Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure

A crucial aspect of mass-mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present results from a new method that reconstructs maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type $\ell_1$-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass-mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artifacts of the reconstruction. Here we present this new formalism, demonstrate the method on illustrative examples, before applying the developed formalism to two observational datasets of the Abel-520 cluster. In our Bayesian framework it is found that neither Abel-520 dataset can conclusively determine the physicality of individual local massive substructure at significant confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data

Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

The size of the training data set is a major determinant of classification accuracy. Neverthe- less, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algo- rithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project

Estimating the number of UK stroke patients eligible for endovascular thrombectomy

Introduction: Endovascular thrombectomy (EVT) is a highly effective treatment for acute ischemic stroke due to large arterial occlusion. Routine provision will require major changes in service configuration and workforce. An important first step is to quantify the population of stroke patients that could benefit. We estimated the annual UK population suitable for EVT using standard or advanced imaging (AI) for patient selection. Patients and Methods: Evidence from randomised control trials and national registries was combined to estimate UK stroke incidence and define a decision-tree describing the EVT eligible population. Results: Between 9,620 and 10,920 UK stroke patients (approximately 10% of stroke admissions) would be eligible for EVT annually. The majority (9,140 to 9,620) would present within 4 hours of onset and be suitable for intravenous thrombolysis. Advanced Imaging would exclude 500 patients presenting within 4 hours, but identify an additional 1,310 patients as eligible who present later. Discussion: Information from randomised control trials and large registry data provided the evidence criterion for 9 of the 12 decision points. The best available evidence was used for 2 decision-points with sensitivity analyses to determine how key branches of the tree affected estimates. Using the mid-point estimate for eligibility (9.6% of admissions) and assuming national EVT coverage, 4,280 patients would have reduced disability. Conclusion: A model combining published trials and register data suggests approximately 10% of all stroke admissions in the UK are eligible for EVT. The use of AI based on current published evidence did not have a major impact on overall numbers, but could alter eligibility status for 16% of cases

How should long-term free-living physical activity be targeted after stroke? A systematic review and narrative synthesis

Abstract Background Increasing physical activity (PA) levels (regular movement such as walking and activities of daily living) and reducing time spent sedentary improves cardiovascular health and reduces morbidity and mortality. Fewer than 30% of independently mobile stroke survivors undertake recommended levels of PA. Sedentary behaviour is also high in this population. We aimed to systematically review the study characteristics and the promise of interventions targeting free-living PA and/or sedentary behaviour in adult stroke survivors. Methods Seven electronic databases were searched to identify randomised controlled trials (≥3-months follow-up) targeting PA and/or sedentary behaviour in adults with first or recurrent stroke or transient ischaemic attack. The quality assessment framework for RCTs was used to assess risk of bias within and across studies. Interventions were rated as “very”, “quite” or “non-promising” based on within- or between-group outcome differences. Intervention descriptions were captured using the TIDieR (Template for Intervention Description and Replication) Checklist. Behaviour change techniques (BCTs) within interventions were coded using the BCT Taxonomy v1, and compared between studies by calculating a promise ratio. Results Nine studies fulfilled the review criteria (N = 717 randomised stroke patients) with a high or unclear risk of bias. None of the studies targeted sedentary behaviour. Six studies were very/quite promising (reported increases in PA post-intervention). Studies were heterogeneous in their reporting of participant age, time since stroke, stroke type, and stroke location. Sub-optimal intervention descriptions, treatment fidelity and a lack of standardisation of outcome measures were identified. Face to face and telephone-based self-management programmes were identified as having promise to engage stroke survivors in PA behaviour change. Optimal intensity of contact, interventionist type and time after stroke to deliver interventions was unclear. Nine promising BCTs (ratios ≥2) were identified: information about health consequences; information about social and environmental consequences; goal setting-behaviour; problem-solving; action planning; feedback on behaviour; biofeedback; social support unspecified; and credible source. Conclusions Future research would benefit from establishing stroke survivor preferences for mode of delivery, setting and intensity, including measurement of physical activity. Interventions need to justify and utilise a theory/model of behaviour change and explore the optimal combination of promising BCTs within interventions

Ligand biological activity predicted by cleaning positive and negative chemical correlations.

Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified

Developing methodology for efficient eelgrass habitat mapping across lidar systems

Super Storm Sandy, the second costliest hurricane in U.S. history, made landfall on the east coast of the U.S. in October 2012. In an attempt to assess the impacts of the storm on coastal ecosystems, several U.S. mapping agencies such as the National Oceanic and Atmospheric Administration (NOAA), the U.S. Geological Survey (USGS), and the U.S. Army Corps of Engineers (USACE) commenced data collection efforts using a variety of remotely-sensed data types including aerial imagery and topobathymetric lidar. The objective of this study was to investigate the applicability of object-based image analysis techniques for benthic habitat mapping. Bathymetry and reflectance data collected by a Riegl VQ-820-G system and the AHAB Chiroptera system along with aerial imagery (Applanix DSS) were compared using an objectbased image analysis (OBIA) technique to classify dense eelgrass beds, mixed sand and macroalgae, and sand habitats. In order to determine the efficacy of this method for benthic habitat classification it was also compared to a manual method of classification from aerial imagery. The resulting habitat maps were compared between systems to determine the feasibility of using one OBIA classification rule set across lidar systems and aerial imagery. Our preliminary results using the Riegl system suggest our methodology correctly classified 85% of benthic habitats. Preliminary results using the Chiroptera also suggests similar accuracy of classification. This methodology will allow streamlined creation of habitat maps for coastal managers and researchers using large sets of data collected by multiple sensors. Testing of this OBIA methodology is ongoing as new data from various sensors becomes available

DUSP1 mRNA modulation during porcine circovirus type 2 and porcine reproductive and respiratory syndrome virus co-infection regulates viruses replication

The effects of porcine circovirus type 2b (PCV2b) and porcine reproductive and respiratory syndrome virus (PRRSV) co-infection in epithelial cells of the swine respiratory tract is unknown. In the present study, the newborn pig trachea cell line NPTr-CD163, which is permissive to both viruses, was persistently infected with PCV2b and then with PRRSV. Viral replication, cell viability, cytokines’ mRNA expression, and modulation of cellular genes expression were evaluated in infected cells. In NPTr-CD163 co-infection model, PCV2b replication was enhanced while PRRSV replication was suppressed. Cell viability was significantly decreased during PCV2b single infection and co-infection compared to mock-infected and PRRSV single infected cells. However, no difference was observed in cell viability between PCV2b and PCV2b/PRRSV infected cells. The IL6, IL8 and IL10 mRNA expression was significantly higher in co-infected cells compared to PCV2b and PRRSV single infected cells. Moreover, the IFN-α/β expression was significantly reduced in co-infected cells compared to PCV2b infected cells whereas it remained higher compared to PRRSV infected cells. The differential gene expression analysis revealed that the mRNA expression level of the cellular gene DUSP1 was significantly higher in all PRRSV infection models compared to PCV2b single infected cells. Knockdown of DUSP1 expression in co-infected cells significantly reduced PCV2b replication, suggesting a role for DUSP1 in PCV2b/PRRSV pathogenesis

Comparison of Intraoperative Fluoroscopy to Postoperative Weight-Bearing Radiographs Obtained 4 to 6 Weeks After Bunion Repair With A Chevron Osteotomy

Background: During operative treatment of bunions, an attempt is made to correct the hallux valgus angle (HVA) and the intermetatarsal angle (IMA). In this study, the HVA and the IMA were measured using intraoperative C-arm fluoroscopic images obtained during surgical treatment of a bunion with chevron osteotomy. These angles were again measured using weight-bearing radiographs obtained 4 to 6 weeks postoperatively. Methods: At our institution, we reviewed medical records of patients who underwent a bunion repair with chevron osteotomy between January 2013 and October 2017. A total of 26 feet from 24 patients were included. Three authors (ALP, TMH, and RAM) measured the HVA and IMA using intraoperative fluoroscopic images and postoperative weight-bearing radiographs (4 measurements per foot; total, 104 measurements). The authors were blinded to their previous angular measurements and to measurements made by the others. An intraclass correlation coefficient was calculated for the HVA and IMA measurements between groups (ie, intraoperative fluoroscopic images and postoperative radiographs) to determine interobserver reliability. We compared the angles measured by the authors between groups and used a paired t test for statistical evaluation. Results: Interobserver difference of the HVA and IMA was low between intraoperative fluoroscopic images and postoperative weight-bearing radiographs (0.98 and 0.79; 0.78 and 0.95, respectively). The measured IMAs were relatively consistent between groups (6.21° and 6.37°, respectively); only two patients had a difference \u3e 3°. There was a greater difference in HVAs between groups (11.5° and 14.2°, respectively). In 11 feet, the HVA was \u3e 5° (range, 5.3-12.7°) in the postoperative radiograph compared to the fluoroscopic image. In one foot, we noted a 7° decrease of the HVA on the postoperative radiograph. The average difference of HVA between groups was 2.6° (P \u3c 0.0001), whereas the IMA was 0.16° (P = 0.002). Conclusions: Interobserver measurements of the HVA and IMA were reliable on both the intraoperative fluoroscopic images and the postoperative weightbearing radiographs. The IMA was similar between groups; however, the HVA was often greater on the postoperative weight-bearing radiographs

Empirical Implementation of Nonparametric First-Price Auction Models

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparamteric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differering number of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.