Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease

Genetic variation in cis-regulatory elements related to splicing machinery and splicing regulatory elements (SREs) results in exon skipping and undesired protein products. We developed a splicing decision model to identify actionable loci among common SNPs for gene regulation. The splicing decision model identified SNPs affecting exon skipping by analyzing sequence-driven alternative splicing (AS) models and by scanning the genome for the regions with putative SRE motifs. We used non-Hispanic Caucasians with neuroimaging, and fluid biomarkers for Alzheimer's disease (AD) and identified 17,088 common exonic SNPs affecting exon skipping. GWAS identified one SNP (rs1140317) in HLA-DQB1 as significantly associated with entorhinal cortical thickness, AD neuroimaging biomarker, after controlling for multiple testing. Further analysis revealed that rs1140317 was significantly associated with brain amyloid-f deposition (PET and CSF). HLA-DQB1 is an essential immune gene and may regulate AS, thereby contributing to AD pathology. SRE may hold potential as novel therapeutic targets for AD

Epidemiologic Responses to Anthrax Outbreaks: A Review of Field Investigations, 1950–2001

We used unpublished reports, published manuscripts, and communication with investigators to identify and summarize 49 anthrax-related epidemiologic field investigations conducted by the Centers for Disease Control and Prevention from 1950 to August 2001. Of 41 investigations in which Bacillus anthracis caused human or animal disease, 24 were in agricultural settings, 11 in textile mills, and 6 in other settings. Among the other investigations, two focused on building decontamination, one was a response to bioterrorism threats, and five involved other causes. Knowledge gained in these investigations helped guide the public health response to the October 2001 intentional release of B. anthracis, especially by addressing the management of anthrax threats, prevention of occupational anthrax, use of antibiotic prophylaxis in exposed persons, use of vaccination, spread of B. anthracis spores in aerosols, clinical diagnostic and laboratory confirmation methods, techniques for environmental sampling of exposed surfaces, and methods for decontaminating buildings

Does Gender Discrimination Impact Regular Mammography Screening? Findings from the Race Differences in Screening Mammography Study

Objective: To determine if gender discrimination, conceptualized as a negative life stressor, is a deterrent to adherence to mammography screening guidelines. Methods: African American and white women (1451) aged 40–79 years who obtained an index screening mammogram at one of five urban hospitals in Connecticut between October 1996 and January 1998 were enrolled in this study. This logistic regression analysis includes the 1229 women who completed telephone interviews at baseline and follow-up (average 29.4 months later) and for whom the study outcome, nonadherence to age-specific mammography screening guidelines, was determined. Gender discrimination was measured as lifetime experience in seven possible situations. Results: Gender discrimination, reported by nearly 38% of the study population, was significantly associated with non-adherence to mammography guidelines in women with annual family incomes of $50,000 or greater (or 1.99, 95% CI 1.33, 2.98) and did not differ across racial/ethnic groups. Conclusions: Our findings suggest that gender discrimination can adversely influence regular mammography screening in some women. With nearly half of women nonadherent to screening mammography guidelines in this study and with decreasing mammography rates nationwide, it is important to address the complexity of nonadherence across subgroups of women. Life stressors, such as experiences of gender discrimination, may have considerable consequences, potentially influencing health prevention prioritization in women

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype

Evaluation of reporting timeliness of public health surveillance systems for infectious diseases

BACKGROUND: Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. METHODS: Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. RESULTS: Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. CONCLUSION: Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted

Bioterrorism-Related Anthrax Surveillance, Connecticut, September–December, 2001

On November 19, 2001, a case of inhalational anthrax was identified in a 94-year-old Connecticut woman, who later died. We conducted intensive surveillance for additional anthrax cases, which included collecting data from hospitals, emergency departments, private practitioners, death certificates, postal facilities, veterinarians, and the state medical examiner. No additional cases of anthrax were identified. The absence of additional anthrax cases argued against an intentional environmental release of Bacillus anthracis in Connecticut and suggested that, if the source of anthrax had been cross-contaminated mail, the risk for anthrax in this setting was very low. This surveillance system provides a model that can be adapted for use in similar emergency settings

A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.Comment: 49 pages, 6 figures, 12 table

Environmental Sampling for Spores of Bacillus anthracis

On November 11, 2001, following the bioterrorism-related anthrax attacks, the U.S. Postal Service collected samples at the Southern Connecticut Processing and Distribution Center; all samples were negative for Bacillus anthracis. After a patient in Connecticut died from inhalational anthrax on November 19, the center was sampled again on November 21 and 25 by using dry and wet swabs. All samples were again negative for B. anthracis. On November 28, guided by information from epidemiologic investigation, we sampled the site extensively with wet wipes and surface vacuum sock samples (using HEPA vacuum). Of 212 samples, 6 (3%) were positive, including one from a highly contaminated sorter. Subsequently B. anthracis was also detected in mail-sorting bins used for the patient’s carrier route. These results suggest cross-contaminated mail as a possible source of anthrax for the inhalational anthrax patient in Connecticut. In future such investigations, extensive sampling guided by epidemiologic data is imperative