A novel polymer acceptor based on the double B←N bridged bipyridine building block is reported. All‐polymer solar cells based on the new polymer acceptor show a power conversion efficiency of as high ...as 6.26% at a photon energy loss of only 0.51 eV.
A diketopyrrolopyrrole‐based conjugated polymer, PDPP‐4FTVT, which exhibits ambipolar transport behavior in air with hole and electron mobilities up to 3.40 and 5.86 cm2 V−1 s−1, respectively, is ...synthesized via direct arylation polycondensation. Incorporation of F‐atoms in β‐positions of thiophene rings dramatically improves the efficiency of direct arylation polycondensation.
Using a “multifluorination” strategy, ambipolar donor–acceptor conjugated polymer with hole and electron mobility (μh and μe) up to 3.94 and 3.50 cm2 V−1 s−1, respectively, and unipolar n‐type ...donor–acceptor conjugated polymers with μe up to 4.97 cm2 V−1 s−1 is synthesized with isoindigo as acceptor units.
Initial observational studies and a systematic review published recently have suggested that non-steroidal anti-inflammatory drug (NSAID) use has the trend to be associated with reduced risk of ...Alzheimer's disease (AD), while results remain conflicting. Thus, we performed an updated meta-analysis to reevaluate the evidence on this association.
Data sources from PUBMED, Embase and Cochrane Library from inception through April 2017 were searched by two independent reviewers. Eligible cohort studies were selected according to predefined keywords. We did a meta-analysis of available study data using a random-effects model to calculate overall relative risks (RRs) for associations between NSAID exposure and AD risk.
From 121 potentially relevant studies, 16 cohort studies including 236,022 participants, published between 1995 and 2016, were included in this systematic review. Meta-analysis demonstrated that current or former NSAID use was significantly associated with reduced risk of AD (RR, 0.81, 95% CI0.70 to 0.94) compared with those who did not use NSAIDs. This association existed in studies including all NSAID types, but not in aspirin (RR, 0.89, 95% CI 0.70 to 1.13), acetaminophen (RR, 0.87, 95% CI 0.40 to 1.91) or non-aspirin NSAID (RR, 0.84, 95% CI 0.58 to 1.23).
Current evidence suggests that NSAID exposure might be significantly associated with reduced risk of AD. However, further large-scale prospective studies are needed to reevaluate this association, especially the associations in individual NSAID type.
The shuttle effect of electrode materials always leads to capacity loss and poor cycle life of batteries. Two‐dimensional (2D) covalent organic frameworks (COFs) with uniform and controllable ...nanopores provide a promising strategy for fabricating ionic sieves to inhibit the shuttle effect. However, the insoluble nature of COFs made it difficult to fabricate compact and ordered membranes of COFs. Herein, we report a novel method for facilely anisotropic ordering of 2D COFs via depositing COFs onto graphene. The resulted double‐layer membranes acting as ionic sieves impressively inhibit the shuttle effect and exhibit versatility to both organic sodium‐ion batteries and Li‐S batteries, leading to high cyclability.
Sieve a little bit: Depositing covalent organic frameworks (COFs) onto a graphene membrane facilely gives anisotropic ordering of 2D COFs. The resulting double‐layer membrane acting as an ionic sieve impressively inhibits the shuttle effect and can be used in both organic sodium‐ion batteries and lithium‐sulfur batteries.
Motor current signal analysis (MCSA) provides an alternative nonintrusive approach to detect mechanical faults by using the fault signature transmitted along the torsional direction through the ...rotor. In existing fault detection methods based on MCSA, the gearbox health condition is monitored through the amplitude of the fault-related sidebands in the lower frequency range of the motor current spectra. However, their practical implementation is challenged by the harmonics resulting from the structural properties of the electrical machines and the inherent system imperfections. This effect is even more severe in case of a drivetrain containing planetary gearboxes due to its more complex assembly. In this paper, the resonance residual technique, which investigates the spectrum region around the resonance frequency where rich fault information may occur, is applied for the first time to MCSA to detect planetary gearbox faults. This proposed approach is verified through both simulation and experiments. A lumped parameter model for an electromechanical drive train with an annulus gear tooth crack is simulated to investigate its effect on the stator current. For experimental verification, a similar 4-kW motor-planetary gearbox- generator test rig is used. The robustness of the proposed method is demonstrated through simulations of a nonlinear finite-element model and experiments under different operating conditions. Furthermore, the effectiveness of the proposed method to extract fault information over the existing methods is also shown.
Efficient organic solar cells (OSCs) often use combination of polymer donor and small molecule acceptor. Herein we demonstrate efficient and thermally stable OSCs with combination of small molecule ...donor and polymer acceptor, which is expected to expand the research field of OSCs. Typical small molecule donors show strong intermolecular interactions and high crystallinity, and consequently do not match polymer acceptors because of large-size phase separation. We develop a small molecule donor with suppressed π-π stacking between molecular backbones by introducing large steric hindrance. As the result, the OSC exhibits small-size phase separation in the active layer and shows a power conversion efficiency of 8.0%. Moreover, this OSC exhibits much improved thermal stability, i.e. maintaining 89% of its initial efficiency after thermal annealing the active layer at 180 °C for 7 days. These results indicate a different kind of efficient and stable OSCs.
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•A NIR fluorescent probe CyOS for detection of hydrazine was reported.•The probe exhibits highly selectivity and sensitivity toward hydrazine (detection limit: 0.78 ppb).•The probe ...has low cytotoxicity and can be applied to image hydrazine in living cells, tissues and mice.
Hydrazine has been identified as an environmental contaminant and a probable human carcinogen for its high toxicity. Thus, the development of detection methods for mapping hydrazine in solution and biosystems, particularly in vivo, is of great significance. Here, through firstly exploited 2-thiophenecarbonyl moiety as a recognition unit for hydrazine, a novel near-infrared (NIR) fluorescent probe CyOS is obtained based on hemicyanine. The probe shows high selectivity towards hydrazine over various amino acids and common ions with a significant fluorescence enhancement at 701 nm in the presence of hydrazine. Further, it has a sufficiently low detection limit (0.78 ppb). And most importantly, due to its good cell membrane permeability and low cytotoxicity, CyOS has been applied to monitor and image hydrazine in living cells, tissues and mice. To the best of our knowledge, it is the first time to visualize hydrazine in deep living tissues (∼90 μm).
Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating ...the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets.
In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, ...these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
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