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Mineralogical and geochemical data are essential for estimating the effects of long-range transport of Asian dust on the atmosphere, biosphere, cryosphere, and pedosphere. However, consistent long-term data sets of dust samples are rare. This study analyzed 25 samples collected during 14 Asian dust events occurring between 2005 and 2018 on the Korean Peninsula and compared them to 34 soil samples (<20 µm) obtained from the Mongolian Gobi Desert, which is a major source of Asian dust. The mineralogical and geochemical characteristics of Asian dust were consistent with those of fine source soils in general. In dust, clay minerals were most abundant, followed by quartz, plagioclase, K-feldspar, calcite, and gypsum. The trace element contents were influenced by the mixing of dust with polluted air and the fractionation of rare earth elements. Time-series analyses of the geochemical data of dust, combined with satellite remote sensing images, showed a significant increase in the Ca content in the dust crossing the Chinese Loess Plateau and the sandy deserts of northern China. Calcareous sediments in the sandy deserts and pedogenic calcite-rich loess are probable sources of additional Ca. Dust-laden air migrating toward Korea mixes with polluted air over East Asia. Gypsum, a minor mineral in source soils, was formed by the reaction between calcite and pollutants. This study describes not only the representative properties of Asian dust but also their variation according to the migration path, fractionation, and atmospheric reactions.

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

This study examines the relationship between residential environmental satisfaction, social capital, and place attachment. Previous studies on place attachment and residential environments have not fully considered the components of residential environments or executed an integrated analysis. This study includes diverse aspects of residential environmental satisfaction such as accessibility, comfort, and safety, which permits a comparison of the influence of each element. Furthermore, this study examined the mediating effect of social capital between residential environments and place attachment. Online surveys were conducted with 750 residents in the metropolitan city of Seoul, Korea, and mediation regression analysis as employed. The results indicated that residential environmental satisfaction had a positive effect on place attachment. In particular, accessibility had the greatest effect on place attachment. Furthermore, social capital had a mediating effect on all sub-components of residential environments. Today, many countries, including China and India, pursue and experience rapid urbanization much like Seoul, which has undergone it over several decades. However, this causes a variety of urban problems that might hinder long-term sustainable development. Therefore, this study suggests that the importance of qualitative development for sustainability should be recognized and incorporated together with quantitative development.

Metal–sulfur batteries (MSBs) provide high specific capacity due to the reversible redox mechanism based on conversion reaction that makes this battery a more promising candidate for next‐generation energy storage systems. Recently, along with elemental sulfur (S8), sulfurized polyacrylonitrile (SPAN), in which active sulfur moieties are covalently bounded to carbon backbone, has received significant attention as an electrode material. Importantly, SPAN can serve as a universal cathode with minimized metal–polysulfide dissolution because sulfur is immobilized through covalent bonding at the carbon backbone. Considering these unique structural features, SPAN represents a new approach beyond elemental S8 for MSBs. However, the development of SPAN electrodes is in its infancy stage compared to conventional S8 cathodes because several issues such as chemical structure, attached sulfur chain lengths, and over‐capacity in the first cycle remain unresolved. In addition, physical, chemical, or specific treatments are required for tuning intrinsic properties such as sulfur loading, porosity, and conductivity, which have a pivotal role in improving battery performance. This review discusses the fundamental and technological discussions on SPAN synthesis, physicochemical properties, and electrochemical performance in MSBs. Further, the essential guidance will provide research directions on SPAN electrodes for potential and industrial applications of MSBs. Sulfurized polyacrylonitrile (SPAN), as a universal sulfur cathode, in which active sulfur moieties are bound to a carbon backbone that can allow all sulfur atoms to become electrochemically active in metal–sulfur batteries. This review covers the key technological developments for the synthesis, physicochemical properties, and electrochemical performances of SPAN.

Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important modalities investigating AMD. Whether concomitant use of fundus and OCT data in DL technique is beneficial has not been so clearly identified. This experimental analysis used OCT and fundus image data of postmortems from the Project Macula. The DL based on OCT, fundus, and combination of OCT and fundus were invented to diagnose AMD. These models consisted of pre-trained VGG-19 and transfer learning using random forest. Following the data augmentation and training process, the DL using OCT alone showed diagnostic efficiency with area under the curve (AUC) of 0.906 (95% confidence interval, 0.891–0.921) and 82.6% (81.0–84.3%) accuracy rate. The DL using fundus alone exhibited AUC of 0.914 (0.900–0.928) and 83.5% (81.8–85.0%) accuracy rate. Combined usage of the fundus with OCT increased the diagnostic power with AUC of 0.969 (0.956–0.979) and 90.5% (89.2–91.8%) accuracy rate. The Delong test showed that the DL using both OCT and fundus data outperformed the DL using OCT alone (P value < 0.001) and fundus image alone (P value < 0.001). This multimodal random forest model showed even better performance than a restricted Boltzmann machine (P value = 0.002) and deep belief network algorithms (P value = 0.042). According to Duncan’s multiple range test, the multimodal methods significantly improved the performance obtained by the single-modal methods. In this preliminary study, a multimodal DL algorithm based on the combination of OCT and fundus image raised the diagnostic accuracy compared to this data alone. Future diagnostic DL needs to adopt the multimodal process to combine various types of imaging for a more precise AMD diagnosis.

Lung cancer shows substantial genetic and phenotypic heterogeneity across individuals, driving a need for personalised medicine. Here, we report lung cancer organoids and normal bronchial organoids established from patient tissues comprising five histological subtypes of lung cancer and non-neoplastic bronchial mucosa as in vitro models representing individual patient. The lung cancer organoids recapitulate the tissue architecture of the primary lung tumours and maintain the genomic alterations of the original tumours during long-term expansion in vitro. The normal bronchial organoids maintain cellular components of normal bronchial mucosa. Lung cancer organoids respond to drugs based on their genomic alterations: a BRCA2-mutant organoid to olaparib, an EGFR-mutant organoid to erlotinib, and an EGFR-mutant/MET-amplified organoid to crizotinib. Considering the short length of time from organoid establishment to drug testing, our newly developed model may prove useful for predicting patient-specific drug responses through in vitro patient-specific drug trials. The clinical efficacy of standard therapy in lung cancer is limited by high level of heterogeneity. Here, the authors report patient-derived lung cancer organoids from different histological subtypes and show them to faithfully recapitulate the histology, genomics, and drug responses of the primary lung tumours.

The 3D multi-cellular tumoroid (MCT) model is an in vivo-like, avascular tumor model that has received much attention as a refined screening platform for drug therapies. Several types of research have been efforted to improve the physiological characteristics of the tumor microenvironment (TME) of the in vivo-like MCTs. Size-controlled MCTs have received much attention for obtaining highly reproducible results in drug screening assays and achieving a homogeneous and meaningful level of biological activities. Here, we describe an effective method for fabricating the size-controlled in vivo-like MCTs using a cell-loss-free (CLF) microwell arrays. The CLF microwell arrays was fabricated by using the simple operation of laser carving of a poly (methyl methacrylate) (PMMA) master mold. We also demonstrated the biophysicochemical effect of tumor microenvironment (TME) resident fibroblasts through the expression of TGFβ, αSMA, Type I-, IV collagen, angiogenesis related markers on tumorigenesis, and confirmed the drug response of MCTs with anti-cancer agents. This technology for the fabrication of CLF microwell arrays could be used as an effective method to produce an in vitro tumor model for cancer research and drug discovery.

Metarhizium anisopliae is a promising alternative to chemical pesticides against pine wilt disease caused by Bursaphelenchus xylophilus . Herein, we investigated the efficacy of modified atmosphere packaging (MAP) to prolong the shelf-life of the M. anisopliae conidia. The effects of various conditions on its stability were also examined. M. anisopliae -inoculated millet grains were treated in a MAP system with different packaging materials (polypropylene, PP; polyethylene terephthalate, PET; ethylene vinyl alcohol, EVOH), gas compositions (high CO 2 atmosphere, ≈ 90%; high O 2 atmosphere, > 95%; high N 2 atmosphere, > 95%; 30% CO 2  + 70% N 2 ; 50% CO 2  + 50% N 2 ; 70% CO 2  + 30% N 2 ), and storage temperatures (4 and 25 °C). Results revealed EVOH film as the best for the preservation of gases at all concentrations for 28 days. MAP treatment in the high-barrier EVOH film under an atmosphere of 30% CO 2  + 70% N 2 achieved 80.5% viability of dried conidia (7.4% moisture content), with 44.2–64.9% viability recorded with the other treatments. Cold storage for technical concentrates formulation promoted extension of shelf-life of MAP-treated conidia. These results imply that MAP under optimized conditions could enhance the shelf-life of fungus-based biopesticides in fungus-colonized substrates formulations.

Face-centered-cubic crystallized super-fine (~ 2 nm in size) wet-ceria-abrasives are synthesized using a novel wet precipitation process that comprises a Ce 4+ precursor, C 3 H 4 N 2 catalyst, and NaOH titrant for a synthesized termination process at temperature of at temperature of 25 °C. This process overcomes the limitations of chemical–mechanical-planarization (CMP)-induced scratches from conventional dry ceria abrasives with irregular surfaces or wet ceria abrasives with crystalline facets in nanoscale semiconductor devices. The chemical composition of super-fine wet ceria abrasives depends on the synthesis termination pH, that is, Ce(OH) 4 abrasives at a pH of 4.0–5.0 and a mixture of CeO 2 and Ce(OH) 4 abrasives at a pH of 5.5–6.5. The Ce(OH) 4 abrasives demonstrate better abrasive stability in the SiO 2 -film CMP slurry than the CeO 2 abrasives and produce a minimum abrasive zeta potential (~ 12 mV) and a minimum secondary abrasive size (~ 130 nm) at the synthesis termination pH of 5.0. Additionally, the abrasive stability of the SiO 2 -film CMP slurry that includes super-fine wet ceria abrasives is notably sensitive to the CMP slurry pH; the best abrasive stability (i.e., a minimum secondary abrasive size of ~ 130 nm) is observed at a specific pH (6.0). As a result, a maximum SiO 2 -film polishing rate (~ 524 nm/min) is achieved at pH 6.0, and the surface is free of stick-and-slip type scratches.

It has been known that the Alfvén velocity plays a significant role in generation and propagation of magnetohydrodynamic (MHD) waves. Until now, however, the global distribution of the Alfvén velocity in the magnetosphere has not been reported. To determine the spatial distribution of the Alfvén velocity, we have statistically examined the THEMIS magnetic field and electron density data obtained in the L (the equatorial geocentric distance to the field line measured in Earth’s radii) range of ~ 4–12 and at all local times near the magnetic equator between − 5° and 5° in magnetic latitude for 2008–2014. We observed a pronounced dawn–dusk asymmetry in the equatorial Alfvén velocity calculated from the THEMIS magnetic field and density data. That is, the dawnside Alfvén velocity is higher than the duskside Alfvén velocity. This asymmetry is due to the duskside bulge in the plasmasphere. The radial profile of the Alfvén velocity shows an increasing function of L between L = 4 and 10 in the dusk sector, while a decreasing function in the dawn sector. By comparing these Alfvén velocity distributions along the local time and radial distance, we discuss the occurrence distribution and propagation of MHD waves in the outer magnetosphere.