structure. With asynchronous dipoles populating the structure, the attenuation of the same portion of the structure had no easily discerned effect on the net field. However, maps of average field power were consistently related to the position of the region of attenuated activity. The locations of regions of attenuated activity were determined by taking the difference between the mean square field pattern obtained when all portions of the cruciform structure were active and the pattern obtained when a portion of the structure was relatively inactive. When activity of the same portions were incremented rather than attenuated, the resulting plot of average power was essentially the same as that of the attenuated portion derived by taking these differences between power distributions. The major conclusions are that the concepts of synchronization and desynchronization have no explanatory power unless the physical conditions under which they occur are specified precisely.(ABSTRACT TRUNCATED AT 400 WORDS)

A patient whose nephrotic syndrome was controlled by steroids and cyclophosphamide was found to have gastric cancer. After removal of the tumor and discontinuing the steroid therapy, no recurrence of the clinical signs of nephrotic syndrome appeared, but moderate proteinuria persisted. The literature regarding the association of nephrotic syndrome with malignancies is reviewed.

A 25-year-old woman was initially seen with hilar adenopathy and bilateral cavitary lesions. No cause for these roentgenographic abnormalities other than sarcoidosis could be determined. In contrast to what is usually found in acute sarcoidosis, the bronchoalveolar lavage fluid from this patient contained a normal number of lymphocytes and an increased number of multinucleated giant cells. These findings raise the possibility that patients with acute sarcoidosis and cavitation have an atypical form of this disease. Even though cavitation in acute sarcoidosis is rare, this disorder must be considered in making the differential diagnosis of cavitary lung disease.

One hundred twenty-two patients with disseminated malignant melanoma were randomized using an unbalanced randomization to receive chlorozotocin, neocarzinostatin, or MeCCNU. Of the 114 evaluable patients, 46 received chlorozotocin, 47 received neocarzinostatin, and 21 received MeCCNU. The response rates to these three drugs were 9%, 4%, and 14% respectively. Median survival times were 4.2, 3.4, and 5.8 months respectively. Toxicity was acceptable with all three agents. Chlorozotocin and neocarzinostatin do not appear to offer any improved response rates over MeCCNU for patients with disseminated malignant melanoma.

Photoradiation, with the use of hematoporphyrin derivative (Hpd) activated by visible light in the red region of the spectrum, was an effective treatment for controlling local and regional chest wall recurrences of breast carcinoma. With sufficient time between iv injection of the drug and local activation with red light, cutaneous and subcutaneous masses were treated effectively without undue damage to overlying and adjacent skin. This high therapeutic ratio resulted from the ability to Hpd to accumulate and/or to be retained to a higher degree in malignant tissue than in many normal tissues. This technique can be used as a primary treatment or upon tumor recurrence following conventional modalities such as surgery, chemotherapy, and radiation therapy.

The effects of ventromedial hypothalamic (VMH) lesions on plasma corticosterone, insulin, and glucose levels were studied in food-restricted and ad lib fed female rats. VMH lesions resulted in significant elevations of corticosterone and insulin levels compared to control values during the first 25 days after surgery. However, unlike insulin values which were generally greater in VMH rats fed ad lib than in food-restricted animals, plasma corticosterone levels were unaffected by level of food intake. Corticosterone and insulin levels were unrelated preoperatively and in sham-operated animals, but were positively correlated (r = .82) by Day 25 in ad lib fed VMH rats. It is concluded that the elevation in plasma glucocorticoids observed in VMH rats is a primary effect of the lesion that is independent of food intake or initial weight gain.

The effect of initial presenting symptoms, degree of tumor differentiation and type of surgical procedure on survival was evaluated in a 20 year review of 42 patients with chondrosarcoma. Therapy of patients presenting initially with a painless mass resulted in a median survival period longer than 13 years versus less than three years of the initial symptom was pain. Median survival time of patients with well differentiated chondrosarcomas was more than ten years compared with 1.2 years if the tumor was less well differentiated. Even if the sarcoma was nonresectable, these factors influenced the survival time. Since chondrosarcomas tended to recur locally rather than to metastasize, a number of patients who had a recurrence after the initial surgical excision could be saved by a more radical secondary operation.