Representativeness of breast cancer cases in an integrated health care delivery system.

BackgroundIntegrated health care delivery systems, with their comprehensive and integrated electronic medical records (EMR), are well-poised to conduct research that leverages the detailed clinical data within the EMRs. However, information regarding the representativeness of these clinical populations is limited, and thus the generalizability of research findings is uncertain.MethodsUsing data from the population-based California Cancer Registry, we compared age-adjusted distributions of patient and neighborhood characteristics for three groups of breast cancer patients: 1) those diagnosed within Kaiser Permanente Northern California (KPNC), 2) non-KPNC patients from NCI-designated cancer centers, and 3) those from all other hospitals.ResultsKPNC patients represented 32 % (N = 36,109); cancer center patients represented 7 % (N = 7805); and all other hospitals represented 61 % (N = 68,330) of the total breast cancer patients from this geographic area during 1996-2009. Compared with cases from all other hospitals, KPNC had slightly fewer non-Hispanic Whites (70.6 % versus 74.4 %) but more Blacks (8.1 % versus 5.0 %), slightly more patients in the 50-69 age range and fewer in the younger and older age groups, a slightly lower proportion of in situ but higher proportion of stage I disease (41.6 % versus 38.9 %), were slightly less likely to reside in the lowest (4.2 % versus 6.5 %) and highest (36.2 % versus 39.0 %) socioeconomic status neighborhoods, and more likely to live in suburban metropolitan areas and neighborhoods with more racial/ethnic minorities. Cancer center patients differed substantially from patients from KPNC and all other hospitals on all characteristics assessed. All differences were statistically significant (p < .001).ConclusionsAlthough much of clinical research discoveries are based in academic medical centers, patients from large, integrated medical centers are likely more representative of the underlying population, providing support for the generalizability of cancer research based on electronic data from these centers

Changes in timber haul emissions in the context of shifting forest management and infrastructure

<p>Abstract</p> <p>Background</p> <p>Although significant amounts of carbon may be stored in harvested wood products, the extraction of that carbon from the forest generally entails combustion of fossil fuels. The transport of timber from the forest to primary milling facilities may in particular create emissions that reduce the net sequestration value of product carbon storage. However, attempts to quantify the effects of transport on the net effects of forest management typically use relatively sparse survey data to determine transportation emission factors. We developed an approach for systematically determining transport emissions using: 1) -remotely sensed maps to estimate the spatial distribution of harvests, and 2) - industry data to determine landscape-level harvest volumes as well as the location and processing totals of individual mills. These data support spatial network analysis that can produce estimates of fossil carbon released in timber transport.</p> <p>Results</p> <p>Transport-related emissions, evaluated as a fraction of transported wood carbon at 4 points in time on a landscape in western Montana (USA), rose from 0.5% in 1988 to 1.7% in 2004 as local mills closed and spatial patterns of harvest shifted due to decreased logging on federal lands.</p> <p>Conclusion</p> <p>The apparent sensitivity of transport emissions to harvest and infrastructure patterns suggests that timber haul is a dynamic component of forest carbon management that bears further study both across regions and over time. The monitoring approach used here, which draws only from widely available monitoring data, could readily be adapted to provide current and historical estimates of transport emissions in a consistent way across large areas.</p

Differentiation of Pre-Ablation and Post-Ablation Late Gadolinium-Enhanced Cardiac MRI Scans of Longstanding Persistent Atrial Fibrillation Patients

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.</p

Functional Locomotor Consequences of Uneven Forefeet for Trot Symmetry in Individual Riding Horses

ABSTRACT: Left-right symmetrical distal limb conformation can be an important prerequisite for a successful performance, and it is often hypothesized that asymmetric or uneven feet are important enhancing factors for the development of lameness. On a population level, it has been demonstrated that uneven footed horses are retiring earlier from elite level competition, but the biomechanical consequences are not yet known. The objectives of this study were to compare the functional locomotor asymmetries of horses with uneven to those with even feet. Hoof kinetics and distal limb kinematics were collected from horses (n = 34) at trot. Dorsal hoof wall angle was used to classify horses as even or uneven (1.5° difference between forefeet respectively) and individual feet as flat (55°). Functional kinetic parameters were compared between even and uneven forefeet using MANOVA followed by ANOVA. The relative influences of differences in hoof angle between the forefeet and of absolute hoof angle on functional parameters were analysed using multiple regression analysis (P<0.05). In horses with uneven feet, the side with the flatter foot showed a significantly larger maximal horizontal braking and vertical ground reaction force, a larger vertical fetlock displacement and a suppler fetlock spring. The foot with a steeper hoof angle was linearly correlated with an earlier braking-propulsion transition. The conformational differences between both forefeet were more important for loading characteristics than the individual foot conformation of each individual horse. The differences in vertical force and braking force between uneven forefeet could imply either an asymmetrical loading pattern without a pathological component or a subclinical lameness as a result of a pathological development in the steeper foot

Deep De-Aliasing for Fast Compressive Sensing MRI

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience. This can also potentially increase the image quality by reducing the motion artefacts and contrast washout. However, once an image field of view and the desired resolution are chosen, the minimum scanning time is normally determined by the requirement of acquiring sufficient raw data to meet the Nyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has been perfectly matched to the MRI scanning sequence design with much less required raw data for the image reconstruction. Inspired by recent advances in deep learning for solving various inverse problems, we propose a conditional Generative Adversarial Networks-based deep learning framework for de-aliasing and reconstructing MRI images from highly undersampled data with great promise to accelerate the data acquisition process. By coupling an innovative content loss with the adversarial loss our de-aliasing results are more realistic. Furthermore, we propose a refinement learning procedure for training the generator network, which can stabilise the training with fast convergence and less parameter tuning. We demonstrate that the proposed framework outperforms state-of-the-art CS-MRI methods, in terms of reconstruction error and perceptual image quality. In addition, our method can reconstruct each image in 0.22ms--0.37ms, which is promising for real-time applications

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF

Biallelic variants in ADARB1, encoding a dsRNA-specific adenosine deaminase, cause a severe developmental and epileptic encephalopathy

Background Adenosine-to-inosine RNA editing is a co-transcriptional/post-transcriptional modification of double-stranded RNA, catalysed by one of two active adenosine deaminases acting on RNA (ADARs), ADAR1 and ADAR2. ADARB1 encodes the enzyme ADAR2 that is highly expressed in the brain and essential to modulate the function of glutamate and serotonin receptors. Impaired ADAR2 editing causes early onset progressive epilepsy and premature death in mice. In humans, ADAR2 dysfunction has been very recently linked to a neurodevelopmental disorder with microcephaly and epilepsy in four unrelated subjects. Methods We studied three children from two consanguineous families with severe developmental and epileptic encephalopathy (DEE) through detailed physical and instrumental examinations. Exome sequencing (ES) was used to identify ADARB1 mutations as the underlying genetic cause and in vitro assays with transiently transfected cells were performed to ascertain the impact on ADAR2 enzymatic activity and splicing. Results All patients showed global developmental delay, intractable early infantile-onset seizures, microcephaly, severe-to-profound intellectual disability, axial hypotonia and progressive appendicular spasticity. ES revealed the novel missense c.1889G&gt;A, p.(Arg630Gln) and deletion c.1245_1247+1 del, p.(Leu415PhefsTer14) variants in ADARB1 (NM_015833.4). The p.(Leu415PhefsTer14) variant leads to incorrect splicing resulting in frameshift with a premature stop codon and loss of enzyme function. In vitro RNA editing assays showed that the p.(Arg630Gln) variant resulted in a severe impairment of ADAR2 enzymatic activity. Conclusion In conclusion, these data support the pathogenic role of biallelic ADARB1 variants as the cause of a distinctive form of DEE, reinforcing the importance of RNA editing in brain function and development