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  1. Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability. 
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  2. AQP7 is one of the four human aquaglyceroporins that facilitate glycerol transport across the cell membrane, a biophysical process that is essential in human physiology. Therefore, it is interesting to compute AQP7's affinity for its substrate (glycerol) with reasonable certainty to compare with the experimental data suggesting high affinity in contrast with most computational studies predicting low affinity. In this study aimed at computing the AQP7-glycerol affinity with high confidence, we implemented a direct computation of the affinity from unbiased equilibrium molecular dynamics (MD) simulations of three all-atom systems constituted with 0.16 M, 4.32 M, and 10.23 M atoms, respectively. These three sets of simulations manifested a fundamental physics law that the intrinsic fluctuations of pressure in a system are inversely proportional to the system size (the number of atoms in it). These simulations showed that the computed values of glycerol-AQP7 affinity are dependent upon the system size (the inverse affinity estimations were, respectively, 47.3 mM, 1.6 mM, and 0.92 mM for the three model systems). In this, we obtained a lower bound for the AQP7-glycerol affinity (an upper bound for the dissociation constant). Namely, the AQP7-glycerol affinity is stronger than 1087/M (the dissociation constant is less than 0.92 mM). Additionally, we conducted hyper steered MD (hSMD) simulations to map out the Gibbs free-energy profile. From the free-energy profile, we produced an independent computation of the AQP7-glycerol dissociation constant being approximately 0.18 mM. 
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