Potential Applications Research Articles

Leveraging artificial intelligence to identify high-risk patients for postoperative sore throat: An observational study.

Postoperative sore throat (POST) is a prevalent complication after general anesthesia and targeting high-risk patients helps in its prevention. This study developed and validated a machine learning model to predict POST. A total number of 834 patients who underwent general anesthesia with endotracheal intubation were included in this study. Data from a cohort of 685 patients was used for model development and validation, while a cohort of 149 patients served for external validation. The prediction performance of random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost) models was compared using comprehensive performance metrics. The Local Interpretable Model-Agnostic Explanations (LIME) methods elucidated the best-performing model. POST incidences across training, validation, and testing cohorts were 41.7%, 38.4%, and 36.2%, respectively. Five predictors were age, sex, endotracheal tube cuff pressure, endotracheal tube insertion depth, and the time interval between extubation and the first drinking of water after extubation. After incorporating these variables, the NN model demonstrated superior generalization capabilities in predicting POST when compared to the XGBoost and RF models in external validation, achieving an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI 0.74-0.89) and a precision-recall curve (AUPRC) of 0.77 (95% CI 0.66-0.86). The model also showed good calibration and clinical usage values. The NN model outperforms the XGBoost and RF models in predicting POST, with potential applications in the healthcare industry for reducing the incidence of this common postoperative complication.

Effects of neratinib on angiogenesis and the early stage of the embryo using chicken embryo as a model.

Angiogenesis is the process of forming new blood capillaries from pre-existing vessels. Even though it is essential during normal development, it plays a major role in cancer progression. Neratinib is a pan-human epidermal growth factor receptor (HER) inhibitor that has recently been approved for the treatment of HER2-positive breast cancer. However, its effects on angiogenesis and embryogenesis remain unknown. This study examined the antiangiogenic effects of neratinib using the chorioallantoic membrane (CAM) of chicken embryos. We also evaluated neratinib's toxicity during the early stages of normal development using the chicken embryos, primary embryonic fibroblasts (EFBs), and human umbilical vein endothelial cells (HUVEC). Our findings revealed that neratinib significantly inhibited the CAM angiogenesis compared to controls by reducing vessel percentage area and the average vessel length. Furthermore, neratinib downregulated vascular endothelial growth factor (VEGF), a key mediator of angiogenesis. At lower concentrations, neratinib was well-tolerated during early stages of normal development. Additionally, EFBs treated with neratinib showed no morphological or viability changes when compared to controls. However, at the highest concentration tested, neratinib treatment reduced HUVEC cell viability. This effect may be associated with the dysregulation of key apoptotic genes, including caspase-3, caspase-8, caspase-9, and the B-cell lymphoma 2 (Bcl2) gene. Our findings indicate a novel potential application of neratinib as an antiangiogenic agent, exhibiting tolerable toxicity in the early stages of embryogenesis.

Recent Progress in the Composites of Perovskite Nanocrystals and II-VI Quantum Dots: Their Synthesis, Applications, and Prospects

Abstract: The remarkable photoelectric characteristics of perovskite nanocrystals (NCs), including high fault tolerance, tunable photoluminescence (PL) emission, and high carrier mobility, contribute to making them especially attractive for photonic and optoelectronic applications. Unfortunately, the poor environmental thermal and light stability set obstacles to their industrial applications. Over the past 40 years, II-VI semiconductor quantum dots (QDs) have achieved many important photophysics findings and optoelectronic applications. Compared with perovskite NCs, II-VI semiconductor QDs still have a relatively weaker molar absorbance coefficient. Whereas, significant enhancement of both the stability and the optical performance of the composites of perovskite NCs and II-VI QDs are of interest for photovoltaic and optoelectronic devices. The composites of perovskite NCs and II-VI QDs come in two primary types: core/shell structures and heterojunction structures. To better understand the composites of perovskite NCs and II-VI QDs, the approaches of synthesis methods, their optoelectronic properties, carrier dynamics and potential applications in solar cells, light emitting diodes (LEDs) and photodetectors are summarized. Furthermore, the unmet problems and the potential applications are also presented.

SiRNA Mediated Downregulation of RhoA Expression Reduces Oxidative Induced Apoptosis in Retinal Ganglion Cells.

Glaucoma is the second leading cause of blindness. Apoptosis of retinal ganglion cells (RGCs) is an important mechanism of glaucomatous optic injury. Rho kinase expression is significantly increased in apoptotic RGCs. This study aimed to investigate the role of RhoA, a Rho GTPase, on the survival of RGCs and further to explore its potential therapeutic applications. RGCs were treated with siRhoA for 24 hours in vitro. Knockdown of RhoA was confirmed with quantitative RT-PCR. Oxidative stress was induced by treating the RGCs with 200 μM of H2O2 for 1 hour, and apoptosis of RGCs was quantified with TUNEL assay in situ, and with flow cytometry. The mRNA expression levels of RhoA, Nogo receptor, caspase 3 and Bcl-2 were evaluated by quantitative RT-PCR, and the protein levels of RhoA, ROCK1, ROCK2, Nogo receptor, caspase 3 and Bcl-2 were evaluated by Western blot. We found siRhoA treatment efficiently downregulated the expression of RhoA in RGCs and protected against H2O2-induced injury in RGCs in vitro. Apoptosis of RGC cells under oxidative stress was quantified in situ using TUNEL assay and confirmed with flow cytometry (FCM). With the knockdown of RhoA, the expression of ROCK1, ROCK2, Nogo Receptor, Casepase-3 were decreased, while the expression of Bcl-2 was increased in both mRNA and protein level. Our data indicated that siRhoA prevented H2O2-induced apoptosis in RGC cells by modulating the RhoA/ROCK pathway. The results suggested that siRhoA may exert potentially effective neuroprotection for RGCs by reducing injury.

The Second Omega Index

Background: The omega index has been recently introduced to identify a variety of topological and combinatorial aspects of a graph with a new viewpoint. As a continuing study of the omega index, by considering the incidence of edges and vertices to the adjacency of the vertices, in this paper, we have introduced the second omega index Ω2 and then computed it over some well-known graph classes. Methods: Many combinatorial counting methods have been utilized in the proofs. The edge partition is frequently used throughout the work. Naturally, some graph theoretical lemmas are also used. Results: In particular, trees having the smallest and largest Ω2 have been constructed. Finally, the second omega index of some derived graphs, such as line graphs, subdivision graphs, and vertex-semitotal graphs, has been presented. Conclusion: Omega invariant has already been explored in many papers. It has been defined in terms of vertex degrees. Vertices correspond to the atoms in a molecule and a calculation, which only depends on the atomic parameters, is not even comparable with a calculation containing both atoms and chemical bonds between them. With this idea in mind, we have evaluated some mathematical properties of the second omega index, which has great potential in chemical applications related to the number of cycles in the molecular graph.

Natural products and synthetic analogs as selective orphan nuclear receptor 4A (NR4A) modulators.

Although endogenous ligands for the orphan nuclear receptor 4A1 (NR4A1, Nur77), NR4A2 (Nurr1), and NR4A3 (Nor-1) have not been identified, several natural products and synthetic analogs bind NR4A members. These studies are becoming increasingly important since members of the NR4A subfamily of 3 receptors are potential drug targets for treating cancer and non-cancer endpoints and particularly those conditions associated with inflammatory diseases. Ligands that bind NR4A1, NR4A2, and NR4A3 including Cytosporone B, celastrol, bis-indole derived (CDIM) compounds, tryptophan/indolic, metabolites, prostaglandins, resveratrol, piperlongumine, fatty acids, flavonoids, alkaloids, peptides, and drug families including statins and antimalarial drugs. The structural diversity of NR4A ligands and their overlapping and unique effects on NR4A1, NR4A2, and NR4A3 suggest that NR4A ligands are selective NR4A modulators (SNR4AMs) that exhibit tissue-, structure-, and response-specific activities. The SNR4AM activities of NR4A ligands are exemplified among the Cytosporone B analogs where n-pentyl-2-[3,5-dihydroxy-2-(nonanoyl)]phenyl acetate (PDNPA) binds NR4A1, NR4A2 and NR4A3 but activates only NR4A1 and exhibits significant functional differences with other Cytosporone B analogs. The number of potential clinical applications of agents targeting NR4A is increasing and this should spur future development of SNR4AMs as therapeutics that act through NR4A1, NR4A2 and NR4A3.

Research Progress of Natural Source Potential Drugs in Reversing Cervical Precancerous Lesions and Human Papillomavirus Negative Conversion

The recent breakthrough in understanding the etiology of cervical cancer has led to increased attention towards the prevention and treatment of this disease. Extensive research has confirmed a strong correlation between persistent infection of the human papillomavirus (HPV) and the development of cervical cancer. Focusing on the pivotal role of HPV infection in the occurrence and progression of cervical cancer, there has been a surge in research aimed at developing drugs that can eradicate HPV and reverse cervical precancerous lesions. Natural drugs have attracted considerable attention in the field of tumor therapy due to their multi-targeted approach, high selectivity, and low toxic and side effects. Therefore, this paper aims to review the research progress of natural drugs in the treatment of HPV infection and cervical precancerous lesions, while also exploring their pharmacological mechanism. The goal is to provide comprehensive information basis for the potential application of natural drugs in the prevention and treatment of cervical cancer.

Carbon Nanoparticles Promoted the Absorption of Potassium Ions by Tobacco Roots via Regulation of K+ Flux and Ion Channel Gene Expression

Background: The regulatory effects of carbon nanomaterials (CNMs) on plant growth and their potential applications in agriculture have attracted a great deal of attention from researchers. CNMs have been shown to promote nutrient absorption and increase plant growth. However, the mechanisms by which CNMs affect plant growth and nutrient absorption are still unknown. Methods: The tobacco seedling biomass, potassium (K+) concentration, and accumulation in hydroponic were investigated to exposure of carbon nanoparticles (CNPs). To directly observe the effect of CNPs on K+ uptake by roots, we employed a noninvasive micro-test technique (NMT) to detect the net flux of K+ on the surface of tobacco roots. The K+-depletion experiment was carried out to explore the kinetic characteristics of K+ absorption, and qRT-PCR was used to monitor the expression levels of the K+ channel gene. Results: The results showed that tobacco seedling biomass significantly improved at 10 mg·L-1 CNP treatments, and K+ concentration and accumulation both in roots and shoots increased with 10 and 20 mg·L-1 CNPs. CNP treatments changed the flow rate of K+ from efflux to influx in tobacco roots; this was observed both in plants cultivated in a CNP-containing medium and after the addition of CNPs to previously untreated plants. A depletion test also showed that CNPs improved the K+ absorption capacity and low-K+ tolerance of tobacco seedlings. Conclusion: CNPs enhanced the K+ absorption capacity and low-K+ tolerance of tobacco seedlings. The promotion of K+ absorption by CNPs was closely related to the activation of K+ influx channel genes and inhibition of the K+ outflow channel gene. The K+ flux response and ion channel gene expression to CNPs in plants reveal the mechanism whereby CNPs promote plant nutrient absorption.

Swift-Eye: Towards Anti-blink Pupil Tracking for Precise and Robust High-Frequency Near-Eye Movement Analysis with Event Cameras.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

Fractal dimension, lacunarity, and Shannon entropy of self-assembled macroscopic copper dendrites

Macroscopic copper dendrites are self-assembled in a porous hydrogel without the application of an external potential. The copper dendrites possess fractal characteristics. The impact of the medium thickness, the initial concentration of copper (II) ions, and the solvent polarity on the evolving copper dendrites are addressed by investigating the fractal dimension, lacunarity, and Shannon entropy (SE) of the structures. The analysis gives a quantitative description of the copper dendritic morphology and its connection to the mechanism of self-assembly. The fractal dimension of the dendrites falls in the range of 1.75–1.85. High self-similar complex systems show low lacunarity and high Shannon entropy, reflecting the low density of gaps and the high level of detail.

Application and Analysis of the Constructive Potential of China’s Digital Public Sphere Education

Application and Analysis of the Constructive Potential of China’s Digital Public Sphere Education

Green electromembrane extraction of morphine by sodium alginate-g-polyacrylamide/agarose interpenetrating polymer network as a membrane

The isolation and pre-concentration of drugs from complex biological matrices prior to analysis remains a significant challenge. This study introduces a novel method for the extraction and determination of morphine from biological fluids utilizing a sodium alginate-g-polyacrylamide/agarose hydrogel electromembrane (EME) coupled with square wave voltammetry (SWV) at a glassy carbon electrode. This innovative approach eliminates the need for organic solvents, promoting a greener analytical methodology. The EME process involves the application of a direct current potential across the hydrogel membrane, facilitating the transport of charged morphine molecules from a donor solution (pH 7.0) to an acceptor solution (pH 3.0) under optimized conditions. Following extraction (70 V, 25 min), the acceptor solution is subjected to SWV analysis in a phosphate buffer solution (pH 6.0). This approach successfully extracts morphine, a highly polar compound (log P = 0.43), without requiring ion-pairing or carrier reagents. The method exhibits impressive limits of detection (LOD) and quantification (LOQ) of 0.036 μg/mL and 0.109 μg/mL, respectively. This work demonstrates the potential of the sodium alginate-g-polyacrylamide/agarose hydrogel EME coupled with SWV for the sensitive determination of morphine in urine and wastewater samples.

GAITBOOST: Boosting gait recognition performance with BAT-extreme learning machines algorithm

Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks.

Efficient data management in Internet of Things: A survey of data aggregation techniques

The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed.

A novel bilateral stream neural network for melt pool monitoring during laser direct energy deposition

Detection and classification methods for the melt pool state in laser direct energy deposition (L-DED) can significantly help predict defects and mechanical properties of L-DED metal parts. Although traditional machine learning algorithms based on physical modeling methods and convolutional neural networks have recently been introduced into melt pool state identification, these methods rely on complex artificially designed features or cannot simultaneously detect defects in multiple dimensions. In this paper, a novel bilateral stream neural network was designed for melt pool identification, which performs defect identification in two label dimensions simultaneously. Two sets of single-channel experiments were designed to collect the dataset captured by a high-speed camera. By cutting the metal parts and marking them with professional equipment operated by professionals, the dataset was labeled according to the bonding condition and dilution rate criteria. Without an additive model structure, the model achieved 95.2% accuracy in identifying defects in the bonding condition and 92.8% in determining deficiencies in the dilution rate. In order to explain the identification mechanism of the model, the CAM method was utilized for the visual display of the model recognition process, which provides a potential application solution for the online monitoring method of the L-DED.

Application of electric potential and Fe(Ⅲ) to stimulate perfluorooctanoic acid (PFOA) degradation using mixed culture anammox granules

Pollution by perfluorooctanoic acid (PFOA), a highly persistent and toxic substance, is commonly addressed with several approaches, but the need for post-treatment affects their environmental and economic sustainability. Biodegradation, which can efficiently degrade micropollutants in water under optimal conditions, offers a promising solution, but faces challenges from the persistence of the C-F bond and the high reactivity of F-. Despite recent use of various microorganisms for PFOA degradation, performance issues persist. In this study, we aimed to simulate PFOA degradation while mixed culture anammox granules oxidize NH4+ and to confirm the effects of electric potential and Fe(Ⅲ) applications on PFOA degradation. A control reactor containing only anammox granules exhibited NH4+ and PFOA removal efficiencies of 11.52% and 19.2%, respectively, with traceable biodegradation at 3.7%. In contrast, a reactor that applied both electric potential and Fe(Ⅲ) demonstrated enhanced removal efficiencies of 41.98% and 50.93%, with traceable biodegradation at 35.03%. Bio-electrochemical properties also improved, showing an average current generation of 331.95 μA and Coulombic efficiency of 141.79 ± 7.6%. This study shows that mixed-culture anammox granules with electric potential and Fe(Ⅲ) can be introduced for efficient PFOA biodegradation and provides the basis for further studies on overcoming the limitations of PFOA biodegradation.

The application of Cornell potential on determining Mass spectra of heavy quarkonium via the Nikiforov–Uvarov method

The mass spectrum of heavy mesons using the Cornell potential is used for studying the quark–antiquark interaction. By appropriately modifying the potential parameters, the established potential models such as Kratzer’s potential and the anharmonic one are applied in the Pekeris approximation. The Schrodinger equation for the Cornell potential setting is solved in this paper via the Nikiforov–Uvarov method. We acquire the confined-state energy spectrum to verify whether the outcome produced by this approach is accurate. The spectrum of energy formulation is used to determine the mass spectra of heavy quarkonium compounds, including charmonium and bottomonium. There is a comparison with various theoretical stances. The outcomes align well with both experimental data and other researchers’ findings.

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model’s reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs’ reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website: https://kugaoyang.github.io/EGL/

Reconfigurable Thick-Panel Structures Based on a Stacked Origami Tube

Abstract Variable crease origami that exhibits crease topological morphing allows a given crease pattern to be folded into multiple shapes, greatly extending the reconfigurability of origami structures. However, it is a challenge to enable the thick-panel forms of such crease patterns to bifurcate uniquely and reliably into desired modes. Here, thick-panel theory combined with cuts is applied to a stacked origami tube with multiple bifurcation paths. The thick-panel form corresponding to the stacked origami tube is constructed, which can bifurcate exactly between two desired modes without falling into other bifurcation paths. Then, kinematic analysis is carried out, and the results reveal that the thick-panel origami tube is kinematically equivalent to its zero-thickness form with one degree-of-freedom (DOF). In addition, a reconfigurable physical prototype of the thick-panel origami tube is produced, which achieves reliable bifurcation control through a single actuator. Such thick-panel origami tubes with controllable reconfigurability have great potential engineering applications in the fields of morphing systems such as mechanical metamaterials, morphing wings, and deployable structures.

Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims at tracking and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this article, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results.