Computer recommendations for an automatic approach and landing system for V/STOL aircraft. Volume 2 - Equations

Automatic approach and landing system for V/STOL aircraf

Two-Loop Calculations with Vertex Corrections in the Walecka Model

Two-loop corrections with scalar and vector form factors are calculated for nuclear matter in the Walecka model. The on-shell form factors are derived from vertex corrections within the framework of the model and are highly damped at large spacelike momenta. The two-loop corrections are evaluated first by using the one-loop parameters and mean fields and then by refitting the total energy/baryon to empirical nuclear matter saturation properties. The modified two-loop corrections are significantly smaller than those computed with bare vertices. Contributions from the anomalous isoscalar form factor of the nucleon are included for the first time. The effects of the implicit density dependence of the form factors, which arise from the shift in the baryon mass, are also considered. Finally, necessary extensions of these calculations are discussed.Comment: 29 pages in REVTeX, 18 figures, preprint IU/NTC 94-02 //OSU--94-11

Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity.We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with 'healthy' data, and once with 'simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.Clinical Relevance - This method remotely establishes health-related data of objective outcome measures of activities of daily living

SOX9 transduction of a human chondrocytic cell line identifies novel genes regulated in primary human chondrocytes and in osteoarthritis

The transcription factor SOX9 is important in maintaining the chondrocyte phenotype. To identify novel genes regulated by SOX9 we investigated changes in gene expression by microarray analysis following retroviral transduction with SOX9 of a human chondrocytic cell line (SW1353). From the results the expression of a group of genes (SRPX, S100A1, APOD, RGC32, CRTL1, MYBPH, CRLF1 and SPINT1) was evaluated further in human articular chondrocytes (HACs). First, the same genes were investigated in primary cultures of HACs following SOX9 transduction, and four were found to be similarly regulated (SRPX, APOD, CRTL1 and S100A1). Second, during dedifferentiation of HACs by passage in monolayer cell culture, during which the expression of SOX9 progressively decreased, four of the genes (S100A1, RGC32, CRTL1 and SPINT1) also decreased in their expression. Third, in samples of osteoarthritic (OA) cartilage, which had decreased SOX9 expression compared with age-matched controls, there was decreased expression of SRPX, APOD, RGC32, CRTL1 and SPINT1. The results showed that a group of genes identified as being upregulated by SOX9 in the initial SW1353 screen were also regulated in expression in healthy and OA cartilage. Other genes initially identified were differently expressed in isolated OA chondrocytes and their expression was unrelated to changes in SOX9. The results thus identified some genes whose expression appeared to be linked to SOX9 expression in isolated chondrocytes and were also altered during cartilage degeneration in osteoarthritis