Role of coronal mass ejections in the heliospheric Hale cycle

[1] The 11-year solar cycle variation in the heliospheric magnetic field strength can be explained by the temporary buildup of closed flux released by coronal mass ejections (CMEs). If this explanation is correct, and the total open magnetic flux is conserved, then the interplanetary-CME closed flux must eventually open via reconnection with open flux close to the Sun. In this case each CME will move the reconnected open flux by at least the CME footpoint separation distance. Since the polarity of CME footpoints tends to follow a pattern similar to the Hale cycle of sunspot polarity, repeated CME eruption and subsequent reconnection will naturally result in latitudinal transport of open solar flux. We demonstrate how this process can reverse the coronal and heliospheric fields, and we calculate that the amount of flux involved is sufficient to accomplish the reversal within the 11 years of the solar cycle

Spacelab system analysis: A study of the Marshall Avionics System Testbed (MAST)

An analysis of the Marshall Avionics Systems Testbed (MAST) communications requirements is presented. The average offered load for typical nodes is estimated. Suitable local area networks are determined

Spacelab system analysis: A study of communications systems for advanced launch systems

An analysis of the required performance of internal avionics data bases for future launch vehicles is presented. Suitable local area networks that can service these requirements are determined

Anatomical knowledge retention in physiotherapy students: A preliminary assessment

Introduction: Anatomical knowledge and understanding are key components of physiotherapy education and practice. Traditionally, anatomy has been taught as a foundation stream within the first year(s) of physiotherapy education. This curricular model is based on the assumption that further learning in subsequent years builds upon the knowledge gained in the early stages of the program. However, the retention rate in all basic sciences has often been called into question. In anatomy, several studies suggest that anatomy knowledge endures considerable attrition, highlighting the need for the evaluation of retention rates. This paper aimed at making a preliminary assessment of the knowledge and retention of anatomy among physiotherapy students. Materials and Methods: We used a carpal bone identification test and assessed 129 first year and 113 fourth year physiotherapy students. Results: 20% of the students managed to identify all bones while 47% were able to identify more than five bones. The best recognised bones were pisiform and scaphoid while the most difficult to identify were trapezium and trapezoid. Conclusion: Overall, first year students performed better than their fourth year counterparts which suggested attrition of anatomical knowledge. Educational strategies based on revision, integration and clinical application of anatomy could contribute towards the decrease of attrition of anatomical knowledge

Evidence for Innate and Adaptive Immune Responses in a Cohort of Intractable Pediatric Epilepsy Surgery Patients.

Brain-infiltrating lymphocytes (BILs) were isolated from resected brain tissue from 10 pediatric epilepsy patients who had undergone surgery for Hemimegalencephaly (HME) (n = 1), Tuberous sclerosis complex (TSC) (n = 2), Focal cortical dysplasia (FCD) (n = 4), and Rasmussen encephalitis (RE) (n = 3). Peripheral blood mononuclear cells (PBMCs) were also isolated from blood collected at the time of the surgery. Cells were immunostained with a panel of 20 antibody markers, and analyzed by mass cytometry. To identify and quantify the immune cell types in the samples, an unbiased clustering method was applied to the entire data set. More than 85 percent of the CD45+ cells isolated from resected RE brain tissue comprised T cells; by contrast NK cells and myeloid cells constituted 80-95 percent of the CD45+ cells isolated from the TSC and the FCD brain specimens. Three populations of myeloid cells made up >50 percent of all of the myeloid cells in all of the samples of which a population of HLA-DR+ CD11b+ CD4- cells comprised the vast majority of myeloid cells in the BIL fractions from the FCD and TSC cases. CD45RA+ HLA-DR- CD11b+ CD16+ NK cells constituted the major population of NK cells in the blood from all of the cases. This subset also comprised the majority of NK cells in BILs from the resected RE and HME brain tissue, whereas NK cells defined as CD45RA- HLA-DR+ CD11b- CD16- cells comprised 86-96 percent of the NK cells isolated from the FCD and TSC brain tissue. Thirteen different subsets of CD4 and CD8 αβ T cells and γδ T cells accounted for over 80% of the CD3+ T cells in all of the BIL and PBMC samples. At least 90 percent of the T cells in the RE BILs, 80 percent of the T cells in the HME BILs and 40-66 percent in the TSC and FCD BILs comprised activated antigen-experienced (CD45RO+ HLA-DR+ CD69+) T cells. We conclude that even in cases where there is no evidence for an infection or an immune disorder, activated peripheral immune cells may be present in epileptogenic areas of the brain, possibly in response to seizure-driven brain inflammation

Phenotype X Herbage Allowance Interactions in Reproduction of First Calf Heifers Grazing Semiarid Rangeland

Cattle are differentially adapted to nutritional environments. The most sensitive measure of adaptation is reproduction of first-calf heifers. We studied the role of maturation rate and milk production on reproductive performance of first-calf heifers allowed different levels of herbage in semiarid rangeland

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl