Detection of Velocity and Diffusion Coefficient Change Points in Single-Particle Trajectories.

The position-time trajectory of a biological subject moving in a complex environment contains rich information about how it interacts with the local setting. Whether the subject be an animal or an intracellular endosomal vesicle, the two primary modes of biological locomotion are directional movement and random walk, respectively characterized by velocity and diffusion coefficient. This contribution introduces a method to quantitatively divide a single-particle trajectory into segments that exhibit changes in the diffusion coefficient, velocity, or both. With the determination of these two physical parameters given by the maximum likelihood estimators, the relative precisions are given as explicit functions of the number of data points and total trajectory time. The method is based on rigorous statistical tests and does not require any presumed kinetics scheme. Results of extensive characterizations, extensions to 2D and 3D trajectories, and applications to common scenarios are also discussed.

Parallelization of Change Point Detection.

The change point detection method ( Watkins , L. P. ; Yang , H. J. Phys. Chem. B 2005 , 109 , 617 ) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics model. The original algorithm, however, has a time complexity of [Formula: see text], where N is the size of the data and is, therefore, limited in its scalability. This paper puts forth a parallelization of change point detection to address these time and memory constraints. This parallelization method was evaluated by applying it to changes in the mean of Gaussian-distributed data and found that time decreases superlinearly with respect to the number of processes (i.e., parallelization with two processes takes less than half of the time of one process). Moreover, there was minimal reduction in detection power. These results suggest that our parallelization algorithm is a viable scheme that can be implemented for other change point detection methods.

Predictive Ability of the Braden QD Scale for Hospital-Acquired Venous Thromboembolism in Hospitalized Children.

PURPOSE: Hospital-acquired venous thromboembolisms (HA-VTEs) are increasingly common in pediatric inpatients and associated with significant morbidity and cost. The Braden QD Scale was created to predict the risk of hospital-acquired pressure injury (HAPI) and is used broadly in children's hospitals. This study evaluated the ability of the Braden QD Total score to predict risk of HA-VTE at a quaternary children's hospital. METHODS: To analyze the predictive potential of the Braden QD Total score and subscores for HA-VTEs, the researchers performed univariate logistic regressions. The increase in a patient's odds of developing an HA-VTE for every 1-point increase in each Braden QD score was evaluated. Each model was evaluated using a 5-fold cross-validated area-under-the-curve of the corresponding receiver operating characteristic curve (AUROC). RESULTS: This study analyzed 27,689 pediatric inpatients. HA-VTE occurred in 135 patients. The odds of HA-VTE incidence increased by 29% (odds ratio 1.29, 95% confidence interval [CI] 1.25-1.34, p < 0.001) for every 1-point increase in a patient's Braden QD Total score. The AUROC was 0.81 (95% CI 0.77-0.85). CONCLUSION: The Braden QD Scale is a predictor for HA-VTE, outperforming its original intended use for predicting HAPI and performing similarly to other HA-VTE predictive models. As the Braden QD Total score is currently recorded in the electronic health records of many children's hospitals, it could be practically and easily implemented as a tool to predict which patients are at risk for HA-VTE.

Patients' Perceptions of Integrated Care Among Medicare Beneficiaries by Level of Need for Health Services.

Requirements for integrating care across providers, settings, and over time increase with patients' needs. Health care providers' ability to offer care that patients experience as integrated may vary among patients with different levels of need. We explore the variation in patients' perceptions of integrated care among Medicare beneficiaries based on the beneficiary's level of need using ordinary least square regression for each of four high-need groups: beneficiaries (a) with complex chronic conditions, (b) with frailties, (c) below 65 with disability, and (d) with any (of the first three) high needs. We control for beneficiary demographics and other factors affecting integrated care, and we conduct sensitivity analyses controlling for multiple individual chronic conditions. We find significant positive associations with level of need for provider support for self-directed care and medication and home health management. Controlling for multiple individual chronic conditions reduces effect sizes and number of significant relationships.

Differences in patient perceptions of integrated care among black, hispanic, and white Medicare beneficiaries.

OBJECTIVE: This study sought to identify potential disparities among racial/ethnic groups in patient perceptions of integrated care (PPIC) and to explore how methodological differences may influence measured disparities. DATA SOURCE: Data from Medicare beneficiaries who completed the 2015 Medicare Current Beneficiary Survey (MCBS) and were enrolled in Part A benefits for an entire year. STUDY DESIGN: We used 4-point measures of eight dimensions of PPIC and assessed differences in dimensions among racial/ethnic groups. To estimate differences, we applied a "rank and replace" method using multiple regression models in three steps, balancing differences in health status among racial groups and adjusting for differences in socioeconomic status. We reran all analyses with additional SES controls and using standard multiple variable regression. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: We found several significant differences in perceived integrated care between Black versus White (three of eight measures) and Hispanic versus White (one of eight) Medicare beneficiaries. On average, Black beneficiaries perceived more integrated support for self-care than did White beneficiaries (mean difference = 0.14, SE = 0.06, P =.02). Black beneficiaries perceived more integrated specialists' knowledge of past medical history than did White beneficiaries (mean difference = 0.12, SE = 0.06, P =.01). Black and Hispanic beneficiaries also each reported, on average, 0.18 more integrated medication and home health management than did White beneficiaries (P <.01 and P <.01). These findings were robust to sensitivity analyses and model specifications. CONCLUSIONS: There exist some aspects of care for which Black and Hispanic beneficiaries may perceive greater integrated care than non-Hispanic White beneficiaries. Further studies should test theories explaining why racial/ethnic groups perceive differences in integrated care.

Neuromuscular diseases associated with Human Immunodeficiency Virus infection.

From the most common distal symmetric polyneuropathy (Bilgrami and O'Keefe, 2014) to the rare motor neuron diseases, HIV infection is associated with pathology at all levels of the peripheral nervous system. HIV infection can cause these conditions due to viral exposure itself, the resulting immune dysregulation, opportunistic infections found in untreated patients, and from the therapy used in treatment of the virus. Before the advent of antiretroviral therapy, 5 neuromuscular diseases associated with HIV often resulted from opportunistic infections. With advances in antiretroviral therapy, the etiologies of neuromuscular complications more frequently become the result of prolonged HIV exposure, comorbid diseases, and side effects of medications. In this article we review the literature on HIV associated neuromuscular diseases, emphasizing the more recent studies in the post antiretroviral era, but also reviewing conditions more prevalent in the pre antiretroviral era which continue to be seen in developing countries and resource poor areas. This discussion includes the following conditions: distal symmetric polyneuropathy, autonomic neuropathy, inflammatory demyelinating polyneuropathy, mononeuropathy, mononeuropathy multiplex, polyradiculopathies, myelopathy, myopathy, motor neuron disease, and antiretroviral treatment related conditions.

Screening for Social Determinants of Health During Primary Care and Emergency Department Encounters.

This cross-sectional study explores characteristics of patients who are screened and who screen positive for social determinants of health (SDOH) needs in different clinical settings within a large integrated health system.

Autoregressive and iterative hidden Markov models for periodicity detection and solenoid structure recognition in protein sequences.

Traditional signal processing methods cannot detect interspersed repeats and generally cannot handle nonstationary signals. In this paper, we propose a new method for periodicity detection in protein sequences to locate interspersed repeats. We first apply the autoregressive model with a sliding window to find possible repeating subsequences within a protein sequence. Then, we utilize an iterative hidden Markov model (HMM) to count the number of subsequences similar to each of the possible repeating subsequences. An iterative HMM search of the potential repeating subsequences can help identify interspersed repeats. Finally, the numbers of repeating subsequences are aggregated together as a feature and used in the classification process. Experiment results show that our method improves the performance of solenoid protein recognition substantially.

Selection and mapping of DNA structural features for short gene recognition.

This paper presents two new approaches for short gene recognition in DNA sequences. Three of fourteen DNA structural features are selected based on their classification power by a modified auto-regressive model method. Experiments on human genome show that the method is superior to existing exon detection algorithms. However, this method requires computing time. To overcome this problem, the DNA structural features are mapped to a set of new values. The three signals generated by the mapped feature values are normalised and averaged before their power spectral density is estimated. The computational complexity is reduced substantially using the feature mapping method.