Ionic Liquids Catalysis for Carbon Dioxide Conversion With Nucleophiles

Carbon dioxide, as a promising C1 synthon, has attracted great interest in organic synthesis. Due to the thermodynamic stability and kinetic inertness of CO2, developing efficient strategies for CO2 activation and subsequent conversion is very crucial. In this context, Ionic liquids (ILs) show great potential for capturing and activating CO2 owing to their unique structures and properties, making them become ideal alternatives to volatile organic solvents and/or catalysts for CO2 transformation. This minireview aims at summarizing ILs-promoted reactions of CO2 with N-nucleophiles (primary amines)/O-nucleophiles (primary alcohols, water). Two catalytic systems i.e., metal/ILs binary systems such as Cu/ILs systems and Ag/ILs systems as well as single ILs systems including anion-functionalized ILs and bifunctionalized ILs have been developed for CO2 catalytic conversion, for instance, carboxylative cyclization of nucleophiles e.g., propargylic alcohols, amines, 2-aminobenzonitriles and o-aminobenzenethiol, and formylation of amines or 2-aminothiophenols with hydrosilanes to afford various value-added chemicals e.g., cyclic carbamates, unsymmetrical organic carbonates, α-hydroxyl ketones, and benzimidazolones. In a word, IL could provide a powerful tool for efficient CO2 utilization

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questions that necessitate complex reasoning and common-sense responses. Despite the growing number of datasets that aim to answer questions over charts, most only address this task in isolation, without considering the broader context of document-level question answering. Moreover, such datasets lack adequate common-sense reasoning information in their questions. In this work, we introduce a novel task named document-level chart question answering (DCQA). The goal of this task is to conduct document-level question answering, extracting charts or plots in the document via document layout analysis (DLA) first and subsequently performing chart question answering (CQA). The newly developed benchmark dataset comprises 50,010 synthetic documents integrating charts in a wide range of styles (6 styles in contrast to 3 for PlotQA and ChartQA) and includes 699,051 questions that demand a high degree of reasoning ability and common-sense understanding. Besides, we present the development of a potent question-answer generation engine that employs table data, a rich color set, and basic question templates to produce a vast array of reasoning question-answer pairs automatically. Based on DCQA, we devise an OCR-free transformer for document-level chart-oriented understanding, capable of DLA and answering complex reasoning and common-sense questions over charts in an OCR-free manner. Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document. We implement and evaluate a set of baselines, and our proposed method achieves comparable results

Development of a prognostic model to identify the metastatic nasopharyngeal carcinoma patients who may benefit from chemotherapy combination PD-1 inhibitor

BackgroundWe aimed to establish a prognostic model to identify suitable candidates for chemotherapy combination PD-1 inhibitor in metastatic nasopharyngeal carcinoma (NPC) patients.Patients and methodsIn this retrospective study, we included 524 patients (192 patients treated with chemotherapy combination PD-1 inhibitor and 332 received chemotherapy alone as first-line regimen) with metastatic NPC between January 2015 and March 2021. We developed a prognostic model to predict progression-free survival (PFS). A model-based trees approach was applied to estimate stratified treatment effects using prognostic scores and two well-matched risk groups (low-risk and high-risk) were created using propensity score matching.ResultsA prognostic nomogram was established with good accuracy for predicting PFS (c-index values of 0.71; 95% confidence interval, 0.66-0.73). The survival curves were significantly different between low-risk and high-risk groups (median PFS: 9.8 vs. 22.8 months, P < 0.001, respectively). After propensity matching analysis, chemotherapy combination PD-1 inhibitor was significantly associated with superior PFS as compared with chemotherapy alone (median PFS, 10.6 versus 9.3 months, P = 0.016) in the high-risk group. However, no significant difference between chemotherapy combination PD-1 inhibitor and chemotherapy was observed (P = 0.840) in the low-risk groups.ConclusionsOur novel prognostic model was able to stratify patients with metastatic NPC into low-risk or high-risk groups and identify candidates for PD-1 inhibitor therapy. These results are expected to be confirmed by a prospective clinical trial

Mechanical-Resonance-Enhanced Thin-Film Magnetoelectric Heterostructures for Magnetometers, Mechanical Antennas, Tunable RF Inductors, and Filters

The strong strain-mediated magnetoelectric (ME) coupling found in thin-film ME heterostructures has attracted an ever-increasing interest and enables realization of a great number of integrated multiferroic devices, such as magnetometers, mechanical antennas, RF tunable inductors and filters. This paper first reviews the thin-film characterization techniques for both piezoelectric and magnetostrictive thin films, which are crucial in determining the strength of the ME coupling. After that, the most recent progress on various integrated multiferroic devices based on thin-film ME heterostructures are presented. In particular, rapid development of thin-film ME magnetometers has been seen over the past few years. These ultra-sensitive magnetometers exhibit extremely low limit of detection (sub-pT/Hz1/2) for low-frequency AC magnetic fields, making them potential candidates for applications of medical diagnostics. Other devices reviewed in this paper include acoustically actuated nanomechanical ME antennas with miniaturized size by 1-2 orders compared to the conventional antenna; integrated RF tunable inductors with a wide operation frequency range; integrated RF tunable bandpass filter with dual H- and E-field tunability. All these integrated multiferroic devices are compact, lightweight, power-efficient, and potentially integrable with current complementary metal oxide semiconductor (CMOS) technology, showing great promise for applications in future biomedical, wireless communication, and reconfigurable electronic systems

Light-Soaking-Free Inverted Polymer Solar Cells with an Efficiency of 10.5% by Compositional and Surface Modifications to a Low-Temperature-Processed TiO2 Electron-Transport Layer.

Compositional modification and surface treatments of a TiO2 film prepared by a low-temperature route are carried out by a new promising method. Inverted polymer solar cells incorporating the post-treated TiO2 :TOPD electron-transport layer achieve the highest efficiency of 10.5%, and more importantly, eliminate the light-soaking problem that is commonly observed in metal-oxide-based inverted polymer solar cells

TLE3 represses colorectal cancer proliferation by inhibiting MAPK and AKT signaling pathways

Primer Sequences used for RT-qPCR (5’ to 3’). (DOCX 13 kb