Progressive cerebral accumulation of tau aggregates is a defining feature of Alzheimer’s disease (AD). A popular theory that seeks to explain the apparent spread of neurofibrillary tangle pathology proposes that aggregated tau is passed from neuron to neuron. Such a templated seeding process requires that the transferred tau contains the microtubule binding repeat domains that are necessary for aggregation. While it is not clear how a protein such as tau can move from cell to cell, previous reports have suggested that this may involve extracellular vesicles (EVs). Thus, measurement of tau in EVs may both provide insights on the molecular pathology of AD and facilitate biomarker development. Here, we report the use of sensitive immunoassays specific for full-length (FL) tau and mid-region tau, which we applied to analyze EVs from human induced pluripotent stem cell (iPSC)-derived neuron (iN) conditioned media, cerebrospinal fluid (CSF), and plasma. In each case, most tau was free-floating with a small component inside EVs. The majority of free-floating tau detected by the mid-region assay was not detected by our FL assays, indicating that most free-floating tau is truncated. Inside EVs, the mid-region assay also detected more tau than the FL assay, but the ratio of FL-positive to mid-region-positive tau was higher inside exosomes than in free solution. These studies demonstrate the presence of minute amounts of free-floating and exosome-contained FL tau in human biofluids. Given the potential for FL tau to aggregate, we conclude that further investigation of these pools of extracellular tau and how they change during disease is merited.

Structural health monitoring (SHM) is used worldwide for managing and maintaining civil infrastructures. SHM systems have produced huge amounts of data, but the effective monitoring, mining, and utilization of this data still need in-depth study. SHM data generally includes multiple types of anomalies caused by sensor faults or system malfunctions that can disturb structural analysis and assessment. In the routine data pre-processing, multiple signal processing techniques are required to detect the anomalies, respectively, which is inefficient. The large variations of extracted features from massive SHM data make the data anomaly detection techniques prone to be over-processed or under-processed. Even with expert intervention, the parameter tuning, associated with multiple data preprocessing methods, is still a challenge, making the procedure expensive and inefficient. In addition, one data anomaly detection technique frequently mis-detects other types of anomaly. In this work, we focus on the anomaly detection in the stage of data pre-processing that little work has been done based on the real-world continuous SHM data with multiclass anomalies. We proposed a novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making. First, we split raw time series data into sections, and visualized the data in time and frequency domain, respectively. Then each section's images were stacked as a single dual-channel image and labeled according to graphical features (multi-2D image space expression). Second, a CNN was designed and trained for data anomaly classification, during which the descriptions and representations of the anomalies' features were learned by convolution. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios on actual acceleration data from an SHM system for a long-span cable-stayed bridge. The results show that our method could detect the multipattern anomalies of SHM data efficiently with high accuracy. The proposed dual-information CNN-based design makes this detection process readily scalable, faster, and more accurate, thereby providing a novel perspective with strong potential for SHM data preprocessing. KEYWORDScomputer vision, convolutional neural network (CNN), data anomaly detection, long-span bridge, structural health monitoring (SHM) | INTRODUCTIONStructural health monitoring (SHM) is used worldwide to manage and maintain civil infrastructure systems by evaluating their structural loads, responses, and real-time performance, and by predicting the future behavior of structures of all types. 1-4 SHM has produced huge amounts of data. For example, the SHM system used for the Sutong Bridge in China, which has 785 sensors, produces 2.5 TB of data each year. The effective mining and utilization of SHM data is an important topic that needs in-depth study. However, researchers have faced challenges because the SHM data generally include multiple types of anomalies caused by sensor fa...

Background: The tau protein plays a central role in Alzheimer’s disease (AD) and there is huge interest in measuring tau in blood and CSF. Methods: We developed a set of immunoassays to measure tau in specimens from humans diagnosed based on current best clinical and CSF biomarker criteria. Results: In CSF, mid-region-detected and N-terminal-detected tau predominated and rose in disease. In plasma, an N-terminal assay (NT1) detected elevated levels of tau in AD and AD-mild cognitive impairment (MCI). Plasma NT1 measurements separated controls from AD-MCI (area under the curve, AUC=0.88) and AD (AUC=0.96) in a Discovery Cohort; and in a Validation Cohort (with AUCs=0.79 and 0.75, respectively). Conclusions: The forms of tau in CSF and plasma are distinct, but in each specimen type the levels of certain fragments are increased in AD. Measurement of plasma NT1 tau should be aggressively pursued as a potential blood-based screening test for AD/AD-MCI.