Potential Applications Research Articles

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.

Financial Arrangement by Employees in Formal Workplace Environment in Nigeria: Case of Cooperative Societies in Obafemi Awolowo University, Ile-Ife, Nigeria

Individual savings in the current day business world and work place have been channelled on a self-arranged/self-managed platform in the nomenclature of cooperatives irrespective of social class and or income group. Cooperatives have become household name in every organisation: its significance is ringing more than bank name now rings. This research analysed the criteria for being a member in cooperatives as well as the extent of satisfaction that members in the cooperatives perceived they had as a result of their membership subscription. Primary data were collected with the aid of structured questionnaire from 396 respondents who are staff of the Ohafenu Awolowo University, Ile-Ife. Results indicated that there was no constraint to being a member inasmuch as the potential applicant is on the University payroll and such a potential member has not violated the floor of limit of take home pay. Method of monthly contribution into cooperative purse is deduction from source through the bursary department. It was also indicated that monthly contribution is based on a member's capacity in terms of how much a person cares to contribute. Eight major areas of members’ investment horizon were identified as having been promoted by cooperative assistance. Eighteen propositions were raised in terms of members’ belief, identification, and satisfaction with their cooperative, six of such propositions had strong support by respondents. Four variables also indicated significant difference among members when tested on t-test scale. Lastly, nine variables were significant in influencing value added for members. In the overall, members were very satisfied with their cooperatives for saving purposes.

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.

Overview of cloning in lactic acid bacteria: Expression and its application of probiotic potential in inflammatory bowel diseases.

Inflammatory bowel disease (IBD) imposes a significant impact on the quality of life for affected individuals. However, there was a current lack of a systematic summary regarding the latest epidemic trends and the underlying pathogenesis of IBD. This highlights the need for a thorough examination of both the epidemiological aspects of IBD and the specific mechanisms by which lactic acid bacteria (LAB) contribute to mitigating this condition. In developed countries, higher incidences and death rates of IBD have been observed, influenced by a combination of environmental and genetic factors. LAB offer significant advantages and substantial potential for enhancing IBD treatment. LAB's capabilities include the production of bioactive metabolites, regulation of gut immunity, protection of intestinal mechanical barriers, inhibition of oxidative damage, and restoration of imbalanced gut microbiota. The review suggests that screening effective LAB using cell models and metabolites, optimizing LAB intake through dose-effect studies, enhancing utilization through nanoencapsulation and microencapsulation, investigating mechanisms to deepen the understanding of LAB, and refining clinical study designs. These efforts aim to contribute to comprehending the epidemic trend, pathogenesis, and treatment of IBD, ultimately fostering the development of targeted therapeutic products, such as LAB-based interventions.

Asymmetric Total Synthesis and Structural Revision of DAT2, an Antigenic Glycolipid from Mycobacterium tuberculosis.

DAT2 is a member of the diacyl trehalose family (DAT) of antigenic glycolipids located in the mycomembrane of Mycobacterium tuberculosis (Mtb). Recently it was shown that the molecular structure of DAT2 had been incorrectly assigned, but the correct structure remained elusive. Herein, the correct molecular structure of DAT2 and its methyl-branched acyl substituent mycolipanolic acid is determined. For this, four different stereoisomers of mycolipanolic acid were prepared in a stereoselective and unified manner, and incorporated into DAT2. A rigorous comparison of the four isomers to the DAT isolated from Mtb H37Rv by NMR, HPLC, GC, and mass spectrometry allowed a structural revision of mycolipanolic acid and DAT2. Activation of the macrophage inducible Ca2+-dependent lectin receptor (Mincle) with all four stereoisomers shows that the natural stereochemistry of mycolipanolic acid / DAT2 provides the strongest activation, which indicates its high antigenicity and potential application in serodiagnostics and vaccine adjuvants.

Synthesis and bio-activity of complex azo compounds: A review on formazans and its complexes

Formazans are well-known complexing agents and form highly stable complexes with verity of metals that are useful for number of pharmaceutical applications. The activity profile of formazans and their complexes are huge that include antibacterial, antifungal, antioxidant, anti-fertility, anti-tubercular, antiviral, anti-inflammatory, anticancer, anti-HIV, and anti-proliferative are among the biological activities covered by the formazans. The structures, synthesis, reactions, and spectral properties of formazans have been studied to highlight their potential applications in a variety of bioactive phenomena and analytical applications. The biological potential of the compounds will be greatly enhanced when substitution is carried out at R and R2. We observed that electron withdrawing groups at para position (if R, R1 & R2 = phenyl ring) are the best substitutions to achieve potent pharmacological activities.

Solid-State Photochemical Cascade Process Boosting Smart Ultralong Room-Temperature Phosphorescence in Bismuth Halides.

Molecular ultralong room-temperature phosphorescence (RTP), exhibiting multiple stimuli-responsive characteristics, has garnered considerable attention due to its potential applications in light-emitting devices, sensors, and information safety. This work proposes the utilization of photochemical cascade processes (PCCPs) in molecular crystals to design a stepwise smart RTP switch. By harnessing the sequential dynamics of photo-burst movement (induced by [2+2] photocycloaddition) and photochromism (induced by photogenerated radicals) in a bismuth (Bi)-based metal-organic halide (MOH), a continuous and photo-responsive ultralong RTP can be achieved. Furthermore, utilizing the same Bi-based MOH, diverse application demonstrations, such as multi-mode anti-counterfeiting and information encryption, can be easily implemented. This work thus not only serves as a proof-of-concept for the development of solid-state PCCPs that integrate photosalient effect and photochromism with light-chemical-mechanical energy conversion, but also lays the groundwork for designing new Bi-based MOHs with dynamically responsive ultralong RTP.

Lowering Entropic Barriers in Triplex DNA Switches Facilitating Biomedical Applications at Physiological pH

AbstractTriplex DNA switches are attractive allosteric tools for engineering smart nanodevices, but their poor triplex‐forming capacity at physiological conditions limited the practical applications. To address this challenge, we proposed a low‐entropy barrier design to facilitate triplex formation by introducing a hairpin duplex linker into the triplex motif, and the resulting triplex switch was termed as CTNSds. Compared to the conventional clamp‐like triplex switch, CTNSds increased the triplex‐forming ratio from 30 % to 91 % at pH 7.4 and stabilized the triple‐helix structure in FBS and cell lysate. CTNSds was also less sensitive to free‐energy disturbances, such as lengthening linkers or mismatches in the triple‐helix stem. The CTNSds design was utilized to reversibly isolate CTCs from whole blood, achieving high capture efficiencies (>86 %) at pH 7.4 and release efficiencies (>80 %) at pH 8.0. Our approach broadens the potential applications of DNA switches‐based switchable nanodevices, showing great promise in biosensing and biomedicine.