Chiropteran types I and II interferon genes inferred from genome sequencing traces by a statistical gene-family assembler.

BACKGROUND: The rate of emergence of human pathogens is steadily increasing; most of these novel agents originate in wildlife. Bats, remarkably, are the natural reservoirs of many of the most pathogenic viruses in humans. There are two bat genome projects currently underway, a circumstance that promises to speed the discovery host factors important in the coevolution of bats with their viruses. These genomes, however, are not yet assembled and one of them will provide only low coverage, making the inference of most genes of immunological interest error-prone. Many more wildlife genome projects are underway and intend to provide only shallow coverage. RESULTS: We have developed a statistical method for the assembly of gene families from partial genomes. The method takes full advantage of the quality scores generated by base-calling software, incorporating them into a complete probabilistic error model, to overcome the limitation inherent in the inference of gene family members from partial sequence information. We validated the method by inferring the human IFNA genes from the genome trace archives, and used it to infer 61 type-I interferon genes, and single type-II interferon genes in the bats Pteropus vampyrus and Myotis lucifugus. We confirmed our inferences by direct cloning and sequencing of IFNA, IFNB, IFND, and IFNK in P. vampyrus, and by demonstrating transcription of some of the inferred genes by known interferon-inducing stimuli. CONCLUSION: The statistical trace assembler described here provides a reliable method for extracting information from the many available and forthcoming partial or shallow genome sequencing projects, thereby facilitating the study of a wider variety of organisms with ecological and biomedical significance to humans than would otherwise be possible.

Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.

In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

Credit scores, cardiovascular disease risk, and human capital.

Credit scores are the most widely used instruments to assess whether or not a person is a financial risk. Credit scoring has been so successful that it has expanded beyond lending and into our everyday lives, even to inform how insurers evaluate our health. The pervasive application of credit scoring has outpaced knowledge about why credit scores are such useful indicators of individual behavior. Here we test if the same factors that lead to poor credit scores also lead to poor health. Following the Dunedin (New Zealand) Longitudinal Study cohort of 1,037 study members, we examined the association between credit scores and cardiovascular disease risk and the underlying factors that account for this association. We find that credit scores are negatively correlated with cardiovascular disease risk. Variation in household income was not sufficient to account for this association. Rather, individual differences in human capital factors—educational attainment, cognitive ability, and self-control—predicted both credit scores and cardiovascular disease risk and accounted for ∼45% of the correlation between credit scores and cardiovascular disease risk. Tracing human capital factors back to their childhood antecedents revealed that the characteristic attitudes, behaviors, and competencies children develop in their first decade of life account for a significant portion (∼22%) of the link between credit scores and cardiovascular disease risk at midlife. We discuss the implications of these findings for policy debates about data privacy, financial literacy, and early childhood interventions.

Reliving, emotions, and fragmentation in the autobiographical memories of veterans diagnosed with PTSD

Fifty veterans diagnosed with posttraumatic stress disorder (PTSD) each recalled four autobiographical memories: one from the 2 years before service, one non-combat memory from the time in service, one from combat, and one from service that had often come as an intrusive memory. For each memory, they provided 21 ratings about reliving, belief, sensory properties, reexperiencing emotions, visceral emotional responses, fragmentation, and narrative coherence. We used these ratings to examine three claims about traumatic memories: a separation of cognitive and visceral aspects of emotion, an increased sense of reliving, and increased fragmentation. There was evidence for a partial separation of cognitive judgments of reexperiencing an emotion and reports of visceral symptoms of the emotion, with visceral symptoms correlating more consistently with scores on PTSD tests. Reliving, but not fragmentation of the memories, increased with increases in the trauma relatedness of the event and with increases in scores on standardized tests of PTSD severity. Copyright © 2004 John Wiley & Sons, Ltd.

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

BACKGROUND: Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. METHODS: We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. RESULTS: The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. CONCLUSIONS: The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets.

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging.

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T2(∗) -weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed.