Cerebral Complications Following Ligation of the Carotid Artery.

For a considerable period of time,ligation of the carotid artery has been used as a therapeutic measure for a variety of serious cranio-cerebral conditions5. Originally it was used as an emergency measure in an effort to control severe hemorrage following lacerations of the largr vessels of the neck and head. During the middle years of the nineteenth century it was attempted for the purpose of relieving cerebral disorders, which were then incurable. However,the operation was discarded because the mortality was high and the permanent symptoms of recovered patients serious5. In the past thirty years,with the increasing safety of surgical measures,the procedure has been reintroduced. The seriousness of carotid ligation was stressed by Pilcher and Thuss,5 who concluded that in from twenty to twenty to twenty-five percent of patients who survive the operation cerebral complications develop. On the basis of controversies on this problem and the interest manifested in the writer an interest was stimulated to re-investigate the effects of carotid ligation on brain tissue

Alien Registration- Lockhart, H W E. (Fort Fairfield, Aroostook County)

https://digitalmaine.com/alien_docs/36500/thumbnail.jp

Calclium-calmodulin regulation of TRPM2 currents

TRPM2 (1507 amino acids), a non-selective cation channel with substantial permeability for Ca2+, is responsive to oxidative stress, and is a mediator of cell death in several cell types. Ca2+-calmodulin has been shown to promote channel activation and inactivation, however the mechanisms are not fully understood. Identifying candidate CaM binding sites using in silico screening, I hypothesized that Ca2+-dependent inactivation (CDI) of TRPM2 is mediated by an intracellular CaM binding domain unique from that of activation (406-415AA). I systematically determined the minimum binding domains for three CaM candidate sites on TRPM2’s intracellular domains using truncated fragments and subsequent CaM-Sepharose pull-downs. TRPM2 with substitution mutations to candidate sites were transfected into HEK293 cells; currents were recorded using 2mM or 0.5mM Ca2+ extracellular fluid and adenosine diphosphate ribose (ADPR) in the patch pipette. Abolished and reduced currents respectively were observed as a result of amino acid substitution to CaM binding regions at 172-187AA and 1087-1101AA of TRPM2. The two identified CaM candidate sites may establish a potential molecular link to CDI of TRPM2

Difficulties in Testing for Covarion-Like Properties of Sequences under the Confounding Influence of Changing Proportions of Variable Sites

The covarion (COV)-like properties of sequences are poorly described and their impact on phylogenetic analyses poorly understood. We demonstrate using simulations that, under an evolutionary model where the proportion of variable sites changes in nonadjacent lineages, log likelihood values for rates across site (RAS) and COV models become similar, making models difficult to distinguish. Further, although COV and RAS models provide a great improvement in likelihood scores over a homogeneous model with these simulated data, reconstruction accuracy of tree building is low, suggesting caution when it is suspected that proportions of variable sites differ in different evolutionary lineages. We study the performance of a recently developed contingency test that detects the presence of COV-type evolution modified for protein data. We report that if proportions of variable sites (pvar) change in a lineage-specific manner such that their distributions in different lineages become sufficiently nonoverlapping, then the contingency test can incorrectly suggest a homogeneous model. Also of concern is the possibility of different proportions of variable sites between the groups being studied. In a study of chloroplast proteins, interpretation of the test is found to be susceptible to different partitioning of taxon groups, making the test very subjective in its implementation. Extreme intergroup differences in the extent of divergence and difference in proportions of variable sites could be contributing to this effect

Spectral and Spin Measurement of Two Small and Fast-Rotating Near-Earth Asteroids

In May 2012 two asteroids made near-miss "grazing" passes at distances of a few Earth-radii: 2012 KP24 passed at nine Earth-radii and 2012 KT42 at only three Earth-radii. The latter passed inside the orbital distance of geosynchronous satellites. From spectral and imaging measurements using NASA's 3-m Infrared Telescope Facility (IRTF), we deduce taxonomic, rotational, and physical properties. Their spectral characteristics are somewhat atypical among near-Earth asteroids: C-complex for 2012 KP24 and B-type for 2012 KT42, from which we interpret the albedos of both asteroids to be between 0.10 and 0.15 and effective diameters of 20+-2 and 6+-1 meters, respectively. Among B-type asteroids, the spectrum of 2012 KT42 is most similar to 3200 Phaethon and 4015 Wilson-Harrington. Not only are these among the smallest asteroids spectrally measured, we also find they are among the fastest-spinning: 2012 KP24 completes a rotation in 2.5008+-0.0006 minutes and 2012 KT42 rotates in 3.634+-0.001 minutes.Comment: 4 pages, 3 figures, accepted for publication in Icaru

Feasibility study of the solar scientific instruments for Spacelab/Orbiter

The feasibility and economics of mounting and operating a set of solar scientific instruments in the backup Skylab Apollo Telescope Mount (ATM) hardware was evaluated. The instruments used as the study test payload and integrated into the ATM were: the Solar EUV Telescope/Spectrometer; the Solar Active Region Observing Telescope; and the Lyman Alpha White Light Coronagraph. The backup ATM hardware consists of a central cruciform structure, called the "SPAR', a "Sun End Canister' and a "Multiple Docking Adapter End Canister'. Basically, the ATM hardware and software provides a structural interface for the instruments; a closely controlled thermal environment; and a very accurate attitude and pointing control capability. The hardware is an identical set to the hardware that flow on Skylab

Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.Comment: 25 pages, 7 figures, accepted for publication in Frontiers in Astronomy and Space Sciences for the Research Topic of Machine Learning in Heliophysics at https://www.frontiersin.org/articles/10.3389/fspas.2020.0003