Analyzing Collective Motion with Machine Learning and Topology

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based in topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.Comment: Published in Chaos 29, 123125 (2019), DOI: 10.1063/1.512549

Beckman Access versus the Bayer ACS:180 and the Abbott AxSYM cardiac Troponin-I real-time immunoassays: an observational prospective study

BACKGROUND: Reliability of cardiac troponin-I assays under real-time conditions has not been previously well studied. Most large published cTnI trials have utilized protocols which required the freezing of serum (or plasma) for delayed batch cTnI analysis. We sought to correlate the presence of the acute ischemic coronary syndrome (AICS) to troponin-I values obtained in real-time by three random-mode analyzer immunoassay systems: the Beckman ACCESS (BA), the Bayer ACS:180 (CC) and the Abbott AxSYM (AX). METHODS: This was an observational prospective study at a university tertiary referral center. Serum from a convenience sampling of telemetry patients was analyzed in real-time for troponin-I by either the BA-CC (Arm-1) or BA-AX (Arm-2) assay pairs. Presence of the AICS was determined retrospectively and then correlated with troponin-I results. RESULTS: 100 patients were enrolled in Arm-1 (38 with AICS) and 94 in Arm-2 (48 with AICS). The BA system produced 51% false positives in Arm-1, 44% in Arm-2, with negative predictive values of 92% and 100% respectively. In Arm-1, the BA and the CC assays had sensitivities of 97% and 63% and specificities of 18% and 87%. In Arm-2, the BA and the AX assays had sensitivities of 100% and 83% and specificities of 11% and 78%. CONCLUSIONS: In real-time analysis, the performance of the AxSYM and ACS:180 assay systems produced more accurate troponin-I results than the ACCESS system

Emergency department spirometric volume and base deficit delineate risk for torso injury in stable patients

BACKGROUND: We sought to determine torso injury rates and sensitivities associated with fluid-positive abdominal ultrasound, metabolic acidosis (increased base deficit and lactate), and impaired pulmonary physiology (decreased spirometric volume and PaO(2)/FiO(2)). METHODS: Level I trauma center prospective pilot and post-pilot study (2000–2001) of stable patients. Increased base deficit was < 0.0 in ethanol-negative and ≤ -3.0 in ethanol-positive patients. Increased lactate was > 2.5 mmol/L in ethanol-negative and ≥ 3.0 mmol/L in ethanol-positive patients. Decreased PaO(2)/FiO(2 )was < 350 and decreased spirometric volume was < 1.8 L. RESULTS: Of 215 patients, 66 (30.7%) had a torso injury (abdominal/pelvic injury n = 35 and/or thoracic injury n = 43). Glasgow Coma Scale score was 14.8 ± 0.5 (13–15). Torso injury rates and sensitivities were: abdominal ultrasound negative and normal base deficit, lactate, PaO(2)/FiO(2), and spirometric volume – 0.0% & 0.0%; normal base deficit and normal spirometric volume – 4.2% & 4.5%; chest/abdominal soft tissue injury – 37.8% & 47.0%; increased lactate – 39.7% & 47.0%; increased base deficit – 41.3% & 75.8%; increased base deficit and/or decreased spirometric volume – 43.8% & 95.5%; decreased PaO(2)/FiO(2 )– 48.9% & 33.3%; positive abdominal ultrasound – 62.5% & 7.6%; decreased spirometric volume – 73.4% & 71.2%; increased base deficit and decreased spirometric volume – 82.9% & 51.5%. CONCLUSIONS: Trauma patients with normal base deficit and spirometric volume are unlikely to have a torso injury. Patients with increased base deficit or lactate, decreased spirometric volume, decreased PaO(2)/FiO(2), or positive FAST have substantial risk for torso injury. Increased base deficit and/or decreased spirometric volume are highly sensitive for torso injury. Base deficit and spirometric volume values are readily available and increase or decrease the suspicion for torso injury

Longitudinal Assessment of the Effect of Atrasentan on Thoracic Bioimpedance in Diabetic Nephropathy:A Randomized, Double-Blind, Placebo-Controlled Trial

BACKGROUND: Fluid retention is a common adverse event in patients who receive endothelin (ET) receptor antagonist therapy, including the highly selective ETA receptor antagonist, atrasentan. OBJECTIVE: We performed longitudinal assessments of thoracic bioimpedance in patients with type 2 diabetes mellitus and nephropathy to determine whether a decrease in bioimpedance accurately reflected fluid retention during treatment with atrasentan. STUDY DESIGN: We conducted a randomized, double-blind, placebo-controlled study in 48 patients with type 2 diabetes mellitus and nephropathy who were receiving stable doses of renin angiotensin system inhibitors and diuretics. METHODS: Patients were randomized 1:1:1 to placebo, atrasentan 0.5 mg, or atrasentan 1.25 mg once daily for 8 weeks. Thoracic bioimpedance, vital signs, clinical exams, and serologies were taken at weeks 1, 2, 4, 6, and 8, with the exception of serum hemoglobin, which was not taken at week 1, and serum brain natriuretic peptide, which was only taken at baseline, week 4, and week 8. RESULTS: Alterations in bioimpedance were more often present in those who received atrasentan than in those who received placebo, though overall differences were not statistically significant. Transient declines in thoracic bioimpedance during the first 2 weeks of atrasentan exposure occurred before or during peak increases in body weight and hemodilution (decreased serum hemoglobin). CONCLUSIONS: We conclude that thoracic bioimpedance did not reflect changes in weight gain or edema with atrasentan treatment in this study. However, the sample size was small, and it may be of interest to explore the use of thoracic bioimpedance in a larger population to understand its potential clinical use in monitoring fluid retention in patients with chronic kidney disease who receive ET receptor antagonists

What should an ideal spinal injury classification system consist of? A methodological review and conceptual proposal for future classifications

Since Böhler published the first categorization of spinal injuries based on plain radiographic examinations in 1929, numerous classifications have been proposed. Despite all these efforts, however, only a few have been tested for reliability and validity. This methodological, conceptual review summarizes that a spinal injury classification system should be clinically relevant, reliable and accurate. The clinical relevance of a classification is directly related to its content validity. The ideal content of a spinal injury classification should only include injury characteristics of the vertebral column, is primarily based on the increasingly routinely performed CT imaging, and is clearly distinctive from severity scales and treatment algorithms. Clearly defined observation and conversion criteria are crucial determinants of classification systems’ reliability and accuracy. Ideally, two principle spinal injury characteristics should be easy to discern on diagnostic images: the specific location and morphology of the injured spinal structure. Given the current evidence and diagnostic imaging technology, descriptions of the mechanisms of injury and ligamentous injury should not be included in a spinal injury classification. The presence of concomitant neurologic deficits can be integrated in a spinal injury severity scale, which in turn can be considered in a spinal injury treatment algorithm. Ideally, a validation pathway of a spinal injury classification system should be completed prior to its clinical and scientific implementation. This review provides a methodological concept which might be considered prior to the synthesis of new or modified spinal injury classifications