Schwannomatosis of the tibial nerve

Schwannoma is the most common type of benign tumor arising from the sheaths of the peripheral nerves. It occurs as a solitary tumor in most cases, but when it appears in multiple forms, it is necessary to differentiate it from plexiform schwannoma, schwannomatosis, neurofibroma and malignant peripheral nerve tumors. The authors experienced schwannomatosis in the tibial nerve without the features of neurofibromatosis type 2, so here we present a case report and literature review

Risk factors for delayed and non-union following transfibular ankle arthrodesis

Background: This study was to identify risk factors associated with delayed union and non-union in patients who underwent transfibular ankle arthrodesis.Methods: This study included 43 patients who underwent ankle arthrodesis using transfibular approach between January 2012 and September 2018 and were followed up for more than 12 months. The patients were divided into two groups according to delayed union or non-union. Group A included patients who had delayed union or non-union and Group B included patients without these complications. Variables that could contribute to non-union including etiologies, age, chronic renal failure, hypertension, diabetes, smoking, pre-operative talus bone quality, pre-operative angulation of the talus and fixation methods were evaluated.Results: The mean time to bone union was 12.7±7.25 weeks. Group A included 12 patients with 5 cases of non-union and 7 cases of delayed union and group B included 31 patients. Infection of the ankle joint (OR, 1.73; p=0.041) was risk factor for non-union and delayed union on the basis of multivariate analysis.Conclusions: We concluded that infection of the ankle joint is the most significant risk factor for delayed union and nonunion in our study. Careful attention should be paid preoperatively, intraoperatively and postoperatively to patients who have this risk factor to obtain a satisfactory surgical outcome

VALIDATION OF OPTIMALLY DESIGNED STATOR-PROPELLER SYSTEM BY EFD AND CFD

The development of energy-saving devices to lower the energy efficiency design index (EEDI) of ships has been actively researched worldwide. One such device is an asymmetric pre-swirl stator, which helps to improve the propulsion efficiency by recovering the rotational energy generated during propeller rotation. Determining the pitch angle is the most important factor in the design of an efficient asymmetric pre-swirl stator. To optimize the pitch angle of an asymmetric pre-swirl stator, this study deals with potential-flow, computational fluid dynamics, and model tests. The model delivered power at a design speed of 24 kt was compared by changing the pitch angle by ±2° with respect to the reference angle designed using a potential-flow program. The commercial code Star-CCM+ was used for the numerical analysis, and the model was also tested in a towing tank at Pusan National University. This study proposes an effective method for determining and verifying the optimal pitch angle of an asymmetric pre-swirl stator

Performance Comparison of Design Optimization and Deep Learning-based Inverse Design

Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints based on the data obtained from simulations or real-world experiments, and then finding the optimal solution from the model using numerical optimization methods. Recent advancements in deep learning-based inverse design methods have made it possible to generate real-time optimal solutions for engineering design problems, eliminating the requirement for iterative optimization processes. Nevertheless, no comprehensive study has yet closely examined the specific advantages and disadvantages of this novel approach compared to the traditional design optimization method. The objective of this paper is to compare the performance of traditional design optimization methods with deep learning-based inverse design methods by employing benchmark problems across various scenarios. Based on the findings of this study, we provide guidelines that can be taken into account for the future utilization of deep learning-based inverse design. It is anticipated that these guidelines will enhance the practical applicability of this approach to real engineering design problems

Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

CRISPR RNAs trigger innate immune responses in human cells

Here, we report that CRISPR guide RNAs (gRNAs) with a 5'-triphosphate group (5'-ppp gRNAs) produced via in vitro transcription trigger RNA-sensing innate immune responses in human and murine cells, leading to cytotoxicity. 5'-ppp gRNAs in the cytosol are recognized by DDX58, which in turn activates type I interferon responses, causing up to similar to 80% cell death. We show that the triphosphate group can be removed by a phosphatase in vitro and that the resulting St-hydroxyl gRNAs in complex with Cas9 or Cpfl avoid innate immune responses and can achieve targeted mutagenesis at a frequency of 95% in primary human CD4(+) T cells. These results are in line with previous findings that chemically synthesized sgRNAs with a 5'-hydroxyl group are much more efficient than in vitro-transcribed (IVT) sgRNAs in human and other mammalian cells. The phosphatase treatment of IVT sgRNAs is a cost-effective method for making highly active sgRNAs, avoiding innate immune responses in human cells.

Use of Magnetic Nanoparticles to Visualize Threadlike Structures Inside Lymphatic Vessels of Rats

A novel application of fluorescent magnetic nanoparticles was made to visualize a new tissue which had not been detectable by using simple stereomicroscopes. This unfamiliar threadlike structure inside the lymphatic vessels of rats was demonstrated in vivo by injecting nanoparticles into lymph nodes and applying magnetic fields on the collecting lymph vessels so that the nanoparticles were taken up by the threadlike structures. Confocal laser scanning microscope images of cryosectioned specimens exhibited that the nanoparticles were absorbed more strongly by the threadlike structure than by the lymphatic vessels. Further examination using a transmission electron microscope revealed that the nanoparticles had been captured between the reticular fibers in the extracellular matrix of the threadlike structures. The emerging technology of nanoparticles not only allows the extremely elusive threadlike structures to be visualized but also is expected to provide a magnetically controllable means to investigate their physiological functions

The ancient phosphatidylinositol 3-kinase signaling system is a master regulator of energy and carbon metabolism in algae

Algae undergo a complete metabolic transformation under stress by arresting cell growth, inducing autophagy and hyperaccumulating biofuel precursors such as triacylglycerols and starch. However, the regulatory mechanisms behind this stress-induced transformation are still unclear. Here, we use biochemical, mutational, and “omics” approaches to demonstrate that PI3K signaling mediates the homeostasis of energy molecules and influences carbon metabolism in algae. In Chlamydomonas reinhardtii, the inhibition and knockdown (KD) of algal class III PI3K led to significantly decreased cell growth, altered cell morphology, and higher lipid and starch contents. Lipid profiling of wild-type and PI3K KD lines showed significantly reduced membrane lipid breakdown under nitrogen starvation (-N) in the KD. RNA-seq and network analyses showed that under -N conditions, the KD line carried out lipogenesis rather than lipid hydrolysis by initiating de novo fatty acid biosynthesis, which was supported by tricarboxylic acid cycle down-regulation and via acetyl-CoA synthesis from glycolysis. Remarkably, autophagic responses did not have primacy over inositide signaling in algae, unlike in mammals and vascular plants. The mutant displayed a fundamental shift in intracellular energy flux, analogous to that in tumor cells. The high free fatty acid levels and reduced mitochondrial ATP generation led to decreased cell viability. These results indicate that the PI3K signal transduction pathway is the metabolic gatekeeper restraining biofuel yields, thus maintaining fitness and viability under stress in algae. This study demonstrates the existence of homeostasis between starch and lipid synthesis controlled by lipid signaling in algae and expands our understanding of such processes, with biotechnological and evolutionary implications.Ministry of Science, ICT and Future Planning 2015M3A6A2065697Ministry of Oceans and Fisheries 2015018