Health Insurance Coverage And Access To Care Among LGBT Adults, 2013-19.

Historically, lesbian, gay, bisexual, and transgender (LGBT) adults have faced barriers to obtaining health insurance coverage, which have contributed to disparities in access to care and health outcomes. The Affordable Care Act (ACA) and the 2015 Supreme Court ruling on marriage equality had the potential to improve access to health insurance for LGBT people. Using data from the nationally representative Health Reform Monitoring Survey, we provide new evidence on trends in coverage and access to care for LGBT and non-LGBT adults between 2013 and 2019. In 2013 LGBT adults were significantly less likely than non-LGBT adults to have insurance coverage and more likely to report difficulty obtaining necessary medical care. Disparities in insurance coverage began to decline in 2014, when the main coverage provisions of the ACA went into effect. By 2017-19, coverage rates for LGBT adults were comparable to those of non-LGBT adults, although significant disparities in access remained.

Assessment of Various Multimodal Fusion Approaches Using Synthetic Aperture Radar (SAR) and Electro-Optical (EO) Imagery for Vehicle Classification via Neural Networks.

Multimodal fusion approaches that combine data from dissimilar sensors can better exploit human-like reasoning and strategies for situational awareness. The performance of a six-layer convolutional neural network (CNN) and an 18-layer ResNet architecture are compared for a variety of fusion methods using synthetic aperture radar (SAR) and electro-optical (EO) imagery to classify military targets. The dataset used is the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, using both original measured SAR data and synthetic EO data. We compare the classification performance of both networks using the data modalities individually, feature level fusion, decision level fusion, and using a novel fusion method based on the three RGB-input channels of a residual neural network (ResNet). In the proposed input channel fusion method, the SAR and the EO imagery are separately fed to each of the three input channels, while the third channel is fed a zero vector. It is found that the input channel fusion method using ResNet was able to consistently perform to a higher classification accuracy in every equivalent scenario.

Assessment of a novel commercial large field of view phantom for comprehensive MR imaging quality assurance of a 0.35T MRgRT system.

Consistent quality assurance (QA) programs are vital to MR-guided radiotherapy (MRgRT), for ensuring treatment is delivered accurately and the onboard MRI system is providing the expected image quality. However, daily imaging QA with a dedicated phantom is not common at many MRgRT centers, especially with large phantoms that cover a field of view (FOV), similar to the human torso. This work presents the first clinical experience with a purpose-built phantom for large FOV daily and periodic comprehensive quality assurance (QUASAR™ MRgRT Insight Phantom (beta)) from Modus Medical Devices Inc. (Modus QA) on an MRgRT system. A monthly American College of Radiology (ACR) QA phantom was also imaged for reference. Both phantoms were imaged on a 0.35T MR-Linac, a 1.5T Philips wide bore MRI, and a 3.0T Siemens MRI, with T1-weighted and T2-weighted acquisitions. The Insight phantom was imaged in axial and sagittal orientations. Image quality tests including geometric accuracy, spatial resolution accuracy, slice thickness accuracy, slice position accuracy, and image intensity uniformity were performed on each phantom, following their respective instruction manuals. The geometric distortion test showed similar distortions of -1.7 mm and -1.9 mm across a 190 mm and a 283 mm lengths for the ACR and MRgRT Insight phantoms, respectively. The MRgRT Insight phantom utilized a modulation transform function (MTF) for spatial resolution evaluation, which showed decreased performance on the lower B0 strength MRIs, as expected, and could provide a good daily indicator of machine performance. Both the Insight and ACR phantoms showed a match with scan parameters for slice thickness analysis. During the imaging and analysis of this novel MRgRT Insight phantom the authors found setup to be straightforward allowing for easy acquisition each day, and useful image analysis parameters for tracking MRI performance.

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

BACKGROUND: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. RESULTS: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. CONCLUSIONS: Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.

The orthodontic-restorative interface: 2. Compensating for variations in tooth number and shape.

UNLABELLED: The aim of this second article in this series of two is to outline a variety of methods which may be used to compensate for variations in tooth shape and number using a combination of orthodontic and restorative approaches. It will also provide an overview of other areas of patient care which necessitate a multi-disciplinary orthodontic/restorative approach. The article will highlight the importance of combined planning from the outset and the close relationship between the different specialties, which must be maintained throughout treatment. The methods of compensating for variations in tooth number and shape will often require contributions from both orthodontist and restorative dentist. It is important that both disciplines are involved in the assessment and treatment planning process so that they know what will be expected of them during the patient's care. Treatment planning in isolation may lead to care being delivered which is below the optimum standard which can be achieved. The orthodontist and restorative dentist are likely to liaise with the patient's general dental practitioner so that he/she can provide the restorative treatment in some cases. CLINICAL RELEVANCE: Great improvements in aesthetics and function may be obtained using an interdisciplinary approach for patients who have variations in tooth number and shape.

The orthodontic-restorative interface: 1. Patient assessment.

UNLABELLED: The first article in this series of two aims to outline the assessment of patients for whom a combined orthodontic-restorative approach would be beneficial. In particular, it will concentrate on the assessment of patients who have hypodontia and tooth size discrepancies. The importance of the aesthetic assessment for these cases will be highlighted. Variations in tooth number and tooth size discrepancy often require a combined treatment planning approach from the orthodontist and restorative dentist. The referring general dental practitioner has a key role in recognizing that this approach may be required and highlighting this in the initial patient referral. It is likely in the more straightforward cases that the GDP will be providing the restorative treatment and so an increased understanding of these cases would be beneficial. In the second paper, treatment options will be presented. CLINICAL RELEVANCE: For patients who require a combined orthodontic/restorative approach, it is important that orthodontic and restorative disciplines liaise closely in the assessment and treatment planning process so that optimal care may be planned.