Preventing emerging infectious diseases : a strategy for the 21st century : overview of the updated CDC plan

Societal, technological, and environmental factors continue to have a dramatic effect on infectious diseases worldwide, facilitating the emergence of new diseases and the reemergence of old ones, sometimes in drug-resistant forms. Modern demographic and ecologic conditions that favor the spread of infectious diseases include rapid population growth; increasing poverty and urban migration; more frequent movement across international boundaries by tourists, workers, immigrants, and refugees; alterations in the habitats of animals and arthropods that transmit disease; increasing numbers of persons with impaired host defenses; and changes in the way that food is processed and distributed. Several recent health events underscore the need for a public health system ready to address whatever disease problems that might arise. For example, in 1997, an avian strain of influenza that had never before infected humans began to kill previously healthy persons in Hong Kong, and strains of Sta phylococcus aureus with diminished susceptibility to the antibiotic vancomycin were reported in Japan and the United States. In addition, researchers recently discovered that a strain of the virus that causes acquired immunodeficiency syndrome (AIDS) had been infecting humans for at least 20 years before AIDS emerged as a worldwide epidemic. Preventing Emerging Infectious Diseases: A Strategy for the 21st Century describes CDC's plan to combat today's infectious diseases and prevent those of tomorrow. It represents the second phase of the effort launched in 1994 with the publication of CDC's Addressing Emerging Infectious Disease Threats: A Prevention Strategy for the United States. This overview of the updated plan outlines specific objectives under four major goals: a) surveillance and response, b) applied research, c) infrastructure and training, and d) prevention and control. Achieving these objectives will enhance understanding of infectious diseases and bolster their detection, control, and prevention. The plan also targets nine categories of problems that cause human suffering and place a burden on society. The aim of this plan is to build a stronger, more flexible U.S. public health system that is well-prepared to respond to known disease problems, as well as to address the unexpected, whether it be an influenza pandemic, a disease caused by an unknown organism, or a bioterrorist attack. The implementation of this plan will require the dedicated efforts of many partners, including state and local health departments, other federal agencies, professional societies, universities, research institutes, health-care providers and organizations, the World Health Organization, and many other domestic and international organizations and groups.September 11, 1998.The following CDC staff members prepared this report: Suzanne Binder, Alexandra M. Levitt, National Center for Infectious Diseases, and National Center for Infectious Diseases Plan Steering Committee.Includes bibliographical references.199

Characterizing the comfort limits of forces applied to the shoulders, thigh and shank to inform exosuit design.

Recent literature emphasizes the importance of comfort in the design of exosuits and other assistive devices that physically augment humans; however, there is little quantitative data to aid designers in determining what level of force makes users uncomfortable. To help close this knowledge gap, we characterized human comfort limits when applying forces to the shoulders, thigh and shank. Our objectives were: (i) characterize the comfort limits for multiple healthy participants, (ii) characterize comfort limits across days, and (iii) determine if comfort limits change when forces are applied at higher vs. lower rates. We performed an experiment (N = 10) to quantify maximum tolerable force pulling down on the shoulders, and axially along the thigh and shank; we termed this force the comfort limit. We applied a series of forces of increasing magnitude, using a robotic actuator, to soft sleeves around their thigh and shank, and to a harness on their shoulders. Participants were instructed to press an off-switch, immediately removing the force, when they felt uncomfortable such that they did not want to feel a higher level of force. On average, participants exhibited comfort limits of ~0.9-1.3 times body weight on each segment: 621±245 N (shoulders), 867±296 N (thigh), 702±220 N (shank), which were above force levels applied by exosuits in prior literature. However, individual participant comfort limits varied greatly (~250-1200 N). Average comfort limits increased over multiple days (p<3e-5), as users habituated, from ~550-700 N on the first day to ~650-950 N on the fourth. Specifically, comfort limits increased 20%, 35% and 22% for the shoulders, thigh and shank, respectively. Finally, participants generally tolerated higher force when it was applied more rapidly. These results provide initial benchmarks for exosuit designers and end-users, and pave the way for exploring comfort limits over larger time scales, within larger samples and in different populations

Combined expression trait correlations and expression quantitative trait locus mapping.

Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein-coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone

Regulation of <i>Riken32G18</i> Inversely Parallels the Regulation of Lipogenic Genes

<div><p>(A) Metabolic regulation of <i>Riken32G18</i>. Expression of lipogenic genes and <i>Riken32G18</i> in adipose tissues of lean versus obese mice.</p><p>(B) Expression of lipogenic genes and <i>Riken32G18</i> in livers of lean versus obese mice.</p><p>(C) Expression of LXRα target genes and <i>Riken32G18</i> in mouse livers treated with LXRα agonist T0901317.</p></div

Expression QTL in the (B6 × BTBR) F₂-<i>ob/ob</i> Cross

<div><p>(A) Expression QTL that regulate gene expression in the (B6 × BTBR) F₂-<i>ob/ob</i> cross. The physical locations of the transcripts are organized on the y-axis. The chromosome regions (x-axis) to which those transcripts are mapped were obtained using the <i>scanone</i> function in the R/qtl package with 5-cM intervals. The gray scale reflects the strength of the linkage signals (LOD scores greater than 8 are scaled to 8). Transcripts appearing on the diagonal are inferred to be <i>cis</i>-regulated. The chromosomes were concatenated to form a 2,300-Mb genome, starting from Chromosome 1 and ending with Chromosome 19.</p><p>(B) Global display of mapping patterns of linkage clusters. The y-axis shows the 6,016 mapping transcripts. The x-axis shows the physical locations to which these transcripts map. The transcripts are grouped based on the hierarchical clustering of linkage mapping patterns across the genome. Darker areas indicate the regions to which traits are comapped or coregulated. The boxes correspond to the hot spots on Chromosomes 2, 10, and 13. The gray scale reflects the strength of the linkage signals (LOD scores greater than 8 are scaled to 8).</p></div

Coordinate Regulation of Genes for GPCR Protein Signaling Pathways

<div><p>(A) GPCR protein signaling pathway genes show coordinated changes across the 60 F₂ mice. The rescaled expression levels of 174 distinct transcripts (y-axis) that belong to the GPCR pathway. The x-axis displays the 60 F₂ mice. The plot shows that the subset of GPCR traits identified by our trait correlation-GO analysis has vertical patterns that indicate coordinate expression across the 60 F₂ mice (<i>P</i> = 1e10⁻⁴ by permutation test).</p><p>(B) Histogram of pairwise correlation coefficients of GPCR traits identified with different seeds.</p><p>(C) Scaled expression levels of traits from one realization of the permuted lists. Thirty-eight lists of correlated traits were randomly sampled from a total of 1,341 correlated trait lists. The list sizes were adjusted to match those of the GPCR lists. The expression values were scaled to have mean intensity = 0 and variance = 1, as in the GPCR list in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020006#pgen-0020006-g002" target="_blank">Figure 2</a>A.</p></div

An Expression Quantitative Trait Locus on Chromosome 2 Regulates Many GPCR Protein Pathway Genes

<div><p>(A) Comapping of GPCR pathway traits to loci on Chromosome 2. The figure shows the peaks on Chromosome 2 versus LOD score for 174 GPCR protein signaling pathway traits. The closed circles correspond to 38 seed traits.</p><p>(B) Comapping of GPCR pathway traits. The figure shows the magnitude (y-axis) versus location of LOD peaks (x-axis is scaled by cM) for 174 GPCR protein signaling pathway traits. The closed circles correspond to the 38 seed traits. The interval mapping LOD profiles for all 38 seeds are included in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020006#pgen-0020006-sg002" target="_blank">Figure S2</a>.</p></div

Lipid Metabolism Pathway Genes Show Coordinated Expression across the F₂ Mice

<p>The rescaled expression levels of 184 distinct transcripts (y-axis) that belong to the lipid metabolism pathway are shown for each of the 60 F₂ mice (x-axis). Plot shows that the subset of lipid metabolism traits identified by our trait correlation-GO analysis has preserved vertical patterns (<i>P</i> = 4e10⁻⁴ by permutation test).</p

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review