Melt crystallization and segmental dynamics of poly(ethylene oxide) confined in a solid electrolyte composite

The isothermal melt crystallization and the corresponding segmental dynamics, of a high molecular weight poly(ethylene oxide) (PEO) confined by Li7La3Zr2O12 (LLZO) particles in solid electrolyte composites, were monitored by differential scanning calorimetry (DSC) and dielectric relaxation spectroscopy (DRS), respectively. Our results show that the overall crystallinity is positively correlated with the surface area of LLZO particles. The primary and secondary crystallization processes are identified by a modified Avrami equation, while two dynamic modes, the α relaxation and α′ relaxation, were in the DRS measurements. The results reveal an unambiguous correlation between the primary crystallization and the α relaxation, while a correlation between the second crystallization and the α′ relaxation concurrently exist in the electrolyte composites. © 2020 Wiley Periodicals, Inc. J. Polym. Sci. 2020, 58, 466–477In a representative polymer‐ceramic composite solid electrolyte, segmental dynamics are closely related to the crystallization processes in the polymer matrix. This nature may significantly impact the performance of the electrolyte, as ionic conductivity in such material relies on segmental motions of the polymer.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153736/1/pola29577_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153736/2/pola29577.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153736/3/pola29577-sup-0001-AppendixS1.pd

Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration

Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions. Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD). In SHENet, a feature encoder converts the input SD-OCT images to deep features, then a graph evolution module predicts the process of disease evolution in high-dimensional latent space and outputs the predicted deep features, and lastly, feature decoder recovers the predicted deep features to SD-OCT images. We further propose an evolution reinforcement module to ensure the effectiveness of disease evolution learning and obtain realistic SD-OCT images by adversarial training. SHENet is validated on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes based on the quantitative and qualitative evaluations. Compared with other generative methods, the generative SD-OCT images of SHENet have the highest image quality. Besides, SHENet achieves the best structure protection and content prediction. Qualitative evaluations also demonstrate that SHENet has a better visual effect than other methods. SHENet can generate post-therapeutic SD-OCT images with both high prediction performance and good image quality, which has great potential to help ophthalmologists forecast the therapeutic effect of nAMD

Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation

You can have your cake and eat it too. Microvessel segmentation in optical coherence tomography angiography (OCTA) images remains challenging. Skeleton-level segmentation shows clear topology but without diameter information, while pixel-level segmentation shows a clear caliber but low topology. To close this gap, we propose a novel label adversarial learning (LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL mainly consists of two designs: a label adversarial loss and an embeddable adjustment layer. The label adversarial loss establishes an adversarial relationship between the two label supervisions, while the adjustment layer adjusts the network parameters to match the different adversarial weights. Such a design can efficiently capture the variation between the two supervisions, making the segmentation continuous and tunable. This continuous process allows us to recommend high-quality vessel segmentation with clear caliber and topology. Experimental results show that our results outperform manual annotations of current public datasets and conventional filtering effects. Furthermore, such a continuous process can also be used to generate an uncertainty map representing weak vessel boundaries and noise

Surface-Modified Phthalocyanine-Based Two-Dimensional Conjugated Metal–Organic Framework Films for Polarity-Selective Chemiresistive Sensing

This corrigendum corrects an omission from the Acknowledgement section. The research leading to the results published in this manuscript was also supported by the project CALIPSOplus under Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020

Surface-Modified Phthalocyanine-Based Two-Dimensional Conjugated Metal–Organic Framework Films for Polarity-Selective Chemiresistive Sensing

Surface-modification of phthalocyanine-based two-dimensional conjugated metal-organic framework (2D c-MOF) films by grafting aliphatic alkyl chains is developed for achieving high-performance polarity-selective chemiresistive sensing toward humidity and polar alcohols. 2D conjugated metal–organic frameworks (2D c-MOFs) are emerging as electroactive materials for chemiresistive sensors, but selective sensing with fast response/recovery is a challenge. Phthalocyanine-based Ni2[MPc(NH)8] 2D c-MOF films are presented as active layers for polarity-selective chemiresisitors toward water and volatile organic compounds (VOCs). Surface-hydrophobic modification by grafting aliphatic alkyl chains on 2D c-MOF films decreases diffused analytes into the MOF backbone, resulting in a considerably accelerated recovery progress (from ca. 50 to ca. 10 s) during humidity sensing. Toward VOCs, the sensors deliver a polarity-selective response among alcohols but no signal for low-polarity aprotic hydrocarbons. The octadecyltrimethoxysilane-modified Ni2[MPc(NH)8] based sensor displays high-performance methanol sensing with fast response (36 s)/recovery (13 s) and a detection limit as low as 10 ppm, surpassing reported room-temperature chemiresistors

Nitroxoline suppresses metastasis in bladder cancer via EGR1/circNDRG1/miR-520h/smad7/EMT signaling pathway

Bladder cancer is one of the most common and deadly cancer worldwide. Current chemotherapy has shown limited efficacy in improving outcomes for patients. Nitroxoline, an old and widely used oral antibiotic, which was known to treat for urinary tract infection for decades. Recent studies suggested that nitroxoline suppressed the tumor progression and metastasis, especially in bladder cancer. However, the underlying mechanism for anti-tumor activity of nitroxoline remains unclear. Methods: CircRNA microarray was used to explore the nitroxoline-mediated circRNA expression profile of bladder cancer lines. Transwell and wound-healing assay were applied to evaluate the capacity of metastasis. ChIP assay was chosen to prove the binding of promotor and transcription factor. RNA-pulldown assay was performed to explore the sponge of circRNA and microRNA. Results: We first identified the circNDRG1 (has_circ_0085656) as a novel candidate circRNA. Transwell and wound-healing assay demonstrated that circNDRG1 inhibited the metastasis of bladder cancer. ChIP assay showed that circNDRG1 was regulated by the transcription factor EGR1 by binding the promotor of host gene NDRG1. RNA-pulldown assay proved that circNDRG1 sponged miR-520h leading to the overexpression of smad7, which was a negative regulatory protein of EMT. Conclusions: Our research revealed that nitroxoline may suppress metastasis in bladder cancer via EGR1/circNDRG1/miR-520h/smad7/EMT signaling pathway

Patterns and functional implications of rare germline variants across 12 cancer types

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine

The Prevalence of Immunologic Injury in Renal Allograft Recipients with De Novo Proteinuria

Post-transplant proteinuria is a common complication after renal transplantation; it is associated with reduced graft and recipient survival. However, the prevalence of histological causes has been reported with considerable variation. A clinico-pathological re-evaluation of post-transplant proteinuria is necessary, especially after dismissal of the term “chronic allograft nephropathy,” which had been considered to be an important cause of proteinuria. Moreover, urinary protein can promote interstitial inflammation in native kidney, whether this occurs in renal allograft remains unknown. Factors that affect the graft outcome in patients with proteinuria also remain unclear. Here we collected 98 cases of renal allograft recipients who developed proteinuria after transplant, histological features were characterized using Banff scoring system. Cox proportional hazard regression models were used for graft survival predictors. We found that transplant glomerulopathy was the leading (40.8%) cause of post-transplant proteinuria. Immunological causes, including transplant glomerulopathy, acute rejection, and chronic rejection accounted for the majority of all pathological causes of proteinuria. Nevertheless, almost all patients that developed proteinuria had immunological lesions in the graft, especially for interstitial inflammation. Intraglomerular C3 deposition was unexpectedly correlated with the severity of proteinuria. Moreover, the severity of interstitial inflammation was an independent risk factor for graft loss, while high level of hemoglobin was a protective factor for graft survival. This study revealed a predominance of immunological parameters in renal allografts with post-transplant proteinuria. These parameters not only correlate with the severity of proteinuria, but also with the outcome of the graft

A discrete model for the geometrically nonlinear mechanics of hard-magnetic slender structures

Hard-magnetic soft (HMS) materials and structures, which can undergo rapid configurational transformation under non-contact magnetic stimuli, have attracted extensive attentions in a wide range of engineering applications, such as soft robotics, biomedical devices and stretchable electronics, etc. In order to realize the full potentials of HMS structures, it is crucial to be able to predict their mechanical (both static and dynamic) responses. In this work, we propose a discrete magneto-elastic rod model with the aim to analyse the mechanical behaviours of slender structures made of HMS materials under magnetic loading. The configuration of a slender object is described by multiply connected nodes and edges, from which the force vector and the associated Hessian matrix are derived by taking the variation of Kirchhoff-like magneto-elastic potentials. The nonlinear dynamical equations of motion are next evaluated through Newton-type optimization, and the static analysis can be obtained using dynamical relaxation method. For verification, we quantitatively compare our numerical results with either analytical solutions or experimental data for several representative cases. Good agreements in all these cases indicate the correctness and accuracy of our discrete model. We further extend the model to take the dipole–dipole interaction and viscous effect into consideration. Finally, as a demonstration, we perform simulations to reproduce the locomotion of a magnetic crawling robot. The developed discrete magneto-elastic model significantly improves the computational efficiency, enabling the practicability of simulating the mechanical, especially dynamic, behaviours of the hard-magnetic slender structures.Ministry of Education (MOE)Nanyang Technological UniversityW.H. acknowledges research funding from the Natural Science Foundation of Jiangsu Province, China (BK20220794). M.L. acknowledges the Presidential Postdoctoral Fellowship from Nanyang Technological University, Singapore. K.J.H. acknowledges the financial supports from Nanyang Technological University, Singapore (Grant M4082428) and Ministry of Education, Singapore under its Academic Research Fund Tier 2 (T2EP50122-0005)