The Importance of N-3 Fatty Acids in Health and Disease

The n-3 fatty acids have important effects to modulate and prevent human diseases, particularly coronary heart disease. Their effects upon the brain later in life to prevent certain disorders such as Alzheimer's Disease are unknown but are certainly worthy of study. Certainly the evidence is now very strong that the n-3 fatty acids are essential for human development in the fetus and infant and are likely to have a role throughout life. The antiarrythmic effect of n-3 fatty acids is a discovery that has great relevance to the prevention of sudden death from ventricular fibrillation in patients with coronary heart disease. Further clinical trials in this area are definitely indicated

Targeted deletion of Fgf9 in tendon disrupts mineralization of the developing enthesis

The enthesis is a transitional tissue between tendon and bone that matures postnatally. The development and maturation of the enthesis involve cellular processes likened to an arrested growth plate. In this study, we explored the role of fibroblast growth factor 9 (Fgf9), a known regulator of chondrogenesis and vascularization during bone development, on the structure and function of the postnatal enthesis. First, we confirmed spatial expression of Fgf9 in the tendon and enthesis using in situ hybridization. We then used Cre-lox recombinase to conditionally knockout Fgf9 in mouse tendon and enthesis (Scx-Cre) and characterized enthesis morphology as well as mechanical properties in Fgf

Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide.Comment: 27 pages, 12 figure

Impact of Temperature Relative Humidity and Absolute Humidity on the Incidence of Hospitalizations for Lower Respiratory Tract Infections Due to Influenza, Rhinovirus, and Respiratory Syncytial Virus: Results from Community-Acquired Pneumonia Organization (CAPO) International Cohort Study

Abstract Background: Transmissibility of several etiologies of lower respiratory tract infections (LRTI) may vary based on outdoor climate factors. The objective of this study was to evaluate the impact of outdoor temperature, relative humidity, and absolute humidity on the incidence of hospitalizations for lower respiratory tract infections due to influenza, rhinovirus, and respiratory syncytial virus (RSV). Methods: This was a secondary analysis of an ancillary study of the Community Acquired Pneumonia Organization (CAPO) database. Respiratory viruses were detected using the Luminex xTAG respiratory viral panel. Climate factors were obtained from the National Weather Service. Adjusted Poisson regression models with robust error variance were used to model the incidence of hospitalization with a LRTI due to: 1) influenza, 2) rhinovirus, and 3) RSV (A and/or B), separately. Results: A total of 467 hospitalized patients with LRTI were included in the study; 135 (29%) with influenza, 41 (9%) with rhinovirus, and 27 (6%) with RSV (20 RSV A, 7 RSV B). The average, minimum, and maximum absolute humidity and temperatur e variables were associated with hospitalization due to influenza LRTI, while the relative humidity variables were not. None of the climate variables were associated with hospitalization due to rhinovirus or RSV. Conclusions: This study suggests that outdoor absolute humidity and temperature are associated with hospitalizations due to influenza LRTIs, but not with LRTIs due to rhinovirus or RSV. Understanding factors contributing to the transmission of respiratory viruses may assist in the prediction of future outbreaks and facilitate the development of transmission prevention interventions

The City of Louisville Encapsulates the United States Demographics

Background: One weakness that applies to all population-based studies performed in the United States (US) is that investigators perform population-based extrapolations without providing objective statistical evidence to show how well a particular city is a suitable surrogate for the US. The objective of this study was to propose and utilize a novel computational metric to compare individual US cities with the US average. Methods: This was a secondary data analysis of publicly available databases containing US sociodemographic, economic, and health-related data. In total, 58 demographic, housing, economic, health behavior, and health status variables for each US city with a residential population of at least 500,000 were obtained. All variables were recorded as proportions. Euclidean, Manhattan, and average absolute difference metrics were used to compare the 58 variables to the average in the US. Results: Oklahoma City, OK, had the lowest distance from the United States, with Euclidean and Manhattan distances in proportion of 0.261 and 1.519, respectively. Louisville, Kentucky, had the second lowest distance for both Euclidean distance and Manhattan distance, with distances of 0.286 and 1.545, respectively. The average absolute differences in proportion for Oklahoma City and Louisville to the US average were 0.026 and 0.027, respectively. Conclusion: To our knowledge, this represents the first study evaluating a method for computing statistical comparisons of United States city sociodemographic, economic, and health-related data with the United States average. Our study shows that among cities with at least 500,000 residents, Oklahoma City is the closest to the United States, followed closely by Louisville. On average, these cities deviate from the US average on any variable studied by less than 3%

Relationship Between Foveal Cone Specialization and Pit Morphology in Albinism

Purpose.Albinism is associated with disrupted foveal development, though intersubject variability is becoming appreciated. We sought to quantify this variability, and examine the relationship between foveal cone specialization and pit morphology in patients with a clinical diagnosis of albinism. Methods. We recruited 32 subjects with a clinical diagnosis of albinism. DNA was obtained from 25 subjects, and known albinism genes were analyzed for mutations. Relative inner and outer segment (IS and OS) lengthening (fovea-to-perifovea ratio) was determined from manually segmented spectral domain-optical coherence tomography (SD-OCT) B-scans. Foveal pit morphology was quantified for eight subjects from macular SD-OCT volumes. Ten subjects underwent imaging with adaptive optics scanning light ophthalmoscopy (AOSLO), and cone density was measured. Results. We found mutations in 22 of 25 subjects, including five novel mutations. All subjects lacked complete excavation of inner retinal layers at the fovea, though four subjects had foveal pits with normal diameter and/or volume. Peak cone density and OS lengthening were variable and overlapped with that observed in normal controls. A fifth hyper-reflective band was observed in the outer retina on SD-OCT in the majority of the subjects with albinism. Conclusions. Foveal cone specialization and pit morphology vary greatly in albinism. Normal cone packing was observed in the absence of a foveal pit, suggesting a pit is not required for packing to occur. The degree to which retinal anatomy correlates with genotype or visual function remains unclear, and future examination of larger patient groups will provide important insight on this issue

Predicting 30-Day Mortality in Hospitalized Patients with Community-Acquired Pneumonia Using Statistical and Machine Learning Approaches

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP. Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine learning methods were used to develop patientlevel prediction models for hospitalized patients with CAP. For each model, nine different statistics were calculated to provide measures of the overall performance of the models. Results: A total of 3249 unique hospitalized patients with CAP were enrolled in the study, 2743 were included in the model building (training) dataset, while the remaining 686 were included in the testing dataset. From the full population, death at 30-days post discharge was documented in 458 (13.4%) patients. All models resulted in high variation in the ability to predict survivors and non-survivors at 30 days. Conclusions: In conclusion, this study suggests that accurate patient-level prediction of 30-day mortality in hospitalized patients with CAP is difficult with statistical and machine learning approaches. It will be important to evaluate novel variables and other modeling approaches to better predict poor clinical outcomes in these patients to ensure early and appropriate interventions are instituted

Short-Run Adjustment Opportunities for Oklahoma Panhandle Farmers

The Oklahoma Agricultural Experiment Station periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311

Elevated Depression Symptoms, Antidepressant Medicine Use, and Risk of Developing Diabetes During the Diabetes Prevention Program

OBJECTIVE—To assess the association between elevated depression symptoms or antidepressant medicine use on entry to the Diabetes Prevention Program (DPP) and during the study and the risk of developing diabetes during the study. RESEARCH DESIGN AND METHODS—DPP participants (n = 3,187) in three treatment arms (intensive lifestyle [ILS], metformin [MET], and placebo [PLB]) completed the Beck Depression Inventory (BDI) and reported their use of antidepressant medication at randomization and throughout the study (average duration in study 3.2 years). RESULTS—When other factors associated with the risk of developing diabetes were controlled, elevated BDI scores at baseline or during the study were not associated with diabetes risk in any arm. Baseline antidepressant use was associated with diabetes risk in the PLB (hazard ratio 2.25 [95% CI 1.38–3.66]) and ILS (3.48 [1.93–6.28]) arms. Continuous antidepressant use during the study (compared with no use) was also associated with diabetes risk in the same arms (PLB 2.60 [1.37–4.94]; ILS 3.39 [1.61–7.13]), as was intermittent antidepressant use during the study in the ILS arm (2.07 [1.18–3.62]). Among MET arm participants, antidepressant use was not associated with developing diabetes. CONCLUSIONS—A strong and statistically significant association between antidepressant use and diabetes risk in the PLB and ILS arms was not accounted for by measured confounders or mediators. If future research finds that antidepressant use independently predicts diabetes risk, efforts to minimize the negative effects of antidepressant agents on glycemic control should be pursued