bluuebunny/crossref_metadata_2025 huggingface dataset:
Liver Magnetic Resonance Elastography: Focus on Methodology, Technique, and Feasibility
Authors: Zerunian Marta , Masci Benedetta , Caruso Damiano , Pucciarelli Francesco , Polici Michela , Nardacci Stefano , De Santis Domenico , Iannicelli Elsa , Laghi Andrea
Published: February 2024

Magnetic resonance elastography (MRE) is an imaging technique that combines low-frequency mechanical vibrations with magnetic resonance imaging to create visual maps and quantify liver parenchyma stiffness. As in recent years, diffuse liver diseases have become highly prevalent worldwide and could lead to a chronic condition with different stages of fibrosis. There is a strong necessity for a non-invasive, highly accurate, and standardised quantitative assessment to evaluate and manage patients with different stages of fibrosis from diagnosis to follow-up, as the actual reference standard for the diagnosis and staging of liver fibrosis is biopsy, an invasive method with possible peri-procedural complications and sampling errors. MRE could quantitatively evaluate liver stiffness, as it is a rapid and repeatable method with high specificity and sensitivity. MRE is based on the propagation of mechanical shear waves through the liver tissue that are directly proportional to the organ’s stiffness, expressed in kilopascals (kPa). To obtain a valid assessment of the real hepatic stiffness values, it is mandatory to obtain a high-quality examination. To understand the pearls and pitfalls of MRE, in this review, we describe our experience after one year of performing MRE from indications and patient preparation to acquisition, quality control, and image analysis.
Access to Dental Care for Children and Young People in Care and Care Leavers: A Global Scoping Review
Authors: Erwin Jo , Horrell Jane , Wheat Hannah , Axford Nick , Burns Lorna , Booth Joelle , Witton Robert , Shawe Jill , Doughty Janine , Kaddour Sarah , Boswell Skye , Devalia Urshla , Nelder Abigail , Paisi Martha
Published: February 2024

Aims: This scoping review aimed to explore three research questions: 1. What is the dental care access for children and young people (CYP) in care and care leavers? 2. What factors influence CYP in care and care leavers’ access to dental care? 3. What pathways have been developed to improve access to oral health care for CYP in care and care leavers? Methods: Five databases (Ovid MEDLINE, Ovid Embase, CINAHL, SocINDEX and Dentistry and Oral Sciences Source) and grey literature sources were systematically searched. Articles relating to CYP in care or care leavers aged 0–25 years old, published up to January 2023 were included. Abstracts, posters and publications not in the English language were excluded. The data relating to dental care access were analysed using thematic analysis. Results: The search identified 942 articles, of which 247 were excluded as duplicates. A review of the titles and abstracts yielded 149 studies. Thirty-eight were eligible for inclusion in the review: thirty-three peer-reviewed articles, one PhD thesis and four grey literature sources. All papers were published from very high or medium Human Development Index countries. The studies indicate that despite having higher treatment needs, CYP in care and care leavers experience greater difficulty in accessing dental services than those not care-experienced. Organisational, psycho-social and logistical factors influence their access to dental care. Their experience of dental care may be impacted by adverse childhood events. Pathways to dental care have been developed, but little is known of their impact on access. There are very few studies that include care leavers. The voices of care-experienced CYP are missing from dental access research. Conclusions: care-experienced CYP are disadvantaged in their access to dental care, and there are significant barriers to their treatment needs being met.
Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model
Authors: Lafhel Majda , Cherifi Hocine , Renoust Benjamin , El Hassouni Mohammed
Published: February 2024

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems.
Fast Model Selection and Hyperparameter Tuning for Generative Models
Authors: Chen Luming , Ghosh Sujit K.
Published: February 2024

Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The performance of generative models depends critically on a good set of hyperparameters. Yet, finding the right hyperparameter configuration can be an extremely time-consuming task. In this paper, we focus on speeding up the hyperparameter search through adaptive resource allocation, early stopping underperforming candidates quickly and allocating more computational resources to promising ones by comparing their intermediate performance. The hyperparameter search is formulated as a non-stochastic best-arm identification problem where resources like iterations or training time constrained by some predetermined budget are allocated to different hyperparameter configurations. A procedure which uses hypothesis testing coupled with Successive Halving is proposed to make the resource allocation and early stopping decisions and compares the intermediate performance of generative models by their exponentially weighted Maximum Means Discrepancy (MMD). The experimental results show that the proposed method selects hyperparameter configurations that lead to a significant improvement in the model performance compared to Successive Halving for a wide range of budgets across several real-world applications.
A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks
Authors: Zhao Kaidi , Han Dingding , Bao Yihong , Qian Jianghai , Yang Ruiqi
Published: February 2024

In the realm of online social networks, the spreading of information is influenced by a complex interplay of factors. To explore the dynamics of one-time retweet information spreading, we propose a Susceptible–Infected–Completed (SIC) multi-information spreading model. This model captures how multiple pieces of information interact in online social networks by introducing inhibiting and enhancement factors. The SIC model considers the completed state, where nodes cease to spread a particular piece of information after transmitting it. It also takes into account the impact of past and present information received from neighboring nodes, dynamically calculating the probability of nodes spreading each piece of information at any given moment. To analyze the dynamics of multiple information pieces in various scenarios, such as mutual enhancement, partial competition, complete competition, and coexistence of competition and enhancement, we conduct experiments on BA scale-free networks and the Twitter network. Our findings reveal that competing information decreases the likelihood of its spread while cooperating information amplifies the spreading of mutually beneficial content. Furthermore, the strength of the enhancement factor between different information pieces determines their spread when competition and cooperation coexist. These insights offer a fresh perspective for understanding the patterns of information propagation in multiple contexts.
Effects of Gender and Age Interaction on Sense of Coherence and Subjective Well-Being of Senior High School Students in Northern Ghana
Authors: Agormedah Edmond Kwesi , Ankomah Francis , Srem-Sai Medina , Nugba Regina Mawusi , Quansah Frank , Hagan John Elvis , Okan Orkan , Dadaczynski Kevin , Schack Thomas
Published: February 2024

Though several studies have established the effect of gender and age on the sense of coherence (SoC) and subjective well-being (SWB) among diverse populations, findings have been varied and inconclusive. These inconsistencies have been attributed to the differences in sample characteristics, methodological and cultural disparities. Thus, the present study assessed the following: (1) gender and age effect on SoC, (2) gender and age effect on SWB, and (3) moderating roles of gender and age in the link between SoC and SWB. Through a stratified sampling technique, 724 selected high school students from secondary schools in Northern Ghana completed the World Health Organization-5 Well-Being and Sense of Coherence instruments. The results showed that female students exhibited higher levels of SoC compared to their male counterparts. Whereas younger male and female students showed no significant difference in SWB levels, older female students, compared to older males, exhibited high levels of SWB. Age significantly moderated the relationship between SoC and SWB. With the same level of SoC, younger students were more likely to exhibit higher SWB compared to older ones. The findings call for sustainable gender- and age-based interventions because students subjectively develop SoC mechanisms for improving their well-being.
Parameter Identification for Maximum Torque per Ampere Control of Permanent Magnet Synchronous Machines under Magnetic Saturation
Authors: Yan Mingyu , Wen Bisheng , Cui Qing , Peng Xiaoyan
Published: February 2024

This paper applies the identified parameters of permanent magnet synchronous machines (PMSMs) for the maximum torque per ampere control (MTPA) under magnetic saturation. The variation in magnet flux with current is determined using a position offset approach while the variation in q-axis inductance with the current is estimated from the d-axis voltage equation afterward. In addition, the d-axis inductance is estimated at standstill by the injection of a small amplitude of high frequency d-axis current. The curve-fitted results of estimated parameters under different saturation conditions are then employed to aid the derivation of MTPA control law. The proposed method is experimentally verified on two prototype PMSMs. Experimental results show that compared with conventional MTPA schemes using fixed values of magnetic parameters, the proposed method can increase maximum output torque by 2.1% and 3.2% on two prototype PMSMs, respectively.
Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak
Authors: Sokolová Zuzana , Harahus Maroš , Juhár Jozef , Pleva Matúš , Staš Ján , Hládek Daniel
Published: February 2024

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.
Two-Timescale Design for RIS-Aided Multicell MIMO Systems with Transceiver Hardware Impairments
Authors: Zhang Shilong , Guo Weiran , Dai Jianxin , Zhu Feng
Published: February 2024

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of the RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at the base stations (BS). The expression of the uplink achievable rate is derived and analyzed. Moreover, the genetic algorithm (GA) is used to optimize the phase shifts of the RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs and reduce the demand for BS antennas, which can significantly reduce the hardware costs at the BS.
Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks
Authors: Lee Shu-Hung , Cheng Chia-Hsin , Lu Kuan-Hsien , Shiue Yeong-Long , Huang Yung-Fa
Published: February 2024

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.
Mouse Data Attack Technique Using Machine Learning in Image-Based User Authentication: Based on a Defense Technique Using the WM_INPUT Message
Authors: Jung Wontae , Hong Sejun , Lee Kyungroul
Published: February 2024

Recently, as the non-face-to-face society persists due to the coronavirus (COVID-19), the Internet usage rate continues to increase, and input devices, such as keyboards and mice, are mainly used to authenticate users in non-face-to-face environments. Due to the nature of the non-face-to-face environment, important personal data are processed, and since these personal data include authentication information, it is very important to protect them. As such, personal information, including authentication information, is entered mainly from the keyboard, and attackers use attack tools, such as keyloggers, to steal keyboard data in order to grab sensitive user information. Therefore, to prevent disclosure of sensitive keyboard input, various image-based user authentication technologies have emerged that allow sensitive information, such as authentication information, to be entered via mouse. To address mouse data stealing vulnerabilities via GetCursorPos() function or WM_INPUT message, which are representative mouse data attack techniques, a mouse data defense technique has emerged that prevents attackers from classifying real mouse data and fake mouse data by the defender generating fake mouse data. In this paper, we propose a mouse data attack technique using machine learning against a mouse data defense technique using the WM_INPUT message. The proposed technique uses machine learning models to classify fake mouse data and real mouse data in a scenario where the mouse data defense technique, utilizing the WM_INPUT message in image-based user authentication, is deployed. This approach is verified through experiments designed to assess its effectiveness in preventing the theft of real mouse data, which constitute the user’s authentication information. For verification purposes, a mouse data attack system was configured, and datasets for machine learning were established by collecting mouse data from the configured attack system. To enhance the performance of machine learning classification, evaluations were conducted based on data organized according to various machine learning models, datasets, features, and generation cycles. The results, highlighting the highest performance in terms of features and datasets were derived. If the mouse data attack technique proposed in this paper is used, attackers can potentially steal the user’s authentication information from various websites or services, including software, systems, and servers that rely on authentication information. It is anticipated that attackers may exploit the stolen authentication information for additional damages, such as voice phishing. In the future, we plan to conduct research on defense techniques aimed at securely protecting mouse data, even if the mouse data attack technique proposed in this paper is attempted.
Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus
Authors: Pan Ronghao , García-Díaz José Antonio , Valencia-García Rafael
Published: February 2024

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.
Resilient Electricity Load Forecasting Network with Collective Intelligence Predictor for Smart Cities
Authors: Bin Kamilin Mohd Hafizuddin , Yamaguchi Shingo
Published: February 2024

Accurate electricity forecasting is essential for smart cities to maintain grid stability by allocating resources in advance, ensuring better integration with renewable energies, and lowering operation costs. However, most forecasting models that use machine learning cannot handle the missing values and possess a single point of failure. With rapid technological advancement, smart cities are becoming lucrative targets for cyberattacks to induce packet loss or take down servers offline via distributed denial-of-service attacks, disrupting the forecasting system and inducing missing values in the electricity load data. This paper proposes a collective intelligence predictor, which uses modular three-level forecasting networks to decentralize and strengthen against missing values. Compared to the existing forecasting models, it achieves a coefficient of determination score of 0.98831 with no missing values using the base model in the Level 0 network. As the missing values in the forecasted zone rise to 90% and a single-model forecasting method is no longer effective, it achieves a score of 0.89345 with a meta-model in the Level 1 network to aggregate the results from the base models in Level 0. Finally, as missing values reach 100%, it achieves a score of 0.81445 by reconstructing the forecast from other zones using the meta-model in the Level 2 network.
Deep Spectral Meshes: Multi-Frequency Facial Mesh Processing with Graph Neural Networks
Authors: Kosk Robert , Southern Richard , You Lihua , Bian Shaojun , Kokke Willem , Maguire Greg
Published: February 2024

With the rising popularity of virtual worlds, the importance of data-driven parametric models of 3D meshes has grown rapidly. Numerous applications, such as computer vision, procedural generation, and mesh editing, vastly rely on these models. However, current approaches do not allow for independent editing of deformations at different frequency levels. They also do not benefit from representing deformations at different frequencies with dedicated representations, which would better expose their properties and improve the generated meshes’ geometric and perceptual quality. In this work, spectral meshes are introduced as a method to decompose mesh deformations into low-frequency and high-frequency deformations. These features of low- and high-frequency deformations are used for representation learning with graph convolutional networks. A parametric model for 3D facial mesh synthesis is built upon the proposed framework, exposing user parameters that control disentangled high- and low-frequency deformations. Independent control of deformations at different frequencies and generation of plausible synthetic examples are mutually exclusive objectives. A Conditioning Factor is introduced to leverage these objectives. Our model takes further advantage of spectral partitioning by representing different frequency levels with disparate, more suitable representations. Low frequencies are represented with standardised Euclidean coordinates, and high frequencies with a normalised deformation representation (DR). This paper investigates applications of our proposed approach in mesh reconstruction, mesh interpolation, and multi-frequency editing. It is demonstrated that our method improves the overall quality of generated meshes on most datasets when considering both the L1 norm and perceptual Dihedral Angle Mesh Error (DAME) metrics.
Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion
Authors: Han Wenhao , Liu Xuemei , Zhang Jianhao , Li Hairui
Published: February 2024

Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.
Multi-Objective Optimization of the Microchannel Heat Sink Used for Combustor of the Gas Turbine
Authors: Zhang Xiaoming , Yang Tao , Chang Zhenyuan , Xu Liang , Xi Lei , Gao Jianmin , Zheng Penggang , Xu Ran
Published: February 2024

This research presents a surrogate model and computational fluid dynamic analysis-based multi-objective optimization approach for microchannel heat sinks. The Non-dominated Sorting Genetic Algorithm is suggested to obtain the optimal solution set, and the Kriging model is employed to lower the simulation’s computational cost. The physical model consists of a coolant chamber, a mainstream chamber, and a solid board equipped with staggered Zigzag cooling channels. Five design variables are examined in relation to the geometric characteristics of the microchannel heat sinks: the length of inlet of the cooling channels, the width of the cooling channels, the length of the “zigzag”, the height of the cooling channels, and the periodic spanwise width. The optimal geometry is established by choosing the averaged cooling effectiveness and coolant mass flow rate which enters the mainstream chamber through the microchannel heat sinks as separate objectives. From the Pareto front, the optimal microchannel heat sinks structures are obtained. Three optimized results are studied, including the maximum cooling effectiveness, minimum coolant mass flow rate, and a compromise between the both objectives; a reference case using the median is compared as well. Numerical assessments corresponding to the four cases are performed, and the flow and cooling performance are compared. Furthermore, an analysis is conducted on the mechanisms that impact the ideal geometric parameters for cooling performance.
Identification of Distribution Network Topology and Line Parameter Based on Smart Meter Measurements
Authors: Wang Chong , Lou Zheng , Li Ming , Zhu Chaoyang , Jing Dongsheng
Published: February 2024

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures.
Proximal Policy Optimization-Based Power Grid Structure Optimization for Reliable Splitting
Authors: Sun Xinwei , Han Shuangteng , Wang Yuhong , Shi Yunxiang , Liao Jianquan , Zheng Zongsheng , Wang Xi , Shi Peng
Published: February 2024

When systems experience a severe fault, splitting, as the final line of defense to ensure the stability of the power system, holds immense significance. The precise selection of splitting sections has become the current focal point of research. Addressing the challenges of a large search space and unclear splitting sections, this paper introduces a grid structure optimization algorithm based on electrical coupling degree. Firstly, employing the theory of slow coherency, a generalized characteristic analysis of the system is conducted, leading to an initial division of coherency groups. Subsequently, an electrical coupling degree index, taking into account the inertia of generators, is proposed. This index can reflect the clarity of grid splitting. Furthermore, a two-layer optimization model for grid structure is constructed, utilizing the Proximal Policy Optimization (PPO) algorithm to optimize the grid structure. This process reduces the size of the splitting space and mitigates the difficulty of acquiring splitting sections. Finally, simulation validation is performed using the IEEE-118-bus system to demonstrate the effectiveness of the proposed optimization algorithm.
Enhanced High-Fructose Corn Syrup Production: Immobilizing Serratia marcescens Glucose Isomerase on MOF (Co)-525 Reduces Co2+ Dependency in Glucose Isomerization to Fructose
Authors: Geng Xu , Li Yi , Wang Ruizhe , Jiang Song , Liang Yingchao , Li Tao , Li Chen , Tao Jin , Li Zhengqiang
Published: February 2024

The escalating demand for processed foods has led to the widespread industrial use of glucose isomerase (GI) for high-fructose corn syrup (HFCS) production. This reliance on GIs necessitates continual Co2+ supplementation to sustain high catalytic activity across multiple reaction cycles. In this study, Serratia marcescens GI (SmGI) was immobilized onto surfaces of the metal-organic framework (MOF) material MOF (Co)-525 to generate MOF (Co)-525-GI for use in catalyzing glucose isomerization to generate fructose. Examination of MOF (Co)-525-GI structural features using scanning electron microscopy-energy dispersive spectroscopy, Fourier-transform infrared spectroscopy, and ultraviolet spectroscopy revealed no structural changes after SmGI immobilization and the addition of Co2+. Notably, MOF (Co)-525-GI exhibited optimal catalytic activity at pH 7.5 and 70 °C, with a maximum reaction rate (Vmax) of 37.24 ± 1.91 μM/min and Km value of 46.25 ± 3.03 mM observed. Remarkably, immobilized SmGI exhibited sustained high catalytic activity over multiple cycles without continuous Co2+ infusion, retaining its molecular structure and 96.38% of its initial activity after six reaction cycles. These results underscore the potential of MOF (Co)-525-GI to serve as a safer and more efficient immobilized enzyme technology compared to traditional GI-based food-processing technologies.
Zinc Protoporphyrin-Rich Pork Liver Homogenates as Coloring Ingredients in Nitrite-Free Liver Pâtés
Authors: Llauger Mar , Guerrero Luis , Arnau Jacint , Morera Afra , Wakamatsu Jun-ichi , Lorenzo José M. , Bou Ricard
Published: February 2024

This study aimed to investigate the coloring ingredient potential of liver homogenates that form Zn protoporphyrin (ZnPP), a natural red pigment, after anaerobic incubation. Liver homogenates were used to develop nitrite-free sterile pork liver pâtés. These homogenates were applied in the formulation of pâtés directly or after centrifugation to obtain a pellet that was highly concentrated in ZnPP. Both the whole homogenate and its insoluble fraction were adjusted to pH 7.5 before their use in the formulation of pâtés with and without antioxidant (0.5% ascorbate plus 0.1% tocopherol) addition. Pâtés formulated with the whole homogenate showed color and texture characteristics that were similar to those of the positive control with nitrite. However, high levels of the insoluble fraction also led to pâtés with improved color characteristics but with a two-fold softened texture. Therefore, the form and amount of ZnPP added played roles in the final appearance of the product. The ZnPP pigment was more stable than heme in the sterilization treatment, and antioxidant addition proved to be unnecessary. The ZnPP-rich ingredients allowed for the preparation of nitrite-free cooked liver pâtés with a stable red color and could thus be potentially applied in other uncured cooked meat products.
In Vivo Diuretic Activity and Anti-Hypertensive Potential of Hibiscus sabdariffa Extract by Inhibition of Angiotensin-Converting Enzyme and Hypertension Precursor Enzymes
Authors: Sanou Abdoudramane , Konaté Kiessoun , Belemnaba Lazare , Sama Hemayoro , Kaboré Kabakdé , Dakuyo Roger , Nitiéma Mathieu , Dicko Mamoudou Hama
Published: February 2024

Aqueous extracts of calyx from Hibiscus sabdariffa (HS) (roselle) are highly appreciated for their nutritional and therapeutic effects, especially as anti-hypertensive substances. This study aimed to evaluate their anti-hypertensive potential through an in vitro inhibition assay of angiotensin-converting enzyme (ACE) and hypertension precursor enzymes and to assess the in vivo diuretic activity of HS. Results showed that HS extract inhibited enzymes belonging to several classes, such as α-amylase, trypsin, chymotrypsin, xanthine oxidase, lipoxygenase, and angiotensin-converting enzyme. In particular, enzymatic kinetics of ACE indicated a competitive inhibition fashion of HS extract. Furthermore, the extracts showed remarkable diuretic and natriuretic effects at doses of 50 mg/kg/bw, 100 mg/kg/b.w, and 200 mg/kg.b.w. These activities can be explained by the high content of phenolic compounds and essential amino acids. Roselle could be a potential source of nutraceuticals and anti-hypertensive bioactive compounds.
Levels, Toxic Effects, and Risk Assessment of Pyrrolizidine Alkaloids in Foods: A Review
Authors: Lu Yu-Shun , Qiu Jing , Mu Xi-Yan , Qian Yong-Zhong , Chen Lu
Published: February 2024

Pyrrolizidine alkaloids (PAs) are naturally occurring secondary metabolites of plants. To date, more than 660 types of PAs have been identified from an estimated 6000 plants, and approximately 120 of these PAs are hepatotoxic. As a result of PAs being found in spices, herbal teas, honey, and milk, PAs are considered contaminants in foods, posing a potential risk to human health. Here, we summarize the chemical structure, toxic effects, levels, and regulation of PAs in different countries to provide a better understanding of their toxicity and risk assessment. With recent research on the risk assessment of PAs, this review also discusses the challenges facing this field, aiming to provide a scientific basis for PA toxicity research and safety assessment.
Optimization of Bioactive Compound Extraction from Saffron Petals Using Ultrasound-Assisted Acidified Ethanol Solvent: Adding Value to Food Waste
Authors: Jabbari Nikoo , Goli Mohammad , Shahi Sharifeh
Published: February 2024

The saffron industry produces large by-products, including petals with potential bioactive compounds, which are cheap and abundant, making them an attractive alternative to expensive stigmas for extracting bioactive components. This study aimed to optimize the extraction conditions of bioactive compounds from vacuum-dried saffron petals using an ultrasound-assisted acidified ethanol solvent. Three factors were considered: ethanol concentration (0–96%), citric acid concentration in the final solvent (0–1%), and ultrasound power (0–400 watt). This study examined the effects of these factors on parameters like maximum antioxidant activity, total anthocyanin content, total phenolic content, and the total flavonoid content of the extraction. This study found that saffron petal extract’s antioxidant activity increases with higher ethanol concentration, citric acid dose, and ultrasound power, but that an increased water content leads to non-antioxidant compounds. Increasing the dosage of citric acid improved the extraction of cyanidin-3-glucoside at different ultrasound power levels. The highest extraction was achieved with 400 watts of ultrasound power and 1% citric acid. Ethanol concentration did not affect anthocyanin extraction. Higher ethanol concentration and greater citric acid concentration doses resulted in the maximum extraction of total phenolic content, with a noticeable drop in extraction at higher purity levels. This study found that increasing the proportion of citric acid in the final solvent did not affect flavonoid extraction at high ethanol concentration levels, and the highest efficiency was observed at 200 watts of ultrasound power. The optimum values of the independent parameters for extracting bioactive compounds from saffron petals included 96% ethanol concentration, 0.67% citric acid concentration, and 216 watts of ultrasound power, resulting in a desirability value of 0.82. This ultrasound-assisted acidified ethanolic extract can be used in the food industry as a natural antioxidant and pigment source.
A Planning Support System for Monitoring Aging Neighborhoods in Germany
Authors: Schaffert Markus , Warch Dominik , Müller Hartmut
Published: February 2024

Many single-family homes built in Germany in the first decades following the Second World War are now occupied by elderly residents. If local conditions are unfavorable, a large number of these buildings may enter the real estate market in a short period of time and put pressure on the local housing market. Planners and decision-makers therefore need detailed spatiotemporal information about these neighborhoods to effectively address and counteract such developments. We present the design and implementation of a planning support system that can generate the required information. The architecture of this newly developed software consists of a composite, multitier framework to perform the complex tasks of data importation, data processing, and visualization. Legally mandated municipal population registers provide the key data for the calculation of indicators as a base for spatiotemporal analyses and visualizations. These registers offer high data quality in terms of completeness, logical consistency, spatial, and temporal and thematic accuracy. We demonstrate the implemented method using population data from a local government in a rural area in southwestern Germany. The results show that the new tool, which relies on open software components, is capable to identify and prioritize areas with particularly high levels of problem pressure. The tool can be used not only for analyses in a local context, but also at a regional level.
From Oblivion to Life: The Recovery of Intangible Cultural Heritage through the Anti-Aircraft Shelters of the Spanish Civil War
Authors: Rosser Pablo , Soler Seila
Published: February 2024

This article examines the rehabilitation of anti-aircraft shelters from the Spanish Civil War in Alicante, Spain. Funded by European resources and managed by local public administration, these shelters have been restored as cultural and tourist attractions. This study aims to analyze their role in preserving and promoting intangible cultural heritage, with a focus on their significance as tangible remnants of a historical period and their reflection on survival practices during the war. This research investigates the impact of public management in rehabilitating these shelters and in disseminating their history and culture. It explores decision-making processes, community engagement, and strategies for promoting cultural tourism. Employing a mixed methodology, this study gathers primary data through interviews with individuals who witnessed the war and secondary data from documentary and bibliographic sources. Findings suggest that the rehabilitation of these shelters has been pivotal in preserving Alicante’s historical memory and cultural heritage. Making the shelters accessible to the public facilitates knowledge transmission about the Spanish Civil War, promotes cultural tourism, and engages the local community in the dissemination of history and culture. This study’s results and conclusions are relevant for academics, professionals, and cultural heritage managers at both national and international levels, offering insights into the effective preservation and promotion of historical sites.
Inhibitors of NAD+ Production in Cancer Treatment: State of the Art and Perspectives
Authors: Ghanem Moustafa S. , Caffa Irene , Monacelli Fiammetta , Nencioni Alessio
Published: February 2024

The addiction of tumors to elevated nicotinamide adenine dinucleotide (NAD+) levels is a hallmark of cancer metabolism. Obstructing NAD+ biosynthesis in tumors is a new and promising antineoplastic strategy. Inhibitors developed against nicotinamide phosphoribosyltransferase (NAMPT), the main enzyme in NAD+ production from nicotinamide, elicited robust anticancer activity in preclinical models but not in patients, implying that other NAD+-biosynthetic pathways are also active in tumors and provide sufficient NAD+ amounts despite NAMPT obstruction. Recent studies show that NAD+ biosynthesis through the so-called “Preiss-Handler (PH) pathway”, which utilizes nicotinate as a precursor, actively operates in many tumors and accounts for tumor resistance to NAMPT inhibitors. The PH pathway consists of three sequential enzymatic steps that are catalyzed by nicotinate phosphoribosyltransferase (NAPRT), nicotinamide mononucleotide adenylyltransferases (NMNATs), and NAD+ synthetase (NADSYN1). Here, we focus on these enzymes as emerging targets in cancer drug discovery, summarizing their reported inhibitors and describing their current or potential exploitation as anticancer agents. Finally, we also focus on additional NAD+-producing enzymes acting in alternative NAD+-producing routes that could also be relevant in tumors and thus become viable targets for drug discovery.
Antineoplastic Activity of 9″-Lithospermic Acid Methyl Ester in Glioblastoma Cells
Authors: Tzitiridou Panagiota , Zoi Vasiliki , Papagrigoriou Theodora , Lazari Diamanto , Sioka Chrissa , Alexiou Georgios A. , Kyritsis Athanassios P.
Published: February 2024

To date, many potent compounds have been found which are derived from plants and herbs and possess anticancer properties due to their antioxidant effects. 9″-Lithospermic acid methyl ester is an effective natural compound derived from the Thymus thracicus Velen. It has been proven that this compound has substantial properties in different diseases, but its effects in cancer have not been thoroughly evaluated. The aim of this work was to study the effects of 9″-Lithospermic acid methyl ester (9″-methyl lithospermate) in U87 and T98 glioblastoma cell lines. Its effects on cellular viability were assessed via Trypan Blue and Crystal Violet stains, the cell cycle analysis through flow cytometry, and cell migration by employing the scratch wound healing assay. The results demonstrated that 9″-methyl lithospermate was able to inhibit cellular proliferation, induce cellular death, and inhibit cell migration. Furthermore, these results were intensified by the addition of temozolomide, the most prominent chemotherapeutic drug in glioblastoma tumors. Further studies are needed to reproduce these findings in animal models and investigate if 9″-lithospermic acid methyl ester represents a potential new therapeutic addition for gliomas.
Special Issue “Role of Apoptosis and Cellular Senescence in Cancer and Aging”
Authors: Tzoneva Rumiana
Published: February 2024

The intention of this Special Issue is to elucidate the role of apoptosis and cellular senescence in different pathological processes, such as cancer and aging [...]
The Role of Rosavin in the Pathophysiology of Bone Metabolism
Authors: Wojdasiewicz Piotr , Turczyn Paweł , Lach-Gruba Anna , Poniatowski Łukasz A. , Purrahman Daryush , Mahmoudian-Sani Mohammad-Reza , Szukiewicz Dariusz
Published: February 2024

Rosavin, a phenylpropanoid in Rhodiola rosea’s rhizome, and an adaptogen, is known for enhancing the body’s response to environmental stress. It significantly affects cellular metabolism in health and many diseases, particularly influencing bone tissue metabolism. In vitro, rosavin inhibits osteoclastogenesis, disrupts F-actin ring formation, and reduces the expression of osteoclastogenesis-related genes such as cathepsin K, calcitonin receptor (CTR), tumor necrosis factor receptor-associated factor 6 (TRAF6), tartrate-resistant acid phosphatase (TRAP), and matrix metallopeptidase 9 (MMP-9). It also impedes the nuclear factor of activated T-cell cytoplasmic 1 (NFATc1), c-Fos, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and mitogen-activated protein kinase (MAPK) signaling pathways and blocks phosphorylation processes crucial for bone resorption. Moreover, rosavin promotes osteogenesis and osteoblast differentiation and increases mouse runt-related transcription factor 2 (Runx2) and osteocalcin (OCN) expression. In vivo studies show its effectiveness in enhancing bone mineral density (BMD) in postmenopausal osteoporosis (PMOP) mice, restraining osteoclast maturation, and increasing the active osteoblast percentage in bone tissue. It modulates mRNA expressions by increasing eukaryotic translation elongation factor 2 (EEF2) and decreasing histone deacetylase 1 (HDAC1), thereby activating osteoprotective epigenetic mechanisms, and alters many serum markers, including decreasing cross-linked C-telopeptide of type I collagen (CTX-1), tartrate-resistant acid phosphatase 5b (TRACP5b), receptor activator for nuclear factor κ B ligand (RANKL), macrophage-colony-stimulating factor (M-CSF), and TRAP, while increasing alkaline phosphatase (ALP) and OCN. Additionally, when combined with zinc and probiotics, it reduces pro-osteoporotic matrix metallopeptidase 3 (MMP-3), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α), and enhances anti-osteoporotic interleukin 10 (IL-10) and tissue inhibitor of metalloproteinase 3 (TIMP3) expressions. This paper aims to systematically review rosavin’s impact on bone tissue metabolism, exploring its potential in osteoporosis prevention and treatment, and suggesting future research directions.