Single-particle fluorescence tracking combined with TrackMate assay reveals highly heterogeneous and discontinuous lysosomal transport in freely orientated axons

Axonal transport plays a significant role in the establishment of neuronal polarity, axon growth, and synapse formation during neuronal development. The axon of a naturally growing neuron is a highly complex and multifurcated structure with a large number of bends and branches. Nowadays, the study of dynamic axonal transport in morphologically complex neurons is greatly limited by the technological barrier. Here, a sparse gene transfection strategy was developed to locate fluorescent mCherry in the lysosome of primary neurons, thus enabling us to track the lysosome-based axonal transport with a single-particle resolution. Thereby, several axonal transport models were observed, including forward or backward transport model, stop-and-go model, repeated back-and-forth transport model, and cross-branch transport model. Then, the accurate single-particle velocity quantification by TrackMate revealed a highly heterogeneous and discontinuous transportation process of lysosome-based axonal transport in freely orientated axons. And, multiple physical factors, such as the axonal structure and the size of particles, were disclosed to affect the velocity of particle transporting in freely orientated axons. The combined single-particle fluorescence tracking and TrackMate assay can be served as a facile tool for evaluating axonal transport in neuronal development and axonal transport-related diseases.

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery

Automated tools for landmarking the internal carotid artery (ICA) bends have the potential for efficient and objective medical image-based morphometric analysis. The two existing algorithms rely on numerical approximations of curvature and torsion of the centerline. However, input parameters, original source code, comparability, and robustness of the algorithms remain unknown. To address the former two, we have re-implemented the algorithms, followed by sensitivity analyses. Of the input parameters, the centerline smoothing had the least impact resulting in 6–7 bends, which is anatomically realistic. In contrast, centerline resolution showed to completely over- and underestimated the number of bends varying from 3 to 33. Applying the algorithms to the same cohort revealed a variability that makes comparison of results between previous studies questionable. Assessment of robustness revealed how one algorithm is vulnerable to model smoothness and noise, but conceptually independent of application. In contrast, the other algorithm is robust and consistent, but with limited general applicability. In conclusion, both algorithms are equally valid albeit they produce vastly different results. We have provided a well-documented open-source implementation of the algorithms. Finally, we have successfully performed this study on the ICA, but application to other vascular regions should be performed with caution.

Machine Learning Prediction and Reliability Analysis Applied to Subsea Spool and Jumper Design

In subsea pipelines projects, the design of rigid spool and jumper can be a challenging and time-consuming task. The selected spool layout for connecting the pipelines to the subsea structures, including the number of bends and leg lengths, must offer the flexibility to accommodate the pipeline thermal expansion, the pipe-lay target box and misalignments associated with the post-lay survey metrology and spool fabrication. The analysis results are considerably affected by many uncertainties involved. Consequently, a very large amount of calculations is required to assess the full combination of uncertainties and to capture the worst-case scenario. Rather than applying the deterministic solution, this paper uses machine learning prediction to significantly improve the efficiency of the design process. In addition, thanks to the fast predictive model using machine learning algorithms, the uncertainty quantification and propagation analysis using probabilistic statistical method becomes feasible in terms of CPU time and can be incorporated into the design process to evaluate the reliability of the outputs. The latter allows us to perform a systematic probabilistic design by considering a certain level of acceptance on the probability of failure, for example as per DNVGL design code. The machine learning predictive modelling and the reliability analysis based upon the probability distribution of the uncertainties are introduced and explained in this paper. Some project examples are shown to highlight the method’s comprehensive nature and efficient characteristics.

Reduction of Drag Resistance by Pressure Drop in Pipeline with 3-Dimensional Design Optimization

The searching for the most optimal pipeline route is a crucial problem in the maritime world because it consumes total designing time by 50%. Also, with different types of ships increases the design complexity. The usual design process has not considered the aspect of distance, cost, obstacles, drag, and pressure reduction in the pipeline very accurately. However, along with algorithms' development to optimize pipeline design, the time can be cut by 40%. This research uses computer-generated Dijkstra's algorithm to optimize pipeline design by considering several constraints in pipe spacing, the number of bends, crossings, pipeline stacks to improve drag reduction, and reducing pressure. This research was conducted to see the effect of pipe mapping on pressure drop, which is too influenced by human decisions that cannot consider bending, crossing, pipe piling, and bending of pipes that are too many to be considered by humans. Helping humans choose pipe mappings with various considerations that can affect pressure drop is advantageous because mapping helps to cut production times and produce a more efficient flow. In this study, this research aims to produce pressure drop by mapping pipes using the Djikstra algorithm by considering bending, crossing, and stacking, which are presented based on the 2-dimensional and 3-dimensional mapping. The data generated in the way of a comparison between drag reduction and pressure drop in pipe design optimization utilizing Dijkstra's and without using the Dijkstra's algorithm with 3-dimensional projections. The result shows the improvement of the drag reduction rate by 8% by decreasing pressure drop by 13%.

Analysis of the effectiveness of constraining metrics of geometric simplification algorithms based on expert evaluation of line detail

The paper reveals dependencies between the character of the line shape and combination of constraining metrics that allows comparable reduction in detail by different geometric simplification algorithms. The study was conducted in a form of the expert survey. geometrically simplified versions of three coastline fragments were prepared using three different geometric simplification algorithms—Douglas-peucker, Visvalingam-Whyatt and Li-Openshaw. Simplification was constrained by similar value of modified hausdorff distance (linear offset) and similar reduction of number of line bends (compression of the number of detail elements). Respondents were asked to give a numerical estimate of the detail of each image, based on personal perception, using a scale from one to ten. The results of the survey showed that lines perceived by respondents as having similar detail can be obtained by different algorithms. however, the choice of the metric used as a constraint depends on the nature of the line. Simplification of lines that have a shallow hierarchy of small bends is most effectively constrained by linear offset. As the line complexity increases, the compression metric for the number of detail elements (bends) increases its influence in the perception of detail. For one of the three lines, the best result was consistently obtained with a weighted combination of the analyzed metrics as a constraint. None of the survey results showed that only reducing the number of bends can be used as an effective characteristic of similar reduction in detail. It was therefore found that the linear offset metric is more indicative when describing changes in line detail.

Vertebrobasilar Artery Geometry and Bisilar Artery Plaques: A High-Resolution Magnetic Resonance Imaging Stydy

Background Atherosclerotic plaques are often present in regions with complicated flow patterns. Vascular morphology plays a role in hemodynamics. In this study, we investigate the relationship between the geometry of the vertebrobasilar artery system and the basilar artery (BA) plaque prevalence.Methods We enrolled 290 patients with posterior circulation ischemic stroke. We distinguished four configurations of the vertebrobasilar artery: Walking, Tuning Fork, Lambda, and No Confluence. The diameter of the vertebral artery (VA) and the number of bends in the intracranial VA segment was assessed using three-dimensional time-of-flight magnetic resonance angiography. We differentiated between multi-bending (≥ 3 bends) and oligo-bending (< 3 bends) VAs. High-resolution magnetic resonance imaging was used to evaluate BA plaques. Logistic regression models examined the relationship between the geometry type and BA plaque prevalence.Results After adjusting for sex, age, body mass index ≥ 28, hypertension, and diabetes mellitus, the Walking, Lambda, and No Confluence geometries were associated with the presence of BA plaque. Patients with multi-bending VAs in both the Walking (71.43%, P = 0.003) and Lambda group (40.43%, P = 0.018) had more plaques compared to patients with oligo-bending VAs in these groups. In the Lambda group, the diameter difference between the VAs was larger in patients with BA plaques than that in patients without BA plaques (1.4 mm vs. 0.9 mm, P < 0.001).Conclusions The Walking, Lambda, and No Confluence geometry, ≥ 3 bends in the VAs, and a large diameter difference between the VAs were associated with the presence of BA plaque.

RESEARCH ON PASSIVE SOIL DEPRESSURIZATION SYSTEMS: STATE OF THE ART AND LAB TEST ON-GOING

There is strong evidence both internationally and in Ireland that the correct installation of passive prevention systems in new buildings is the most cost-effective way of protecting the population against radon. Previous work considering membranes, granular fill material in the aggregate layer beneath the slab and sump system has been conducted in Ireland to improve the protection of buildings from radon. The implications of research on passive sumps potential to reduce radon concentrations are significant, as if it can be shown that the installation of passive sumps in Irish building is effective; this could constitute a low-cost, passive, sustainable method for minimizing radon levels in buildings. On-going experimental tests investigating the performance of different common cowls used for passive soil depressurization systems are presented, in addition to the impact of different vertical heights and horizontal lengths of pipe with a number of bends investigated.