1st International round robin on EL imaging: automated camera calibration and image normalisation

Results from the first international Round Robin on electroluminescence (EL) imaging of PV devices are presented. 17 Laboratories across Europe, Asia and the US measured EL images of ten commercially available modules and five single-cell modules. This work presents a novel automated camera calibration and image scaling routine. Its performance is quantified through comparing intensity deviation of corrected images and their cell average. While manual calibration includes additional measurement of lens distortion and flat field, the automated calibration extracts camera calibration parameters (here: lens distortion, and vignetting) exclusively from EL images. Although it is shown that the presented automated calibration outperforms the manual one, the method proposed in this work uses both manual and automated calibration. 501 images from 24 cameras are corrected. Intensity deviation of cell averages of every measured device decreased from 10.3 % (results submitted by contributing labs) to 2.8 % (proposed method), For three images the image correction produced insufficient results and vignetting correction failed for one camera, known of having a non-linear camera sensor. Surprisingly, largest image quality improvements are achieved by spatially precise image alignment of the same device and not by correcting for vignetting and lens distortion. This is due to overall small lens distortion and the circumstance that, although vignetting caused intensity reduction of more than 50%, PV devices are generally positioned in the image centre in which vignetting distortion is lowest

The grand-average ERPs for the different conditions.

<p>The figure depicts the ERPs for (A) the safe driver group and (B) the dangerous driver group. The time windows analyzed for the two effects in the four conditions are marked as grey boxes.</p

The mean reaction times (ms) for the emotional Stroop task under each condition.

<p>Error bars are included.</p

The number of previous crashes in each of the three groups and negativity bias.

<p>Error bars are included.</p

Supplemental Material - Endoscopic Lumbar Interbody Fusion, Minimally Invasive Transforaminal Lumbar Interbody Fusion, and Open Transforaminal Lumbar Interbody Fusion for the Treatment of Lumbar Degenerative Diseases: A Systematic Review and Network Meta-Analysis

Supplemental Material for Endoscopic Lumbar Interbody Fusion, Minimally Invasive Transforaminal Lumbar Interbody Fusion, and Open Transforaminal Lumbar Interbody Fusion for the Treatment of Lumbar Degenerative Diseases: A Systematic Review and Network Meta-Analysis by Xijian Hu, Lei Yan, Xinjie Jin, Haifeng Liu, Jing Chai, and Bin Zhao in Global Spine Journal</p

VennPainter: A Tool for the Comparison and Identification of Candidate Genes Based on Venn Diagrams

<div><p>VennPainter is a program for depicting unique and shared sets of genes lists and generating Venn diagrams, by using the Qt C++ framework. The software produces Classic Venn, Edwards’ Venn and Nested Venn diagrams and allows for eight sets in a graph mode and 31 sets in data processing mode only. In comparison, previous programs produce Classic Venn and Edwards’ Venn diagrams and allow for a maximum of six sets. The software incorporates user-friendly features and works in Windows, Linux and Mac OS. Its graphical interface does not require a user to have programing skills. Users can modify diagram content for up to eight datasets because of the Scalable Vector Graphics output. VennPainter can provide output results in vertical, horizontal and matrix formats, which facilitates sharing datasets as required for further identification of candidate genes. Users can obtain gene lists from shared sets by clicking the numbers on the diagram. Thus, VennPainter is an easy-to-use, highly efficient, cross-platform and powerful program that provides a more comprehensive tool for identifying candidate genes and visualizing the relationships among genes or gene families in comparative analysis.</p></div

Comparison of BioVenn, Venny, jVenn, VennDiagrams, VennTure, and VennPainter.

<p>Comparison of BioVenn, Venny, jVenn, VennDiagrams, VennTure, and VennPainter.</p

Output shared datasets.

<p>The Horizontal, Vertical and Matrix formats of output datasets. <b>(a)</b> Matrix format, first row contains all datasets and the first column contains all dataset elements; remaining columns denote if an element belongs to the dataset. Matrix used to construct a network. <b>(b)</b> Horizontal format, each line represents one intersection shared by datasets, which are listed before the colon. <b>(c)</b> Vertical format, identical to the Horizontal format with exchanged columns and rows.</p

Venn diagrams and Nested Venn in VennPainter.

<p><b>(a)</b> Classic Venn diagram depicting from one to five datasets. <b>(b)</b> Edwards’ Venn diagrams for from two to six datasets. <b>(c)</b> Nested Venn diagrams showing from five to eight variables. Nested Venn diagrams uses single-level Classic Venn diagrams to construct multi-level ones, which are easier to interpret than other forms of Venn diagrams when the datasets reaches more than six.</p

Venn diagrams for seven sets.

<p><b>(a)</b> Edward’s Venn diagram constructed with cogwheels, which become smaller with increasing numbers of sets. With seven sets, this made it hard to fill intersections with a number. <b>(b)</b> Venn diagram constructed with irregular curves. Some intersections are unclear. <b>(c)</b> Nested Venn diagram places its intersections more evenly and regularly, which facilitates accurate interpretation.</p