Solvation Effect: The Cornerstone of High-Performance Battery Design for Commercialization-Driven Sodium Batteries.

Sodium batteries (SBs) emerge as a potential candidate for large-scale energy storage and have become a hot topic in the past few decades. In the previous researches on electrolyte, designing electrolytes with the solvation theory has been the most promising direction is to improve the electrochemical performance of batteries through solvation theory. In general, the four essential factors for the commercial application of SBs, which are cost, low temperature performance, fast charge performance and safety. The solvent structure has significant impact on commercial applications. But so far, the solvation design of electrolyte and the practical application of sodium batteries have not been comprehensively summarized. This review first clarifies the process of Na+ solvation and the strategies for adjusting Na+ solvation. It is worth noting that the relationship between solvation theory and interface theory is pointed out. The cost, low temperature, fast charging, and safety issues of solvation are systematically summarized. The importance of the de-solvation step in low temperature and fast charging application is emphasized to help select better electrolytes for specific applications. Finally, new insights and potential solutions for electrolytes solvation related to SBs are proposed to stimulate revolutionary electrolyte chemistry for next generation SBs.

LncRNA LCPAT1 is involved in DNA damage induced by CSE.

Cigarette smoking plays an important role in the process of lung cancer, during which DNA damage is proved to be involved. Non-coding RNAs are found to be involved in the DNA damage and repair processes induced by cigarette smoke. In the present study, we investigated the role of lncRNA LCPAT1 in DNA damage caused by CSE in Beas-2B cells. Our results indicate that LCPAT1, through RCC2 is involved in the CSE-induced DNA damage providing new insight into the lung carcinogenesis related to cigarette smoking.

SNPs in LncRNA genes are associated with non-small cell lung cancer in a Chinese population.

BACKGROUND: It has indicated that single nuclear polymorphisms (SNPs) in the regions encoding non-coding transcripts are associated with lung cancer susceptibility. In a previous microarray study, we identified 13 differentially expressed long non-coding RNAs (lncRNAs) in non-small cell lung cancer (NSCLC) and associations of SNPs in these lncRNA genes with lung cancer were unknown. We conducted a case-control study to address this issue. METHODS: Using the TaqMan method, we genotyped 17 SNPs located in the 13 lncRNA genes in 1294 cases with NSCLC and 1729 healthy controls. Unconditional logistic regression and Cox proportional hazards regression were used to analyze the associations of these SNPs with NSCLC risk and patient survival, respectively. These analyses were also repeated in subgroups of cases and controls stratified by gender, age group, smoking status, disease stage, and histological type. RESULTS: We identified three SNPs associated with NSCLC risk. For SNP rs498238, CC genotype was associated with lower risk compared to TT genotype (adjusted OR = 0.33, 95%CI: 0.11-0.97, P = 0.043). For rs16901995, CT/TT genotypes were associated with lower risk compared to CC genotype in non-smokers (adjusted OR = 0.78, 95%CI: 0.62-0.98, P = 0.035). Variant genotypes in rs219741 were associated with NSCLC risk in young patients, and the adjusted OR was 1.47 (95%CI: 1.03-2.10, P = 0.033) when compared to the wild genotype. No SNPs were found to be associated with patient overall survival in the study. CONCLUSION: The study suggests that some genetic polymorphisms in the lncRNA genes may influence the risk of NSCLC among Chinese.

LncRNA LCPAT1 Mediates Smoking/ Particulate Matter 2.5-Induced Cell Autophagy and Epithelial-Mesenchymal Transition in Lung Cancer Cells via RCC2.

BACKGROUND/AIMS: Ecological studies have shown that air pollution and prevalence of cigarette smoking are positively correlated. Evidence also suggests a synergistic effect of cigarette smoking and PM2.5 exposure (Environmental Particulate Matter ≤ 2.5 µm in diameter) on lung cancer risk. We aimed to evaluate the interaction between smoking prevalence and PM2.5 pollution in relation to lung cancer mortality and determine its underlying mechanisms in vitro. METHODS: "MOVER" method was used to analyze the interaction between smoking prevalence and PM2.5 pollution in relation to lung cancer mortality. Cell autophagy and malignant behaviors induced by cigarette smoke extract (CSE) and PM2.5 exposure were examined in vitro. Gene expression was examined by qRT-PCR and western blot. RNA and protein interaction was determined using a RNA binding protein immunoprecipitation assay. RESULTS: An increased risk for lung cancer death (RERI (the relative excess risk) =0.28) was observed with a synergistic interaction between cigarette smoking and PM2.5 pollution. Cell migration, invasion, EMT (epithelial-mesenchymal transition) and autophagy were elevated when lung cancer cells were treated with CSE and PM2.5 in combination. A lncRNA, named lung cancer progression-association transcript 1 (LCPAT1), was up-regulated after the treatment of CSE and PM2.5, and knocking down the lncRNA impaired the effect of CSE and PM2.5 on lung cancer cells. In addition, LCPAT1 was shown to bind to RCC2, and RCC2 mediated the effect of LCPAT1 on cell autophagy, migration, invasion and EMT in lung cancer. CONCLUSIONS: Our results suggest that combined exposure to CSE and PM2.5 induces LCPAT1 expression, which up-regulates autophagy, and promotes lung cancer progression via RCC2.

Effect of Calcipotriol on IFN-γ-Induced Keratin 17 Expression in Immortalized Human Epidermal Keratinocyte Cells.

BACKGROUND Calcipotriol ointment has been demonstrated to be a very safe and effective topical drug for psoriasis. This study aims to investigate the effect of calcipotriol on IFN-γ-induced keratin 17 (K17) expression in a human keratinocyte cell line (HaCaT), which is a widely accepted as a mimic in vitro model for psoriasis. MATERIAL AND METHODS We used Western blot, immunofluorescence staining, and luciferase reporter system assays to evaluate the expression of K17 and the possible underlying mechanisms. RESULTS Administration of IFN-γ (125-1000 U) increased K17 expression in a dose-dependent manner, and 250 U/ml IFN-γ significantly elevated K17 expression. The experimental results showed that calcipotriol at concentrations of 10^-7 M and 10^-5 M suppressed the IFN-γ-induced K17 expression by 58.10% and 70.68%, respectively. Through immunofluorescence staining and luciferase reporter assay, we found that Vitamin D Response Element (VDRE) affected IFN-activated site (Gamma-activated sequence, GAS) function at the transcriptional level and was involved in the inhibition of K17 expression. CONCLUSIONS Our data suggest that calcipotriol downregulates IFN-γ-mediated K17 expression in keratinocytes in a dose-dependent manner via VDRE effect GAS function. The inhibitory effect of calcipotriol on K17 expression may be a potential mechanism and function in the treatment psoriasis.

Favored Amorphous LixSi Process with Restrained Volume Change Enabling Long Cycling Quasi-solid-state SiOx anode.

Silicon-based anodes are becoming promising materials due to their high specific capacity. However, the intrinsically large volume change brought about by the alloying reaction results in the crushing of the active particles and destruction of the electrode structure, which severely limits its practical application. Various structured and modified silica-based anodes exhibit improved cycling stability and the demonstrated ability to mitigate their volume changes through interfacial and binder strategies. However, the issue of large volume changes in silicon-based anodes remains. Herein, we report a gel polymer electrolyte (GPE) prepared through an in situ thermal polymerization process that is suitable for SiOx anode materials and achieving long-term cycling stability. GPE-based cells essentially mitigate the volume change of SiOx anodes by guiding the unique lithiation/delithiation mechanism that tends to favor the formation and delithiation of amorphous-LixSi (a-LixSi) with smaller volume change, thereby mitigating electrode damage and cracking, and achieving the significant improvement in cycling performance. The prepared GPE-SiOx cells retained 693.80 mAh g-1 reversible capacity after 450 cycles at 500 mA g-1. In addition, the prelithiation process was incorporated to mitigate capacity fluctuations and improve the Initial Coulombic Efficiency (ICE), and a reversible capacity of 641.90 mAh g-1 was retained after 480 cycles.

An explainable deep learning framework for characterizing and interpreting human brain states.

Deep learning approaches have been widely adopted in the medical image analysis field. However, a most of existing deep learning approaches focus on achieving promising performances such as classification, detection, and segmentation, and much less effort is devoted to the explanation of the designed models. Similarly, in the brain imaging field, many deep learning approaches have been designed and applied to characterize and predict human brain states. However, these models lack interpretation. In response, we propose a novel domain knowledge informed self-attention graph pooling-based (SAGPool) graph convolutional neural network to study human brain states. Specifically, the dense individualized and common connectivity-based cortical landmarks system (DICCCOL, structural brain connectivity profiles) and holistic atlases of functional networks and interactions system (HAFNI, functional brain connectivity profiles) are integrated with the SAGPool model to better characterize and interpret the brain states. Extensive experiments are designed and carried out on the large-scale human connectome project (HCP) Q1 and S1200 dataset. Promising brain state classification performances are observed (e.g., an average of 93.7% for seven-task classification and 100% for binary classification). In addition, the importance of the brain regions, which contributes most to the accurate classification, is successfully quantified and visualized. A thorough neuroscientific interpretation suggests that these extracted brain regions and their importance calculated from self-attention graph pooling layer offer substantial explainability.

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.

EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level. However, present classification performance is still far from satisfactory. We have identified two major focal points for improvements: one is about organizing the input EEG signals, and the other is related to the design of the RNN architecture. To optimize the above-mentioned issues and achieve better brain state classification performance, we propose a novel multi-clip random fragment strategy-based interactive bidirectional recurrent neural network (McRFS-IBiRNN) model in this work. This model has two advantages over previous methods. First, the McRFS component is designed to re-organize the input EEG signals to make them more suitable for the RNN architecture. Second, the IBiRNN component is an innovative design to model the RNN layers with interaction connections to enhance the fusion of bidirectional features. By adopting the proposed model, promising brain states classification performances are obtained. For example, 96.97% and 99.34% of individual and group level four-category classification accuracies are successfully obtained on the EEG motor/imagery dataset, respectively. A 99.01% accuracy can be observed for four-category classification tasks with new subjects not seen before, which demonstrates the generalization of our proposed method. Compared with existing methods, our model outperforms them with superior results. Overall, the proposed McRFS-IBiRNN model demonstrates great superiority in differentiating brain states on EEG signals.

A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging.

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test-retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the "semantic gap" between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application.

Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks.

Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective. In addition, an effective framework not only jointly studies the brain function and structure at a group-level, but also retains the individual differences among the subjects. In this study, a novel dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework is proposed to differentiate preterm and term infant brains and characterize the corresponding biomarkers. This framework adopts the DICCCOL system as the initialized graph node of GNN for each subject, utilizing both functional and structural profiles and effectively retaining the individual differences. To be specific, functional magnetic resonance imaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. Our results successfully demonstrate that the proposed framework can effectively differentiate the preterm and term infant brains. Furthermore, the self-attention-based mechanism can accurately calculate the attention score and recognize the most significant biomarkers. In this study, not only 87.6% classification accuracy is observed for the developing Human Connectome Project (dHCP) dataset, but also distinguishing features are explored and extracted. Our study provides a novel and uniform framework to differentiate brain disorders and characterize the corresponding biomarkers.

Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection.

Preterm is a worldwide problem that affects infants' lives significantly. Moreover, the early impairment is more than limited to isolated brain regions but also to global and profound negative outcomes later, such as cognitive disorder. Therefore, seeking the differences of brain connectome between preterm and term infant brains is a vital step for understanding the developmental impairment caused by preterm. Existing studies revealed that studying the relationship between brain function and structure, and further investigating their differentiable connectomes between preterm and term infant brains is a way to comprehend and unveil the differences that occur in the preterm infant brains. Therefore, in this article, we proposed a novel canonical correlation analysis (CCA) with locality preserving projection (LPP) approach to investigate the relationship between brain functional and structural connectomes and how such a relationship differs between preterm and term infant brains. CCA is proposed to study the relationship between functional and structural connections, while LPP is adopted to identify the distinguishing features from the connections which can differentiate the preterm and term brains. After investigating the whole brain connections on a fine-scale connectome approach, we successfully identified 89 functional and 97 structural connections, which mostly contributed to differentiate preterm and term infant brains from the functional MRI (fMRI) and diffusion MRI (dMRI) of the public developing Human Connectome Project (dHCP) dataset. By further exploring those identified connections, the results innovatively revealed that the identified functional connections are short-range and within the functional network. On the contrary, the identified structural connections are usually remote connections across different functional networks. In addition, these connectome-level results show the new insights that longitudinal functional changes could deviate from longitudinal structural changes in the preterm infant brains, which help us better understand the brain-behavior changes in preterm infant brains.

Association between sex hormones regulation-related SNP rs12233719 and lung cancer risk among never-smoking Chinese women.

BACKGROUND: The mechanism of rapidly increased non-small cell lung cancer (NSCLC) among never-smoking Chinese women has not been elucidated. Ovarian sex steroid hormones have been suggested to counteract lung cancer development, and sex hormone-binding globulin (SHBG) is essential in sex hormones regulation. This study aims to exploring single nucleotide polymorphisms (SNPs) in genomic regions associated with SHBG concentrations that contributed to never-smoking female NSCLC. METHODS: Candidate genes were selected by a genome-wide association (GWAS) meta-analysis and gene expression profiles of never-smoking NSCLC of Chinese women. The candidate SNPs limited to common minor allele frequency (MAF), missense variant, ethnic heterogeneous distribution, and SNPs were genotyped using the TaqMan method. A two-stage case-control design was adopted for exploration and validation of associations between candidate SNPs and risk of NSCLC. All participants were never-smoking Chinese women. Chi-square test and multivariate logistic regression were applied. RESULTS: Beginning with 12 genomic regions associated with circulating SHBG concentrations and gene expression profiles from never-smoking NSCLC in Chinese women, candidate SNP rs12233719 and rs7439366 both located in candidate gene UGT2 B7, which may be related to circulating SHBG concentrations and cancer risk, were identified. A two-stage case-control study was conducted in Shenyang and Tianjin represented as the training stage and validation stage, respectively. Under the dominant model, compared to individuals with the wild G/G genotype, the adjusted OR of those with the T allele was 1.58 (95% CI: 1.15-2.16) in Chinese Shenyang training set, and was 1.49 (95% CI: 1.02-2.18) in Chinese Tianjin validation set, both accompanied with a significant trend relationship consistently. UGT2B7 was upregulated in female NSCLC patients' tumor tissues and was associated with a poor prognosis in NSCLC. CONCLUSION: Our findings indicated that a sex hormones regulation-related SNP rs12233719 was associated with never-smoking female lung cancer risk, which might partially explain NSCLC-susceptibility in Chinese women.

Silica nanoparticles enhance germ cell apoptosis by inducing reactive oxygen species (ROS) formation in Caenorhabditis elegans.

Silica nanoparticles (SiO2 NPs) are widely used in daily life and can enter organisms through several pathways, often causing unpredictable toxicity. Although SiO2 NPs are known to cause damage to the respiratory system, little is known about their oral toxicity, and their potential harm to the reproductive system is unclear. In this study, we used a Caenorhabditis elegans model to clarify SiO2 NPs oral toxicity in vivo and explore their effect on the reproductive system. We exposed C. elegans to 0.25, 0.5 and 1 mg /mL SiO2 NPs for 24 hr. Our results showed that SiO2 NPs exposure for 24 hr did not affect nematode survival rates, but did affect, to varying degrees, the reproduction, development, and movement of nematodes, with nematode fecundity being the most sensitive to SiO2 NPs toxicity. The NPs exposed group showed enhanced germ cell apoptosis and increased oxidative stress as seen through an increase in ROS and malondialdehyde (MDA), and decrease in reduced glutathione (GSH). N-acetyl-L-cysteine (NAC), an antioxidant, negated SiO2 NPs effect on germ cells and restored nematodes reproductive ability. We also found that SiO2 NPs could affect the expression of genes related to metal detoxification, oxidative stress, and apoptosis. The expression of metallothionein coding genes mtl-1 and mtl-2 changed most significantly among the tested genes. We demonstrated that SiO2 NPs could enhance germ cell apoptosis by inducing oxidative stress, providing a new area for studies of the mechanism of SiO2 NP toxicity.

MicroRNA Biomarker hsa-miR-195-5p for Detecting the Risk of Lung Cancer.

BACKGROUND: Lung cancer is one of the leading diagnosed cancers worldwide, and microRNAs could be used as biomarkers to diagnose lung cancer. hsa-miR-195 has been demonstrated to affect the prognosis of NSCLC (non-small-cell lung cancer) in a previous study. However, the diagnostic value of hsa-miR-195-5p in lung cancer has not been investigated. METHODS: To evaluate the ability of hsa-miR-195-5p to diagnose lung cancer, we compared the expression of hsa-miR-195-5p in lung cancer patients, COPD patients, and normal controls. Receiver operating characteristic (ROC) curve analysis was performed to investigate the sensitivity and specificity of hsa-miR-195-5p. Coexpression network and pathway analysis were carried out to explore the mechanism. RESULTS: We found that hsa-miR-195-5p had lower expression in lung cancer and COPD patients than in normal controls, and the AUC was 0.92 for diagnosing lung cancer. hsa-miR-143 correlated with hsa-miR-195-5p, and by combining these two microRNAs, the AUC was 0.97 for diagnosing lung cancer. CONCLUSIONS: hsa-miR-195-5p may act as a biomarker that contributes to the diagnosis of lung cancer and the detection of its high-risk population.

SNP rs17079281 decreases lung cancer risk through creating an YY1-binding site to suppress DCBLD1 expression.

Genome-wide association studies (GWAS) have identified numerous genetic variants that are associated with lung cancer risk, but the biological mechanisms underlying these associations remain largely unknown. Here we investigated the functional relevance of a genetic region in 6q22.2 which was identified to be associated with lung cancer risk in our previous GWAS. We performed linkage disequilibrium (LD) analysis and bioinformatic prediction to screen functional SNPs linked to a tagSNP in 6q22.2 loci, followed by two case-control studies and a meta-analysis with 4403 cases and 5336 controls to identify if these functional SNPs were associated with lung cancer risk. A novel SNP rs17079281 in the DCBLD1 promoter was identified to be associated with lung cancer risk in Chinese populations. Compared with those with C allele, patients with T allele had lower risk of adenocarcinoma (adjusted OR = 0.86; 95% CI: 0.80-0.92), but not squamous cell carcinoma (adjusted OR = 0.99; 95% CI: 0.91-1.10), and patients with the C/T or T/T genotype had lower levels of DCBLD1 expression than those with C/C genotype in lung adenocarcinoma tissues. We performed functional assays to characterize its biological relevance. The results showed that the T allele of rs17079281 had higher binding affinity to transcription factor YY1 than the C allele, which suppressed DCBLD1 expression. DCBLD1 behaved like an oncogene, promoting tumor growth by influencing cell cycle progression. These findings suggest that the functional variant rs17079281C>T decreased lung adenocarcinoma risk by creating an YY1-binding site to suppress DCBLD1 expression, which may serve as a biomarker for assessing lung cancer susceptibility.