Very rapid long-distance sea crossing by a migratory bird

Landbirds undertaking within-continent migrations have the possibility to stop en route, but most long-distance migrants must also undertake large non-stop sea crossings, the length of which can vary greatly. For shorebirds migrating from Iceland to West Africa, the shortest route would involve one of the longest continuous sea crossings while alternative, mostly overland, routes are available. Using geolocators to track the migration of Icelandic whimbrels (Numenius phaeopus), we show that they can complete a round-trip of 11,000 km making two non-stop sea crossings and flying at speeds of up to 24 m s-1; the fastest recorded for shorebirds flying over the ocean. Although wind support could reduce flight energetic costs, whimbrels faced headwinds up to twice their ground speed, indicating that unfavourable and potentially fatal weather conditions are not uncommon. Such apparently high risk migrations might be more common than previously thought, with potential fitness gains outweighing the costs

A machine learning approach to predict cellular uptake of pBAE polyplexes

The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size and polydispersity. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal in silico screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease

BACKGROUND: While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers. METHODS: Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10%  40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4. RESULTS: Based on CV and ICC, segmental Ecc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice Ecc and global Ell provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice Err, CV was in the fair-good range, and ICC was in the good-excellent range. CONCLUSIONS: Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental Ecc, global Ell, and torsion measurements in healthy subjects and heart disease patients

RgoogleMaps and loa: unleashing R graphics power on map tiles

The RgoogleMaps package provides (1) an R interface to query the Google and the OpenStreetMap servers for static maps in the form of PNGs, and (2) enables the user to overlay plots on those maps within R. The loa package provides dedicated panel functions to integrate RgoogleMaps within the lattice plotting environment. In addition to solving the generic task of plotting on a map background in R, we introduce several specific algorithms to detect and visualize spatio-temporal clusters. This task can often be reduced to detecting over-densities in space relative to a background density. The relative density estimation is framed as a binary classification problem. An integrated hotspot visualizer is presented which allows the efficient identification and visualization of clusters in one environment. Competing clustering methods such as the scan statistic and the density scan other higher detection power at a much larger computational cost. Such clustering method can then be extended using the lattice trellis framework to provide further insight into the relationship between clusters and potentially influential parameters. While there are other options for such map `mashups' we believe that the integration of RgoogleMaps and lattice using loa can in certain circumstances be advantageous, e.g., by providing a highly intuitive working environment for multivariate analysis and flexible testbed for the rapid development of novel data visualizations

4D Flow MR Imaging to Improve Microwave Ablation Prediction Models: A Feasibility Study in an In&nbsp;Vivo Porcine Liver.

PURPOSE: To characterize the effect of hepatic vessel flow using 4-dimensional (4D) flow magnetic resonance (MR) imaging and correlate their effect on microwave ablation volumes in an in&nbsp;vivo non-cirrhotic porcine liver model. MATERIALS AND METHODS: Microwave ablation antennas were placed under ultrasound guidance in each liver lobe of swine (n&nbsp;= 3 in each animal) for a total of 9 ablations. Pre- and post-ablation 4D flow MR imaging was acquired to quantify flow changes in the hepatic vasculature. Flow measurements, along with encompassed vessel size and vessel-antenna spacing, were then correlated with final ablation volume from segmented MR images. RESULTS: The linear regression model demonstrated that the preablation measurement of encompassed hepatic vein size (β&nbsp;= -0.80 ±&nbsp;0.25, 95% confidence interval [CI] -1.15 to -0.22; P&nbsp;= .02) was significantly correlated to final ablation zone volume. The addition of hepatic vein flow rate found via 4D flow MRI (β&nbsp;= -0.83 ± 0.65, 95% CI -2.50 to 0.84; P&nbsp;= .26), and distance from antenna to hepatic vein (β&nbsp;= 0.26 ±&nbsp;0.26, 95% CI -0.40 to 0.92; P&nbsp;= .36) improved the model accuracy but not significantly so (multivariate adjusted R2&nbsp;= 0.70 vs univariate (vessel size) adjusted R2&nbsp;= 0.63, P&nbsp;= .24). CONCLUSIONS: Hepatic vein size in an encompassed ablation zone was found to be significantly correlated with final ablation zone volume. Although the univariate 4D flow MR imaging-acquired measurements alone were not found to be statistically significant, its addition to hepatic vein size improved the accuracy of the ablation volume regression model. Pre-ablation 4D flow MR imaging of the liver may assist in prospectively optimizing thermal ablation treatment