Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

SPANA: Spatial decomposition analysis for cellular-scale molecular dynamics simulations.

The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized-Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large-scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments.

Characterization of cytoskeletal and structural effects of INF2 variants causing glomerulopathy and neuropathy.

Focal segmental glomerulosclerosis (FSGS) is a common glomerular injury leading to end-stage renal disease. Monogenic FSGS is primarily ascribed to decreased podocyte integrity. Variants between residues 184 and 245 of INF2, an actin assembly factor, produce the monogenic FSGS phenotype. Meanwhile, variants between residues 57 and 184 cause a dual-faceted disease involving peripheral neurons and podocytes (Charcot-Marie-Tooth CMT/FSGS). To understand the molecular basis for INF2 disorders, we compared structural and cytoskeletal effects of INF2 variants classified into two subgroups: One (G73D, V108D) causes the CMT/FSGS phenotype, and the other (T161N, N202S) produces monogenic FSGS. Molecular dynamics analysis revealed that all INF2 variants show distinct flexibility compared to the wild-type INF2 and could affect stability of an intramolecular interaction between their N- and C-terminal segments. Immunocytochemistry of cells expressing INF2 variants showed fewer actin stress fibers, and disorganization of cytoplasmic microtubule arrays. Notably, CMT/FSGS variants caused more prominent changes in mitochondrial distribution and fragmentation than FSGS variants and these changes correlated with the severity of cytoskeletal disruption. Our results indicate that CMT/FSGS variants are associated with more severe global cellular defects caused by disrupted cytoskeleton-organelle interactions than are FSGS variants. Further study is needed to clarify tissue-specific pathways and/or cellular functions implicated in FSGS and CMT phenotypes.

Tropical cyclone dataset for a high-resolution global nonhydrostatic atmospheric simulation.

This dataset is a time series of tropical cyclones simulated using the high-resolution Nonhydrostatic Icosahedral Atmospheric Model (NICAM). By tracking tropical cyclones from 30 years of simulation data, 2,463 tracks that include the life stages of precursors (pre-TCs), tropical cyclones (TCs), and post-tropical cyclones (post-TCs), if any, were extracted. Each track data includes the time, latitude, longitude, maximum wind speed, minimum pressure, elapsed time since onset, and life-stage label of the tropical cyclone. The numbers of steps (6 h) for pre-TCs, TCs, and post-TCs were 45,288, 55,206, and 37,312, respectively. The dataset for each step also consists of atmospheric field data of multiple physical quantities, such as outgoing longwave radiation at the top-of-the-atmosphere, sea level pressure, sea surface temperature, specific humidity at 600 hPa, and zonal and meridional winds at 850 and 200 hPa over a 1000 km2 area that includes a tropical cyclone at its center. This dataset can be used to develop machine-learning models for the detection, intensity prediction, and cyclogenesis prediction of tropical cyclones.

BePLi Dataset v1: Beach Plastic Litter Dataset version 1 for instance segmentation of beach plastic litter.

Marine plastic pollution is a pressing global issue nowadays. To address this problem, automated image analysis techniques that can identify plastic litter are necessary for scientific research and coastal management purposes. The Beach Plastic Litter Dataset version 1 (BePLi Dataset v1) comprises 3709 original images taken in various coastal environments, along with instance-based and pixel-level annotations for all plastic litter objects visible in the images. The annotations were compiled in the Microsoft Common Objects in Context (MS COCO) format, which was partially modified from the original format. The dataset enables the development of machine-learning models for instance-level and/or pixel-wise identification of beach plastic litter. All original images in the dataset were extracted from beach litter monitoring records operated by the local government of Yamagata Prefecture in Japan. Litter images were taken in different backgrounds, such as sand beaches, rocky beaches, and tetrapods. The annotations for instance segmentation of beach plastic litter were made manually, and were given for all plastics objects, including PET bottles, containers, fishing gear, and styrene foams,all of which were categorized in a single class "plastic litter". Technologies developed using this dataset have the potential to enable further scalability for the estimation of plastic litter volume. This would help researchers, including individuals, and the the government to monitor or analyze beach litter and the corresponding pollution levels.

Formation of extramembrane ß-strands controls dimerization of transmembrane helices in amyloid precursor protein C99.

The 99-residue C-terminal domain of amyloid precursor protein (APP-C99), precursor to amyloid beta (Aß), is a transmembrane (TM) protein containing intrinsically disordered N- and C-terminal extramembrane domains. Using molecular dynamics (MD) simulations, we show that the structural ensemble of the C99 monomer is best described in terms of thousands of states. The C99 monomer has a propensity to form ß-strand in the C-terminal extramembrane domain, which explains the slow spin relaxation times observed in paramagnetic probe NMR experiments. Surprisingly, homodimerization of C99 not only narrows the conformational ensemble from thousands to a few states through the formation of metastable ß-strands in extramembrane domains but also stabilizes extramembrane α-helices. The extramembrane domain structure is observed to dramatically impact the homodimerization motif, resulting in the modification of TM domain conformations. Our study provides an atomic-level structural basis for communication between the extramembrane domains of the C99 protein and TM homodimer formation. This finding could serve as a general model for understanding the influence of disordered extramembrane domains on TM protein structure.

Angiotensin-Converting Enzyme Inhibitor-Induced Angioedema Following Posterior Reversible Encephalopathy Syndrome in a Child: A Case Report.

Angioedema is a rare but potentially lethal side effect of angiotensin-converting enzyme inhibitors (ACEIs). Most ACEI-induced angioedema (ACEI-AE) cases have been reported in adults, with few reports of cases in children. Posterior reversible encephalopathy syndrome (PRES) is a neurological disorder that presents with acute onset of symptoms, including headache, vomiting, visual disturbances, and seizures. We report the case of a patient who developed ACEI-AE after developing PRES during the treatment of steroid-resistant nephrotic syndrome. ACEI-AE is very rare, especially in children, but can be life-threatening if swelling of the tongue or the throat blocks the airway. Whenever ACEIs are used, even in children, clinicians should be aware of the possibility of the occurrence of ACEI-AE, particularly when accompanied by dry cough. Moreover, bradykinin may be associated with PRES onset in patients with ACEI-AE and may be a risk factor for PRES.

Experimental and theoretical investigations into the mechanism of interactions between membrane-bound fatty acids and their binding protein: A model system to investigate the behavior of lipid acyl chains in contact with proteins.

The interaction of proteins with hydrophobic ligands in biological membranes is an important research topic in the life sciences. The hydrophobic nature of ligands, especially their lack of water solubility, often makes it difficult to experimentally investigate their interactions with proteins, thus hampering quantitative evaluation based on thermodynamic parameters. The fatty acid-binding proteins, particularly FABP3, discussed in this review can recognize fatty acids, a primary component of membrane lipids, with high affinity. The precise three-dimensional structure of fatty acids and related ligands bound in FABP3 and their interaction with the binding pocket will contribute to the understanding of accurately determining physicochemical factors that cause the expression of affinity between protein surfaces and lipids in biological membranes. During the research of FABP3, we encountered many of the problems that were widely implicated in experiments dealing with hydrophobic ligands. To address these issues, we developed experimental methodologies using X-ray crystallography, calorimetry, and surface plasmon resonance. Using these methods and computational approaches, we have obtained several insights into the interaction of hydrophobic ligands with protein binding sites. Structural and functional studies of FABP potentially lead to a better understanding of the interaction between lipids and proteins, and thus, this protein may provide one of the model systems for investigating substance transport across cell membranes and inner membrane systems.

Proteinuria from first-morning urine in a child due to brace treatment for adolescent idiopathic scoliosis.

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.

Different acyl-CoA:diacylglycerol acyltransferases vary widely in function, and a targeted amino acid substitution enhances oil accumulation.

Triacylglycerols (TAGs) are the major component of plant storage lipids such as oils. Acyl-CoA:diacylglycerol acyltransferase (DGAT) catalyzes the final step of the Kennedy pathway, and is mainly responsible for plant oil accumulation. We previously found that the activity of Vernonia DGAT1 was distinctively higher than that of Arabidopsis and soybean DGAT1 in a yeast microsome assay. In this study, the DGAT1 cDNAs of Arabidopsis, Vernonia, soybean, and castor bean were introduced into Arabidopsis. All Vernonia DGAT1-expressing lines showed a significantly higher oil content (49% mean increase compared with the wild-type) followed by soybean and castor bean. Most Arabidopsis DGAT1-overexpressing lines did not show a significant increase. In addition to these four DGAT1 genes, sunflower, Jatropha, and sesame DGAT1 genes were introduced into a TAG biosynthesis-defective yeast mutant. In the yeast expression culture, DGAT1s from Arabidopsis, castor bean, and soybean only slightly increased the TAG content; however, DGAT1s from Vernonia, sunflower, Jatropha, and sesame increased TAG content >10-fold more than the former three DGAT1s. Three amino acid residues were characteristically common in the latter four DGAT1s. Using soybean DGAT1, these amino acid substitutions were created by site-directed mutagenesis and substantially increased the TAG content.

Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding.

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world's seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.

The BeachLitter dataset for image segmentation of beach litter.

This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models. The beach litter images were obtained from coastal environment surveys conducted between 2011 and 2019 by the Yamagata Prefectural Government, Japan. These images were originally obtained owing to the reporting guidelines concerning regular coastal-environmental-cleanup and beach-litter-monitoring surveys. Based on these images, the Japan Agency for Marine-Earth Science and Technology created 3500 images comprising eight classes of semantic masks for beach litter detection [1].

Pixel-level image classification for detecting beach litter using a deep learning approach.

Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.

Impact of acute kidney injury on overall survival in children and young adults undergoing allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Acute kidney injury (AKI) is a complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Increasing severity of AKI is associated with an increased risk of death. However, the impact of AKI in patients with malignant versus nonmalignant disease has not been reported. We investigated the incidence of AKI within the first 100 days after allo-HSCT and the impact of AKI on both 3-year overall survival (OS) and cumulative incidence of death after allo-HSCT in all patients and in patients with/without malignant primary diseases. METHODS: We performed a retrospective analysis of 107 consecutive pediatric and young adult patients who received their first allo-HSCT. AKI was classified into three grades according to the Acute Kidney Injury Network classification system. RESULTS: The cumulative incidences of AKI stages 1-3, 2-3, and 3, at day 100 after allo-HSCT were 34.6% (95% confidence interval [CI], 25.7%-43.6%), 17.8% (95% CI, 11.2%-25.6%), and 3.7% (95% CI, 1.2%-8.6%), respectively. OS was reduced for patients with AKI compared with patients without AKI (60.4% vs. 79.6%, p = .038). The cumulative incidence of death in the AKI group with nonmalignant disease was significantly higher than that in the no-AKI group (44.4% vs. 0%, p = .003). CONCLUSION: AKI after allo-HSCT was not only a frequent event but also related to reduced OS. We recommend that all patients receiving allo-HSCT, especially patients with nonmalignant diseases, be closely monitored for AKI.

Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps.

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

CO2 -responsive CCT protein interacts with 14-3-3 proteins and controls the expression of starch synthesis-related genes.

CO2 -responsive CCT protein (CRCT) is a positive regulator of starch synthesis-related genes such as ADP-glucose pyrophosphorylase large subunit 1 and starch branching enzyme I particularly in the leaf sheath of rice (Oryza sativa L.). The promoter GUS analysis revealed that CRCT expressed exclusively in the vascular bundle, whereas starch synthesis-related genes were expressed in different sites such as mesophyll cell and starch storage parenchyma cell. However, the chromatin immunoprecipitation (ChIP) using a FLAG-CRCT overexpression line and subsequent qPCR analyses showed that the 5'-flanking regions of these starch synthesis-related genes tended to be enriched by ChIP, suggesting that CRCT can bind to the promoter regions of these genes. The monomer of CRCT is 34.2 kDa; however, CRCT was detected at 270 kDa via gel filtration chromatography, suggesting that CRCT forms a complex in vivo. Immunoprecipitation and subsequent MS analysis pulled down several 14-3-3-like proteins. A yeast two-hybrid analysis and bimolecular fluorescence complementation assays confirmed the interaction between CRCT and 14-3-3-like proteins. Although there is an inconsistency in the place of expression, this study provides important findings regarding the molecular function of CRCT to control the expression of key starch synthesis-related genes.

Heterozygous missense variant in TRPC6 in a boy with rapidly progressive infantile nephrotic syndrome associated with diffuse mesangial sclerosis.

Transient receptor potential channel C6 encoded by TRPC6 is involved in slit diaphragm formation in podocytes, and abnormalities of the TRPC6 protein cause various glomerular diseases. The first identified pathogenic variant of TRPC6 was found to cause steroid-resistant nephrotic syndrome that typically developed in adulthood and then slowly led to end-stage renal disease, along with a renal pathology of focal segmental glomerulosclerosis. Here, we report a patient with rapidly progressing infantile nephrotic syndrome and a heterozygous missense TRPC6 variant. The patient, a 2-year-old Japanese boy, developed steroid-resistant nephrotic syndrome at age 11 months. His renal function deteriorated rapidly, and peritoneal dialysis was introduced at age 1 year and 6 months. His renal pathology, obtained at age 1 year and 1 month, was consistent with diffuse mesangial sclerosis (DMS). Clinical exome analysis and custom panel analysis for hereditary renal diseases revealed a reported heterozygous missense variant in TRPC6 (NM_004621.5:c.523C > T:p.Arg175Trp). This is the first report of a patient with a TRPC6-related renal disorder associated with DMS.

Assessment of kidney function using inulin-based and estimated glomerular filtration rates before and after allogeneic hematopoietic stem cell transplantation in pediatric patients.

BACKGROUND: Accurate evaluation of kidney function before and after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is important for both informed decision making and detection of chronic kidney disease. However, to the best of our knowledge, no report has evaluated the glomerular filtration rate (GFR) in pediatric patients who underwent HSCT using the gold standard GFR measurement, as well as inulin-based GFR (iGFR). METHODS: We assessed iGFR before and after allo-HSCT to evaluate the impact of allo-HSCT on GFR in a prospective cohort study of 17 pediatric patients. We also assessed the accuracy and bias of the values of estimated GFR (eGFR) calculated using serum creatinine (Cr), cystatin C (CysC), beta-2 microglobulin (ß2 MG), 24-h creatinine clearance (24hCcr), and the full chronic kidney disease in children (CKiD) index that combines Cr, CysC, and blood urea nitrogen-based equations with iGFR as a reference to identify the most reliable equation for GFR. RESULTS: There was no significant difference between the values before and after allo-HSCT. CKiD CysC-, 24hCcr-, and full CKiD-based values showed good within 30% (P30) accuracy (80.6%, 79.3%, and 80.6%, respectively), but only 24hCcr and full CKiD had good mean bias (8.5% and 8.9%, respectively) and narrow 95% limits of agreement (-32.2 to 52.7 mL/min/1.73 m2 and -29.3 to 47.4 mL/min/1.73 m2 , respectively) compared with the corresponding iGFR. CONCLUSION: There was no significant impact of allo-HSCT on GFR in our cohort. The most reliable equations for pediatric patients with allo-HSCT were eGFR-24hCcr and eGFR-full CKiD.