Algorithms with Area under the Curve for Daily Urinary Estrone-3-Glucuronide and Pregnanediol-3-Glucuronide to Signal the Transition to the Luteal Phase

Background and Objectives: Home fertility assessment methods (FAMs) for natural family planning (NFP) have technically evolved with the objective metrics of urinary luteinizing hormone (LH), estrone-3-glucuronide (E3G) and pregnanediol-3-glucuronide (PDG). Practical and reliable algorithms for timing the phase of cycle based upon E3G and PDG levels are mostly unpublished and still lacking. Materials and Methods: A novel formulation to signal the transition to the luteal phase was discovered, tested, and developed with a data set of daily E3G and PDG levels from 25 women, 78 cycles, indexed to putative ovulation (day after the urinary LH surge), Day 0. The algorithm is based upon a daily relative progressive change in the ratio, E3G-AUC/PDG-AUC, where E3G-AUC and PDG-AUC are the area under the curve for E3G and PDG, respectively. To improve accuracy the algorithm incorporated a three-fold cycle-specific increase of PDG. Results: An extended negative change in E3G-AUC/PDG-AUC of at least nine consecutive days provided a strong signal for timing the luteal phase. The algorithm correctly identified the luteal transition interval in 78/78 cycles and predicted the start day of the safe period as: Day + 2 in 10/78 cycles, Day + 3 in 21/78 cycles, Day + 4 in 28/78 cycles, Day + 5 in 15/78 cycles, and Day + 6 in 4/78 cycles. The mean number of safe luteal days with this algorithm was 10.3 ± 1.3 (SD). Conclusions: An algorithm based upon the ratio of the area under the curve for daily E3G and PDG levels along with a relative PDG increase offers another approach to time the phase of cycle. This may have applications for NFP/FAMs and clinical evaluation of ovarian function.

Color multi-focus image fusion based on transfer learning

Multi-focus image fusion is a technique that integrates the focused areas in a pair or set of source images with the same scene into a fully focused image. Inspired by transfer learning, this paper proposes a novel color multi-focus image fusion method based on deep learning. First, color multi-focus source images are fed into VGG-19 network, and the parameters of convolutional layer of the VGG-19 network are then migrated to a neural network containing multilayer convolutional layers and multilayer skip-connection structures for feature extraction. Second, the initial decision maps are generated using the reconstructed feature maps of a deconvolution module. Third, the initial decision maps are refined and processed to obtain the second decision maps, and then the source images are fused to obtain the initial fused images based on the second decision maps. Finally, the final fused image is produced by comparing the Q ABF metrics of the initial fused images. The experimental results show that the proposed method can effectively improve the segmentation performance of the focused and unfocused areas in the source images, and the generated fused images are superior in both subjective and objective metrics compared with most contrast methods.

Digital Biomarkers for the Objective Assessment of Disability in Neurogenic Thoracic Outlet Syndrome

Neurogenic thoracic outlet syndrome (nTOS) is a musculoskeletal disorder in which compression of the brachial plexus between the scalene muscles of the neck and the first rib results in disabling upper extremity pain and paresthesia. Currently there are no objective metrics for assessing the disability of nTOS or for monitoring response to its therapy. We aimed to develop digital biomarkers of upper extremity motor capacity that could objectively measure the disability of nTOS using an upper arm inertial sensor and a 20-s upper extremity task that provokes nTOS symptoms. We found that digital biomarkers of slowness, power, and rigidity statistically differentiated the affected extremities of patients with nTOS from their contralateral extremities (n = 16) and from the extremities of healthy controls (n = 13); speed and power had the highest effect sizes. Digital biomarkers representing slowness, power, and rigidity correlated with patient-reported outcomes collected with the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and the visual analog scale of pain (VAS); speed had the highest correlation. Digital biomarkers of exhaustion correlated with failure of physical therapy in treating nTOS; and digital biomarkers of slowness, power, and exhaustion correlated with favorable response to nTOS surgery. In conclusion, sensor-derived digital biomarkers can objectively assess the impairment of motor capacity resultant from nTOS, and correlate with patient-reported symptoms and response to therapy.

Colourlab Image Database: Geometric Distortions

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.

An Objective Metric of Individual Health And Aging For Population Surveys

Background: Generalized, biomarker-based metrics of health status have numerous applications in fields ranging from sociology and economics to clinical research. We recently proposed a novel metric of health status based on physiological dysregulation measured as a Mahalanobis distance (DM) among clinical biomarkers. While DM was not particularly sensitive to the choice of biomarkers, it required calibration when used in different populations, making it difficult to compare findings across studies. To facilitate its use, here we aimed to identify and validate a standard version of DM that would be highly stable across populations, while using fewer biomarkers drawn exclusively from common blood panels. Methods: Using three datasets, we identified nine-biomarker (DM9) and seventeen-biomarker (DM17) versions of DM, choosing biomarkers based on their consistent levels across populations. We validated them in a fourth dataset. We assessed DM stability within and across populations by looking at correlations of DM versions calibrated using different populations or their demographic subsets. We used regression models to compare these standard DM versions to allostatic load and self-assessed health in their association with diverse health outcomes. Results: DM9 and DM17 were highly stable across population subsets (mean r = 0.96 and 0.95, respectively) and across populations (mean r = 0.94 for both). Performance predicting health outcomes was competitive with allostatic load and self-assessed health, though performance of these markers were somewhat variable for different health outcomes. Conclusions: Both DM9 and DM17 are highly stable within and across populations, supporting their use as objective metrics of health status. DM17 performs slightly better than DM9 and at least as well as other comparable metrics, but requires more biomarkers. The metrics we propose here are easy to measure with data that are available in a wide array of panel, cohort, and clinical studies.

Standardised Metrics and Methods for Synthetic Tabular Data Evaluation

Synthetic Tabular Data Generation (STDG) is a potentially valuable technology with great promise to augment real data and preserve privacy. However, prior to adoption, an empirical assessment of synthetic tabular data (STD) is required across the three dimensions of resemblance, utility, and privacy, trying to find a trade-off between them. A lack of standardised and objective metrics and methods has been found targeting this assessment in the literature and neither an organised pipeline or process for coordinating this evaluation has been identified. Therefore, in this work we propose a collection of metrics and methods to evaluate STD in the previously defined dimensions, presenting a meaningful orchestration of them and a pipeline unifying all of them. Additionally, we present a methodology to categorise STDG approaches performance for each dimension. Finally, we conducted an extensive analysis and evaluation to verify the usability of the proposed pipeline across six healthcare-related datasets, using four STDG approaches. The results of these analyses showed that the proposed pipeline can effectively be used to evaluate and benchmark the STD generated with one or more different STDG approaches, helping the scientific community to select the most suitable approaches for their data and application of interest.