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Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling
Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.
Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders
Introduction This study aimed to investigate the psychometric properties of the Affective Lability Scale-short form (ALS-SF) among Chinese patients with mood disorders, and to compare ALS-SF subscale scores between patients with major depressive disorder (MDD) and patients with bipolar disorder (BD) depression. Methods A total of 344 patients with mood disorders were included in our study. Participants were measured through a set of questionnaires including the Chinese version of ALS-SF, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and NEO-Five Factor Inventory (NEO-FFI). Exploratory factor analysis and confirmatory factor analysis were applied to examine the psychometric properties of ALS-SF. Besides, correlation and regression analyses were performed to explore the relationship between affective lability and depression, anxiety, and neuroticism. Independent samples t -tests were used to compare the subscale scores of ALS-SF between the MDD and BD depression groups. Results Results of factor analysis indicated that the model of ALS-SF was consistent with ALS-SF. The ALS-SF showed a solid validity and high internal consistency (Cronbach’s alpha = 0.861). In addition, each subscale of ALS-SF was significantly correlated with PHQ-9, GAD-7, and NEO-FFI neuroticism subscale, except for the anger subscale showed no significant correlation with PHQ-9. Besides, the depression/elation and anger factor scores in patients with BD depression were higher than in patients with MDD. Conclusion Our study suggests that the Chinese version of ALS-SF has good reliability and validity for measuring affective lability in Chinese patients with mood disorders. Assessing affective lability would assist clinicians to distinguish between MDD and BP depression and may decrease the risks of misdiagnosis.
Design and Simulation of a MEMS Structure for Electrophoretic and Dielectrophoretic Separation of Particles by Contactless Electrodes
Rapid identification of pathogenic bacterial species is an important factor in combating public health problems such as E. coli contamination. Food and waterborne pathogens account for sickness in 76 million people annually (CDC). Diarrheagenic E. coli is a major source of gastrointestinal illness. Severe sepsis and Septicemia within the hospital environment are also major problems. 75 1,000 cases annually with a 30-50% mortality rate (Crit Care Med, July '01, Vol. 29, 1303-10). Patient risks run the continuum from fever to organ failure and death. Misdiagnosis or inappropriate treatment increases mortality. There exists a need for rapid screening of samples for identification of pathogenic species (Certain E. coli strains are essential for health). Critical to the identification process is the ability to isolate analytes of interest rapidly. This poster discusses novel devices for the separation of particles on the basis of the dielectric properties, mass and surface charge characteristics is presented. Existing designs involve contact between electrode surfaces and analyte medium resulting in contamination of the electrode bearing elements Two different device designs using different bulk micromachining MEMS processes (PolyMUMPS and a PyrexBIGold electrode design) are presented. These designs cover a range of particle sizes from small molecules through eucaryotic cells. The application of separation of bacteria is discussed in detail. Simulation data for electrostatic and microfluidic characteristics are provided. Detailed design characteristics and physical features of the as fabricated PolyMUMPS design are provided. Analysis of the simulation data relative to the expected performance of the devices will be provided and subsequent conclusions discussed.
Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme
A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred
Symptomatic Plasmodium vivax Infection in Rwanda
Here, we report a Plasmodium vivax infection in a Rwandan child misdiagnosed with Plasmodium falciparum and administered artemether-lumefantrine. Antigen detection revealed an absence of P falciparum histidine-rich protein 2 (HRP2) and presence of Plasmodium vivax lactate dehydrogenase. Nested and real-time polymerase chain reactions verified that the sample only contained P vivax deoxyribonucleic acid.