FAA Capstone Program: Phase II Baseline Report (Southeast Alaska)

This report provides the Federal Aviation Administration (FAA) with information on air safety and aviation infrastructure in southeast Alaska as of December 31, 2002. The data will establish a baseline to enable the University of Alaska Anchorage (UAA) to conduct an independent evaluation of how the Capstone program affects aviation safety in the region. The FAA contracted with UAA’s Institute of Social and Economic Research and Aviation Technology Division to do a variety of training and evaluation tasks related to the Capstone program. The program is a joint effort of industry and the FAA to improve aviation safety and efficiency in select regions of Alaska, through government-furnished avionics equipment and improvements in ground infrastructure.Federal Aviation Administration (Alaskan Region

Making the Case for Accelerated Withdrawal of Aducanumab

The controversial approval in June 2021 by the Food and Drug Administration (FDA) of aducanumab (marketed as Aduhelm), Biogen's monoclonal antibody for patients with Alzheimer's disease, raises significant concerns for the dementia field and drug approval process, considering its lack of adequate evidence for clinical efficacy, safety issues, and cost. On 15 December 2021, an international group of clinicians, basic science experts, psychological and social science researchers, lay people with lived experience of dementia, and advocates for public health met to discuss making a recommendation for whether aducanumab's approval should be withdrawn. Attendees considered arguments both in favor of and in opposition to withdrawal and voted unanimously to recommend that the FDA withdraw its approval for aducanumab and to support the Right Care Alliance's filing of a formal Citizen Petition to this effect

Interrater reliability of motor severity scales for hemifacial spasm

To compare the inter-rater reliability (IRR) of five clinical rating scales for video-based assessment of hemifacial spasm (HFS) motor severity. We evaluated the video recordings of 45 HFS participants recruited through the Dystonia Coalition. In Round 1, six clinicians with expertise in HFS assessed the participants\u27 motor severity with five scales used to measure motor severity of HFS: the Jankovic rating scale (JRS), Hemifacial Spasm Grading Scale (HSGS), Samsung Medical Center (SMC) grading system for severity of HFS spasms (Lee\u27s scale), clinical grading of spasm intensity (Chen\u27s scale), and a modified version of the Abnormal Involuntary Movement Scale (Tunc\u27s scale). In Round 2, clinicians rated the same cohort with simplified scale wording after consensus training. For each round, we evaluated the IRR using the intraclass correlation coefficient [ICC (2,1) single-rater, absolute-agreement, 2-way random model]. The scales exhibited IRR that ranged from poor to moderate ; the mean ICCs were 0.41, 0.43, 0.47, 0.43, and 0.65 for the JRS, HSGS, Lee\u27s, Chen\u27s, and Tunc\u27s scales, respectively, for Round 1. In Round 2, the corresponding IRRs increased to 0.63, 0.60, 0.59, 0.53, and 0.71. In both rounds, Tunc\u27s scale exhibited the highest IRR. For clinical assessments of HFS motor severity based on video observations, we recommend using Tunc\u27s scale because of its comparative reliability and because clinicians interpret the scale easily without modifications or the need for consensus training

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors

Systematic identification of protein-drug interaction networks is crucial to correlate complex modes of drug action to clinical indications. We introduce a novel computational strategy to identify protein-ligand binding profiles on a genome-wide scale and apply it to elucidating the molecular mechanisms associated with the adverse drug effects of Cholesteryl Ester Transfer Protein (CETP) inhibitors. CETP inhibitors are a new class of preventive therapies for the treatment of cardiovascular disease. However, clinical studies indicated that one CETP inhibitor, Torcetrapib, has deadly off-target effects as a result of hypertension, and hence it has been withdrawn from phase III clinical trials. We have identified a panel of off-targets for Torcetrapib and other CETP inhibitors from the human structural genome and map those targets to biological pathways via the literature. The predicted protein-ligand network is consistent with experimental results from multiple sources and reveals that the side-effect of CETP inhibitors is modulated through the combinatorial control of multiple interconnected pathways. Given that combinatorial control is a common phenomenon observed in many biological processes, our findings suggest that adverse drug effects might be minimized by fine-tuning multiple off-target interactions using single or multiple therapies. This work extends the scope of chemogenomics approaches and exemplifies the role that systems biology has in the future of drug discovery