bluuebunny/crossref_metadata_2025 huggingface dataset:
In Silico, In Vitro, and In Vivo Investigations on Adapalene as Repurposed Third Generation Retinoid against Multiple Myeloma and Leukemia
Authors: Boulos Joelle C. , Chatterjee Manik , Shan Letian , Efferth Thomas
Published: August 2023

The majority of hematopoietic cancers in adults are incurable and exhibit unpredictable remitting-relapsing patterns in response to various therapies. The proto-oncogene c-MYC has been associated with tumorigenesis, especially in hematological neoplasms. Therefore, targeting c-MYC is crucial to find effective, novel treatments for blood malignancies. To date, there are no clinically approved c-MYC inhibitors. In this study, we virtually screened 1578 Food and Drug Administration (FDA)-approved drugs from the ZINC15 database against c-MYC. The top 117 compounds from PyRx-based screening with the best binding affinities to c-MYC were subjected to molecular docking studies with AutoDock 4.2.6. Retinoids consist of synthetic and natural vitamin A derivatives. All-trans-retinoic acid (ATRA) were highly effective in hematological malignancies. In this study, adapalene, a third-generation retinoid usually used to treat acne vulgaris, was selected as a potent c-MYC inhibitor as it robustly bound to c-MYC with a lowest binding energy (LBE) of −7.27 kcal/mol, a predicted inhibition constant (pKi) of 4.69 µM, and a dissociation constant (Kd value) of 3.05 µM. Thus, we examined its impact on multiple myeloma (MM) cells in vitro and evaluated its efficiency in vivo using a xenograft tumor zebrafish model. We demonstrated that adapalene exerted substantial cytotoxicity against a panel of nine MM and two leukemic cell lines, with AMO1 cells being the most susceptible one (IC50 = 1.76 ± 0.39 µM) and, hence, the focus of this work. Adapalene (0.5 × IC50, 1 × IC50, 2 × IC50) decreased c-MYC expression and transcriptional activity in AMO1 cells in a dose-dependent manner. An examination of the cell cycle revealed that adapalene halted the cells in the G2/M phase and increased the portion of cells in the sub-G0/G1 phase after 48 and 72 h, indicating that cells failed to initiate mitosis, and consequently, cell death was triggered. Adapalene also increased the number of p-H3(Ser10) positive AMO1 cells, which is a further proof of its ability to prevent mitotic exit. Confocal imaging demonstrated that adapalene destroyed the tubulin network of U2OS cells stably transfected with a cDNA coding for α-tubulin-GFP, refraining the migration of malignant cells. Furthermore, adapalene induced DNA damage in AMO1 cells. It also induced apoptosis and autophagy, as demonstrated by flow cytometry and western blotting. Finally, adapalene impeded tumor growth in a xenograft tumor zebrafish model. In summary, the discovery of the vitamin A derivative adapalene as a c-MYC inhibitor reveals its potential as an avant-garde treatment for MM.
Flavonoids on the Frontline against Cancer Metastasis
Authors: Eltahir Sarah , Ahmad Aamir
Published: August 2023

Metastasis is the leading cause of death in cancer patients [...]
Editorial: The Application of Proteogenomics to Urine Analysis for the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study
Authors: Gamallat Yaser , Bismar Tarek A.
Published: August 2023

In this editorial context, we aim to leverage the potential of proteogenomics, which integrates genomic and proteomic data, to discover novel biomarkers that can aid in the diagnosis and management of prostate cancer. We highlight the importance of proteogenomics for understanding the functional consequences of somatic mutations in cancer and demonstrating how proteogenomic analysis can provide insights into the effects of genetic alterations on the proteomic landscape and identify potential therapeutic targets. This article also emphasizes the potential of urine analysis for the detection of prostate cancer. Overall, our editorial paper provides general insights on the application of proteogenomics to urine analysis for the identification of novel biomarkers of prostate cancer.
RET-Altered Cancers—A Tumor-Agnostic Review of Biology, Diagnosis and Targeted Therapy Activity
Authors: Desilets Antoine , Repetto Matteo , Yang Soo-Ryum , Sherman Eric J. , Drilon Alexander
Published: August 2023

RET alterations, such as fusions or mutations, drive the growth of multiple tumor types. These alterations are found in canonical (lung and thyroid) and non-canonical (e.g., gastrointestinal, breast, gynecological, genitourinary, histiocytic) cancers. RET alterations are best identified via comprehensive next-generation sequencing, preferably with DNA and RNA interrogation for fusions. Targeted therapies for RET-dependent cancers have evolved from older multikinase inhibitors to selective inhibitors of RET such as selpercatinib and pralsetinib. Prospective basket trials and retrospective reports have demonstrated the activity of these drugs in a wide variety of RET-altered cancers, notably those with RET fusions. This paved the way for the first tumor-agnostic selective RET inhibitor US FDA approval in 2022. Acquired resistance to RET kinase inhibitors can take the form of acquired resistance mutations (e.g., RET G810X) or bypass alterations.
Cancer and Nutrients
Authors: Tuli Hardeep Singh , Yerer Mukerrem Betul , Yadav Vikas
Published: August 2023

Editorial: Over the last few decades, the scientific community has recognized the enormous potential of bioactive dietary nutrients/components in the management and prevention of cancer [...]
Dielectric Spectroscopy Shows a Permittivity Contrast between Meningioma Tissue and Brain White and Gray Matter—A Potential Physical Biomarker for Meningioma Discrimination
Authors: Kordić Anton , Šarolić Antonio
Published: August 2023

The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools.
Electrochemical Degradation of Diuron by Anodic Oxidation on a Commercial Ru0.3Ti0.7O2 Anode in a Sulfate Medium
Authors: de Faria Lucas B. , Teixeira Guilhermina F. , Alves Andréia C. F. , Linares José J. , Oliveira Sérgio B. , Motheo Artur J. , Colmati Flavio
Published: August 2023

This work presents the electrochemical degradation of the herbicide Diuron by anodic oxidation on a Ti/Ru0.3Ti0.7O2 metal mixed oxide anode using sulfate as the electrolyte. The study includes the influence of Diuron concentration and current density on anodic oxidation. The results evidence a first-order degradation, with the highest capacity achieved at 40 mA cm−2 and at an initial Diuron concentration of 38 mg L−1. Nevertheless, in terms of efficiency and energy demand, the operation at 10 mA cm−2 is favored due to the more efficient and less energy-consuming condition. To discern the optimum design and operation conditions, this work presents the results of a preliminary technical–economic analysis, demonstrating that, to minimize the total costs of the system, it is recommended to seek the most efficient conditions, i.e., the conditions demanding the lowest applied charges with the highest Diuron degradation. At the same time, attention must be given to the required cell voltage to not increase excessively the operating costs.
Adverse Effects Associated with the First Dose of the Pfizer–BioNTech COVID-19 Vaccine in Healthcare Workers from Mexico: A Case Serie from Passive Surveillance
Authors: Pérez-Nieto Orlando Rubén , Alonso-Bello Cesar Daniel , Deloya-Tomas Ernesto , Del Carpio-Orantes Luis , Maya-Mejía María de Lourdes , Medina Úrsula , Almendra-Pegueros Rafael , Phinder-Puente Marian Elizabeth
Published: August 2023

Almost a year after the declaration of the pandemic due to the SARS-CoV-2 virus which causes the COVID-19 disease and the need to contain the progression and treatment, the promising option was designing an effective and safe vaccine to reach a state of massive immunity. The first vaccine approved was the one produced by Pfizer–BioNTech, and its application started in December 2020. Within days of the first applications, 0.2% of adverse events were reported. Herein, a series of 26 cases with the manifestation of adverse events related to the application of the first dose of the BNT162b2 vaccine from Pfizer–BioNTech in healthcare workers from Mexico. Of these cases, only five patients were classified with a certainty of anaphylaxis; two of them presented seizures, and their management is described individually. After the examination of all the cases, the symptoms were resolved. In Mexico and around the globe, the vaccination process continues, and the report of possible AEFIs is still needed to contribute to the pharmacovigilance of this new vaccine and improve its safety profile.
Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges
Authors: Paja Wiesław
Published: August 2023

Relevant attribute selection in machine learning is a key aspect aimed at simplifying the problem, reducing its dimensionality, and consequently accelerating computation. This paper proposes new algorithms for selecting relevant features and evaluating and selecting a subset of relevant objects in a dataset. Both algorithms are mainly based on the use of a fuzzy approach. The research presented here yielded preliminary results of a new approach to the problem of selecting relevant attributes and objects and selecting appropriate ranges of their values. Detailed results obtained on the Sonar dataset show the positive effects of this approach. Moreover, the observed results may suggest the effectiveness of the proposed method in terms of identifying a subset of truly relevant attributes from among those identified by traditional feature selection methods.
Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection
Authors: Mai Tai Tan , Crane Martin , Bezbradica Marija
Published: August 2023

The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.
Misunderstanding Flight Part 2: Epistemology and the Philosophy of Science
Authors: Wild Graham
Published: August 2023

Flight has become a common everyday occurrence. We have engineered ever more efficient and reliable aircraft, facilitating safe transportation around the world. However, from the education literature on the topic of lift, online discussions, and YouTube, it becomes apparent that there are underlying pedagogical issues. The 2003 New York Times article by Chang and the 2020 Scientific American article by Regis conclude that no one really understands flight. These claims are made without regard for the underpinning science and engineering responsible for the modern aviation industry. Although, it does beg the question, why is there confusion about how wings work? Several factors have conspired together, resulting in this confusion. Fluid mechanics is a complex topic that stumped legends of physics and mathematics for centuries. It also contains paradoxes, exacerbating the complexity. However, the central thesis of this work is that knowledge about aerodynamics is not easy to construct due to two main factors. First, there are epistemological traps that directly lead to fallacious conclusions. Second, representativeness heuristics incorrectly apply behaviors of visible water to invisible air. While many assume they know how wings work, if they do not understand why there is confusion, rather than dismissing it, confusion will endure.
Research and Pedagogies for Early Math
Authors: Clements Douglas H. , Lizcano Renee , Sarama Julie
Published: August 2023

The increasing interest in early childhood mathematics education for decades has increased the need for empirically supported pedagogical strategies. However, there is little agreement on how early math might best be taught. We draw from the empirical literature to paint a picture of research-based and research-validated pedagogical approaches and strategies for teaching early math. Most approaches share core characteristics, including concern for children’s interests and engagement and for working on content matched to children’s level of thinking. Learning trajectories are an especially useful organizing structure because they combine and integrate educational goals, development of children’s thinking, and empirically supported pedagogical strategies. Therefore, they help teachers interpret what the child is doing, thinking, and constructing, and offer instructional activities that extend children’s mathematical thinking. Simultaneously, teachers can see instructional strategies from the child’s perspective, offering meaningful and joyful opportunities to engage in learning.
Named Entity Recognition for Few-Shot Power Dispatch Based on Multi-Task
Authors: Tan Zhixiang , Chen Yan , Liang Zengfu , Meng Qi , Lin Dezhao
Published: August 2023

In view of the fact that entity nested and professional terms are difficult to identify in the field of power dispatch, a multi-task-based few-shot named entity recognition model (FSPD-NER) for power dispatch is proposed. The model consists of four modules: feature enhancement, seed, expansion, and implication. Firstly, the masking strategy of the encoder is improved by adopting whole-word masking, using a RoBERTa (Robustly Optimized BERT Pretraining Approach) encoder as the embedding layer to obtain the text feature representation, and an IDCNN (Iterated Dilated CNN) module to enhance the feature. Then the text is cut into one Chinese character and two Chinese characters as a seed set, the score for each seed is calculated, and if the score is greater than the threshold value ω, they are passed to the expansion module as candidate seeds; next, the candidate seeds need to be expanded left and right according to offset γ to obtain the candidate entities; finally, to construct text implication pairs, the input text is used as a premise sentence, the candidate entity is connected with predefined label templates as hypothesis sentences, and the implication pairs are passed to the RoBERTa encoder for the classification task. The focus loss function is used to alleviate label imbalance during training. The experimental results of the model on the power dispatch dataset show that the precision, recall, and F1 scores of the recognition results in 20-shot samples are 63.39%, 61.97%, and 62.67%, respectively, which is a significant performance improvement compared to existing methods.
A Novel Traffic Obfuscation Technology for Smart Home
Authors: Zhang Shuo , Shen Fangyu , Liu Yaping , Yang Zhikai , Lv Xinyu
Published: August 2023

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.
A Low-RCS 2D Multi-Layer Van Atta Array at X-Band
Authors: Chen Lei , Qin Mingjuan , Zou Lin , Zhang Tianling
Published: August 2023

This paper presents a novel approach to reducing radar cross section (RCS) using a 2D multi-layer Van Atta array based on the phase cancellation principle. By controlling the phase of transmission lines using wideband phase shifters, the proposed array can achieve significant RCS reduction at a wide frequency range of 8 GHz to 11 GHz. Both theoretical calculations and experimental measurements were conducted to evaluate the performance of the Van Atta array with phase shifters. Results showed significant RCS reduction from various incident angles, demonstrating the effectiveness of the proposed design in achieving wideband RCS reduction at the X-band.
Automatic Word Length Selection with Boundary Conditions for HIL of Power Converters
Authors: García-Vellisca Mariano Alberto , Gómez Muñoz Carlos Quiterio , Martínez-García María Sofía , de Castro Angel
Published: August 2023

Hardware-in-the-loop (HIL) is a common technique used for testing in power electronics. It draws upon FPGAs (field-programmable gate arrays) because they allow for reaching real-time simulation for mid-high switching frequencies. FPGA area and delay are keys to reaching a compromise between performance and accuracy. To minimize area and delay, signal word length (WL) is critical. Furthermore, the input and output’s WL should be carefully chosen because these signals come from ADCs (analog-to-digital converters) or go to DACs (digital-to-analog converters). In other words, the role of ADCs and DACs is the boundary condition when assigning all the signal WLs in an HIL model. This research presents an automatic method for computing the signal WLs in the corresponding model by considering input/output boundary conditions. This automatic method needs a single simulation to decide both the integer and fractional width of every signal. Our method accelerates the process, showing an advantage over manual methods and those requiring multiple simulations. The proposed method is applied to create all the WL assignments to the signals involved in a fixed-point coded buck converter model, which shows its feasibility.
Safe and Trustful AI for Closed-Loop Control Systems
Authors: Schöning Julius , Pfisterer Hans-Jürgen
Published: August 2023

In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
Research on Automatic Emergency Braking System Based on Target Recognition and Fusion Control Strategy in Curved Road
Authors: Zhang Lin , Yu Zhidong , Xu Xiaowei , Yan Yunbing
Published: August 2023

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort.
Electric Analysis of the Maritime Application High-Frequency Magnetohydrodynamic Thruster
Authors: Kan Kin Lung Jerry , Cheng Ka Wai Eric , Zhuang Hai-Chen
Published: August 2023

A magnetohydrodynamic (MHD) thruster is the next-generation electric jet engine for maritime applications. It eliminates the moving mechanical components that make the noises and reduces physical harm to sea creatures. This paper finds that aluminum electrodes have higher conductivity and less capacitive value in a KCl solution than the 316 stainless steel and zinc in MHD applications. Further, the AC operation can mitigate the power loss during electrolysis and power loss while on the water. The new optimal coil design with the enclosed-type ferrite layout of the MHD thruster is addressed by this simulation study. The AC operation and electric drive with a Lorentz force analysis will be demonstrated. Lastly, a verification experiment that pushes the KCl solution at 3 cm/s will be interpreted by the prototype to display the electric operation detail.
Review of Surface Charge Accumulation on Insulators in DC Gas-Insulated Power Transmission Lines: Measurement and Suppression Measures
Authors: Liang Fangwei , Luo Hanhua , Fan Xianhao , Li Xuetong , Wang Xu
Published: August 2023

Gas-insulated power transmission lines (GILs) can replace cables and overhead transmission lines, playing an important role in DC transmission systems. However, the influence of surface charge accumulation on insulation reliability cannot be ignored as the operational voltage of the DC GIL increases. In this paper, the measurement methods for the insulator surface potential are summarized, including, dust maps, the Pockels effect method, and the electrostatic probe method. Then, a typical surface charge inversion algorithm is introduced. The main influencing factors of surface charge accumulation are analyzed, such as the applied voltage, insulation gas, insulator shape, and temperature. The charge accumulation pathway is revealed. Furthermore, methods for inhibiting the accumulation of surface charges and promoting the dissipation of accumulated charges are introduced to reduce the surface charges on insulators. Finally, the development direction of DC GIL insulators is predicted. We anticipate that the online monitoring of surface charge distribution, clarifying the percentage of charge accumulation pathways, and optimizing the insulator casting process will be the research directions for the insulator surface charge topic in the future. This article provides a comprehensive understanding of the surface charges of GIL insulators and a reference for the insulation design of DC GILs.
Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis
Authors: Saad Mostafa M. , Menon Ramanunni Parakkal , Eicker Ursula
Published: August 2023

Decarbonizing buildings is crucial in addressing pressing climate change issues. Buildings significantly contribute to global greenhouse gas emissions, and reducing their carbon footprint is essential to achieving sustainable and low-carbon cities. Retrofitting buildings to become more energy efficient constitutes a solution. However, building energy retrofits are complex processes that require a significant number of simulations to investigate the possible options, which limits comprehensive investigations that become infeasible to carry out. Surrogate models can be vital in addressing computational inefficiencies by emulating physics-based models and predicting building performance. However, there is a limited focus on investigating feature engineering and selection methods and their effect on the model’s performance and optimization. Feature selection methods are considered effective with interpretable models such as multi-variate linear regression (MVLR) and multiple adaptive regression splines (MARS) for achieving stable prediction stability. This study proposes a modelling framework to create, optimize, and improve the performance of surrogate predictive models for energy consumption, carbon emissions, and the associated cost of building energy retrofit processes. The investigated feature selection methods are wrapper and embedded methods such as backward-stepwise feature selection (BSFS), recursive feature elimination (RFE), and Elastic Net embedded regularization in order to provide insights into the model’s behavior and optimize the model’s performance. The most accurate surrogate models developed achieved a mean absolute percentage error (MAPE) of 0.2–1.8% compared to the used test data. In addition, when calculated for a million samples, all developed surrogate models reduced the computational time by one-thousand-fold compared to physics-based models. The study’s findings pave the way towards low-computational accurate models that can comprehensively predict building performance in near real-time, ultimately leading to identifying decarbonization measures at scale.
Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer
Authors: Meteab Wisam Kareem , Alsultani Salwan Ali Habeeb , Jurado Francisco
Published: August 2023

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.
Metabolomic and Transcriptomic Analyses Reveal the Effects of Grafting on Nutritional Properties in Eggplant
Authors: Yan Yaqin , Wang Wuhong , Hu Tianhua , Hu Haijiao , Wang Jinglei , Wei Qingzhen , Bao Chonglai
Published: August 2023

Grafting has a significant impact on the botany properties, commercial character, disease resistance, and productivity of eggplants. However, the mechanism of phenotypic modulation on grafted eggplants is rarely reported. In this study, a widely cultivated eggplant (Solanum. melongena cv. ‘Zheqie No.10’) was selected as the scion and grafted, respectively, onto four rootstocks of TOR (S. torvum), Sa (S. aculeatissimum), SS (S. sisymbriifolium), and Sm64R (S. melongena cv. ‘Qiezhen No. 64R’) for phenotypic screening. Physiological and biochemical analysis showed the rootstock Sm64R could improve the fruit quality with the increasing of fruit size, yield, and the contents of total soluble solid, phenolic acid, total amino acid, total sugar, and vitamin C. To further investigate the improvement of fruit quality on Sm64R, a transcriptome and a metabolome between the Sm64R-grafted eggplant and self-grafted eggplant were performed. Significant differences in metabolites, such as phenolic acids, lipids, nucleotides and derivatives, alkaloids, terpenoids, and amino acids, were observed. Differential metabolites and differentially expressed genes were found to be abundant in three core pathways of nutritional qualities, including biosynthesis of phenylpropanoids, phospholipids, and nucleotide metabolism. Thus, this study may provide a novel insight into the effects of grafting on the fruit quality in eggplant.
An Efficient Prephenate Dehydrogenase Gene for the Biosynthesis of L-tyrosine: Gene Mining, Sequence Analysis, and Expression Optimization
Authors: Ji Anying , Bao Pengfei , Ma Aimin , Wei Xuetuan
Published: August 2023

L-tyrosine is a key precursor for synthesis of various functional substances, but the microbial production of L-tyrosine faces huge challenges. The development of new microbial chassis cell and gene resource is especially important for the biosynthesis of L-tyrosine. In this study, the optimal host strain Bacillus amyloliquefaciens HZ-12 was firstly selected by detecting the production capacity of L-tyrosine. Subsequently, the recombinant expression of 15 prephenate dehydrogenase genes led to the discovery of the best gene, Bao-tyrA from B. amyloliquefaciens HZ-12. After the overexpression of Bao-tyrA, the L-tyrosine yield of the recombinant strain HZ/P43-Bao-tyrA reach 411 mg/L, increased by 42% compared with the control strain (HZ/pHY300PLK). Moreover, the nucleic acid sequence and deduced amino acid sequence of the gene Bao-tyrA were analyzed, and their conservative sites and catalytic mechanisms were proposed. Finally, the expression of Bao-tyrA was regulated through a promoter and 5′-UTR sequence to obtain the optimal expression elements. Thereby, the maximum L-tyrosine yield of 475 mg/L was obtained from HZ/P43-UTR3-Bao-tyrA. B. amyloliquefaciens was applied for the first time to produce L-tyrosine, and the optimal prephenate dehydrogenase gene Bao-tyrA and corresponding expression elements were obtained. This study provides new microbial host and gene resource for the construction of efficient L-tyrosine chassis cells, and also lays a solid foundation for the production of various functional tyrosine derivatives.
SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network
Authors: Qiu Mengqing , Tang Le , Wang Jinghong , Xu Qingshan , Zheng Shouguo , Weng Shizhuang
Published: August 2023

The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.
Impact of the COVID-19 Lockdown on the Body Composition and Physical Performance of Athletes: A Systematic Review with Meta-Analysis and Meta-Regression
Authors: Rosa Bruno Viana , Maldonado Alberto Jimenez , de Araújo Ayrton Oliveira , Neves Lucas Melo , Rossi Fabricio Eduardo
Published: August 2023

Sporting events were cancelled, and sports training was banned to prevent the spread of COVID-19. These changes during the COVID-19 pandemic decreased the physical activity levels, increased sedentary time, and also impaired the mental health of elite and sub-elite athletes. The impact on body composition and physical performance is not clear, however, especially considering a systematic review with meta-analysis. Thus, our objective was to conduct a review in accordance with the PRISMA Statement studies published in scientific journals (PubMed, Web of Science, or Scopus databases) that investigated the effect that social distancing during the COVID-19 pandemic had on the physical performance (muscle power, cardiorespiratory capacity, and sprint) or body composition (body weight, percentage of fat, fat mass, and fat-free mass) of athletes. Data from 24 studies indicate that, throughout the global lockdown, the athletes maintained muscle power, cardiorespiratory capacity, and sprint, and prevented significant changes in fat mass and fat-free mass. However, the total body weight (meta-analysis with 18 studies), showed a significant increase (p = 0.006), with a small ES = 0.12; 95% CI = 0.04 to 0.21. Furthermore, the time of follow-up, level of training, and the age of the athletes were possible moderators of these effects. The data reinforce the importance of general strength and endurance exercises sessions to maintain physical fitness during non-competitive periods or due to the mandatory lockdown.
Analysis of Driving Factors in the Intention to Use the Virtual Nursing Home for the Elderly: A Modified UTAUT Model in the Chinese Context
Authors: Ren Zongwei , Zhou Guangmin
Published: August 2023

As a unique form of mobile health service (m-Health) for elderly care in China, the virtual nursing home is considered a reliable alternative to the traditional model of home-based care, but services from virtual nursing homes are infrequently used by the elderly in practice. Thus, this study aims to measure the driving factors affecting the behavioral intention to use the virtual nursing home by designing a research framework that combined the unified theory of acceptance and use of technology (UTAUT) with the technology acceptance model (TAM). Furthermore, a new variable, conformist mentality, is proposed as a moderator. In order to validate the model, a well-structured questionnaire using mature scales was used, and the linear regression analysis method was conducted on 200 valid data samples collected during a field study in Harbin, China. The results show that performance expectancy, effort expectancy, and social influence significantly affect behavioral intention. However, the effect of facilitating conditions is not significant. Moreover, performance expectancy and effort expectancy have a positive effect on attitude toward use, and attitude toward use not only affects behavioral intention but also plays a mediating role in the effect of performance expectancy and effort expectancy on behavioral intention. This study also innovatively proposes and confirms conformist mentality as a moderator to strengthen the driving effect of social influence on behavioral intention. This is the first time that conformist mentality is introduced as a moderator in a study on the behavioral perception and acceptance of virtual nursing homes among Chinese older adults. Based on these findings, this study offers theoretical contributions and management implications that are conducive to the sustainable development of virtual nursing homes, thereby making extensive contributions to this field. Additionally, it also aids in the contextual expansion of the UTAUT model.
What Influenced Hanoi’s Apartment Price Bubble between 2010 and 2021?
Authors: Le Phuong Lan , Do Anh Tuan , Pham Anh Ngoc
Published: August 2023

This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors.
MicroRNA-22 Is a Key Regulator of Lipid and Metabolic Homeostasis
Authors: Panella Riccardo , Petri Andreas , Desai Bhavna N. , Fagoonee Sharmila , Cotton Cody A. , Nguyen Piercen K. , Lundin Eric M. , Wagshal Alexandre , Wang Da-Zhi , Näär Anders M. , Vlachos Ioannis S. , Maratos-Flier Eleftheria , Altruda Fiorella , Kauppinen Sakari , Paolo Pandolfi Pier
Published: August 2023

Obesity is a growing public health problem associated with increased risk of type 2 diabetes, cardiovascular disease, nonalcoholic fatty liver disease (NAFLD) and cancer. Here, we identify microRNA-22 (miR-22) as an essential rheostat involved in the control of lipid and energy homeostasis as well as the onset and maintenance of obesity. We demonstrate through knockout and transgenic mouse models that miR-22 loss-of-function protects against obesity and hepatic steatosis, while its overexpression promotes both phenotypes even when mice are fed a regular chow diet. Mechanistically, we show that miR-22 controls multiple pathways related to lipid biogenesis and differentiation. Importantly, genetic ablation of miR-22 favors metabolic rewiring towards higher energy expenditure and browning of white adipose tissue, suggesting that modulation of miR-22 could represent a viable therapeutic strategy for treatment of obesity and other metabolic disorders.
Expression of Prostaglandin Genes and β-Catenin in Whole Blood as Potential Markers of Muscle Degeneration
Authors: Wajda Anna , Bogucka Diana , Stypińska Barbara , Radkowski Marcin Jerzy , Targowski Tomasz , Dudek Ewa , Kmiołek Tomasz , Modzelewska Ewa , Paradowska-Gorycka Agnieszka
Published: August 2023

Prostaglandin signaling pathways are closely related to inflammation, but also muscle regeneration and processes associated with frailty and sarcopenia, whereas β-catenin (CTNNB1 gene) as a part of Wnt signaling is also involved in the differentiation of muscle cells and fibrosis. The present study analyzed the association between selected prostaglandin pathway genes and clinical parameters in patients with sarcopenia and frailty syndrome. The present study was conducted on patients with sarcopenia, frailty syndrome, and control older patients (N = 25). Additionally, two healthy controls at the age of 25–30 years (N = 51) and above 50 years old (N = 42) were included. The expression of the PTRGER4, PTGES2 (COX2), PTGS2, and CTNNB1 genes in whole blood was checked by the qPCR method. The serum cytokine levels (IL-10, TNFα, IFN-y, IL-1α, IL-1β) in patients and controls were checked by the Q-Plex Human Cytokine Panel. The results showed a significant effect of age on PTGER4 gene expression (p = 0.01). A negative trend between the appendicular skeletal muscle mass parameter (ASSM) and the expression of PTGER4 has been noted (r = −0.224, p = 0.484). PTGES2 and PTGS2 expressions negatively correlated with creatine phosphokinase (r = −0.71, p = 0.009; r = −0.58, p = 0.047) and positively with the functional mobility test timed up and go scale (TUG) (r = 0.61, p = 0.04; r = 0.63, p = 0.032). In the older control group, a negative association between iron levels and the expression of PTGS2 (r = −0.47, p = 0.017) was observed. A similar tendency was noted in patients with sarcopenia (r = −0.112, p = 0.729). A negative trend between appendicular skeletal muscle mass (ASMM) and PTGER4 seems to confirm the impairment of muscle regeneration associated with sarcopenia. The expression of the studied genes revealed a trend in associations with the clinical picture of muscular dystrophy and weakening patients. Perhaps PTGS2 and PTGES2 is in opposition to the role of the PTGER4 receptor in muscle physiology. Nevertheless, further, including functional studies is needed.
Comprehensive Analysis of Photoreceptor Outer Segments: Flow Cytometry Characterization and Stress-Driven Impact on Retinal Pigment Epithelium Phagocytosis
Authors: Liang Haoqian , Wu Qiang , Guo Xinzheng Victor , Chan Linda , Mao Tin , Stella Cinzia , Guilbaud Axel , Camperi Julien
Published: August 2023

Phagocytosis is one of the key functions of retinal pigment epithelium (RPE) cells, which maintain photoreceptor health by removing photoreceptor outer segments (POSs) that are regularly shed. A deficiency in RPE function to phagocytose POSs may lead to vision loss in inherited retinal diseases and eventually to age-related macular degeneration (AMD) with geographic atrophy. Significant progress has been made in the field of cell replacement therapy for AMD using stem-cell-derived RPE. To test their function, RPE cells are incubated with purified bovine POSs for the demonstration of efficient binding, internalization, and digestion of POSs. Here, we present an image-based method to measure phagocytosis activity by using POSs labeled with a pH-sensitive fluorescent dye, which has low fluorescence at neutral pH outside of the cell and high fluorescence at low pH inside the phagosome. Further, we introduce a unique flow-cytometry-based method for the characterization of POSs by measuring specific markers for POSs such as rhodopsin and opsin. Using this method, we demonstrated a comparable quality of several bovine POS isolation batches and a reliable assessment of POS quality on RPE phagocytosis assay performance when subjected to different stress conditions. This work provides new tools to characterize POSs and insight into RPE phagocytosis assay development for the functional evaluation of RPE cells in the field of cell replacement therapy.