Perspectives For Applications Research Articles

A Perspective of Fabrication and Applications of MXene–Metal Hybrids

A Perspective of Fabrication and Applications of MXene–Metal Hybrids

A Case Study of 2D Bi2O2Se Nanoplate Near‐Infrared Photodetectors from the Perspective of Practical Applications

AbstractOver the past several decades infrared (IR) photodetectors have received wide attention due to their important applications. 2D materials, distinguished by their unique electronic structures, ultimate dimensional confinement, and robust light‐matter interactions, provide a promising candidate for fabricating future IR photodetectors. However, there is a lack of reports concerning the practical industrial applications of these 2D photodetectors, despite that some of these 2D photodetectors have demonstrated performance exceeding that of commercial photodetectors. In this work, a case study on 2D Bi2O2Se nanoplate near‐infrared photodetectors from the perspective of practical applications is presented. With the characterization method used for nano detectors, the 2D Bi2O2Se photodetector exhibits a responsivity of 212.5 A W−1, a specific detectivity of 4.99 × 1011 Jones and an external quantum efficiency of 26 887.68% at wavelength of 980 nm, while with the traditional characterization method the photodetector shows a responsivity of 0.13 A W−1 and a specific detectivity of 2.26 × 106 Jones and an external quantum efficiency of 17.87% at wavelength of 900 nm. The Bi2O2Se nanoplate photodetector also presents good passive imaging quality in the near‐infrared wavelength region. These results suggest the great potential of Bi2O2Se nanoplate photodetectors for practical applications.

Chitosan-Based Hydrogels as Antibacterial/Antioxidant/Anti-Inflammation Multifunctional Dressings for Chronic Wound Healing.

Due to repeated microbial infection, persistent inflammation, excessive oxidative stress, and cell dysfunction, chronic wounds are difficult to heal, posing a serious threat to public health. Therefore, developing multifunctional wound dressings that can regulate the complex microenvironment of chronic wounds and enhance cellular function holds great significance. Recently, chitosan has emerged as a promising biopolymer for wound healing due to its excellent biocompatibility, biodegradability, and versatile bioactivity. The aim of this review is to provide a comprehensive understanding of the mechanisms of delayed chronic wound healing and discuss the healing-promoting properties of chitosan and its derivatives, such as good biocompatibility, antibacterial activity, hemostatic capacity, and the ability to promote tissue regeneration. On this basis, the potential applications of chitosan-based hydrogels are summarized in chronic wound healing, including providing a suitable microenvironment, eliminating bacterial infections, promoting hemostasis, inhibiting chronic inflammation, alleviating oxidative stress, and promoting tissue regeneration. In addition, the concerns and perspectives for the clinical application of chitosan-based hydrogels are also discussed.

Coriolopsis trogii MUT3379: A Novel Cell Factory for High-Yield Laccase Production

Coriolopsis trogii is a basidiomycete fungus which utilizes a large array of lignin-modifying enzymes to colonize and decompose dead wood. Its extracellular enzymatic arsenal includes laccases, i.e., polyphenol oxidases of relevant interest for different industrial applications thanks to their ability to oxidize a diverse range of natural and synthetic compounds. In this work, the production of laccases in C. trogii MUT3379 was explored and improved. From an initial production of ca. 10,000 U L−1, the fermentation process was gradually optimized, reaching a final yield of ca. 200,000 U L−1. An SDS-PAGE analysis of the secretome highlighted the presence of a main protein of ca. 60 kDa showing laccase activity, which was designated as Lac3379-1 once its primary sequence was established by tandem mass spectrometry. The characterization of Lac3379-1 revealed a remarkable enzymatic stability in the presence of surfactants and solvents and a diversified activity on a broad range of substrates, positioning it as an interesting tool for diverse biotechnological applications. The high-yield and robust production process indicates C. trogii MUT3379 as a promising cell factory for laccases, offering new perspectives for industrial applications of lignin-modifying enzymes.

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.

Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks. This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in health settings. A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and to derive the properties of the resulting estimators. From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity. This paper contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data, by adapting these methods to the context of heterogeneous health data and by clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics.

Fungal Proteins: Sources, Production and Purification Methods, Industrial Applications, and Future Perspectives

Fungal Proteins: Sources, Production and Purification Methods, Industrial Applications, and Future Perspectives

Least Squares Estimation of Multifactor Uncertain Differential Equations with Applications to the Stock Market

Multifactor uncertain differential equations are powerful tools for studying dynamic systems under multi-source noise. A key challenge in this study is how to accurately estimate unknown parameters based on the framework of uncertainty theory in multi-source noise environments. To address this core problem, this paper innovatively proposes a least-squares estimation method. The essence of this method lies in constructing statistical invariants with a symmetric uncertainty distribution based on observational data and determining specific parameters by minimizing the distance between the population distribution and the empirical distribution of the statistical invariant. Additionally, two numerical examples are provided to help readers better understand the practical operation and effectiveness of this method. In addition, we also provide a case study of JD.com’s stock prices to illustrate the advantages of the method proposed in this paper, which not only provides a new idea and method for addressing the problem of dynamic system parameter estimation but also provides a new perspective and tool for research and application in related fields.

Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global Climate Models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as Perfect Prognosis (PP) and Model Output Statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of eXplainable Artificial Intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differ between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM-dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications.

Generating grid double-scroll attractors from magnetized SC-CNN and its application in image encryption

Cellular neural network has attracted widespread attention due to its easy hardware implementation and general application in information processing. In this paper, a new memristive State-Controlled-CNN (MSC-CNN) model is established by introducing two novel flux-controlled memristors into a three-cell network, where one of which simulates the electromagnetic radiation and the other characterizes the coupling weight. The present MSC-CNN can generate multi-double-scroll attractors (MDSAs) in one direction or grid multi-double-scroll attractors (GMDSAs). Stability analysis shows that each asymptotically stable equilibrium point of the memristors serves as index-1 saddle-focus in the MSC-CNN, which induces the corresponding connecting bond orbits between two scrolls. Thus, the number of GMDSAs can be flexibly adjusted by varying the number of the asymptotically stable equilibrium points of the memristors. Moreover, the memristive coupling strength also controls the number of MDSAs. Through numerical simulations, complex dynamic behaviors such as extreme multistability and amplitude control are also observed. Then, an image encryption scheme based on the proposed MSC-CNN is designed from the application perspective. Finally, a microcontroller-based hardware system is implemented to verify the effectiveness of numerical simulations.

Production of bacterial protease via solid-state cultivation with agro-industrial by-products and eco-friendly perspectives of enzyme application

Production of bacterial protease via solid-state cultivation with agro-industrial by-products and eco-friendly perspectives of enzyme application

Exploring the Mechanism of Topical Application of Clematis Florida in the Treatment of Rheumatoid Arthritis through Network Pharmacology and Experimental Validation

Clematis Florida (CF) is a folk medicinal herb in the southeast of China, which is traditionally used for treating osteoarticular diseases. However, the mechanism of its action remains unclear. The present study used network pharmacology and experimental validation to explore the mechanism of CF in the treatment of rheumatoid arthritis (RA). Liquid chromatography–mass spectrometry (LC-MS/MS) identified 50 main compounds of CF; then, their targets were obtained from TCMSP, ETCM, ITCM, and SwissTargetPrediction databases. RA disease-related targets were obtained from DisGeNET, OMIM, and GeneCards databases, and 99 overlapped targets were obtained using a Venn diagram. The protein–protein interaction network (PPI), the compound–target network (CT), and the compound–potential target genes–signaling pathways network (CPS) were constructed and analyzed. The results showed that the core compounds were screened as oleanolic acid, oleic acid, ferulic acid, caffeic acid, and syringic acid. The core therapeutic targets were predicted via network pharmacology analysis as PTGS2 (COX-2), MAPK1, NF-κB1, TNF, and RELA, which belong to the MAPK signaling pathway and NF-κB signaling pathway. The animal experiments indicated that topical application of CF showed significant anti-inflammatory activity in a mouse model of xylene-induced ear edema and had strong analgesic effect on acetic acid-induced writhing. Furthermore, in the rat model of adjuvant arthritis (AA), topical administration of CF was able to alleviate toe swelling and ameliorate joint damage. The elevated serum content levels of IL-6, COX-2, TNF-α, IL-1β, and RF caused by adjuvant arthritis were reduced by CF treatment. Western blotting tests showed that CF may regulate the ERK and NF-κB pathway. The results provide a new perspective for the topical application of CF for treatment of RA.

A Comprehensive Review on the Antioxidant and Anti-Inflammatory Bioactives of Kiwi and Its By-Products for Functional Foods and Cosmetics with Health-Promoting Properties

Kiwi’s increased popularity as a healthy fruit with several agro-food applications has increased the amount of bio-waste produced like leaf, peel, and seed by-products, usually combined to form a kiwi pomace, which increases the environmental footprint of kiwi fruit and waste management costs. The aim of the present study is to thoroughly review and outline the nutritional content and bioactive components of both kiwi fruit and its by-products, as well as the innovative approaches to obtain and valorize kiwi’s bioactives, phytochemicals, vitamins, and nutrients in several functional food products, nutraceuticals, and cosmetics applications with health-promoting properties. The antioxidant and anti-inflammatory properties and mechanisms of action of the extracted polyphenols, flavonoids, flavones, organic acids, and other bioactive components in both the fruit and in its functional products are also elucidated. Emphasis is given to those bioactive ingredients and extracts from kiwi by-products that can be valorized in various functional foods, supplements, nutraceuticals, nutricosmetics, cosmeceuticals, and cosmetics-related applications, with antioxidant and anti-inflammatory health-promoting properties. Characteristic examples with reported health benefits are the functional kiwi fruit jelly (FKJ),fermented kiwi fruit products like wine, starchy kiwi fruit flour (SKF), and kiwi-derived functional protein bars, cheese and flour, as well as several nutraceuticals and functional cosmetics with kiwi bioactives improving their antioxidant, antiaging, and photoprotective properties, collagen synthesis, skin density, hydration, elasticity, and the wound healing process, while beneficially reducing skin roughness, wrinkles, hyperpigmentation, keratinocyte death, and DNA and cell damage. The limitations and future perspectives for these kiwi bioactive-based applications are also discussed.

Exploring the Mechanism of Topical Application of Clematis Florida in the Treatment of Rheumatoid Arthritis through Network Pharmacology and Experimental Validation

Clematis Florida (CF) is a folk medicinal herb in the southeast of China, which is traditionally used for treating osteoarticular diseases. However, the mechanism of its action remains unclear. The present study used network pharmacology and experimental validation to explore the mechanism of CF in the treatment of rheumatoid arthritis (RA). Liquid chromatography–mass spectrometry (LC-MS/MS) identified 50 main compounds of CF; then, their targets were obtained from TCMSP, ETCM, ITCM, and SwissTargetPrediction databases. RA disease-related targets were obtained from DisGeNET, OMIM, and GeneCards databases, and 99 overlapped targets were obtained using a Venn diagram. The protein–protein interaction network (PPI), the compound–target network (CT), and the compound–potential target genes–signaling pathways network (CPS) were constructed and analyzed. The results showed that the core compounds were screened as oleanolic acid, oleic acid, ferulic acid, caffeic acid, and syringic acid. The core therapeutic targets were predicted via network pharmacology analysis as PTGS2 (COX-2), MAPK1, NF-κB1, TNF, and RELA, which belong to the MAPK signaling pathway and NF-κB signaling pathway. The animal experiments indicated that topical application of CF showed significant anti-inflammatory activity in a mouse model of xylene-induced ear edema and had strong analgesic effect on acetic acid-induced writhing. Furthermore, in the rat model of adjuvant arthritis (AA), topical administration of CF was able to alleviate toe swelling and ameliorate joint damage. The elevated serum content levels of IL-6, COX-2, TNF-α, IL-1β, and RF caused by adjuvant arthritis were reduced by CF treatment. Western blotting tests showed that CF may regulate the ERK and NF-κB pathway. The results provide a new perspective for the topical application of CF for treatment of RA.

Zebrafish Maintenance Conditions Affect Behavioral and Biochemical Biomarkers: A Possible Interfering Factor on the Research Results.

Over the years, scientific research with fish models has grown at a rapid pace, and issues such as animal welfare are becoming increasingly important in various areas of animal husbandry and experimentation. Here, we evaluated whether Danio rerio behavior is affected by long-term maintenance (75 days) in an enriched environment or a chronic stress (CS) situation. In addition, we evaluated some biochemical parameters related to redox status. We concluded that long-term maintenance of zebrafish in enriched environment might induce an anxiety-like behavior pattern when these fish are faced with an acute subsequent stressor. These anxiety results, the increased school cohesion, and the absence of oxidative damage allow us to hypothesize that the fish maintained in environmental enrichment (EE) situation is more reactive, showing a strong protective reaction to the stress. From an applicable perspective, we show that both too much stress and too little stress are not ideal for zebrafish stocks. In CS situations, fish can habituate and might not respond optimally to test conditions. In opposite, the low stress promoted by environmental enrichment also renders the fish incapable of dealing with occasional stressors optimally, because now even normal conditions appear stressful to them and may elicit fear behaviors they normally would not exhibit.

Virtual Interviews in the Post-COVID-19 Emergency Era: Insights from the Gastroenterology Fellowship Applicants and Programs.

Virtual interviews are still recommended for fellowship applications, 3years after the beginning of the COVID-19 pandemic. Improving equity by reducing the cost for the applicants has been the most important reason for continuing virtual interviews. However, some argue that important information may be missed in a virtual setting. Our objective was to assess the perspective of Gastroenterology (GI) fellowship interviewers and applicants toward virtual interviews. We designed two different anonymous surveys directed at GI programs and GI applicants who were interviewed for GI fellowship programs from 2020 to 2022 and matched to a GI program. Survey links were emailed to the Program Directors (PDs) and Program Coordinators via the AGA listserv starting in January 2023. A descriptive analysis was performed using Excel, and Fisher's exact tests were performed using R version 4.3.1. Sixty-one applicants and 79 interviewers responded to our survey. More than 80% of applicants strongly agreed (n = 36; 59%) and agreed (n = 14; 23%) that they would prefer in-person interviews if money was not an issue. When applicants were asked about the interview format in order of their preference, "in-person, hybrid, virtual" was the most popular answer (n = 16; 26.2%). Most interviewers (n = 47; 59.5%) do not prefer virtual interviews over in-person interviews. Furthermore, some interviewers were rarely able to judge the applicants' interpersonal (n = 17; 21.5%) and ethical skills (n = 16; 20.3%). These results differed according to the type of interviewer (p = 0.013 and 0.018, respectively). Based on our survey, most programs still prefer in-person interviews. Despite the several advantages of virtual interviews, the majority of applicants would prefer an in-person setting if the financial burden was not a factor. Nonetheless, many applicants think that the cost savings outweigh all the disadvantages associated with virtual interviews. The lack of empathy, personal connections, and engagement may impact the ability of interviewers to judge and ultimately rank a candidate. The virtual interview is here to stay, and we need input from the applicants and the interviewers to make the process more productive.

Virtual Interviews in the Post-COVID-19 Emergency Era: Insights from the Gastroenterology Fellowship Applicants and Programs.

Virtual interviews are still recommended for fellowship applications, 3years after the beginning of the COVID-19 pandemic. Improving equity by reducing the cost for the applicants has been the most important reason for continuing virtual interviews. However, some argue that important information may be missed in a virtual setting. Our objective was to assess the perspective of Gastroenterology (GI) fellowship interviewers and applicants toward virtual interviews. We designed two different anonymous surveys directed at GI programs and GI applicants who were interviewed for GI fellowship programs from 2020 to 2022 and matched to a GI program. Survey links were emailed to the Program Directors (PDs) and Program Coordinators via the AGA listserv starting in January 2023. A descriptive analysis was performed using Excel, and Fisher's exact tests were performed using R version 4.3.1. Sixty-one applicants and 79 interviewers responded to our survey. More than 80% of applicants strongly agreed (n = 36; 59%) and agreed (n = 14; 23%) that they would prefer in-person interviews if money was not an issue. When applicants were asked about the interview format in order of their preference, "in-person, hybrid, virtual" was the most popular answer (n = 16; 26.2%). Most interviewers (n = 47; 59.5%) do not prefer virtual interviews over in-person interviews. Furthermore, some interviewers were rarely able to judge the applicants' interpersonal (n = 17; 21.5%) and ethical skills (n = 16; 20.3%). These results differed according to the type of interviewer (p = 0.013 and 0.018, respectively). Based on our survey, most programs still prefer in-person interviews. Despite the several advantages of virtual interviews, the majority of applicants would prefer an in-person setting if the financial burden was not a factor. Nonetheless, many applicants think that the cost savings outweigh all the disadvantages associated with virtual interviews. The lack of empathy, personal connections, and engagement may impact the ability of interviewers to judge and ultimately rank a candidate. The virtual interview is here to stay, and we need input from the applicants and the interviewers to make the process more productive.

An Integrated Geometric Obstacle Avoidance and Genetic Algorithm TSP Model for UAV Path Planning

In this paper, we propose an innovative approach for the path planning of Uninhabited Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising missions in advance and a geometrically based obstacle avoidance algorithm (QuickNav) for avoiding obstacles along the optimised path. The proposed approach addresses the key problem of determining an optimised trajectory for UAVs that covers multiple waypoints by enabling efficient obstacle avoidance, thus improving operational safety and efficiency. The study highlights the numerous challenges for UAV path planning by focusing on the importance of both global and local path planning approaches. To find the optimal routes, the GA utilises multiple methods of selection to optimise trajectories using the Cartesian Coordinate System (CCS) data transformed from a motion capture system. The QuickNav algorithm applies linear equations and geometric methods to detect obstacles, guaranteeing the safe navigation of UAVs and preventing real-time collisions. The proposed methodology has been proven useful in reducing the total distance travelled and computing times and successfully navigating UAVs across different scenarios with varying numbers of waypoints and obstacles, as demonstrated by simulations and real-world UAV flights. This comprehensive approach provides advantageous perspectives for real-world applications in a variety of operational situations and improves UAV autonomy, safety, and efficiency.

Revealing the Limitation Induced by Hydroxyl in Regulating Solvation Structure of Zn2+ and Overcoming Challenges with Hybrid Additives towards Highly StableZinc Anodes.

In the field of electrolyte design for aqueous zinc-ion batteries (AZIBs), additives containing hydroxyl have been demonstrated to effectively modulate the solvation structure of Zn2+. However, reported studies typically focus solely on the effectiveness of hydroxyl while neglecting the issues that emerge during solvation structure regulation. The strong electron-attracting capability of Zn2+ attracts electrons from the oxygen in hydroxyl, thereby weakening the strength of hydroxyl, the hydrogen evolution reaction (HER) is also pronounced. This work innovatively reveals the limitation of hydroxyl-containing additives and proposes a synergistic regulation strategy based on hybrid additives. Arginine with a high isoelectric point is introduced into the electrolyte system containing hydroxyl additives. The protonation effect and electrostatic attraction of arginine enable it to absorb protons at the anode released by the weakened hydroxyl, thereby compensating for the limitation of hydroxyl additives. Under the synergistic action of hybrid additives, the Zn|Zn battery achieved stable deposition/stripping for over 1200 hours under 10 mA cm-2 and 10 mAh cm-2. Moreover, the Zn|Cu battery cycled for over 570 hours with a high Coulombic efficiency of 99.82%. This study presents a pioneering perspective for the further application of AZIBs.

Construction of terminology system in public health and preventive medicine

Public Health and Preventive Medicine, as a first-level discipline within the field of medical sciences, plays a crucial role in standardizing terminology for the purposes of discipline construction, technological advancement, and societal progress. Given the current macro-environment of social development, establishing and perfecting a standardized terminology system for Public Health and Preventive Medicine is not only a necessity for the discipline itself but also a pressing need for the nation and the world. This paper focuses on the development environment and current status of the Public Health and Preventive Medicine discipline system. It explores the current state, challenges, and future prospects of building a terminology system for Public Health and Preventive Medicine from the perspectives of terminology evaluation, standardized application, and discipline construction.

Review of few-shot learning application in CSI human sensing

Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its application faces challenges due to limited generalizability, necessitating frequent data recollection and neural network retraining for adaptation to new environments. To address these limitations, some researchers introduced few-shot learning into Wi-Fi sensing applications because it offers a promising solution with its ability to achieve remarkable performance in novel scenarios using minimal training samples. Despite its potential, a comprehensive review of its applications within this domain remains absent. This study endeavors to fill this gap by exploring prominent Wi-Fi sensing applications that incorporate few-shot learning, aiming to delineate their key features. We categorize few-shot learning approaches into three distinct methodologies: transfer learning, metric learning, and meta-learning, based on their neural network training strategies. Through this classification, we examine representative systems from an application perspective and elucidate the principles of few-shot learning implementation. These systems are evaluated in terms of learning methodology, data modality, and recognition accuracy. Finally, this paper highlights the challenges and future directions for few-shot learning in Channel State Information (CSI) based human sensing, providing a valuable resource for researchers in the field of Wi-Fi human sensing leveraging few-shot learning.