Electrospun polyetherimide nanofiber mat-reinforced, permselective polyvinylalcohol composite separator membranes for rechargeable zinc-air batteries

Department of Energy Engineering (Battery Science and Technology)Despite the commercial success of primary Zinc (Zn)-air batteries, rechargeable Zn-air batteries are still far behind meaningful performance levels. Among numerous challenges facing rechargeable Zn-air batteries, from the material point of view, separator membranes should not be underestimated, along with other battery components such as anodes, cathodes and electrolytes. More particularly, crossover of soluble zincate (Zn(OH)42-) ions through separator membranes from Zn anode to air cathode, which significantly affects electrochemical performance of Zn-air cells, has hardly been addressed. Here, as a facile and scalable strategy to resolve the separator membrane-related issues, we demonstrate a new class of electrospun nanofiber mat-reinforced permselective composite membranes (referred to as ERC membranes) and explore their potential contribution to development of rechargeable Zn-air cells in terms of transport phenomena of hydroxyl (OH-) and Zn(OH)42- ions. The ERC membrane is fabricated by impregnating polyvinyl alcohol (PVA) into electrospun polyetherimide (PEI) nanofiber mat. The PEI nanofiber mat acts as a compliant framework to endow dimensional stability and mechanical strength. The PVA matrix, after being swelled with electrolyte solution, provides ion size (OH- vs. Zn(OH)42-)-dependent conductive pathways. This architecture/material uniqueness of the ERC membrane effectively suppresses permeation of bulky Zn(OH)42- ions without impairing OH- conduction, thereupon achieving exceptional cycle capacity retention of Zn-air cells far beyond those accessible with conventional microporus polyolefin separators. The ERC membrane featuring the ion size exclusion-based permselectivity opens a new membrane-driven opportunity that leads us closer toward rechargeable Zn-air batteries.ope

Effect of a New Prokinetic Agent DA-9701 Formulated with Corydalis Tuber and Pharbitidis Semen on Cytochrome P450 and UDP-Glucuronosyltransferase Enzyme Activities in Human Liver Microsomes

DA-9701 is a new botanical drug composed of the extracts of Corydalis tuber and Pharbitidis semen, and it is used as an oral therapy for the treatment of functional dyspepsia in Korea. The inhibitory potentials of DA-9701 and its component herbs, Corydalis tuber and Pharbitidis semen, on the activities of seven major human cytochrome P450 (CYP) enzymes and four UDP-glucuronosyltransferase (UGT) enzymes in human liver microsomes were investigated using liquid chromatography-tandem mass spectrometry. DA-9701 and Corydalis tuber extract slightly inhibited UGT1A1-mediated etoposide glucuronidation, with 50% inhibitory concentration (IC50) values of 188 and 290 μg/mL, respectively. DA-9701 inhibited CYP2D6-catalyzed bufuralol 1′-hydroxylation with an inhibition constant (Ki) value of 6.3 μg/mL in a noncompetitive manner. Corydalis tuber extract competitively inhibited CYP2D6-mediated bufuralol 1′-hydroxylation, with a Ki value of 3.7 μg/mL, whereas Pharbitidis semen extract showed no inhibition. The volume in which the dose could be diluted to generate an IC50 equivalent concentration (volume per dose index) value of DA-9701 for inhibition of CYP2D6 activity was 1.16 L/dose, indicating that DA-9701 may not be a potent CYP2D6 inhibitor. Further clinical studies are warranted to evaluate the in vivo extent of the observed in vitro interactions

Intelligent CCTV Surveillance Based on Sound Recognition and Sound Localization

CCTV is used for many purposes, especially for surveillance and fortraffic condition monitoring. This paper proposesan intelligent CCTV system that tracks sound events based on sound recognition and sound localization. From the experimental results, it is evident that the proposed method can be successfully used for the intelligent CCTV system of CCTV

Insight into highly conserved H1 subtype-specific epitopes in influenza virus hemagglutinin

Influenza viruses continuously undergo antigenic changes with gradual accumulation of mutations in hemagglutinin (HA) that is a major determinant in subtype specificity. The identification of conserved epitopes within specific HA subtypes gives an important clue for developing new vaccines and diagnostics. We produced and characterized nine monoclonal antibodies that showed significant neutralizing activities against H1 subtype influenza viruses, and determined the complex structure of HA derived from a 2009 pandemic virus A/Korea/01/2009 (KR01) and the Fab fragment from H1-specific monoclonal antibody GC0587. The overall structure of the complex was essentially identical to the previously determined KR01 HA-Fab0757 complex structure. Both Fab0587 and Fab0757 recognize readily accessible head regions of HA, revealing broadly shared and conserved antigenic determinants among H1 subtypes. The beta-strands constituted by Ser110-Glu115 and Lys169-Lys170 form H1 epitopes with distinct conformations from those of H1 and H3 HA sites. In particular, Glu112, Glu115, Lys169, and Lys171 that are highly conserved among H1 subtype HAs have close contacts with HCDR3 and LCDR3. The differences between Fab0587 and Fab0757 complexes reside mainly in HCDR3 and LCDR3, providing distinct antigenic determinants specific for 1918 pdm influenza strain. Our results demonstrate a potential key neutralizing epitope important for H1 subtype specificity in influenza virus

Separatrix modes in weakly deformed microdisk cavities

Optical modes in deformed dielectric microdisk cavities often show an unexpected localization along unstable periodic ray orbits. We reveal a new mechanism for this kind of localization in weakly deformed cavities. In such systems the ray dynamics is nearly integrable and its phase space contains small island chains. When increasing the deformation the enlarging islands incorporate more and more modes. Each time a mode comes close to the border of an island chain (separatrix) the mode exhibits a strong localization near the corresponding unstable periodic orbit. Using an EBK quantization scheme taking into account the Fresnel coefficients we derive a frequency condition for the localization. Observing far field intensity patterns and tunneling distances, reveals small differences in the emission properties. © 2017 Optical Society of America.1

Cdk4 Regulates Recruitment of Quiescent β-Cells and Ductal Epithelial Progenitors to Reconstitute β-Cell Mass

Insulin-producing pancreatic islet β cells (β-cells) are destroyed, severely depleted or functionally impaired in diabetes. Therefore, replacing functional β-cell mass would advance clinical diabetes management. We have previously demonstrated the importance of Cdk4 in regulating β-cell mass. Cdk4-deficient mice display β-cell hypoplasia and develop diabetes, whereas β-cell hyperplasia is observed in mice expressing an active Cdk4R24C kinase. While β-cell replication appears to be the primary mechanism responsible for β-cell mass increase, considerable evidence also supports a contribution from the pancreatic ductal epithelium in generation of new β-cells. Further, while it is believed that majority of β-cells are in a state of ‘dormancy’, it is unclear if and to what extent the quiescent cells can be coaxed to participate in the β-cell regenerative response. Here, we address these queries using a model of partial pancreatectomy (PX) in Cdk4 mutant mice. To investigate the kinetics of the regeneration process precisely, we performed DNA analog-based lineage-tracing studies followed by mathematical modeling. Within a week after PX, we observed considerable proliferation of islet β-cells and ductal epithelial cells. Interestingly, the mathematical model showed that recruitment of quiescent cells into the active cell cycle promotes β-cell mass reconstitution in the Cdk4R24C pancreas. Moreover, within 24–48 hours post-PX, ductal epithelial cells expressing the transcription factor Pdx-1 dramatically increased. We also detected insulin-positive cells in the ductal epithelium along with a significant increase of islet-like cell clusters in the Cdk4R24C pancreas. We conclude that Cdk4 not only promotes β-cell replication, but also facilitates the activation of β-cell progenitors in the ductal epithelium. In addition, we show that Cdk4 controls β-cell mass by recruiting quiescent cells to enter the cell cycle. Comparing the contribution of cell proliferation and islet-like clusters to the total increase in insulin-positive cells suggests a hitherto uncharacterized large non-proliferative contribution

Blind Biological Sequence Denoising with Self-Supervised Set Learning

Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeatedly using a high-throughput long-read platform to generate multiple subreads, or noisy observations of the same sequence. Denoising these subreads with alignment-based approaches often fails when too few subreads are available or error rates are too high. In this paper, we propose a novel method for blindly denoising sets of sequences without directly observing clean source sequence labels. Our method, Self-Supervised Set Learning (SSSL), gathers subreads together in an embedding space and estimates a single set embedding as the midpoint of the subreads in both the latent and sequence spaces. This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence. In experiments on simulated long-read DNA data, SSSL methods denoise small reads of $\leq 6$ subreads with 17% fewer errors and large reads of $>6$ subreads with 8% fewer errors compared to the best baseline. On a real dataset of antibody sequences, SSSL improves over baselines on two self-supervised metrics, with a significant improvement on difficult small reads that comprise over 60% of the test set. By accurately denoising these reads, SSSL promises to better realize the potential of high-throughput DNA sequencing data for downstream scientific applications

Abnormal state diagnosis model tolerant to noise in plant data

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants. ? 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)