Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule-guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi-media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features

Senescence risk score: a multifaceted prognostic tool predicting outcomes, stemness, and immune responses in colorectal cancer

Colorectal cancer (CRC) remains a primary cause of cancer mortality globally, necessitating precise prognostic indicators for effective clinical management. Our study introduces the Senescence Risk Score (SRRS), based on several senescence-related genes (SRGs), a potent prognostic tool designed to measure cellular senescence in CRC. The higher SRRS predicts a poorer prognosis, providing a novel and efficient approach to patient stratification. Notably, we found that SRRS correlates with methylation and mutation variations, and increased immune infiltration in the tumor microenvironment, thus revealing potential therapeutic targets. We also discovered an inverse relationship between SRRS and cell stemness, which could have significant implications for cancer treatment strategies. Utilizing bioinformatics resources and machine learning, we identified LIMK1 and WRN as key genes associated with SRRS, further enhancing its prognostic value. Importantly, the modulation of these genes significantly impacts cellular senescence, proliferation, and stemness in CRC cells. In summary, our development of SRRS offers a powerful tool for CRC prognosis and paves the way for novel therapeutic strategies, underscoring its potential in transforming CRC patient management

On-chip integrated graphene aptasensor with portable readout for fast and label-free COVID-19 detection in virus transport medium

Graphene field-effect transistor (GFET) biosensors exhibit high sensitivity due to a large surface-to-volume ratio and the high sensitivity of the Fermi level to the presence of charged biomolecules near the surface. For most reported GFET biosensors, bulky external reference electrodes are used which prevent their full-scale chip integration and contribute to higher costs per test. In this study, GFET arrays with on-chip integrated liquid electrodes were employed for COVID-19 detection and functionalized with either antibody or aptamer to selectively bind the spike proteins of SARS-CoV-2. In the case of the aptamer-functionalized GFET (aptasensor, Apt-GFET), the limit-of-detection (LOD) achieved was about 103 particles per mL for virus-like particles (VLPs) in clinical transport medium, outperforming the Ab-GFET biosensor counterpart. In addition, the aptasensor achieved a LOD of 160 aM for COVID-19 neutralizing antibodies in serum. The sensors were found to be highly selective, fast (sample-to-result within minutes), and stable (low device-to-device signal variation; relative standard deviations below 0.5%). A home-built portable readout electronic unit was employed for simultaneous real-time measurements of 12 GFETs per chip. Our successful demonstration of a portable GFET biosensing platform has high potential for infectious disease detection and other health-care applications

Study on the Frost Resistance of Composite Limestone Powder Concrete against Coupling Effects of Sulfate Freeze–Thaw

Concrete in saline or coastal settings exposed to freezing temperatures is frequently affected by coupling actions of sulfate assault and freeze–thaw degradation, reducing the service life of concrete structures significantly. This study conducted an accelerated freeze–thaw cycle test in pure water and Na2SO4 solution with a mass proportion of 5% to examine the coupling impact of sulfate freeze–thaw on the frost resistance of composite limestone powder (CLP) concrete. Combined with SEM and XRD methods, the performance degradation mechanisms of composite limestone powder (CLP) concrete in coupling sulfate freeze–thaw conditions were analyzed with a microscopic point of view. The findings demonstrated that limestone powder has a filling effect but the activity is low. When the content is 10~20%, the chemical response is higher than the physical response. The pozzolanic effect of fly ash and slag can improve the pore structure and improve the compactness of concrete. The “superposition effect” of limestone powder, fly ash, and slag can improve the frost resistance of CLP concrete. The scenario of salt freezing cycles has negative effects that are worse than those of water freezing cycles on the antifreeze performance of CLP concrete, including apparent morphology, mass loss, relative dynamic modulus of elasticity, and compressive strength. Sulfate’s activation effect boosts slag’s activity effect, which significantly promotes the antifreeze performance of concrete subjected to salt frozen cycles over water frozen cycles. The freeze–thaw damage model of CLP concrete under coupling sulfate freeze–thaw is established through theorem analysis and experiment statistics, laying a theoretical framework for the popularization and use of this concrete

A lightweight ship target detection model based on improved YOLOv5s algorithm.

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas

A lightweight ship target detection model based on improved YOLOv5s algorithm

Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas

Hybrid Structures of Sisal Fiber Derived Interconnected Carbon Nanosheets/MoS2/Polyaniline as Advanced Electrode Materials in Lithium-Ion Batteries

In this work, we designed and successfully synthesized an interconnected carbon nanosheet/MoS2/polyaniline hybrid (ICN/MoS2/PANI) by combining the hydrothermal method and in situ chemical oxidative polymerization. The as-synthesized ICNs/MoS2/PANI hybrid showed a “caramel treat-like” architecture in which the sisal fiber derived ICNs were used as hosts to grow “follower-like” MoS2 nanostructures, and the PANI film was controllably grown on the surface of ICNs and MoS2. As a LIBs anode material, the ICN/MoS2/PANI electrode possesses excellent cycling performance, superior rate capability, and high reversible capacity. The reversible capacity retains 583 mA h/g after 400 cycles at a high current density of 2 A/g. The standout electrochemical performance of the ICN/MoS2/PANI electrode can be attributed to the synergistic effects of ICNs, MoS2 nanostructures, and PANI. The ICN framework can buffer the volume change of MoS2, facilitate electron transfer, and supply more lithium inset sites. The MoS2 nanostructures provide superior rate capability and reversible capacity, and the PANI coating can further buffer the volume change and facilitate electron transfer

Endogenous IAA affected fluoranthene accumulation by regulating H+-ATPase and SOD activity in ryegrass

This study explores the role of endogenous indole-3-acetic acid (IAA) in modulating plant responses to pollution stress and its effect on pollutant accumulation, with a focus on fluoranthene (Flu) in ryegrass. To elucidate the mechanism, we employed an IAA promoter (α-aminobutyric acid [α-AB]) and an IAA inhibitor (naphthylphthalamic acid [NPA]) to regulate IAA levels and analyze Flu uptake characteristics. The experimental setup included a Flu treatment group (ryegrass with Flu addition) and a control group (ryegrass without Flu). Our findings demonstrate that Flu treatment enhanced IAA content and plant growth in ryegrass compared to the control. The Flu+AB treatment further enhanced these effects, while the Flu+NPA treatment exhibited a contrasting trend. Moreover, Flu+AB treatment led to increased Flu accumulation, in contrast to the inhibitory effect observed with Flu+NPA treatment. Flu treatment also enhanced the activities of key antioxidant enzymes (SOD, POD, CAT) and increased soluble sugar and protein levels, indicative of enzymatic and nonenzymatic defense responses, respectively. The Flu+AB treatment amplified these responses, whereas the Flu+NPA treatment attenuated them. Significantly, Flu treatment raised H+-ATPase activity compared to the control, an effect further elevated by Flu+AB treatment and diminished by Flu+NPA treatment. A random forest analysis suggested that Flu accumulation dependency varied under different treatments: it relied more on H+-ATPase activity under Flu+AB treatment and more on SOD activity under Flu+NPA treatment. Additionally, Flu+AB treatment boosted the transpiration rate in ryegrass, thereby increasing the Flu translocation factor, a trend reversed by Flu+NPA treatment. This research highlights crucial factors influencing Flu accumulation in ryegrass, offering potential new avenues for controlling the gathering of contaminants within plant systems

Redox-detecting deep learning for mechanism discernment in multi-redox cyclic voltammograms

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within multi-redox cyclic voltammograms. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers’ productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory