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Imaging FCS and FCCS generate high-resolution quantitative data over large numbers of pixels that can be used to measure a range of molecular characteristics, including concentration, diffusion rates and interactions. Single-plane illumination (SPIM) or total internal reflection fluorescence (TIRF) microscopes can be combined with fast and single-molecule-sensitive cameras to allow spatially resolved fluorescence (cross-) correlation spectroscopy (FCS or FCCS, hereafter referred to FCS/FCCS). This creates a powerful quantitative bioimaging tool that can generate spatially resolved mobility and interaction maps with hundreds to thousands of pixels per sample. These massively parallel imaging schemes also cause less photodamage than conventional single-point confocal microscopy–based FCS/FCCS. Here we provide guidelines for imaging FCS/FCCS measurements on commercial and custom-built microscopes (including sample preparation, setup calibration, data acquisition and evaluation), as well as anticipated results for a variety of in vitro and in vivo samples. For a skilled user of an available SPIM or TIRF setup, sample preparation, microscope alignment, data acquisition and data fitting, as described in this protocol, will take ∼1 d, depending on the sample and the mode of imaging.

Dust storms in the earth's major desert regions significantly influence microphysical weather processes, the CO₂-cycle and the global climate in general. Recent increases in the spatio-temporal resolution of remote sensing instruments have created new opportunities to understand these phenomena. However, the scale of the data collected and the inherent stochasticity of the underlying process pose significant challenges, requiring a careful combination of image processing and statistical techniques. Using satellite imagery data, we develop a statistical model of atmospheric transport that relies on a latent Gaussian Markov random field (GMRF) for inference. In doing so, we make a link between the optical flow method of Horn and Schunck and the formulation of the transport process as a latent field in a generalized linear model. We critically extend this framework to satisfy the integrated continuity equation, thereby incorporating a flow field with nonzero divergence, and show that such an approach dramatically improves performance while remaining computationally feasible. Effects such as air compressibility and satellite column projection hence become intrinsic parts of this model. We conclude with a study of the dynamics of dust storms formed over Sanaran Africa and show that our methodology is able to accurately and coherently track storm movement, a critical problem in this field.

This paper presents a technique for finding the surface normal of an object from a set of images obtained under different lighting positions. The method presented is based on the principles of Photometric Stereo (PS) combined with Optimum Experimental Design (OED) and Parameter Estimation (PE). Unclear by the approach of photometric stereo, and many models based thereon, is how to position the light sources. So far, this is done by using heuristic approaches this leads to suboptimal and non-data driven positioning of the light sources. But what if the optimal positions of the light sources are calculated for photometric stereo? To this end, in this contribution, the effect of positioning the light sources on the quality of the normal vector for PS is evaluated. Furthermore, a new approach in this direction is derived and formulated. For the calculation of the surface normal of a Lambertian surface, the approach based on calibrated photometric stereo; for the estimation the optimal position of the light sources the approach is premised on parameter estimation and optimum experimental design. The approach is tested using synthetic and real-data. Based on results it can be seen that the surface normal estimated with the new method is more detailed than with conventional methods.

We address the vessel segmentation problem by building upon the multiscale feature learning method of Kiros et al., which achieves the current top score in the VESSEL12 MICCAI challenge. Following their idea of feature learning instead of hand-crafted filters, we have extended the method to learn 3D features. The features are learned in an unsupervised manner in a multi-scale scheme using dictionary learning via least angle regression. The 3D feature kernels are further convolved with the input volumes in order to create feature maps. Those maps are used to train a supervised classifier with the annotated voxels. In order to process the 3D data with a large number of filters a parallel implementation has been developed. The algorithm has been applied on the example scans and annotations provided by the VESSEL12 challenge. We have compared our setup with Kiros et al. by running their implementation. Our current results show an improvement in accuracy over the slice wise method from 96.66\\(\\)1.10% to 97.24\\(\\)0.90%.

Comparing different algorithms for segmenting glass fibers in industrial computed tomography (CT) scans is difficult due to the absence of a standard reference dataset. In this work, we introduce a set of annotated scans of short-fiber reinforced polymers (SFRP) as well as synthetically created CT volume data together with the evaluation metrics. We suggest both the metrics and this data set as a reference for studying the performance of different algorithms. The real scans were acquired by a Nikon MCT225 X-ray CT system. The simulated scans were created by the use of an in-house computational model and third-party commercial software. For both types of data, corresponding ground truth annotations have been prepared, including hand annotations for the real scans and STL models for the synthetic scans. Additionally, a Hessian-based Frangi vesselness filter for fiber segmentation has been implemented and open-sourced to serve as a reference for comparisons.

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 {\\mu}m isotropic) and low (8.3 {\\mu}m isotropic) resolution using deep learning architectures. We performed experiments on both synthetic and real CT scans and evaluated deep fully convolutional architectures with both 2D and 3D kernels. Our artificial neural networks outperform existing methods at both medium and low resolution scans.

Dust storms in the earth's major desert regions significantly influence microphysical weather processes, the CO\\(_2\\)-cycle and the global climate in general. Recent increases in the spatio-temporal resolution of remote sensing instruments have created new opportunities to understand these phenomena. However, the scale of the data collected and the inherent stochasticity of the underlying process pose significant challenges, requiring a careful combination of image processing and statistical techniques. In particular, using satellite imagery data, we develop a statistical model of atmospheric transport that relies on a latent Gaussian Markov random field (GMRF) for inference. In doing so, we make a link between the optical flow method of Horn and Schunck and the formulation of the transport process as a latent field in a generalized linear model, which enables the use of the integrated nested Laplace approximation for inference. This framework is specified such that it satisfies the so-called integrated continuity equation, thereby intrinsically expressing the divergence of the field as a multiplicative factor covering air compressibility and satellite column projection. The importance of this step -- as well as treating the problem in a fully statistical manner -- is emphasized by a simulation study where inference based on this latent GMRF clearly reduces errors of the estimated flow field. We conclude with a study of the dynamics of dust storms formed over Saharan Africa and show that our methodology is able to accurately and coherently track the storm movement, a critical problem in this field.

Complete lymph node dissection is recommended in patients with positive sentinel lymph node biopsy results. To date, the effect of complete lymph node dissection on prognosis is controversial. In the DeCOG-SLT trial, we assessed whether complete lymph node dissection resulted in increased survival compared with observation. In this multicentre, randomised, phase 3 trial, we enrolled patients with cutaneous melanoma of the torso, arms, or legs from 41 German skin cancer centres. Patients with positive sentinel lymph node biopsy results were eligible. Patients were randomly assigned (1:1) to undergo complete lymph node dissection or observation with permuted blocks of variable size and stratified by primary tumour thickness, ulceration of primary tumour, and intended adjuvant interferon therapy. Treatment assignment was not masked. The primary endpoint was distant metastasis-free survival and analysed by intention to treat. All patients in the intention-to-treat population of the complete lymph node dissection group were included in the safety analysis. This trial is registered with ClinicalTrials.gov, number NCT02434107. Follow-up is ongoing, but the trial no longer recruiting patients. Between Jan 1, 2006, and Dec 1, 2014, 5547 patients were screened with sentinel lymph node biopsy and 1269 (23%) patients were positive for micrometastasis. Of these, 483 (39%) agreed to randomisation into the clinical trial; due to difficulties enrolling and a low event rate the trial closed early on Dec 1, 2014. 241 patients were randomly assigned to the observation group and 242 to the complete lymph node dissection group. Ten patients did not meet the inclusion criteria, so 233 patients were analysed in the observation group and 240 patients were analysed in the complete lymph node dissection group, as the intention-to-treat population. 311 (66%) patients (158 in the observation group and 153 in the dissection group) had sentinel lymph node metastases of 1 mm or less. Median follow-up was 35 months (IQR 20–54). Distant metastasis-free survival at 3 years was 77·0% (90% CI 71·9–82·1; 55 events) in the observation group and 74·9% (69·5–80·3; 54 events) in the complete lymph node dissection group. In the complete lymph node dissection group, grade 3 and 4 events occurred in 15 patients (6%) and 19 patients (8%) patients, respectively. Adverse events included lymph oedema (grade 3 in seven patients, grade 4 in 13 patients), lymph fistula (grade 3 in one patient, grade 4 in two patients), seroma (grade 3 in three patients, no grade 4), infection (grade 3 in three patients, no grade 4), and delayed wound healing (grade 3 in one patient, grade 4 in four patients); no serious adverse events were reported. Although we did not achieve the required number of events, leading to the trial being underpowered, our results showed no difference in survival in patients treated with complete lymph node dissection compared with observation only. Consequently, complete lymph node dissection should not be recommended in patients with melanoma with lymph node micrometastases of at least a diameter of 1 mm or smaller. German Cancer Aid.

Marine silicate alteration plays a key role in the global carbon and cation cycles, although the timeframe of this process in response to extreme weather events is poorly understood. Here we investigate surface sediments across the Peruvian margin before and after extreme rainfall and runoff (coastal El Niño) using Ge/Si ratios and laser-ablated solid and pore fluid Si isotopes (δ 30 Si). Pore fluids following the rainfall show elevated Ge/Si ratios (2.87 µmol mol −1 ) and δ 30 Si values (3.72‰), which we relate to rapid authigenic clay formation from reactive terrigenous minerals delivered by continental runoff. This study highlights the direct coupling of terrestrial erosion and associated marine sedimentary processes. We show that marine silicate alteration can be rapid and highly dynamic in response to local weather conditions, with a potential impact on marine alkalinity and CO 2 -cycling on short timescales of weeks to months, and thus element turnover on human time scales. This study identifies the rapidness of marine mineral reactions, directly after an extreme rainfall event. The reactions have the potential to affect marine cation and CO 2 cycling, impacting element turnover on human time scales