Performance of implantable loop recorders. Role of R vector and detection algorithms

SummaryRecently, the clinical entity embolic stroke of undetermined source (ESUS) has been defined for patients with ischemic strokes, where neither a cardioembolic nor a non-cardiac source can be detected. These patients may suffer from asymptomatic atrial fibrillation (AF), terminating spontaneously and thus eluding detection. Implantable loop recorders (ILR) with automatic AF detection algorithms can detect short-lasting, subclinical AF. The aim of this study was to prospectively assess and predict AF detection in patients with ESUS using ILR with daily remote interrogation. Patients with acute ESUS received an ILR, were seen every 6 months and additionally interrogated their ILR daily using remote monitoring. The incidence of AF detection was assessed and parameters which might predict AF detection (clinical and from magnetic resonance tomography) were analysed. ILR implantation was performed in 123 patients on average 20 days after stroke. During a mean follow-up of 12.7±5.5 months, AF was documented and manually confirmed in 29 of 123 patients (23.6%). First AF detection occurred on average after 3.6±3.4 months of monitoring. Patients with AF were on average older, had a higher CHA2DS2-VASc score and more often cerebral microangiopathy. In conclusion, AF can be documented in approximately 25% of patients with the diagnosis of ESUS after careful work-up within a year of monitoring by an ILR and daily remote interrogation. This had important therapeutic consequences (initiation of anticoagulation for secondary stroke prevention) in these patients.

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On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles

10.1101/000315 ◽

2013 ◽

Author(s):

Ying-Wooi Wan ◽

Claire Mach ◽

Genevera I. Allen ◽

Matthew Anderson ◽

Zhandong Liu

Keyword(s):

Ovarian Cancer ◽

Cancer Survival ◽

Human Cancer ◽

The Cancer Genome Atlas ◽

Detection Algorithms ◽

Level 3 ◽

Cancer Genome Atlas ◽

Ovarian Cancer Survival ◽

Generation Sequencing

Dysregulated microRNA (miRNA) expression is a well-established feature of human cancer. However, the role of specific miRNAs in determining cancer outcomes remains unclear. Using Level 3 expression data from the Cancer Genome Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12 miRNAs that are associated with survival when miRNAs were profiled in the same specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly, only 1 miRNA transcript is associated with ovarian cancer survival in both datasets. Our analyses indicate that this discrepancy is due to the fact that miRNA levels reported by the two platforms correlate poorly, even after correcting for potential issues inherent to signal detection algorithms. Further investigation is warranted.

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Object Detectors’ Convolutional Neural Networks backbones : a review and a comparative study

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2021/039112021 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1379-1386

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Object Detection ◽

Convolutional Neural Networks ◽

Crucial Role ◽

Extended Version ◽

Backbone Networks ◽

Detection Algorithms ◽

Wide Range

Computer vision is a scientific field that deals with how computers can acquire significant level comprehension from computerized images or videos. One of the keystones of computer vision is object detection that aims to identify relevant features from video or image to detect objects. Backbone is the first stage in object detection algorithms that play a crucial role in object detection. Object detectors are usually provided with backbone networks designed for image classification. Object detection performance is highly based on features extracted by backbones, for instance, by simply replacing a backbone with its extended version, a large accuracy metric grows up. Additionally, the backbone's importance is demonstrated by its efficiency in real-time object detection. In this paper, we aim to accumulate the crucial role of the deep learning era and convolutional neural networks in particular in object detection tasks. We have analyzed and have been concentrating on a wide range of reviews on convolutional neural networks used as the backbone of object detection models. Building, therefore, a review of backbones that help researchers and scientists to use it as a guideline for their works.

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Towards a Network Psychometrics Approach to Assessment: Simulations for Redundancy, Dimensionality, and Loadings

10.31234/osf.io/84kgd ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexander P. Christensen

Keyword(s):

Alternative Explanation ◽

Network Models ◽

Assessment Instrument ◽

Assessment Instruments ◽

Detection Algorithms ◽

Psychological Attributes ◽

Confirmatory Factor ◽

Psychometric Tool ◽

Modern Psychometrics

Research using network models in psychology has proliferated over the last decade. The popularity of network models has largely been driven by their alternative explanation for the emergence of psychological attributes—observed variables co-occur because they are causally coupled and dynamically reinforce each other, forming cohesive systems. Despite their rise in popularity, the growth of network models as a psychometric tool has remained relatively stagnant, mainly being used as a novel measurement perspective. In this dissertation, the goal is to expand the role of network models in modern psychometrics and to move towards using these models as a tool for the validation of assessment instruments. This paper presents three simulation studies and an empirical example that are designed to evaluate different aspects of the psychometric network approach to assessment: reducing redundancy, detecting dimensions, and estimating loadings. The first simulation evaluated two novel approaches for determining whether items are redundant, which is a key component for the accuracy and interpretation of network measures. The second simulation evaluated several different community detection algorithms, which are designed to detect dimensions in networks. The third simulation evaluated an adapted formulation of the network measure, node strength, and how it compares to factor loadings estimated by exploratory and confirmatory factor analysis. The results of the simulations demonstrate that network models can be used as an effective psychometric tool and one that is on par with more traditional methods. Finally, in the empirical example, the methods from the simulations are applied to a real-world dataset measuring personality. This example demonstrated that these methods are not only effective, but they can validate whether an assessment instrument is consistent with theoretical and empirical expectations. With these methods in hand, network models are poised to take the next step towards becoming a robust psychometric tool.

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Evaluating the role of community detection in improving influence maximization heuristics

Social Network Analysis and Mining ◽

10.1007/s13278-021-00804-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

László Hajdu ◽

Miklós Krész ◽

András Bóta

Keyword(s):

Community Detection ◽

Real Life ◽

Detection Algorithm ◽

Influence Maximization ◽

Overlapping Community Detection ◽

Detection Algorithms ◽

Overlapping Community ◽

Community Detection Algorithm ◽

The Cost

AbstractBoth community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.

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Quantification of hormone pulsatility via an approximate entropy algorithm

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1992.262.5.e741 ◽

1992 ◽

Vol 262 (5) ◽

pp. E741-E754 ◽

Cited By ~ 61

Author(s):

S. M. Pincus ◽

D. L. Keefe

Keyword(s):

Hormone Secretion ◽

Clinical Endocrinology ◽

Approximate Entropy ◽

Direct Measure ◽

Foreseeable Future ◽

Pulse Detection ◽

Detection Algorithms ◽

Potential Applicability ◽

Data Points

Approximate entropy (ApEn) is a recently developed formula to quantify the amount of regularity in data. We examine the potential applicability of ApEn to clinical endocrinology to quantify pulsatility in hormone secretion data. We evaluate the role of ApEn as a complementary statistic to widely employed pulse-detection algorithms, represented herein by ULTRA, via the analysis of two different classes of models that generate episodic data. We conclude that ApEn is able to discern subtle system changes and to provide insights separate from those given by ULTRA. ApEn evaluates subordinate as well as peak behavior and often provides a direct measure of feedback between subsystems. ApEn generally can distinguish systems given 180 data points and an intra-assay coefficient of variation of 8%. This suggests ApEn as applicable to clinical hormone secretion data within the foreseeable future. Additionally, the models analyzed and extant clinical data are both consistent with episodic, not periodic, normative physiology.

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Atrial fibrillation post central retinal artery occlusion: Role of implantable loop recorders

Pacing and Clinical Electrophysiology ◽

10.1111/pace.13990 ◽

2020 ◽

Vol 43 (9) ◽

pp. 992-999

Author(s):

Ryan A. Watson ◽

Jennifer Wellings ◽

Rittu Hingorani ◽

Tingting Zhan ◽

Daniel R. Frisch ◽

...

Keyword(s):

Atrial Fibrillation ◽

Central Retinal Artery Occlusion ◽

Retinal Artery Occlusion ◽

Artery Occlusion ◽

Central Retinal Artery ◽

Retinal Artery ◽

Implantable Loop Recorders

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Towards a Network Psychometrics Approach to Assessment: Simulations for Redundancy, Dimensionality, and Loadings

10.31237/osf.io/7u9bn ◽

2020 ◽

Author(s):

Alexander P. Christensen

Keyword(s):

Alternative Explanation ◽

Network Models ◽

Assessment Instrument ◽

Assessment Instruments ◽

Detection Algorithms ◽

Psychological Attributes ◽

Confirmatory Factor ◽

Psychometric Tool ◽

Modern Psychometrics

Research using network models in psychology has proliferated over the last decade. The popularity of network models has largely been driven by their alternative explanation for the emergence of psychological attributes—observed variables co-occur because they are causally coupled and dynamically reinforce each other, forming cohesive systems. Despite their rise in popularity, the growth of network models as a psychometric tool has remained relatively stagnant, mainly being used as a novel measurement perspective. In this dissertation, the goal is to expand the role of network models in modern psychometrics and to move towards using these models as a tool for the validation of assessment instruments. This paper presents three simulation studies and an empirical example that are designed to evaluate different aspects of the psychometric network approach to assessment: reducing redundancy, detecting dimensions, and estimating loadings. The first simulation evaluated two novel approaches for determining whether items are redundant, which is a key component for the accuracy and interpretation of network measures. The second simulation evaluated several different community detection algorithms, which are designed to detect dimensions in networks. The third simulation evaluated an adapted formulation of the network measure, node strength, and how it compares to factor loadings estimated by exploratory and confirmatory factor analysis. The results of the simulations demonstrate that network models can be used as an effective psychometric tool and one that is on par with more traditional methods. Finally, in the empirical example, the methods from the simulations are applied to a real-world dataset measuring personality. This example demonstrated that these methods are not only effective, but they can validate whether an assessment instrument is consistent with theoretical and empirical expectations. With these methods in hand, network models are poised to take the next step towards becoming a robust psychometric tool.

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Odor motion sensing enables complex plume navigation

10.1101/2021.09.29.462473 ◽

2021 ◽

Author(s):

Nirag Kadakia ◽

Mahmut Demir ◽

Brenden T. Michaelis ◽

Matthew A. Reidenbach ◽

Damon A. Clark ◽

...

Keyword(s):

Motion Detection ◽

Critical Role ◽

Natural Environments ◽

Orthogonal Direction ◽

Directional Information ◽

Insect Navigation ◽

Detection Algorithms ◽

Bilateral Differences ◽

Odor Signals

ABSTRACTInsects can detect bilateral differences in odor concentration between their two antennae, enabling them to sense odor gradients. While gradients aid navigation in simple odor environments like static ribbons, their role in navigating complex plumes remains unknown. Here, we use a virtual reality paradigm to show that Drosophila use bilateral sensing for a distinct computation: detecting the motion of odor signals. Such odor direction sensing is computationally equivalent to motion detection algorithms underlying motion detection in vision. Simulations of natural plumes reveal that odor motion contains valuable directional information absent from the airflow, which Drosophila indeed exploit when navigating natural plumes. Olfactory studies dating back a century have stressed the critical role of wind sensing for insect navigation (Flügge, 1934; Kennedy and Marsh, 1974); we reveal an entirely orthogonal direction cue used by flies in natural environments, and give theoretical arguments suggesting that this cue may be of broad use across the animal kingdom.

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