Brass Blast with special guest Georgia Brass Band

Kennesaw State University School of Music presents Brass Blast with special guest, Georgia Brass Band.https://digitalcommons.kennesaw.edu/musicprograms/1375/thumbnail.jp

Performance of the Christchurch, New Zealand Cathedral during the M7.1 2010 Canterbury earthquake

The Catholic Cathedral is classified as a category 1 listed heritage building constructed largely of unreinforced stone masonry, and was significantly damaged in the recent Canterbury earthquakes of 2010 and 2011. In the 2010 event the building presented slight to moderta damage, meanwhile in the 2011 one experienced ground shaking in excess of its capacity leading to block failures and partial collapse of parts of the building, which left the building standing but still posing a significant hazard. In this paper we discuss the approach to develop the earthquake analysis of the building by 3D numerical simulations, and the results are compared/calibrated with the observed damage of the 2010 earthquake. Very accurate records were obtained during both earthquakes due to a record station located least than 80 m of distance from the building and used in the simulations. Moreover it is included in the model the soil structure interaction because it was observed that the ground and foundation played an important role on the seismic behavior of the structure. A very good agreement was found between the real observed damage and the nonlinear dynamic simulations described trough inelastic deformation (cracking) and building´s performance.University of AucklandPolytechnical University of Guadalajar

Impact of cytological sampling on EGFR mutation testing in stage III-IV lung adenocarcinoma

Objectives. There have been advances in the identification and understanding of molecular subsets of lung cancer, defined by specific oncogenic aberrations. A number of actionable genetic alterations have been identified, such as the epidermal growth factor receptor (EGFR) mutation. We aimed to establish the reasons why patients were not undergoing EGFR mutation testing at the time of histological diagnosis. Methods. The records of 70 patients with advanced adenocarcinoma of the lung managed through a single multidisciplinary team at a single institution were reviewed. Data were collected on method of tumour sample collection, whether this was sent for EGFR testing, and the result. Results. Seventy patients were identified. In 21/25 (84%) cases, cytological sampling was sufficient for EGFR mutation analysis, compared with 40/45 (89%) cases with histological sampling. EGFR mutation testing was not carried out in 22/70 (31.4%) patients. There was insufficient tumour sample for EGFR testing in 9/22 (40.9%) patients. Other reasons for not testing included poor patient fitness and problems in the diagnostic pathway. Conclusions. In this series, cytological tumour sampling was not the predominant reason why cancers failed to have EGFR mutation status established

Computational Analyses in Support of Sub-scale Diffuser Testing for the A-3 Facility

A unique assessment of acoustic similarity scaling laws and acoustic analogy methodologies in predicting the far-field acoustic signature from a sub-scale altitude rocket test facility at the NASA Stennis Space Center was performed. A directional, point-source similarity analysis was implemented for predicting the acoustic far-field. In this approach, experimental acoustic data obtained from "similar" rocket engine tests were appropriately scaled using key geometric and dynamic parameters. The accuracy of this engineering-level method is discussed by comparing the predictions with acoustic far-field measurements obtained. In addition, a CFD solver was coupled with a Lilley's acoustic analogy formulation to determine the improvement of using a physics-based methodology over an experimental correlation approach. In the current work, steady-state Reynolds-averaged Navier-Stokes calculations were used to model the internal flow of the rocket engine and altitude diffuser. These internal flow simulations provided the necessary realistic input conditions for external plume simulations. The CFD plume simulations were then used to provide the spatial turbulent noise source distributions in the acoustic analogy calculations. Preliminary findings of these studies will be discussed

Exploring the reactivity of 2-trichloromethylbenzoxazoles for access to substituted benzoxazoles

The reactivity of 2-trichloromethylbenzoxazoles towards various nucleophiles, under metal free or iron-catalyzed conditions, for the synthesis of substituted benzoxazoles is described. These methods allow for selective substitution at either the 2- or 2’- position of the benzoxazoles using the same starting materials / reagents. This approach allows for the controlled synthesis of a variety of key derivatives from a single 2-trichloromethylbenzoxazole starting material

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes