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CONTEXT: Next-generation sequencing (NGS) analysis of sporadic medullary thyroid carcinoma (sMTC) has led to increased detection of somatic mutations, including RET M918T, which has been considered a negative prognostic indicator. OBJECTIVE: This study aimed to determine the association between clinicopathologic behavior and somatic mutation identified on clinically motivated NGS. METHODS: In this retrospective cohort study, patients with sMTC who underwent NGS to identify somatic mutations for treatment planning were identified. Clinicopathologic factors, time to distant metastatic disease (DMD), disease-specific survival (DSS), and overall survival (OS) were compared between somatic mutations. RESULTS: Somatic mutations were identified in 191 sMTC tumors, including RET M918T (53.4%), other RET codons (10.5%), RAS (18.3%), somatic RET indels (8.9%), and RET/RAS wild-type (WT) status (8.9%). The median age at diagnosis was 50 years (range, 11-83); 46.1% were female. When comparing patients with RET M918T, RET-Other, and RET WT (which included RAS and RET/RAS WT), there were no differences in sex, TNM category, systemic therapy use, time to DMD, DSS, or OS. On multivariate analysis, older age at diagnosis (HR 1.05, P < .001; HR 1.06, P< .001) and M1 stage at diagnosis (HR 3.17, P = .001; HR 2.98, P = .001) were associated with decreased DSS and OS, respectively, but mutation cohort was not. When comparing RET M918T to RET indels there was no significant difference in time to DMD, DSS, or OS between the groups. CONCLUSION: Somatic RET mutations do not portend compromised DSS or OS in a cohort of sMTC patients who underwent clinically motivated NGS.

With support from QuarkNet, high school students investigated the effect of overburden on muon flux by collecting data from Fermilab's Main Injector Neutrino Oscillation Search (MINOS) tunnel. Muon flux varied due to a change in overburden created by a 103-meter deep access shaft. A profile of muon flux as a function of distance from the access shaft was measured by observing muon flux from different locations within the tunnel.

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The 'bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera. Author summary: To navigate through the world animals require knowledge of the direction they are facing. Insects keep track of this "head direction" with a population of 'compass' neurons. These cells can use internal measures of angular velocity to maintain the heading estimate but this becomes inaccurate over time and needs to be stabilised by environmental cues. In this work we produce a spiking neural network model replicating the connectivity between regions of the insect brain known to be involved in keeping track of heading and the neurons which are responsible for bringing sensory information into the circuit. We show that the model replicates the dynamics of visual learning from experiments where flies learn simple visual stimuli. Then using panoramic videos of complex natural environments, we show that the learned mapping between the current estimate of heading in the compass neurons and the features of the visual scene can maintain and enforce the correct heading estimate. [ABSTRACT FROM AUTHOR]