Two-dimensional polymer nanosheets as a high-performance organic anode for sodium-ion batteries.

Organic compounds have become a potentially important choice for a new generation of energy-storage electrode materials due to their designability, flexibility, green sustainability, and abundance. However, the applications of organic electrode materials are still limited because of their dissolution in electrolytes and low electrical conductivity, which in turn cause poor cycling stability. Here, for the first time, we report 2-amino-4-thiazole-acetic acid (ATA) and its sodium salt, sodium 2-amino-4-thiazol-derived polymer (PATANa), as an anode. The PATANa showed a two-dimensional (2D) nanosheet structure, offering a larger contact area with the electrolyte and a shorter ion-migration path, which improved the ion-diffusion kinetics. The polymer showed excellent cycling stability and outstanding rate capability when tested as an anode for sodium-ion batteries (SIBs). It could deliver a high reversible specific capacity of 303 mA h g-1 at 100 mA g-1 for 100 cycles and maintain a high discharge capacity of 190 mA h g-1 after 1000 long cycle numbers even at a high current density of 1000 mA g-1. This approach of salinizing the polymer opens a new way to develop anode materials for sodium-ion batteries.

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes.

Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016-2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

A serum piRNA signature as promising non-invasive diagnostic and prognostic biomarkers for colorectal cancer.

Purpose: Piwi-interacting RNAs (piRNAs) are a novel class of small non-coding RNAs, which are not easily degraded but detectable in human body fluids. Recent studies have shown that aberrant piRNA expression is a signature feature across multiple tumor types. However, the expressions of piRNAs in serum of tumor patients and their potential clinical values remain largely unclear. Patients and methods: High-throughput sequencing was performed to investigate the serum piRNA profiles, followed by evaluations in serum samples of 220 colorectal cancer (CRC) patients and 220 healthy controls using reverse transcription quantitative real-time PCR (RT-qPCR). Biomarker panels including piRNA-based Panel I and carcinoembryonic antigen (CEA)-based Panel II, were developed by logistic regression model, and their diagnostic potentials were compared. Fagan's nomogram was plotted to promote clinical application. Results: We identified five differentially expressed serum piRNAs (piR-001311, piR-004153, piR-017723, piR-017724 and piR-020365), which, when combined in the piRNA-based Panel I, outperformed the CEA-based Panel II (P<0.001) and could detect CRC with an area under the receiver operating characteristic curve of 0.867. In addition, Kaplan-Meier analysis showed that patients with low serum piR-017724 level had worse overall survival (OS) and progression-free survival (PFS). In multivariate Cox regression analysis, serum piR-017724 was an independent prognostic factor for OS and PFS (P<0.05). Conclusion: Our findings suggest serum piRNA expression signatures have potential for use as biomarkers for CRC detection and to predict prognosis at the time of diagnosis.