Lithium Storage Mechanism: A Review of Perylene Diimide N-Substituted with a 1,2,4-Triazol-3-yl Ring for Organic Cathode Materials.

The demand for lithium-ion batteries (LIBs) has increased rapidly. However, commercial inorganic-based cathode materials have a low theoretical capacity and inherent disadvantages, such as high cost and toxicity. Redox-active organic cathodes with a high theoretical capacity, eco-friendly properties, and sustainability have been developed to overcome these limitations. Herein, perylene diimide derivatives N-substituted with 1,2,4-triazol-3-yl rings (PDI-3AT) were developed to apply as a cathode material for LIBs. The PDI-3AT cathode exhibited discharge capacities of 85.2 mAh g-1 (50 mA g-1 over 100 cycles) and 64.5 mAh g-1 (500 mA g-1 over 1000 cycles) with ratios to the theoretical capacities of 84 and 64%, respectively. Electrochemical kinetics analysis showed capacitive behaviors of the PDI-3AT cathode with efficient pathways for lithium-ion transport. Also, the activation step of the PDI-3AT cathode was demonstrated by improving the charge transfer resistance and lithium-ion diffusion coefficient during the initial few charge-discharge cycles. Furthermore, DFT calculations at the B3LYP/6-311+G** level and ex situ analysis of various charge states of the PDI-3AT electrode using attenuated total reflection Fourier transform infrared (ATR FT-IR) analysis, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were conducted for the further study of the lithium-ion storage mechanism. The results showed that the lithiation process formed the lithium enolate (═C-O-Li) coordinated with the N atoms of the 1,2,4-triazole ring. It is expected that our study results will encourage the production and use of redox-active perylene diimide derivatives as next-generation cathode materials.

A subset of macrophages and monocytes in the mouse bone marrow express atypical chemokine receptor 1.

Duffy antigen receptor for chemokines (DARC)/CD234, also known as atypical chemokine receptor 1 (ACKR1), is a seven-transmembrane domain protein expressed on erythrocytes, vascular endothelium, and a subset of epithelial cells (Peiper et al., 1995). Previously, we reported that ACKR1 was expressed in bone marrow macrophages. ACKR1 interacts with CD82 on long-term repopulating hematopoietic stem cells (LT-HSCs) to maintain the dormancy of LT-HSCs during homeostasis (Hur et al., 2016). We also demonstrated that ACKR1 interacts with CD82 in HSCs from human umbilical cord blood (hUCB). These findings demonstrated that CD82 is a functional surface marker of LT-HSCs and this molecule maintains LT-HSC quiescence by interactions with ACKR1-expressing macrophages in mice and humans.

Correction to: Deep user identification model with multiple biometric data.

An amendment to this paper has been published and can be accessed via the original article.

The Key Role of DNA Methylation and Histone Acetylation in Epigenetics of Atherosclerosis.

Atherosclerosis, which is the most common chronic disease of the coronary artery, constitutes a vascular pathology induced by inflammation and plaque accumulation within arterial vessel walls. Both DNA methylation and histone modifications are epigenetic changes relevant for atherosclerosis. Recent studies have shown that the DNA methylation and histone modification systems are closely interrelated and mechanically dependent on each other. Herein, we explore the functional linkage between these systems, with a particular emphasis on several recent findings suggesting that histone acetylation can help in targeting DNA methylation and that DNA methylation may control gene expression during atherosclerosis.

Deep user identification model with multiple biometric data.

BACKGROUND: Recognition is an essential function of human beings. Humans easily recognize a person using various inputs such as voice, face, or gesture. In this study, we mainly focus on DL model with multi-modality which has many benefits including noise reduction. We used ResNet-50 for extracting features from dataset with 2D data. RESULTS: This study proposes a novel multimodal and multitask model, which can both identify human ID and classify the gender in single step. At the feature level, the extracted features are concatenated as the input for the identification module. Additionally, in our model design, we can change the number of modalities used in a single model. To demonstrate our model, we generate 58 virtual subjects with public ECG, face and fingerprint dataset. Through the test with noisy input, using multimodal is more robust and better than using single modality. CONCLUSIONS: This paper presents an end-to-end approach for multimodal and multitask learning. The proposed model shows robustness on the spoof attack, which can be significant for bio-authentication device. Through results in this study, we suggest a new perspective for human identification task, which performs better than in previous approaches.