Tau misprocessing to form aggregates and other toxic species has emerged as a major feature in our developing understanding of the etiology and pathogenesis of Alzheimer's Disease (AD). The significance of tau misprocessing in AD has been further emphasized by recent studies showing that tau can be secreted from neurons via exosomes and may itself be an important agent in the spreading of neurofibrillary lesions within the brain. Tau secretion occurs most readily under disease-associated conditions in cellular models, suggesting that cellular changes responsible for secretion, possibly including tau oligomerization, could play a key role in the propagation of neurofibrillary lesions in neurodegenerative disease. Here we show that overexpression of 4R0N human tau in neuroblastoma cells recruits mitochondrial and axonogenesis-associated proteins relevant to neurodegeneration into the exosomal secretion pathway via distinct mechanisms. The recruitment of mitochondrial proteins appears to be linked to autophagy disruption (exophagy) in multiple neurodegenerative conditions but but has few known direct links to AD and tau. By contrast, the involvement of synaptic plasticity and axonogenesis markers is highly specific to both tau and AD and may be relevant to the reactivation of developmental programs involving tau in AD and the recently demonstrated ability of secreted tau to establish tissue distribution gradients in CNS neuropil. We also found a highly significant correlation between genes that are significantly downregulated in multiple forms of AD and proteins that have been recruited to exosomes by tau, which we interpret as strong evidence for the central involvement of tau secretion in AD cytopathogenesis. Our results suggest that multiple cellular mechanisms may link tau secretion to both toxicity and and neurofibrillary lesion spreading in Alzheimer's disease and other tauopathies.

Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the multi-label classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multilabel classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative (FN) and false positive (FP) results. To address this gap, we define a method of creating the multi-label confusion matrix (MLCM) based on three proposed categories of multi-label problems. Once establishing the shortcomings of current methods for identifying FN and FP, we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.

Background: Long-duration exploration missions (LDEMs), such as voyages to Mars, will present unique medical challenges for astronaut crews, including communication delays and the inability to return to Earth early. Medical events threaten crewmember lives and increase the risk of mission failure. Managing a range of potential medical events will require excellent technical and nontechnical skills (NTSs). We sought to identify medical events with potential for rescue, range them according to the potential impact on crew health and mission success during LDEMs, and develop a list of NTSs to train for management of in-flight medical events. Materials and methods: Twenty-eight subject matter experts with specializations in surgery, medicine, trauma, spaceflight operations, NTS training, simulation, human factors, and organizational psychology completed online surveys followed by a 2-d in-person workshop. They identified and rated medical events for survivability, mission impact, and impact of crewmember NTSs on outcomes in space. Results: Sudden cardiac arrest, smoke inhalation, toxic exposure, seizure, and penetrating eye injury emerged as events with the highest potential mission impact, greatest potential for survival, and that required excellent NTS for successful management. Key NTS identified to target in training included information exchange, supporting behavior, communication delivery, and team leadership/followership. Conclusions: With a planned Mars mission on the horizon, training countermeasures need to be developed in the next 3-5 y. These results may inform policy, selection, medical

The purpose of our study was to examine the association between obesity and gait mechanics in older adults with knee osteoarthritis (OA). Subjects were 101 older adults (25 males and 76 females) with knee OA. High-speed video analysis and a force platform were used to record sagittal view lower extremity kinematic data and ground reaction forces. Increased body mass index (BMI) was significantly related to both decreases in walking velocity and knee maximum extension. There were no significant relationships between BMI and any of the hip or ankle kinematic variables. BMI was directly related to vertical force minimum and maximum values, vertical impulse, and loading rate. Increases in braking and propulsive forces were significantly correlated with increased BMI. Maximum medially and laterally directed ground reaction forces were positively correlated with BMI. Our results suggests that, in subjects with knee OA, obesity is associated with an alteration in gait.

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