Search

A first-morning urine test for screening urinary protein is important for distinguishing whether asymptomatic proteinuria, which is a common finding in school-aged children, is caused due to kidney disease or not. We report the case of a 12-year-old Japanese girl who was referred to our pediatric department for asymptomatic proteinuria detected during a school urinary screening. Proteinuria was found only on the first-morning urinalysis and not on the routine urinalysis. The patient had been diagnosed with adolescent idiopathic scoliosis and treated with a nighttime brace. As excess protein was not detected on urinalysis of the first-morning urine sample that was collected after a night without the brace, proteinuria due to the brace treatment for scoliosis was diagnosed. The present case revealed that brace treatment can cause proteinuria. Even if a first-morning urine is positive for protein, an unexpected cause can trigger asymptomatic proteinuria in a growing child.

Ocean data have been improved with the enhancement of observed values and the evolution of computational technologies. It has also been verified based on the reproducibility of various ocean phenomena. Mode water is one of the indicators for assessing ocean data because of its special properties. However, its definition differs for each ocean data. Besides, its observation is primarily performed by 2D analysis using the cutting plane of the ocean space. Therefore, reproducibility of the ocean space may have not been fully examined. Here, this paper presents a visual analysis tool for the feature of ocean data based on the 3D shape comparison of the mode water regions among three ocean datasets. Our comparison is based on similarity measure from shape appearances of the mode water regions extracted as isosurfaces. Users can interact with shape similarity data and a pair of isosurfaces. Our visualization tool supports to easily explore the relationship of different variable thresholds that are used for conditions of the mode water region and observe the specified parts of the pair in the 3D space. We demonstrate the availability and potential benefit of this approach through three examples that searched for the best conditions and expert feedback.

Background Mutations in the Wilms tumor 1 gene cause a spectrum of podocytopathy ranging from diffuse mesangial sclerosis to focal segmental glomerulosclerosis. In a considerable fraction of patients with Wilms tumor 1 mutations, the distinctive histology of immune-complex-type glomerulonephritis has been reported. However, the clinical relevance and etiologic mechanisms remain unknown. Case presentation A 5-year-old child presented with steroid-resistant nephrotic range proteinuria. Initial renal biopsy revealed predominant diffuse mesangial proliferation with a double-contour and coexisting milder changes of focal segmental glomerulosclerosis. Immunofluorescence and electron microscopy revealed a full-house-pattern deposition of immune complexes in the subendothelial and paramesangial areas. Serial biopsies at 6 and 8 years of age revealed that more remarkable changes of focal segmental glomerulosclerosis had developed on top of the initial proliferative glomerulonephritis. Identification of a de novo Wilms tumor 1 splice donor-site mutation in intron 9 (NM_024426.6:c.1447 + 4C > T) and 46,XY-gonadal dysgenesis led to the diagnosis of Frasier syndrome. Conclusions Our findings, together with those of others, point to the importance of heterogeneity in clinicopathological phenotypes caused by Wilms tumor 1 mutations and suggest that immune-complex-mediated membranoproliferative glomerulopathy should be considered as a histological variant.

We propose a deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation is used for training two-dimensional deep convolutional neural networks (CNNs). The CNNs are trained with 50,000 TCs and their precursors and 500,000 non-TC data for binary classification. Ensemble CNN classifiers are applied to 10 year independent global OLR data for detecting precursors and TCs. The performance of the CNNs is investigated for various basins, seasons, and lead times. The CNN model successfully detects TCs and their precursors in the western North Pacific in the period from July to November with a probability of detection (POD) of 79.9–89.1% and a false alarm ratio (FAR) of 32.8–53.4%. Detection results include 91.2%, 77.8%, and 74.8% of precursors 2, 5, and 7 days before their formation, respectively, in the western North Pacific. Furthermore, although the detection performance is correlated with the amount of training data and TC lifetimes, it is possible to achieve high detectability with a POD exceeding 70% and a FAR below 50% during TC season for several ocean basins, such as the North Atlantic, with a limited sample size and short lifetime.

Because of improvements in offshore tsunami observation technology, dispersion phenomena during tsunami propagation have often been observed in recent tsunamis, for example the 2004 Indian Ocean and 2011 Tohoku tsunamis. The dispersive propagation of tsunamis can be simulated by use of the Boussinesq model, but the model demands many computational resources. However, rapid progress has been made in parallel computing technology. In this study, we investigated a parallelized approach for dispersive tsunami wave modeling. Our new parallel software solves the nonlinear Boussinesq dispersive equations in spherical coordinates. A variable nested algorithm was used to increase spatial resolution in the target region. The software can also be used to predict tsunami inundation on land. We used the dispersive tsunami model to simulate the 2011 Tohoku earthquake on the Supercomputer K. Good agreement was apparent between the dispersive wave model results and the tsunami waveforms observed offshore. The finest bathymetric grid interval was 2/9 arcsec (approx. 5 m) along longitude and latitude lines. Use of this grid simulated tsunami soliton fission near the Sendai coast. Incorporating the three-dimensional shape of buildings and structures led to improved modeling of tsunami inundation.

Abscisic acid (ABA) is a phytohormone that regulates many physiological functions, such as plant growth, development and stress responses. The MAPK cascade plays an important role in ABA signal transduction. Several MAPK and MAPKK molecules are reported to function in ABA signaling; however, there have been few studies related to the identification of MAPKKK upstream of MAPKK in ABA signaling. In this study, we show that an Arabidopsis MAPKKK, MAPKKK18 functions in ABA signaling. The expression of MAPKKK18 was induced by ABA treatment. Yeast two-hybrid analysis revealed that MAPKKKK18 interacted with MKK3, which interacted with C-group MAPK, MPK1/2/7. Immunoprecipitated kinase assay showed that the 3xFlag-tagged MAPKKK18, expressed in Arabidopsis plants, was activated when treated with ABA. These results indicate the possibility that the MAPK cascade is composed of MAPKKK18, MKK3 and MPK1/2/7 in ABA signaling. The transgenic plants overexpressing MAPKKK18 (35S:MAPKKK18) and its kinase negative mutant (35S:MAPKKK18 KN) were generated, and their growth was monitored. Compared with the WT plant, 35S:MAPKKK18 and 35S:MAPKKK18 KN showed smaller and bigger phenotypes, respectively. Senescence of the rosette leaves was promoted in 35S:MAPKKK18, but suppressed in 35S:MAPKKK18 KN. Furthermore, ABA-induced leaf senescence was accelerated in 35S:MAPKKK18. These results suggest that MAPKKK18 controls the plant growth by adjusting the timing of senescence via its protein kinase activity in ABA dependent manners.