Spatiotemporal anomaly detection: streaming architecture and algorithms

Includes bibliographical references.2020 Summer.Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of anomaly detection has been extensively studied by mathematicians, statisticians, economists, engineers, and computer scientists. One open research question remains the design of distributed cloud-based architectures and algorithms that can accurately identify anomalies in previously unseen, unlabeled streaming, multivariate spatiotemporal data. With streaming data, time is of the essence, and insights are perishable. Real-world streaming spatiotemporal data originate from many sources, including mobile phones, supervisory control and data acquisition enabled (SCADA) devices, the internet-of-things (IoT), distributed sensor networks, and social media. Baseline experiments are performed on four (4) non-streaming, static anomaly detection multivariate datasets using unsupervised offline traditional machine learning (TML), and unsupervised neural network techniques. Multiple architectures, including autoencoders, generative adversarial networks, convolutional networks, and recurrent networks, are adapted for experimentation. Extensive experimentation demonstrates that neural networks produce superior detection accuracy over TML techniques. These same neural network architectures can be extended to process unlabeled spatiotemporal streaming using online learning. Space and time relationships are further exploited to provide additional insights and increased anomaly detection accuracy. A novel domain-independent architecture and set of algorithms called the Spatiotemporal Anomaly Detection Environment (STADE) is formulated. STADE is based on federated learning architecture. STADE streaming algorithms are based on a geographically unique, persistently executing neural networks using online stochastic gradient descent (SGD). STADE is designed to be pluggable, meaning that alternative algorithms may be substituted or combined to form an ensemble. STADE incorporates a Stream Anomaly Detector (SAD) and a Federated Anomaly Detector (FAD). The SAD executes at multiple locations on streaming data, while the FAD executes at a single server and identifies global patterns and relationships among the site anomalies. Each STADE site streams anomaly scores to the centralized FAD server for further spatiotemporal dependency analysis and logging. The FAD is based on recent advances in DNN-based federated learning. A STADE testbed is implemented to facilitate globally distributed experimentation using low-cost, commercial cloud infrastructure provided by Microsoft™. STADE testbed sites are situated in the cloud within each continent: Africa, Asia, Australia, Europe, North America, and South America. Communication occurs over the commercial internet. Three STADE case studies are investigated. The first case study processes commercial air traffic flows, the second case study processes global earthquake measurements, and the third case study processes social media (i.e., Twitter™) feeds. These case studies confirm that STADE is a viable architecture for the near real-time identification of anomalies in streaming data originating from (possibly) computationally disadvantaged, geographically dispersed sites. Moreover, the addition of the FAD provides enhanced anomaly detection capability. Since STADE is domain-independent, these findings can be easily extended to additional application domains and use cases

An Empirical Analysis of the Propensity of Academics to Engage in Informal University Technology Transfer

Formal university technology transfer mechanisms, through licensing agreements, research joint ventures, and university-based startups, have attracted considerable attention in the academic literature. Surprisingly, there has been little systematic empirical analysis of the propensity of academics to engage in informal technology transfer. This paper presents empirical evidence on the determinants of three types of informal technology transfer by faculty members: knowledge transfer, joint publications with industry scientists, and consulting. We find that male and tenured faculty members are more likely to engage in all three forms of informal technology transfer. We also find that academics who allocate a relatively higher percentage of their time to grants-related research are more likely to engage in informal commercial knowledge transfer.

Assessment of a plasma amyloid probability score to estimate amyloid positron emission tomography findings among adults with cognitive impairment

Importance: The diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology. Objective: To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-β (Aβ) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status. Design, Setting, and Participants: This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020. Exposures: Amyloid detected in blood and by positron emission tomography (PET) imaging. Main Outcomes and Measures: The main outcome was the diagnostic performance of plasma Aβ42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: All 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aβ42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aβ42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aβ42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology. Conclusions and Relevance: These findings suggest that this blood biomarker test could allow for distinguishing individuals with brain amyloid-positive PET findings from individuals with amyloid-negative PET findings and serve as an aid for Alzheimer disease diagnosis

Lean Years

Contributors: Kim Deitch, Leslie, John Pound, Mike Royer, Trina, Chris Warner. Barry Siegel and Bruce Simon, editors. Member: United Cartoon Workers of America. The Adler Archive of Underground Comix, Gift of Bill Adler.https://digitalcommons.risd.edu/specialcollections_adlerarchive_undergroundcomix/1085/thumbnail.jp

First-in-man evaluation of 124I-PGN650: A PET tracer for detecting phosphatidylserine as a biomarker of the solid tumor microenvironment

Purpose: PGN650 is a F(ab′) 2 antibody fragment that targets phosphatidylserine (PS), a marker normally absent that becomes exposed on tumor cells and tumor vasculature in response to oxidative stress and increases in response to therapy. PGN650 was labeled with 124 I to create a positron emission tomography (PET) agent as an in vivo biomarker for tumor microenvironment and response to therapy. In this phase 0 study, we evaluated the pharmacokinetics, safety, radiation dosimetry, and tumor targeting of this tracer in a cohort of patients with cancer. Methods: Eleven patients with known solid tumors received approximately 140 MBq (3.8 mCi) 124 I-PGN650 intravenously and underwent positron emission tomography–computed tomography (PET/CT) approximately 1 hour, 3 hours, and either 24 hours or 48 hours later to establish tracer kinetics for the purpose of calculating radiation dosimetry (from integration of the organ time-activity curves and OLINDA/EXM using the adult male and female models). Results: Known tumor foci demonstrated mildly increased uptake, with the highest activity at the latest imaging time. There were no unexpected adverse events. The liver was the organ receiving the highest radiation dose (0.77 mGy/MBq); the effective dose was 0.41 mSv/MBq. Conclusion: Although 124 I-PGN650 is safe for human PET imaging, the tumor targeting with this agent in patients was less than previously observed in animal studies

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

A prospective trial comparing FDG-PET/CT and CT to assess tumor response to cetuximab in patients with incurable squamous cell carcinoma of the head and neck

Computed tomography (CT), the standard method to assess tumor response to cetuximab in incurable squamous cell carcinoma of the head and neck (SCCHN), performs poorly as judged by the disparity between high disease control rate (46%) and short time to progression (TTP) (70 days). F-18 fluorodeoxyglucose positron emission tomography (FDG-PET)/CT is an alternative method to assess tumor response. The primary objective of this prospective trial was to evaluate the metabolic response of target lesions, assessed as the change in maximum standardized uptake value (SUV(max)) on FDG-PET/CT before and after 8 weeks (cycle 1) of cetuximab. Secondary objectives were to compare tumor response by CT (RECIST 1.0) and FDG-PET/CT (EORTC criteria) following cycle 1, and determine TTP with continued cetuximab administration in patients with disease control by CT after cycle 1 but stratified for disease control or progression by FDG-PET/CT. Among 27 patients, the mean percent change of SUV(max) of target lesions after cycle 1 was −21% (range: +72% to −81%); by FDG-PET/CT, partial response (PR)/stable disease (SD) occurred in 15 patients (56%) and progression in 12 (44%), whereas by CT, PR/SD occurred in 20 (74%) and progression in 7 (26%). FDG-PET/CT and CT assessments were discordant in 14 patients (P = 0.0029) and had low agreement (κ = 0.30; 95% confidence interval [CI]: 0.12, 0.48). With disease control by CT after cycle 1, median TTP was 166 days (CI: 86, 217) if the FDG-PET/CT showed disease control and 105 days (CI: 66, 159) if the FDG-PET/CT showed progression (P < 0.0001). Median TTP of the seven patients whose post cycle 1 CT showed progression compared to the 12 whose FDG-PET/CT showed progression were similar (53 [CI: 49, 56] vs. 61 [CI: 50, 105] days, respectively). FDG-PET/CT may be better than CT in assessing benefit of cetuximab in incurable SCCHN

Radioactive Iodine Therapy Decreases Recurrence in Thyroid Papillary Microcarcinoma

Background. The most appropriate therapy for papillary microcarcinoma (PMC) is controversial. Methods. We reviewed the therapy and outcome of 407 patients with PMC. Results. Three hundred-eighty patients underwent total thyroidectomy, and 349 patients received I-131 therapy. The median followup was 5.3 years. Forty patients developed recurrent disease. On univariate analysis, development of disease recurrence was correlated with histological tumor size > 0.8 cm (P = 0.0104), age < 45 years (P = 0.043), and no I-131 therapy (P < 0.0001). On multivariate analysis, histological tumor size > 0.8 cm, positive lymph nodes, and no I-131 therapy were significant. The 5-year RFS for patients treated with I-131 was 95.0% versus 78.6% (P < 0.0001) for patients not treated with I-131. Patients with lymph node metastasis who did not receive I-131 had a 5-year RFS of 42.9% versus 93.2% (P < 0.0001) for patients who received I-131. Conclusions. Recommend I-131 remnant ablation for patients with PMC, particularly patients with lymph node metastasis